Greyworld White Balancing with Low Computation Cost for On- Board Video Capturing

Size: px
Start display at page:

Download "Greyworld White Balancing with Low Computation Cost for On- Board Video Capturing"

Transcription

1 reyword Whte aancng wth Low Computaton Cost for On- oard Vdeo Capturng Peng Wu Yuxn Zoe) Lu Hewett-Packard Laboratores Hewett-Packard Co. Pao Ato CA USA Abstract Whte baancng s a process commony used n dgta photography to estmate the umnant then compensate the coor appearance so that the whte coor appears as whte regardess of the varety n the umnaton condton when the pcture was captured. n ths paper we propose a ow computaton cost approach to reaze whte baancng for onboard dgta vdeo capturng. The reyword approach has been wdey adopted consdered as the bass for many automatc whte baancng agorthms. When the reyword approach s apped to dgta vdeos each vdeo frame n the sequence s treated ndependenty. The two modues n the approach namey the mutper estmaton the frame modfcaton are repeated on every frame wthout takng nto account the tempora correaton across adjacent frames. n fact when a segment of a vdeo cp s captured under a fxed ghtng condton f the objects n the scene do not move suffcenty rapdy the mutpers for dfferent frames often possess strong correaton. Such an observaton eads to the foowng two deas to reduce the overa computaton cost for whte baancng on-board vdeos: ) Detectng keepng track of the umnant change across frames to determne f the mutper estmaton for a new frame s necessary 2) nstead of usng the mutpers to modfy one entre frame a bock-based approach s proposed to compare bocks of the current frame wth the co-ocated ones n the prevous frame to perform coor modfcaton on seected bocks. Usng our proposed approach the computatona compexty s reduced whe the performance s mantaned. As a trade-off we need to mantan the frame buffer as we as the mutper buffer. ntroducton Whte baancng s a process commony used n dgta photography to estmate the umnant then compensate the coor appearance so that the whte coor appears as whte regardess of the varety n the umnaton condton when the pcture was captured. n ths paper we propose a ow computaton cost approach to reaze whte baancng for on-board dgta vdeo capturng. The reyword approach has been wdey adopted consdered as the bass for many automatc whte baancng agorthms. Most consumer) vdeos may endure a varety of umnatons wthn a short tme wndow. The reyword approach determnes the chromatcty of the umnant from the average of a the pxes n one mage assumng that the average coor of the scene s gray that any departure from ths average n the mage s caused by the umnant. When the reyword approach s apped to dgta vdeos each vdeo frame n the sequence s treated ndependenty. The two modues n the approach namey the mutper estmaton the frame modfcaton are repeated on every frame wthout takng nto account the tempora correaton across adjacent frames. n fact when a segment of a vdeo cp s captured under a fxed ghtng condton f the objects n the scene do not move suffcenty rapdy the mutpers for dfferent frames often possess strong correaton. Such an observaton eads to the foowng two deas to reduce the overa computaton cost for whte baancng on-board vdeos: ) Detectng keepng track of the umnant change across frames to determne f the mutper estmaton for a new frame s needed 2) nstead of usng the mutpers to modfy one entre frame a bock-based approach s proposed to compare bocks of the current frame wth those co-ocated ones n the prevous frame A subset of bocks n the current frame are then dentfed to represent the porton of the current frame that are dfferent from the prevous frame n terms of the umnant The coor modfcaton s then ony performed on those dentfed bocks. Usng our proposed approach the computatona compexty s reduced whe the performance s mantaned. Compared wth other reyword-based whte baancng approaches for vdeo capturng our proposed technque has the dstngushed advantage on the computaton cost. Through takng the tempora correaton across adjacent frames pus the consstency of umnant nto account the proposed technque can ower the amount of mutpcaton operatons nvoved n the coor modfcaton. As a trade-off we need to mantan the frame buffer as we as the mutper buffer. reyword-ased Whte aance Technque for On-board Dgta Vdeo Capture reyword Agorthm for Whte aance Varous agorthms have been deveoped to estmate adjust umnatons. Consderng the computatona compexty of the agorthm the fact that consumer) vdeo capturng may have dfferent umnatons wthn a snge vdeo cp we many nvestgate the reyword approach n ths paper. n the reyword approach the chromatcty of the umnant s determned from the average of a the pxes n one mage. t Socety for magng Scence Technoogy

2 assumes that the average coor of the scene s gray that any departure from ths average n the mage s caused by the umnant. Specfcay gven an mage n the space ettng represent the mage pxes n the channes at poston the reyword agorthm searches for a transform n the coor space α = β γ that appes to the The mutpers α β γ are obtaned ) components. α = δ / E ) 2.) β = δ / E ) 2.2) γ = δ / E ) 2.3) where E ) E ) E ) respectvey are the mean of δ = mn{ E ) E ) E ). 3) The modfed mage usng greayword s then obtaned as: x = α 4.) x = β 4.2) x γ =. 4.3) reyword-ased Whte aance for Vdeo Capture ) ) { ) +) +) { +) +2) Fgure Fundamenta procedure of reyword-based whte baance for vdeo capture n [] [2] usng the reyword-based whte baance approach for st/vdeo capture s addressed. As ustrated n Fgure n dgta vdeo capture a vdeo cp s the output of capturng a sequence of frames contnuousy wthn a certan tme perod. The whte baance scheme descrbed n [] [2] s essentay to appy the reyword approach as ntroduced n the prevous secton to each of the frame n the sequence to generate a modfed frame sequence as the fna captured vdeo. More +2) { +2) +m) +m) { +m) specfcay for the th frame ) the mutper estmaton ) bock shown n Fgure utzes Eq. 2.) 2.2) 2.3) to P = α β γ such a set of compute a set of mutpers ) { ) mutpers are used to modfy the th frame usng Eq. 4.) 4.2) 4.3) to obtan the corrected frame ). A Low Computaton Cost reyword Technque for On oard Vdeo Capture n the scheme descrbed n Fgure each frame n the sequence s treated ndependenty. The mutper estmaton frame modfcaton are repettvey conducted on each frame wthout consderng the tempora correaton across adjacent frames. n fact when a segment of vdeo cp s captured under a fxed ghtng condton the objects n the scene do not move rapdy the mutpers often possess strong correaton. Such an observaton eads to the foow two deas to reduce the computaton cost: ) Detect the umnaton change across frames to determne f the mutper estmaton s needed 2) nstead of usng the mutpers to modfy the entre frame a bock-based approach s proposed to compare bocks of the current frame wth those co-ocated ones n the prevous frame to dentfy a subset of bocks n the current frame These dentfed bocks represent the porton of the current frame that s dfferent from the prevous frame n terms of the umnaton condton. The coor modfcaton s then ony performed on the dentfed bocks. umnaton Change Detecton To detect the change of umnaton across frames the coor hstogram s wdey used as descrbed n [3]. ven a frame ) et H j) denote ts coor hstogram where j = 2 s the tota number of bns used to form the hstogram. The dstance between the hstograms of two adjacent frames ) + s obtaned as: ) SD = j= H j) H. 5) + j) f the dfference SD s arger than a gven threshod T then an umnaton change s decared. To set the threshod t usuay requres the cacuaton of the mean μ the stard devaton σ of SD over ) = 2 m. The threshod can be obtaned as: T = μ + ασ. 6) A typca vaue for α s 5 or 6. For the on-board vdeo capture t s not reastc to compute the mean μ the stard devaton σ over the entre vdeo cp to be captured for that requres the bufferng of a the captured frames. nstead an adaptve umnaton change detecton agorthm has been proposed n whch ony a sma number of hstograms need to be buffered. A smar approach has been suggested n [4] for the purpose of vdeo summarzaton. Let H denote the hstogram buffer whch keeps K hstograms. The adaptve umnaton change detecton agorthm s descrbed as foows: CSH'2008: nternatona Conference on magng Scence Hardcopy 28

3 Step : For frame ) = K obtan H et Step 2: H = K H Obtan SD = j= H j) H + j) = 0 K 2 Step 3: Obtan μ = mean SD ) σ = std SD ) TH H H = μ + ασ H Step 4: Set = K + Step 5: Obtan H from ) Step 6: Obtan SD Step 7: f SD ' > TH between ) ' = j= H K H j) H j) decare umnaton change go to Step 8 ese go to Step 8 Step 8: Set H = H+ = 0 K 2 an H = ) K H = SD+ Step 9: Set SD = 0 K 3 ' SD K 2 = SD Step 0: Obtan μ = mean SD ) σ = std SD ) TH H H = μ + ασ H Step : Set = K + 2 go to Step 5. H Adoptng the above agorthm each nput frame ) = K + can be examned to determne whether t s at the boundary of an umnaton change. To everage the tempora correaton of the mutpers under the same or smar umnaton we ony perform the mutper estmaton when the frame s determned as the boundary frame. More specfcay we mantan a mutper buffer to keep track of the mutpers used for modfyng the current frame. The vaues of mutpers w be updated through mutper estmaton ony when the ncomng frame s decared to be the boundary of umnance change. Otherwse the buffered mutpers w be used to modfy the ncomng frame. ock-ased Coor Correcton frame ) dvded to bocks) Compute mean E) E2) E3) E2) E22) E23) E3) E32) E33) Fgure 2 ustraton of bock-based computaton of means E) f frames are dentfed as beongng to the same umnance condton the same set of { P ) = α β γ ) s used throughout a these frames to perform the coor modfcaton as descrbed n Eq. 4.) 4.2) 4.3). Further we can everage the tempora correaton of consecutve frames to reduce the computaton cost. We frst dvde ) ) ) nto bocks of equa sze. Then when computng the mean of ) ) ) not ony E )) E )) E )) are computed but aso the mean of each bock of the ) ) ) as ustrated n Fgure 2. Suppose for p ) p = we dvde t nto M N bocks. The mean computaton w resut a mean of the entre p ) means of bocks E p m where m = M n = N. To perform bock-based modfcaton we examne the mean E p m wth E p m the foowng procedure s conducted: Procedure for bock-based coor modfcaton: ven p ) p ) E p m E p m for bock m of p ) { f not E m E m E m E m E m E m ) then { for a the pxes beongng to bock bock m of p ) compute bock m of p ) ) x = α x ) x = β x ) x = γ x ese { for a the pxes beongng to bock bock m of p ) compute bock m of ) x x x x = x = x =. The dea behnd ths procedure s that f the bock dfferences on channes across frames are a very sma we consder t as a strong ndcaton that the two bocks ocated n m n frame p ) p ) are very smar. Snce we use the same set of mutpers we can safey set the bock m n frame p ) to be the same as that of p ). f the bock dfferences are not a very sma we just perform the bock-based modfcaton usng the buffered mutpers. n ths way for p Socety for magng Scence Technoogy

4 bocks that have consstent pxe vaues across consecutve frames we reduce the computaton by skppng the pxe modfcaton that nvoves the mutpcaton operaton. As a trade off we need to keep the frame buffer the mutper buffer. The Compete Scheme Fgure 3 ustrates the compete scheme of appyng ths ow computaton cost reyword technque for on board vdeo capture. n genera gven an nput frame ) we frst examne whether the frame s at the boundary of a umnance change by appyng the adaptve umnance change detecton scheme. f t s we perform the mutper computaton to refresh the mutper buffer use such a mutper to modfy the entre frame for whte baance. Otherwse the nput frame s consdered to share the same umnance wth the prevous frame we use the mutper n the buffer to perform bock-based modfcaton. ) Adaptve umnant change detecton Change detected Yes No ead mutper buffer Mutper estmaton Update mutper buffer ock based modfcaton Frame based modfcaton ) Fgure 3 Proposed ow computaton cost reyword-based whte baancng approach for on-board vdeo capture a) Orgna vdeo frames b) After whte baancng usng our proposed approach Expermenta esuts Concusons Fgure 4 Test sequence Ths paper has addressed a ow computaton cost reywordbased whte baance technque for on board vdeo capture. As CSH'2008: nternatona Conference on magng Scence Hardcopy 283

5 compared wth other reyword-based whte baance technque for vdeo capture the proposed technque has the dstngush advantage on the computaton cost. y takng the tempora correaton of frames consstency of umnaton nto account the proposed technque can ower the amount of mutpcaton operatons nvoved n the coor modfcaton. eferences [] Yoon Km June-Sok Lee A.W. Moraes Sung-Jea Ko A Vdeo Camera System wth Enhanced Zoom Trackng Auto Whte aance EEE Transactons on Consumer Eectroncs vo. 48 no. 3 pg ) [2] June-Sok Lee You-Young Jung yung-soo Km Sung-Jea Ko An Advanced Vdeo Camera System wth obust AF AE AW Contro EEE Transactons on Consumer Eectroncs vo. 47 no. 3 pg ) [3] HongJang Zhang Yhong ong S.W. Smoar Shuang Yeo Tan Automatc Parsng of News Vdeo Proceedngs of the nternatona Conference on Mutmeda Computng Systems pg ) [4] Arnon Amra a Ashourb Savtha Srnvasan Automatc eneraton of Conference Vdeo Proceedngs Journa of Vsua Communcaton mage epresentaton vo. 5 No. 3 pg ) [5] Homer H. Chen Chun-Hung Shen Pe-Shan Tsa Edge-ased Automatc Whte aancng wth Lnear umnant Constrant Proceedngs of SPE nternatona Conference on Vdeo Communcatons mage Processng VCP) San Jose CA USA vo pg. 6508D- 6508D ) Author ography Peng Wu receved hs PhD n Eetrca Engneerng from Unversty of Caforna Santa arbara 200). Snce then he has worked n the magng Technoogy Department at HP Labs n Pao Ato CA. Hs work has focused mutmeda content anayss mutmeda systems. Yuxn Zoe) Lu receved her PhD n eectrca engneerng from Purdue Unversty West LafayetteN 2004). She was wth Noka esearch Center n rvng TX from Aug through Dec as a esearch Engneer was wth Sun Labs n Meno Park CA from Dec through Aug as a Staff Engneer. Snce Aug she has been wth HP Labs n Pao Ato CA as a esearch Scentst. Zoe s major research nterests have been focused on vdeo compresson meda anayss Socety for magng Scence Technoogy

A finite difference method for heat equation in the unbounded domain

A finite difference method for heat equation in the unbounded domain Internatona Conerence on Advanced ectronc Scence and Technoogy (AST 6) A nte derence method or heat equaton n the unbounded doman a Quan Zheng and Xn Zhao Coege o Scence North Chna nversty o Technoogy

More information

Research on Complex Networks Control Based on Fuzzy Integral Sliding Theory

Research on Complex Networks Control Based on Fuzzy Integral Sliding Theory Advanced Scence and Technoogy Letters Vo.83 (ISA 205), pp.60-65 http://dx.do.org/0.4257/ast.205.83.2 Research on Compex etworks Contro Based on Fuzzy Integra Sdng Theory Dongsheng Yang, Bngqng L, 2, He

More information

Multispectral Remote Sensing Image Classification Algorithm Based on Rough Set Theory

Multispectral Remote Sensing Image Classification Algorithm Based on Rough Set Theory Proceedngs of the 2009 IEEE Internatona Conference on Systems Man and Cybernetcs San Antono TX USA - October 2009 Mutspectra Remote Sensng Image Cassfcaton Agorthm Based on Rough Set Theory Yng Wang Xaoyun

More information

Associative Memories

Associative Memories Assocatve Memores We consder now modes for unsupervsed earnng probems, caed auto-assocaton probems. Assocaton s the task of mappng patterns to patterns. In an assocatve memory the stmuus of an ncompete

More information

Pop-Click Noise Detection Using Inter-Frame Correlation for Improved Portable Auditory Sensing

Pop-Click Noise Detection Using Inter-Frame Correlation for Improved Portable Auditory Sensing Advanced Scence and Technology Letters, pp.164-168 http://dx.do.org/10.14257/astl.2013 Pop-Clc Nose Detecton Usng Inter-Frame Correlaton for Improved Portable Audtory Sensng Dong Yun Lee, Kwang Myung Jeon,

More information

Numerical Investigation of Power Tunability in Two-Section QD Superluminescent Diodes

Numerical Investigation of Power Tunability in Two-Section QD Superluminescent Diodes Numerca Investgaton of Power Tunabty n Two-Secton QD Superumnescent Dodes Matta Rossett Paoo Bardea Ivo Montrosset POLITECNICO DI TORINO DELEN Summary 1. A smpfed mode for QD Super Lumnescent Dodes (SLD)

More information

Xin Li Department of Information Systems, College of Business, City University of Hong Kong, Hong Kong, CHINA

Xin Li Department of Information Systems, College of Business, City University of Hong Kong, Hong Kong, CHINA RESEARCH ARTICLE MOELING FIXE OS BETTING FOR FUTURE EVENT PREICTION Weyun Chen eartment of Educatona Informaton Technoogy, Facuty of Educaton, East Chna Norma Unversty, Shangha, CHINA {weyun.chen@qq.com}

More information

MARKOV CHAIN AND HIDDEN MARKOV MODEL

MARKOV CHAIN AND HIDDEN MARKOV MODEL MARKOV CHAIN AND HIDDEN MARKOV MODEL JIAN ZHANG JIANZHAN@STAT.PURDUE.EDU Markov chan and hdden Markov mode are probaby the smpest modes whch can be used to mode sequenta data,.e. data sampes whch are not

More information

REAL-TIME IMPACT FORCE IDENTIFICATION OF CFRP LAMINATED PLATES USING SOUND WAVES

REAL-TIME IMPACT FORCE IDENTIFICATION OF CFRP LAMINATED PLATES USING SOUND WAVES 8 TH INTERNATIONAL CONERENCE ON COMPOSITE MATERIALS REAL-TIME IMPACT ORCE IDENTIICATION O CRP LAMINATED PLATES USING SOUND WAVES S. Atobe *, H. Kobayash, N. Hu 3 and H. ukunaga Department of Aerospace

More information

Structure and Drive Paul A. Jensen Copyright July 20, 2003

Structure and Drive Paul A. Jensen Copyright July 20, 2003 Structure and Drve Paul A. Jensen Copyrght July 20, 2003 A system s made up of several operatons wth flow passng between them. The structure of the system descrbes the flow paths from nputs to outputs.

More information

The Minimum Universal Cost Flow in an Infeasible Flow Network

The Minimum Universal Cost Flow in an Infeasible Flow Network Journal of Scences, Islamc Republc of Iran 17(2): 175-180 (2006) Unversty of Tehran, ISSN 1016-1104 http://jscencesutacr The Mnmum Unversal Cost Flow n an Infeasble Flow Network H Saleh Fathabad * M Bagheran

More information

Neural network-based athletics performance prediction optimization model applied research

Neural network-based athletics performance prediction optimization model applied research Avaabe onne www.jocpr.com Journa of Chemca and Pharmaceutca Research, 04, 6(6):8-5 Research Artce ISSN : 0975-784 CODEN(USA) : JCPRC5 Neura networ-based athetcs performance predcton optmzaton mode apped

More information

WAVELET DOMAIN VIDEO INFORMATION EMBEDDING

WAVELET DOMAIN VIDEO INFORMATION EMBEDDING WAVELET DOMAIN VIDEO INFORMATION EMBEDDING Mng Yang, Monca Trfas, Candce Trutt, and Guoun Xong Department of Mathematca and Computer Scences Jacksonve State Unversty, Jacksonve, AL 3665 {myang, atrfas,

More information

Tracking with Kalman Filter

Tracking with Kalman Filter Trackng wth Kalman Flter Scott T. Acton Vrgna Image and Vdeo Analyss (VIVA), Charles L. Brown Department of Electrcal and Computer Engneerng Department of Bomedcal Engneerng Unversty of Vrgna, Charlottesvlle,

More information

ON AUTOMATIC CONTINUITY OF DERIVATIONS FOR BANACH ALGEBRAS WITH INVOLUTION

ON AUTOMATIC CONTINUITY OF DERIVATIONS FOR BANACH ALGEBRAS WITH INVOLUTION European Journa of Mathematcs and Computer Scence Vo. No. 1, 2017 ON AUTOMATC CONTNUTY OF DERVATONS FOR BANACH ALGEBRAS WTH NVOLUTON Mohamed BELAM & Youssef T DL MATC Laboratory Hassan Unversty MORO CCO

More information

The Synchronous 8th-Order Differential Attack on 12 Rounds of the Block Cipher HyRAL

The Synchronous 8th-Order Differential Attack on 12 Rounds of the Block Cipher HyRAL The Synchronous 8th-Order Dfferental Attack on 12 Rounds of the Block Cpher HyRAL Yasutaka Igarash, Sej Fukushma, and Tomohro Hachno Kagoshma Unversty, Kagoshma, Japan Emal: {garash, fukushma, hachno}@eee.kagoshma-u.ac.jp

More information

[WAVES] 1. Waves and wave forces. Definition of waves

[WAVES] 1. Waves and wave forces. Definition of waves 1. Waves and forces Defnton of s In the smuatons on ong-crested s are consdered. The drecton of these s (μ) s defned as sketched beow n the goba co-ordnate sstem: North West East South The eevaton can

More information

Approximate merging of a pair of BeÂzier curves

Approximate merging of a pair of BeÂzier curves COMPUTER-AIDED DESIGN Computer-Aded Desgn 33 (1) 15±136 www.esever.com/ocate/cad Approxmate mergng of a par of BeÂzer curves Sh-Mn Hu a,b, *, Rou-Feng Tong c, Tao Ju a,b, Ja-Guang Sun a,b a Natona CAD

More information

QUARTERLY OF APPLIED MATHEMATICS

QUARTERLY OF APPLIED MATHEMATICS QUARTERLY OF APPLIED MATHEMATICS Voume XLI October 983 Number 3 DIAKOPTICS OR TEARING-A MATHEMATICAL APPROACH* By P. W. AITCHISON Unversty of Mantoba Abstract. The method of dakoptcs or tearng was ntroduced

More information

Nested case-control and case-cohort studies

Nested case-control and case-cohort studies Outne: Nested case-contro and case-cohort studes Ørnuf Borgan Department of Mathematcs Unversty of Oso NORBIS course Unversty of Oso 4-8 December 217 1 Radaton and breast cancer data Nested case contro

More information

The Application of BP Neural Network principal component analysis in the Forecasting the Road Traffic Accident

The Application of BP Neural Network principal component analysis in the Forecasting the Road Traffic Accident ICTCT Extra Workshop, Bejng Proceedngs The Appcaton of BP Neura Network prncpa component anayss n Forecastng Road Traffc Accdent He Mng, GuoXucheng &LuGuangmng Transportaton Coege of Souast Unversty 07

More information

IMAGE DENOISING USING NEW ADAPTIVE BASED MEDIAN FILTER

IMAGE DENOISING USING NEW ADAPTIVE BASED MEDIAN FILTER Sgnal & Image Processng : An Internatonal Journal (SIPIJ) Vol.5, No.4, August 2014 IMAGE DENOISING USING NEW ADAPTIVE BASED MEDIAN FILTER Suman Shrestha 1, 2 1 Unversty of Massachusetts Medcal School,

More information

Regulation No. 117 (Tyres rolling noise and wet grip adhesion) Proposal for amendments to ECE/TRANS/WP.29/GRB/2010/3

Regulation No. 117 (Tyres rolling noise and wet grip adhesion) Proposal for amendments to ECE/TRANS/WP.29/GRB/2010/3 Transmtted by the expert from France Informal Document No. GRB-51-14 (67 th GRB, 15 17 February 2010, agenda tem 7) Regulaton No. 117 (Tyres rollng nose and wet grp adheson) Proposal for amendments to

More information

Image Classification Using EM And JE algorithms

Image Classification Using EM And JE algorithms Machne earnng project report Fa, 2 Xaojn Sh, jennfer@soe Image Cassfcaton Usng EM And JE agorthms Xaojn Sh Department of Computer Engneerng, Unversty of Caforna, Santa Cruz, CA, 9564 jennfer@soe.ucsc.edu

More information

REDUCTION OF CORRELATION COMPUTATION IN THE PERMUTATION OF THE FREQUENCY DOMAIN ICA BY SELECTING DOAS ESTIMATED IN SUBARRAYS

REDUCTION OF CORRELATION COMPUTATION IN THE PERMUTATION OF THE FREQUENCY DOMAIN ICA BY SELECTING DOAS ESTIMATED IN SUBARRAYS 15th European Sgna Processng Conference (EUSIPCO 27), Poznan, Poand, September 3-7, 27, copyrght by EURASIP REDUCTION OF CORRELATION COMPUTATION IN THE PERMUTATION OF THE FREQUENCY DOMAIN ICA BY SELECTING

More information

L-Edge Chromatic Number Of A Graph

L-Edge Chromatic Number Of A Graph IJISET - Internatona Journa of Innovatve Scence Engneerng & Technoogy Vo. 3 Issue 3 March 06. ISSN 348 7968 L-Edge Chromatc Number Of A Graph Dr.R.B.Gnana Joth Assocate Professor of Mathematcs V.V.Vannaperuma

More information

Regularized Discriminant Analysis for Face Recognition

Regularized Discriminant Analysis for Face Recognition 1 Regularzed Dscrmnant Analyss for Face Recognton Itz Pma, Mayer Aladem Department of Electrcal and Computer Engneerng, Ben-Guron Unversty of the Negev P.O.Box 653, Beer-Sheva, 845, Israel. Abstract Ths

More information

Solution of Linear System of Equations and Matrix Inversion Gauss Seidel Iteration Method

Solution of Linear System of Equations and Matrix Inversion Gauss Seidel Iteration Method Soluton of Lnear System of Equatons and Matr Inverson Gauss Sedel Iteraton Method It s another well-known teratve method for solvng a system of lnear equatons of the form a + a22 + + ann = b a2 + a222

More information

SIMPLE LINEAR REGRESSION

SIMPLE LINEAR REGRESSION Smple Lnear Regresson and Correlaton Introducton Prevousl, our attenton has been focused on one varable whch we desgnated b x. Frequentl, t s desrable to learn somethng about the relatonshp between two

More information

Optimum Selection Combining for M-QAM on Fading Channels

Optimum Selection Combining for M-QAM on Fading Channels Optmum Seecton Combnng for M-QAM on Fadng Channes M. Surendra Raju, Ramesh Annavajjaa and A. Chockangam Insca Semconductors Inda Pvt. Ltd, Bangaore-56000, Inda Department of ECE, Unversty of Caforna, San

More information

WAVELET-BASED IMAGE COMPRESSION USING SUPPORT VECTOR MACHINE LEARNING AND ENCODING TECHNIQUES

WAVELET-BASED IMAGE COMPRESSION USING SUPPORT VECTOR MACHINE LEARNING AND ENCODING TECHNIQUES WAVELE-BASED IMAGE COMPRESSION USING SUPPOR VECOR MACHINE LEARNING AND ENCODING ECHNIQUES Rakb Ahmed Gppsand Schoo of Computng and Informaton echnoogy Monash Unversty, Gppsand Campus Austraa. Rakb.Ahmed@nfotech.monash.edu.au

More information

THE VIBRATIONS OF MOLECULES II THE CARBON DIOXIDE MOLECULE Student Instructions

THE VIBRATIONS OF MOLECULES II THE CARBON DIOXIDE MOLECULE Student Instructions THE VIBRATIONS OF MOLECULES II THE CARBON DIOXIDE MOLECULE Student Instructons by George Hardgrove Chemstry Department St. Olaf College Northfeld, MN 55057 hardgrov@lars.acc.stolaf.edu Copyrght George

More information

Cyclic Codes BCH Codes

Cyclic Codes BCH Codes Cycc Codes BCH Codes Gaos Feds GF m A Gaos fed of m eements can be obtaned usng the symbos 0,, á, and the eements beng 0,, á, á, á 3 m,... so that fed F* s cosed under mutpcaton wth m eements. The operator

More information

Errors for Linear Systems

Errors for Linear Systems Errors for Lnear Systems When we solve a lnear system Ax b we often do not know A and b exactly, but have only approxmatons  and ˆb avalable. Then the best thng we can do s to solve ˆx ˆb exactly whch

More information

Module 3 LOSSY IMAGE COMPRESSION SYSTEMS. Version 2 ECE IIT, Kharagpur

Module 3 LOSSY IMAGE COMPRESSION SYSTEMS. Version 2 ECE IIT, Kharagpur Module 3 LOSSY IMAGE COMPRESSION SYSTEMS Verson ECE IIT, Kharagpur Lesson 6 Theory of Quantzaton Verson ECE IIT, Kharagpur Instructonal Objectves At the end of ths lesson, the students should be able to:

More information

Econ107 Applied Econometrics Topic 3: Classical Model (Studenmund, Chapter 4)

Econ107 Applied Econometrics Topic 3: Classical Model (Studenmund, Chapter 4) I. Classcal Assumptons Econ7 Appled Econometrcs Topc 3: Classcal Model (Studenmund, Chapter 4) We have defned OLS and studed some algebrac propertes of OLS. In ths topc we wll study statstcal propertes

More information

EEL 6266 Power System Operation and Control. Chapter 3 Economic Dispatch Using Dynamic Programming

EEL 6266 Power System Operation and Control. Chapter 3 Economic Dispatch Using Dynamic Programming EEL 6266 Power System Operaton and Control Chapter 3 Economc Dspatch Usng Dynamc Programmng Pecewse Lnear Cost Functons Common practce many utltes prefer to represent ther generator cost functons as sngle-

More information

A Novel Feistel Cipher Involving a Bunch of Keys supplemented with Modular Arithmetic Addition

A Novel Feistel Cipher Involving a Bunch of Keys supplemented with Modular Arithmetic Addition (IJACSA) Internatonal Journal of Advanced Computer Scence Applcatons, A Novel Festel Cpher Involvng a Bunch of Keys supplemented wth Modular Arthmetc Addton Dr. V.U.K Sastry Dean R&D, Department of Computer

More information

Statistical analysis using matlab. HY 439 Presented by: George Fortetsanakis

Statistical analysis using matlab. HY 439 Presented by: George Fortetsanakis Statstcal analyss usng matlab HY 439 Presented by: George Fortetsanaks Roadmap Probablty dstrbutons Statstcal estmaton Fttng data to probablty dstrbutons Contnuous dstrbutons Contnuous random varable X

More information

Simulated Power of the Discrete Cramér-von Mises Goodness-of-Fit Tests

Simulated Power of the Discrete Cramér-von Mises Goodness-of-Fit Tests Smulated of the Cramér-von Mses Goodness-of-Ft Tests Steele, M., Chaselng, J. and 3 Hurst, C. School of Mathematcal and Physcal Scences, James Cook Unversty, Australan School of Envronmental Studes, Grffth

More information

Image Processing for Bubble Detection in Microfluidics

Image Processing for Bubble Detection in Microfluidics Image Processng for Bubble Detecton n Mcrofludcs Introducton Chen Fang Mechancal Engneerng Department Stanford Unverst Startng from recentl ears, mcrofludcs devces have been wdel used to buld the bomedcal

More information

LOW BIAS INTEGRATED PATH ESTIMATORS. James M. Calvin

LOW BIAS INTEGRATED PATH ESTIMATORS. James M. Calvin Proceedngs of the 007 Wnter Smulaton Conference S G Henderson, B Bller, M-H Hseh, J Shortle, J D Tew, and R R Barton, eds LOW BIAS INTEGRATED PATH ESTIMATORS James M Calvn Department of Computer Scence

More information

Lower bounds for the Crossing Number of the Cartesian Product of a Vertex-transitive Graph with a Cycle

Lower bounds for the Crossing Number of the Cartesian Product of a Vertex-transitive Graph with a Cycle Lower bounds for the Crossng Number of the Cartesan Product of a Vertex-transtve Graph wth a Cyce Junho Won MIT-PRIMES December 4, 013 Abstract. The mnmum number of crossngs for a drawngs of a gven graph

More information

Lecture 4: November 17, Part 1 Single Buffer Management

Lecture 4: November 17, Part 1 Single Buffer Management Lecturer: Ad Rosén Algorthms for the anagement of Networs Fall 2003-2004 Lecture 4: November 7, 2003 Scrbe: Guy Grebla Part Sngle Buffer anagement In the prevous lecture we taled about the Combned Input

More information

Lower Bounding Procedures for the Single Allocation Hub Location Problem

Lower Bounding Procedures for the Single Allocation Hub Location Problem Lower Boundng Procedures for the Snge Aocaton Hub Locaton Probem Borzou Rostam 1,2 Chrstoph Buchhem 1,4 Fautät für Mathemat, TU Dortmund, Germany J. Faban Meer 1,3 Uwe Causen 1 Insttute of Transport Logstcs,

More information

A parametric Linear Programming Model Describing Bandwidth Sharing Policies for ABR Traffic

A parametric Linear Programming Model Describing Bandwidth Sharing Policies for ABR Traffic parametrc Lnear Programmng Mode Descrbng Bandwdth Sharng Poces for BR Traffc I. Moschoos, M. Logothets and G. Kokknaks Wre ommuncatons Laboratory, Dept. of Eectrca & omputer Engneerng, Unversty of Patras,

More information

Example: Suppose we want to build a classifier that recognizes WebPages of graduate students.

Example: Suppose we want to build a classifier that recognizes WebPages of graduate students. Exampe: Suppose we want to bud a cassfer that recognzes WebPages of graduate students. How can we fnd tranng data? We can browse the web and coect a sampe of WebPages of graduate students of varous unverstes.

More information

A NEW DISCRETE WAVELET TRANSFORM

A NEW DISCRETE WAVELET TRANSFORM A NEW DISCRETE WAVELET TRANSFORM ALEXANDRU ISAR, DORINA ISAR Keywords: Dscrete wavelet, Best energy concentraton, Low SNR sgnals The Dscrete Wavelet Transform (DWT) has two parameters: the mother of wavelets

More information

The Leak Detection of Heating Pipe Based on Multi-Scale Correlation Algorithm of Wavelet

The Leak Detection of Heating Pipe Based on Multi-Scale Correlation Algorithm of Wavelet Sensors & Transducers Vo. 5 Speca Issue December 03 pp. 80-88 Sensors & Transducers 03 by IFSA http://www.sensorsporta.com The Lea Detecton of Heatng Ppe Based on ut-scae Correaton Agorthm of Waeet Xufang

More information

VQ widely used in coding speech, image, and video

VQ widely used in coding speech, image, and video at Scalar quantzers are specal cases of vector quantzers (VQ): they are constraned to look at one sample at a tme (memoryless) VQ does not have such constrant better RD perfomance expected Source codng

More information

Supporting Information

Supporting Information Supportng Informaton The neural network f n Eq. 1 s gven by: f x l = ReLU W atom x l + b atom, 2 where ReLU s the element-wse rectfed lnear unt, 21.e., ReLUx = max0, x, W atom R d d s the weght matrx to

More information

Inthem-machine flow shop problem, a set of jobs, each

Inthem-machine flow shop problem, a set of jobs, each THE ASYMPTOTIC OPTIMALITY OF THE SPT RULE FOR THE FLOW SHOP MEAN COMPLETION TIME PROBLEM PHILIP KAMINSKY Industra Engneerng and Operatons Research, Unversty of Caforna, Bereey, Caforna 9470, amnsy@eor.bereey.edu

More information

Correspondence. Performance Evaluation for MAP State Estimate Fusion I. INTRODUCTION

Correspondence. Performance Evaluation for MAP State Estimate Fusion I. INTRODUCTION Correspondence Performance Evauaton for MAP State Estmate Fuson Ths paper presents a quanttatve performance evauaton method for the maxmum a posteror (MAP) state estmate fuson agorthm. Under dea condtons

More information

For now, let us focus on a specific model of neurons. These are simplified from reality but can achieve remarkable results.

For now, let us focus on a specific model of neurons. These are simplified from reality but can achieve remarkable results. Neural Networks : Dervaton compled by Alvn Wan from Professor Jtendra Malk s lecture Ths type of computaton s called deep learnng and s the most popular method for many problems, such as computer vson

More information

Kernel Methods and SVMs Extension

Kernel Methods and SVMs Extension Kernel Methods and SVMs Extenson The purpose of ths document s to revew materal covered n Machne Learnng 1 Supervsed Learnng regardng support vector machnes (SVMs). Ths document also provdes a general

More information

Improvement of Histogram Equalization for Minimum Mean Brightness Error

Improvement of Histogram Equalization for Minimum Mean Brightness Error Proceedngs of the 7 WSEAS Int. Conference on Crcuts, Systems, Sgnal and elecommuncatons, Gold Coast, Australa, January 7-9, 7 3 Improvement of Hstogram Equalzaton for Mnmum Mean Brghtness Error AAPOG PHAHUA*,

More information

Chapter 5. Solution of System of Linear Equations. Module No. 6. Solution of Inconsistent and Ill Conditioned Systems

Chapter 5. Solution of System of Linear Equations. Module No. 6. Solution of Inconsistent and Ill Conditioned Systems Numercal Analyss by Dr. Anta Pal Assstant Professor Department of Mathematcs Natonal Insttute of Technology Durgapur Durgapur-713209 emal: anta.bue@gmal.com 1 . Chapter 5 Soluton of System of Lnear Equatons

More information

A General Column Generation Algorithm Applied to System Reliability Optimization Problems

A General Column Generation Algorithm Applied to System Reliability Optimization Problems A Genera Coumn Generaton Agorthm Apped to System Reabty Optmzaton Probems Lea Za, Davd W. Cot, Department of Industra and Systems Engneerng, Rutgers Unversty, Pscataway, J 08854, USA Abstract A genera

More information

Short-Term Load Forecasting for Electric Power Systems Using the PSO-SVR and FCM Clustering Techniques

Short-Term Load Forecasting for Electric Power Systems Using the PSO-SVR and FCM Clustering Techniques Energes 20, 4, 73-84; do:0.3390/en40073 Artce OPEN ACCESS energes ISSN 996-073 www.mdp.com/journa/energes Short-Term Load Forecastng for Eectrc Power Systems Usng the PSO-SVR and FCM Custerng Technques

More information

Single-Source/Sink Network Error Correction Is as Hard as Multiple-Unicast

Single-Source/Sink Network Error Correction Is as Hard as Multiple-Unicast Snge-Source/Snk Network Error Correcton Is as Hard as Mutpe-Uncast Wentao Huang and Tracey Ho Department of Eectrca Engneerng Caforna Insttute of Technoogy Pasadena, CA {whuang,tho}@catech.edu Mchae Langberg

More information

LECTURE 21 Mohr s Method for Calculation of General Displacements. 1 The Reciprocal Theorem

LECTURE 21 Mohr s Method for Calculation of General Displacements. 1 The Reciprocal Theorem V. DEMENKO MECHANICS OF MATERIALS 05 LECTURE Mohr s Method for Cacuaton of Genera Dspacements The Recproca Theorem The recproca theorem s one of the genera theorems of strength of materas. It foows drect

More information

A Three-Phase State Estimation in Unbalanced Distribution Networks with Switch Modelling

A Three-Phase State Estimation in Unbalanced Distribution Networks with Switch Modelling A Three-Phase State Estmaton n Unbaanced Dstrbuton Networks wth Swtch Modeng Ankur Majumdar Student Member, IEEE Dept of Eectrca and Eectronc Engneerng Impera Coege London London, UK ankurmajumdar@mperaacuk

More information

= z 20 z n. (k 20) + 4 z k = 4

= z 20 z n. (k 20) + 4 z k = 4 Problem Set #7 solutons 7.2.. (a Fnd the coeffcent of z k n (z + z 5 + z 6 + z 7 + 5, k 20. We use the known seres expanson ( n+l ( z l l z n below: (z + z 5 + z 6 + z 7 + 5 (z 5 ( + z + z 2 + z + 5 5

More information

Graph Reconstruction by Permutations

Graph Reconstruction by Permutations Graph Reconstructon by Permutatons Perre Ille and Wllam Kocay* Insttut de Mathémathques de Lumny CNRS UMR 6206 163 avenue de Lumny, Case 907 13288 Marselle Cedex 9, France e-mal: lle@ml.unv-mrs.fr Computer

More information

Sampling Theory MODULE VII LECTURE - 23 VARYING PROBABILITY SAMPLING

Sampling Theory MODULE VII LECTURE - 23 VARYING PROBABILITY SAMPLING Samplng heory MODULE VII LECURE - 3 VARYIG PROBABILIY SAMPLIG DR. SHALABH DEPARME OF MAHEMAICS AD SAISICS IDIA ISIUE OF ECHOLOGY KAPUR he smple random samplng scheme provdes a random sample where every

More information

Sampling-based Approach for Design Optimization in the Presence of Interval Variables

Sampling-based Approach for Design Optimization in the Presence of Interval Variables 0 th Word Congress on Structura and Mutdscpnary Optmzaton May 9-4, 03, Orando, orda, USA Sampng-based Approach for Desgn Optmzaton n the Presence of nterva Varabes Davd Yoo and kn Lee Unversty of Connectcut,

More information

Microwave Diversity Imaging Compression Using Bioinspired

Microwave Diversity Imaging Compression Using Bioinspired Mcrowave Dversty Imagng Compresson Usng Bonspred Neural Networks Youwe Yuan 1, Yong L 1, Wele Xu 1, Janghong Yu * 1 School of Computer Scence and Technology, Hangzhou Danz Unversty, Hangzhou, Zhejang,

More information

NEW ASTERISKS IN VERSION 2.0 OF ACTIVEPI

NEW ASTERISKS IN VERSION 2.0 OF ACTIVEPI NEW ASTERISKS IN VERSION 2.0 OF ACTIVEPI ASTERISK ADDED ON LESSON PAGE 3-1 after the second sentence under Clncal Trals Effcacy versus Effectveness versus Effcency The apprasal of a new or exstng healthcare

More information

Copyright 2017 by Taylor Enterprises, Inc., All Rights Reserved. Adjusted Control Limits for P Charts. Dr. Wayne A. Taylor

Copyright 2017 by Taylor Enterprises, Inc., All Rights Reserved. Adjusted Control Limits for P Charts. Dr. Wayne A. Taylor Taylor Enterprses, Inc. Control Lmts for P Charts Copyrght 2017 by Taylor Enterprses, Inc., All Rghts Reserved. Control Lmts for P Charts Dr. Wayne A. Taylor Abstract: P charts are used for count data

More information

DISTRIBUTED PROCESSING OVER ADAPTIVE NETWORKS. Cassio G. Lopes and Ali H. Sayed

DISTRIBUTED PROCESSING OVER ADAPTIVE NETWORKS. Cassio G. Lopes and Ali H. Sayed DISTRIBUTED PROCESSIG OVER ADAPTIVE ETWORKS Casso G Lopes and A H Sayed Department of Eectrca Engneerng Unversty of Caforna Los Angees, CA, 995 Ema: {casso, sayed@eeucaedu ABSTRACT Dstrbuted adaptve agorthms

More information

Calculation of time complexity (3%)

Calculation of time complexity (3%) Problem 1. (30%) Calculaton of tme complexty (3%) Gven n ctes, usng exhaust search to see every result takes O(n!). Calculaton of tme needed to solve the problem (2%) 40 ctes:40! dfferent tours 40 add

More information

Predicting Model of Traffic Volume Based on Grey-Markov

Predicting Model of Traffic Volume Based on Grey-Markov Vo. No. Modern Apped Scence Predctng Mode of Traffc Voume Based on Grey-Marov Ynpeng Zhang Zhengzhou Muncpa Engneerng Desgn & Research Insttute Zhengzhou 5005 Chna Abstract Grey-marov forecastng mode of

More information

On the correction of the h-index for career length

On the correction of the h-index for career length 1 On the correcton of the h-ndex for career length by L. Egghe Unverstet Hasselt (UHasselt), Campus Depenbeek, Agoralaan, B-3590 Depenbeek, Belgum 1 and Unverstet Antwerpen (UA), IBW, Stadscampus, Venusstraat

More information

Chapter 13: Multiple Regression

Chapter 13: Multiple Regression Chapter 13: Multple Regresson 13.1 Developng the multple-regresson Model The general model can be descrbed as: It smplfes for two ndependent varables: The sample ft parameter b 0, b 1, and b are used to

More information

Tutorial 2. COMP4134 Biometrics Authentication. February 9, Jun Xu, Teaching Asistant

Tutorial 2. COMP4134 Biometrics Authentication. February 9, Jun Xu, Teaching Asistant Tutoral 2 COMP434 ometrcs uthentcaton Jun Xu, Teachng sstant csjunxu@comp.polyu.edu.hk February 9, 207 Table of Contents Problems Problem : nswer the questons Problem 2: Power law functon Problem 3: Convoluton

More information

A PROBABILITY-DRIVEN SEARCH ALGORITHM FOR SOLVING MULTI-OBJECTIVE OPTIMIZATION PROBLEMS

A PROBABILITY-DRIVEN SEARCH ALGORITHM FOR SOLVING MULTI-OBJECTIVE OPTIMIZATION PROBLEMS HCMC Unversty of Pedagogy Thong Nguyen Huu et al. A PROBABILITY-DRIVEN SEARCH ALGORITHM FOR SOLVING MULTI-OBJECTIVE OPTIMIZATION PROBLEMS Thong Nguyen Huu and Hao Tran Van Department of mathematcs-nformaton,

More information

CHAPTER 5 NUMERICAL EVALUATION OF DYNAMIC RESPONSE

CHAPTER 5 NUMERICAL EVALUATION OF DYNAMIC RESPONSE CHAPTER 5 NUMERICAL EVALUATION OF DYNAMIC RESPONSE Analytcal soluton s usually not possble when exctaton vares arbtrarly wth tme or f the system s nonlnear. Such problems can be solved by numercal tmesteppng

More information

corresponding to those of Heegaard diagrams by the band moves

corresponding to those of Heegaard diagrams by the band moves Agebra transformatons of the fundamenta groups correspondng to those of Heegaard dagrams by the band moves By Shun HORIGUCHI Abstract. Ths paper gves the basc resut of [1](1997),.e., a hande sdng and a

More information

AGC Introduction

AGC Introduction . Introducton AGC 3 The prmary controller response to a load/generaton mbalance results n generaton adjustment so as to mantan load/generaton balance. However, due to droop, t also results n a non-zero

More information

Low Complexity Soft-Input Soft-Output Hamming Decoder

Low Complexity Soft-Input Soft-Output Hamming Decoder Low Complexty Soft-Input Soft-Output Hammng Der Benjamn Müller, Martn Holters, Udo Zölzer Helmut Schmdt Unversty Unversty of the Federal Armed Forces Department of Sgnal Processng and Communcatons Holstenhofweg

More information

A Dissimilarity Measure Based on Singular Value and Its Application in Incremental Discounting

A Dissimilarity Measure Based on Singular Value and Its Application in Incremental Discounting A Dssmarty Measure Based on Snguar Vaue and Its Appcaton n Incrementa Dscountng KE Xaou Department of Automaton, Unversty of Scence and Technoogy of Chna, Hefe, Chna Ema: kxu@ma.ustc.edu.cn MA Lyao Department

More information

6 Supplementary Materials

6 Supplementary Materials 6 Supplementar Materals 61 Proof of Theorem 31 Proof Let m Xt z 1:T : l m Xt X,z 1:t Wethenhave mxt z1:t ˆm HX Xt z 1:T mxt z1:t m HX Xt z 1:T + mxt z 1:T HX We consder each of the two terms n equaton

More information

A Robust Method for Calculating the Correlation Coefficient

A Robust Method for Calculating the Correlation Coefficient A Robust Method for Calculatng the Correlaton Coeffcent E.B. Nven and C. V. Deutsch Relatonshps between prmary and secondary data are frequently quantfed usng the correlaton coeffcent; however, the tradtonal

More information

Experience with Automatic Generation Control (AGC) Dynamic Simulation in PSS E

Experience with Automatic Generation Control (AGC) Dynamic Simulation in PSS E Semens Industry, Inc. Power Technology Issue 113 Experence wth Automatc Generaton Control (AGC) Dynamc Smulaton n PSS E Lu Wang, Ph.D. Staff Software Engneer lu_wang@semens.com Dngguo Chen, Ph.D. Staff

More information

Statistical Evaluation of WATFLOOD

Statistical Evaluation of WATFLOOD tatstcal Evaluaton of WATFLD By: Angela MacLean, Dept. of Cvl & Envronmental Engneerng, Unversty of Waterloo, n. ctober, 005 The statstcs program assocated wth WATFLD uses spl.csv fle that s produced wth

More information

Neuro-Adaptive Design - I:

Neuro-Adaptive Design - I: Lecture 36 Neuro-Adaptve Desgn - I: A Robustfyng ool for Dynamc Inverson Desgn Dr. Radhakant Padh Asst. Professor Dept. of Aerospace Engneerng Indan Insttute of Scence - Bangalore Motvaton Perfect system

More information

Inductance Calculation for Conductors of Arbitrary Shape

Inductance Calculation for Conductors of Arbitrary Shape CRYO/02/028 Aprl 5, 2002 Inductance Calculaton for Conductors of Arbtrary Shape L. Bottura Dstrbuton: Internal Summary In ths note we descrbe a method for the numercal calculaton of nductances among conductors

More information

Formulas for the Determinant

Formulas for the Determinant page 224 224 CHAPTER 3 Determnants e t te t e 2t 38 A = e t 2te t e 2t e t te t 2e 2t 39 If 123 A = 345, 456 compute the matrx product A adj(a) What can you conclude about det(a)? For Problems 40 43, use

More information

VARIATION OF CONSTANT SUM CONSTRAINT FOR INTEGER MODEL WITH NON UNIFORM VARIABLES

VARIATION OF CONSTANT SUM CONSTRAINT FOR INTEGER MODEL WITH NON UNIFORM VARIABLES VARIATION OF CONSTANT SUM CONSTRAINT FOR INTEGER MODEL WITH NON UNIFORM VARIABLES BÂRZĂ, Slvu Faculty of Mathematcs-Informatcs Spru Haret Unversty barza_slvu@yahoo.com Abstract Ths paper wants to contnue

More information

The Study of Teaching-learning-based Optimization Algorithm

The Study of Teaching-learning-based Optimization Algorithm Advanced Scence and Technology Letters Vol. (AST 06), pp.05- http://dx.do.org/0.57/astl.06. The Study of Teachng-learnng-based Optmzaton Algorthm u Sun, Yan fu, Lele Kong, Haolang Q,, Helongang Insttute

More information

Single-Facility Scheduling over Long Time Horizons by Logic-based Benders Decomposition

Single-Facility Scheduling over Long Time Horizons by Logic-based Benders Decomposition Sngle-Faclty Schedulng over Long Tme Horzons by Logc-based Benders Decomposton Elvn Coban and J. N. Hooker Tepper School of Busness, Carnege Mellon Unversty ecoban@andrew.cmu.edu, john@hooker.tepper.cmu.edu

More information

18.1 Introduction and Recap

18.1 Introduction and Recap CS787: Advanced Algorthms Scrbe: Pryananda Shenoy and Shjn Kong Lecturer: Shuch Chawla Topc: Streamng Algorthmscontnued) Date: 0/26/2007 We contnue talng about streamng algorthms n ths lecture, ncludng

More information

Supplementary Material: Learning Structured Weight Uncertainty in Bayesian Neural Networks

Supplementary Material: Learning Structured Weight Uncertainty in Bayesian Neural Networks Shengyang Sun, Changyou Chen, Lawrence Carn Suppementary Matera: Learnng Structured Weght Uncertanty n Bayesan Neura Networks Shengyang Sun Changyou Chen Lawrence Carn Tsnghua Unversty Duke Unversty Duke

More information

CONTRAST ENHANCEMENT FOR MIMIMUM MEAN BRIGHTNESS ERROR FROM HISTOGRAM PARTITIONING INTRODUCTION

CONTRAST ENHANCEMENT FOR MIMIMUM MEAN BRIGHTNESS ERROR FROM HISTOGRAM PARTITIONING INTRODUCTION CONTRAST ENHANCEMENT FOR MIMIMUM MEAN BRIGHTNESS ERROR FROM HISTOGRAM PARTITIONING N. Phanthuna 1,2, F. Cheevasuvt 2 and S. Chtwong 2 1 Department of Electrcal Engneerng, Faculty of Engneerng Rajamangala

More information

Reactive Power Allocation Using Support Vector Machine

Reactive Power Allocation Using Support Vector Machine Reactve Power Aocaton Usng Support Vector Machne M.W. Mustafa, S.N. Khad, A. Kharuddn Facuty of Eectrca Engneerng, Unverst Teknoog Maaysa Johor 830, Maaysa and H. Shareef Facuty of Eectrca Engneerng and

More information

A Hybrid Variational Iteration Method for Blasius Equation

A Hybrid Variational Iteration Method for Blasius Equation Avalable at http://pvamu.edu/aam Appl. Appl. Math. ISSN: 1932-9466 Vol. 10, Issue 1 (June 2015), pp. 223-229 Applcatons and Appled Mathematcs: An Internatonal Journal (AAM) A Hybrd Varatonal Iteraton Method

More information

ISSN: ISO 9001:2008 Certified International Journal of Engineering and Innovative Technology (IJEIT) Volume 3, Issue 1, July 2013

ISSN: ISO 9001:2008 Certified International Journal of Engineering and Innovative Technology (IJEIT) Volume 3, Issue 1, July 2013 ISSN: 2277-375 Constructon of Trend Free Run Orders for Orthogonal rrays Usng Codes bstract: Sometmes when the expermental runs are carred out n a tme order sequence, the response can depend on the run

More information

An Interactive Optimisation Tool for Allocation Problems

An Interactive Optimisation Tool for Allocation Problems An Interactve Optmsaton ool for Allocaton Problems Fredr Bonäs, Joam Westerlund and apo Westerlund Process Desgn Laboratory, Faculty of echnology, Åbo Aadem Unversty, uru 20500, Fnland hs paper presents

More information

Analysis of Bipartite Graph Codes on the Binary Erasure Channel

Analysis of Bipartite Graph Codes on the Binary Erasure Channel Anayss of Bpartte Graph Codes on the Bnary Erasure Channe Arya Mazumdar Department of ECE Unversty of Maryand, Coege Par ema: arya@umdedu Abstract We derve densty evouton equatons for codes on bpartte

More information

Some Comments on Accelerating Convergence of Iterative Sequences Using Direct Inversion of the Iterative Subspace (DIIS)

Some Comments on Accelerating Convergence of Iterative Sequences Using Direct Inversion of the Iterative Subspace (DIIS) Some Comments on Acceleratng Convergence of Iteratve Sequences Usng Drect Inverson of the Iteratve Subspace (DIIS) C. Davd Sherrll School of Chemstry and Bochemstry Georga Insttute of Technology May 1998

More information