p - CITEE 2015 - Universitas Gadjah Mada

Transcription

Number 2
ISSN: 2085-6350
PROCEEDINGS OF
CONFERENCE ON
INFORMATION TECHNOLOGY
AND ELECTRICAL ENGINEERING
INTERNATIONAL SESSION
Signals, Systems, and Circuits
DEPARTMENT OF ELECTRICAL ENGINEERING
FACULTY OF ENGINEERING
GADJAH MADA UNIVERSITY
Conference on Information Technology and Electrical Engineering (CITEE)
Organizer
Steering Commitee
• Adhi Susanto (UGM)
• Hamzah Berahim (UGM)
• Thomas Sri Widodo (UGM)
• Dadang Gunawan (UI)
• Heri Mauridi (ITS)
• Yanuarsyah Harun (ITB)
• Anto Satrio Nugroho (BPPT)
• Son Kuswadi (PENS)
Advisory Board
• Tumiran (UGM)
• Lukito Edi Nugroho (UGM)
• Anto Satrio Nugroho (BPPT)
• Son Kuswadi (PENS)
General Chair
• Bambang Sutopo
Organizing Chairs
• Risanuri Hidayat
• Sri Suning Kusumawardhani
• Ridi Ferdiana
• Adha Imam Cahyadi
• Budi Setiyanto
Program Chairs
• Prapto Nugroho
• Agus Bejo
• Cuk Supriyadi Ali Nandar (BPPT)
• Yusuf Susilo Wijoyo
Publication Chair
• Enas Dhuhri K
Finance Chairs
• Eny Sukani Rahayu
• Maun Budiyanto
• Roni Irnawan
Secretariats
• Astria Nur Irfansyah
• Lilik Suyanti
YOGYAKARTA, AUGUST 4, 2009
Conference on Information Technology and Electrical Engineering (CITEE) 2009
FOREWORD
First of all, praise to Almighty God, for blessing us with healthy and ability to come here, in the
Conference of Information and Electrical Engineering 2009 (CITEE 2009). If there is some noticeable wisdoms
and knowledge must come from Him.
I would like to say thank you to all of the writers, who come here enthusiastically to share experiences and
knowledge. Without your contribution, this conference will not has a meaning.
I also would like to say thank you to Prof. Dadang Gunawan from Electrical Engineering, University of
Indonesia (UI), Prof. Yanuarsyah Haroen from Electrical Engineering and Informatics School, Bandung Institute
of Technology, ITB, Prof. Mauridhi Hery Purnomo from Electrical Engineering Department, Surabaya Institute
of Technology (ITS). And also Prof. Takashi Hiyama from Kamamoto University, Japan, Thank you for your
participation and contribution as keynote speakers in this conference.
This conference is the first annual conference held by Electrical Engineering Department, Gadjah Mada
University. We hope, in the future, it becomes a conference of academics and industries researchers in the field of
Information Technology and Electrical Engineering around the world. We confine that if we can combine these
two fields of sciences, it would make a greater impact on human life quality.
According to our data, there are 140 writers gather here to present their papers. They will present 122 titles
of papers. There are 47 papers in the field of Electrical Power Systems, 53 papers in the area of Systems, Signals
and Circuits, and 22 papers in Information Technology. Most of these papers are from universities researchers.
We hope, the result of the proceedings of this conference can be used as reference for the academic and
practitioner researchers to gain
At last, I would like to say thank you to all of the committee members, who worked hard to prepare this
conference. Special thanks to Electrical Engineering Department, Gadjah Mada University, of supporting on
facilities and funds.
Thank you and enjoy the conference, CITEE 2009, and the city, Yogyakarta
August, 4Th, 2009
Bambang Sutopo
Electrical Engineering Dept., Fac. of Engineering, GMU
Proceedings of CITEE 2009
Number 2
ISSN: 2085-6350
Table of Contents
Organizer
Foreword
Table of Contents
Schedule
ii
iii
v
vii
KEYNOTE
Social Intelligent on Humanoid Robot: Understanding Indonesian Text Case Study
Mauridhi Hery Purnomo (Electrical Engineering Department, ITS, Indonesia)
1
Signal Processing: Video Compression Techniques
Dadang Gunawan (Electrical Engineering Department, University of Indonesia)
4
Intelligent Systems Application to Power Systems
Prof. Takashi Hiyama (Kumamoto University, Japan)
-
INTERNATIONAL SESSION: Signals, Systems, and Circuits
Analysis and Simulation of Bus-Clamping PWM Techniques Based on Space Vector Approach
Ms.M.Thanuja, Mrs. K. SreeGowri
7
Medical Image Processing of Proximal Femur X-Ray for Osteoporosis Detection
Riandini, Mera Kartika Delimayanti, Donny Danudirdjo
15
Performance Evaluation of Free-Space Optical Communication System on Microcell Networks in Urban Area
Purnomo Sidi Priambodo, Harry Sudibyo and Gunawan Wibisono
22
A Measure of Vulnerability for Communication Networks: Component Order Edge Connectivity
A. Suhartomo, Ph.D.
27
The Speech Coder at 4 kbps using Segment between Consecutive Peaks based on the Sinusoidal Model
Florentinus Budi Setiawan
31
Automatic Abnormal Waves Detection from the Electroencephalograms of Petit Mal Epilepsy Cases to Sort Out the Spikes Fp1 Fp2, the Sharps, the Polyphase Based on Their Statistical Zerocrossing
Siswandari N, Adhi Susanto, Zainal Muttaqin
35
Studies on the Limitation of Fighter Aircraft Maneuver Caused by Automatic Control Design
Okky Freeza Prana Ghita Daulay, Arwin Datumaya Wahyudi Sumari
39
Feature Extraction and Selection on Osteoporosis X-Ray Image for Content Based Image Retrieval (CBIR) Purposes
Usman Balugu, Ratnasari Nur Rohmah, Nurokhim
45
The Implementation of Turbo Encoder and Decoder Based on FPGA
Sri Suning Kusumawardani and Bambang Sutopo
51
BER Performance Analysis of PAM and PSM for UWB Communication
Risanuri Hidayat
55
Hardware Model Implementation of a Configurable QAM Mapper-Demapper for an Adaptive Modulation OFDM System
Budi Setiyanto, Astria Nur Irfansyah, and Risanuri Hidayat
61
Hardware Model Implementation of a Baseband Conversion, Chip Synchronization, and Carrier Synchronization Technique for a
Universal QAM System
Budi Setiyanto, Mulyana, and Risanuri Hidayat
69
Comparison Study of Breast Thermography and Breast Ultrasonography for Detection of Breast Cancer
Thomas Sri Widodo, Maesadji Tjokronegore, D. Jekke Mamahit
75
Adaptive Polynomial Approximation for Gravimetric Geoid: A case study using EGM96 and EIGEN-GL04C Geopotential
Development
Tarsisius Aris Sunantyo, Muhamad Iradat Achmad
78
Cerebellar Model Associative Computer (CMAC) for Gravimetric Geoid study based on EGM96 and EIGEN-GL04C Geopotential
Development
Muhamad Iradat Achmad, Tarsisius Aris Sunantyo, Adhi Susanto
83
SCHEDULE
Tuesday, August 4, 2009
07.30 – 08.00:
08.00 – 08.15:
Registration
Opening
1. Welcome speech by conference chairman
2. Speech by GMU’s Rector
08.15 – 09.20:
PLENARY SESSION
Prof. Takashi Hiyama (Kumamoto University, Japan): Intelligent Systems Application to
Power Systems
Prof. Dr. Mauridhi Hery Purnomo (Electrical Engineering Department,ITS, Indonesia):
Social Intelligent on Humanoid Robot: Understanding Indonesian Text Case Study
Prof. Dr. Dadang Gunawan (Electrical Engineering Department, University of
Indonesia): Signal Processing: Video Compression Techniques
Prof. Dr. Yanuarsyah Haroen (Electrical Engineering and Informatics School, ITB,
Indonesia): Teknologi Sistem Penggerak dalam WahanaTransportasi Elektrik
09.20 – 09.30: Break
PARALLEL SESSION
INTERNATIONAL SESSION (Room 1, 2)
Room: 1
Time
Group
Country/City
09.30 – 09.45
P
Malaysia
09.45 – 10.00
10.00 – 10.15
10.15 – 10.30
10.30 – 10.45
10.45 – 11.00
11.00 – 11.15
11.15 – 11.30
11.30 – 11.45
11.45 – 12.00
12.00 – 13.00
13.00 – 13.45
13.15 – 13.30
P
P
P
Papua
Medan
Bandung
P
P
P
P
P
Bandung
Surabaya
Surabaya
Surabaya
Yogyakarta
P
P
Surabaya
Surabaya
13.30 – 13.45
I
Surabaya
13.45 – 14.00
14.00 – 14.15
14.15 – 14.30
14.30 – 14.45
14.45 – 15.00
15.00 – 15.15
15.15 – 15.30
I
I
Surabaya
Surabaya
I
I
P
P
Yogyakarta
Surakarta
Yogyakarta
Yogyakarta
Author(s) or Presenter(s)
Zulkarnain Lubis, Ahmed N. Abdalla, Samsi bin MD said .Mortaza bin
Mohamed
Adelhard Beni Rehiara
Zakarias Situmorang
Kartono Wijayanto, Yanuarsyah Haroen
Coffee Break
Hermagasantos Zein
Ali Musyafa, Soedibjo, I Made Yulistiya Negara , Imam Robandi
Buyung Baskoro, Adi Soeprijanto, Ontoseno Penangsang
Eko Prasetyo, Boy Sandra, Adi Soeprijanto
T. Haryono, Sirait K.T., Tumiran, Hamzah Berahim
Lunch Break
Dimas Anton Asfani, Nalendra Permana
Dimas Anton Asfani, Iman Kurniawan, Adi Soeprijanto
F.X. Ferdinandus, Gunawan, Tri Kurniawan Wijaya, Novita Angelina
Sugianto
Arya Tandy Hermawan, Gunawan, Tri Kurniawan Wijaya
Herman Budianto, Gunawan, Tri Kurniawan Wijaya, Eva Paulina Tjendra
Coffee Break
Bambang Soelistijanto
Munifah, Lukito Edi Nugroho, Paulus Insap Santosa
Nurcahyanto, T. Haryono, Suharyanto.
Agni Sinatria Putra, Tiyono, Astria Nur Irfansyah
Notes:
1. P: Electrical Power Systems; S: Signals, Systems, and Circuits; I: Information Technology
2. Paper titles are listed in Table of Contents
Department of Electrical Engineering, Faculty of Engineering, Gadjah Mada University
Room: 2
Time
09.30 – 09.45
09.45 – 10.00
10.00 – 10.15
10.15 – 10.30
10.30 – 10.45
10.45 – 11.00
11.00 – 11.15
11.15 – 11.30
11.30 – 11.45
11.45 – 12.00
12.00 – 13.00
13.00 – 13.45
13.15 – 13.30
13.30 – 13.45
13.45 – 14.00
14.00 – 14.15
14.15 – 14.30
14.30 – 14.45
14.45 – 15.00
15.00 – 15.15
15.15 – 15.30
Group
S
S
S
S
Country/City
INDIA
Jakarta
Jakarta
Jakarta
I
I
I
S
S
Yogyakarta
Yogyakarta
Lampung
Semarang
Semarang
S
S
S
S
S
Yogyakarta
Yogyakarta
Yogyakarta
Yogyakarta
Yogyakarta
S
S
S
S
Yogyakarta
Yogyakarta
Yogyakarta
Yogyakarta
Author(s) or Presenter(s)
Ms.M.Thanuja, Mrs. K. SreeGowri
A. Suhartomo
Riandini, Mera Kartika Delimayanti, Donny Danudirdjo
Coffee Break
Arwin Datumaya Wahyudi Sumari, Adang Suwandi Ahmad
Arwin Datumaya Wahyudi Sumari, Adang Suwandi Ahmad
Sumadi, S; Kurniawan, E.
Siswandari N, Adhi Susanto, Zainal Muttaqin
Lunch Break
Thomas Sri Widodo, Maesadji Tjokronegore, D. Jekke Mamahit
Tarsisius Aris Sunantyo, Muhamad Iradat Achmad
Muhamad Iradat Achmad, Tarsisius Aris Sunantyo, Adhi Susanto
Usman Balugu, Ratnasari Nur Rohmah, Nurokhim
Coffee Break
Sri Suning Kusumawardani and Bambang Sutopo
Risanuri Hidayat
NATIONAL SESSION (Room 3, 4, 5, 6, 7)
Yogyakarta, August 4, 2009
Proceedings of CITEE, August 4, 2009
-1
Keynote
Social Intelligent on Humanoid Robot:
Understanding Indonesian Text
Case Study
Mauridhi Hery Purnomo
Electrical Engineering Department-Institut Teknologi Sepuluh November
Surabaya 60111, Indonesia
[email protected]
Abstract— Social affective and emotion are required on
humanoid robot performance to make the robot be more
human. Social intelligent are the individual ability to manage
relationship with other agents and act wisely based on
previous learning experiences. Here, social intelligent is
intended to understand Indonesian text. How the computation
process, as well as affective interaction, emotion expression of
the humanoid robot to the human statement. This process is a
highly adaptive complex approximation, dependently on its
entire situation and environment.
Keywords—social, affective, emotion, intelligent, computing
(key words)
I. INTRODUCTION
Social and interactive behaviors are necessary
requirements in wide implementation areas and contexts
where robots need to interact and collaborate with other
robots or humans. The nature of interactivity and social
behavior in robot and humans is a complex model.
An experimental robot platform KOBIE, which provides
a simulation tool for emotion expression system includes an
emotion engine was developed. The simulation tool
provides a visualization interface for the emotion engine
and expresses emotion through an avatar. The system can be
used in the development of cyber characters that use
emotions or in the development of an apparatus with
emotion in a ubiquitous environment [1].
To improve the understandability and friendliness in
human-computer interfaces and media contents, a
Multimodal Presentation Markup Language (MPML) is
developed. MPML is a simple script language to make
multi-modal presentation contents using animated
characters for presenters [2].
Other effort in the robot head which uses arm-type
antennae, eye-expression, and additional exaggerating parts
for dynamic emotional expression is also developed. The
robot head is developed for various and efficient emotional
expressions in the Human-Robot interaction field. The
concept design of the robot is an insect character [3].
In regard to artificial cognitive, iCub humanoid robot
systems is developed. The system is open-systems 53
degree-of-freedom cognitive humanoid robot, 94 cm tall,
the same size as a three year-old child. Able to crawl on all
fours and sit up, its hands will allow dexterous
manipulation, and its head and eyes are fully articulated. It
has visual, vestibular, auditory, and haptic sensory
capabilities [4].
An innovative integration of interactive group learning,
multimedia technology, and creativity used to enhance the
learning of basic psychological principles was created.
This system is based on current robotic ideology calling for
the creation of a PowerPoint robot of the humanoid type
that embodies the basic theories and concepts contained in a
standard psychological description of a human being [5].
Now days, not only visual and auditory information are
used in media and interface fields but also multi-modal
contents including documents such like texts. Thus, in this
paper, a part of result on emotion expression and
environment through understanding Indonesian text, as
affective interaction between a human and a robot is
explored.
The paper is organized as follows. In Section 2, the
general emotion and expression system on life-like agent is
presented. Section 3 describes the experimental on
Indonesian text classification. In Section 4, the preliminary
result in emotion classification of Indonesian article text is
discussed.
II.
EMOTION AND EXPRESSION ON LIFE-LIKE AGENT
Social Computing, Social Agent, and Life-likeness
Many Psychologists have studied a definition and
classification of emotions, therefore, so many classification
methods of emotions and expressions. However, we need to
choose categories of emotions that are suitable expressed by
robot, as well as the well-known Ekman’s 6 basic emotion
expressions model can be used.
In social computing life-like characters are the key, and
the affective functions create believability. To articulate
synthetic emotions can be presented as; personalities, human
interactive behavior or presentation skills. The personalities;
by means of body movement, facial display, and the
coordination of the embodied conversational behavior of
multiple characters possibly including the user. Personality
is key to achieving life-likeness
Some Applications of Life-Like Character
Life-like characters are synthetic agents apparently living
on the screen of computers. Life-like character can be
implemented as virtual tutors and trainers in interactive
learning environments. On the web as an information expert,
presenter, communication partners, and enhancing the search
engine. The other application as actors for entertainment, in
ISSN: 2085-6350
2
Keynote
online communities and guidance systems as personal
representatives.
Early characterization of the emotional and believable
character was raised by Joseph Bates. He said, the portrayal
of emotions plays a key role in the aim to create believable
characters, one that provides the illusion of life, and thus
permits the audience’s suspension of disbelief. In game and
animation, suspension of disbelief is very important, for
instance as: synthetic actors, non-player characters, and
embodied conversational agents.
Figure 1 is an example how to train machine (agent) in
order responsive to the external and adequate response. The
case study is, Indonesian text classification. There are two
types of machine learning, supervised and unsupervised
learning.
Figure 2 show a block diagram process of embodied
conversational agent.
IV.
Emotion and personality are often seen as the affective
bases of believability, and sometimes the broader term social
is used to characterize life-likeness.
•
III.
CLASSIFICATION SYSTEM FOR INDONESIAN TEXT
Information growth, including texts are faster than
human ability, thus help system is quite necessary. For
instance as the following illustration;
y
The Recent study, which used Web searches in 75
different languages to sample the Web, determined
that there were over 11.5 billion (1012) Web pages
in the publicly indexable Web as of the end of
January 2005
y
As of March 2009, the indexable web contains at
least 25.21 billion pages
y
On July 25, 2008, Google software engineers Jesse
Alpert and Nissan Hajaj announced that Google
Search had discovered one trillion unique URLs
y
As of May 2009, over 109.5 million websites
operated
feature
extractor
..features..
test
input
language
dependent
NLP tools
feature
extractor
..features..
machine
learning
classifier
model
•
“When I nearly walked on a blindworm and then
saw it crawl away” → disgust
•
“When I was involved in a traffic accident” → fear
•
“I do not help out enough at home” → guilt
•
“Passing an exam I did not expect to pass” → joy
•
“Failing an examination” → sadness
•
“When, as an adult I have been caught lying or
behaving badly” → shame
The classification is divided into six (6) classes of
emotion: disgust, shame, anger, sadness, joy and fear.
Each class has 200 text files, data: “as-is”; DataNot:
pre-processing only handles “not”. Split ratio 0.5 shows
f-measure scores 0.59
label
language
dependent
NLP tools
The following sentences are sample of statements,
and some emotion expressions;
“When a car is overtaking another and I am forced
to drive off the road” → anger
Based on some statements and the emotion expression as
mentioned above in Indonesian text, the preliminary
classification results are shown in the table 1, figure 3 and
figure 4.
(a) training phase
training
input
EMOTION CLASSIFICATION FROM INDONESIAN
ARTICLE TEXT
Pre-processing Steps
predicted
label
(b) prediction phase
Figure 1. Example of Indonesian Text Classification
Text Classification (TC) techniques usually ignore stopwords and case of input text. In pre-processing step, stopwords removal can be applied.
Stop-words such as “not”, “in”, “which” and exclamation
marks (“!”) usually do not affect categorization of text.
Text
Input
Knowledge
Base
Response
User
(Human)
Text-based
Conversational Agent
Information
Retrieval
Text
Classification
Text
Mining
Figure 2. Knowledge from Free (Unstructured) Text
The illustrations as mentioned above explain the
essential of help system especially in Indonesian text. We
have developed a system for understanding and classifying
Indonesian text, and the block diagram as shown in figure 1
and figure 2.
ISSN: 2085-6350
TABLE I. NAÏVE BAYES CLASSIFICATION INTO 4 CLASS
Free
Text
Classifica
tion ratio
(%)
Usual & original text
Text without stop
words
20
71.41
69.81
40
73.33
71.3
60
74.40
71.3
80
75.33
74.05
Accuracy (%)
Example:
-
Microsoft released Windows → categorized as
“news”.
-
Microsoft has not released Windows yet → still
categorized as “news”.
Input text is converted
classifications, as example:
to
-3
Keynote
lowercase
on
emotion
DataNot
-
“I do like you.” ≠ “I do not like you!”
70
-
“I do not like you.” ≠ “I DO NOT LIKE YOU!!”
65
60
F-Measure
Naive Bayes
70
55
50
65
Multinomial NB
45
NB
Precision
60
40
55
0
0,1
0,2
0,4
0,5
0,6
0,7
0,8
0,9
1
Rasio data
50
Data
DataNot
With pre processing
45
Figure 4. Results Recapitulation of Indonesian Text Classification
40
40
45
50
55
60
65
70
Recall
V.
Multinomial NB
70
65
60
55
REFERENCES
Data
DataNot
50
[1]
45
40
[2]
40
45
50
55
CONCLUDING REMARKS
The preliminary results of emotion expression and related
environment through Indonesian text are described. We
develop an Indonesian conversational agent system includes
an emotion expression engine, that will used in the game
engine. The use of emotion on Indonesian text is expected
to the improvement of expressiveness of understanding and
actions. The research are still underway, so many possibility
to improve our future works and making the system more
life-like.
non bayesian
Precision
0,3
60
65
70
Recall
[3]
Multinomial non bayesian
Figure 3. Emotion Classification of Indonesian Text
[4]
Data
65
[5]
60
C Park, J W Ryu, J Kim, S Kang, J Sohn, YJ Cho, “Emotion
Expression and Environment Through Affective Interaction”
Proceedings of the 17th World Congress The International Federation
of Automatic Control,Seoul, Korea, July 6-11, 2008 .
K Kushida, Y Nishimura, et al.“Humanoid Robot Presentation
through Multimodal Presentation Markup Language MPML-HR”
AAMAS’05, Utrecht, Netherlands, July 25-29, 2005.
H Song and DS Kwon, “Design of a Robot Head with Arm-type
Antennae for Emotional Expression”, International Conference on
Control, Automation and Systems in COEX, Seoul, Korea Oct. 1720, 2007.
G Sandini, G Metta, and D Vernon,”The iCub Cognitive Humanoid
Robot:An Open-System Research Platform for Enactive Cognition”,
M. Lungarella et al. (Eds.): 50 Years of AI, Festschrift, LNAI 4850,
Springer-Verlag Berlin Heidelberg, pp. 359–370, 2007.
James L. Anderson and Erin M. Applegarth, “The Psychological
Robot: A New Tool for Learning, 3rd ed., International Journal of
Teaching and Learning in Higher Education 2007, Volume 19,
Number 3, 305-314
F-Measure
55
50
Multinomial NB
45
Naive Bayes
40
0
0,1
0,2
0,3
0,4
0,5
0,6
0,7
0,8
0,9
1
Rasio data
Without pre processing
ISSN: 2085-6350
4
Keynote
Signal Processing:
Video Compression Techniques
Dadang Gunawan
Electrical Engineering Department, University of Indonesia
In our information society, signal processing has been
created a significant effect. Signal processing can be found
everywhere: in home appliances, in Cell Phone, TVs,
Automobile, GPSs, Modem Scanner, and All kind of
Communication Systems and Electronic Devices. Modern
cell phones are indeed a most typical example – within
these small wonders, voice, audio, image, video and
graphics are processed and enhanced based on decades of
media signal processing research.
Technological advancement in recent years has
proclaimed a new golden age for signal processing [1].
Many exciting directions, such as bioinformatics, human
language, networking, and security, are emerging from
traditional field of signal processing on raw information
content. The challenge in the new era is to transcend from
the conventional role of processing in low level, waveformlike signal to the new role of understanding and mining the
high-level, human-centric semantic signal and information.
Such a fundamental shift has already taken place in limited
areas of signal processing and is expected to become more
pervasive in coming years of research in more areas of
signal processing.
One of the huge applications of signal processing is
exploited as video compression. Nowadays, video
applications such as digital laser disc, electronic camera,
videophone and video conferencing systems, image and
interactive video tools on personal computers and
workstations, program delivery using cable and satellite,
and high-definition television (HDTV) are available for
visual communications. Many of these applications,
however, require the use of data compression because visual
signals require a large communication bandwidth for
transmission and a large amounts of computer memory for
storage [2][3]. In order to make the handling of visual
signals cost effective it is important that their bandwidth be
compressed as much as possible. Fortunately, visual signals
contain a large a mount of statistically and psychovisually
redundant information [4]. By removing this unnecessary
information, the amount of data necessary to adequately
represent an image can be reduced.
The removal of unnecessary information generally can
be achieved by using either statistical compression
techniques or psychovisual compression techniques. Both
techniques result in a loss information, but in the former the
loss may be recovered by signal processing such as filtering
and inter or intra-polation. In the later, information is in fact
discarded, but in way that is not perceptible to a human
observer. The later techniques offer much greater levels of
ISSN: 2085-6350
compression but it is no longer possible to perfectly
reconstructed the original image [4]. While the aim in
psychovisual coding is to keep these differences at an
imperceptible level, psychovisual compression inevitably
involves a tradeoff between the quality of the reconstructed
image and the compression rate achieved. This tradeoff can
often be assessed using mathematical criteria, although a
better assessment is in general provided by human observer.
The applications of image data compression, in general,
are primarily in the transmission and storage of information.
In transmission, applications such as broadcast television,
teleconferencing, videophone, computer-communication,
remote sensing via satellite or aircraft, etc., require the
compression techniques to be constrained by the need
For the real time compression and on-line consideration
which tends to severely limit the size and hardware
complexity. In storage applications such as medical images,
educational and business documents, etc., the requirements
are less stringent because much of the compression
processing can be done off-line. However, he
decompression or retrieval should still be quick and
efficient to minimize the response time [5].
All images of interest usually contain a considerable
amount of statistically and subjectively superfluous
information [6]. A statistical image compression technique
exploits statistical redundancies in the information in the
image. This technique reduces the amount of data to be
transmitted or to be stored in an image without any
information being lost. The alternative is to discard the
subjective redundancies in an image, which leads to
psychovisual image compression. These psychovisual
techniques rely on properties of the Human Visual
characteristic system (HVS) to be determined which
features will not be noticed by human observer.
There are numerous way to achieve compression in
statistical image compression techniques such as Pulse
Code Modulation (PCM), Differential PCM (DPCM),
Predictive Coding, Transform Coding, Pyramid Coding and
Subband Coding, as well as Psychovisual Coding
techniques. Statistical compression techniques all use a
form of amplitude quantization in their algorithms to
improve compression performance. Simple quantization
alone, however, is not the most efficient or flexible
techniques to combine with a statistical compression
algorithm [7]. The combination of quantization and
psychophysics, on the other hand has the potential to
remove most subjectively redundant information efficiently
Keynote
from an image, a process which is based on the actual
behavior of the HVS. Furthermore, subband coding and
pyramid coding schemes can be combined with visual
psychophysics-based compression techniques, since both of
these statistical schemes break the original image data down
into separate frequency bands. A process that similar to the
bandpass filter characteristics of the HVS and can be used
to quantized the information in each band depending on the
relative frequency band.
Transform coding is able to achieve optimum statistical
compression ratios, especially the Discrete Cosine
Transform (DCT). Much research has been performed in
combining the DCT transform coding and visual
psychophysics-based compression techniques [8][9][10]
resulting in a higher compression ratio and good
reconstruction of the original image.
Image compression techniques mentioned above,
involve spatial correlations in single frames where
redundancies are exploited either statistically or
subjectively, are known as intraframe coding techniques.
Interframe coding techniques known video compression, by
contrast, attempt to exploit the redundancies produced by
temporal correlation as well as spatial correlations in
successive video signals. These techniques hold the promise
of significantly greater reduction in the data required to
transmit the video signal as compared to interframe coding.
The simplest interframe coding technique is called
“conditional replenishment [11][12][13]. This technique
bases the coding scheme on the previous frame and is also
often called predictive coding. In the conditional
replenishment technique, only pixels the values of which
have changed significantly since the last frame, as
compared to a certain threshold, are transmitted. Another
technique, which still uses predictive coding from previous
frame, is adaptive intra-inter-frame prediction [14]. In this
technique, interframe prediction is used for scenes in
images where there is little motion, while intraframe
prediction is used for areas where this is much motion. The
switching between intra- and inter-frame prediction or a
combination of both, is usually controlled by the signal
changes of previously transmitted pixels so that no
overhead control information need to sent. The prediction
error can be quantized and transmitted for every pixel or can
be thresholded into predictable and non-predictable pixels
[15].
Adaptive prediction displacement of a moving object
which is based on information obtained from successive
frames is known as Motion Compensation. This scheme
was studied by [16] and [17] by measuring small
displacements based on very simple model of moving
objects in a stationary background scene for segmentation
purposes. A later refinement developed by [18][19][20] led
to one set of techniques known as Pel Recursive Motion
Compensation, which recursively adjusts the translational
estimates at every pixel or every small block of pixels. [21]
developed another technique known Block Matching
Motion Estimation. This technique estimates the location of
a block of pixels in the current frame by using a search in a
-5
confined window defined in the previous frame. Location of
the block results in the displacement vector for that block.
Different search methods have been proposed to avoid an
exhaustive search [22][23][24][25].
In order to produce a higher compression ratio,
transform coding has been applied to video coding, and can
be carried out as a three-dimensional transform [26] or in an
interframe coding scheme [27][28]. In the latter case motion
compensation can be performed in either the spatial domain
or the frequency domain. Transform coding can also be
combined with predictive coding so that the transform
coefficients from intraframe transformations of the previous
frame can be used to predict the transform coefficients of
the current frame [29]. CCITT H.261 Recommendations
[30], JPEG standard [31] and the MPEG draft [32], are also
DCT transform based and intra-inter-frame adaptive with
optional motion compensation. Their schemes result in a
blocking effect for low bit rates. Wavelet transform coding
can
effectively
eliminate
this
blocking
effect
[33][34][35][36][37] because the wavelet bases in adjacent
subbands overlap one another. Another advantage of
wavelet transform coding is that it is very similar to
subband coding. Wavelet transform combined by
psychovisual coding resulted a very good performance in
term of compression ratio and reconstructed images [4].
Since then, the DCT is replaced to the WT in order to
achieve high compression algorithms and good quality
reconstructed images, and has been adopted to be standard.
These standards are ITU standard for H-261, H-263, H-264;
ISO/IEC for JPEG, JPEG-2000, MPEG-2, MPEG-4, and
MPEG-7.
However, there is inevitably space for
improvements and extension within this area of research,
such as a hybrid system by using combining transform
method and Fuzzy, Neural Network, etc. For instance, the
TEMATICS Team has been developed some algorithm and
practical for analysis and modeling of video sequences;
sparse representations, compression and interaction with
indexing; Distributed source & Joint Source-Channel
Coding, etc [38].
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[2]
Dadang Gunawan, “Interframe Coding and Wavelet
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[3]
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[4]
Dadang Gunawan & D.T. Nguyen, “Psychovisual Image
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[6]
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York, 1988.
[7]
David L McLarent, “Video and Image Coding for
broadband ISDN”, Ph.D. Thesis, University of Tasmania,
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J. C. Candy, “Transmitting television as Clusters of Frame
to frame Differences”, The Bell System Technical Journal,
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[B. G. Haskel, F. W. Mount and C. Candy, “Interframe
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Zurich Seminar on digital Communication, pp. 39 – 45,
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– University of London, 1989.
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Moving Objects from television Signals”, IEEE
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Displacements of Television Images”, IEEE Transaction
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A. N. Netravali & J. D. Robbins, “Motion Compensated
television Coding ; Part 1”, The Bell-System Technical
Journal, Vol. 58, pp. 631 – 670, Mar. 1979.
C. Cafforio & F Rocca, “The Differential method for
Motion Estimation”, Image Science Processing & Dynamic
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124, 1983.
J. D. Robbins & A. N. Netravali, “Recursive motion
compensation : A Review”, mage Science Processing &
Dynamic Scene Analysis, Springer Verlag, New York, pp.
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ISSN: 2085-6350
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526, May 1984.
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Block matching Algorithm for Motion Compensated
Coding”, Proceeding IEEE ICASSP, pp. 25.4.1 – 25.4.4,
1987.
M. Ghanbari, “The Cross Search Algorithm for Motion
Compensation”, IEEE Transaction on Communication,
Vol. 38, pp. 950 – 953, Jul. 1990.
M. Gotze & G Ocylock, “An Adaptive Interframe
Transform Coding System for Images”, proceeding IEEE
ICASSP 82, pp. 448 – 451, 1982.
J. R. Jain & A. K. Jain, “Displacement measurement & Its
Application in Interframe Image Coding”, IEEE
Dec. 1981.
J. A. Roese, W. K. Pratt & G. S. Robinson, “Interframe
Cosine Transform Image Coding, “ IEEE Transaction on
Communication, Vol. 25, pp. 1329 – 1338, Nov. 1977.
J. A. Roese, “Hybrid Transform predictive Image Coding
in Image Transmission Techniques, Academic Press, new
York, 1979.
CCITT H.261 Recommendations, “Video Codec for
Audiovisual Services at p x 64 kbps”, 1989.
G. Wallace, “The JPEG Stil Picture Compression
Standard”, Communication ACM, Vol. 34, pp. 30 – 44,
Apr. 1991.
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Multimedia Applications”, Communication ACM, Vol. 34,
pp. 46 – 58, Apr. 1991.
S. G. Mallat, “A Theory for Multiresolution Signal
Decomposition : the Wavelet Representation” IEEE
Transaction on Pattern Analysis & Machine Intelligent,
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S. G. Mallat, “Multifrequency Channel Decomposition of
Image and Wavelet Models”, IEEE Transaction on ASSP,
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O Riol & M. Vetterli, “Wavelet & Signal Processing”,
IEEE Signal Processing Magazine, Vol. 8, pp. 14 – 38,
Oct. 1991.
Y. Q. Zhang & S. Zafar, “Motion-Compensated Wavelet
Transform Coding for Color Video Compression”, IEEE
Transaction on Circuit & Systems for Video Technology,
Vol. 2, pp. 285 – 296, Sep. 1992.
S. Zafar, Y. Q. Zhang & B. Jabbari, “Multiscale Video
Representation Using Multiresolution Compensation &
Wavelet Decompostion“, IEEE Journal Selected Area in
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Project Team Tematics, “Activity Report”, INRIA, 2008.
7 Proceedings of CITEE, August 4, 2009
Analysis and Simulation Of Bus-Clamping
PWM Techniques Based On Space Vector
Approach
1
2
Ms.M.Thanuja1
Mrs. K. SreeGowri2
PG-Student, Dept. of Electrical and Electronics, RGMCET, Nandyal, India.
Assoc. Professor, Dept of Electrical and Electronics, RGMCET, Nandyal, India.
Abstract:
Conventional space vector pulse width modulation
employs conventional switching sequence, which divides
the zero vector time equally between the two zero states
in every subcycle.Existing bus-clamping PWM
techniques employ clamping sequences, which use only
one zero state in a subcycle.In the present work a new
set of BCPWM dealing with a special type of switching
sequences, termed as “double-switching
clamping
sequences”, which use only one zero state and an active
vector repeats twice in a sub cycle, will be proposed.
It is shown analytically that the proposed
BCPWM techniques result in reduced harmonic
distortion in the line currents over CSVPWM as well as
existing BCPWM techniques at high modulation indices
for given a average switching frequency. This work
deals with Analysis and Simulation of “double-switching
clamping sequences” in terms of stator flux ripple and
line current harmonic distortion. Simulation is done on
v/f
controlled
Induction
Motor
drive
in
MATLAB/SIMULINK environment.
Index Terms—Bus clamping pulse width modulation
(BCPWM),discontinuous PWM, harmonic distortion,
induction motor drives,PWM inverters, space vector
PWM, stator flux ripple, switching sequences.
v
I. INTRODUCTION:
oltage source inverter fed induction motors are widely
used in variable speed applications. The harmonic
distortion in the motor phase currents must be low for
satisfactory operation of
the
motor drive.
The
harmonic
distortion in the current is determined by the switching
frequency and PWM Technique is employed. The switching
frequency cannot be increased beyond a certain range due to
practical limitations. The distortion is reduced at a given
switching frequency by a good design of PWM Technique.
Specially designed PWM Techniques have been
reported for high power applications where the inerter
switching frequency is low. For switching frequency much
higher than the maximum fundamental frequency, several
modulation functions and frequency modulation of carrier
have been investigated. This project focuses on developing
and evaluating new real time PWM techniques for voltage
source inverters.
SPWM and CSVPWM are very popular real time
techniques. CSVPWM and THIPWM lead to higher line
side voltages for given dc bus voltage compare to SPWM.
These technique results in less harmonic distortion in motor
currents than SPWM at a given line voltage. Discontinuous
modulation methods lead to reduction in distortion at higher
line voltages over a CSVPWM for a given average
switching frequency. This paper proposes high performance
HSVPWM, which further reduce the distortion in the line
currents over comparable real-time technique at a given
average switching frequency. The superiority in
performance of proposed techniques is established
theoretically as well as experimentally.
With SPWM, CSVPWM and THIPWM, every phase
switches once in a sub-cycle or half carrier signal. This
paper explores novel switching sequence that switch ‘a’
phase twice in a sub-cycle, while switching second phase
once and clamping the third phase. This paper brings out all
such possible sequences (including two new sequences),
which results same average switching frequency as
CSVPWM for a given sampling frequency.
Real-time PWM techniques balance the reference
volt-seconds and applied volt-second over every sub cycle
or half carrier cycle. The multiplicity of possible switching
sequences provides a choice in the selection of switching
sequences in every sub cycle. The proposed hybrid PWM
techniques employ the sequence which results in the lowest
rms current ripples over given subcycle, out of given set of
sequences. Consequently the total rms current ripple over
fundamental cycle is reduced
II.SWITCHING SEQUENCES OF INVERTER Conference on Information Technology and Electrical Engineering (CITEE)
ISSN: 2085-6350 Proceedings of CITEE, August 4, 2009 8
duration for which the active state is applied in a sub cycle
must satisfy (1).
4) The total duration for which the zero vector (either using
the zero state 0 or the zero state 7) is applied in a sub cycle
must satisfy (1).
5) Only one phase must switch for a state transition.
6) The total number of switching’s in a subcycle must be
less
than or equal to three. This ensures that the average
switching frequency is less than or equal to that of
CSVPWM for a given sampling Frequency.
Fig.1. Two level inverter circuit diagram
A three-phase voltage source inverter has eight switching
states as shown in Fig. 1. The two zero states (−−− and
+++), which short the motor terminals, produce a voltage
vector of zero magnitude as shown in the figure. The other
six states, or the active states, produce an active voltage
vector
each. These active vectors divide the space vector plane into
six sectors and are of equal magnitude as shown. The
magnitudes are normalized with respect to the dc bus
voltage.
From the Volt-time balance principle T1, T2 and Tz can be
given as
T1 = Vref * Ts *
sin (60° − α )
sin (60°)
T2 = Vref * Ts *
sin (α )
sin (60°)
Tz = Ts − T1 − T2
(2)
Fig.2. Inverter states and voltage vectors of three-phase
Inverter
The six active space vectors are represented by the
following expression:
(1)
In space vector-based PWM, the voltage reference, which is
sampled once in every subcycle, TS. Given a sampled
reference vector of magnitude VREF and angle α in sector I
as shown in Fig. 2, the dwell times of active vector 1, active
vector 2 and zero vector in the subcycle are given by T1, T2,
and TZ, respectively, in CSVPWM divides TZ equally
between 0 and 7, and employs the switching sequence 0-12-7 or 7-2-1-0 in a subcycle in sector I. The conditions to be
satisfied by a valid sequence in sector I are as follows.
1) The active state 1 and the active state 2 must be applied
at least once in a subcycle.
2) Either the zero state 0 or the zero state 7 must be applied
at least once in a subcycle.
3) In case of multiple application of an active state, the total
ISSN: 2085-6350
Fig.3. Different possible switching sequences in sector I
Conference on Information Technology and Electrical Engineering (CITEE) 9 Proceedings of CITEE, August 4, 2009
Fig.4. PWM Gate signals when the reference vector
sitting in sector-I (0127)
Sequence 7212 leads to clamping of R -phase to the
positive dc bus, while sequence 0121 results in clamping of
B-phase to the negative dc bus. Both sequences result in Y phase switching twice in a sub cycle. Hence, sequences
0121 and 7212 are termed “double-switching clamping
sequences” here. The sequences illustrated in Fig. 3 are
employed in sector I. The equivalent sequences in the other
sectors are as listed in Table I. The PWM gating signals for
the CSVPWM is shown in Fig 4.
number of DPWM methods. If
δ = 0, -pi/6, -pi/3, then
DPWM1, DPWM2 and DPWM3 can be obtained
respectively. The modulation waveforms of the different
PWM methods are as shown in Fig.5. 1
TABLE I. SWITCHING SEQUENCES IN SIX SECTORS
III. MODERN PWM TECHNIQUES
The modern PWM methods can be
separated into two groups and those are:
•
•
Continuous PWM(CPWM) methods
Discontinuous PWM(DPWM) methods
In discontinuous one the modulation wave of a phase has
at least one segment which is clamped to the positive or
negative dc bus for at most a total of 1200 (over a
fundamental cycle).Where as in continuous PWM there is
no clamping in the modulation wave.
The expressions for the modulation signals are
given as
M in =
Fig.5.Modulation waveforms of the various PWM
methods
The conventional SVPWM algorithm employs equal
division of zero voltage vector times within a sampling
period or sub cycle. However, by utilizing the freedom of
zero state division, various DPWM methods can be
generated. GDPWM algorithm, which
uses the utilization of the freedom of zero state time
division. In this proposed method the zero state time will be
shared between two zero states as T0 for V0 and T7 for V7
respectively, and T0 , T7 can be expressed as:
2V in
2 µ V min 2 (µ − 1)V max
+ (1 − 2µ ) −
+
Vdc
Vdc
Vdc
i=a, b, c
(5)
(4)
The selection of µ gives rise to an infinite number of PWM
modulations. To obtain the generalized discontinuous
modulation signal, µ is given as :
μ =1-0.5[1+sgn (cos3 (ωt+δ))]
(3)
When µ = 0, any one of the phases is clamped to
positive dc bus for 1200 and then DPWMMAX is obtained.
When µ = 1, any one of the phases is clamped to negative
dc bus for 1200 and then DPWMMIN is obtained. If µ = 0.5,
then the SVPWM algorithm is obtained. Similarly, the
variation of modulation phase angle δ yields to infinite
Fig.6.Existing
clamp).
bus-clamping
PWM
technique
(300
IV. BUS-CLAMPING PWM TECHNIQUES
A popular existing bus-clamping method clamps
every phase during the middle 300 duration in every quarter
cycle of its fundamental voltage. This technique, termed as
and “300 clamp.” This employs sequences 721, 127, in the
first half, and 012, 210, in the second half of sector I as
shown in Fig. 6.
(6)
In Fig. 7(a), every phase is clamped
continually for 600 duration in every half cycle of the
fundamental voltage waveform. These techniques can be
termed “continual clamping” techniques. In Fig. 7(b), the
600 clamping duration is split into one interval of width in
the first quarter cycle and another interval of (600-γ) in the
next quarter in every half cycle. Since the clamping duration
is split into two intervals, these techniques are termed “split
clamping” PWM techniques. Fig. 8(a) and (b) present
average pole voltage waveforms that illustrate the two types
of clamping for γ= 450 .
Fig.9.Simulated Phase and line voltages waveforms of
the two level inverter
VOLTS /HZ CONTROL TECHNIQUE: Fig.7. Existing bus-clamping PWM techniques
(a) continual clamping type (b) split clamping type.
This is the most popular method of Speed control
because of simplicity. The Flux and Torque are also
function of frequency and voltage respectively the
magnitude variation of control variables only. The air gap
voltage of induction motor is
φag
=
Eag
f
≈
V
f
Speed is varied by varying the frequency; maintain v/f
constant to avoid saturation of flux. With constant v/f ratio,
motor develops a constant maximum torque.
Fig.8. Average pole voltage over a fundamental cycle for
VREF = 0.75 corresponding to (a) continual clamping
and (b) split clamping both with γ=450.
The design of the inverter phase voltage and common mode
voltages for different pulse sequences are:
ISSN: 2085-6350
INDUCTION MOTOR MODELLING:
Among the various reference frames, V/F uses the
stationary reference frame. Hence, in this work, the
induction motor model is developed in the stationary
reference frame, which is also known as Stanley reference
frame. Rotor and stator voltages and their flux linkages are
vds = Rs ids +
vqs = Rs iqs +
dψ ds
dt
dψ qs
ψ qs = Ls iqs + Lm iqr
dt
vdr = Rr idr + ω rψ qr +
vqr = Rr iqr − ω rψ dr +
dψ dr
dt
dψ qr
ψ ds = Ls ids + Lm idr
ψ qr = Lr iqr + Lm iqs
ψ dr = Lr idr + Lm ids
dt
The electromagnetic torque of the induction motor is given
by
dω m
2 dω r
= TL + J
Te = TL + J
dt
P dt
Q1 = [cos (α) −Vref] *T1 Q2 = [cos (60° − α) − Vref] *T2
QZ = −Vref*TZ
D = sin (α)*T1.
Expressions for RMS Stator Flux Ripple:
The rms Stator flux ripples different sequences employed
and their respective vector diagram of d-axis and q-axis
ripples shown in Fig 12.
[
]
1
1
2 T
2
2 T
2
F0127
= (0.5Qz ) z + (0.5Qz ) + 0.5Qz (0.5Qz +Q1) + (0.5Qz + Q1) 1 +
3
2Ts 3
Ts
[
]
1
(0.5Qz +Q1)2 −(0.5Qz +Q1)(0.5Qz ) +(−0.5Qz )2 T2 +
3
Ts
The Electromechanical equation of induction drive is given
by
1
(−0.5Qz )2 Tz + 1 D2 (T1 +T2 )
3
2Ts 3
Ts
3⎛P⎞
T e= ⎜ ⎟(ψ ds iqs −ψ qs ids )
2⎝ 2 ⎠
(7a)
[
]
1 T 1
2 T
2
F012
= Qz2 z + Qz2 + Qz (Qz + Q1 ) + (Qz + Q1 ) 1
3 Ts 3
Ts
1
1 (T + T )
2T
+ (Qz + Q1 ) 2 + D2 1 2
3
Ts 3
Ts
(7b)
[
]
1 T 1
2 T
2
= Qz2 z + Qz2 + Qz (Qz + Q2 ) + (Qz + Q2 ) 2
F721
3 Ts 3
Ts
+
Fig.10. Speed and Torque characteristics with respect to
time
1
(Qz + Q2 )2 T1 + 1 D2 (T1 + T2 )
3
Ts 3
Ts
(7c)
[
]
1 T 1
2 T
2
F7212
= Qz2 z + Qz2 + Qz (Qz + 0.5Q2 ) + (Qz + 0.5Q2 ) 2 +
3 Ts 3
2Ts
[
]
1
(Qz + 0.5Q2 )2 − (Qz + 0.5Q2 )0.5Q2 + (− 0.5Q2 )2 T1 +
Ts
3
1
(− 0.5Q2 )2 T2 + 1 (0.5D)2 (T1 + T2 )
Ts
3
2Ts 3
.
.
Fig.11. Block diagram of V/F controlled
based IM drive
2
=
F0121
BCPWM
+
V. ANALYSIS OF HARMONIC DISTORTION
The generalized stator q-axis and d-axis flux ripples are
as shown below.
(7d)
+
[
]
1 2 Tz 1 2
2 T1
+ Q z + Q z (Q z + 0.5Q1 ) + (Q z + 0.5Q1 )
Qz
3
2Ts
Ts 3
[
]
1
(Q z + 0.5Q1 )2 − (Q z + 0.5Q1 )0.5Q1 + (− 0.5Q1 )2 T2
3
Ts
1
(− 0.5Q1 )2 T1 + 1 (0.5 D )2 (T1 + T2 )
3
2Ts 3
Ts
.
(7e)
Fig. 13. Comparison of rms stator flux ripples due to
CS-0127, S1-012, S2-721, S3-0121, S4-7212, S5-1012 and
S6-2721 at different modulation indices.
A. Analysis of Existing BCPWM Techniques:
Sequence 012 leads to less RMS current ripple over
a subcycle than 721 in the first half of the sector, and vice
versa in the second half of the sector.
F012(α) < F721(α) , 00 < α < 300
(8a)
F012(α) > F721(α) , 300 < α < 600
(8b)
F012(α) = F721(600- α)
(9)
Fig.12.Stator flux ripple vector over a subcycle for
sequences (a) 0127, (b) 012, (c) 721, (d) 0121 and (e)
7212.
Fig.14.Measured no-load current waveforms at
VREF=0.85 for Existing BCPWM techniques.
ISSN: 2085-6350
techniques. The switching energy loss in a subcycle in an
inverter leg is proportional to the phase current and the
number of switchings of the phase (n) in the given subcycle.
The normalized switching energy loss per subcycle (ESUB)
in an inverter leg is defined in (1a), where i1 is the
fundamental phase current, Im is the peak phase
fundamental current and Φ is the line-side power factor
angle. Table II Measured Values of ITHD for Existing
BCPWM
No-Load Current THD
γ=300
γ =450
continual clamping
5.32%
4.72%
Split Clamping
4.61%
5.01%
E SUB =
n i1
Im
= n sin (ωt − Φ )
(12a) Π
E SUB (AV ) =
1
E SUB dωt
Π ∫0
(12b)
Fig.15.Measured no-load current waveforms
VREF=0.85 for Proposed BCPWM techniques.
at
Fig.16. Variation of normalized switching loss ESUB over
a fundamental cycle for CSVPWM.
B. Analysis of Proposed BCPWM Techniques:
F0121(α) < F7212(α) , 00 < α < 300
(10a)
F0121(α) > F7212(α) , 300 < α < 600
(10b)
F0121(α) = F7212(600- α)
(11)
Table III Measured Values of ITHD for Proposed
BCPWM
No-Load Current THD
γ=300
γ=450
continual clamping
3.91%
3.46%
Split Clamping
2.66%
3.04%
VI INVERTER SWITCHING LOSSES
This section presents a comparison of inverter
switching losses due to CSVPWM, existing BCPWM
(a)
(b)
(c)
(d)
Fig. 17. Variation of normalized switching loss ESUB over
a fundamental cycle for Existing BCPWM techniques.
Average Switching Loss for CSVPWM = 0.6366
Table IV Measured Values of Average Switching Loss
Average Switching Loss
Φ=00
Φ =900
(a) 0.318
----
-----
(b) 0.4776
(c) 0.4041
(d) 0.4041
continual clamping
(i) γ=300
(ii)
γ=600
Split Clamping
γ=300
VI. CONCLUSIONS
A class of bus-clamping PWM (BCPWM)
techniques, which employ only the double-switching
clamping sequences, is proposed. The proposed BCPWM
techniques are studied, and are compared against
conventional space vector PWM (CSVPWM) and existing
BCPWM techniques at a given average switching
frequency. The proposed families of BCPWM techniques
result in less line current distortion than CSVPWM and the
existing BCPWM techniques at high line voltages close to
the highest line side voltage during linear modulation. The
analysis presented explains the difference in distortion due
to the different techniques. The study classifies both the
existing BCPWM and the proposed BCPWM techniques
into two categories, namely continual clamping methods
and split clamping methods, depending on the type of
clamping adopted. It is shown that split clamping methods
are better than continual clamping ones in terms of line
current distortion. In terms of switching losses, continual
clamping is better at high power factors, while split
clamping is superior at low power factors. REFERENCES
[1]“Advanced Bus-Clamping PWM Techniques Based on
Space Vector Approach” G. Narayanan, Member, IEEE,
ISSN: 2085-6350
Harish K. Krishnamurthy, Di Zhao, and Rajapandian
Ayyanar, Member, IEEE,2006.
[2] J. Holtz, “Pulse width modulation—A survey,” IEEE
Trans Ind. Electron., vol. 39, no. 5, pp. 410–420, Dec.
1992.
[3] J. Holtz, “Pulse width modulation for electronic power
conversion,” Proc. IEEE, vol. 82, no. 8, pp. 1194–1214,
Aug. 1994.
[4] D. G. Holmes and T. A. Lipo, Pulse Width Modulation
for Power Converters: Principle and Practice. New York:
Wiley, 2003.
[5] V. Blasko, “Analysis of a hybrid PWM based on
modified space-vector and triangle-comparison methods,”
IEEE Trans.
Ind. Appl., vol. 33, no. 3, pp. 756–764,
May/Jun. 1997.
[6] D. Zhao, G. Narayanan, and R. Ayyanar, “Switching
loss characteristics of sequences involving active state
division space vector based PWM,” in Proc. IEEE
APEC’04, 2004, pp. 479–485.
ACKNOWLEDGEMENT
We express our sincere thanks to TEQIP for providing us good
lab facilities. A heart full and sincere gratitude to our beloved
parents for their tremendous motivation and moral support.
M.Thanuja was born in Nandyal. She has completed her
B.Tech at R.G.M. College of Engg and Techng. affiliated to
J.N.T.U. Hyderabad in 2002. She is currently doing M.Tech
at RGMCET,Nandyal, affiliated to J.N.T.U. Anantapur. She
is working in areas power electronics and Power electronics
control of ac drives.
E-mail: [email protected]
K.SreeGowri received the B.Tech degree from SVU
College of Engineering, Tirupati in 1997, the M.Tech
degree from RGM College of Engineering and Technology,
Nandyal and is currently pursuing the Ph.D. in Electrical
Engineering Department, JNTU, and Hyderabad. She is
currently an Associate Professor in the Department of EEE
in RGMCET, Nandyal, A.P. Her areas of interest include
Power Electronics, pulse width modulation techniques, AC
Drives and Control.
Conference on Information Technology and Electrical Engineering (CITEE) Proceedings of CITEE, August 4, 2009
15
Medical Image Processing of Proximal Femur
X-Ray for Osteoporosis Detection
Riandini, Mera Kartika Delimayanti
Politeknik Negeri Jakarta, Electrical Engineering Department
Kampus UI Depok, Indonesia 16424
[email protected]
Donny Danudirdjo
Institut Teknologi Bandung
Jl. Ganesha 10 Bandung, Indonesia 40132
Abstract— Osteoporosis is one of the generative (aging
process) diseases which occurs because of the rise of one’s life
expectancy from the age of 60s to 74s or more. This disease
attacks not only women but also men. The worst impact of it is
death. Although it is very important to detect it very early to
avoid the worst impact of it, an early effective process of
osteoporosis detection is clinically difficult and costly to do.
The only method of doing so which is relatively cheap is the
analysis of x-ray image (radiology) instead of DEXA (Dual
Energi X-Ray Absorptiometri) which long has been used as a
golden standard. But, this process may lead to subjectivity
since doctors (medical staffs) do so with naked eyes. This paper
describes a series of algorithm plans for medical image
analysis with the support of Femur Proximal x-ray image as
data input. Algorithm plans which are going to be developed
are a combination of Gabor algorithm, Wavelet algorithm,
Fractal algorithm and algorithm (analysis) mainly used to
enhance the quality of an image through the omission of
vertical lines artefact on the image, edge detection on the
image and image rotation. The outcome of the plan is going to
be implemented within GUI-based (Graphical User Interface)
computer application which is user-friendly and usable for
helping doctors diagnose osteoporosis.
Keywords—medical image analysis, x-ray image, proximal
femur, osteoporosis, algoritma.
I. INTRODUCTION
Osteoporosis is a degenerative disease that takes place in
many places around the world. The high prevalence of
osteoporosis in Indonesia is inseparable from the rise of life
expectancy, from 59.8 in 1990 up to 67 at present. An
effective standard instrument largely used nowadays to
detect osteoporosis is Dual Energy X-Ray Absorptiometri
(DEXA). A significant shortcoming of the instrument is that
it is very expensive and the number of it is limited not only
at rural areas but also in big cities over Indonesia. Besides,
the operational expenditure of the tool is totally high. One
alternate solution to the problem is the implementation of xray radiology image detecting instrument. However, it still
brings two primary weaknesses: (1). Data processing
subjectivity – a physician analyzes and interprets the same
data several times with different results; (2) Data processing
with the naked eye may lead to ignorance of detailed
information on a radiology image. In order to deal with
those weaknesses, this paper is going to describe a system
of medical image processing algorithm with a purpose of
assisting doctors for early detection of osteoporosis. The
system applies femur proximal x-ray image in its operation.
The algorithm operation of image texture analysis involves
determination of optimum textural features of the image and
the supporting classification methods of the image texture.
The system used in the operation is then used by physicians
to assist them diagnose osteoporosis.
II. OSTEOPOROSIS
Osteoporosis is a situation when there is an over-limit
decrease of bone density and structural malfunction of bone
micro-structure system that makes the bone system unable
to prevent bone fracture from minimal trauma. The decrease
of bone mass occurs because the velocity of bone formation
process by osteoblas cell is not capable of equalizing the
velocity of bone surface erodibility by osteoklas cell.
Histopalogically, osteoporosis indicates the decrease of
cortex density and the declining amount as well as size of
bone trabucela [3].
Osteoporosis is suffered much more by women than men
in that about 50 percent of menopause women are suffering
from osteoporosis. However, recent research has shown that
it is no longer dominated by old-aged people. Youngsters
are likely to get suffered from it if they don’t have
appropriate eating habit [6].
The number of osteoporosis cases in Indonesia is quite
high. Prevalence of it has reached 19.7 percent. This
percentage is believed to keep increasing due to the rising
life expectancy and the improvement of the quality of
people’s health. Osteoporosis is categorized as a
degenerative disease which is commonly suffered by old
people and may lead to physical disability and death. Thus,
the early prevention of it is very crucial.
Efforts to prevent osteoporosis are closely related to the
early bone strength diagnosis and treatment. The standard
method of diagnosing the bone density is well known as
DEXA (Dual Energy X-Ray Absorptiometry). In Indonesia,
the application of the method faces two problems: (1) fairly
expensive diagnosis and treatment, and (2) lack of medical
instruments supporting the diagnosis process within some
hospitals in big cities of the country. An alternate method to
cope with the problem, which is aimed at doing trabekula
style treatment, is Singh Index.
ISSN: 2085-6350
16
Fig 2.1. illustrates the way of determining Singh Index on
femur x-ray image
Index value determination is commonly done by an
orthopedic specialist. The outcome of the index value is
strongly influenced by a specialist expertise and inseparable
from his/her subjectivity. If he/she is doing the evaluation at
two distinct times, he/she may come up with different
outcomes. Therefore, the presence of the diagnosing system
of trabekula texture of the femur x-ray image is in urgent
need to help doctors do early osteoporosis diagnosis and
detection.
III. TEXTURE ANALYSIS Texture is a two dimension intrinsic character of an image
correlated with the level of roughness, granularity, and the
regularity of pixel arrangement. Texture analysis runs
through observing neighboring model within pixels in a
spatial domain and is related to the need of the image
characters extraction. In this research, an observation of
textural characteristics of x-ray radiology image (connected
to level of osteoporosis) is carried out with the support of
four methods: co-occurrence analysis, Gabor filter, wavelet
transform, and fractal analysis. These four methods are used
in an attempt to obtain the most optimum method and result
in an accurate diagnosis outcome. There are eight image
characters used in the research, and in each method involves
at least one character. Character classification process
works through the support of artificial neuron system with
an outcome of Singh index value of the radiology image
that is observed.
A.
Cooccurrence Analysis Co-occurrence analysis is the simplest analyzing
method of the image texture. This analysis is completed
ISSN: 2085-6350
through two stages: (1) establishing co-occurrence matrix as
a representation of the probability of neighboring
interaction between two pixels in certain distance (d) and
angle orientation (θ), (2) determining character as a function
of the co-occurrence matrix.
Co-occurrence is two concurrent events, i.e., the
number of the occurrence of one level pixel value as a
neighbor of another one level pixel value in certain distance
(d) and angle orientation (θ). Distance is valued in Pixel and
orientation in Degree. There are four angle orientations with
45° angle interval, which are 0°, 45°, 90°, and 135°. On the
other hand, distance within pixels is valued 1 pixel.
Co-occurrence matrix is a square matrix with the
number of elements equal to a square of the number of pixel
intensity on an image. Each spot (p, q) within a cooccurrence matrix oriented to θ has a possibility of pixel
occurrence with value amounting p that is neighboring with
another pixel with the value amounting q in distance d and
angle orientation θ and (180−θ). Figure 3.1 illustrates the
formation of a co-occurrence matrix.
With the outcomes of matrix co-occurrence above, we
can determine statistical character of orde 2 that represents
an observed image. Haralick proposed a number of textural
characters which are likely to be extracted from a matrix cooccurrence [1]. In this research, six textural characters of an
image are used: Angular Second Moment, Contrast,
Correlation, Variance, Inverse Difference Moment, and
Entropy.
1.) Angular Second Moment (ASM) This character is used to show the level of homogeneity of
an image
(1)
{p(i, j )}2
ASM =
∑∑
i
j
Where p(i,j) represents values of row i and column j on a
co-occurrence matrix.
2.) Contrast (CON) This character is used to show the spread (moment of
inertia) of all elements on an image matrix. If the spread
(location) is located far away from the main diagonal line
on the matrix, then it has a big contrast value. Visually,
contrast value is the level of greyness variedness on an
image.
⎡
⎤
CON = ∑ k 2 ⎢∑∑ p (i, j )⎥
k
⎣ i j
⎦
(2)
i− j = k
17
ENT2 = −∑∑ p (i, j ) ⋅ 2 log p (i, j )
i
(6)
j
B. Gabor Filter Human’s visualizing ability system to distinguish
different image textures is based on the ability to identify a
variety of spatial frequencies and orientations of an image’s
textures observed. Gabor Filter is one of the filters that can
represent human’s visualizing ability system in isolating
spatial frequencies and orientations of an image’s textures.
This character enables Gabor Filter to be used as an
application of an image texture observation [4].
Spatially, Gabor function is sinusoida function which is
modulated by Gaussian function. An impulse response of a
two-dimension complex Gabor filter is shown below:
⎧⎪ 1 ⎡ x 2 y 2 ⎤ ⎫⎪
(7)
1
h( x, y) =
Fig 3.1.Illustration of the matrix co-occurrence formation on an
image: (a) image input (b) intensity value of an image input (c) outcome of
0° co-occurrence matrix (d) outcome of 45° co-occurrence matrix (e)
outcome of 90° co-occurrence matrix (f) outcome of 135° co-occurrence
matrix
2πσ xσ y
exp⎨− ⎢ 2 + 2 ⎥ ⎬ exp( j 2πFx)
⎪⎩ 2 ⎢⎣σ x σ y ⎥⎦ ⎪⎭
Illustration of a Gabor filter within a spatial domain is
presented in Figure 3.2.
3.) Correlation (COR)
Correlation is used to show the level of linear interdependence of greyness on an image and thus indicates the
presence of linear structure on the image.
COR =
∑∑ (ij ). p(i, j ) − µ
i
x
µy
j
σ xσ y
Fig 3.2. Impulse Response of a two-dimension Gabor Filter
(3)
4.) Variance (VAR)
Variance is used to present variedness of elements of a cooccurrence matrix. An image with low degree of greyness
has a small value of variedness.
(4)
VAR =
(i − µ x )( j − µ y ) p(i, j )
∑∑
i
j
Within a spatial frequency domain, a Gabor filter is
represented
as
shown
below:
H (u, v) = exp{− 2π 2 (u − F ) 2 σ x2 + v 2σ y2 }
(8)
[
]
Illustration of a Gabor filter within a spatial frequency
domain is presented in Figure 3.3
5.) Inverse Different Moment (IDM)
Inverse Different Moment (IDM) is mainly used to show
homogeneity of an image with certain similar degree of
greyness. A homogeneous image will have a bigger IDM
value than a heterogonous image.
IDM = ∑∑
i
j
1
p(i, j )
1 + (i − j ) 2
(5)
6.) Entropy (ENT)
Entropy is used to show the level of shape irregularity. An
image with a big ENT value is the one that has even degree
of greyness; meanwhile, the image with a small ENT value
has uneven degree of greyness (varied degree of greyness).
Fig 3.3. Parameter of a Gabor Filter within a Spatial Frequency Domain
There are six parameters that must be present in the
implementation of a Gabor filter. They are F, θ, σx, σy, BF,
and Bθ .
ISSN: 2085-6350
18
•
Frequency (F) and orientation (θ) define the
location of a filter center
•
BF and Bθ represent constants of the width of a
frequency tape and the angular range of a filter
•
Variabel σx is closely related to a response of -6 dB
for a spatial frequency component
•
Variabel σy is closely connected with a response of
-6dB for an angular component
σy =
•
ln 2
2πF tan( Bθ / 2)
Value used
Symbol
research
in
the
2 2 2 2 2
,
,
,
,
21 2 2 2 3 2 4 2 5
1 octave
Angular tape width
Bθ
45°
Frequency Spacing
SF
1 oktaf
Angular Spacing
Sθ
45°
Orientation
Θ
0°, 45°, 90°, 135°
N
∑∑ x(m, n)
2
k
⎣
J +1,k
J +1,k
∑
j =0
j +1,k
j +1,k
⎥
⎦
In which coefficient c0,k has been defined already and
coefficients cj+1,n and dj+1,n at scale j+1 are related to
coefficient cj,k at scale j through a formula:
c j +1,n = ∑ c j ,k h(k − 2n )
(14a)
d j +1,n = ∑ d j ,k g (k − 2n)
(14b)
k
In which equation 0 ≤ j ≤ J applies.
The main wavelet is established by scale function φ(t)
and wavelet ψ(t) which comply with two-scale relation as
shown in a below formula:
(15a)
φ ( x ) = 2 ∑ h(k ) φ (2 x − k )
ψ ( x ) = 2 ∑ g (k ) φ (2 x − k )
(15b)
k
With requirement:
k
g (k ) = (− 1) h(1 − k )
(10)
i =1 j =1
This research uses frequency tape width (BF) and middle
frequency distance (SF) of one octave, and angular tape
width (Bθ) and angular distance (Sθ) of 45°. These values are
highly likely to reflect characteristics of human’s
visualizing system.
C. Wavelet Transform Textural analysis with wavelet transform works through
decomposition of image input, used to observe the content
of image information within several sub-bands.
Decomposition process applying discrete wavelet transform
(DWT) is conducted in stages, each of which will result in 4
sub-band matrix – one matrix of approximation coefficient
A and three detailed matrix (horizontal H, vertical V, and
diagonal D). If the process is conducted in a narrower range
of frequencies, then a matrix of approximation coefficient A
is likely to be re-decomposed into 4 smaller sub-band
matrix [5].
ISSN: 2085-6350
∑⎢
0,k 0,k
k
After Gabor character is obtained, then extraction of energy
character is likely to carry out. This is defined as follow:
M
(12)
k
BF
1
MN
∑
k
Frequency tape width
E ( x) =
1 ⎛ x−b⎞
ψ⎜
⎟
a ⎝ a ⎠
ψ a ,b ( x ) =
0
Table 3.1. Six Parameters of a Gabor Filter
Middle
frequency
F
(normalized)
Where wavelet ψa,b is counted from the main wavelet
through translation and dilation
Wavelet decomposition at level J is written below
J
⎡
⎤ (13)
f (x) = c φ (x) = c φ (x) + d ψ (x)
(9)
Position (F, θ) and tape width (σx, σy) of a Gabor
filter within a frequency domain have to be
determined accurately to obtain correct textural
information. A middle frequency of a canal filter
must be close to a frequency of an image texture
characteristic.
Parameter
Continual wavelet transform for signal 1-D f(x) is
obviously defines as follow:
(11)
Wa f (b ) = ∫ f ( x )ψ a*,b ( x )dx
(15c)
Coefficient h(k) is one of the main wavelet functions
like Haar, Daubechies, Coiflets, Symlets, or Biorthogonal.
Practically, discrete wavelet transform is calculated by
applying a bank filter inseparable from function f(x) or
image function I(x).
Ln (bi , b j ) = [ H x * [ H y * Ln−1 ]↓2,1 ]↓1, 2 (bi , b j ) (16a)
Dn1 (bi , b j ) = [ H x * [G y * Ln −1 ]↓ 2,1 ]↓1, 2 (bi , b j ) (16b)
Dn 2 (bi , b j ) = [G x * [ H y * Ln−1 ]↓2,1 ]↓1, 2 (bi , b j ) (16c)
Dn3 (bi , b j ) = [G x * [G y * Ln −1 ]↓2,1 ]↓1, 2 (bi , b j ) (16d)
In which an asterisk * is a convolution operator, while ↓2,1
(↓1,2) is a sub-sampling operator along a row (or column)
and L0 = I(x) defines a genuine image. H and G are low pass
and band-pass h(k) and g(k)) filters on a horizontal or
vertical angle.
Ln is obtained from filtration of low-pass by h(k) and thus
regarded as an approximation image at scale n. Dni is
obtained from filtration of band-pass by g(k) on certain
angle and therefore contains detailed angle information at
scale n. It is then defined as a detailed image. A genuine
image I is represented by a group of sub-images at various
scales: {Ld, Dni}i=1,2,3; n=1..d which represents a compilation
of multi-scale sub-images with certain depth d from an
image I.
19
Types of main wavelet applied in this research include
Daubechies, Coiflets, and Symlets, and the character used is
normalized energy character of a sub-image Dni defined as
the below formula:
E ni =
1
∑ ( Dni (b j , bk )) 2
N j ,k
(17)
With a condition that N represents the total number of
wavelet coefficients of a sub-image Dni. Physically, this
wavelet energy character shows the spread of energy along
frequency axis towards scale and orientation.
D. Fractal Analysis Fractal geometry is a concept to explain geometrical
dimensions of objects that have scaling features/characters
or self-resemblance. This is frequently found on natural
object as well as human body. Different from Euclidean
geometry, fractal geometry is recognized for the value of
object dimension in fraction. In Euclidean geometry
concept, the smaller the scale within the measuring
instrument is used, the more precise the measurement
process of object dimension will be. In contrast, this
condition doesn’t apply to fractal objects. Therefore, it is
necessary to have fractal analysis methods. In this research,
two fractal analysis methods are used to obtain information
about bone textural characters: semi-variance method and
the method with Fourier transform approach [2].
1.) Semi variance
Bone texture analysis with semi-variance technique is
implemented on the basis of the analysis of neighboring
correlation among pixels in certain orientation. In this
technique, the value of semi-variance SV (θ, h) is calculated
from the sum of the subtractions of one pixel intensity and
its neighboring intensity on an image in distance h and
orientation θ. The orientation values commonly used in
most research are both 0° and 90° as shown below
SV (θ , h) =
1
2 (18)
∑(I (xi , y) − I (xi − h cosθ , y − h sinθ ))
2N N
According to the above formula, exponent Hurst is
determined by the observation of coefficient modulus of
Fourier transform from input signals and the observation of
dense curve gradient of spectral power on logs scale
towards signal frequency. Fractal analysis is done through
the observation of spectral power S(u,v) as
(21)
S 2 (u , v ) = F (u , v ) F * (u , v )
The value of spectral power obtained is subsequently
averaged on all angular orientations and this results in
S(|ω|). Signal dimension is determined from logarithm curve
slope S(|ω|) towards logarithm |ω|
(22)
D SA = 2 − slope
IV. EXPERIMENT
Experiment data is obtained as 37 x-ray film images
collected through taking images of 37 women with Singh
index of 2-5. The images are taken with analog x-ray
instrument, with output of film (radiograph). Each film is
then scanned to be digital data with scanner of 400 dpi
resolution and 8 bit intensity depth. Afterwards, digital data
achieved is processed in a set of the following stages: (1)
determination of RoI, (2) enhancement of image quality, (3)
image character extraction, and (4) classification. The result
of classification is eventually compared with the diagnosis
result carried out by a radiologist before.
A. RoI Determination
The process of RoI (Region of Interest) determination is
aimed at obtaining particular observation areas within
human body which are suitable for osteoporosis
interpretation. One of the areas commonly observed by
doctors to diagnose osteoporosis is Ward triangle. In this
research, the observation area is a square area of 256 × 256
pixels, including Ward triangle. Physically, this size
represents a square with length of 1.6256 cm each. RoI area
used in this research is illustrated in Figure 4.1.
In which N defines the size of an image influenced by the
number of pixels within it.
The calculation of fractal dimension value DSV(θ) from
the slope of logarithm curve SV(θ,h) toward logarithm h is
reflected by the following formula
DSV (θ ) =
4 − slope
2
(19)
2.) Fourier Transform
One of the most popular fractal signals is a signal fBm
(fractional Brownian motion). Signal fBm is largely
recognized because it is non-stationary, self-similar,
derivative-less, and represented by a scaling parameter
called Hurst exponent. Signal fBm is regarded having Hurst
exponent with the value H, in which the equation 0 < H < 1
applies, if the spectral density of the signal power complies
with the following formula [12]
Sx ~
1
f
2 H +1
(20)
Fig 4.1 llustration of RoI area used in the research
For easy use and interface purposes, RoI determination
is carried out in two stages:
• In stage one, users are required to push the central
point of RoI square.
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20
•
In stage two, users are obligated to decide the
angular point of a square, which is the point
directing to body axis (proximal direction).
Figure 4.2. shows illustration of RoI determination process
within the program created
Input and output of image quality enhancement processing
are illustrated in Figure 4.4. below:
Fig 4.4. Image Quality Enhancement Processing:
Input (left) and Output (Right)
C.
Fig 4.2. Illustration of RoI determination process
B. Image Quality Enhancement The second stage of digital data processing is image
quality enhancement. This process is implemented through
two important methods (median filtering and contrast
stretching) toward a RoI image of 256 × 256 pixels. Median
filtering is intended to reduce noise disturbance to the
image, while contrast stretching to standardize image
intensity. The former method is used with the support of
checking window of 5 × 5 pixels, and the latter one runs by
following the below equation:
I ′( x, y ) =
I ( x, y ) − µ (I ( x, y ))
× 95 + 160 (23)
max (I ( x, y )) − µ (I ( x, y ))
The above equation defines that I’ is pixel intensity that has
been processed and I initial intensity. Operators µ and max
refer to average and maximum operators. Number 160
represents expected average intensity for RoI which has
been processed; meanwhile, number 95 is the dynamic
intensity as a result of substraction of the value µ and the
highest intensity value within RoI. Both numbers are
achieved on the basis of a series of image quality
enhancement experiments towards 111 RoI areas used in the
research. Figure 4.3. illustrates the change of histogram
shape in the process of image quality enhancement
Fig 4.3 Change of intensity histogram at RoI area in contrast stretching,
before (left) and after (right)
ISSN: 2085-6350
Character Extraction Character extraction process is an integrated part of
trabekula textural analysis. This process is applied to an
image of which the quality has been enhanced. With the
four methods described previously, the research comes up
with 175 textural characters:
• Co-occurrence analysis: 6 features
•
Gabor filter: 20 characters, achieved by combining
4 orientations with 5 middle frequency values
(normalized)
•
Wavelet transform: 104 characters as a
combination of 2 coefficients (approximation and
detail), 13 kinds of the main wavelet, and 4
observation levels
•
Fractal analysis: 45 characters, obtained from a
combination of 2 methods (semi-variance and
fourier methods), each of which is enriched with
30 and 15 resolutions.
D. Classification Classification process is carried out with the help of
artificial neural system, exercised with the use of exercising
data in the form of 111 RoI images. Testing is done toward
740 testing sampels. Before classification process is
conducted, selection of certain characters significantly
correspondent with Singh index is done. By the selection,
there are 8 characters used in the research:
1. Level 3 approximation image energy in wavelet
transform with the use of the main wavelet Symlets
of orde 4.
2. Level 3 approximation image energy in wavelet
transform with the use of the main wavelet Symlets
of orde 5.
3. Combined fractal dimension in semi-variance
method with the experiment value of 50 pixels.
4. Vertical fractal dimension in semi-variance method
with the experiment value of 50 pixels.
5. Horizontal fractal dimension in Fourier analysis
method with the experiment value of 12 Fourier
coefficients.
6. Image energy in Gabor filter with the orientation of
135° and normalized middle frequency F = 2−4.5.
21
7.
Level 2 approximation image energy in wavelet
transform with the use of the main wavelet
Symlets of orde 3.
8. Inverse Different Moment (IDM) in co-occurrence
analysis.
The exercise of the artificial neural system is run using
propagation algorithm of Levenberg Marquardt with the
use of Matlab software. The structure of the artificial neural
system is composed with 3 layers. The first layer (input
layer) consists of 8 neurons; the second layer 12 neurons;
and the third layer (output layer) 6 neurons (equal to the
number of grades within Singh index).
V. RESULT Testing of 740 data samples (i.e., RoIs which have been
determined before as testing samples) results in textural
character classifications as shown in Table 5.1.
Table 5.1. Result of Singh Index Classification with the help of the
artificial neural system
Results of neural system diagnosis
by radiologists
Results of neural system diagnosis
In summary, the classification process ran quite well in
that 96,62 % of testing data (715) was classified properly;
3,11 % (23) with diagnosis error of one grade Singh index;
and 0,27% (2) with diagnosis error of 2 grade Singh index.
VI.
CONCLUSION This research succeeded in creating initial construction
and implementation of trabekula textural analysis method of
femur x-ray image, meaning that it was successful in
helping doctors do early osteoporosis detection. In this
research, a series of experiments and analyses were
conducted using various methods of image analysis in an
effort to identify trabekula textural characters in femur
proximal bone correlated with osteoporosis detection.
Image analysis methods were a combination of cooccurrence technique, Gabor filter, wavelet transform, and
fractal analysis. The implementation and testing of the
neural system resulted in system potentials that might help
early osteoporosis detection process now that 96,62% of
data samples were accuraely detected (similar to the result
of diagnosis by doctors/radiologists).
1
2
3
4
5
6
[1]
1
0
0
0
0
0
0
[2]
2
0
39
1
0
0
0
3
0
0
111
7
2
0
4
0
0
0
345
15
0
5
0
0
0
13
207
0
6
0
0
0
0
0
0
[3]
[4]
[5]
[6]
REFERENCES R.M. Haralick, K. Shanmugam, I. Dinstein, Textural Features for
Image Classification, IEEE Transactions on Systems, Man, and
Cybernetics, Vol.SMC-3, No.6, pp.610-621, 1973.
J.C. Lin, S. Grampp, T. Link, M. Kothari, D.C. Newitt, D. Felsenberg, S.
Majumdar, Fractal Analysis of Proximal Femur Radiographs:
Correlation with Biomechanical Properties and Bone Mineral
Density, Osteoporos Int, 9:516-524, 1999.
B. Sankaran, Osteoporosis, Mumbai: Novelty Printers, 2000.
---------, Modul Praktikum Analisis Tekstur, Imaging & Image
Processing Research Group, Institut Teknologi Bandung
T.R. Mengko, J.T. Pramudito, Texture Analysis of Proximal Femur
Radiographs for Osteoporosis Assessment, WSEAS Transaction on
Computers, Vol.3, No.1, 2004
S.W. Simson, M. Hidayat, Bone Architecture, Matrix Property and
Fracture Risks, Pertemuan Ilmiah Tahunan Perhimpunan
Osteoporosis Indonesia (PEROSI), 2007
ISSN: 2085-6350
22
Optical Communication System on Microcell
Networks in Urban Area
Department of Electrical Engineering, Faculty of Engineering, Universitas Indonesia
Depok, Jawa Barat 16424, Indonesia
Email : [email protected]
HP : 0813-81153343
Abstract— The free-space optical communication (FSOC)
system is a new kind of technological engineering based on
the existing fiber-optic technology to obtain a breakthrough
technology which is wireless, flexible, less expensive and
large
bandwidth
capacity.
Free-space
optical
communications is a line-of-sight (LOS) technology that
transmits a modulated beam of visible or infrared light
through the atmosphere for broadband communications.
The entire components used in this optical free-space
communication system are taken and modified from the
existing fiber-optics system. Similar to fiber optical
communications, free-space optics uses a light emitting
diode (LED) or laser semiconductor diode source for data
transmission. Free-space optical communications offer data
rates comparable to fiber optical communications at a
fraction of the deployment cost of microwave or fiber-optics
links. The other advantages are flexible installation and
extremely narrow laser beam widths provide no limit to the
number of free-space optical links that may be installed in a
given location. Thus advantages are justified and suitable to
be implemented on microcell networks in urban areas.
The fundamental limitation of free-space optical
communications arises from the atmosphere through which
it propagates. Free-space optical communication systems
can be severely affected by fog and atmospheric turbulence.
The main design challenges in the free-space optical
communications are solving to overcome the effects of thus
fog, scintillation and beam wander.
In this paper, the authors elaborate optical free-space
propagation parameters such as attenuation, polarization
effect, Rayleigh scattering and possible fading. Further, the
mathematical model is introduced for each component and
path of the communication system. The model will be used
to design the optimal optical free-space communication
system implemented on microcell networks in urban area.
Keywords—free-space
optical communication, atmospheric propagation, atmospheric turbulence, spatial
diversity reception
I. INTRODUCTION
Free-space optical communications (FSOC) is a lineof-sight (LOS) technology where a modulated beam of
visible or infrared light is transmitted through the
atmosphere for broadband communications [1]. Similar to
fiber optical communications, free-space optical
ISSN: 2085-6350
communication uses a light emitting diode (LED) or laser
source for data transmission [2]. The beam is collimated and
transmitted through space or atmosphere rather than being
guided through a fiber-optics cable. Free-space optical
communications offer data rates comparable to fiber optical
communications at a fraction of the deployment cost of
microwave or fiber-optics links. The other advantages of
this technology are flexible installation and extremely
narrow laser beam widths provide no limit to the number of
free-space optical links that may be installed in a given
location. Thus advantages are justified and suitable to be
implemented on microcell networks in urban area.
At this time, this technology can be installed licensefree worldwide, can be installed in less than a day. This
line-of-sight technology approach uses invisible beams of
light to provide optical bandwidth connections [3]. It's
capable of sending up to 1.25 Gbps of data, voice, and video
communications simultaneously through the air — enabling
fiber-optic connectivity without requiring physical fiberoptic cable. This optical connectivity doesn't require
expensive fiber-optic cable or securing spectrum licenses
for radio frequency (RF) solutions. While fiber-optic
communications gained worldwide acceptance in the
telecommunications industry, FSOC is still considered
relatively new. This FSOC technology approach has a
number of advantages:
1.
2.
3.
4.
5.
Requires no RF spectrum license.
Easy to upgrade, and its open interfaces support
equipment from a variety of vendors.
It is immune to radio frequency interference or
saturation.
It can be deployed behind windows, eliminating the
need for costly antenna building such as for RF
technology.
No fiber-optic cables deployment required.
A. Applications:
FSOC technology can be applied for two different
implementations. The first are applications for space
communications between satellites, spacecrafts and deepspace between planets. The second are applications for
terrestrial communications, similar to point-to-point or lineof-sight in microwave communication technology. For
space communications, there are some facts that free-space
optical communications will enable space missions to return
10 to 100 times more data with 1% of the antenna area of
current state-of-the-art communications systems, while
utilizing less mass and power. Data can also be exchanged
between a more remote spacecraft and a station on /or near
Earth. For example, planetary probes can generate a lot of
image data, and a major challenge is to send large amount
of data back to Earth. Until recently, radio links operating
e.g. in the X band or Ka band were the only available
technology. The basic advantage of the optical technology
over radio links is that the much shorter wavelength allows
for a much more directional sending and receiving of
information. In technical terms, the antenna gain can be
much higher. This is particularly important for bridging
interplanetary distances. In the future FSOC will become
the fundamental communication technology for deep-space
communications.
The second are applications for terrestrial
communications and omni-wireless, such as point-to-point
communications between base-stations in cellular networks
and omni Wi-Fi Local Area Networks (LANs). When the
traffics increase, the cellular networks need to migrate
become microcellular networks where the cell areas is about
less than 1-km2. The number of cellular base-stations
(BTSs) increases in large number, the consequence is to
deploy the communication channels between BTSs. Fiberoptics deployments are not flexible and very expensive for
short distance between BTSs, while point-to-point
microwaves do not give a good large bandwidth. The
answer is FSOC, which gives us flexibility such as point-topoint microwaves, large bandwidth, easy deployment, not
heavy and inexpensive investment. Applications for
terrestrial are more challenging than applications for space
communications, because terrestrial applications facing the
effects of atmospheric turbulence.
The fundamental limitation of free-space optical
communications for terrestrial links arises from the
atmospheric medium through which it propagates. Freespace optical communication systems can be severely
affected by fog and atmospheric turbulence [4]. Fog is vapor
composed of water droplets, which are only a few hundred
microns in diameter but can modify light characteristics or
completely hinder the passage of light through a
combination of absorption, scattering, and reflection. This
can lead to a decrease in the power density of the
transmitted beam, decreasing the effective distance of a
free-space optical link. The atmospheric turbulence is
mostly due to wind and temperature gradient, causes the
index of refraction gradient by time and space. This
refractive index gradient deflects and diverge the light
beam, while the turbulence-wind will cause the light beam
wandering.
The items that limit the FSOC performance, then listed
in the following systematical parameters:
1) Absorpsion occurs when suspended water molecules in
the terrestrial atmosphere absorb photons. This causes a
decrease in the power density (attenuation) of the FSO
beam and directly affects the availability of a system.
Absorption occurs more readily at some wavelengths
23
2)
3)
4)
5)
than others. However, the use of appropriate power,
based on atmospheric conditions, and use of spatial
diversity (multiple beams within an FSO-based unit)
helps maintain the required level of network
availability.
Scattering is caused when the wavelength collides with
the scattering-particles. The physical size of the
scattering-particles determines the type of scattering.
When the particles are smaller than the wavelength, this
is known as Rayleigh scattering. When the particles are
of comparable size to the wavelength, this is known as
Mie scattering. When the particles are much larger than
the wavelength, this is known as non-selective
scattering. In scattering — unlike absorption — there is
no loss of energy, only a directional redistribution of
energy that may have significant reduction in beam
intensity for longer distances.
Polarization-rotation is caused by water molecules in
the terrestrial atmosphere act like dipole-molecules
with arbitrary orientation. Statistically it change the
polarization of the light beam.
Scintillation is the temporaland spatial variation in
light intensity caused by atmospheric turbulence. Such
turbulence is caused by wind and temperature gradients
that create pockets of air with rapidly varying densities
and, therefore, fast-changing indices of optical
reflection. These air pockets act like lenses with timevarying properties and can lead to sharp increases in the
bit-error-rates of free-space optical communication
systems, particularly in the presence of direct sunlight.
Beam-Wandering arises when turbulent wind current
(eddies) larger than the diameter of the transmitted
optical beam cause a slow, but significant,
displacement of the transmitted beam. Beam wander
may also be the result of seismic activity that causes a
relative displacement between the position of the
transmitting laser and the receiving photodetector.
Another concern in the implementation of FSOC is
safety, because the technology uses lasers for transmission.
The proper use and safety of lasers have been discussed
since FSO devices first appeared in laboratories more than
three decades ago. The two major concerns involve eye
exposure to light beams and high voltages within the light
systems and their power supplies. Strict international
standards have been set for safety and performance, comply
with the standards.
The main design challenges in FSOC are solving to
overcome the effects of thus absorption, scattering,
polarization rotation, scintillation and beam-wandering,
which are due to the existence of fog and atmospheric
turbulence. The first step prior to overcome the combination
of atmospheric disadvantage parameters is by creating
propagation model as explained in the next section.
B. Transmission Issues in Terrestrial Free Space Optical
Communications
Particularly for large transmission distances, it is
essential to direct the energy of the sender accurately in the
ISSN: 2085-6350
24
form of a well-collimated coherence light beam. For long
distance (5-km) applications with large bandwidth, it is
suggested to use lasers that have narrower bandwidth and
long coherence-length. However, for applications those
require inexpensive investment with shorter distance and
smaller bandwidth, it is sufficient to use collimated LED.
The collimated light beam is required in order to limit the
loss of power between the sender and the receiver due to
beam expansion. However, the one needs large beam radius
with high-quality optical front-wave to keep the receiver in
line with the transmitter when beam-wandering happens.
The one should use a diffraction-limited light source and a
large high-quality optical telescope for collimating a large
beam radius. Due to the short wavelength of light, the beam
divergence of an optical transmitter can be much smaller
than that of a radio or microwave source of similar size.
Using a frequently used term in the area of radio
transmission, the antenna gain can be much higher for
optical transmitters over 100 dB even for moderate
telescope diameters of e.g. 25Ԝcm.
Fig. 1: Setup for a Simplex FSOC system. Although the
transmitter signal is approximately collimated, part of the
transmitted power may miss the detector.
It is also advantageous to have a large high-quality
optical telescope on the side of the receiver. It is essential
not only to collect as much of the sender's power as
possible, but also to minimize disturbing influences, e.g.
from background light, which introduces noise and thus
reduces the data transmission capacity. Both high sensitivity
and high directionality can be achieved by using a large
high-quality telescope at the receiver end. High
directionality also requires high precision in the alignment
of the sender and receiver.
An important issue is the power budget of a free-space
link, including the transmitter's power and all power losses.
The remaining power at the receiver largely determines the
possible data transmission rate, even though this is also
influenced by the modulation format, the acceptable bit
error rate, and various noise sources, in particular laser
noise, pre-amplifier noise, excess noise in the receiver (e.g.
an avalanche photodiode), and background light. The latter
can often be efficiently suppressed with additional narrowband optical filter and high-quality telescope, since the
optical bandwidth of the signal is fairly limited, whereas
background light is usually very broadband.
Severe challenges can arise from the effects of
atmospheric disturbances such as clouds, dust and fog,
which can cause not only strong signal attenuation but also
inter-symbol interference. To solve this problem, it is
suggested to use sophisticated techniques of forward error
correction embedded in the datalink layer, which allow for
reliable high-capacity optical links even through thick
clouds.
II.
SPATIAL DIVERSITY RECEPTION MODEL TO
IMPROVE PERFORMANCE
In this section, the authors will elaborate a statistical
model of spatial diversity reception to overcome the effects
of atmospheric turbulence. Spatial diversity reception,
which has been introduced for RF applications, is a
potential method to overcome the degradation due to
atmospheric turbulence [5-9]. Application of spatial diversity
reception in FSOC has been introduced in some researches
[7-9]
. The performance of spatial diversity reception on
FSOC has been studied and proposed [8], Assuming that
atmospheric-turbulence fading is uncorrelated at each of the
optical receivers. In order for this assumption reasonable,
the spacing between receivers should exceed the fading
correlation length in the plane of the receivers. It may be
hard to satisfy this assumption in practical situation, for any
reasons. Available space may not permit enough receivers
spacing. In power-limited links, which employ collimated
beam with large diameter, the receiver spacing required
obtaining uncorrelated fading, may exceed the beam
diameter.
A. Maximum-Likelihood (ML) Diversity Detection on
Turbulence Channels
In the case of spatial diversity reception with nreceivers, the received signal is presented by n-component
vector. Taking account of correlation between the receivers,
bit-error probability equation for maximum-likelihood in [ 4]
is modified as follows:
r
⎡ n r2 ⎤ n
1
P r | Off = exp ⎢ −∑ i ⎥ ∏
⎣ i =1 Ni ⎦ 1 π Ni
(
)
(
⎡ n r − η I e 2 X i −2 E[ X i ]
r
uur
i
0
P r | Off = ∫ f uuXr X • exp ⎢ − ∑
⎢ i =1
uur
Ni
X
⎣⎢
(
)
( )
) ⎤⎥ •
(1)
2
⎥
⎦⎥
n
∏
1
dX i
π Ni
(2)
Where Ni / 2 is noise covariance of the i-th receiver and
exp{− 12 ⎣⎡( X 1 − E [ X 1 ])
uur
f uuXr X =
( )
⎡ ( X 1 − E [ X 1 ]) ⎤
⎢
⎥
− E [ X n ]) ⎦⎤} C X−1 ⎢
⎥
⎢ X − E[X ] ⎥
n )⎥
⎢⎣( n
⎦
n/2
( 2π ) | C X |1/ 2
(X
n
(3)
CX is the covariance matrix of the log-amplitude in the nreceivers. The likelihood function
ISSN: 2085-6350
r
P r | On
r
Λ r = r
P r | Off
(
(
()
= ∫ f uuXr
uur
X
25
)
)
where
(4)
(
⎡ n r − η I e2 X i − 2 E[ X i ]
uur
0
i
X • exp ⎢ −∑
⎢ i =1
Ni
⎢⎣
( )
)
2
− ri ⎤ uur
⎥dX
⎥
⎥⎦
2
uur
⎛ T ⎞
P Bit Error X = P ( Off ) Q ⎜ th ⎟ + P ( On )
⎝ 2N ⎠
⎛ η I 0 e 2 X1 − 2 E[ X1 ] + e 2 X 2 − 2 E[ X 2 ] − Tth
Q⎜
⎜
2N
⎝
(
)
(
)
(10)
⎞
⎟
⎟
⎠
B. Numerical Simulations for Multi Receivers
The ML detector uses the decision rule
r > On .
Λ r
1
< Off
()
Since the
log-amplitude follows a joint log-normal distribution,
calculation of the likelihood function in (4) involve multidimensional integration. It is emphasized that this decision
rule has been derived under the assumption that the
receiving side knows the fading correlation but not the
instantaneous fading state.
The bit-error probability of the ML receiver is given by
Pb = P(Off) . P(Bit Error| Off) + P(On) . P(Bit Error|On) (5)
where P(Bit Error| Off) and P(Bit Error| On) denote the biterror probabilities when the transmitted bit is off and on,
respectively. Without considering inter-symbol interference,
which can be ignored when the bit rate is not high and
multipath effects are not pronounced, we have:
P ( Bit error Off ) =
(
)
∫
r
r
p r Off d r
∫
r
r
p r On d r
r
Λ r >1
()
In this section, we intend to present numerical
simulations of the performance of spatial-diversity
reception/detection with multi-receivers and dual-receivers
as well. However, up to this Conference submission
deadline our research group has not finished the numerical
simulation yet. The structure of multi-receiver link that is
represented by dual-receiver is illustrated by Fig.2. As
described above, the ML receiver (Fig.2a) has full
knowledge of the turbulence-correlated fading matrix CX,
while the EGC receiver (Fig.2b) has knowledge only the
marginal distribution of fading at the receivers. It is
assumed that E[I1] = E[I2] = E[I] and N1 = N2 = N and
define the electrical signal-to-noise ratio SNR = (ηE[I]2)/N.
(6)
and
P ( Bit error On ) =
r
Λ r <1
()
(
)
(7)
To evaluate the optimal ML diversity detection scheme, we
compare it with the conventional equal-gain combining
(EGC) scheme [ ]. In the EGC scheme, it is assumed that
the receiving side has knowledge of the marginal
distribution of the channel fading, in every receiver, but has
no knowledge of the fading correlation or the instantaneous
fading state. For each single individual receiver output, it is
able to find an optimum threshold τi. Then the EGC detector
adds together the n-receivers output with equal gains and
compares the sum to the threshold
n
Tth = ∑τ i
(8)
i =1
The error probability of EGC is
uur
uur uur
Pb = ∫ f uuXr X P Bit Error | X d X
( ) (
uur
X
III.
)
(9)
CONCLUSION
Free space optical communications through atmosphere
is under intensive researches, where various methods have
been
proposed
to
overcome
turbulence-effects
communication signal fading. In this paper we introduce
Fig. 2: Dual-receiver model on atmospheric turbulence channels
with correlated turbulence fading. (a) Maximum-likelihood
detection. (b) Equal-Gain combining with threshold detection.
To obtain simulation results, it is assumed that E[X]= 0
and σX = 0.1, varying the normalized correlation ρd from 0
to 0.9. It is also compared to set up value δX = 0.25. We
expect that turbulence correlated fading will cause a larger
degradation of bit error probability when the standard
deviation noise δX is larger. Hipothetically, with tworeceivers, ML detection achieves a better performance than
EGC for a given SNR. The advantage of ML over EGC
method is more pronounced when the correlation ρd
between the two receivers is high. It is more reasonable
when the SNR is high, then the errors are caused mainly by
turbulence-correlated fading, as opposite to the noise.
spatial diversity reception model for free space optical
communications. This model can be use to analyze and later
on to overcome the effects due to turbulence-correlated logamplitude fluctuations.
ISSN: 2085-6350
26
ACKNOWLEDGMENT
This research is supported by Universitas Indonesia
Research Grant called RUUI-2009, Nb.DRPM/RUUI
Unggulan/2009/I/3342 .
[5]
G. L. Stuber, Principles ofMobile Communication. New York: Kluwer
Academic, 1996.
[6]
J. G. Proakis, Digital Communication, 3rd ed. New York:
McGraw-Hill, 1995.
[7]
M. Srinivasan and V. Vilnrotter, “Avalanche photodiode arrays for
optical communication receivers,”, NASA TMO Progress Rep. 42144, 2001.
[8]
M. M. Ibrahim and A. M. Ibrahim, “Performance analysis of optical
receivers with space diversity reception,” Proc. IEE—Commun., vol.
143, no. 6, pp. 369–372, December 1996.
[9]
X. Zhu and J. M. Kahn, “Maximum-likelihood spatial-diversity
reception on correlated turbulent free-space optical channels,” presented
at the IEEE Conf. on Global Commun., San Francisco, CA, Nov.–Dec.
27–1, 2000.
REFERENCES
[1]
V. W. S. Chan, “Optical Space Communications”, IEEE J. Sel. Top.
Quantum Electron. 6 (6), 959 (2000)
[2]
O. Caplan, “Laser communication transmitter and receiver design”, J.
Opt. Fiber Commun. Rep. 4, 225 (2007)
[3]
K. F. Büchter et al., “All-optical Ti:PPLN wavelength conversion
modules for free-space optical transmission links in the midinfrared”, Opt. Lett. 34 (4), 470 (2009)
[4]
Xiaoming Zhu and Joseph M. Khan, “Free Space Optical
Communication Through Atmospheric Turbulence Channels,” IEEE
Transactions on Communications, Vol. 50, No. 8, August 2002
ISSN: 2085-6350
27
A Measure of Vulnerability For Communication Networks:
Component Order Edge Connectivity
A. Suhartomo, Ph.D.
Department of Electrical Engineering
President University
[email protected],
[email protected]
Abstract: Graph theoretical techniques provide a
convenient tool for the investigation of a communication
network’s vulnerability to failure. When there are point to
point connections, a communication network can be
modeled by a graph. A measure of the vulnerability of a
network to link failure is the so-called edge connectivity of
the graph. We introduce a new vulnerability parameter,
generalization of edge connectivity, called “Component
Order Edge Connectivity”. It is the smallest number of
edges that must be removed in order to ensure that all
resulting components have fewer nodes than some given
threshold value. We then derive formulas for λc( k ) for stars,
paths, cycles, and complete graphs; but no formula has been
found for an arbitrary graph G.
Keywords: edge connectivity; minimum degree; kcomponent edge connectivity; edge-failure set, edge-failure
state
1.
Introduction
Networks such as ring fiber optic, satellite
communication, terrestrial microwave, and social all can be
modeled using graph-theoretic construct.
Network
vulnerability
is
an
important
consideration in network design. The utilization of
communication networks has grown tremendously in the
last decade, for activities such as transmitting voice, data,
and images around the world. With the widespread
dependence upon such networks, it becomes important to
find topologies that yield a high level of reliability and a
low level of vulnerability to disruption.
It is desirable to consider quantitative measures of
a network’s vulnerability. To obtain such measures we
model the network by a graph in which the station terminals
are represented by the nodes of the graph and the links are
represented by the edges.
The problem, in which edges are subject to failure but nodes
are not will be investigated here as a new vulnerability
parameter of networks called “k-component order edge
connectivity” or “component order edge connectivity”.
This is a departure from the classical edge failure model,
but we make use of edge connectivity and are concerned
with how these parameters interrelate.
2.
Network vulnerability in practice
Graph theoretic techniques provide a convenient
tool for the investigation of the vulnerability of a
communication network to damage from natural or enemy –
induced damage. Thus, a communication system can often
be represented as interconnection of stations and links. The
assumption is that the system is subject to natural failure or
enemy attack aimed at isolating stations from each other.
The problems of determining the vulnerability and
designing communication network which are invulnerability
to enemy attack are of paramount importance. As yet,
neither the analysis nor synthesis problems have been
completed proved, although a number of practical results
have been discovered. One difficulty immediately
encountered in vulnerability studies is lack of a completely
appropriate vulnerability criterion.
In this paper, we attempt to address this deficiency
by introducing a new parameter: the minimum number of
edges that must be deleted in order to isolate a given
number of nodes, called component order edge
connectivity.
3.
Graph Theory
Background
and
Network
Vulnerability
This section introduces graph theoretic concepts
which are fundamental to understanding network
vulnerability, and surveys the relevant existing work in the
field.
A graph G = (V,E), or G, consists of a finite nonempty set of nodes V and a set E of two element subsets of
V. If {u , v} ∈ E , we say that {u , v} is incident at the
nodes u and v, and that u and v are adjacent. If
| V |= n, | E |= e , G is referred to as an (n,e) graph; n is
the order of G and e is size of G.
A specific example of a graph G is given by
V = {v0 , v1 , v 2 , v3 , v 4 , v5 , v6 , v7 } , and
E = {{v0 , v1 }, {v0 , v 2 }, {v0 , v3 }, {v1 , v5 },
{v 2 , v5 }, {v 2 , v 4 }, {v3 , v 4 }, {v 4 , v5 }, {v 4 , v6 },
{v5 , v7 }, {v 6 , v7 }}.
If it is convenient to do so, we usually represent
the graph pictorially, e.g., Figure 1 depicts the graph given
in the exam
ISSN: 2085-6350
28
v1
v0
v2
v5
v7
neighborhood of a node v, denoted by N(v), is the set of
nodes which are adjacent to v. The degree of a node is thus
the cardinality of its neighborhood, i.e., deg v = |N(v)|. The
minimum degree among the nodes of G is denoted δ (G) =
min{deg v| v ∈ V} and the maximum degree is denoted
∆ (G) = max{deg v| v ∈ V}.
Since each edge has two end-nodes, the sum of the
degrees is exactly twice the number of edges:
n
∑ deg v
v3
v4
v6
(1)
⎢ 2e(G ) ⎥
⎣ n ⎥⎦
δ (G ) ≤ ⎢
(2)
⎡ 2e(G ) ⎤
.
∆ (G ) ≥ ⎢
⎢ n ⎥⎥
(3)
and
•
Connectedness
A desirable property for networks such as
communication networks is that each station be able to send
messages to all other stations, if not directly then by
relaying through other stations. In terms of the graph that
models the network, the desired property is that the graph is
connected. Therefore, we introduce some basic notions
pertinent to connectedness.
A walk in a graph G is an alternating sequence of
nodes and edges,
v0 , x1 , v1 , x 2 , L , v n −1 , x n , v n , where xi = {vi −1 , vi } for
i = 1, L , n.
It is closed if v0 = v n , and is open otherwise. Since G has
no multiple edges or loops, it suffices to suppress the edges
and just list the nodes in order of appearance.
A path in a graph is walk in which no node is
repeated. If a walk is closed, then it is a cycle provided
v0 , v1 ,L, v n −1 are distinct and n ≥ 3 .
A graph G is said to be connected if for all pairs of
distinct nodes u and v there exists a walk (equivalently a
path) joining u and v. A disconnecting set of a graph is a set
of edges which renders the graph disconnected upon
removal.
In the labeled graph G of Figure 2.1.,
v0,v3,v4,v2,v4,v5,v7 is a walk, which is not a path, v0,v2,v4,v5,v7
is a path, and v2,v4,v5,v2 is a cycle. The graph G is connected
and {{v5 , v 7 }, {v 4 , v 6 }} is a disconnecting set.
•
Subgraphs
A graph H = (V(H),E(H)) is a subgraph of a graph
G, denoted H ⊆ G, if
V(H) ⊆ V(G) and E(H) ⊆ E(G). If V(H) = V(G), then H is
called a spanning subgraph. Therefore, a spanning
subgraph may be viewed as a graph obtained by the removal
of a set of edges. Observe that G is connected if and only if
G has one component.
Degrees
The degree of a node v in a graph G, denoted by
deg v, is the number of edges incident with v. In a (n,e)
graph, 0 ≤ deg v ≤ n-1 for every node v. The
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= 2e(G ) .
Hence
Figure 1. A pictorial representation of a graph.
•
i
1
4.
Classical Network Vulnerability
The parameters (node) connectivity and edge
connectivity are used to measure the “vulnerability” of a
network (graph) to disconnection upon failure of nodes or
edges, respectively. Specifically, the (node) connectivity
κ (G ) is the minimum number of nodes required to be
removed so that the surviving subgraph is disconnected or
trivial (i.e., a single node). The edge connectivity λ (G ) is
the minimum number of edges required to be removed so
that the surviving graph is disconnected. He also proved that
⎢ 2e ⎥
⎣ n ⎥⎦
κ (G ) ≤ λ (G ) ≤ δ (G ) ≤ ⎢
(4)
Figure 2 provides an example of this result.
Figure 2. A graph with
κ (G ) = 1, λ (G ) = 2,
⎢ 2e ⎥
δ (G ) = 3, ⎢ ⎥ = 3
⎣n⎦
In many networks, disconnection may not
guarantee that the network can no longer perform the
function that it was designed for. As long as there is at least
one connected piece which is large enough, the network
may still be considered operational. Hence there are
inadequacies inherent in using connectivity or edge
connectivity as a measure of vulnerability. A concrete
example of this deficiency is show in Figure 3. The graph G
29
of order 1,000,001 has κ (G ) = λ (G ) = 1 . However the
subgraphs G – v and G – {u,v} have components of order
999,999 and 1,000,000, respectively.
component order edge connectivity of G, denoted by
λ(ck ) (G ) or simply λ(kc ) , is defined to
λ(ck ) (G ) = min{| F |: F ⊆ E , F is k - component
be
edge-failure set}, i.e., all component of G – F have order
≤ k −1.
u
v
K 10 6
Definition 3. A set of edges F of graph G is
Figure 3. A graph with
λ (G ) = 1
It is reasonable to consider a model in which it is
not necessary that the surviving subgraph is connected so
long as it contains a component of some predetermined
order. Component order edge connectivity is the minimum
number of edges required to be removed so that the
surviving subgraph contains no component or order at least
some prescribed threshold.
5.
Component Order Edge Connectivity
•
The Traditional Edge-Failure Model
In the traditional edge-failure model it is assumed
that nodes are perfectly reliable but edges may fail. When a
set of edges F fail we refer to F as an edge-failure set and
the surviving subgraph G – F as an edge-failure state if G –
F is disconnected.
Definition 1. The edge connectivity of G, denoted by
λ (G ) or simply λ , is defined to be λ (G ) = min{
| F |: F ⊆ E , F is an edge-failure set }, i.e., G – F
disconnected.
One drawback of the traditional edge-failure model
is that the graph G – F is an edge-failure state if it is
disconnected and no consideration is given to whether or
not there exists a “large” component which in itself may
be viable. In this paper we introduced a new edge-failure
model, the k-component edge-failure model.
•
The New Edge-Failure Model
It is reasonable to consider a model in which it is not
necessary that the surviving edges form a connected
subgraph as long as they form a subgraph with a
component of some predetermined order. Thus we
introduce a new edge-failure model, the
k-component order edge-failure model. In this model,
when a set of edges F fail we refer to F as a kcomponent edge-failure set and the surviving subgraph
G – F as a
k-component edge-failure state if G - F contains no
component of order at least k, where k
is a
predetermined threshold value.
λ(kc ) -edge set
if and only if it is a k-component order edge-failure set and
| F |= λ(ck ) .
Next we compute
λ(ck ) (G ) for specific type of
graphs. The first type of graph we consider is the star,
K 1,n −1 . Deletion of any set of m edges results in a subgraph
consisting of m+1 components, one isomorphic to K 1, n −m −1
and the remaining components isolated nodes. Therefore a
k-component edge-failure state exists if the component
K 1, n −m −1 contains at most k-1 nodes. Thus n − m ≤ k − 1
or n − k + 1 ≤ m . Since component order edge
connectivity is the minimum number of edges whose
removal results in a k-component edge-failure state we
obtain the following result:
Theorem 1: Given
2 ≤ k ≤ n, λ(ck ) ( K1,n−1 ) = n − k + 1
The next type of graph we consider is the path on
n nodes, Pn . Starting at a pendant edge label the edges
consecutively from 1 to n – 1. Let F be the set of edges
whose label is divisible by k – 1. The deletion of the edges
in F creates |F| + 1 components, the first |F| having order
exactly k-1 and the last order at most k-1 (but at least 1). We
also note that if fewer edges are removed then there will be
fewer components and thus by the Pigeonhole Principle,
there must be at least one component of order at least k.
⎢ n − 1⎥
we have:
Since |F| = ⎢
⎣ k − 1⎥⎦
Theorem 2: Given 2 ≤ k ≤ n,
⎢ n − 1⎥
.
⎣ k − 1⎥⎦
λ(ck ) ( Pn ) = ⎢
Theorem 3. Let Tn be a tree on n nodes. Then for any
value k, 2 ≤ k ≤ n,
⎢ n − 1⎥
(k )
(k )
(k )
⎢⎣ k − 1⎥⎦ = λc ( Pn ) ≤ λc (Tn ) ≤ λc ( K 1, n −1 ) = n − k + 1
The next type of graph considered is the cycle on n
nodes, C n . The formula for
similar manner to that of
λ(ck ) (C n ) can be derived in a
λ(ck ) ( Pn ) . When one edge of the
Definition 2. Let 2 ≤ k ≤ n be a predetermined threshold
value. The k-component order edge-connectivity or
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30
cycle is removed, it becomes the path with n nodes, Pn .
and
since
λ(ck ) (C n ) = λ(ck ) ( Pn ) + 1 ,
⎡ n ⎤
⎢ n − 1⎥
⎢⎣ k − 1⎥⎦ + 1 = ⎢⎢ k − 1⎥⎥ we have the following result:
Thus
Theorem 4. Given 2 ≤ k ≤ n,
⎡ n ⎤
.
⎢ k − 1⎥⎥
λ(ck ) (C n ) = ⎢
The last type of graph considered in this section is
the complete graph on n nodes, K n . Let F ⊆ E ( K n ) be
a
λ(kc ) -edge set set. We can compute |F| by calculating the
maximum number of edges that can remain in the kcomponent edge-failure state K n − F . It is easy to see
that any edge in F must have its endpoints in two different
components of K n − F ; thus each component of
K n − F must itself be complete. Thus we have the
following:
2 ≤ k ≤ n,
⎛ n ⎞ ⎢ n ⎥⎛ k − 1⎞ ⎛ r ⎞
λ(ck ) ( K n ) = ⎜⎜ ⎟⎟ − ⎢
⎥⎦⎜⎜ 2 ⎟⎟ − ⎜⎜ 2 ⎟⎟ ,
2
k
−
1
⎣
⎝ ⎠
⎝
⎠ ⎝ ⎠
n
⎢
⎥
where n = ⎢
(k − 1) + r , 0 ≤ r ≤ k − 2 .
⎣ k − 1⎥⎦
Theorem 5. Given
References
[1] Gross, D., Boesch, F., Kazmierczak, L., Suffel, C.,
Suhartomo, A.: “Component Order Edge Connectivity
– An Introduction”, Congressus Numerantium 178
(2006), pp. 7-14.
[2] Gross, D., Boesch, F., Kazmierczak, L., Suffel, C.,
Suhartomo, A.: “Bounds For Component Order Edge
Connectivity”, Congressus Numerantium 185 (2007),
pp. 159-171.
ISSN: 2085-6350
[3] Suhartomo, A.: “Component Order Edge Connectivity:
A Vulnerability Parameter
For Communication
Networks”, Ph.D Dissertation, Stevens Institute of
Technology, Hoboken – NJ, USA, 2007
[4] Frank Boesch, Daniel Gross, L. William Kazmierczak,
John T. Saccoman, Charles Suffel, A. Suhartomo.: “A
Generalization of A Theorem of Chartrand”, Network
– 2009-DOI 10.1002/net
Biography:
In 1975 – 1977, Antonius Suhartomo received training
courses in Radio Transmission of Microwave Terrestrial
and Satellite from PT. Telekomunikasi Indonesia
(TELKOM), and from 1977 – 2004 Antonius was an
employee of PT. TELKOM. He received a B.S in Physics
from University of North Sumatera, Medan – Indonesia in
1984, followed by a Master (M.Eng.Sc.) in OpticalElectronic and Laser Applications from University of
Indonesia, Jakarta – Indonesia in 1993. He also received a
Master (M.M.) in Management from Graduate School of
Management, Institute Management Telkom, Bandung –
Indonesia in 1994. At PT. TELKOM from 1977 – 2004, he
was assigned in many departments under TELKOM
business such as in Engineering Division, Training
Division, R & D, Institute of Technology Telkom, and
Maintenance Service Center Division before he took his
Ph.D. He received his Ph.D. in Electrical and Computer
Engineering Department majoring in Electrical Engineering
from Stevens Institute of Technology, Hoboken – NJ, USA
in 2007. He is currently in-charge in several positions such
as Secretary Department of Electrical Engineering, Head of
Research and Development Center, and Director Office of
Residence Life at President University Kota Jababeka –
Cikarang, Bekasi – Indonesia starting from year 2008..
31
The Speech Coder at 4 kbps using Segment
between Consecutive Peaks based on the
Sinusoidal Model
Electrical Engineering, Soegijapranata Catholic University,
Semarang 50234, Indonesia
[email protected]
Abstract— Communications equipment needs to work at
low bit rate, that can reduce the need of transmission
bandwidth. The proposed speech coder generates coded signal
at the rate of 4 kbps with low complexity. Thus, the
transmission channel can be used for great number of
communications connection. The low bit rate speech coder can
be realized by using segmental sinusoidal model. By using this
model, signal parameters are used to generate the synthetic
speech signal.. In this paper, we proposed a sinusoidal model
by using the peak of signal amplitude, called as segmental
sinusoidal model. The proposed coder is combined with
waveform interpolation model and codebook, thus it can work
at low bit rate. The coder has MOS score of 3.8 (out of 5). It
means that the perception quality is fairly good. The output
data rate of encoder is 4 kbps or bellow, with complexity less
than 10 MIPS(Million Instruction per Second).
Keywords—analysis,
frequency,
period, segmental, sinusoidal, synthesis
I.
interpolation,
peak,
INTRODUCTION
The number of transmission channel for communication
become limited due to increase of the communication
channel usage. Limited channel capacity had endorsed all
side more efficient. Speech signal data rate can be developed
by using encoder to obtain the simpler information. The
research aim is obtained the low rate speech signal encoder
with high quality.
The research aim is obtaining coding algorithm on the
rate of 4 kbps with high perception quality, on Mean
Opinion Score 3,5 - 4. Research consists of encoder and
decoder development at conversation frequency between 300
Hz and 3400 Hz. Coder system can be applied for speech
signal storage and it can be implemented at digital signal
processor system. On the other hand, coder can be applied
into communication system using digital signal
controller.The main contribution of the research is obtaining
the method for decreasing speech signal data rate at 4 kbps.
The other contribution is saving the storage media capacity.
The number of saving is 93% compared to G.711 speech
coder. The coder can be implemented into speech
communication hardware like cellular phone, cordless
phone, VoIP, multimedia system, secret communication and
many other communication system.
The speech coder system consists of encoder and
decoder. The encoder function is encoding the speech signal
at rate 4 kbps and the decoder function is converting the
coded signal into estimation speech signal approximate with
the original speech signal. Main part of encoder is
compression algorithm with segmental sinusoidal method for
speech signal analysis and waveform interpolation to reduce
data rate. Differentiation of the voiced signal and the
unvoiced signal is useful for compression method choosing.
The voiced, the unvoiced, the pitch, the segment, the
decimation, and the formant parameters are coded into
codebook. Decoder consists of parameters synthesis block
and quality enhancing block. The coded signal is synthesised
by using sinusoidal signal generator based on transmitted
parameters. Code-vectors are generated from the transmitted
codebooks index. Reconstructed speech signal enhancement
is developed as a four parallel of band-pass filters for
increasing the formant signal and decreasing the valleys
between formants.
The research consists of speech signal algorithm
development at the rate of 4 kbps, coder software simulation
and hardware realization at digital signal processor. The
simulation is developed by using the Borland C++ software
at 4.5 version. Software simulation consists of speech signal
acqusition, speech signal coder, speech signal reconstruction,
speech signal reproduction as results of the signal
reconstruction and performance tests. The hardware
implementation is applied at digital signal processor starter
kit (DSK) TMS320VC5416 from Texas Instrument by using
Code Composed Studio software. The simulation results are
implemented into DSK TMS320VC5416 hardware.
Based on the development and experiment results, I
conclude that speech signal coder at 4 kbps have realized by
using segmental signal model, waveform interpolation and
codebook usage. Near toll perception quality have realized
by using the voiced and the unvoiced differentiation,
harmonic signal of the sinusoidal model and postfilter
implementation. Coder system have applied for speech
signal storage and it have been realized by using a hardware
based on digital signal processor. The number of the
memory needed for encoder and decoder routines saving is
less than 16 KB, so that it is realizable for digital signal
processor system implementation. The coder system also
have been applied for communication system and it can be
realized by using the custom integrated circuit design. The
number of instruction is less than 6 million per second (6
MIPS), so that it can be implemented into the recent realtime
system that work at speed of more than 160 MIPS.
Suggestion for the futher research is coder implementation at
the digital signal controller, that consists of DSP, memory,
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32
ADC and DAC. So, it can be used as encoder and decoder at
application scale.
II.
SINUSOIDAL MODEL
Sinusoidal model can be developed to obtain less
parameters, so that the signal data rate can be reduced. This
model is called as segmental sinusoidal model. By using this
model, there are two harmonic signal to estimate the original
signal between two consecutive peaks, (maximum to
minimum or minimum to maximum). Peak means minimum
peak or maximum peak on the frame. Therefore, one
segment means part of signal between maximum peak and
consecutive minimum peak or part of signal between
minimum peak and consecutive maximum peak.
Time distance between i-th maximum peak and
consecutive minimum peak called as period information, and
denoted by pd(i). Maximum peak or minimum peak called as
peak information, and denoted by pk(i). Peak information is
obtained by detecting the maximum peaks and minimum
peaks over the frame observed. Period information is
obtained by counting the time distance between the
consecutive peaks. The proposed method is a process on
time domain. On the extreme waveform coding, signal is
fetched on its peaks [1]. On the one frame with length of N,
there are M maximum peaks and L minimum peaks. On this
frame, there are large number sinusoidal signal components.
It can be written as :
K
s(n) = ∑ ak cos(ωk (n)n + φk (n))
k =0
(1)
for : 0 < k < K-1, n = 0,1,2 … N-1 , K<N
The first and second coefficients (k=0 and k=1) are used
as components to reconstruct the estimated signal from one
maximum peak until the consecutive minimum peak or from
one minimum peak until the consecutive maximum peak.
This is the equation for estimated signal
s( n) = a0 + a1 cos(ω1 ( n) + φ1 ( n))
(2)
The estimated signal over the frame is a train of the spv
and svp for i=0 until i=I-1. based on the previous
explanation, for pk (0) > pk(1), the reconstructed signal using
the segmental sinusoidal model can be written as :
sr (i, n) =
⎞
pk (i ) + pk (i + 1) pk (i ) − pk (i + 1) ⎛ π .( n − nk (i ))
+
+ i.π ⎟⎟
cos⎜⎜
2
2
p d (i )
⎝
⎠
(3)
for i = 0,1,2 …(I-1)
If pk(0) < pk(1), the reconstructed signal using the
segmental sinusoidal model can be written as :
s r (i , n ) =
⎛ π .( n − nk (i ))
⎞
p k (i ) + p k (i + 1) p k (i ) − p k (i + 1)
+
+ (i + 1).π ⎟⎟
cos⎜⎜
2
2
p d (i )
⎝
⎠
for i = 0,1,2 …(I-1)
ISSN: 2085-6350
(4)
III.
SPEECH CODER AT 4 KBPS
In this paper, a speech signal encoder at 4 kbps and
bellow has been designed using several blocks and
algorithms. Detail of the encoder is shown in fig. 6. The
encoder contains existing signal detector, windowing
process, and pitch detector. The next blocks are voiced and
unvoiced classificator, sinusoidal based coder, and formant
coder.There are some operation mode of the encoder system
depends on the kind of signal to obtain the high performance
of coding system [2-7]. There are two different operation
modes: silent operation mode and signal operation mode.
The signal operation mode consists of vibrating mode
operation and non-vibrating mode operation. Input signal is
speech signal in 16-bit PCM format at 8 kHz frequency
sampling. The first block is signal buffer with 30 ms length.
The next block is existing signal detector. Then the 30 ms
signal will be detected its pitch period width. Based on pitch
period information, signal would be classified into vibrating
and non-vibrating signal. If it is less than 160 samples, the
signal in buffer is called as vibrating signal. Then, if it is
more than 160 samples, it is called as non-vibrating signal.
The next process is depend on the kind of signal. For
vibrating signal (voiced), characteristic signal [8][9] have to
be held. One pitch period of signal is quantized using
segmental sinusoidal model. The formant information for
each pitch period is kept to obtain the variation information
changing for 30 ms. The next block is codebook index
searching based on periods, peaks, and formants. All of the
coded parameter are sent to the decoder with rate 4 kbps or
less than 4 kbps, depends on the kind of the speech signal.
The speech input signal exist are detected by using
existing signal detector. The signals are buffered with length
of 30 ms. The detector identify the input speech signals
whether there are signals exist or there are no signals exist. If
there are no signals exist, they are called as silence. A sign is
transmitted into decoder to inform this condition, so that the
decoder is not process the signal during 30 ms. But if there
are signals exist, the encoding process is continued with
pitch detecting process.
Pitch is the useful parameter in encoding process. Based
on the pitch value, we would identify the signals whether
voiced or unvoiced. Human speech pitch period of voiced
part is vary from 2.5 ms until 20 ms, depend on the gender
and the age. Men tend to have the longer pitch period than
women and children [10]. The pitch period is detected by
using autocorrelation process. The first step, the buffered
signal is detected on its peak. Based on the peak value, it can
be found the threshold for the center clip process. The
threshold is half of the peak value over entire signal in the
buffer. The speech signals on the buffer are clipped, so that
we would reduce computation complexity. The clipped
signals are processed in autocorrelation computation. The
autocorrelation process would result two kind patterns.
There are peak-valley-peak pattern and peak-valley pattern.
The pitch value is detected based on the distance between
peaks of the peak-valley-peak pattern. The peak-valley
pattern indicates that the signals are unvoiced.
s(n)
33
Buffer
30 ms
sb
Signal
detector
voiced and
unvoiced
decision
svad
svad
suv
sv
cpk
Tp Characteristic
signal
Formant
codebook
FFT
sFFT
cFn
sss
Peak Code
searching
cpk
vpk
Tp
Pitch
detector
Spectral
Smoothing
Peak
codebook
sp
Decimation
Period
codebook
M
u
l
t
i
p
l
e
x
i
n
g
D
i
Segmental
quantization
Tp
vpd
cpd
Period code
searching
cpd
c(n)
Fn
Formant code
searching
Figure 1. Diagram of the Encoder
The voiced and the unvoiced signals are classified by
using the pitch detection process results. The autocorrelation
results pattern are used as reference to identify the signals
whether voiced or unvoiced. If the pattern is peak-valleypeak and if the distance between peaks is longer than 2.5 ms
but less than 20 ms, it means that the signals is voiced. Then,
if the pattern is peak-valley or peak-valley-peak with
distance between peaks is longer than 20 ms, it means that
the signal is unvoiced. The voiced and unvoiced signals
would process in the different methods. The unvoiced
signals would be process without referring the pitch period,
then the voiced signals would process based on the pitch
period.
The voiced signals are fetched on the one pitch period
that representing entire voiced signals on the buffer. The one
pitch period signals are called as characteristic signal in
waveform interpolative signal terminology [8][9]. The
length of the characteristics signals is referred as the pitch
period. The characteristic signal is quantized on its peaks
and periods by using segmental sinusoidal model. For the
unvoiced signal, the decimation process is implemented to
obtain the smaller size of signal. Then the peak and period
quantization is applied. Based on the segmental sinusoidal
model, peaks and periods information is extracted. The
processed signal would be generated by using the peaks and
periods quantization.
The peak information size is reduced by applying 10
look-up tables. The look-up table is also called as codebook.
The codebook is trained by using the peak information codevector. Large amount of the peak information code-vectors
are trained with k-means algorithm to obtain the peaks
codebook. The index number of the peak codebook is varied
from 6 to 10 to obtain the optimum process. The period
information size is also reduced by applying look-up table.
The index number of the peak codebook is also varied from
6 to 10 to obtain the optimum process. The period accuracy
has to maintain to obtain the good receiver perception on the
decoder side. Formant information is important for postfiltering process. The post-filter is arranged as four adaptive
band-pass filters to increase the formants and decrease the
valley between formants to enhance perception. The
formants location is detected by applying the FFT and
smoothing filter. Then the low-passed spectra are detected
on its peaks location. The peaks location is send to receiver
to set the center frequency on the appropriate band-pass
filter.
Parameters that have to be sent to the receiver are peaks,
periods, pitch, formants, segment, and decimation. The
peaks information needs 16-56 bits, the period information
needs 6-54 bits, pitch needs 7 bits, segment information
needs 6 bits and the formants information needs 0-14 bits.
The maximum total coded signal bits resulted for one frame
(30 ms) is 120 bits. Thus, the coded speech signals data rate
is 4 kbps.
Signal
detector
silence
Se
Ss
c(n)
D
e
m
u
l
t
i
p
l
e
x
i
n
g
cpd
cpk
Sr
Unvoiced
reconstruction
sˆ( n )
+
SF
Sr
postfilter
Voiced
reconstruction
Tp , i, D
Fn
Figure 2. Diagram of the Decoder
In decoder, the coded signal is reconstructed to obtain the
speech signal approximation with near toll quality at rate 4
kbps or bellow. If the coded signal is detected as silence, the
decoder will generate silence over a frame length i.e. 30 ms.
If, it is an existing speech signal, the decoder will generate
the speech signal depends on the type of signal. Unvoiced
signal is generated using the sinusoidal parameters. For
voiced signal, there is a special processing, that is postfiltering process. The post-filter will enhance performance of
the voiced signal by increasing the formant peaks and
reducing the valley between formant. The encoder sends
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34
speech signal information as a multiplexed parameter. In the
decoder, parameters are de-multiplexed to obtain the useful
information for signal reconstruction. Parameters would be
generated are peaks, periods information, formants
information, pitch, number of segment, and decimation.
The signal detector would identify the kind of signal. If
there is a silence, detector would not work because. The
unvoiced signal will be reconstructed if it is detected as
unvoiced. The peaks and periods are used to generate the
unvoiced signal. The next process is interpolation process
with ratio D, inverting of the decimation process on the
encoder. The voiced signal is reconstructed by generating the
characteristic signal along the 30 ms segment. The number
of characteristic signal along this segment varies between 1.5
until 12, depends on the pitch period. The characteristic
signal is generated by using the segmental sinusoidal model
from peaks and periods information. The reconstructed
signal, especially the voiced signal is passed into post-filter.
The post-filter proposed is a train of four adaptive band-pass
filters. The center frequency of each filter is changed every
30 ms segment. The center frequencies are detected by
encoder, and then they are sent to decoder. Then the comb
filter and compensation filter are applied to improve the
hearing perception quality [11][12].
The resulted reconstructed signal by sinusoidal approach
seems smoother than the original that had arbitrary form
between one peak to the consecutive peak. Nevertheless,
roughness of original signal means containing high
frequency component. Therefore, the spectral power is
reduced on the high frequency component. Unfortunately,
increasing of the number of peak would decrease the
dynamic range of period changing variation for each
segment. Thus, compression ratio would be increased to
compensate decreasing of compression ratio caused by the
number of peaks.
IV.
EXPERIMENTAL RESULTS
The proposed speech coder has been simulated in a
personal computer using a 4kbps coder program that was
specially developed by using C++ programming language.
The resulted mean opinion score (MOS) test measured for
15 Indonesian phrase in computer simulation is 3.8 (out of
5). It is tested on 46 peoples with variation on gender,
background and age. The coder complexity is less than 15
MIPS, comparable with others kind of speech coder that
needs 0.01 MIPS until 90 MIPS. It need less than 30 kB for
encoder and 10 kB for decoder.
Then, the coder is implemented on digital signal
processor starter kit TMS320VC5416, using a 4 kbps coder
program specially developed for the DSP system, based on
Code Composer Studio ver.2. Based on the experimental
results, hearing perception of the reconstructed signal is
fairly good. By using digital signal processor starter kit, the
MOS test score is 3.3 (out of 5), because of reducing the
number of codebooks. The resulted mean opinion score
(MOS) test measured for 15 Indonesian phrase in computer
ISSN: 2085-6350
simulation is 3.8 (out of 5). It is tested on 46 peoples with
variation on gender, background and age. The coder
complexity is less than 15 MIPS. It need less than 16 kB for
encoder and 3 kB for decoder. Therefore, it is expected that
the proposed low bit rate speech coder with fairly good
MOS test score and low complexity will be suitable for
voice communication and telemedicine applications during
and after disaster cases.
Figure 3. MOS test results
V.
CONCLUSION
Speech signal could be coded into 4 kbps rate and
decoded with high quality of the human perception based on
segmental sinusoidal model. The maximum MOS score is
3.8 on Indonesian words. The coder complexity for the
digital signal processor implementation is low, it needs less
than 10 MIPS.
REFERENCES
[1]
[2]
Ridkosil F., Extreme Waveform Coding, US Pattent
TF Quatiery, RJ.McAulay, Speech Transforma tions Based on a
Sinusoidal Representation, IEEE TASSP, vol. ASSP-34, no. 6, 1986
[3] RJ.McAulay, TF Quatiery, Speech, Analysis/ Synthesis Based on a
SinusoidalRepresentation, IEEE TASSP, vol. ASSP-34, no. 4, 1986
[4] T. Abe, dan M. Honda, Sinusoidal Model Based On Instantaneous
Frequency Attractor, TSALP vol 14 No.4, 2006
[5] R. Boyer, dan Abed-Meraim, K, AudioModeling Based on Delayed
Sinusoids, TSAP vol 12, No.2, 2004.
[6] CO. Etemoglu, dan V. Cuperman, V, Matching Pursuit Sinusoidal
Speech Coding, TSAP vol 11, No.5, 2003.
[7] GH. Hotho, dan RJ. Sluijter, A Narrowband Low Bit Rate Sinusoidal
Audio and Speech Coder, Company Research - Philips Electronics
Nederland, 2003.
[8] Gottesman, O. dan Gersho, A. (2000) : High Quality Enhanced
Waveform Interpolative Coding at 2.8 kbps, Proceedings of
International Conference on Acoustics, Speech, and Signal
Processing, 0-0.
[9] Gottesman, O. dan Gersho, A. (2001) : Enhanced Waveform
Interpolative Coding at Low Bit-Rate, IEEE Transactions on Speech
and Audio Processing, 9, 1-13.
[10] JR. Deller, JG. Proakis, JHL. Hansen, Discrete-Time Processing of
Speech, Macmillan Publishing Company, New York, 1993.
[11] FB. Setiawan, S. Soegijoko, Sugihartono, S. Tjondronegoro,
Perbaikan Mutu Sinyal Suara Tercampur Derau Berdasarkan Bentuk
Formant, SITIA Proceedings, Surabaya, 2006
[12] FB. Setiawan, S. Soegijoko, Sugihartono, dan S. Tjondronegoro, A
Low Bit Rate Speech Coder using Segmental Sinusoidal Model for
Disaster and Emergency Telemedicine, Journal of eHealth
Technology and Application, September 2008.
35
AUTOMATIC ABNORMAL WAVES DETECTION
FROM THE ELECTROENCEPHALOGRAMS OF PETIT MAL EPILEPSY CASES TO SORT
OUT THE SPIKES FP1 - FP2, THE SHARPS, THE POLYPHASE BASED ON THEIR
STATISTICAL ZEROCROSSING
Siswandari N, Adhi Susanto,
Electrical engineering UNDARIS,Electrical engineering UGM
[email protected], [email protected]
Zainal Muttaqin
neurosurgent epilepsy medical Undip
[email protected]
ABSTRACT
To explore the merits of the widespread digital signal
processing on EEG waves the standard analyses of
autocorelation, Fourier transform and zero-crossings were
applied on the seizure related waves of a large number of
epilepsy patients.
Patient consist of 267M,140F, ages 3-55 years. Clinical
status: epileptic (97M,46F: EEG with spike;39M,
28Fwithout); clinical status: nonepileptic: (93M, 41F EEG
with spike ; 38M, 25F without). Numerical data were
acquired with EEG dump diagnostic Biologic system at
Sarjito hospital Yogyakarta etc, 2002-2009. The wave
components were sorted out according to their amplitudes
and time span between zerocrossings.
The experimental results show a promising application of
the scheme, since practically all are in line with those
assessed by specialists in this field. Utilizing the hardware
and software facilities at hand, marking the st arts and ends
of abnormal waves could be done with +39µV threshold.
The zerocrossings detection could automatically
distinguished according to the 20ms–70ms time period for
the “spikes” (109M,53F) ,70ms-120ms for “sharps” (101M,
51F), and the existence of multiple peaks for
“polyphase”(57M,36F).
The research carried out so far was to find the prospect of
this digital signal processing on EEG waves to support the
doctors' work in this field.
Keywords
: EEG, spikes, sharp
I. INTRODUCTION
The clinical electroencephalographer correlates central
nervous system functions as well as dysfunction and
diseases with certain paterns of the electroencephalogram
(EEG) on an empirical basis, Obviously, this method has
been found Valuable in clinical practice
The
International
Federation
of
societies
for
electroencephalograpy and clinical Neurophysiology (
committee chaired by G.E Chatrian, see reference for
IFSECN, 1974 proposed the following definition
1. Alpha rhythm, EEG wave pattern that has
frequency 8 -13 Hz, with amplitude 20 – 200 µV.
This wave pattern can be found in almost all
normal people who are in awaked condition but
quiet.
2. Beta rhythm, wave pattern that has frequency 13
Hz, sometime in certain condition can reach 50 Hz.
Rhytmical beta activity is encountered chiefly over
the frontal and central regions, it usually does not
exceed 35 Hz.
3. Theta rhythm, wave pattern that has frequency 4 7 Hz. This wave appears in children but sometime
appears in adult who are in frustrated condition.
4. Delta rhythm, wave pattern that has frequency
under 4 Hz, sometime only appears once in two or
three seconds. This wave happens in people
sleeping tight.
The EEG utility available in a hospital have not been able to
detect spike, sharp, and polyphase automatically only in
subjectively perception and manually counting. Therefore, it
needs a program to differentiate spike, sharp and polyphase
automatically.
However, it should be noticed that a small part of EEG inter
epilepsy attack does not show any difference by using the
autocorrelation and cross correlation EEG signal. Moreover,
FFT is able to show the previous signal exploration.
II.
MODEL,
ANALYSIS,
DESIGN,
AND
IMPLEMENTATION
A. Research Material
Numerical data were acquired with EEG dump
diagnostic Biologic system at Dr. Sardjito hospital
Yogyakarta etc, 2002- by changing the file of EEG
dump diagnostic into the form of .txt. format.
B. Tools
Computer with Matlab 6.5 software using Windows XP
System
C. How To Do
The research is done with the following stages:
a. First, the collection of data cm is done by
observing the EEG qualitative diagnostic and
interpretation of its abnormal wave.
Data can be classified into:
1. Clinical status : epileptic with spike
2. Clinical status epileptic without spike
3. Clinical status : non epileptic with spike
4. Clinical status non epileptic (normal)
without spike
b. Digital data taken from the EEG biologic system of
Dr. Sardjito hospital Yogyakarta, based on Mark
Dump Utility.
To determine the wave form of spike, sharp and
polyphase in EEG, it uses zero crossing time with t0,
t1, t2, …. Etc.
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36
T1 = t1 - t 0
PSD or Power Septral Density, defined as power spectrum
quotation frequency.
T 2 = t 2 - t1
Pxx (ω ) =
T3 = t3 - t2
etc…………..(.1)
ten take the balance.
S xx ( f )
S xx (ω )
and Pxx ( f ) =
fs ...(4)
2π
III. RESULT
2.1 Interictal Paroxysmal Pattern
Spike
According to IFSECN (1974), a spike is a transient, clearly
distinguished from the background activity, with pointed
peak at conventional paper speed and a duration from 20 to
under 70 msec, the main component is generally negative,
Amplitude is variable.
The distinction from the background activity is based on
wave morphology and amplitude. In many cases, spikes
stand out against the background because of their high
voltage. If the voltage of spikes and background activity is
approximately equal, the faster character ( short duration) of
the spike is its distinctive feature.
Sharp
According to IFSECN (1974), a sharp is a transient, clearly
distinguished from the background activity, with pointed
peak at conventional paper speed and duration of 70 - 200
msec. the main component is generally negative relative to
other areas.
Jasper(1941) pointed out that the rising phase of the sharp
waveis of the same order of magnitude as in spikes, but the
descending phase is prolonged.
Figure : Spectrogram signal FP1 – FP2 for 379s.
2.2 Signal appearance by using spectrogram:
The example of signal EEG from a patient name A who is
diagnosed epilepsy petit mal taken in electrode FP1– FP2.
The purpose of spectral estimation is to explain about power
distribution which is contained in signal at certain range
frequency, according to the amount of limited data
quotation. This spectrum estimation can use to analyze
EEG’s power spectrum signal to see abnormal symptom
that happen in activity of nerve human being brain.
To see signal power spectrum clearly can be done by
dividing signal become some parts and then in each part of
the signal search their PSD. For example FP1.-FP2 signal
recorded for 6 minutes, 1.009 seconds with the quotation
frequency 256, will be separated into some parts where each
part consists of 256 data quotations, with overlapping 200
data, by using 2D image (spectrogram) the result will be
seen as figure 1.
1.
Power spectrum from a stationer random process xn,
mathematically correlating with Fourier transformation
from its row correlation, in form of normalization
frequency, this correlation can be written as:
S xx (ω ) =
∞
∑R
m = −∞
xx
( m ) e − jωm
.…..(2)
Sxx is power spectrum, Rxx is auto correlation, equivalent
above can be expressed as frequency function f (in Hertz)
by using correlation ω = 2πf/fs, with fs is quotation
frequency.
S xx ( f ) =
∞
∑R
m = −∞
ISSN: 2085-6350
xx
(m) e − 2πj f m / fs
…(3.)
The biggest figure box is power spectrum representation in
the form of 2D with abscissa asserts quotation time and
coordinate asserts frequency content with color that
represent power per set of frequency in certain time. In that
figure can be seen that frequency content with high power
happens in frequency between 0 to 30 Hz (red). When the
symptom of epilepsy appears in that frequency area become
higher (flatten red). In that figure also visible a signal
trouble that appears from power supply with frequency 50
Hz and 1.0000 Hz (orange ribbon) that appears in whole
signal.
This second is special characteristic that seen by medic in
electrode FP1 – FP2 appears 4 1.00 with very high
amplitude, 3 1.00 with average amplitude, with pattern 1.00
sharp then multi 1.00 sharp 4.00 then multi 1.00 with very
high amplitude. It approves that patient epilepsy petit mal
seen from medical science that in 1.00 seconds appear 31.00
37
is not be proven (or possible to be said not always). 1.00
µ
V happens in a last
with high amplitude about 65.20
second in elapsed time 00 : 03:48 – 00 :03 :49. In this
second is special characteristic of abnormal wave of petit
mal, doctor usually sees symptom clinic at first, EEG just
help to build diagnose.
Figure 4 EEG , FP1 – FP2 time elapsed 00:04:51 – 00:04:52
Tabel 1 : Zerocrossing method EEG FP1 – FP2 time elapsed 00:04:51 –
00:04:52
low
Figure 2: EEG Bipolar 19 channel
This pattern has been described as “petit mal absence
epilepsy “. Classical 3/sec spike wave complexes are widely
known
even
outside
the
community
of
electroencephalographers at FP1 – FP2 figure 1. It is
officially termed “ spike and slow wave complex “ , this
term comprises all types of spike wave complexes, which
listed separately because of markedly differing associated
clinical epileptological conditions.
Figure 3 :
00:05:00
signal FP1 – FP2
time elapsed 00:04:51 –
high
Duration
(mS)
Amplitu
de( µ V)
Code
1.00
6.00
19.53
6.80
0
1.00
21.00
34.00
50.78
7.60
4.00
poliphase
1.00
44.00
48.00
15.63
21.60
0
1.00
69.00
83.00
54.69
9.60
4.00
poliphase
1.00
93.00
114.00
82.03
15.60
4.00
poliphase
1.00
114.00
114.00
0
1.00
122.00
128.00
23.44
43.00
1.00 spike
1.00
135.00
137.00
7.81
2.40
0.00
1.00
145.00
193.00
187.50
45.00
4.00
poliphase
1.00
202.00
206.00
15.63
16.40
0
1.00
222.00
257.00
136.72
91.00
2.00 sharp
1.00
0
0
Applying Zerocrossing of Equation (1) to the detection of
epileptic spikes, sharp and poliphase . Initially, with Code
1.00 = spike, code 2.00 = sharp, code 4.00 = poliphase with
frequencies sampling fs = 256. The x axis of this output
represented the sample data n , i.e. time. The y axis of this
output represented amplitude. For table 1, low = 122.00,
high = 128.00, duration = 23,44 mS, amplitude = 43 µ V,
Code = 1.00 spike,
ISSN: 2085-6350
38
4. CONCLUSION
1.
2.
3.
4.
The EEG wave figure shown by the recent EEG
utility can be seen from other aspect such as
autocorrelation and power spectrum dense.
The figure of spike, sharp and polyphase can be
seen in detail using zero crossing in software
automatically.
Therefore,
many
pattern
introduction might appear into sharp, spike and
polyphase.
The study can lead to the prediction of spike and
sharp waves emerge since they cannot be seen
using the ordinary EEG record.
The epilepsy sensor implant can be made in order
to plan software and hardware installation for early
warning system of an attack. The attack early
warning system can detect an attack 10 minutes
before it happens. The alarm will automatically
ring. It can happen if there is cooperation between
medical and engineering department.
REFERENCES
1 . Ernst Neidermeyer, M.D and Fernando Lopes Da Silva,
M.D, February 1982, “ Electroencephalography Basic
Principles, Clinical Applications, and Related Fields”,
Williams and Wilkins, Baltimore USA.
2 A Barreto et al.,October.1993, “Intraoperative Fokus
localization System based Spatio-Temporal EcoG
[]
[]
[3]
Analysis”, Proc. XV Amnual Intl.Conf of the IEEE
Engineering in Medicine and Biology Society.
Mahar Mardjono, 1992, Simposium Diagnosis dini
Epilepsi, MDK/ Vol. 11. No. 01/Januari 1992, Jakarta.
. Alan V. Oppenheim and Ronald W. Schafer, 1994,
“Digital Signal Processing”, Prentice Hall of India,
New Delhi.
. Erik Bolviken, Geir Storvik, Gutorm Hogasen and Pal
G. Larsson, 2004, “Spike detection in EEG through
random line segments”, The project has been
performed at the Norwegian Computing Center
supported by Royal Norwegian Council for Scientific
and Industrial Research.
Lim Shih Hui , 2003, “Interictal and Ictal Epileptiform
Discharges”, National Neuroscience Institute,
Singapore.
Hamid Hassanpour, Moestefa Mesbah and Boualem
Boashash, 2004,” EEG Spike detection Using TimeFrequency Signal Analysis”, 2004 IEEE.
Shoichiro Nakamura, 1993, “Applied Numerical
methods ”, Prentice-Hall International, Inc USA
10.John G. Webster , 1995, “Medical Instrumentation
Application and Design” John Wiley & Sons,Inc, New
York.
[4]
[5]
[6]
[7]
[8]
.
ISSN: 2085-6350
39
Studies on the Limitation of
Fighter Aircraft Maneuver
Caused by Automatic Control Design
Departemen Elektronika, Akademi Angkatan Udara
Jl. Laksda Adisutjipto, Yogyakarta – 55002
Abstract— In 1950, fighter aircraft were not able to make a
shape maneuver in very high speed. Later, it became possible
in 1980 caused by progress in technology. However, the
maneuver was unstable. The control systems couldn’t perform
perfectly and became very expensive when it applied to fighter
aircraft. In this paper, we analyzed a non-linear and a linear
model of fighter aircraft. We used Sweden JAS-39 fighter
aircraft. As we know that this fighter aircraft can perform
shape maneuver in supersonic speed during flight. To perform
this kind maneuver, it needs good control system to assist pilot
when “drive” the aircraft. Based on this problem, we design a
linear controller based on a non-linear model of fighter
aircraft and applied it to the dynamic non-linear model of
fighter aircraft. Finally, we inspected it performance based on
the angle of attack and the sideslip angle of fighter aircraft.
sensor information, the control system computes the control
surface deflections to be produced. This information is sent
through electrical wires to the actuators located at the
control surfaces, which in turn realize the desired
deflections. This is known as fly-by-wire technology. Based
on this technology, we derive a non-linear and a linear
approximation model from the dynamic model of fighter
aircraft. We used Sweden Air Force fighter aircraft JAS-39
Gripen as fighter aircraft model that shows in Fig. 1 for this
study. Here p s , q s , rs are stability-axes angular velocity, α
is angle of attack, and β is sideslip angle of fighter aircraft.
Some parameter on this model we assumed to be constant.
Keywords—automatic control system, fighter aircraft, nonlinear and linear models
I.
INTRODUCTION In 1950, fighter aircraft were not able to make a shape
maneuver in very high speed. Later, it became possible in
1980 caused by progress in technology. However, the
maneuver was unstable. The control systems couldn’t
perform perfectly and became very expensive when it
applied to fighter aircraft. In this study, we analyzed a nonlinear and a linear model of fighter aircraft. We design a
linear controller based on a non-linear model of fighter
aircraft and applied it to the dynamic non-linear model of
fighter aircraft. Finally, we inspected it performance based
on the angle of attack and the sideslip angle of fighter
aircraft. When inspected the aircraft maneuver ability, we
also inspected the possibility using linear controller to nonlinear model. We assumed, with linear controller that built
from linear model, it can control the non-linear model of the
aircraft as good as non-linear controller. Also, by using
linear controller, we can save more time and cost to build
and produce it at near future.
Figure 1. Fighter Aircraft Dynamic Model.
A. NonLinear Model In a non-linear model, angle of attack and sideslip angle of
fighter aircraft assumed to be the variable of model. As
system input, we put stability-axes angular velocity. We
collect the fighter aircraft model parameter from the
previous section and write the result in a form suitable for
control systems. Equation (1) is the fighter aircraft force
equations.
II.
FIGHTER AIRCRAFT PRIMER
During the early years of flight technology, the pilot
was in direct control of the aircraft control surfaces. These
where mechanically connected to the pilot's manual inputs.
In modern aircraft, the pilot inputs are instead fed to a
control system. Based on the pilot inputs and available
ISSN: 2085-6350
40
・ 1
V = ( − D + Fcosαcosβ + mg1 )
m
・
1
α = q − (pcosα + rsinα )tanβ +
( − L − Fsinα + mg 2 )
mVcosβ
・
1
β = psinα − rcosα +
(Y − Fcosαsinβ + mg 3 )
mV
.
(1)
g1 = g( − cos α cos β sin θ + sin β cos θ sin φ + sin α cos β cos θ cos φ )
g 2 = g(cosαcosθcosφ + sinαsinθ )
g 3 = g(cosβcosθsinφ + sinβcosαsinθ - sinαsinβcosθcosφ )
Equation (2) is the fighter aircraft dynamic non-linear
model of fighter aircraft that we will use for designing the
controller.
・
β = − rs +
1
mV
・
rs = u 3
1
( − L( α ) - Fsin α + mg 2 )
mVcos β
(4)
where A and b are given by
F − mgsin θ
mV
A 12 =
)
A 21 = 0
mgcos θ cos φ − L( α )
mV
A 22 = − (
b11 = 1
b12 = 0
b 21 = 0
b 22 = −1
F − mgsin θ
mV
− ps
)
where the parameters value which we used in this research
that named body mass, force, gravity acceleration, and
velocity of fighter aircraft are
.
m = 9,100 [kg]
2
g = 9.8 [m/ s ]
III.
(Y( β ) - Fcos α sin β + mg 3 )
(2)
where
g 2 = g(cos α cos θ cos φ + sin α sin θ )
g 3 = g(cos β cos θ sin φ + sin β cos α sin θ - sin α sin β cos θ cos φ )
Later, we created the fighter aircraft non-linear control
system inputs based on bakstepping method and bode
diagram based on (2). Backstepping method is a method
that built from subsystems that radiate out from an
irreducible subsystem that can be stabilized using some
other method. Because of this recursive structure, the
designer can start the design process at the known-stable
system and feedback new controllers that progressively
stabilize each outer subsystem. The process terminates
when the final external control is reached.
A. Linear Model In linear model, we assumed angle of attack and sideslip
angle is zero and roll angular velocity assumed to be
constant during calculation. This made the calculation of
problem easier to solve. Equation (3) is basic model of
linear equation.
・
x = Ax + bu
⎡・⎤ ⎡A A ⎤⎡α⎤ ⎡b b ⎤⎡q ⎤
⎢α・⎥ = ⎢ 11 12 ⎥⎢ ⎥ + ⎢ 11 12 ⎥⎢ s ⎥
A
A
b
b
r
β
⎣⎢β⎦⎥ ⎣ 21 22⎦⎣ ⎦ ⎣ 21 22⎦⎣ s ⎦
A11 = − (
where the contribution due to gravity are given by
・
・
p s = u1 q s = u 2
・
α = q s − p s tan β +
Equation (4) is the state variable result from
linearization of the fighter aircraft dynamic equation
according to (3).
F = 54,000 [N]
V = 165.811 [m/s]
FIGHTER
AIRCRAFT CONTROLLER
We designed non-linear controller based on a non-linear
model and a linear controller based on linear model. We
used Scilab/Scicos program to design and simulate the
system. Scilab/Scicos program is a numerical computation
software package similar to MATLAB/SIMULINK that has
been released by Institut National de Recherché en
Informatique et Automatique (INRIA) from France since
1990.
A. NonLinear Controller We referred to [1] when designed non-linear controller
using Backstepping method based on non-linear model.
According to the non-linear model in (1), (5), (6), and (7)
are non-linear controller inputs using Backstepping method.
u1 = k ps (ps
ref
u 2 = − k α,2 (q s + k α,1 (α − α
(5)
− ps )
ref
) + f α (α
u 3 = k β,2 ( − rs + k β,1β +
ref
gcosθsinφ
V
, y α ))
(6)
)
(7)
Fig. 2 is fighter aircraft non-linear model bode diagram
using Backstepping method.
(3)
ISSN: 2085-6350
41
k = −r
+
rs −
ref
kβ ,1
+
ps
ref
+
−
k ps
−
1
s
u3
1
s
・
-1
β
+
u2
−
α
・
Equation (8) is fighter aircraft linear model equation
using enlarged system method that we used to solve the
matrix problem during build the feedback gain.
・
⎡ v(t) ⎤
x = A x (t) + bu(t) +
⎢⎣ r(t) ⎥⎦
(8)
where
b=
⎡b ⎤
⎢⎣0 ⎥⎦
⎡ F − mgsinθ mgcosθgcos − L(α ref )
ref
− ps
)
⎢− (
mV
mV
⎢
0⎤
F − mgsinθ
=⎢
)
0
−
(
0⎥⎦ ⎢
mV
⎢
−1
0
⎢
0
−1
⎣
⎡1 0 ⎤
⎢0 − 1⎥
=⎢
⎥
⎢0 0 ⎥
⎢⎣0 0 ⎥⎦
0
0
0
0
According to linear model in (8), I made linear model
input as shown in (9).
u = kx
(9)
where k is feedback gain. We used Riccati Equation as
shown in (10).
A T P + PA − r − 1 PBB
T
P+ Q = 0
(10)
This equation used to build feedback gain which means the
controller in linear model and applied it to the linear model
inputs as shown in (11).
u = −r
−1 T
B Px
Figure 3. Linear System
A. MANEUVER AND SIMULATION In this session, we combine three control models and
controllers to inspect each combination performance.
They are:
• Non-linear model with non-linear controller.
1
s
B. Linear Controller We used linear quadratic regulator method when
designed linear controller based on linear model of fighter
aircraft. Meanwhile, to design a servo system that suitable to
appointed angle of attack and sideslip angle, we used the
Enlarged system method.
⎡A
⎢⎣− c
Equation (12) is feedback gain from linear model. Fig. 3 is
fighter aircraft linear model bode diagram based on (8).
Here, roll angular velocity assumed to zero because it has
less effect both in angle of attack and sideslip angle
maneuver.
α
Figure 2. Non-linear System
A=
(12)
tan β
+
qs ref −
β
−1 T
B P
•
Linear model with linear controller.
•
Non-linear model with linear controller.
These three-combination-and-simulation goals was to
inspect the differences on each combination, search the best
combination for the model, and look forward about
possibility to use new combination between model and
controller. Meanwhile, we also figure out each combination
effect to fighter aircraft maneuver during flight.
Fig. 4 and Fig. 5 (a) is non-linear model with non-linear
controller, (b) is linear model with linear controller, and (c)
is non-linear model with linear controller simulation result.
Fig. 6 is non-linear model with linear controller which has
been increased in feedback gain. In this result from top to
below showed roll angular velocity, angle of attack targeted
value, sideslip angle targeted value, angle of attack result,
⎤
0⎥ and sideslip angle result.
⎥
Fig. 4 described the simulation from fighter aircraft
0⎥
⎥ maneuver without roll maneuver. Here we inspect each
0⎥
controller design effect to fighter aircraft maneuver in
0⎥⎦
simply maneuver environment. Fig. 4 (a) and (b) mostly
showed similar movement simulation result especially in
angle of attack (AoA) or pitch angle. But, Fig. 4 (b) showed
that sideslip angle grew bigger. It may be said that the
adjustment of the control loop for the sideslip angle is more
necessary. In (c) the result was able to made similar
movement especially in angle of attack (AoA) or pitch
angle. But, it has too much noise with it. It was started when
pilot gave the input to aircraft and continued even pilot did
not give any input. Meanwhile, sideslip angle result turned
unstable at all. Even pilot did not give any input, result
showed movement that indicate aircraft did not work
normally. When we increased input continuously, angle of
attack (AoA) showed less noise on it. Meanwhile, noise on
sideslip angle and last combination (non-linear model with
linear controller) getting bigger continuously.
(11)
refer to (11), the feedback gain for this model is
ISSN: 2085-6350
42
ps
ref
α ref
+
1
s
−
β ref
+
−
1
s
ps
Gain
（
ｋ）
α
qs
Control
rs
System
β
Fig. 5 described the simulation from fighter aircraft
maneuver with roll maneuver. We inspect each controller
from first and second simulation, this combination did
not show good result. We increased feedback gain
controller by increased heaviness for the state (Q) to
stabilize system. Fig. 7 (a) gave the best result among them.
When I set Q = diag (100, 100, 1, 1), angle of attack (AoA)
and sideslip angle result showed closer to input. If we grew
feedback gain, angle of attack (AoA) result getting more
unstable and could not reach the same value as input.
Meanwhile, sideslip angle result showed better because it
could reach the same value as input. Even after we set the
heaviness for the state (Q) extremely when designed linear
controller, it couldn’t improve the result at all when applied
to non-linear model.
Therefore, it might be said that linear controller that built
from linear model, was not able to control the non-linear
model of fighter aircraft and fighter aircraft maneuver
performance received big influence by it control system.
V. CONCLUDING REMARKS AND FURTHER WORKS On this study, we guessed it whether cannot control the
non-linear model of the fighter in a linear controller.
In addition, we thought it to be useful when we
implemented it because the result did not become
expensive, less design time, and many other positive results.
However, in this study, we simulated it and inspected it, but
our assumed result did not appear.
Finally, there was only two ways controller design this
far. First, non-linear controller designed by it own system
and linear controller designed by it own system. Each
controller can only control it own system. Also, it is better
to use non-linear controller for fighter aircraft non-linear
model than use linear controller that built from linear
model.
As reference for next research is make a computer
program to control servo or stepper based on this research.
This is to prove that the simulation result can be
implemented on reality or not.
ISSN: 2085-6350
design effect to fighter aircraft maneuver in complex
maneuver environment. Fig. 5 (a) and (b) showed similar
result with first simulation result. In angle of attack (AoA)
or pitch angle result, Fig. 5 (a) and (b) showed similar result
for input and output. It described that was no problem
during control the aircraft. Aircraft could perform maneuver
as well as input that was given by pilot. Meanwhile, noise
that occurred in sideslip angle was getting bigger that result
in first simulation. It showed that more complex input that
pilot give, the result will become more complex. Fig. 5 gave
same result as first simulation.
Fig. 6 described the simulation from combination of
non-linear model and linear controller. According to result
For our further works, we would like to try another method
to resolve all the problems that appear during research. As
an example, we would like to rebuild non-linear controller
using Lyapunov method. Lyapunov method allow us to
analyze the stability from a system, in this case is fighter
aircraft stability. By using lyapunov method, we can
compare the result between it when applied to build
controller and the controller using Backstepping method.
From this result we can compare which one has the best
result, linear controller, non-linear controller using
Backstepping method, or non-linear controller using
Lyapunov method. We also will try new model with same
research. Next model for research is F-16 Fighting Falcon.
We will simulate same simulation and compare the result
with JAS-39 Gripen result.
ACKNOWLEDGMENT First of all, we want to thank our mighty God. Also we want
to thank National Defense Academy of Japan Department of
Electronic and Electrical Engineering, especially Control
System Research Group for hereby giving me the
opportunity to perform research. We also want to thank my
supervisor Professor Sasaki Keigo for guidance and
expertise within non-linear and linear control system,
respectively. We also want to thank Governor of Indonesian
Air Force Academy and all member of Electrical
Department for support during this research.
[1]
[2]
[3]
[4]
[5]
REFERENCES O. Harkegard, “Flight Control Design Using Backstepping”,
Linkopings University, 2001
M. Najafi & R. Nikoukhah, "Modeling and simulation of differential
equations in Scicos", pp.177-185, The Modelica Associations,
September 4 th − 5 th , 2006
B. Etkin & L.D. Reid, "Dynamics of flight: stability and control",
AAIA, 1996.
Y.C. Yamaguchi, "Fundamentals of Aerospace Engineering", pp.5158, 2003-2008
O.F.P.G.
Daulay,
"制御器設計に起因する戦闘機の旋回性能限界に関する研究”,
National Defense Academy of Japan, 2009
ps
43
ref
[ rad / s ]
α
ref
[rad ]
β
ref
[rad ]
α
[rad ]
β
[rad ]
(a) ΣN
(b) ΣL
(c) ΣNL
Figure 4. Simulation result without roll maneuver
ps
ref
[ rad / s ]
α
ref
[rad ]
β
ref
α
[rad ]
[rad ]
β
[rad ]
(a) ΣN
(b) ΣL
(c) ΣNL
Figure 5. Simulation result with roll maneuver
ISSN: 2085-6350
44
ps
ref
[ rad / s ]
α
ref
[rad ]
β
ref
[rad ]
α
[rad ]
β
[rad ]
(a) Q=diag(100,100,1,1)
(b) Q=diag(1k,1k,1,1)
(c) Q=diag(10k,10k,1,1)
Figure 6. Simulation result with increased feedback gain (ΣNL)
ISSN: 2085-6350
45
Feature Extraction and Selection on Osteoporosis
X-Ray Image for Content Based Image Retrieval
(CBIR) Purposes
Usman Balugu1,2, Ratnasari Nur Rohmah1,3
1
Jurusan Teknik Elektro, UGM
2
Politeknik Bandung, 3UMS Surakarta
Nurokhim
PTLR, Batan
City PostCode, Indonesia
[email protected]
Abstract— This paper presents results on experiment in
extracting feature of osteoporosis X-Ray image and finding the
right feature or features to perform as descriptor in ContentBased Image Retrieval (CBIR) Systems. Consider that
descriptor of each image will represent the content of each
image, the decision which features of image will be the best
representation of image content, is very important. Data in this
experiments are digitized X-Ray proximal femur images
already known osteoporosis level based on Index Singh (grade
3 – grade 6) due to simple verification on classification. We
used statistical approach and log-polar wavelet transform for
extracting textural feature from image. On statistical
approach, we use grey-level co-occurance matrix (GLCM)
methods to extract feature and used Contrast and Uniformity
as quantity measured. We use Norm-1 as energy wavelet
signature measured. Experiment on feature selection conduct
by applying feature vector on classification process.
Experiment’s results shows that two dimension feature vector
using combination of Contrast on GLCM and Norm-1 on
approximation component of log-polar wavelet transformed
image, is the best choice image descriptor’s. Log-polar
transforms proven effective in reducing wavelet energy error
of rotated and scaled image. Improvement in classification
accuracy also shown that, the use of log-polar transform prior
to wavelet transform is useful in classification study.
Keywords—osteoporosis, descriptor, GLCM, log-polar,
wavelet
I.
INTRODUCTION
Application computer in telemedicine has improved
medical service in healthcare area. One of the applications
is in applying databases on medical information such as
medical record or medical image. Recently, compare to
common method textual-base image retrieval, CBIR is
considered as the more appropriate choice method for image
retrieval in medical image databases. This method retrieves
images from database using information directly derived
from the contents of image themselves, rather than from
accompanying text or annotation [1].
As a basic principle of CBIR, images are internally
represented by numerical features, which are extracted
directly from image. These features are stored in the
database, as a descriptor, along with the images, for rapid
access. At retrieval time, the user presents a sample image,
and the system computes the numerical features, compares
them to those stored in the database, and returns all images
with similar features. It is obvious that the quality of
response depends on descriptor and the distance or
similarity measure that is used to compare features from
different images.
This paper presents results on experiment in extracting
feature of osteoporosis X-Ray image and choosing the right
feature or features to perform as descriptor in ContentBased Image Retrieval (CBIR) Systems. Consider that
descriptor of each image will represent the content of each
image, the decision which features of image will be the best
representation of image content, is very important. We used
statistical approach Grey level co-occurance matrix
(GLCM) and log-polar wavelet transform for extracting
image feature. Selection on image feature is conduct by
choosing feature vector gives optimum accuracy on image
classification test. Data in this experiments are digitized XRay proximal femur images already known osteoporosis
level based on Index Singh (grade 3 – grade 6) due to
simple verification on classification validity.
II.
FEATURES EXTRACTION AND SELECTION
A. Textural Feature on Osteoporosis X-Ray Image
Osteoporosis is characterized by an absolute decrease in
the amount of bone to a level below that required for
mechanical support of normal activity and by the occurrence
of non-traumatic skeletal fracture [2]. Bone structure can be
estimated by observing the change of trabecular pattern in
proximal femur radiograph. The observation of trabecular
pattern change for diagnosis of osteoporosis was first
proposed in the 1960s using radiographs of proximal femur.
The diagnosis was known as Singh Index grading system.
In this research, we used this structure pattern as image
feature. We used 4 grades of Singh indexed image (grade 3
to grade 6) from women patients between 45 – 65 years of
ISSN: 2085-6350
46
age. Image feature extraction is done by image textural
analyzing which will produce feature vector of each image.
This feature vector is used as descriptor on the image
retrieval systems. We used statistical approach and log-polar
wavelet transform for extracting textural feature from image.
B. Feature Extraction Using GLCM
On statistical approach we use GLCM methods to extract
feature. As X-Ray image represent as grey-level image, this
method is considered as suitable method. From an image, f,
with L possible intensity levels, GLCM is a matrix G whose
element g ij is the number of times that pixel pairs with
intensities zi and z j occur in f , in position specified by an
operator Q , where 1 ≤ i , j ≤ L [3]. The operator Q is an
operator that defines the position of two image pixels
relative to each others. The total number, n, of pixel pairs
that satisfy Q is equal to the sum of the elements of G.
Then, the quantity
g ij
pij =
(1)
n
given N × N image f ( x , y ) is transforms into polar form
S × [N 2] image p(a, r) and then logarithm functions are
applied to this polar form to obtain S × R log-polar
image lp ( i , j ) .
Polar form S × [ N 2 ] image p ( a, r ) of N × N image
f ( x , y ) is compute as follows:
⎛⎡N ⎤ ⎡
⎛ 2πa ⎞⎤ ⎞⎟ (5)
⎛ 2πa ⎞⎤ ⎡ N ⎤ ⎡
p (a, r ) = f ⎜⎜ ⎢ ⎥ + ⎢r cos⎜
⎟⎥
⎟⎥, ⎢ ⎥ − ⎢r sin ⎜
⎝ S ⎠⎦ ⎟⎠
⎝ S ⎠⎦ ⎣ 2 ⎦ ⎣
⎝⎣ 2 ⎦ ⎣
for a = 0 , L , S − 1 , and r = 0, L , [N 2] − 1 , and S × R logpolar image lp(i, j ) is computed as follows:
⎛ ⎡ log 2 ( j + 2 )
lp ( i , j ) = p ⎜⎜ i , ⎢
⎝ ⎣ log 2 ( R + 2 )
⎡N
⋅ .⎢
⎣2
⎤ ⎤ ⎞⎟
⎥⎥ ⎟
⎦⎦ ⎠
(6)
for i =0,L,S −1, and j = 0, L , R − 1 .
is an estimate of the probability that a pair of points that
satisfy Q will have value zi , z j . These probabilities are in
(
)
the range [0, 1] and their sum is 1:
K K
∑ ∑ pij = 1
(2)
i =1 j =1
where K is the row (or column) dimension of square matrix
G.
Because G depends on Q, the presence of intensity
texture pattern can be detected by choosing an appropriate
position operator and analyzing the elements of G. In this
research position operator defined as “one pixel immediately
to the right”. As for descriptor that useful for characterizing
the content of G, we choose two descriptors, Contrast and
Uniformity. Contrast is a measure of intensity contrast
between a pixel and its neighbor over the entire image [3].
The range of values is 0 (when G is constant) to (K – 1)2.
Contrast = ∑ ∑ (i − j )2 pij
K K
i = i j =1
K K
(b) Image (a) rotated
by 10 degree
(c) Image (a) scaled by
110%
(d) Log-polar
transformed of (a)
(e) Log-polar
transformed of (b)
(f) Log-polar
transformed of (c)
(3)
Uniformity (also called Energy) is a measure of uniformity
in the range [0, 1]. Uniformity is 1 for a constant image.
Uniformity = ∑ ∑ pij2
(a) Original image
Figure 1. Original, rotated, and scaled image and their log-polar
transformed.
(4)
i =1 j =1
Wavelet Transform for textural feature extraction
C. Feature Extraction Using Log-Polar Wavelet
Transforms
Log Polar Transform
Log-polar transform is used to eliminate the rotation
and scale effect in the input image by converting the image
into corresponding log-polar image (Fig 1). Such log-polar
image is rotation invariant and nearly scale invariant [4].
This transform algorithm is divided into two major steps. A
ISSN: 2085-6350
Discrete Wavelet Transform (DWT) useful for image
analysis due to wavelets having finite duration which
provides both the frequency and spatial locality and
efficient implementation [4].
The hierarchical onedimensional DWT uses pair of high-pass and low-pass
filters derived from wavelet functions to decompose the
original signal into two subbands: details and
approximation, respectively. In 2D-DWT analysis, an
image is split into an approximation and three detail images.
The approximation image is then itself split into a second-
47
level approximation and detail images, and the process is
recursively repeated. The tree details images as well as the
approximation images can also be split.
The standard 2D-DWT can be described by a pair of
quadrature mirror filters (QMF) H and G [5]. The filter H is
a low-pass filter with a finite impulse response denoted by
h(n). And, the high-pass G with a finite impulse response is
defined by:
g ( n ) = ( − 1) n h (1 − n ), for all n.
(7)
keep one column out of two
keep one row out of two
The low-pass filter is assumed to satisfy the following
conditions for orthonormal peresentation:
for all j ≠ 0
∑ h(n)h(n + 2 j) = 0,
(8)
convolve with filter X
Figure 2. Decomposition of a discrete image
image by using the conjugate filters H and G.
n
∑ h(n )
2
(9)
=1
n
∑ h(n) g (n + 2 j ) = 0,
for all j
(10)
n
Forward 2D-DWT using Fast Wavelet Transform
(FWT) algorithm of a N × M discrete image x up to level
p + 1 is recursively defined in terms of the coefficients of
level p as follows:
(11)
C4pk+,1(i , j ) =
h(m )h(n )Ckp, ( m + 2i , n + 2 j )
∑∑
m
n
C4pk++11, ( i , j ) = ∑∑ h(m )h(n )Ckp, ( m + 2i , n + 2 j )
(12)
C4pk++12, ( i , j ) = ∑∑ h(m )h(n )Ckp, ( m + 2i , n + 2 j )
(13)
C4pk++13, ( i , j ) = ∑∑ h(m)h(n )Ckp, ( m + 2i , n + 2 j )
(14)
m
m
m
Ckp into four quarter-size
n
n
n
Log-polar Wavelet Transform
Wavelets transforms provide good multiresolution
analytical tools for texture analysis and classification [6][7].
Experimental results show that this approach can achieve a
high accuracy rate [4]. However, this approach assumes
that the texture images have the same orientation and scale,
which is not realistic for most practical application. A
number works address the problem on rotation and scale on
image. One on these works that shows promising results is
using log-polar transforms on image [4][8] and combined
wavelet packet transforms. Considered that image data in
this research are obtained from scanning X-Ray analog
image, which usually subject to certain random skew angles
and scaling, we use this method prior to wavelets transforms
on image texture-based feature extraction [7] (Fig. 3). As
comparison, we also build feature extraction systems
without applying log-polar transform (Fig. 4). Quantities
calculated as features in this approach are the norm-1 e from
all component of wavelet decomposed image
(approximation, vertical, horizontal, and diagonal
component); e A , ev , eH , eD .
where C0 ( i , j ) = x( i , j ) is given by the intensity levels of the
0
e=
image x.
1 N
∑ Ck
N 2 k =1
2
(15)
In practice, the image x has only a finite number of
pixels. Different methods such as symmetric, periodic or
zero padding could be used for boundary handling. At each
p
step, we decompose the image Ck into four quarter-size
images
C4pk+1 ,
C4pk++11 ,
C4pk++12 , and C4pk++13 . This
decomposition algorithm can be illustrated by the block
diagram in Fig. 2.
Figure 3. Feature extraction using GLMC and Log-polar Wavelet
Transform
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48
III.
EXPERIMENT AND RESULTS
A. Experiment and Result in Log-polar Transform effect in
Wavelet Energy Signature
Figure 4. Feature extraction using GLMC and Wavelet Transform
D. Feature Selection
As mention in sub section above feature extraction for
image is conduct using GLCM approach and wavelet
transforms (Fig. 3 and Fig. 4). That is means that for each
image we have six quantities measured as image feature.
Not all of the measure features above are considered as
descriptor. In this paper, feature vector as descriptor is a
two-dimension vector using combination of two features,
one from statistical approach and another from wavelet
transform. From these eight combinations we define which
feature vector as descriptor by trial and error (Fig. 5).
Experiment in log-polar transform is conduct to ensure
positive impact of this transform in feature extraction. As
mention in sub section above that image data in this research
are obtained from scanning X-Ray analog image, which
usually subject to certain random skew angles and scaling.
Experiment results shows that log-polar transform reducing
error in wavelet energy measured on rotated image (Fig. 6).
On the other hands, this transform doesn’t that much
significant effect on scaled image, especially for
approximation image (Fig. 7). However, generally this logpolar transform has positive effect in reducing MSE of
wavelet energy signature from all decomposed image.
(a) Approximation image
(b) Horizontal detail image
(c) Vertical detail image
(d) Diagonal detail image
Test on image classification is conduct on some sample
data using each combination of two features. A simple
minimum distance classifier method was chosen for this
testing process. Feature vectors giving optimum accuracy
on classification is selected as descriptor.
Figure 6. Mean Square Error of wavelet energy measured
from rotated image.
(a) Approximation image
(b) Horizontal detail
(c) Vertical detail
(d) Diagonal detail
Figure 5. Flowchart on feature vector selection process
Figure 7.
Mean Square Error of wavelet energy measured
from scaled image
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49
B. Experiment and Result in Feature Selection and impact
of the log-polar transform.
Experiment on feature selection shows that only four
feature
vectors: [e A Contrast ]T ,
[e A Uniformity ]T ,
and [eD Uniformity]T , for system using logpolar transform prior to wavelet transform, that result in
clustering in classification process (Fig. 8). In systems not
applying log-polar transform, four feature vectors:
[e A Contrast ]T , [e A Uniformity ]T , [eH Uniformity]T , and
[eV
[eV
Uniformity ]T ,
wavelet transform is the best choice for image descriptors.
This result gives higher accuracy rate 86.25% of all classes
compare to accuracy rate 83.75% for the same feature
vectors in system without applying log-polar transform.
These facts also show that applying of log-polar transforms
has its benefits. Table I and Table II shows classification
accuracy based on feature vectors.
TABLE I.
CLASSIFICATION ACCURACY ACHIEVED ON
CLASSIFICATION PROCESS USING FOUR FEATURE VECORS DERIVED FROM
GLMC AND LOG-POLAR WAVELET TRANSFORM FEATURE EXTRACTION
Uniformity]T
shows clustering in classification process
(Fig. 9). Boundary lines are performed based on feature
vectors (descriptor) of 37 images in databases.
Classes
Grade 3
Grade 4
Grade 5
Grade 6
⎡ eA ⎤
⎢
⎥
⎣Contrast ⎦
(a) Feature vector:
(b) Feature vector:
eA
⎡
⎤
⎢
⎥
⎣Uniformity ⎦
eV
⎡
⎤
⎢
⎥
⎣Uniformity ⎦
(d) Feature vector:
(a) Feature vectors:
(c) Feature vectors:
Grade 3
Grade 4
Grade 5
Grade 6
eD
⎡
⎤
⎢
⎥
⎣Uniformity ⎦
Figure 8. Decision boundary of minimum distance classifier based on
feature vectors from feature extraction process
applying log-polar wavelet transform.
⎡ eA ⎤
⎢
⎥
⎣Contrast ⎦
(b) Feature vectors:
eA
⎡
⎤
⎢
⎥
⎣Uniformity ⎦
eH
⎡
⎤
⎢
⎥
⎣Uniformity ⎦
(d) Feature vectors:
eV
⎡
⎤
⎢
⎥
⎣Uniformity ⎦
Figure 9. Decision boundary of minimum distance classifier based on
feature vectors from feature extraction process
applying wavelet transform
eA
⎡
⎤
⎢
⎥
⎣Uniformity ⎦
eV
⎡
⎤
⎢
⎥
⎣Uniformity ⎦
eD
⎡
⎤
⎢
⎥
⎣Uniformity ⎦
91.67
70.00
83.33
100.00
75.00
80.00
83.33
100.00
66.67
60.00
66.67
100.00
75.00
30.00
25.00
100.00
Classification accuracy (%) for feature vectors:
⎡ eA ⎤
⎢
⎥
⎣Contrast ⎦
eA
⎡
⎤
⎢
⎥
⎣Uniformity ⎦
eH
⎡
⎤
⎢
⎥
⎣Uniformity ⎦
eV
⎡
⎤
⎢
⎥
⎣Uniformity ⎦
91.67
60.00
83.33
100.00
75.00
70.00
83.33
100.00
83.33
30.00
25.00
33.33
75.00
30.00
25.00
100.00
IV.
CONCLUSION
Combination on log-polar wavelet energy signature and
contrast on GLCM as two-dimension vector descriptor, give
sufficient accuracy for the CBIR purposes. Log-polar
transforms proven effective in reducing wavelet energy error
of rotated and scaled image. Improvement in classification
accuracy shown that, the use of log-polar transform prior to
wavelet transform is useful in classification study.
However, only small database used for verification in
this research, and most of data has already confirmed its
index Sigh. Further works would be applying bigger
database and more indistinct indicated data to find the best
image feature to represent of image content as descriptor.
Moreover, feature vector dimension would probably need to
be increased in larger database.
ACKNOWLEDGMENT
F. A. Author thanks to Mr. J. Tjandra Pramudityo for
giving copy on data of osteoporosis X-Ray images used in
this research.
REFERENCES
[1]
Among all those combination, features vectors consist
of Contrast on GLCM and Norm-1 on approximation
component [e A Contrast ]T in system using log-polar
⎡ eA ⎤
⎢
⎥
⎣Contrast ⎦
TABLE II.
CLASSIFICATION ACCURACY ACHIEVED ON
CLASSIFICATION PROCESS USING FOUR FEATURE VECORS DERIVED FROM
GLMC AND WAVELET TRANSFORM FEATURE EXTRACTION
Classes
(c) Feature vector:
Classification accuracy (%) for feature vectors:
El-Naqa, I., Yang, Y., Galatsanos, N. P., & Wernick, M. N., “A
similarity learning approach to content-based image retrieval:
ISSN: 2085-6350
50
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ISSN: 2085-6350
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Image Database.”, Proceeding of International Conference on
Rural Information and Communication Technology 2009, pp.
199–202, July 2009
51 Proceedings of CITEE, August 4, 2009
The Implementation of Turbo Encoder and Decoder
Based on FPGA
Sri Suning Kusumawardani * and Bambang Sutopo †
* Dept. of Electrical Engineering, Fac. of Engineering, Gadjah Mada University, Jl. Grafika 2, Yogyakarta,
Indonesia
† Dept. of Electrical Engineering, Fac. of Engineering, Gadjah Mada University, Jl. Grafika 2, Yogyakarta,
Indonesia
Abstract – Turbo Code was proposed in 1993 by Berrou,
Glavieux, and Thitimajashima, who reported excellent
coding gain results (Berrou, 1993), approaching
Shannonian predictions. The turbo code uses two
Recursive Systematic Convolutional (RSC) encoders
separated by an interleaver. There are two main
decoding techniques which one of them is Soft Output
Viterbi Algorithm (SOVA). SOVA is a modified Viterbi
Algorithm making it is able to use a-priori information
between SOVA decoders. Reliability estimation, loglikelihood algebra, and soft channel outputs are the
fundamentals for SOVA decoding. The iterative SOVA
decoding algorithm is used to increase the performance
of turbo code. Field Programmable Gate Array (FPGA) is
a reprogrammable digital chip which is considered as
very fast and easy to modify chip making it suitable for
prototyping products. Based on above considerations,
FPGA (EPF10K10LC84) is choosen to implement the
Turbo Code.
There are 10 units to be implemented; encoder, 8
units composing a decoder, and control unit. Both the
design process and the implementation use MAX+plus II
software.
The result shows that the simulation works well,
although it cannot to be implemented in a single chip.
The circuit needs 1553 LEs which is much bigger than
the available LEs on EPF10K10LC84. The
implementation results show it is able to correct a
maximum of 5 errors bits.
Viterbi Algorithm (SOVA). SOVA is a modified Viterbi
Algorithm making it is able to use a-priori information
between SOVA decoders. Reliability estimation, loglikelihood algebra, and soft channel outputs are the
fundamentals for SOVA decoding. The iterative SOVA
decoding algorithm is used to increase the performance of
turbo code.
FPGA is a reprogrammable chip. A design in FPGA
can be automatically converted from gate level into layout
structure by place and route software.
This research is our preliminary research on
implementation Turbo Code in FPGA.
II. BASIC THEORY
Recursive Systematic Convolutional (RSC) encoder
RSC is developed from Convolution encoder. RSC
.
, and
encoder written as
are code generators, G1 is feedback polynomial
generator and G2 is forward polynomial generator. Figure 1
show us Recursive Systematic Convolutional encoder block
diagram with constraint length (K) = 3 and ak is obtained
from equation (1).
(1)
Where g1 = g1i if uk = dk and g2 = g2i if vk = dk
Keywords - Turbo Code, RSC Encoder, SOVA Decoder,
Soft Output, FPGA (Field Programmable Gate Array)
I. INTRODUCTION
Error Correcting Control is very important in modern
communication systems which one of them is Turbo Code.
Turbo Code was proposed in 1993 by Berrou, Glavieux, and
Thitimajashima, who reported excellent coding gain results
(Berrou, 1993), approaching Shannonian predictions. The
turbo code uses two Recursive Systematic Convolutional
(RSC) encoders separated by an interleaver. There are two
main decoding techniques which one of them is Soft Output
Figure 1. Recursive Systematic Convolutional Encoder
SOVA decoder
Turbo decoder is a modified Viterbi algorithm. The
aim of this modification is to make Viterbi algorithm
provides soft output values. These values are given to one of
SOVA decoders as a-priori information in order to
increasing the decoder performance. SISO algorithm
receives a-priori information and provides a-posteriori as
output (Figure 2).
information and error series into serial output. There are two
main parts to be implemented: Information series and error
series generator. The truth table of information series
generator is given by Table 1.
Table 1. Truth Table of Information Series Generator
Figure 2. SISO Decoder
There are two modifications in Viterbi algorithm to
make SOVA decoders able to provide soft output:
1. Defined ML path, SOVA uses former matrix path
information and a-priori information to define the ML
path or chosen path.
2. Update process, SOVA must be able to provide soft
output (a-posteriori) which is used as a-priori input
for another SOVA.
Based on the truth table in Table 1, the VHDL
program of Information series generator is shown in Figure
3.
SOVA decoder use three kinds of information which is
different from Viterbi:
1. A-priori
A-priori information is encoded bit probability which
is known before encoding process. Obtained from
another decoder’s output and called as intrinsic
information.
2. Extrinsic
Extrinsic information is encoded bit probability which
is obtained from a-posteriori by deleting the effect of
and a-priori
received systematic information
.
information
3. A-posteriori
A-posteriori information is state series probability
which is obtained based on soft channel output
.
information and a-priori information
III. EXPERIMENTAL RESULTS
Turbo Encoder and Decoder designed using
MAX+plus II software. The designs are implemented using
VHDL hardware programming language. Turbo Codes
System is divided into 5 main units: parallel into serial
converter (information series and error series generator),
encoder, decoder, serial into parallel converter, and control
unit.
Parallel into Serial Converter
Encoding and Decoding in Turbo codes are processed
in serial way but information and error series are given in
parallel, so we need parallel into serial converter to convert
ISSN: 2085-6350 Figure 3. VHDL Program for Information Series Generator
The truth table of error series generator is given by
Table 2.
Table 2. Truth Table of Error Series Generator
Encoder
Figure 5 shows the block diagram of Turbo encoder.
Figure 5. Turbo Encoder Block Diagram
Turbo encoder is obtained from two registers (Flip
Flop D) which is designed in VHDL programming language
as shown in Figure 6.
Based on the truth table in Table 2, the VHDL
program of Information series generator is shown in Figure
4.
Figure 6. VHDL Program for FFD
Decoder
SOVA decoders can be implemented by dividing it
into 8 sub units: Branch Metric Generator (BMG), Add
Compare and Select Unit (ACSU), Delta Path Metric
(DeltaPM), Maximum State Select Unit (MSSU), Track
Back Unit (TBU), Path Equivalence Detector (PED),
Reliability Measurement Unit (RMU), and A- posteriori
Measuring Unit.
Serial into Parallel Converter
Serial into Parallel circuit is given by Figure 7.
Figure 4. VHDL Program for Error Series Generator
Figure 7. Serial into Parallel Converter Circuit
A-posteriori Circuit and Decoded Bit
Figure 8 shows us the circuit which is provide aposteriore result and decoded bit.
and encoded bit are determined at TBU. TBU needs the
biggest LE, 558 LE or 96.88% of EPF10K10LC84’s
maximum capacity.
IV. CONCLUSIONS
The Turbo Codes implementation results show it is
able to correct random error up to 5 bits. SOVA decoders
can be implemented by dividing it into 8 sub units: Branch
Metric Generator (BMG), Add Compare and Select Unit
(ACSU), Delta Path Metric (DeltaPM), Maximum State
Select Unit (MSSU), Track Back Unit (TBU), Path
Equivalence Detector (PED), Reliability Measurement Unit
(RMU), and A-posteriori Measuring Unit.
Chosen metric path and encoded bit are determined at
TBU. TBU needs the biggest LE, 558 LE or 96.88% of
EPF10K10LC84’s maximum capacity.
Figure 8. A-posteriore Circuit
Control Unit
Figure 9 shows us the circuit which is provide control
signal to control some units and components.
Figure 9. Control Signal Generator Circuit
The Turbo Codes implementation results show it is
able to correct random error up to 5 bits. Chosen metric path
ISSN: 2085-6350 REFERENCES
[1] Altera Corporation, January 2003, “FLEX 10K
Embedded Programmable Logic Device Family”, Data
Sheet version 2.2, Altera Corporation.
[2] Altera Corporation, January 2003, “FLEX 10K
Embedded Programmable Logic Device Family”, Data
Sheet version 4.2, Altera Corporation.
[3]Altera Corporatio, July 2002, ”Turbo Encoder/Decoder”,
Megacore Function User Guide Version 1.1.2 Rev. 1,
Altera Corporation.
[4] Berrou, C., 1993, Turbo Codes: Some Simple Ideas for
Efficient Communications.
[5]Hagenauer, J., Hoeher, P., 1989, “A Viterbi with SoftDecision Outputs and its Applications”, German
Aerospace research Establishement, Institute for
Communiaction Technology, West-Germany.
[6]Hanzo,L., Liew, T.H., Yeap, B.L., 2002, “Turbo Coding,
Turbo Equalisation and Space-Time Coding”,
Department of Electronics and Computer Science,
University of Southampton, UK.
[7]Huang, F., May 29, 1997, “Evaluation of Soft Output
Decoding for Turbo Codes”, Master's thesis, Virginia
Polytechnic Institute and State University, Blacksburg,
Virginia.
[8]Kusumawardani S.S., 2001, “Implementasi Sandi BCH
(15,5) dengan FPGAXC4013”, EE UGM, Yogyakarta.
[9]Lin,S. dan Castello, 1983, “Error Control Coding:
Fundamental
and
Apliccation”,
Prentice-Hall
International Inc, Englewood Cliff, New Jersey.
Conference on Information Technology and Electrical Engineering (CITEE) Proceedings of CITEE, August 4, 2009
55
BER Performance Analysis of PAM and PSM for
UWB Communication
Risanuri Hidayat
Electrical Engineering Department
Faculty of Engineering Gadjah Mada University
Yogyakarta 55281, Indonesia
[email protected]
Abstract— This paper proposes BER analysis of Impulse
Radio Ultra Wideband (IR-UWB) pulse modulations. A
Gaussian monocycle as UWB pulse is modulated using Pulse
Amplitude Modulation (PAM) and Pulse Shape Modulation
(PSM). The channel model is generated from a modified S-V
model. Bit-error rate (BER) is measured over several of bit
rates. The result shows that PAM gives better performance
than PSM in bit rates and SNR. Moreover, as standard of
speed has been given for UWB, both modulations is
appropriate with high bit rates in LOS channel.
where Tc = 1 / f c is the width of the pulse. The values A1 and
A2 are amplitudes. The Federal Communications
Commission (FCC) has recently approved the deployment of
UWB on an unlicensed basis in the 3.1–10.6 GHz band
subject to a modified version of Part 15.209 rules as in Fig. 1
[1]. The essence of this regulation is to limit the power
spectral density (PSD) measured in a 1–MHz bandwidth at
the output of an isotropic transmit antenna.
Keywords— IR-UWB, S-V Channel Model, LOS NLOS,
PAM, PSM
I.
INTRODUCTION
Since FCC released UWB frequency band [1], UWB
performance for communication has been studied [2]-[13],
including binary modulations for impulse radio include pulse
amplitude modulation (PAM) [3] and pulse shape
modulation (PSM) [4]. BER performance for impulse radio
ultra wideband (IR-UWB) systems in multipath propagation
channels has also been introduced [5][6][7][8][9]. In order to
realize actual condition, a statistical model is established for
the ultra-wide bandwidth in a simulation [10][11], and the
channel model using a Saleh-Valenzuela (S-V) [12] that
showed multipath arriving in clusters has been modified for
UWB signal application [13].
This paper presents the analysis of modulations of UWB
through S-V channel that has been modified for UWB
communications [13] and AWGN noise especially in bit
rates and BER by a simulation. PAM and PSM are
implemented for this purpose. Data sent by the transceiver
are converted to modulated UWB pulse and are then
received by the receiver. At the receiver the pulse is then
converted to data again. The comparison between the data
bits sent by transmitter and the data bits received by receiver
represents the BER values.
II.
UWB PULSE AND ITS MODULATIONS
Some references have represented several formulas of
Gaussian pulse [14] as an IR-UWB. A monocycle pulse is
the first derivative of Gaussian pulse. The Gaussian pulse
has formula (1), while the monocycle is given in (2).
x ( t ) = A1 .e
⎛π t ⎞
−2⎜
⎟
⎝ Tc ⎠
2
x
⎟
− 2 ⎜⎜
dx ( t )
T ⎟
(t ) =
= A 2 t .e ⎝ c ⎠
dt
A number of modulation schemes may be used with IRUWB systems. The potential modulation schemes include
both orthogonal and antipodal schemes. Some common
methods of modulation are to invert the called as pulse
amplitude modulation (PAM) [3] and pulse shape
modulation (PSM) [4] [15], which requires special pulse
shapes to be generated which are orthogonal to each other.
A. Pulse Amplitude Modulation
PAM is implemented by binary pulse modulation, which
is presented using two antipodal Gaussian pulses. The
transmitted binary baseband pulse that is modulated
information signal (t) is expressed as [3]
x(t ) = d j .wtr (t )
(3)
where wtr(t) represents the UWB pulse waveform, j
represents the bit transmitted that d j = −1, j = 0 and
d j = +1, j = 1
(1)
⎛ πt ⎞
(1 )
Figure 1. UWB spectral mask and FCC Part 15 limits
2
(2)
Fig 2 (a) shows the pulse amplitude modulation of the
Gaussian monocycle pulse waveform expressed in (2) with
Tc = 2.22x10-1 ns. Fig. 2 (b) shows the normalized Power
Spectral Density (PSD) of the derivative of pulse. When a
pulse is transmitted, due to the derivative characteristics of
ISSN: 2085-6350
56
the antenna, the output of the transmitter antenna can be
modelled by the first derivative of the pulse [3]. The
derivative can be achieved by the basics of differential
calculus
x (1) ( t ) =
lim
dt → 0
x ( t + dt ) − x ( t )
dt
(4)
PSD of the deterministic power signal, w(t), is
P( f ) =
lim
T →∞
⎛ X(f)2 ⎞
⎜
⎟
⎜
⎟
T
⎝
⎠
(a) PSM pulse shapes
(5)
where T is the pulse spacing interval. X(f) is the Fourier
transform of the pulse, i.e. x(t). P(f) has units of watts per
hertz. When X(f) is attained, the peak emission frequency,
i.e. fM, can be found as the frequency at the maximum value
of |X(f)|. The normalized PSD is used to comply with the
FCC spectral mask of the pulse that is transmitted by
antenna [1][16][17]. The normalized PSD can be defined as
follows
(b) Frequency domain
P( f ) =
X(f)
2
X ( fM )
2
(6)
Fig. 2 (b) shows that the pulses have normalized PSD in
3.1-10.6 GHz frequency band that fulfil the FCC rules of
UWB communication as shown in Fig. 1.
B. Pulse Shape Modulation
Pulse shape modulation (PSM) uses different, orthogonal
waveforms to represent bit ‘0’ and ‘1’. The transmitted pulse
can be represented as
Figure 3. Pulse waveforms used for PSM modulation and its frequency
domain
x(t ) = (1 − d j ) wtr( 0) (t ) + d j wtr(1) (t )
(7)
where dj is defined as in (8) and wtr(0) (t ) and wtr(1) (t ) represent
two different pulse waveforms.
Fig. 3 shows the pair of PSM pulses and it’s PSD. The
pulse waveforms apply the Gaussian monocycle pulse
defined as in (2) with Tc = 2.22x10-1 ns. The second
waveform is the derivative of (2). It means that the pulse
shapes are the first and the second derivatives of the
Gaussian pulse. These two waveforms are orthogonal
according to the definition given by the cross correlation of
the two waveforms,
+∞
ρ c (τ ) | t =0 =
∫w
tr
(t ).wtr (t + τ )dt = 0
(8)
−∞
Fig. 3 (b) shows that both of the PSM pulses have the
normalized PSD that comply with the FCC spectral mask.
(a) BPAM pulse shapes
III. CHANNEL MODEL
Since UWB waveforms can be up to 7.5 GHz wide, for
example, any paths separated by more than about 133 psec.
(equivalent to 4 cm path length difference) can be
individually resolved at the receiver [18]. The realistic
channel for IEEE 802.15 study group 3a has been developed
by Saleh-Valenzuela (S-V model) and proposed for the real
indoor channel model, where the clusters and rays are multipath components [13]. This multi-path channel can be
expressed as
(b) Frequency domain
Figure 2. PAM pulse shapes for ‘1’ and ‘0’ bits and its frequency domain
ISSN: 2085-6350
L
57
K
h(t ) = ∑∑ α k ,l δ (t − Tl − τ k ,l )
l =0 k =0
(9)
where α k,l
is the multipath gain coefficient, Tl is the
delay of the lth cluster, and τ k,l is the delay of the kth
multipath component relative to the lth cluster arrival time
( Tl ).
As suggested in [13], α k,l = p k,l β k,l is adopted,
where p k,l is equally likely to take on the values of +/-1,
and β k,l is the lognormal fading term, due to the
simplicity of the real channel coefficients, and to avoid the
ambiguity of phase for an UWB waveform.
The proposed channel model uses the definitions as
previously described in the S-V model, and is repeated here
for completeness. Tl is the arrival time of the first path of the
l-th cluster; τk,l is the delay of the k-the path within the l-th
cluster relative to the first path arrival time, Tl; Λ is cluster
arrival rate; and λ = ray arrival rate, i.e., the arrival rate of
path within each cluster.
Therefore, τ 0l = Tl . The distribution of cluster arrival
time and the ray arrival time are given by
p(Tl Tl −1 ) = Λexp ⎡⎣−Λ(Tl −Tl −1 ) ⎤⎦ , l > 0
(
)
(
)
p τk,l τ(k−1),l = λ exp ⎡⎣−λ τk,l −τ(k−1),l ⎤⎦ , k > 0
(10)
The channel coefficients are defined as α k ,l = p k ,l β k ,l ,
β k ,l is obtained by this expression
20 log10( β k ,l ) ∝ Normal( µ k ,l , σ 2 )
or
IV. SIMULATION METHOD
Fig. 4 shows the block diagram of the transmitter and
receiver communication. Transmitter converts the data bits
to UWB pulses and the pulse are transmitted through the
antenna. There is a channel model between the transmitter
and the receiver. At receiver, the pulses are received by
antenna and LNA (Low Noise Amplifier) becomes uf(t).
Finally, the pulses are detected using the template pulse. The
Gaussian monocycle pulse expressed in (2) with Tc =
2.22x10-1 ns is implemented. PAM and PSM modulations
are implemented using (3) and (7), respectively.
A channel is available between transceiver and receiver.
An UWB channel model is derived from the modified SalehValenzuela model suitable for home environment with both
LOS and NLOS as described previously. There are 5 key
parameters that define the model: Λ = cluster arrival rate; λ
= ray arrival rate, i.e., the arrival rate of path within each
cluster; Γ = cluster decay factor; γ = ray decay factor; and σ
= standard deviation of lognormal fading term (dB). All
parameters are taken into account for channel modeling as
formulated in (10)-(13).
This paper uses one channel model generated from S-V
model using the parameters as shown in Table I. The
equation in (9)-(13) and Table I derived from and based on
Intel measurements [13] are used to generate a channel
model. The generated channel model is shown in Fig 10.
It should be noted that in an ideal environment the
received pulse shape is the second derivative of the signal
transmitted due to differentiator effect in transmitter and
receiver antennas. However, the amplitude of correlation
result between the first and the third derivatives of the
Gaussian pulse is 80% of the autocorrelation of the received
waveform [3]. This means that using the transmitted
waveform as the template, the performance of the correlator
is reduced by less than 1 dB. Therefore, the receiver still can
use the same template waveform to demodulate the signals.
(11)
β k ,l = 10 n / 20
where
n ∝ Normal( µ l , σ 2 )
[ ]
E β k2,l = Ω 0 e −Tl / Γ e
(12)
−τ k ,l / γ
Tl is the excess delay of bin l and Ω 0 is the mean power of
Figure 4. UWB pulse communication
the first path of the first cluster, and p k ,l is +/-1. Then µl is
TABLE I.
given by
Model Parameters
Λ (1/nsec)
λ (1/nsec)
Ѓ (nsec)
γ (nsec)
σ (dB)
µl =
10 ln( Ω 0 ) − 10Tl / Γ − 10τ k ,l / γ
ln(10 )
−
σ 2 ln(10)
20
(13)
CHANNEL MODEL PARAMETERS.
LOS
1/60
1/0.5
16
1.6
4.8
NLOS
1/11
1/0.35
16
8.5
4.8
ISSN: 2085-6350
58
it is much thicker. The bit rates drops drastically in the
NLOS channel model. Note that in order to draw the line in
Fig. 7, the value of log (10-6) is given instead of BER = 0,
since log zero is infinity.
(a) LOS
(a) PAM over LOS channel
(b) NLOS
Figure 5. The generated channel model
V. SIMULATION RESULTS
The implementation based on the block diagram in Fig. 4
shows the UWB pulse communication. The transceiver
converts bits into UWB pulses, then the UWB pulses are
reconverted the pulse back into bits by receiver. The
comparison between the bits sent by transceiver and received
by receiver gives BER value.
(b) PSM over LOS channel
The Gaussian monocycle pulse defined in (2) is
implemented with Tc = 2.22x10-1 ns and PAM and PSM
modulations are applied as in (3) and (7). The channel is
considered to analyze the bit rates of each pulse over the
channel. The channel models, both LOS and NLOS using
parameter in Table I are implemented as shown in Fig. 5.
Wireless communication systems that use frequency
band between 3.1-10.6 GHz. GSM/UMTS uses 0.9-1.8GHz
is rare, while WLAN uses 2.5 GHz. Only 802.11a system
uses frequency band 5 GHz inside this UWB frequency
standard. Therefore, some AWGN are included in the
simulation.
Our experiments apply 104 bits sent from the transceiver
to receiver, and later being analyzed. The bits are sent in
Mbps bit rates. The various bit rates and its BER can be seen
in Fig. 7. Fig. 6 shows the result of the BER of PAM and
PSM pulse modulation of IR_UWB through the LOS and
NLOS channel models. Fig. 6 shows that the influence of
inter multipaths occurs in higher bit rates. The LOS channel
model in Fig. 5 (a) has the last significant paths that cross a
10 dB threshold (NP10dB) at 0.5x10-7 sec and it has only 5
multipaths. It gives low BER until hundreds Mbps.
Meanwhile, the NLOS channel has many multipaths that
influence the pulses, so that it can not be sent in high bit
rates. The NLOS channel model in Fig. 5 (b) has the last
significant paths that cross a 10 dB threshold (NP10dB) at
around 1.0x10-7 sec and it has around 33 multipaths, so that
ISSN: 2085-6350
(c) PAM over NLOS channel
(d) PSM over NLOS channel
Figure 6.
BER of the UWB pulses through the LOS and NLOS channel
models
Fig. 6 (a) shows data sent using PAM over the LOS
channel model. The result shows that SNR 4 and 8 dB
AWGN give high BER. SNR 12 dB gives no error (or error
lower than 10-4) until 800 Mbps. BER increases drastically
after 800 Mbps.
Fig. 6 (b) shows data sent using PSM over the LOS
channel model. SNR 16 and 20 dB AWGN give high BER.
SNR 24 dB gives error in 10-3 level until 800 Mbps and BER
increases afterwards. SNR 28 dB has no error (below 10-4)
until 800 Mbps, and the value of BER increases drastically
afterwards. Therefore, this modulation needs higher SNR
than the other modulations.
Different from the LOS channel that has much higher bit
rates, the NLOS channel gives lower bit rates for the
communication. In Fig. 6 (c), data are sent using PAM over
NLOS channel model. It has high BER until SNR 12 dB.
SNR 16 dB has no error (or error lower than 10-4) within 10
Mbps, gives BER in 10-3 level for bit rates below 30 Mbps
and BER increases drastically after 30 Mbps. SNR 20 dB
has no error (or error lower than 10-4) until 30 Mbps, but it
has higher BER afterwards.
In Fig. 6 (d), data are sent using PSM over the NLOS
channel model. It has high BER until SNR equals to 28 dB.
SNR 32 dB gives BER in 10-4 level for 10 Mbps and BER in
10-3 level until 30 Mbps. It has also high BER for above 30
Mbps.
Comparing among the modulations, the result shows that
for the same channel, PAM is more robust of AWGN than
PSM, both in LOS and NLOS channels. From the results, it
can be said that UWB pulse communication is appropriate in
LOS channel, i.e., has high bit rates (hundreds Mbps).
Nevertheless, this communication can be used until several
Mbps in NLOS channel model.
TABLE II.
LOS
PAM
PSM
NLOS
PAM
PSM
ESTIMATE OF HIGHEST BIT RATES (HBR)
Bit rates (Mbps)
SNR 16 dB
SNR 12 dB
800
800
Bit rates (Mbps)
SNR 32 dB
SNR 20 dB
SNR 16 dB
30
30
30
30
SNR 28 dB
800
800
Table II shows estimation of highest bit rates without
error (or error less than 10-4 level is tolerable). If the less
BER is desired, then lower bit rate can be implemented.
However, in high SNR environment, the high bit rate can
still be implemented. It shows that PAM modulation over
LOS channel gives the best performance. By looking at the
UWB reference standard and compare to the others as shown
in Table III [19], it seems that this communication model is
preferable for the LOS channel, but PSM needs high SNR
environment.
Based on Fig. 6(a)-(d), some samples of bit rate are
chosen to take a closer look at each modulation over LOS
and NLOS channels. Based on Table III, 400 Mbps is chosen
for all modulations for the LOS channel. However, since the
maximum bit rates for NLOS channel is only 30 Mbps, this
rate is chosen.
59
There are 104 bits sent from the transceiver to receiver in
Fig. 9. The figure shows the BER performance of the UWB
pulses through the LOS and NLOS channel models in the
chosen bit rates. Fig 13 shows that PAM is more robust both
in LOS and NLOS channels.
TABLE III.
COMPARISON OF UWB BIT RATE WITH OTHER
STANDARDS.
Speed (Mbps)
480
200
110
90
54
20
11
10
1
Standard
UWB, USB 2.0
UWB (4 m minimum)
UWB (10 m minimum)
Fast Ethernet
802.11a
802.11g
802.11b
Ethernet
Bluetooth
When 400 Mbps is chosen for LOS channel model, it
means that the pulse is sent in 2.5ns periodic time. PAM has
BER in 10-3 level for SNR 8 dB, and low BER (in 10-4
level or lower) afterwards. Fig. 7 shows that data
communication using PAM that needs BER 10-5 or less can
be applied in SNR 12 dB or more.
PPM has BER in 10-2 level within SNR 10 dB, and in
10-4 level for SNR 10 dB. It can be estimated that using
PPM, BER 10-5 or less can be applied in SNR 14 dB or
more.
PSM has BER in 10-2 level within SNR 22 dB, BER in
10-3 level for SNR 24 dB, and BER in 10-4 level for SNR
26 dB. It can be estimated that using PPM, BER 10-5 or less
can be applied in SNR 30 dB or more.
When 30 Mbps is chosen for NLOS channel model, it
means that the pulse is sent in 0.33 ns periodic time. PAM
has BER in 10-2 level for SNR 12 dB, BER in 10-4 for SNR
14 dB, and low BER (lower than 10-4 level) afterwards. Fig.
7 shows that data communication using PAM that needs
BER 10-5 or less can be applied in SNR 16 dB or more.
PSM has BER in 10-2 level within SNR 28 dB, BER in
10-3 level for SNR 30 dB, and BER in 10-4 level for SNR
32 dB. It can be estimated that using PPM, BER 10-5 or less
can be applied in SNR 34 dB or more.
VI.
CONCLUSION
The BER analysis of the Impulse Radio Ultra Wide Band
pulse communication over modified S-V channel model is
presented. The pulse uses Gaussian monocycle pulse. The
channel model uses a modified S-V model introduced by
Intel. PAM and PSM are applied for modulation, and BER is
measured over several bit rates. The result shows that all
modulation are appropriate for both LOS and NLOS
channel, but PAM gives the best performance in bit rates and
SNR. Moreover, as standard of speed has been given for
UWB, the communication is appropriate with high bit rates
in LOS channel.
ACKNOWLEDGMENT
The authors would like to thank the anonymous
reviewers for their constructive comments and insights that
help to improve this paper.
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[5]
[6]
[7]
[8]
[9]
[10]
(a)
[11]
[12]
[13]
[14]
[15]
[16]
(b)
Figure 7. BER performances over channels
[17]
REFERENCES
[1]
[2]
[3]
[4]
[1] FCC 04-48 “First Report and Order in the Matter of Revision of
Part 15 of the Commission’s Rules Regarding Ultra-wideband
Transmission Systems.” Feb.14, 2002.
[2] R. A. Scholtz, “Multiple access with time-hopping impulse
modulation,” Proc. MILCOM ’93, vol. 2, pp. 447-450, 1993.
[3] I. Oppermann, M. Hamalainen, and J. Iinatti, “UWB Theory and
Applications”,John Wiley & Sons, West Sussex, 2004.
[4] L. B. Michael, M. Ghavami, and R. Kohno, “Multiple pulse
generator for ultra-wideband communication using Hermite
polynomial based orthogonal pulses,” in IEEE Conf. Ultra Wideband
Systems and Technologies, Dig. Papers, Baltimore, MD, 2002, pp.
47–51.
ISSN: 2085-6350
[18]
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[5] W. Xu, R. Yao, Z. Guo, W. Zhu, and Z. Zhou, “A power efficient
M-ary orthogonal pulse polarity modulation for TH-UWB system
using modified OVSF codes”, in Proc. IEEE Global
Telecommunications Conf. (GLOBECOM), San Francisco, CA, Dec.
1–5, 2003, vol. 1, pp. 436–440.
[6] S.-C. Lin and T.-D. Chiueh, “Performance analysis of impulse
radio under timing jitter using M-ary bipolar pulse waveform and
position modulation”, in IEEE Conf. Ultra Wideband Systems and
Technologies, Dig. Papers, Reston, VA, Nov. 16–19, 2003, pp. 121–
125.
[7] D. J. Clabaugh, M. A. Temple, R. A. Raines, and C. M. Canadeo,
“UWB multiple access performance using time hopped pulse position
modulation with biorthogonal signaling”, in IEEE Conf. Ultra
Wideband Systems and Technologies, Dig. Papers, Reston, VA, Nov.
16–19, 2003, pp. 330–333.
[8] L. Ge, G. Yue, and S. Affes, "On the BER Performance of PulsePosition Modulation UWB Radio in Multipath Channels", Proc. IEEE
Conference on Ultra Wideband Systems and Technologies, pp. 231234, May 2002.
[9] G. Yue, L. Ge, and S. Li, “Performance of UWB time-hopping
spread-spectrum impulse radio in multipath environments”, Proc.
IEEE Semiannual Vehicular Technology Conference (VTC '03), vol.
3, pp. 1644–1648, April, 2003.
[10] J. Karedal, S. Wyne, P. Almers, F. Tufvesson and A.F. Molisch,
“Statistical Analysis of the UWB Channel in an Industrial
Environment”, Proc. IEEE Vehicular Technology Conference (VTC
2004), pp. 81-85, Sept. 2004.
[11] D. Cassioli, M. Win and A. Molisch, "The Ultra-Wide
Bandwidth Indoor Channel: From Statistical Model to Simulations",
IEEE Journal on Selected Areas in Communications, Vol. 20, No. 6,
pp. 1247-1257, 2002.
[12] A. M. Saleh, R. A. Valenzuela, “A statistical model for indoor
multipath propagation”, IEEE Journal on Selected Areas in
Communications, SAC-5 no.2, pp.128- 137, 1987.
[13] J. R. Foerster, Q. Li, “UWB Channel Modeling Contribution
from Intel”, IEEE P802.15 Working Group for Wireless Personal
Area
Networks
(WPANs),
June
2002,
http://www.ieee802.org/15/pub/2002/Jul02/ 02279r0P802-15_SG3aChannel-Model-Cont-Intel.doc
[14] X. Chen, and S. Kiaei, “Monocycle shapes for ultra wideband
system”, Proc. IEEE International Symposium on Circuits and
Systems, vol. 1, pp. 597-600, May 2002.
[15] R. S. Dilmaghani, M. Ghavami, B. Allen, and H. Aghvami,
“Novel UWB pulse shaping using prolate spheroidal wave
functions”, in Proc. IEEE Personal, Indoor and Mobile Radio
Communications Conf. (PIMRC), Beijing, China, Sep. 7–10, 2003,
vol. 1, pp. 602–606.
[16] G. Breed, “A Summary of FCC Rules for Ultra Wideband
Communications,”2005,http://www.highfrequencyelectronics.com/Ar
chives/ Jan05/
[17] S. Hongsan, P. Orlik, A.M. Haimovich, L. J. Jr. Cimini, and Z.
Jinyun, “On the Spectral and Power Requirements for UltraWideband Transmission”, Proc. IEEE on Communications, vol. 1,
pp. 738 – 742, May 2003.
[18] A.F. Molisch, J.R. Foerster, M. Pendergrass, “Channel models
for Ultra wideband personal area networks”, IEEE Wireless
Communications, vol. 10, Issue 6, pp.14-21, 2003.
[19] M. Ghavami, L. B. Michael, R. Kohno, “Ultra Wideband
Signals and Systems in Communication Engineering”, John Wiley &
Sons, West Sussex, 2004.
4ll
61
Hardware Model Implementation of a
Configurable QAM Mapper-Demapper for an
Adaptive Modulation OFDM System
Electrical Engineering Department, Faculty of Engineering, Gadjah Mada University
Jl. Grafika 2 Yogyakarta, 55281, Indonesia
[email protected]
Abstract—This research implemented a configurable
4/16/64/256 quadrature amplitude modulation (QAM) mapperdemapper as a part of an adaptive modulation orthogonal
frequency division multiplexing (OFDM) hardware model. The
model is developed as a learning-equipment. Therefore, all
electronic components are selected from the general purpose
types, and performance quality is beyond the target. Mapper
and demapper are a sequential logic circuit controlled by a
synchronized cyclic finite state machine (FSM). The standalone latency generated by mapper and demapper are as long
as 8.5 and 0.5 bits duration, respectively. But, this 0.5 bit
demapper latency will not appear when this mapper-demapper
couple is integrated to the other parts in the QAM or OFDM
model. This mapper-demapper can be configured to form a 4,
16, 64, or 256 orders QAM system, and capable for supporting
a bit rate up to about 4 Mbps.
Keywords—adaptive modulation, OFDM, QAM
If one bit duration is Tb, then both I(t) and Q(t) have
constant value during every T = 2NTb duration.
B. Conceptual OFDM
At the K sub-channels OFDM system transmitter-side,
the original bit stream, d(t), is split to K sub-streams. Each
sub-stream is QAM modulated. Thus, conceptually, the
OFDM transmitter can be visualized in Figure 1.
I. INTRODUCTION
In any communication system, the transmission power
and frequency bandwidth are strictly limited, while the bitrate, noise-immunity, and fading-robustness should be
optimized. OFDM employing QAM is one of suitable
techniques for compromising such resource constrain in one
side and performance objective in other side [1, 4]. It is (and
will be) widely applied in recent (and future) wired and
wireless technologies [2 – 6]. Adaptive modulation is a
scheme intended to optimize the performance.
A configurable 4/16/64/256-QAM hardware model has
been implemented in this research. It is a part of an adaptive
modulation OFDM hardware model. The model is developed
as a learning-equipment. Therefore, all electronic
components are selected from the general purpose types, and
performance quality is beyond the target.
II. REVIEW OF THEORETHICAL BACKGROUND
A. M-QAM
At the M-QAM (M = 22N, N = 1, 2, 3, …) transmitterside, the original bit stream, b(t), is broken-down to 2N-by2N bits segments. Each 2N-bit segment is further split to two
N-bit length groups. Alternate splitting is more suggested
than sequential. In such splitting scheme, the first and second
groups are built of N even-numbered bits and the N oddnumbered bits, respectively. Those two groups are converted
to in-phase and quadrature baseband analog signals notated
as I(t) and Q(t), respectively. Using M(t) = I(t) – jQ(t), the
QAM signal can be expressed as
{
(
s(t ) = Re M (t )exp j 2πf t
c
)}
Figure 1. Conceptual OFDM transmitter visualization
According to equation (1), this OFDM signal can be
expressed as
(
)
K
s' (t ) = Re ∑ M (t )exp j 2πf t = Re(s(t ))
k
k
k =1
(2)
In equation (2), each Mk(t) has constant value during the
symbol duration of TM = 2KNTb. To preserve the
orthogonality, the sub-carrier frequency, fk for all k, must be
an integer multiply of fM = 1/TM. Figure 1 is not feasible to
be realized (implemented) directly.
C. Feasibly-Realized OFDM
Let fk = fc + kfM. By choosing fc a sufficiently high
integer multiply of fM, the summation index (k) in equation
(2) must not be started from a positive integer. By changing
the summation index, the complex-valued signal in equation
(2), can be expressed as
(
K −1
s(t ) = ∑ M (t )exp j 2πf t
k
k
k =0
)
(3)
Sampling s(t) as many as K times during TM, or with
sampling interval of Ts = TM/K, gives
( )
K −1
s( nT ) = ∑ M nT exp( j 2πkn / K )
s
k s
k =0
(4)
(1)
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62
For any k, Mk(nTs) has constant value, that is Mk(nTs) =
M(k) for all n in this k. Normalizing Ts = 1 and replacing
Mk(nTs) with M(k), yield
K −1
s( n) = ∑ M (k )exp( j 2πkn / K ) = IDFT{M (k )}
k =0
(5)
B. Principles of the OFDM Hardware
According to the previous feasible-realized OFDM
review, principle of OFDM transmitter can be modelled as
shown in Figure 4, and the associated receiver in Figure 5.
Equation (5) shows that the complex-valued OFDM
signal is the inverse discrete Fourier transform (IDFT) of the
M(k) = Mk(t) for mTM < t < (m + 1)TM. Using inverse fast
Fourier transform (IFFT) processor, equation (5) can be
implemented efficiently. Therefore, transmitting the
complex-valued OFDM signal equation (5) is more feasible
rather than transmitting the real-valued one equation (2).
Figure 4. Principle of OFDM transmitter
D. Adaptive Modulation OFDM
Bit error rate (BER) increases as the Eb/No decreases.
Effort which can be done to prevent the Eb/No does not fall
below the desired level without increasing the power and
still occupying the overall available bandwidth is decreasing
the operating QAM order. OFDM in which its QAM order is
changeable like this is called adaptive modulation OFDM.
III. PRINCIPLES FOR IMPLEMENTATION
Figure 5. Principle of OFDM receiver
All of the implemented models are constructed as a
trainer, and all of the digital integrated circuit components
are chosen from 74LSxx TTL series. All of the design,
simulation, and lay-out works are aided by OrCAD Release
9.1 software package.
Figure 4 and 5 are generic form. For real applications,
additional sections may be inserted to improve the system
performance [4 – 6].
IV. IMPLEMENTATION OF MAPPING TECHNIQUE
A. Principles of the QAM Hardware
According to the previous M-QAM theoretical review,
principle of QAM transmitter can be modelled as shown in
Figure 2, and the corresponding receiver in Figure 3.
Figure 2. Principle of QAM transmitter
Previous work has been implemented a fix 16-QAM
mapper for four channels OFDM [7].
The QAM mapper implemented in this research is a
configurable (selectable) 4, 16, 64, or 256 orders. Block
diagram of this mapper is shown in Figure 6. In this figure,
notation bIN and m are used rather than b(k) and m(k),
respectively. The QAM symbol, m, takes a complex form as
m = I + jQ, where I and Q represent in-phase and quadrature
components, respectively. This diagram shows two parts: the
signal flow section and control circuit section at the upper
and lower parts, respectively. The signal flow part consists
of one bit-by-bit serial-to-parallel converter, a pair of bit-bybit gate, a pair of 4-bit latch, and a pair of optional format
adapter sections.
Figure 3. Principle of QAM receiver
QAM mapper and demapper designs implemented in this
research refer to those models. For adaptive modulation, the
mapper and demapper must be configurable. In this research,
they can be configured to form as a 4, 16, 64, or 256 orders.
Figure 2 and 3 are generic form. For real applications,
additional sections may be inserted to improve the system
performance.
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Figure 6. Block diagram of the implemented configurable QAM mapper
63
A. Signal Flow Part
Following the S/P, there are two identical arms: in-phase
(I) and quadrature (Q). The main signal flow part will be
elaborated further using Figure 7.
Figure 9. I-arm 2 MSBs gate section output for 256-QAM
Figure 7. Signal flow part, from S/P to latch section
The bit-by-bit S/P employs an 8-bit serial-in parallel-out
(SIPO) shift register 74LS164 preceded by an 74LS74 D-FF
for synchronizing the input bits. According to Figure 7, the
nth-stage output bit is the (8 – n) bits delayed version of the
incoming serial bit. The first stage S/P output bit (b7) is
shown in Figure 8. The strobe pulse (Stb) displayed in this
figure is for 64-QAM. This figure also shows that bits are
shifted at the beginning of their duration.
Figure 10. I-arm 2 LSBs gate section output for 256-QAM
Figure 8. Bit clock, 64-QAM strobe pulse, input bit stream, and the first
stage S/P output bit
Bit-by-bit gate section consists of four AND gates using
IC 74LS08. Each bit coming from S/P is AND-ed with its
associate gating logic (G) generated by the control circuit,
according to the selected QAM order. Operation performed
by this section is illustrated in Table I. As examples, Figure
9 to 12 describe Table I for 256-QAM. The highest delay is
eight bits long, which is experienced by the MSB (b0), due to
the S/P operation as explained before.
Figure 11. Q-arm 2 MSBs gate section output for 256-QAM
TABLE I. OPERATION OF THE BIT-BY-BIT GATE SECTION
QAM
Order
4
16
64
256
G3
1
1
1
1
Gating Logic
G2
G1
G0
0
0
0
1
0
0
1
1
0
1
1
1
B3
b0
b0
b0
b0
Output Bits
B2
B1
0
0
b2
0
b2
b4
b2
b4
B0
0
0
0
b6
Figure 12. Q-arm 2 LSBs gate section output for 256-QAM
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64
The two 4-bit latch (IC 74LS374) sections provide valid
bit (parallel bits) immediately after strobe. As listed in Table
I, the un-gated bit (bits) will also be presented as 0 at the bitby-bit gate sections output, and so at the latch output. As
examples, Figure 13 and 14 show the latched desired bits
(D3 – D2) output for 16-QAM.
Figure 9 to 14 also show that strobe pulse arrives at the
second half of the bit duration. Therefore, the latch sections
generate an additional delay of half bit duration.
The 4-bit parallel data format from these latches may not
be match with the format required by the IFFT section.
Therefore, a format adapter is necessary. Such required
formats may be unsigned number, signed number, 2’s
complement, or others. In this research, the format adapter is
a proportional expander converting the latch output to 8-bit
format. Its operation principle is shown in Figure 15 and
Table II.
For 4, 16, and 256 orders, these proportional expansions
are perfectly performed. For 64-order, the 8-bit format is
obtained by puncturing its 9-bit LSB. However, a slight
distortion occurs. The seven multiplexers in Figure 15 are
implemented using four dual multiplexers IC 74LS153.
Reason of employing magnitude expander is explained
as follows. In fact, the latch section output word-length is 1,
2, 3, or 4 for 4, 16, 64, or 256 orders, respectively. In other
words, its magnitude range is depended on the selected
QAM order, due to the difference in the number of bits. The
higher the order, the larger the range is. If the latch output
like this is fed directly to the IFFT block, then this block
must be adjusted according to the selected QAM order.
Briefly, it needs an ‘adjustable’ IFFT block. This scheme is
impractical, because increasing this block complexity.
Figure 13. I-arm latched desired bits for 16-QAM
By expanding the N-bit word-length to its proportional 8bit one, the dynamic range seen by the IFFT block is equal,
regardless the selected QAM order.
B. Control Circuit Part
The control circuit part will be elaborated further using
Figure 16. The selected QAM orders are determined by the
Y1Y0 bits. The gating logic encoder operation is described
by Table III, Figure 17, and Figure 18.
Figure 14. Q-arm latched desired bits for 16-QAM
Figure 16. Block diagram of the control circuit part
TABLE III. OPERATION OF THE GATING LOGIC ENCODER
QAM
Order
4
16
64
256
Figure 15. Principle of the 4-to-8 bit format adapter
TABLE II.
QAM
Order
4
16
64
256
q7
B3
B3
B3
B3
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OPERATION OF THE FORMAT ADAPTER
q6
B3
B2
B2
B2
Format Adapter Output
q5
q4
q3
q2
B3
B3
B3
B3
B3
B2
B3
B2
B1
B3
B2
B1
B1
B0
B3
B2
q1
B3
B3
B3
B1
q0
B3
B2
B2
B0
Select
Y1
0
0
1
1
Y0
0
1
0
1
G3
1
1
1
1
Gating logic
G2
G1
0
0
1
0
1
1
1
1
G0
0
0
0
1
According to this table, two NOT gates (IC 74LS04),
four AND gates (IC 74LS08), and three OR gates (IC
74LS32) are needed to realize it.
Frequency divider is implemented using a cascaded two
4-bit binary counters (IC 74LS161) and produces two clock
signals with frequency as high as 2fb and fb, respectively.
Synchronization, if necessary, is possible by clearing the
counters momentary at the synchronizing pulses timing
instants. Figure 19 shows an example of the exciting and
produced clock signals. Figure 9 to 14 informs that strobe
pulse is nothing other than the QAM symbol clock. It is
generated by a finite-state machine (FSM) producing the
required pattern shown in Figure 20.
65
strobes are decoded from S7 and S15, and the 256-QAM is
decoded from S15 only. Generating the 64-QAM strobe by
decoding the S11, of course, needs a special trick, different
from the others. Block diagram of this FSM is illustrated in
Figure 21. The 4-bit binary counter is IC 74LS161, and the
multiplexer is IC 74LS153.
Figure 20. Required strobe pulses timing diagram
Figure 17. Gating logic encoder operation (2 MSBs)
Figure 21. Block diagram of the strobe pulse generator
Timing relationships among the strobe pulse and other
signals have been shown in several previous figures. A
comparison of the four strobe signals is shown in Figure 22.
Figure 18. Gating logic encoder operation (2 LSBs)
Figure 22. Comparison of the four strobe signals
V. IMPLEMENTATION OF DEMAPPING TECHNIQUE
Figure 19. Example of exciting and produced clock signals
Figure 20 shows that the bit clock and QAM symbol
clock (strobe pulse) are appear at the bit beginning and
ending instants, respectively. These strobe pulses are formed
by decoding the FSM output for the appropriate states. The
strobe pulses for 4, 16, 64, and 256 orders arrive once at
every 4, 8, 12, and 16 states, respectively. Therefore, the
FSM must be able to produce 16 states: S0, S1, …, S15. For 4,
16, and 256 orders, this FSM is let to run freely. The 4-QAM
strobes are decoded from S3, S7, S11, and S15. The 16-QAM
The demapper must be compatible to the mapper. Block
diagram of the implemented configurable (4/16/64/256)
QAM demapper is shown in Figure 23. This diagram
indicates two differences with Figure 6. First, the parallel-toserial (P/S) converter not only needs bit clock, but also load
pulse. Second, the demapper operation timing must be
synchronized to the arriving in-phase (I’) and quadrature
(Q’) signals.
A. Signal Flow Part
The signal flow part block diagram is shown in Figure
24. If the format adapter at the mapper side is just an
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66
expander, as implemented in this research, then the format
adapter in the demapper must be a compressor. It does not
need any electronic circuit, because it just takes the four
MSBs and leaves the remainder four LSBs.
Figure 23. Block diagram of the implemented configurable QAM
demapper
The P/S is implemented using an 8-bit parallel-input
serial-output (PISO) shift register 74LS165. Relation
between load pulse and serial output at P/S (for 256-QAM)
is shown in Figure 27.
Figure 26. Bit-by-bit gate parallel output bits for 16-QAM when its
parallel input is 1111
Figure 27. Relation between load pulse and serial output (256-QAM)
Figure 24. Signal flow part, from latch to P/S sections
The 4-bit latch and bit-by-bit gate sections are exactly
similar to the other ones at the mapper, so those sections
need not be explained again. Figure 25 shows that the Ds
input bits are latched to Bs output bits at the strobe pulse
time instants.
B. Control Circuit Part
In addition to generating signals similar to the signals
generated by the mapper control circuit, the demapper
control circuit must also generate the load pulse and must be
synchronized to the arriving symbol. It is the half bit
duration shifted and inverted version of the strobe pulse, and
can simply be generated by a manner illustrated in Figure 28.
Figure 29 shows the relation between strobe and load pulse
for 256-QAM.
Figure 28. Load pulse generation
If the selected QAM order is changed dynamically, the
strobe pulse is also able to track this variation, as shown in
Figure 30. At a fix bit rate, the higher the QAM order, the
lower the strobe rate is.
Figure 25. Latching instants of the parallel input bits
The bit-by-bit gate section operation is illustrated in
Figure 26. In this figure, the selected QAM order is 16, and
all parallel input bits are set to 1. The two MSBs are gated,
and the remainder two LSBs are un-gated (set to 0 at output).
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Synchronizing pulse is used to synchronize the demapper
bit clock, strobe, and load by resetting all of the running
counters. Therefore, the arriving instants of this
synchronizing pulse must be at the symbol transition
instants, and not necessarily at every transition time. The
synchronized bit clock and strobe pulse are shown in Figure
31 and 32.
67
VI. MAPPING-DEMAPPING PERFORMANCE
Mapper-demapper compatibility is observed by
arranging the equipments as shown in Figure 33. External bit
source generates either a regular or pseudorandom binary
stream (PRBS). The synchronization pulse generator
simulator is a real synchronization circuit which has also
been constructed in chain with research, but the discussion is
beyond the scope of this paper. Therefore, in Figure 33, this
circuit is represented by a dash-lined box.
Figure 29. Relation between strobe and load pulse for 256-QAM
Figure 33. Compatibility test set-up
Figure 30. Effect of QAM order dynamic variation to the strobe pulse
Figure 34. Demapper output bit stream compared with mapper input bit
stream for 256-QAM
Figure 31. Pre and post-synchronized bit clock
Figure 35. Demapper output bit stream compared with mapper input bit
stream for 64-QAM
Figure 34 shows the demapper output (recovered) bit
stream (bOUT) compared with the mapper input (original) bit
stream for 256-QAM.
Figure 32. Pre and post-synchronized strobe pulse
As explained above, the mapper parallel output is
delayed from its original input bit stream as long as 8.5 bits,
regardless what the QAM order is. In fact, this parallel-bit is
ready to be read by the demapper, because at the same time,
it has been available at the demapper input latch. But, to read
these available bits, demapper must wait for the next strobe
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pulse. In synchronized condition, for 256-QAM, this waited
pulse will appear again eight bits duration later. So, these
parallel bits are latched-in to the demapper 8.5 + 8 = 16.5
bits later than the mapper original serial bit input. At this
time, these bits are available at the demapper P/S input and
waiting for be loaded to the P/S. The waited load pulse is
generated half bits later. Therefore, the complete delay is
16.5 + 0.5 = 17 bits, as shown in Figure 34.
Another similar result, for 64-QAM, is shown in Figure
35. In 64-QAM, the waited next strobe will appear again six
bit durations later, so the complete delay becomes 8.5 + 6 +
0.5 = 15 bits.
It must be noticed that the demapper half bit duration
delay is just a pseudo-delay, provided that this mapperdemapper pair section is operated in a complete
configuration shown in Figure 4 and Figure 5. In such
complete configuration, this half bit delay According to
those explanations, it can be concluded that the demapper
does not generate latency. On the other hand, the mapper
really generates latency as long as 8.5 bits.
All observations described above are performed at about
80 kbps bit rate. The control circuits have also been tested to
operate at about 4 Mbps bit rate, and the result is shown in
Figure 36.
VII. CONCLUSION
A hardware model implementing a technique of QAM
mapping-demapping for an adaptive modulation OFDM
system has been constructed in this research. These models
are constructed as a learning-equipment. The operating
QAM order can be selected as 4, 16, 64, or 256.
The mapper maps the incoming serial bit to the
corresponding I-Q pair QAM symbol. Both I and Q symbols
are expanded to 8-bit parallel format. The demapper demaps
the recovered I-Q pair 8-bit parallel format back to serial bit
output. Due to the configurability property required, the
mapper and demapper control circuits may be best
implemented as an FSM. The mapper generates latency as
long as 8.5 bits duration. The 0.5 bit latency generates by the
mapper just a pseudo-latency, because it will not appear in a
complete OFDM configuration operation. This mapperdemapper model is also capable supporting the bit rate up to
about 4 Mbps.
ACKNOWLEDGMENT
Idea of implementing this hardware model is also
supported
by
Mr.
Mulyana,
a
member
of
Telecommunication and High Frequency Laboratory at the
Electrical Engineering Department, Faculty of Engineering,
Gadjah Mada University. Construction and testing of this
model is completed by Amirul Azis Ashidiqy, Ibnu Gholib,
and Nurcahyo Wibowo, students at this department.
REFERENCES
[1]
[2]
[3]
[4]
[5]
Figure 36. Bit clock and strobe pulses for 4 Mbps bit rate
Figure 36 also shows that the signal waveforms are
seriously distorted, even though the levels are still in the
TTL permitted range. Nonetheless, operation in this rate is
possible.
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[6]
[7]
Wilson, S.G., 1996, Digital Modulation and Coding, Prentice-Hall
International, Inc., Upper Saddle River, New Jersey.
Leon-Garcia, A., and I. Widjaja, 2004, Communication Networks
Fundamental Concepts and Key Architectures, McGrawHill, New
York.
Orthman, F., 2005, WiMAX Handbook Building 802.16 Wireless
Network, McGraw-Hill Companies, Inc., New York.
Schulze, H., and C. Luders, 2005, Theory and Applications of OFDM
and CDMA Wideband Wireless Communications, John Wiley &
Sons, Ltd., Chichester.
Stuber, G.L., J.R. Barry, S.W. Mclaughlin, Y.G. Li, M.A. Ingram,
and T.G. Pratt, 2004, Broadband MIMO-OFDM Wireless
Communications, Proceedings of the IEEE, Volume 92, Number 2,
February 2004.
van Nee, R., and R. Prasad, 2000, OFDM for Wireless Multimedia
Communications, Artech House Publisher, London.
Mulyana and B. Setiyanto, 2008, Implementasi Model PerangkatKeras Pemeta-Pengawapeta Bit-QAM dan Modulator-Demodulator
I/Q OFDM, Laporan Penelitian DIKS, Fakultas Teknik UGM,
Yogyakarta.
69
Hardware Model Implementation of a
Baseband Conversion, Chip Synchronization,
and Carrier Synchronization Technique
for a Universal QAM System
Electrical Engineering Department, Faculty of Engineering, Gadjah Mada University
Jl. Grafika 2 Yogyakarta, 55281, Indonesia
[email protected]
Abstract—Baseband converters, chip synchronizer, and
carrier synchronizer sections hardware model of a universal
quadrature amplitude modulation (QAM) system have been
implemented in this research. They are developed as a part of
an adaptive modulation orthogonal frequency division
multiplexing (OFDM) learning-equipment. Therefore, all
types, and performance quality is beyond the target. The
transmitter baseband converter is a digital-to-analog (DAC)
circuit. The receiver baseband converter is a cascaded
integrate-and-dump (I/D), sample-and-hold (S/H), and analogto-digital converter (ADC) circuit controlled by a triggered
non-cyclic finite state machine (FSM). Chip synchronization is
carried-out by detecting the abrupt amplitude changes of the
received signal. Synchronizing the carrier phase is carried-out
by shortly speeding-up or slowing-down the receiver local
voltage controlled oscillator (VCO), provided that no frequency
offset occurs. The ADC component limits the chip rate as high
as about 10 kcps.
Keywords—quadrature modulation, DAC, ADC, chip
synchronization, carrier synchronization
I. INTRODUCTION
Due to its compromise bandwidth efficiency and noiseimmunity [1], QAM is (and will be) widely applied in recent
(and future) wired and wireless technologies, either in single
or multi carrier system [2 – 6]. Examples of application
employing QAM are terrestrial microwave transmission and
almost all of orthogonal frequency division multiplexing
(OFDM)-based systems.
The QAM system baseband converter, chip synchronizer,
and carrier phase synchronizer sections have been
implemented in this research. Further, these constructed
sections are prepared to be integrated with the other sections
to form a complete adaptive modulation OFDM model. The
model will be packed as a learning-equipment. Therefore, all
types, and performance quality is beyond the target.
signals, modulate two orthogonal carrier signals, and finally,
transmitted as a QAM signal, s(t). Receiver-side recovers the
bit stream from the received QAM signal, r(t).
Figure 1. General form of QAM transmitter and receiver sides
Various processes transforming bits to chip and vice
versa are beyond the scope of this research. Therefore,
Figure 1 represents them as a dash-lined box. For single
carrier QAM, this process just organizes the serial bits to
their corresponding chip. For OFDM system, this process is
complicated.
The receiver attempts to extract or estimate I(t) and Q(t)
from the received signal, r(t), by carrying-out the
correlations
1 kT
I ' (t ) = Iˆ(t − kT ) =
r (t ) cos(2πf t )dt
∫
0
T (k − 1)T
and
Q' (t ) = Qˆ (t − kT ) =
II. REVIEW OF THEORETHICAL BACKGROUND
General form of a QAM transmitter and receiver sides
can be modelled in Figure 1. At the transmitter-side, the
input bit stream, b(nTb), is transformed to chip symbols
M(kT) = Re(kT) - jIm(kT), with Tb and T = NTb represent the
bit and chip durations, respectively. In general, each chip of
Re and Im symbols consists of N parallel bits. These chip
streams are converted to I(t) and Q(t) analog baseband
(1.a)
1 kT
r (t ) sin (2πf t )dt
∫
0
T (k − 1)T
(1.b)
Optimum estimation result is achieved when during the
integration interval, those two carrier signals are orthogonal
each other. This orthogonal property is guaranteed when f0 =
k/T (k = 1, 2, 3, …).
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70
III. IMPLEMENTATION OF BASEBAND CONVERSION
TECHNIQUE AT THE TRANSMITTER-SIDE
In Figure 1, baseband converter at the transmitter-side is
represented simply as a digital-to-analog converter (DAC)
only. There are two identical converters, for I (in-phase) and
Q (quadrature) arms. The complete configuration of each
converter is shown in Figure 2. For generality, the Re and Im
symbols are represented as D(kT), and the I and Q signals
are represented as a(t). The 8-bit DAC (IC 0800) is followed
by a current-to-voltage (I/V) converter. Level adjuster (LA)
is provided to adjust the final analog output signal range and
level. I/V and LA are based on operational amplifiers LF356.
Figure 5. Both converters output signal when the input is a free running
4-bit binary counter output
Figure 2. Configuration of baseband converter at the transmitter-side
Figure 3 shows an example of the DAC output voltage
(v(t)) when its first three MSBs are varied and the others are
fixed to be in zero state. This figure informs that the DAC is
able to track the input variation.
IV. IMPLEMENTATION OF THE BASEBAND CONVERSION
TECHNIQUE AT THE RECEIVER-SIDE
At the receiver-side, baseband converter also consists of
signal flow part and control circuit as shown in Figure 6.
Figure 6. Block diagram of the baseband converter and multiplier
Figure 3. Effect of input bits variation to analog output voltage of the
DAC-I/V section
Effect of level adjustment on the final converter output
voltage is shown in Figure 4. The LA can be exploited as a
level shifter, an amplitude scaling (attenuation or
amplification), or both.
A. Signal Flow Part
The correlator and ADC in Figure 1 can be explored
further as shown in Figure 7. Correlator section consists of
multiplier and integrate-and-dump (I/D) circuits. The
multiplier section is beyond the scope of this research, and
has ever been implemented [7]. Baseband converter consists
of I/D, sample-and-hold (S/H), level adjuster (LA), and
ADC circuits. There are also two identical converters, for I
and Q arms.
Figure 7. Correlator and ADC sections
The I/D circuit is desired to produce
Figure 4. Effect of level adjustments on the converter output
Both converters linearity and dynamic response have
been observed by driving the DAC with the output of a 4-bit
binary counter. The result is shown in Figure 5.
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t
b(t ) = A ∫ u (τ )dτ , kδ < t < k (δ + T )
kδ
(2)
where A is a constant, k is an integer, and T is the chip
duration, so kδ is the instants when the dump pulse arrives.
The S/H samples b(t) at t = kσ, where kσ is the instants
71
when sample pulse arrives, and holds it until the next
consecutive sample pulse. Thus,
c(t ) = b(kσ )
(3)
Referring to equation (2), optimal result is obtained when
δ =0, and so does σ. Realizing δ =0 and σ = 0 is possible,
but impractical. In this research, δ =0 and σ = 0-, implying
As shown in Figure 9 and 10, sample and dump pulses
are bipolar (positive and negative) voltage.
Level adjuster (LA) is an inverting differential amplifier
circuit using operational amplifier LF 356. Operation
performed by this section is demonstrated by the signal
waveforms as shown in Figure 11.
that the integration result is sampled momentary prior to the
next consecutive integration period.
Principles of I/D and S/H circuits are illustrated in Figure
8, left and right part, respectively. Operational amplifiers
used in this figure are LF355. Operation performed by the
I/D section is demonstrated by the signal waveforms as
shown in Figure 9. This figure informs that integration is
started at the dump pulse instant.
Figure 11. Signal waveforms related to level adjuster (LA) operation
Figure 8. Integrate-and-dump and sample-and-hold circuits
This research employs IC 0808 successive approximation
register (SAR) type ADC. This IC needs a start pulse to
trigger convertion and clock. ADC starts converting at the
start pulse instant. The ADC end-of-conversion (EOC) status
signal goes high when once conversion has finished.
Operation performed by this section is demonstrated by the
signal waveforms as shown in Figure 12 and 13.
Figure 9. Signal waveforms related to I/D operation
Operation performed by the S/H section is demonstrated
by the signal waveforms as shown in Figure 10. This figure
informs that integrator output is sampled momentarily prior
to the next integration interval.
Figure 12. ADC clock, start pulse, and EOC status
Figure 13. Signal waveforms related to ADC operation
Figure 10. Signal waveforms related to S/H operation
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72
The ADC is followed by an 8-bit buffer 74LS373. The
buffer holds the last ADC output until refreshed by the next
consecutive result, as shown in Figure 14.
Figure 14. Signal waveforms related to buffer operation
Ideally, end-to-end baseband conversion test should be
carried-out by building connecting the DAC output to the
ADC input, exciting the DAC with a fast-varying 8-bit
parallel word, and observing the 8-bit ADC output. This
procedure required a 16-traces oscilloscope as a measuring
instrument. This instrument has not been available.
Figure 16 and 17 inform the ADC-DAC couple operate
properly, indicated by the DAC output which is able to track
the ADC input. The slower the ADC input varies, the more
similar the DAC output is, of course. In these tests, the ADC
is forced to operate at its maximum conversion rate as high
as 10000 conversions per second as stated in data-sheet.
B. Control Circuit
Block diagram of control circuit is shown in Figure 18.
Crystal oscillator generates 4.9 MHz clock. Frequency
divider is a free running 4-bit binary counter. It divides the
4.9 MHz exciting clock to produce 1.225 MHz ADC clock
and 306.25 triggered counter clock. Triggered counter is an
up-and-stop 4-bit binary counter. It starts when triggered and
stops when reaches its maximum state. The incoming
external triggering pulse may be too wide for directly
triggering the counter. Pulse shaper transforms this pulse to
an as narrow as desired pulse. Sample, start, and dump
pulses are formed by decoding three consecutive states, S0,
S1, and S2, respectively.
Without loss of significance, a simpler procedure is
created by reversing the configuration, as shown in Figure
15. The results are shown in Figure 16 and 17.
Figure 15. ADC-DAC compatibility test set-up
Figure 18. Block diagram of the control circuit
The frequency divider and triggered counter are
implemented using IC 74LS161. Figure 19 shows the clock
signals provided by frequency divider.
Figure 16. ADC-DAC link test result for 100 Hz sinusoidal signal
Figure 19. Frequency divider available outputs
External supplied triggering pulse may be too wide to be
directly fed to the triggered counter. Therefore, a pulse
shaper is inserted to form this pulse as narrow as desired.
The pulse shaper is a cascaded two monostable
multivibrators (MMV) IC 74LS123. Pulse position and
width are adjusted by the first and second stages MMV,
respectively.
Figure 17. ADC-DAC link test result for 1 kHz signal
ISSN: 2085-6350
External trigger, sample, start, and dump pulses are
shown in Figure 20. Sample and dump pulses shown in this
figure are in unipolar (zero and positive polarities) format.
As shown in Figure 9 and 10, these two pulses should be in
bipolar (negative and positive polarities) format. Therefore, a
unipolar-to-bipolar converter (UBC) circuit is inserted. UBC
circuit is simply an analog comparator circuit.
73
Relation between the received OFDM-signal with the
synchronizing pulse is shown in Figure 23. This pulse is
further used for synchronizing, not for exciting, the other
related circuits. Therefore, the appearance instants of this
pulse must be the chip transition instants, and not necessary
be at every chip transition. On the other, inter chip-transition
appearance is prohibited.
Figure 20. Trigger, sample, start, and sample pulses
V. IMPLEMENTATION OF CHIP SYNCHRONIZATION
TECHNIQUE
Example of a QAM signal waveform is shown in Figure
21 [7]. This figure shows that at every chip transition time,
signal amplitude and/or phase changes are very possible.
Figure 23. Chip synchronized pulse compared to the simulated received
OFDM signal
Above explanation informs that the chip-transition
detection like this is also applicable for general non constantamplitude modulation systems.
VI.
IMPLEMENTATION OF CARRIER SYNCHRONIZATION
TECHNIQUE
Referring Figure 1, adopting equation (1.a) and (1.b) for
estimating the real and imaginary parts chip gives
Figure 21. Examples of a QAM signal waveform
In this research, chip synchronization is performed by
searching for the abrupt change of received signal amplitude.
The pulse synchronizer generates a narrow pulse when such
change is detected. Block diagram of this chip synchronizer
is shown in Figure 22.
Figure 22. Block diagram of the chip synchronizer circuit
The envelope detector is a diode-capacitor circuit, similar
to one which is used in AM radio broadcasting receiver. The
differentiator is a capacitor-resistor (C-R) circuit, and its
output contains the dominant desired pulse contaminated by
other ripple pulses. Level threshold must be adjusted to
discard these ripples and extract the desired pulse. Level
threshold adjuster is just an analog comparator circuit. The
pulse width is adjusted by a pulse shaping circuit. The pulse
shaper is implemented using a monostable multivibrator IC
74LS123.
R̂e(t − kT ) =
1 kT
r (t ) cos(2πf t )dt
∫
0
T (k − 1)T
Îm(t − kT ) =
1 kT
r (t ) sin ( 2πf t )dt
∫
0
T (k − 1)T
(3.a)
and
(3.b)
Equation (3.a) and (3.b) inform that estimation requires a
synchronous local oscillator signals: cos (2πf0t) and sin
(2πf0t).
Carrier recovery technique [8] commonly used in M-ary
modulation system principally is adoptable for QAM if the
chip word-length (number of bits per chip) is known by the
receiver. For OFDM system, this requirement is unrealistic,
because it means that the receiver must know the number of
bits per point of the transmitter IFFT output. Therefore, this
research proposes a carrier synchronization technique which
is independent to the chip word-length. This technique will
be explained as follows.
Let the receiver has been successful to generate the
desired frequency. The next problem is synchronizing its
phase. In usual practical application, a special time-slot is
provided where a specific real and imaginary parts pattern
are transmitted to guide the receiver in synchronizing its
phase. Using signal identification, the receiver know whether
its synchronous condition has been reached or not yet.
The proposed technique in this research is illustrated in
Figure 24. The voltage controlled oscillator (VCO) is
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74
controlled by a positive, zero, or negative voltage through an
analog multi-plexer. The control logic is generated by
another smart circuit responsible for signal identification as
explained above. This smart circuit is not presented here, and
beyond the scope of this research.
Figure 24. Principle of the carrier phase synchronization
Control circuit must know whether the VCO is being
lead or lag. Delaying or advancing the VCO is performed by
shortly slowing it down or speeding it up, that is by driving it
with a negative or positive voltage, respectively. The VCO is
left to be in free running condition by driving it with zero
control voltage.
Preliminary experiment using IC 4046 has shown that
varying the control voltage from -180 mV to +180 mV
varies almost linearly the frequency from 450 kHz to 550,
with a free running or centre frequency of about 500 kHz.
The VCO output waveforms are shown in Figure 25.
cascaded integrate-and-dump, sample-and-hold, analog
signal level adjuster, and 8-bit analog-to-digital converter
(ADC) with its suitable control circuits. Chip
synchronization is carried-out by detecting the abrupt
changes in the received signal amplitude. This section
consists of envelope detector, differentiator, threshold level
adjuster, and pulse shaper circuits. Carrier synchronization is
performed by adopting the delay-locked loop (DLL)
technique commonly employed in a spread-spectrum system
pseudorandom code tracking circuit. This section consists of
a voltage difference detector, a lowpass filter (LPF), and a
voltage controlled oscillator (VCO). These sections are
implemented as a trainer, and operate at the chip rate as high
as about 10 kcps. The chip rate and carrier frequency are
constrained mainly by the ADC and VCO, respectively.
ACKNOWLEDGMENT
Idea of implementing this hardware model is also
supported by Mr. Astria Nur Irfansyah, a member of Basic
Electronics Laboratory at the Electrical Engineering
Department, Faculty of Engineering, Gadjah Mada
University. Construction and testing of this model is
completed by Amirul Azis Ashidiqy, Ibnu Gholib, and
Nurcahyo Wibowo, students at this department.
REFERENCES
[1]
[2]
[3]
[4]
[5]
Figure 25. VCO output at various control voltage level
VII. CONCLUSION
[6]
A hardware model implementing a technique of
baseband conversion, chip synchronization, and carrier
synchronization for an OFDM system has been constructed
in this research. At the transmitter-side, the baseband
converter section is a pair of cascaded 8-bit digital-to-analog
converter (DAC) and analog signal level adjuster circuits. At
the receiver-side, this baseband converter is a pair of
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[7]
[8]
Wilson, S.G., 1996, Digital Modulation and Coding, Prentice-Hall
International, Inc., Upper Saddle River, New Jersey.
Leon-Garcia, A., and I. Widjaja, 2004, Communication Networks
Fundamental Concepts and Key Architectures, McGrawHill, New
York.
Orthman, F., 2005, WiMAX Handbook Building 802.16 Wireless
Network, McGraw-Hill Companies, Inc., New York.
Schulze, H., and C. Luders, 2005, Theory and Applications of OFDM
and CDMA Wideband Wireless Communications, John Wiley &
Sons, Ltd., Chichester.
Stuber, G.L., J.R. Barry, S.W. Mclaughlin, Y.G. Li, M.A. Ingram,
and T.G. Pratt, 2004, Broadband MIMO-OFDM Wireless
Communications, Proceedings of the IEEE, Volume 92, Number 2,
February 2004.
van Nee, R., and R. Prasad, 2000, OFDM for Wireless Multimedia
Communications, Artech House Publisher, London.
Mulyana and B. Setiyanto, 2008, Implementasi Model PerangkatKeras Pemeta-Pengawapeta Bit-QAM dan Modulator-Demodulator
I/Q OFDM, Laporan Penelitian DIKS, Fakultas Teknik UGM,
Yogyakarta.
Ha, T. T., 1990, Digital Satellite Communications, 2nd ed., Mc-Graw
Hill, Singapore.
75
Comparison Study of Breast Thermography and
Breast Ultrasonography for Detection of Breast
Cancer
Thomas Sri Widodo1), Maesadji Tjokronegore2), D. Jekke Mamahit3)
1) &3)
Electrical Dept., Eng. Fac., Gadjah Mada University
2)
Medicine Fac., Gadjah Mada University
Abstracti-Breast thermography is a non-invasive diagnostic
technique that allows the examiner to visualize and quantify
changes in skin surface temperature. The objective of this
research is to compare breast thermography and breast
ultrasonography during abnormal physiology monitoring period
for early stage cancer detection.
For this research the breast thermogram of four patients
are compared with breast sonogram of the same patients with
known breast diseases. The results show that thermography can
be used to detect breast cancer with all stages with exception of
cysts disease in the breast.
Key words: thermography, sonopgraphy, breast, cancer
I. Introduction
Thermography may be defined as a method of mapping
surface temperature variations that depends entirely upon
radiated energy in the infrared spectrum. A detector within
the apparatus converts infrared radiation to an electrical
output proportional to the emitted radiation [1].
The procedure is based on the principle that the
chemical and blood vessel activity in both pre-cancerous
tissue and the area surrounding a developing breast cancer
is almost higher than in the normal breast. Since precancerous and cancerous masses are highly metabolic
tissues, they need an abundant supply of nutrients to
maintain their growth. In order to do this they increase
circulation to their cells by sending out chemicals to keep
existing blood vessels open, recruit dormant vessels and
create new ones (neoangiogenesis). This process results in
an increase in regional surface temperatures of the breast [2,
3].
Criteria for the abnormal breast thermogram may be
derived into vascular and nonvascular thermal patterns. The
number of vessels should be relatively equal bilaterally .
Vessels that are tortuous or serpiginous, or tend to cluster
should be considered abnormal. Nonvascular criteria
include the edge sign, which is an asymmetric flattened
breast contour or localized protrusion with in the breast.
Since there is a high degree of thermal symmetry in the
normal body, subtle abnormal temperature asymmetry can
be easily identified [1, 2].
II. Category and Stage of Tumor
There are five thermo-biological (TH) categories for
breast thermogram. [4]:
TH1 - Normal uniform non vascular.
TH2 - Normal uniform vascular. There
symmetrical streaks of thermal findings (blood vessels) in
both breast. This increase in metabolism is caused by a
relative progresterone deficiency (estrogen dominance).
TH3 – Equivocal (questionable). There is single hot blood
vessel in one breast (right or left). This thermal finding will
need to be watched over time for change. If it is remain
stable, or improves, this finding is normal for the
physiology of the patient.
TH4 – Abnormal. There is an increased temperature area
(metabolism) of one breast. Especially significant is the
highly vascular area.
TH5 – Severely abnormal. There is an intensely increased
temperature (metabolism) of the entire one breast. This is
possible an inflammatory carcinoma.
The currently accepted staging scheme for breast cancer
is the TNM classification. The five tumor T classification
values are Tis, T1, T2, T3, and T4 [5].
Tx – Primary tumor cannot be
assessed.
T0 – No evidence of primary tumor.
Tis - Carcinoma in situ.
T1 - Tumor 2 cm or less in its
greatest dimension
T2 - Tumor more than 2 cm but no
more than 5 cm in its greatest
dimension.
T3 - Tumor more than 5 cm in its
greater dimension.
T4 - Tumor of any size with direct
extension to chest wall or skin.
III. Comparison Results with Breast Ultrasonography
Thermogram images of four patients with different
stages were imaged using an infrared camera (Fluke
Thermal Imager Model I 120) in an hospital. These images
are then compared with the ultrasonography images
(sonogram) of the same patients which are shown in Figure
1 to 4.
IV. Conclusion
There is correlation between thermo biological breast
category and breast cancer stage
for breast cancer
classification. Non uniform thermal findings in both breast
indicates a breast cancer in one breast. This comparison
study shows that breast thermography can be used to detect
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76
a breast cancer. Cyst in the breast can not be detected using
breast thermography.
Figure 3. (a) There is an increased temperature area (metabolism) of left
breast (TH4). (b). Left breast sonogram shows benign tumor without
malignancy (T2).
Figure 1. (a) Entirely breast thermogram shows normal uniform
nonvascular (TH1). (b) and (c) Right and left breast sonogram show small
cysts (T0).
Figure 2. (a) Entire breast thermogram shows a thermal finding at the
upper part of the right breast (TH3). (b) Right breast sonogram shows a
mass of disfuse dextra sinistra of 0.8x0.4 cm (T1) with malignancy and
metastasis.
Figure 4. (a) There is an intensely increased temperature (metabolism) of
the entire left breast (TH5). (b) Left breast sonogram shows a malignancy
tumor with micro calcification of 3.5 cm (T3).
ISSN: 2085-6350
References
[1]. Carl J.D’Orsi,MD & Richard E.Wilson,MD, 1983,
Carcinoma of the breast : Diagnosis and
Treatment, Boston/Toronto
[2]. Anonium, 2003., Breast Health,
Meditherm Inc
[3]. Gros C., Gautherie M., 1980, Breast Thermography
and Cancer Risk Prediction, Cancer
77
[4].
Anonymous,
2007,
Breast
Thermography,
[email protected].
[5].
Anonymous, 2009, Breast Cancer Classification,
Wilkipedia
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78
Adaptive Polynomial Approximation for
Gravimetric Geoid: A case study using EGM96 and
EIGEN-GL04C Geopotential Development
Tarsisius Aris Sunantyo
Geodetic Department, Faculty of Engineering, Gadjah Mada University.
55000, Indonesia
[email protected]
Muhamad Iradat Achmad
Electrical Department, Faculty of Engineering, Gadjah Mada University
55000, Indonesia
[email protected]
Abstract— In this paper, adaptive polynomial approximation
method to model the gravimetric geoid is presented. The
polynomial approximation model to arrange training of input
is for adaptive system. Training data pairs (input and output)
were compiled from latitude and longitude data sequences as
input training, and an associated geopotential developments
datum on each spatial position as output training. By preceded
centering of latitude and longitude data, input training formed
with following appropriate formula is the polynomial terms.
Adaptation process used LMS (least mean square) algorithm
in weight updating, and after training session, approximation
of desired target was computed by reloading weights into the
polynomial model. Model of assessment test used was to
validate adaptive model by comparing residual distance of
consecutive point from both geopotential developments data
and respective adaptive model in geocentric coordinates
system. Using geopotential developments data around of
Merapi and Merbabu as a case study, the results show that the
residual distance between geopotential developments data and
respective adaptive model are about 0.0014417 m (in total
absolute value with standard deviation is about 4.6906x10-5 m)
using EGM96 geoid and about 0.0014468 m (in total absolute
value with standard deviation is about 4.702x10-5 m) using
EIGEN-GL04C geoid. Better result could be achieved by
adding more training session with smaller gain factor.
Keywords—
Adaptive
polynomial
approximation,gravimetric geoid, geopotential developments, and model
assesment.
I. INTRODUCTION
One of the fundamental problems in geodesy is to define
the shape and size of the earth and its gravity field taking
into account its temporal variation. Representation of the
shape of the earth can be carried out by several methods;
one of them is by geoid. Geoid is an equipotential surface of
the earth at mean sea level (Heiskanen and Moritz, 1967).
The use of precise geoid related data, in particular its
undulation, is widespread in all branches of geodesy and it
is often analyzed in other Earth sciences for example in
geophysics, oceanography, as well as in civil engineering
(Zhicai and Yong-qi, 2002).
ISSN: 2085-6350
Figure 1. Distribution of the Indonesian active volcanoes as the Ring of
Fire (Red color is active volcano) (Sunantyo, 2008)
Indonesia, as an archipelago, located partly on the
Eurasian plate, which is subducted by the three major
plates: the Indo-Australian plate in the south and in the
west; the Pacific plate in the East and the Philippine Sea
plate in the north. These subduction zones around Indonesia
create a ring of volcanoes, which is called “the Ring of
Fire” (see Figure 1). A total of 129 active volcanoes exist,
and one of them is Merapi as a result of the subductions.
Purbawinata et al.,(1997) state that the subduction zone is
marked by a chain of active and dormant volcanoes which
spreads along Sumatra, Java, Bali, Lombok, Sulawesi to the
eastern part of Indonesian. North of Merapi volcano is
Merbabu volcano which has not had activities more than
200 years. These two volcanoes are located in Yogyakarta
city and Central Java (Purbawinata et al., (1997)). These
two volcanoes are very often to be used as a research area
too many scientists (i.e. geophysicist, geologist, geodesist
etc) to have a comprehensive understanding about them.
Merapi volcano has been labeled as one of 15 high risk
volcanoes by the International Decade of Natural Disaster
Reduction program (IDNDR) of UNESCO (Sunantyo,
2008). The integration a multidisciplinary approach
(geodesy, geology, geophysics, seismology etc) concerning
to understand the structure and volcanic activities of Merapi
in more detail is very important and urgent (Zschau et al.,
1998). In physical geodesy aspect, gravimetric geoid
determination in these two volcanoes using geopotential
developments is discussed in this research.
79
[ ]
Both the estimation of Cost function ξ k = E ε k ≅ ε k used
2
2
in computing instant gradient at the kth steps, ∇ε k , and gain
factor µ , are used in weight updating
2
w k +1 = w k − µ∇ (ε k2 ) .
(5)
Gradient operator ∇(•) defined as column vector
⎡ ∂ (• )
∇(• ) = ⎢
⎣⎢ ∂w1,k
∂ (• )
∂w2 ,k
∂ (• ) ⎤
L
⎥ (6)
∂w p ,k ⎦⎥
T
and the ith element of gradient vector is
∂ (ε k2 )
∂ε k
= 2ε k
∂wi ,k
∂wi ,k
Figure 2. Mechanical block of current adaptive system (Widrow and
Stearns, 1985)
II. ADAPTIVE SYSTEM DESIGN
Input training of adaptive system is formed by following
associated formula in the polynomial terms. For the 4th order
of 2D polynomial (Sunantyo, 2008):
h = c0,0 + c1,0 λ + c0,1ϕ + c 2,0 λ 2 + c1,1 λϕ + c0,2ϕ 2 +
(1)
c3,0 λ + c2,1 λ ϕ + c1,2 λϕ + c0,3ϕ +
3
2
2
3
c 4,0 λ + c3,1 λ ϕ + c2,2 λ ϕ + c1,3 λϕ + c0,4ϕ .
4
3
2
2
3
4
where λ, φ and h are longitude, latitude, and geopotential
developments respectively, we have 15 terms (including
constant term), and 15 parameters. For convenience, we use
a1, a2,…,a14 to represent the terms: λ, φ, …, φ4 and w0, w1,
w2, …, w14 for parameters: c0,0, c1,0, c0,1, …, c0,4. By preceded
centering of latitude (=φ) and longitude (=λ) data, input
training vector arranged by following appropriate formula in
each polynomial terms. For example, the fifth term of kth
input training given by
(
)(
a4 k = λk − λ ϕ k − ϕ
)
(2)
The kth output of training data, hk, is an associated
geopotential developments datum on spatial position (λk, φk).
Mechanical block of current adaptive system shown in Fig. 1
where k is time index, system output fk denotes an
approximation of desired output zk, and residu εk gives the
difference between zk and fk. Weight updating that has a
basic form (scalar input vector), gives new weight vector
wk+1, and guarantees that adaptation process always parallel
to input vector. Output system at time index k is given by
fk =
L
∑
wik a ik
(5)
i =0
and residue estimation
ε k = zk − fk .
(4)
(7)
By using (4) in (7) and noting that fk is independent of wk,
we obtain
∂ (ε k2 )
∂f k
(8)
= −2ε k
∂wi ,k
∂wi ,k
Using (3) in (8), we get
∂ (ε k2 )
= −2ε k ai ,k ,
∂wi ,k
and gradient vector become
∇ (ε k2 ) = −2ε k a k
[
where a k = a1,k
a 2 ,k
L a p ,k
(9)
]
T
(10)
is input vector at time
index k. Substituting (10) in (5) we get
w k +1 = w k + 2 µ ε k a k .
(11)
This is referred to the LMS algorithm shown in Fig. 1 as the
dash line block. For N training data presentations, we define
input matrix
A = [a 1 a 2 K a n K a N ] T (12)
where n is data index and using it to estimate gain factor as
0.01
µ≅
.
(13)
trace(E[A T A])
where E[.] is expectation operator.
III. MODEL ASSESMENT TEST
Model assessment test was used to validate model. This
test compares the distance between two consecutive points
given by both geopotential developments data and its
adaptive model in geocentric system (Sunantyo, 2008).
Both residual distance (in total absolute value) and
respective standard deviation are used to asses the modeling
capability of adaptive system. For two consecutive points in
geodetic system, (λi ,ϕ i , hi ) and (λi +1 ,ϕ i +1 , hi +1 ) , the distance
(in meter) is given by
d=
(x
− xi ) + ( yi +1 − yi ) + (z i +1 − z i ) (14)
2
i +1
2
2
where
⎛ π ⎞ ⎛ π ⎞
x = (N ′ + h ) cos⎜ ϕ
⎟ cos⎜ λ
⎟,
⎝ 180 ⎠ ⎝ 180 ⎠
⎛ π ⎞ ⎛ π ⎞
y = (N ′ + h ) cos⎜ ϕ
⎟ sin⎜ λ
⎟,
⎝ 180 ⎠ ⎝ 180 ⎠
z = (N ′(1 − e 2 ) + h )sin ϕ ,
K
N′ =
,
1 − e 2 sin 2 ϕ
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K = 6378137,
e = 8.1819190842622x10-2.
Difference between the distance given by gravimetric
data dh and one other given by adaptive model df (called
residual distance) which defined as
∆d = d z − d a ,
(15)
and has a total value
∑ ∆d =
N −1
∑ ∆d
j
,
(16)
j =1
with standard deviation given by
∑(
)
1
2
2 ⎞
⎛ 1 N −1
σ ∆d = ⎜⎜
∆d j − ∆d ⎟⎟ ,(17)
⎝ N − 1 j =1
⎠
represents the closeness of model from its desired target.
Denominator (N-1) in (17) referred to the number of
elements of residual vector related to the consecutive points
selecting.
IV. IMPLEMENTATION
The two kinds of geopotential developments data are
EGM96 and EIGEN-GL04C, located in 48 different stations
around Merapi and Merbabu volcanoes. The datasets consist
of longitude, latitude, and an associated geoid height from
both EGM96 and EIGEN-GL04C. At the design stage, we
use (2) to center both longitude (= λ ) and latitude (= ϕ )
data, and arrange them following the polynomial terms in (1)
to be input training data, while in the output side we use two
sets of geopotential developments data to be our desired
target. Using (12), it was defined input matrix
A ∈ ℜ48×15 where 48 rows and 15 columns referred to the
number of data presentations and the number of weights
respectively. Substituting input matrix A in (13), then is was
got gain factor µ = 0.0095. Now, it was ready to develop an
algorithm to handle our modeling task.
Algorithm of gravimetric geoid modeling using adaptive
polynomial approximation is explained as follows:
Input:
Latitude (= ϕ ), longitude (= λ ), geopotential
developments height (=h), gain factor (= µ ), and
maximum iteration (=kmax).
end
4. Computing output vector f:
for n=1,2,…,48,
fn=anTwkmax
end
V. RESULT AND DISCUSSION
In the first part, distribution of TTG and its corresponding
geoid using EGM96 and EIGEN-GL04C were discussed,
while the second part analyzes are contour lines of geoid
model and difference between geoid data and its model of
EGM96 and EIGEN-GL04C. The third part is model of
assessment using EGM96 and EIGEN-GL04C model for
the 1D plotting of, data and model in the distance axis,
distance of consecutive points and residual distance.
Figure 2. Distribution of TTG and its corresponding geoid using EGM96.
The Figure 2. show that distribution of TTG TTG and
its corresponding geoid using EGM96 that all of TTG from
Yogyakarta, Muntilan, Surakarta are mostly up (plus) and
TTG from Surakarta to Yogyakarta are mostly down
(minus).
Output: Approximation of h (=f).
1. Centering ϕ , λ ∈ ℜ 48 :
ϕ c = ϕ − ϕ and λ c = λ − λ .
2. Arranging input training based on polynomial terms:
a n ∈ ℜ15 , n = 1,2,..., 48. .
3. Training:
k=0,w0=0
while k<kmax
for n=1,2,…,48
fn=anTwk
εn=hn-fn
wk+1=wk+2µεnan
end
k=k+1
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Figure 3. Distribution of TTG and its corresponding geoid using EIGENGL04C
The Figure 3. show that distribution of TTG TTG and its
corresponding geoid using EIGEN-GL04C that all of TTG
from Yogyakarta, Muntilan, Surakarta are mostly up (plus)
and TTG from Surakarta to Yogyakarta are mostly down
(minus).
81
model gives smaller both residual distance and its standard
deviation than another geoid model, EIGEN GL04C.
Concerning with the Figures 2 and 3 that the distribution of
TTG TTG and its corresponding geoid using EGM96 and
EIGEN-GL04C are very similar (there is no significant
difference between them).
Figure 6. Model assesment using EGM96 model for
residual distance
Figure 4. Contour lines of gravimetric geoid model and difference between
geoid data and its model of EGM96
Figure 7. Model assessment using EIGEN-GL04C model for residual
distance
VI.
CONCLUSION
The conclusion of this research are summarized as
follows:
1.
Figure 5. Contour lines of gravimetric geoid model and difference between
geoid data and its model of EIGEN-GL04C
2.
Figures 4 and 5 show that the pattern of contour line of
gravimetric geoid height is very similar. It is supposed that
there is no significant difference contour line using EGM96
and EIGEN-GL04C. The geoid height between them are
also continue
Figures 6 and 7 show the results of model assesment
procedure related to the geoid model contoured in Figure 4
and 5, respectively. In that procedure, distance of
consequtive points in cartesian system are calculated from
both data and model. Residual distance which are defined as
difference distance between data and model are then used to
asses the model. From the results, we know that EGM96
3.
Adaptive polynomial approximation for
gravimetric Geoid height using EGM96 and
EIGEN-GL04C Geopotential Development is very
similar. It is supposed that there is no significant
difference contour line using EGM96 and EIGENGL04C.
The gravimetric geoid height using EGM96 and
EIGEN-GL04C are continue.
The distribution of TTG TTG and its
corresponding gravimetric geoid using EGM96
and EIGEN-GL04C are very similar
REFERENCES
[1]
[2]
Heiskanen, W. and Moritz, H., 1967, Physical Geodesy, Freeman,
San Francisco. Hofmann-Wellenhof B. And Moritz, 2006, Physical
Geodesy, Second Edition, Springer, Wien NewYork.
Pubawinata, M.A., Radomopurbo A., Sinulingga I. K., Sumarti S.,
and Suharno, 1997, Merapi Volcano A Guide
Book, The
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82
[3]
[4]
Volcanological Survey of Indonesia, Directorate General of Geology
and Mineral Resources, Bandung, Indonesia
Sunantyo, Modeling of Local Geoid using Adaptive Scheme: A Case
Study around Merapi and Merbabu Volcanoes, Central Jawa,
Indonesia, Doctoral Dissertation, Faculty of Engineering, Gadjah
Mada University, 2008.
Widrow, B., and Stearns, S. D., 1985, Adaptive Signal Processing,
Prentice Hall Inc.
ISSN: 2085-6350
[5]
[6]
Zachau J., Sukhyar, R., Purbawinata, M.A., Lühr, and Westerhaus,
M., 1998, Project MERAPI – interdisciplinary research at a high-risk
volcano, In : Decade–Volcanoes Under Investigation, Deutsche
Geophysikalische Gesellschaft, Sonderband III/1998.
Zhicai L. and Yong-qi C, 2002, Precise determination of Hongkong
Geoid using heterogeneous data, FIG XXII International Congress,
Washington, D.C. U.S.A.
83
Cerebellar Model Associative Computer (CMAC)
for Gravimetric Geoid study based on EGM96 and
EIGEN-GL04C Geopotential Development
1)
Muhamad Iradat Achmad1), Tarsisius Aris Sunantyo2)
[email protected]
2)
Geodetic Department, Faculty of Engneering, Gadjah Mada University.
[email protected]
3)
Adhi Susanto3)
Abstract— In this paper, cerebellar model associative
computer (CMAC) algorithm is proposed to be used for geoid
study. This algorithm based on the mapping scheme of input
space that transforms input similarity in the input space into
associated levels in the associated cells space. Similar inputs
mapped to similar levels, while dissimilar inputs associated to
mutual independent cells. Associated levels given by
overlapping base functions selected represent a subset of
“feature” to be parameterized using adaptive linear combiner
(ALC) structure where a set of “feature” impersonate as input
vector in the back side, parameter or weight vector in the
middle side, and an output in the front side. ALC structure
adjusts weights value using LMS (least mean square) recursion
in the training (or analyzing) session, and accumulates
weighted input to get an output in the synthesizing session.
Desired output using in weights adjustment are any values
from the output space that correspond to respective values in
the input space. Model assessment test used to validate CMAC
model by comparing residual distance of consecutive points
from both EGM96 and EIGEN-GL04C geopotential global
development is data and respective CMAC model in geocentric
coordinates system. By utilization of EGM96 and EIGENGL04C geopotential global development data sets acquired
from selected points of geometric geoid in around of Merapi
and Merbabu volcanoes, it is simulated the proposed
algorithm to study geoid. The results show that difference
between data and model based on EGM96 geopotential global
development are about 0.00026296 m in total absolute value
with standard deviation about 8.3856 x 10(6) m, and based on
EIGEN-GL04C geopotential global development is about
0.00027139 m in total absolute value with standard deviation
about 8.5441 m. These results use 1x106 epoch in training
session with gain factor mu=1.0638 x 10(4) m better result
could be achieved by adding more training session with
smaller gain factor.
Keywords—cerebellar
model
associative
computer
(CMAC), adaptive linear combiner, least mean square,
EGM96 and EIGEN-GL04C geopotential global development.
I.
INTRODUCTION
Gravimetric geoid study is a fundamental part in geodesy
and other earth sciences in case to define the shape and size
of the earth and its gravity field including its temporal
variation. Many applications such as EWARS (early warning
system) for disaster, oil and gas discovery, land
management, and many more, utilizate geoid information to
perform specific and dedicated operation in its
implementation. This is the facts to be the reason of our
study.
One of many tasks in gravimetric study is the modeling
of gravimetric geoid that has the same meaning with the
surface fitting in which two predicting variables positioned
as input and one response variable as the function of two
input variables to be an output. For the temporal variation of
the surface, we need to add an input time variable, but this is
not the case presented here. Related to the surface fitting,
there are many research available in text books and
references. Most of them use the generalizing of least square
method where prior knowledge given in input design
suspected as the characteristics of the two-dimensional (2D)
plant handled.
In this paper, we propose a gravimetric geoid modeling
method that differs with the previous method in the way to
incorporate prior knowledge to the modeling system. The
method uses an algorithm that exploits the generalization
and learning capability of CMAC (cerebellar model
associative computer, cerebellar model arithmetic computer,
or cerebellar model articulation controller). CMAC is an
associative memory neural network based on the mapping
scheme of input space that transforms input similarity in the
input space into associated levels in the associated cells
space. Similar inputs mapped to similar levels, while
dissimilar inputs associated to mutual independent cells.
CMAC is interpreted as a look up table method [1], and has
been used in the areas of control [2]-[6], pattern recognition
[7], signal and image processing [8]-[10]. Other research
related to the structure and learning of CMAC investigated
in [11]-[16].
In the proposed method, CMAC used to learn a non
linear mapping from a spatial position into the corresponding
geoid value. A geoid position is quantized by a number of
quantization levels, and the CMACs input taken from
quantizers output, associated to a set of association cells by
the input mapping scheme. Training (or analyzing) session
uses the association cells and the corresponding geoid value
to be a set of respective input and output training. By using
the LMS recursion, a set of weights adjusts until a number of
epoch reached. Syntesizing session accumulates the
weighted inputs to approximate desired output.
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II.
CMAC
In this section, we give a brief description of CMAC
with emphasis on generalization and learning capability. In
the proposed method, CMAC is used to learn a nonlinear
mapping from a spatial position into the corresponding geoid
value. Thus, we limit our discussion to a two-input singleoutput CMAC, although a general CMAC is capable of
learning a multi-input multi-output nonlinear function.
Input Mapping Scheme
Let the two-dimensional (2D) input of a nonlinear
function be (x,y), where x and y are a set of L real numbers,
{x,y}∈RL, in the closed interval, [xmin, xmax] and [ymin, ymax],
respectively. The input x is quantized by 1Q diferent uniform
quantizers with quantization interval 1Q, 1ui (i=0,1,2,…,U)
where 1u0≤xmin and 1uU≥xmax. The pre-superscript “1” used in
1
Q and in all symbols in this paper denotes the input
dimension. The decision level indexs of 1ui and 1ui+1 are
given by
prior the design. Equation (3) gives a number of g address
from overall address
p=
∑p
j
.
(7)
j
On each associated address, the association levels is
defined by
A.
⎛
⎞
− (g 2 4 )
⎟⎟
aki , j = exp⎜⎜ i
i
⎝ ( k − (Z a − 0.5 g ))((Z a + 0.5 g ) − k ) ⎠
(8)
where Za is the center of the covered inputs region in terms
of ik (i=1,2).
Adaptive Linear Combiner (ALC)
The structure of ALC is shown in Fig. 1. There is an
input vector, a set of adjustable weights, a summing unit, and
a single output. An input vector, a, with elements a1, a2, …,
⎢ x −1 u 0 1 ⎥
ap, has a number of g non zero elements where g is integer in
1
( Q )⎥ + 1
k =⎢1
(1)
1
the open interval (0,p). These inputs are parameterized by a
⎣ uU − u 0
⎦
corresponding sub set of adjustable weights, wj∈w, j=1, 2,
…, g, and summed up by the linear combiner to get a single
1
1
where k is a set of L positive integer in interval [1, Q]. The
output, y. The combiner is called “linear” because for a fixed
corresponding dequantization levels of (1) are given by
setting of the weights its output is a linear combination of the
input components. However, when the weights are in the
1
process of being adjusted, the output of the combiner is no
2( k ) + 1 1
~
(2) longer a linear function of input. Given an input vector to
x =1 u 0 +
× ( uU − 1 u 0 ) .
1
2( Q )
ALC be in the column form as
x is called the quantization error
Difference between x and ~
of the dimension of x. Similarly, the input y is also quantized
in the same way as in x. By changing in (1) and in (2), the
pre-superscript from “1” to “2”, the quantization interval
from “u” to “v”, and the index from “M” to “N”, we get a set
of L positive integer , 2k, in interval [1,2Q] and the
corresponding dequantization levels, ~y . Difference between
y and ~
y is called the quantization error of the dimension of
y.
B.
[
a n = a 1n
a 2n
L a pn
]
T
(9)
Let the two dimensional (2D) input of CMAC be (1k,2k)
where 1k and 2k are positive integers in [1,1Q] and [1,2Q]
respectively. A set of g address associated by these inputs is
given by
fa j = p j + (1 f j ) + (1 c j )(2 f j );
where
p j = (1 c j )( 2 c j ) ,
⎡ Q − dj ⎤
cj = ⎢
⎥ +1 ,
g
⎢
⎥
i
i
⎡
⎤
k
d
−
j
i
fj = ⎢
⎥ , i=1,2.
⎢ g ⎥
i
Figure 1. Adaptive linear combiner (ALC).
j = 1, K , g (3)
(4)
i
i
(5)
(6)
The parameters of CMAC, idj and g, are displacement and
generalization parameters, respectively. They are defined
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In this notation T stands for transpose so an, is actually a
column vector. Any non zero elements in an, amn≠0∈an, is
given by (8). Here we use both subscript m and n for the
element index of input vector and the iteration (and or data
persentation) index, respectively. The corresponding set of
adjustable weights is also in the column form as
[
w n = w1n
]
w2 n L w pn . (10)
T
Given an input vector an and a weight vector wn, a single
output of ALC on the nth iteration is given by
yn = a n w n =
T
g
∑a
jn
85
w jn .
(11)
j =1
At the first right of equal sign in (11), the overall elements of
the weighted input including zero elements is summed up,
while in the second one, only g non zero elements to be
summed to get the same output, yn.
C. Learning Scheme and Generalization
The multi input-single output mapping of ALC is
determined by the stored weights. The weights are adjusted
based on the least mean square algorithm so that the
mapping achieves the desired function given a priori. Let the
output of ALC for the input vector a be y, the corresponding
training datum be h, and the output error between y and h be
ε. Given an input vector an and the training datum be hn, the
weights wjn (j=1,2,…,g) associated with the input are
adjusted according to
w j ( n+1) = w jn + δ jn ;
j = 1,2, L , g
(a)
(12)
where
δ jn = 2µε n a jn
ε n = hn − y n
The parameter µ is a gain factor that governs a learning
speed. Let the output error for the input an after the updating
be ε+. Then we have
ε n = hn − a Tn (w n + 2µε n a n )
+
= hn − yn − 2µε n a Tn a n
= (1 − 2µa Tn a n )ε n .
To make ε n+ ≤ ε n , the parameter µ is selected so it is in the
interval
0<µ ≤
1
.
T
2a n a n
(13)
The significant feature of CMAC is that the learning
scheme changes the output values for the nearby inputs,
because each of subregions that formed by amn∈an covers g2
inputs, ik (i=1,2). Therefore, similar inputs lead to similar
outputs even for untrained inputs. This property is called
generalization, which is of great use in the CMAC-based
modelling. Moreover, we can control the degree of
generalization by changing the size of g. A larger g gives
larger subregion and wider generalization region. This is to
be the reason so g is called a generalization parameter.
Fig. 2(a) shows the generalization region of CMAC with
g=4. We shall give a training signal to the input h(5,6). The
input then specifies four subregions denoted by the squares,
and four weights stored in the subregions are updated. The
number of updated weights for each input is represented by
“1”, “2”, “3”, and “4”. The outputs indicated by “4”, “3”,
(b)
Figrue 2. Generalization property of CMAC. (a) g=4. (b) g=2.
“2”, and “1” are increased by (2µε-aj), (3µε-aj/2), (µε-aj), and
(µε-aj/2), respectively, where ε- is the output error for the
input (5,6) before the updating and aj is the corresponding
association levels with respect to the covered inputs (1k,2k).
We see that the CMAC outputs are changed even for
untrained inputs. For comparisson, Fig. 2(b) shows the
generalization region of a CMAC with g=2. In this case, two
weights are updated, and the outputs indicated by “2” and
“1” are increased by (2µε-aj) and (µε-aj), respectively. We
see that the generalization region becomes smaller than the
previous one.
III.
CMAC-BASED MODELING
Fig. 3 shows a structure of the proposed modelling
system. Here, an input pair (x,y) is quantized by an interval
quantizer to get a quantized quantity pair, (1k,2k). The input
mapping uses both 1k and 2k to generate the association
address, m. Those quantized quantity pair is also utilizated to
calculate the association levels in m address, am. ALC then
uses the association levels am and an output h to be its
training data pair. After a set of weights adjusted in the
training session of ALC, the association levels weighted by a
fixed set of weights is summed up to get an approximation
~
of the desired output, h . Because of using a quantized
quantity pair in the input mapping, the dequantizer then
x and
needs to dequantize them to get the quantized input, ~
~y , that corresponds to the output h~ in h~ (~
~
x, y ).
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IV.
Figure 3. Generalization property of CMAC. (a) g=4. (b) g=2.
The gravimetric geoid data can be represented in general
form as h(x,y) where the spatial position, x and y, is referred
to longitude and latitude, respectively, and the gravimetric
geoid, h, is the geoid height at position (x,y). Given a set of L
positions, {x,y}∈RL, where x in [xmin,xmax] and y in
[ymin,ymax]. Let the selected ROI (region of interest) for the
dimension of x and y be in interval [1u0,1uM] and [1v0,1vN]
respectively so the ROI covers all position in datasets. By
deciding a number of 1Q and 2Q quantization levels for each
of ROI intervals, quantizer (1) will give a set of L positive
integer {1k,2k} where 1k in [1,1Q] and 2k in [1,2Q]. Equation
(3) then utilizated those quantized quantities, 1k and 2k, to
associate a number of g address vector as the element indexs
of a n ∈ R p (n=1,2,…,L). Those quantized quantities are also
used by (8) to calculate the corresponding association levels
in each associated address. ALC then uses both association
vector an and geoid height h be its input-output training. By
selecting a gain factor, µ, so it is in (13), a set of adjustable
weights, w ∈ R p , is trained by ALC using (12) until some
stoping criteria achieved. Given a fixed set of adjusted
weights, w*∈ R p , and an association vector, a n ∈ R p ,
~
n=1,2,…,L, an approximation of geoid height, hn , is
~
calculated by (11). The quantity hn is not only an
approximation value of true geoid height hn, but its
corresponding position, (~
x, ~
y ) , given by dequantizer (2) is
also an approximation value of true position (x,y).
The last statements in the previous paragraph confirms
the using of model assesment that incorporate the
~
approximated values in both h and (~
x , ~y ) . Reference [17]
was given a respective procedure in model assesment that
used by this paper. In that procedure, data and model in
geodetic coordinat system are transformed into geocentric
cartesian system and then distance of consequtive points in
the new coordinat system are calculated. Residual distances
between model and data are used to asses the model.
As shown in Fig. 2, generalization regions can be control
by using a pre-defined value of generalization parameter, g.
In “unpublished” [18], generalization g is pre-defined so it is
a positive integer in [1,max(1Q,2Q)). By first ploting the
distribution of TTG (tanda tinggi geodesi) to see its
coverage area and defining properly ROI so it covers all
TTG, then generalization g is approximately defined in such
so its value is equal or larger than the maximum distance
between the ROI frames and its respective TTG area frames.
For comparing purpose in this paper, we implement two
CMACs with different generalization parameter in the same
maximum quantization level of both x and y. Here we define
CMAC with g=73 and g=123 in the maximum quantization
level 1Q=2Q=256.
ISSN: 2085-6350
RESULT AND DISCUSSION
In this section, the distribution of TTG points around
Merapi and Merbabu volcanoes are first explained. Those
TTGs are then processed by the proposed method and the
results which are consist of contour lines of corresponding
geoid heights model given by two CMACs with different g,
wil be discussed in the second part of this section. Model
assesments are then presented in the last part and also closed
this section.
Fig. 4(a) and (b) show the distribution of TTG points and
its corresponding EGM96 and EIGEN-GL04C geoid height,
respectively. In geoid height plotting, difference of geoids by
its minimum value are used instead of its original one to
visually enhance its spatial distribution of geoid in each
TTG. In those figures we then know that TTGs which are lie
in the path from Surakarta to Yogyakarta, are mostly have
minimal geoid heights, while TTGs which are lained in the
path from around Muntilan to near Ambarawa are mostly
have maximal and uniform geoid heights. Different with
(a)
(b)
Figrue 4. Distribution of TTG and its corresponding geoid height, (a)
EGM96, (b) EIGEN-GL04C.
87
(a)
(a)
(b)
(b)
Figure 5. Contour lines of geoid model and difference between geoid
data and its model using CMAC with g=73, (a) EGM96, (b) EIGENGL04C
Figure 6. Contour lines of geoid model and difference between geoid
data and its model using CMAC with g=123, (a) EGM96, (b) EIGENGL04C.
TTGs in two paths explained previously, TTGs which are
lained in the path from Bawen to Boyolali are have
fluctuative geoid heights.
of extrapolating points to share their “knowledge” received
from respective trained points. Vertical lines that figured on
each TTGs in Fig. 5 and 6 are difference between geoid data
and its model. From those lines we visually know that
CMAC with smaller g has a smaller difference. This visually
assesment will be explored quantitatively in the next
discussion.
Fig. 5 and 6 show contour lines of geoid models which
are given by CMAC with g=73 and g=123, respectively. In
both figures, EGM96 model are placed in the up side, while
another one are in the bottom side. All figures use the same
resolution in contour lines calculating, that is, a number of
64 contour levels is defined on peak-to peak values of geoid
model. Those figures show that the geoid models given by
CMAC with smaller g has more peaks (top and bottom) than
others in geoid models given by CMAC with larger g.
CMAC with smaller g generalize a small area around trained
points and a narrow valley at near the volcanoes shown in
Fig. 5(a) and (b) is caused by the overlapping of
generalization regions is only in their edges. Different
situations as shown in Fig. 6(a) and (b) are given by CMAC
with larger g. In this situations, generalization regions which
almost covered all trained points are functioned as a bridge
Fig. 7 and 8 show the results of model assesment
procedure related to the geoid model displaying on Fig. 5
and 6, respectively. In that procedure, distance of
consequtive points in geocentric cartesian system are
calculated from both data and model. Residual distance
which are defined as difference distance between data and
model are then used to asses the model. As we have
expected, CMAC with smaller g has smaller residual
distance than other ones given by CMAC with larger g. For
CMAC with g=73, residual distance of EGM96 model is
about 0.00025 m in total absolut value and standard
deviation is about 7.47x10-6 m. These two values are lower
ISSN: 2085-6350
88
than another ones given by CMAC with g=123. This CMAC
produces EGM96 model with residual distance is about
0.00069 m and standard deviation is about 1.94x10-5 m. Fig.
7 and 8 are also showed that using the same CMAC, residual
distance that was given by EIGEN-GL04C models is larger
then it was given by EGM96 models.
(a) EGM96
(a) EGM96
(b) EIGEN-GL04C
Figure 8. Model assesment in using CMAC with g=123
REFERENCES
(b) EIGEN-GL04C
Figure 7. Model assesment in using CMAC with g=73
V.
[1]
[2]
CONCLUSION
In this paper, it is presented that CMAC was a method to
propose gravimetric geoid using EGM96 and EIGENGL04C geopotential developments. The advantages of the
proposed method are summarized as follows.
1) It is very well in local gravimetric geoid response.
[3]
[4]
2) It does not need to redesign when the datasets updated.
3) Spatial resolution is known and customized before the
development process, even if the datasets are limited.
CMAC is inherently capable of learning a multi-input multioutput non linear function. Using three input single output
CMAC’s, the proposed method for 2D non linear function
can straightforwardly be applied to 3D system such as
temporal variation of 2D gravity field. The extenxion will be
shown elsewhere.
[5]
[6]
[7]
[8]
[9]
ISSN: 2085-6350
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melalui Implementasi CMAC sebagai Penyandi Laju Bit Rendah,
Laporan Hasil Penelitian untuk Disertasi, Program Studi Teknik
Elektro, Fakultas Teknik, Universitas Gadjah Mada, Mei, 2009.
ISSN: 2085-6350

p - CITEE 2015 - Universitas Gadjah Mada

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