March 2010 PROCEEDINGS OF THE FIRST INTERNATIONAL CONFERENCE ON GREEN COMPUTING AND THE SECOND AUNjSEED-NET REGIONAL CONFERENCE ON ICT ,_~ ~ (2)K M I T L











2-3 March 2010

PROCEEDINGS OF

THE FIRST INTERNATIONAL CONFERENCE ON GREEN COMPUTING

AND

THE SECOND AUNjSEED-NET

REGIONAL CONFERENCE ON ICT

,_~

~

K M I T L

- . . ,

_,,-

_~

, , . _ _ - ' ,

A U N / S E E O - N e t

j I C A )

E

F .

?ARTMENT OF ELECTRICAL ENGINEERING AND INFORMATION TECHNOLOGY

FA~l HTV OJ=' 1=[\1~If\U::I=QIf\Ir-

~-



The First International Conference on Green Computing and The Second AUN/SEED-Net Regional Conference on leT







Stee Commi Gene

• Lu

Organi

•

^Litasar

•

^Risanuri

•

^Sarjiya

•

^M.Isnaen

•

Wahyu Dcwanto

•

^Suharya

• ^F.

^Dana

•

Ridi Fcrdian

•

^Astria

• ^{Eni Suka}

• ^{Avrin Nu}

•

^{Budi Setiy}

•

Husni Rois Ali

• ^Igi

^Ardiy

Secreta







ii YOGYAKARTA. 2 - 3 March 2010 Dent. of FF and IT r,MII

 Tumiran (Gadjah Mada University)

 Kazuhiko Hamamoto (Tokai University)

 Anantawat Kunakorn (King Mongkut's Institute of Technology Ladkrabang)

 Bambang Sutopo (Gadjah Mada University)



ICGC-RCICT 2010

Table of Contents

Inner Cover

Organizing Committee Foreword

. chedule

Table of Contents

ParI I: SPECIAL LECTURE

ISSN: 2086-4868

ii

111

V

i x



_Lee Energy Effici

iii

Lee Ex

f{2 MlMO

Lee Virt

#3 Computing

l.ec Ubiquitou

#4

Lee Hi

#5 Lee Fil

#6 Part 2: TEC CAM

III CAM

#2

CAM

#3 INA

#1 IND

#1 JPN

#1

JPN

#2

LAO

#1

Analysis or Single-Phase Passive and Active EMl Filter Performance for Induction Motor Drive Chanthea KIIlIII, Werachet Khan-gnern, Masaaki Kanda

Dynamic Modeling and Control ora SEPIC Converter in Discontinuous Conduction Model Vuthchhay Eng and Chanin Bunlaksananusorn

Prototype of Sound-based News on Demand Application in Mutli-languages for partial/fully illiterates

;1 nnanda Thavymony RATH

Experiences in Implementing General-Purpose Applications on CUDA Achmad 1117(.1111 Kistijantoro

Epitomizing Green Computing

P. U. Bhalchaudra , Dr.SD'Khamitkar, N.K.Deshmukh , S.N.Lokflande

Range-Free Localization Algorithm Using Distance Deviation Weighted Factor in Wireless Sensor Network

Thida Zin Mvint, Tomooki Ohtsuki

Stereo-Based Ground Plane Estimation: A Reliable Approach for Low Texure Stereo Images Sach Thanh LE, Kazuhiko HAMAlYJOTO, Kiyoaki A TSUTA, Shozo KONDO

3D Reconstruction from X-ray Fluoroscopy using Exponential Opacity Setting for Differential Volwne Rendering

Khamphong Khongsomboon, Phoumy Indarak, Saykhong Saynasine, Kazuhiko HAMAMOTO, ShozoKONDO

41

45

52

56

60

68

74

82

,.1 ....•... _1. __ '- .•.. f") •••• ~A ________L ""AI"\

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ISSN: 2086-4868

-

---

.ccc-scrcr

2010

MAS

#1

MAS

#2 MAS

#3

MAS

#4 MYA

#1

MYA

#2

PI-II

#1

PI-II

#2

SIN

#1 SIN

#2 SIN

#3 THA

#1

-rllA

#2

Flat Fibre Technology: Towards a Truly Flexible, Distributed Optical Sensor for Environmental Protection and Preservation

F.R. Mahamd Adikan, S.R. Sandoghchi

Compact Bismuth-based Erbium-doped Fiber Amplifier With Wide-band Operation S. W. Harun, X. S. Cheng, H. Ahmad

Adopting Variable Strength Interaction Testing: Some Practical Issues Kamal Z. Zamli, Mohammed f. Younis

Optimum Operation of Pump at Water Treatment Plant for Achieving Energy Saving Mohamed Aris Rantlan, A III in Parvizi, Mohamad Rom Tatnjis

Sustainable Development of ICT for Rural Areas in Myanmar Khin Mar Soc

Green in ICT Utilization for Sustainable Development of Myanmar Soe Soe Khaing

KineSpc1l2 A Full VAK Approach in Learning Spelling

Rose Ann Sa/e. MOllica Dimaiwut, Ma. Rowena SO/WIIO, Rommel Feria

Event Driven Rcconfigurable Architecture for Real-time Multiple Human Motion Tracking and Profiling

Cesar A. Llorente

Fair End-to-end Bandwidth Allocation (FEBA) Algorithms Observation and Improvement Nhan 11. Truong, Trang T T. Do, Duong Tran

Cross Directional Rectangle Search for Fast Block-Matching Motion Estimation Binh P. Nguyen, Trang T. T Do

A High-Accuracy and High-Speed 2-D 8x8 Discrete Cosine Transform Design Trang T. T Do, Binh P. Nguyen

EITor Concealment in H.264 Spatial Scalable Video using Improved Motion Estimation Simon Jude Q. /,0111, Supavadee Aramvith

Evaluation of Blind Modulation Detection in Adaptive OFDM Systems

Donekeo LAKANCHANH, Shingo YOSH1ZAWA, Suthichai NOPPANAKEEPONG, Yoshikazu MIYANAGA

8 8

92

9 6

102

107

112

117

121

126

132 135 140

142 THA An Algorithm for Q-Pactor Evaluation A Case Study on 40 Gbps Hybrid Amplified Optical 147

#3 Transmission System Nayot Kurukitkoson THA

114 TI-I A

#5 THA

#6 THA

#7 VIE III

On-line Verification Algorithm for Flexible Interval Representation System Napat Rujeerapaiboon, Athasit Surarerks

Active Contour Using Local Region Based Force with Adaptive Length Search Line Sopon Pumeechanya, Charnchai Pluempitiriyawej, Saowapak Thongvigitmanee Running Network Intrusion Detection System on a Recycled Personal Computer

Pornchai Korpraserttaworn and Surin Kittitornkun

Directional Filtered Fingerprint Images: An Investigation and Evaluation of Minutiae Consistency Keokanlaya Sihalath, Somsat Choomchuay, Kazuhiko Hamamoto

A Proposal of' Internet-based Monitoring and Control Systems Using OPC UA Nguyen Thi Thanh Tu, Bui QIIOC Khanh, Huynh Quyet Thang

152 157

16 3 167

172

tcoc-acrcr

2010

VIE

#2

Jaka

=1

\ladu '-'I

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=1

ISSN: 2086-4868

Providing Qualified Intensional Answers Using Fuzzy Concept Hierarchies Phong Tuan Ngo, Phuong Hong Nguyen, Anh Kim Nguyen

VIE 1;'3

Credibility Checking-Based Scheduling Schemes lor Desktop Grid Computing Son Hong Ngo, Masaru Fukushi. Xiaohong Jiang

VIE

#4

New Method to Implement Intelligent Street Lighting System in Vietnam KIwi Phan-Dinh

VIE -"'5

High-throughput Pattern Matching Engine for Network Intrusion Detection System Tran Ngoc Thinh, SI/I"il1 Kittitornkun

Bali

={

SMS-Based Technology for Data School Collecting in Bali

1 Ketut Gede Danna Putra, 1 Putu Agung Bayupati, AA. Kompiang Oka Sudana

Design and Implementation of Solar Tracker in Solar Energy Conversion Systems Hac/ian Sa/ria Utania, Endah Setyaningsih, John Son

Jaka

=2

The Assesment of Quality of Service (QoS) in IP/MPLS Network Yulianus, Riri Fitri Sari

Smile Stages Recognition in Orthodontic Rehabilitation Using 2D-PCA Feature Extraction Rima Tri Wahyuningrum, Mauridhi Hery Purnomo, J Ketut Eddy Purnama

.:::1

The Internet Protocol Design Framework to Real Time Communication Application Development Mingsep Sampebua, Lukito Edi Nugroho, Jazi Eko Istiyanto

P~!

k

=1

Reliability Mcasurcm ent of Internet Services Deris Stiawan, Abdul Hanan, Mohd. Yazid Idrus

P; Ie

=2

An Integrated Model of Collaborative Learning in Higher Education Rendra Gustriansyah

?apu

=.:{

The Evaluation of Non-fundamental Frequency Apparent Power and Fundamental Unbalanced Load Apparent Power Measurements According to IEEE Standard 1459-2000 in the Big Consumers

Maurits A. Paath, Suharyanto, Tum iran, Avrin Nul' Widyastuti

z; 1 Data Authentication in Network Forensic Using MD5 and CRC32 Method lrwan Sentbiring, Jazi Eko Istiyanto

Application of Decision Support System to Determine the Level of Drug-Resistance Tuberculosis Sari Wijayanti, Adi Setiya Dwi Grahito, Kuwat Triyana

erna image Processing Intelligent System Iris Eye Real-Time Compound Distribution Planning At Eye 257

=2 Center Sultan Agung Hospital Ida Widihastuti, Sari Ayu Wulandari

Sura Energy-Efficient Protocol of Wireless Sensor Network using Carrier Signaling 264

;: I A. Sjainsjiar Rachman, Wirawan, Gamantyo Hendrantoro

Sura Performance Evaluation of Adaptive Coded Modulation and Maximal Ratio Combining in Millimeter- 272

#2 Wave Fixed Cellular System Under

the

Impact

of Rain

Attenuation and Interference in Tropical Region

S. Tahcfulloh, Suwadi, G. Hendrantoro

Sura Efficiency Comparison on Eclat, FP-Tree, and Top-Down Algorithms in Search Lvltemsets 276

#3 Gunawan, 1.K.E Purnama

Sura Collocation Detection for Indonesian 281

#4 Gunawan, Andy Raharja Tanaya

177

.18 6

190

19 4

199

205

209 214 217 225

230

237

245

253

ISSN:

2086-4868

leGe-ReleT 2010

Sura Optimization of Size and Location of Capacitor Banks on Distribution Systems Using an Improved 285 11-5 Adaptive Genetic Algorithm

Patria Julian/a, Imam Robandi

Sura

#6

Sura

#7

Sura

#S

Position Control of Manipulator's Links Using Artificial Neural Network with Backpropagation 290 Training Algorithm

Thiang, I-Jal1(/J:1' Kho,\'\FClI1l0, Tall Hendra Sutanto

Maximum Power Point Tracker Using Buck-Boost Converter as Variable Resistance 295 Vita Lystianingrum, Hcndra Hardiansyah, Mochamad Ashar!

Filtering of normal. and laryngectomiced patient voices using ANFIS: A Preliminary evaluation of 299 ANFIS algorithm compared to LPF, median, and average Filter

Andy Noortjahja, Tri Arief Sardjono, .MH Purnomo

ET

E I

HI

....•

_

^.

.,.

h i Sura

#9

Compression and Reconstruction of Digital Dental Image on Fuzzy Transform Ingrid Nurtanio. I Ketut Eddv Purnama, Mauridhi He!')' Purnomo

304

Sura Simulation of Desicion Support System for Pricing Grid Enterprise in Renderring Farm Using Neural 308

#10 Network

Lilik An[/(III, Tommv A/II/(I marif Haryanto, Buddy Setiawan, Darda Insan Alain, I Ketut Edi

P u r n a ma, Mocluunmad Hariadi, Mauridhi Hery Purnomo

Sura :112

Sura

=13

:\AU

,12

ISTA

L'NY iTl UNY

#'2

LNY

"'3

TET!

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TET!

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Design and Performance Analysis of Speed Controller in Induction Motor with Sliding Mode Control Mardlijah, Lusiana Prastiwi, M Hery Purnomo

High Performance of Nonlinear DC Motor Speed Control using Backpropagation Neural Network Saidah, Mohamad Ashari, Mauridhi Heri Purnomo

Knowledge Sharing in Knowledge-Growing-based Systems

Arwin Datumava Wuhyttd! Sumari. Adang Suwattdi Ahmad, Aciek Ida Wuryandari, Jaka Semblring Savings Time Execution Prima Numbers Generator Using Bit-Array Structure

Rakhmat Hidayat, A. Rida lsmu Windyarto, Herdjunanto, Samladji, Himawan. H Design and

Implementation

of M-Leaming for Increasing Flexibility of Learning System

Gutot SOl/to,I'O, Edy Sutanta, Rifianto Suryawan

Providing User Quality of Service Specification Cor Communities with Low Connectivity Ratna Wardani. F. Soesianto, Lukito Edi Nugroho, Ahmad Ashari

Electrol.arynx, Esopahgus, and Normal Speech Classification using Gradient Discern, Gradient discern with momentum and learning rate, and Levenberg-Marquardt Algorithm

Fatchul Arifin, Tri AriefSardjono, Mauridhi Hery

Analysis of Green Computing Strategy in University: Analytic Network Process (ANP) Approach Handaru Jati

Website Quality Evaluation Comparison: An Empirical Study in Asia Handaru Jati

Adaptive LMS Noise Cancellation of Wideband Vehicle's Noise Signals

Sri Arttin! DH'i Prasetyowati, Adhi Susanto, Thomas Sriwidodo, Jazi Eko Istiyanto

Design of Power Plant Boiler Temperature Monitoring Application using Sound Card as i\DC and Data Acquisition Device

Udayanto Dwi Atmoio, Gotama Edo Priambodo, Umar Sidiq An NCIQs, Suharyanto, AVl'ill Nur Wh{wlsllili

320

325

329

334

340

345

35 1

358

365

372

379

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= Sura Backpropagation Neural Network based Reference Control Modeling for Mobile Solar Tracker on a 314

;111 Large Ship

Budh)' Setiawan. Mochaniad A shari, Mauridhi Hery 1'111'1101110



ISSN: 2086-4868 Technical Paper Proceedings of leGe·:-



:

Abstract--Vehicle's noise signals In'C random and periodic signals. The Mnjority of the spectrum components are on the low frequencies, but there are some high frequecies that appeared as sharp signals.

These properties causes difficulties in the application of the Adaptive Least Mean Square (LMS) Noise Cancelling process especially in finding the optimum step size

u .

It call be simulated by some sharp signal that purposely created and then compared to the sine signal. This can be understood since the wider the signal spectrum is the closer its parameter to the white noise, which contributes to the overall adaptation error,

Keywords: Adaptive LMS Noise Cancelling, error, spectrum

l. iNTRODUCTION

Vehicle's noise signals consist of random as well as periodic signals and exhibit some sharps appearances. The Majority of the spectrum components are on the low frequencies. The high frequencies components were perceived II sizzling. That properties causes difficulties in the application of the Adaptive Least Mean Square (LMS) Noise Cancelling process especially in finding of the optimum step size

Jl,

This condition can be simulated with some sharp signals that purposely made and then compared to the sine wave before the real LMS Adaptive process for vehicle's signals,

11. ;\NALYTICS

desired reponse signal. This is accomplishe , comparing the output with the desired response t , an "error" signal and then adjusting or optimizir., weight vector to minimize this error signal, The, signal Yk, is simply substracted by the desired signa. . produce the error signal [; k '

G'"

= d, - Yk

=d" -Xk

T

W

=d" -WTX"

~ 2 T T T

E:k

=d , +W X"X"W-2d

k

X

k

W

MSE=q

= E[li/l =

E[d

k2J

+ E[WTXkX~'Wl- 2E[d"X:W]

Using

R=E/X"

Xk'lj and

P=E/OkXkL

then

;; =

E{8

k2

] =E{d

k2

] + WTRW _2pTW

(2)

R is the auto-correlation of x, and P

is the crt', .

correlation between dkand xi.

The gradient of the MSE can be

expressed as follows:

or The basic theoretical calculations were

based on

the following several formulas. The first formula was the linear combiner:

8;;/

1 8 w

2

V;; = 2RW-2P

(3)



Vl'lk

Adaptive LMS Noise Cancellation of Wide band Vehicle's Noise Sign

(Penghapusan Isyarat Berspektrum Lebar dengan LMS Adaptif)

Sri Arttini Dwi Prasetyowati" Adhi Susanto"? Thomas Sriwidodo '0), Jazi Eko Istiyanto .*")

')Post Graduate Student of Electrical Engineering, Faculty of Technology UGM

"')Lecturers of Electrical Engineering, Faculty of Technology UGM

••• ) Lecturer of Electronical and Instrumentation, Faculty ofMlPA, UGM

the adaptation process based on a performance law, the weight vector of the linear combiner is adjusted to cause the output, .\'k to agree as closely as possible with the

(1) In

"

T

o fine the m inim um M SE , th e w eight vector

"

m u st b e o pt i m al ( W i') or th e gr a di e nt M S E e q u al s to 0:

V

; ;

=

0

=

2 R W

* - 2

P

=> W,:, = R IP

⁽⁴⁾

Then Newton's method can be generalized as the Newtoi Algorithm:

W

kll

= W" - Jill. IV C;k

(5) If Xk and

(h

are unknown signals, the matrix H can not be used in thin algorithm, The method of Steepest Descent is expressed by the following algorithm (Steepest



Proceedings of ICGC-:::;':::, .:: - ~ -

Descent Algorithm), ::' ., .. _ determines the step <.:; ._.' ,.

reciprocal signal pow cr:

WI<+1

==

"'j; - l-l-:- "

To develop ,.:~ ... : .. :"; ;; .. :;,'::-';;1111 using the previous methods. :::~ ;:'.~.~: "::; ~,:' .; = E[ ck -] is , replaced by the di;'I.-!rc;" 2::, bet\', eel' the short-term averages of £; -. H,'\'."

-

cr. to develop the LMS Algorithm. it takes £,,: as the estimate of ~k ' Then, at each iteration in the adaptive process, the gradient cs tim ate is

'1~::: Vs/::: 2c','I7{;'j_ :::

2&k'l(d

k-WkXk)::: -2cl;xl; (7)

From (6) and (7) Then LMS Adptivc Algorithm is Wk!1 = WI,

+ 2j.iX

k

f,k

eX) This simple realisation of the algorithm takes much longer lime to reach the optimum value.

Ill. DESCRIPTION

The signal refercns x is the signal with high frequency components (sharp signal), and d is the same signal x whieh was delayed some samples. The LMS Adaptive Predictive can not make the error

c

^close

enough to zero or the output y close to d. This is due to the fact that sharp signals contain vast spectrum components.

Figure I up to 3 shows the sharp signal and the optimal adaptation. It could be seen that the number of Jelayed samples correspond to better shape of output.

I' ;\ '

/

^{'; i}

\

, v

\/ \:

^{I ,} _! ^I

'J

Figure 1. Sharp Signal

"

.1.

\

^{: \}

ⁱ

i

f

^\

i

I I

\

i ¹

I

\

\ I

Figure 2. Output y for dclay J samples, n =47,

f1

^=O,Ol,

and L=5.

-:'ht: weight values at the last iteration were -0.0765, 0.0973,0,2968,0.4455, 0.3334. But

c

can not come close to zero but keeps oscillating.

__ •••..• 1. __ •. _ ~ " •• _. __ ._ ",., ••..•• r..

ISSN: 2086·4868

,

'

/\

I \

I -. \

"

'1 ^.'

, "

,

I

,

,A

" ^ll~

Figure 3: Error G' must be close to zero

A .. Sine Signal with One or More Frequencies

Figure 4.a shows the sine signal with three (3) periods. Figure 4.b and 4.c shows the results of LMS adaptive predictive scheme with L"" 3, delay one sample,

and j.J. =0,00l.

, I ~ '. , ••

r--~;

~-.~.- ~~~!

"\'

-'-'-"'n;~

'\

:' ^'

.~

^{: }}

_, ! "

_\

I

':1 \ ·

^;

^\"

/ \'1

: \ 1

' :1 I

' '1 _{I:' .}

^I·

_I

^l

\:1

1.1, ^{I;' •.}

\ , ^" \1

\

"

.'" ^{I' ! " IJ.} " ^{l' ,} Figure 4,a. Sine With 3 Period

--- I

j

I

: 1 i

.! I

..

^:"

Figure 4.b Error

c:

for L=3, delay == J. P =0,0 I.

Figure 4.c Output y

"

ISSN: 2086-4868 Technical Paper Proceedings of ICGC-? :

,

: fi·

"I! .

t_, \ I \





Figure 5.a Error

e

[or L=3, delay =2,

f.l

=0,01.

, I

Figure S.b. Output y for L=<\ delay=2,

Jl

=0,01.

Figure 7.a Signal Error (

e )

^{dengan L}

=:_:.

Jl =0,02.

'j

? \

:.!.:' :._:... •

..,-- .

.' 'I

_{, I}

^{I \}

":1'

_{i > !}

f

":l \ ^I

~ ~.

-

..

.. ^,

-~~- .

Figure 7.b: Output y With L=3, delay=C .. , Tabell: f..L lind L for Sinus One Frequez •





_{", ~. 1 •.} 1: .". Delay L

~

^iN

e

I (Sal1mle)

,

1 3 0,01 0,353) Not konve~"l.

yet 2 3 0,01 0,3310 Koll."~l·i-=l:

: (the]~ are no oscillation) 3 3 0,01 O,30~ Not

kOllve~ell yet

I

4 3 0,01 0;2775 Not kcal'.'e~en

I

y,;,t i

3 3 O,C[2 0;2816 Not kC'lwe~en

I

:ret 3 4 0,01 0,2303 Not

kOlI\'ll~el\

v .• t 3 ^j O,ol ^0,19':4 Korrlerg<:n

(thel~ "'''' no oscillation) :I 3 0,02 O~1293 ICo:rwerom (theJ.'e are no o~iIJation.) Figure 6.b: Output y lor 10=3. delay=3.

Ii

=0,02

LMS adaptive prediction with varied L, delay, and

f.l

arc shown at Figure 5 lip to Figure 7, and the numerical result arc shown in

Table

I.

From Table I it can be concluded tL' optimum value was reached for

Ii

= 0,02, delay l':

(2) sample, and L = 3.

A combination of more than one sine s~=

needed different optimum values of

IL,

L, and

c_

\

I i

\ I

\ I

Figure 6.a: L=3, tunda=J,

!l

==0,02.





Proceedings of ::.:;.: '::.: :- Figure 8 up i(' ;: .: -

adaptive predict: .. ' . observation.

It..::':- .

for adaptive pr~" .-

/-t

=0,02. delay·~,~·,-·

Technical Paper '::':::.>

of LMS

. _ ,. ;'. ,::;~ list of the

. . .: .. :_: ::::: optimum value - . ':::::',: :-.:nc;; arc L = 6

...

- '

,

j I

1

".;. "

Figure 8.a: x=sin(2 TC 10k).

^"

n

^{t, I}

^'--i

^C

-'--'-"-

, ^\

^{! ,}

f \

I I

^{I r}

^I

^{i I}

i \ ^{I "}

\ I \

I I

!

^r

^{I .}

I ^I

! i

^I

,·i

^I

\

I I

'.'[t I ^,

^{I ,}

I ^{I I} : I I

l ^{\ i}

1 I

, r \

J \

^I. I I

'.1 " ^"

^{,I • I- ., ."}

Figure S.«: y=sinf

n

10k),

"

.--_- 1\

n

^Ii

,\

^,\

I' "

i\

,

^{\ I}

,

^\ i

\

,

^{I i i; j\} !

'.

^{i \; 1 I} \., ^I

,

^{: I}

^{\ I}

.1 ^;

\

^,;

'

^\i

^I ':

..

','i.'

)

Figure S.c: x

+ y

"",'" ... 1

J.

Figure 9.a: Error & for delay 2 samples, L=3,

p

'""0,01.

l , I

\ I·

. I .,

\1 , ,

. .

: I

-

--

^::; ..

~ _-

_.

~.l:-e 9.h: Output y for delay 2 samples, L == 3,

J.l

O,Ol,

- .~ '_0,. •.•... _~._;. ~~!..: ':. ':~.:'~. _.

,

. ,

, i\ ^j

',.. "I

,

,

, I

,

:

\ ' r

"

ISSN: 2086-4868

Figure 10.a:

s

^f I 1 . or ue ay 4 samples, L

=

3,

Jl

= 0,01.

, J-'---_'::_::":' :~_-::._;:..__.~~-

n'.r " i.

i " ' '

i'

"I' ,/\/' I Ii ," : \

. \. v I

^{j;; I I'}

'. i { \ ( If ;

!

\J t \ ;

't .

^{\i '}

., :;!----;.l~. --_.

---_

----:-"~'-.--. ^•.

-

....

~

^-;

F' . rgure LO.b: Output

y

for delay 4 samples L = 3

J-1

= 0,01. "

~:! ----.:.:.!...:.:.~.'

.:~.- _ ..

···_'·-l

. .

j .. --_,,,, _ _j

,

,,'

Figure II.a Error t: for delay 2 samples. L ~ 6. Jl = 0,02.

~ J\

\

"\' \ i \

..

_{, \.i'j}

v\ V . \

l ., ,

I i

\

...j, ". ~ " .•..•. ". ,,' - 4! .. ;

, I

\

I

I I \

I,

I \

.... "

J

j.

, I

I' ,)

Figure ll.h Output Y for delay 2 samples, L"" 6,

f.l

= 0,02.

akarta, 2 - 3 Marcl1 2010 Dept. of EE and IT, GMU

375

 

ISSN: 2086·4868 Technical Paper Proceedings of leG ~.: .

Frekucnsl

Tunda L

/-' w ^s Yfoutput) Dalam

Sampel

1 3 ^0,01 0,6023 Osc

Mirip

f-._--

-

^besar

^x+y

2 3 0,01 O,35:!3 Osc Mirip kecil

x+y

3 3 0,01

-

0,0253 Osc Mirip (terkccil) besar

x+y

4 3 0,01 -O.27X6 Osc Mirip besar

x+y

2 4 ⁱ 0,01 0,3822 Osc Mirip kecil

x+y

2 3

(),021

(),3 5 17 Osc Mirip

kecil

x+y

3 3 ^0,02 -O,J014 Osc Mirip bcsar

x+y

--.

1 6 0,02 O,57~q Osc Mirip besar

x+y

2 6 0,02 0,3506 Osc Mil'ip

terkecll

x+y

3 ⁶ 0,02 0,1085

Osc

^Mirip .

._- ---

^besar

^x+y

2 6 0,01 O,33<)~{ Ose Mirip

!

^kecil

x+y

-_-_."

B. Sawtooth Signal

Similar process was carried for the sawtooth signal. Figures 12 show the representing resulting signals.

,

.

Figure 12.a: Sawtooth Signal -

Figure 12.c: Error 8 with L=S.

P

^=.

Figure 12.d: Error

s

with L=3,

Jt

=0,( .:

'. ! ,. ~

....

--

^..•..

-~--

'I

ⁱ

. il.

. . 'I

!I.... .. .. _, " ... - .. --~ -- ..•..•... .

'I'

, ,

Figure 12.e: Error

c·

with L=3, /-' =0.1

' .•.. _1" •.

·.· .•..

'J "':".' (

.••.•.•

I~' • ~·I'.~ ·1~~~'U· ., ,

Figure 12.f: Error

I>

with L=3,

f..l =0.2

";

I I'

f ~

.t

_{'., .• _ l} ! ""

Figure 12.g: Error F. with

L=3, /-' =(),3







Proceedings of ICGC-RCICT 2010

c r,

,

Technical Paper ISSN: 2086-4868

j !

'1 ',,'111 -. ·.f '':'t

Figure 12.h: Error [;' with L=3,

J.l

=0,35

It is difficult to fine the optimum value for sawtooth signal especially to fine the optimum

J.l

so that the eror (

c )

closed to zero.

C. UvlS Adaptive Predictionfor Vehocle's Signal

LMS adaptive prediction process with 40.000 samples after low pass filtering 15 kHz. Figure 13 and Figure 14 show the errors of the adaptive prediction for the desired outputs

wi

th L ~ 10 and

J.l

= 0,001.

Figure 13.a: Diesel's Signal

'1 ." .. _,,_, _, 1

" 6 .JIJ

./

"e l3.b: Error E for desired input equals to referens

::n L == 10 and ft =-=0,001 for Diesel's Signal

, l

1~I!I\I'IIIlPlIt ~

.\

I _{, .}

• ,.,,<t

Figure 13.c:Oulpul y for desired input equals to refcrens with L == 10 dan fl =0,001, for Motocycle's Signal

Figure 14.a: Motorcycle's Signal.

. , .-~ ... -- .. : .. -...::. .. :.._:':::.:, .. ·_··_··

... -"1

I

·1

1 ..

__ J

• 1

:/

Figure 14.b: Error

e

for desired input equals to referens with L = 10 and fl =0,001 for Motorcycle's Signal.

)1'

Figure 14.c:Output y fur desired input equals to refcrens with L = 10 dun

J.l

=0,00 I, for Motorcycle's Signal.

D. Spectra a/The Signals

Figure 15 up to 1 S show the spectra of several signals to show the frequencies of each signal. Spectrum of the sine signal are in the low frequency range while the spectrum of the sharp signals and vehicle's signal are in the low as well as high frequency ranges.

..

_.":.'J

figll~:~-'i5: Saw(o(;th' s Spectrum

Yogyal~arta, 2 - 3 March 2010 Dept. of EE and IT, GMU 37

7

ISSN: 2086-4868 Technical Paper Proceedings of ICGC-RC : - _

"1 Widrow, B., dan S.D. Stearns, Adaptive

Processing, Prentice-Hall, Inc., Eng.,

Clifts,

New Jersey, 1985.

Figure j 6: Sine's Spectrum

':;:

...

^,

-

.---

Figure 17: Spectrum of Signal of Figure 3.a

'"_ ..,J

Figure j 8: Spectrum of Diesel 's Signal

IV. CONCLUSION

It is difficult for high frequency signals (sharp signals) (0 be completely adapted by LMS algorithm, because the contain vas! ranges

or

informations while the number of weight are limited.

REFERENCES

Hines, W.W, Montgomery, D.C., Goldsman, D.M dan Borror, C.M., Probahility and Statistics ^ill Engineering. Fourth Edition, John Wiley and

SOilS, Inc, 2003.

lfeachor, Emmanuel, C. Jervis. Barrie, W" "Digital Signal Processing: A Practical Approach", Addison- Wesley Publishing Company, 1993.

Mooncn, M., dan Ian.P .. All Introduction to Adaptive Signal Processing. Course Notes, 1998-1999.