Statistical Approach for Offline Handwritten Signature Verification


 Theresa Laurel Arnold
 2 years ago
 Views:
Transcription
1 Journal of Computer Scence 4 (3): , 2008 ISSN Scence Publcatons Statstcal Approach for Offlne Handwrtten Sgnature Verfcaton 2 Debnath Bhattacharyya, 1 Samr Kumar Bandyopadhyay, 2 Poulam Das, 2 Debashs Ganguly and 2 Swarnendu Mukherjee 1 Department of Computer Scence and Engneerng, Unversty of Calcutta, Rajabazar Scence College, Inda 2 Department of Computer Scence and Engneerng, Hertage Insttute of Technology, Kolkata, Inda Abstract: Sgnatures were consdered an mportant tool for authentcatng the dentty of human bengs. So, sgnature verfcaton was one of the bggest uses for that. We proposed an algorthmc approach for the verfcaton of handwrtten sgnatures by applyng some statstcal methods. The research work was based on the collecton of set of sgnatures from whch an average sgnature was obtaned based on our algorthm and then takng decson of acceptance after analyzng the correlaton n between the sample sgnature and the average sgnature. Key words: Sgnatures, authentcatng, statstcal methods, acceptance, analyzng, correlaton, sample sgnature and average sgnature INTRODUCTION Sgnatures are composed of specal characters and flourshes and therefore most of the tme they can be unreadable. Also ntrapersonal varatons and the dfferences make t necessary to analyze them as complete mages and not as letters and words put together [1]. As sgnatures are the prmary mechansm both for authentcaton and authorzaton n legal transactons, the need for research n effcent automated solutons for sgnature recognton and verfcaton has ncreased n recent years. Varous methods have already been ntroduced n ths feld and applcaton of Statstcal models s one of them n ths regard. Usng statstcal knowledge, we can easly perform the relaton, devaton, etc between two or more data tems. Strctly speakng, to fnd out the relaton between some set of data tems we generally follow the concept of Correlaton Coeffcents. In general statstcal usage, correlaton or correlaton refers to the departure of two varables from ndependence, although correlaton does not mply causaton. Our approach s based on the above concept. To verfy an entered sgnature wth the help an average sgnature, whch s obtaned from the set of, prevously collected sgnatures, we have followed the concept of correlaton to fnd out the amount of dvergence n between them. Based on some predefned dvergence, we have taken the decson of acceptance of the entered sgnature. RELATED WORKS Handwrtten Sgnature Identfcaton both offlne and onlne s a classcal work area n the lne of Computer Scence and Technology snce last two decades. Numerous approaches have been proposed for Handwrtten Sgnature Identfcaton, Recognton and Authentcaton systems. Apart from the all, the approach whch makes our attracton s the applcaton of Artfcal Neural Network at the tme of dentfcaton. An Artfcal Neural Network s traned to dentfy patterns among dfferent suppled handwrtng samples. Handwrtten sgnature samples are consdered nput for the artfcal neural network model and typcally weghts also suppled for recognton. In March, 2007, Debnath Bhattacharyya, Samr Kumar Bandyopadhyay and Poulam Das [12] have proposed a new recognton technque; an Artfcal Neural Network s traned to dentfy patterns among dfferent suppled handwrtng samples. Handwrtten sgnature samples are consdered nput for that artfcal neural network model and typcally weghts also suppled for recognton [2]. Correspondng Author: Debnath Bhattacharyya, Computer Scence and Engneerng Department, Hertage Insttute of Technology, Kolkata , Inda 181
2 J. Computer Sc., 4 (3): , 2008 Another mportant method for offlne sgnature verfcaton s the applcaton of Hdden Markov s rule. Justno, Bortolozz and Sabourn proposed an offlne sgnature verfcaton system usng Hdden Markov Model [3]. Hdden Markov Model (HMM) s one of the most wdely used models for sequence analyss n sgnature verfcaton. Handwrtten sgnature s a sequence of vectors of values related to each pont of sgnature n ts trajectory. Therefore, a well chosen set of feature vectors for HMM could lead to the desgn of an effcent sgnature verfcaton system. Applcaton of Support Vector machne (SVM) at the tme of sgnature verfcaton s also a new dmenson n ths feld. Emre Ozgunduz, Tuln enturk and M. Elf Karslıgl has proposed an algorthmc approach accordng to whch offlne sgnature verfcaton and recognton can be done by Support Vector Machne [4]. Statstcal approach for offlne handwrtten sgnature verfcaton also drves our attenton towards t. In ths method, generally usng some statstcal tool a devaton n between testng sample and the predefned samples s calculated and based on that value decson s taken up. Debnath Bhattacharyya, Samr Kumar Bandyopadhyay and Deepskha Chaudhury, 2007, proposed a scheme where the Standard Devaton for each byte of the Tranng Image Fles (sample sgnatures) s computed and then each correspondng byte of Test Sgnature s compared to check whether t falls wthn the range of (Mean ± Standard Devaton). If 70% cases match, then the Test Sgnature s accepted [5]. X = E (X) = (X )/N For extractng the mean of data values a constant value, named as avgsgnthresoldvalue, s compared wth the actual mean to take the decson for selecton between the bnary values to be placed and ths constant value s to be calculated after conductng a survey on a test data set of known features and as per the strctness n the securty concern of the nsttute n concern. From the bnary data value calculated as above the Avg_Sgn s generated n turn followng the same algorthm. The algorthm for verfcaton of handwrtten sgnatures s requred an addtonal nput of Sample_Sgn, whch s to be tested for verfcaton of acceptance or rejecton. Here, another constant value s mantaned, named as decsonvalue, whch s also calculated and set by professonal statstcan and securty admnstrator decdng the securty concern and polces of the organzaton after conductng surveys and testng over some sample expermental data set wth already known results. The algorthm compares between two bnary data sets obtaned after analyss of Avg_Sgn and Sample_Sgn and calculates out the correlaton coeffcent, r xy between them usng the followng statstcal formula: r = (S X Y X Y ) X,Y 2 2 1/ / 2 ((S X ( X ) ) ((S Y ( Y ) ) In turn, r xy s compared wth the decsonvalue and accordngly return TRUE or FALSE as for acceptance or rejecton respectvely. MATERIALS AND METHODS The algorthm proposed n ths paper s bascally deals wth the scheme of handwrtten sgnature verfcaton n an offlne system, but t can also be realzed as the verfcaton method of extended onlne verfcaton of sgnatures for forgery control and securty purpose. The algorthmc approach mentoned n the followng sectons has the flexblty of choosng the number of sgnatures,.e., no_of_sgn for testng purpose to generate a sgnature as Avg_Sgn contanng the specalzed mean features set from the test sgnatures set. After collectng the sgnatures for testng, the algorthm converts them nto a set of 2D arrays of bnary data values0 and 1. From these bnary arrays usng statstcal methods of calculatng expected mean an average data set s calculated usng the formula gven by: 182 Saohsv_avgpccalc (No of sgn, sgn1_pc,..): Ths s the man functon n our algorthm. Ths functon wll be used for the verfcaton of the handwrtten sgnature by comparng wth a standard sgnature, whch s obtaned by applyng statstcal analyss on a set of sgnatures. Ths functon wll take Number of total sgnatures and correspondng sgnatures as argument and fnally t wll output the decson for the acceptance of the gven sgnature: Declare one 2D array namely statdatainputsgn [no_of_sgn] [ndexrowmajor] and a 1D array as statdataavgsgn [ndexrowmajor], frst one stored bnary data value for the number of sample sgns gven nput and second one holds bnary data values depctng and descrbng desred AVG_SIGN Consder the sample sgn gven nput as SIGN _PIC and analyze ts pxels
3 If the pxel value corresponds to colour whte then correspondng data value wll be zero and f the colour s black that sgn or scratch s present then t wll be 1 n statdatainputsgn [] Step back to Step2 f any other sample test sgns are avalable else goes to next step Now calculate the sum of bnary values for each rowmajorindex of all the sgns and then dvde each one by no_of_sgn to calculate expected mean Compare the mean for each ndex wth the constant avgsgnthresoldvalue calculated as conductng survey, f mean s greater and equals to the constant values, then correspondng value n AVG_SIGN wll be 1 else 0 whch s to be stored n 1D array of statdataavgsgn[] n row major fashon From the statdataavgsgn[] form new pcture as AVG_SIGN n such a fashon that pxel wll be of whte colour f value correspondng to an ndex n array s zero and black f value s 1 Saohsv_decson (avg_sgn, sample_sgn): Ths module wll be used for takng the decson whether the nput sgnature matches wth the average sgnature, whch s obtaned n the man functon. Ths functon wll take average sgnature and sample sgnature as arguments and fnally t wll output the decson for the acceptance of the gven sgnature: Declare two 1D arrays namely statdataavgsgn [ndexrowmajor] and statdatasamplesgn [ndexrowmajor], where frst one stores bnary data corresponds to AVG_SIGN and second one for SAMPLE_SIGN Consder the sample sgn gven nput as SAMPLE_SIGN and analyze ts pxels If the pxel value corresponds to colour whte then correspondng data value wll be zero and f the colour s black that sgn or scratch s present then t wll be 1 n statdatasamplesgn [] Consder the average sgn gven nput as AVG_SIGN and analyze ts pxels If the pxel value corresponds to colour whte then correspondng data value wll be zero and f the colour s black that sgn or scratch s present then t wll be 1 n statdataavgsgn [] Now calculate correlaton coeffcent, r xy wth bvarate data set x and y taken as statdataavgsgn [] and statdatasamplesgn [] respectvely Compare r xy wth a constant value decsonvalue, calculated by statstcans and analyst keepng J. Computer Sc., 4 (3): , securty concern of organzaton. If former exceeds later then return TRUE else return FALSE RESULTS AND DISCUSSIONS The Algorthm stated n above secton conssts of two dstnct dvsons. a. Average Sgnature calculaton, b. Decson: comparson between Average Sgnature and Sample Sgnature to fnd the correlaton between them. Complexty analyss of the stated algorthm: For converson from btmap pctures nto 1D array: Consderng the rowsze and columnsze as wdth and heght of the pxel matrx of the btmap pctures depctng the sgnatures, the total number of pxels,.e., sze of the pxel matrx s gven by (row sze column sze). So, for converson from pxels to bnary data as per suggested by algorthm, the tme complexty requred for each pcture s gven by O (row sze column sze) O(szeOfPxelMatrx) and thus total tme complexty of converson for no_of_sgn s gven by O (no_of_sgn row Sze column sze). For calculaton of AVG_SIGN: For calculaton of mean out of no_of_sgn number of pctures correspondng to the sample sgnatures total tme complexty ncurred s as follow: O (no_of_sgn row sze column sze) For SAOHSV_DECISION: In ths module correlaton coeffcent s beng calculated between two bvarate data sets of bnary values correspondng to AVG_SIGN and SAMPLE_SIGN respectvely. The tme taken n ths procedure s of order of O (row sze column sze). Here n mplementaton of the above stated procedures through programs no extras varable spaces are requred to be allocated n memory, so consderng the space complexty of the algorthm, ths s an nplace algorthm. Test Results: In mplementaton of the algorthm proposed n above secton database of 15 sgnatures for each of 100 dfferent users s consdered. Out of these 15 sgnatures, one Avg_Sgn for each of the 100 users s calculated usng the procedure descrbed n Secton III.A. Testng s done here wth two traned forgery sgnatures and two true acceptable sgnatures, whch are sent as Sample_Sgn to the argument of the procedure of Secton III.B. The Array of 15 sgnatures for an arbtrarly chosen user from database s represented n the Fg. 1.
4 J. Computer Sc., 4 (3): , 2008 Sgn1_pc Sgn2_pc Sgn15_pc Fg. 1: Set of Sample Sgnatures Table 2: Analyzed value and Rejected sample Sgnatures Avg_sgn Sample_Sgn Correlaton Coeffcent, r x,y Sample1_false Sample2_false (Rejecton: r xy < 0.50) CONCLUSION Fg. 2: Stat Data Avg Sgn (After pxel by pxel transformaton of nput sgnatures nto bnary data values and calculatng ther mean) Sample1_true Sample2_true Sample1_false Sample2_false Fg. 3: Sample Sgnatures Table 1 : Analyzed values for accepted sample sgnatures Avg_sgn Sample_Sgn Correlaton Coeffcent, r x,y Sample1_true Sample2_true (Acceptance: r xy > = 0.50) The 1D array of bnary data value correspondng to the Avg_Sgn of the above taken user s same as n Fg. 2. The Sample_Sgn s taken as varable argument for the monochrome btmap pctures, namely Sample1_true, Sample2_true, Sample1_false, Sample2_false and t s shown as n Fg. 3. The test results of decson functons for the above gven data set are shown n Table 1 and Amongst the dfferent bometrc authentcaton schemes for securty verfcaton ncludng voce detecton, retna scan, fngerprnt verfcaton, handwrtten sgnature verfcaton, durng montory transactons and other securty polces both onlne and offlne, s ncreasngly becomng popular. Our motvaton behnd ths paper s to mplement a smple statstcal approach for such handwrtten sgnature verfcaton avodng all such complextes of handlng a huge database of monochrome pctures correspondng to sgnatures of each ndvdual. Here to avod complex mage processng methods lke thnnng, scalng and other morphologcal schemes, the sgnatures taken n the form of monochrome btmap mages are frstly converted nto 1D data arrays of bnary values  0 and 1. Then, Avg_Sgn s calculated followng Statstcal formula for Expected Mean, though t gves the same result f the formula for R.M.S.,.e., Root Mean Square s beng followed. The Recognton scheme s based on extensve Statstcal Analyss of Correlaton Coeffcent between bvarate data set. In mplementaton of proposed algorthm to constant factors carry major mpact on the valdty of the method and the strength of the verfcaton les n the effcency of selecton of these constant parameters, namely avg Sgn Thresold Value and decson value. We hope that our Study and Research wll defntely be focused to extend the above gven approach from offlne detecton scheme to onlne one through realzaton of neural networks and artfcal systems. REFERENCES 1. Velez, J.F., A. Sanchez and A.B. Moreno, Robust offlne sgnature verfcaton usng compresson networks and postonal cuttngs. Proceedngs of 2003 IEEE Sgnal Processng Socety Workshop on Neural Networks, September, 2003, Toulouse, France, pp: Samr Kumar Bandyopadhyay, Debnath Bhattacharyya and Poulam Das, A Flexble ANN System for Handwrtten Sgnature Identfcaton, Proceedngs of the Internatonal MultConference of Engneers and Computer Scentsts 2007 Volume II, IMECS '07, March 2123, 2007, Hong Kong, Lecture Notes n Engneerng and Computer Scence, pp
5 J. Computer Sc., 4 (3): , Justno, E.J.R., F. Bortolozz and R. Sabourn, Offlne sgnature verfcaton usng hmm for random, smple and sklled forgeres. 6th Internatonal Conference on Document Analyss and Recognton, September, 2001, Seattle, WA, USA, pp: Emre Ozgunduz, Tüln enturk and M. Elf Karslıgl, Offlne sgnature verfcaton and recognton by support vector machne. Euspco 2005, 48 September, 2005, Antalya, Turkey, pp co2005/defevent/papers/cr2010.pdf 5. Debnath Bhattacharyya, Samr Kumar Bandyopadhyay and Deepskha Chaudhury, Handwrtten sgnature authentcaton scheme usng ntegrated statstcal analyss of bcolor mages, IEEE ICCSA 2007 Conference, Kuala Lumpur, Malaysa, August 2629, pp:
Biometric Signature Processing & Recognition Using Radial Basis Function Network
Bometrc Sgnature Processng & Recognton Usng Radal Bass Functon Network Ankt Chadha, Neha Satam, and Vbha Wal Abstract Automatc recognton of sgnature s a challengng problem whch has receved much attenton
More informationThe Development of Web Log Mining Based on ImproveKMeans Clustering Analysis
The Development of Web Log Mnng Based on ImproveKMeans Clusterng Analyss TngZhong Wang * College of Informaton Technology, Luoyang Normal Unversty, Luoyang, 471022, Chna wangtngzhong2@sna.cn Abstract.
More informationOffline Verification of Hand Written Signature using Adaptive Resonance Theory Net (Type1)
Internatonal Journal of Sgnal Processng Systems Vol, No June 203 Offlne Verfcaton of Hand Wrtten Sgnature usng Adaptve Resonance Theory Net (Type) Trtharaj Dash Veer Surendra Sa Unversty of Technology,
More informationGRAVITY DATA VALIDATION AND OUTLIER DETECTION USING L 1 NORM
GRAVITY DATA VALIDATION AND OUTLIER DETECTION USING L 1 NORM BARRIOT JeanPerre, SARRAILH Mchel BGI/CNES 18.av.E.Beln 31401 TOULOUSE Cedex 4 (France) Emal: jeanperre.barrot@cnes.fr 1/Introducton The
More informationGender Classification for RealTime Audience Analysis System
Gender Classfcaton for RealTme Audence Analyss System Vladmr Khryashchev, Lev Shmaglt, Andrey Shemyakov, Anton Lebedev Yaroslavl State Unversty Yaroslavl, Russa vhr@yandex.ru, shmaglt_lev@yahoo.com, andrey.shemakov@gmal.com,
More informationA Secure PasswordAuthenticated Key Agreement Using Smart Cards
A Secure PasswordAuthentcated Key Agreement Usng Smart Cards Ka Chan 1, WenChung Kuo 2 and JnChou Cheng 3 1 Department of Computer and Informaton Scence, R.O.C. Mltary Academy, Kaohsung 83059, Tawan,
More informationForecasting the Direction and Strength of Stock Market Movement
Forecastng the Drecton and Strength of Stock Market Movement Jngwe Chen Mng Chen Nan Ye cjngwe@stanford.edu mchen5@stanford.edu nanye@stanford.edu Abstract  Stock market s one of the most complcated systems
More information"Research Note" APPLICATION OF CHARGE SIMULATION METHOD TO ELECTRIC FIELD CALCULATION IN THE POWER CABLES *
Iranan Journal of Scence & Technology, Transacton B, Engneerng, ol. 30, No. B6, 789794 rnted n The Islamc Republc of Iran, 006 Shraz Unversty "Research Note" ALICATION OF CHARGE SIMULATION METHOD TO ELECTRIC
More informationCan Auto Liability Insurance Purchases Signal Risk Attitude?
Internatonal Journal of Busness and Economcs, 2011, Vol. 10, No. 2, 159164 Can Auto Lablty Insurance Purchases Sgnal Rsk Atttude? ChuShu L Department of Internatonal Busness, Asa Unversty, Tawan ShengChang
More informationIntelligent VoiceBased Door Access Control System Using AdaptiveNetworkbased Fuzzy Inference Systems (ANFIS) for Building Security
Journal of Computer Scence 3 (5): 274280, 2007 ISSN 15493636 2007 Scence Publcatons Intellgent VoceBased Door Access Control System Usng AdaptveNetworkbased Fuzzy Inference Systems (ANFIS) for Buldng
More informationSingle and multiple stage classifiers implementing logistic discrimination
Sngle and multple stage classfers mplementng logstc dscrmnaton Hélo Radke Bttencourt 1 Dens Alter de Olvera Moraes 2 Vctor Haertel 2 1 Pontfíca Unversdade Católca do Ro Grande do Sul  PUCRS Av. Ipranga,
More informationFace Verification Problem. Face Recognition Problem. Application: Access Control. Biometric Authentication. Face Verification (1:1 matching)
Face Recognton Problem Face Verfcaton Problem Face Verfcaton (1:1 matchng) Querymage face query Face Recognton (1:N matchng) database Applcaton: Access Control www.vsage.com www.vsoncs.com Bometrc Authentcaton
More informationLETTER IMAGE RECOGNITION
LETTER IMAGE RECOGNITION 1. Introducton. 1. Introducton. Objectve: desgn classfers for letter mage recognton. consder accuracy and tme n takng the decson. 20,000 samples: Startng set: mages based on 20
More informationA FASTER EXTERNAL SORTING ALGORITHM USING NO ADDITIONAL DISK SPACE
47 A FASTER EXTERAL SORTIG ALGORITHM USIG O ADDITIOAL DISK SPACE Md. Rafqul Islam +, Mohd. oor Md. Sap ++, Md. Sumon Sarker +, Sk. Razbul Islam + + Computer Scence and Engneerng Dscplne, Khulna Unversty,
More informationMultivariate EWMA Control Chart
Multvarate EWMA Control Chart Summary The Multvarate EWMA Control Chart procedure creates control charts for two or more numerc varables. Examnng the varables n a multvarate sense s extremely mportant
More information8.5 UNITARY AND HERMITIAN MATRICES. The conjugate transpose of a complex matrix A, denoted by A*, is given by
6 CHAPTER 8 COMPLEX VECTOR SPACES 5. Fnd the kernel of the lnear transformaton gven n Exercse 5. In Exercses 55 and 56, fnd the mage of v, for the ndcated composton, where and are gven by the followng
More informationAPPLICATION OF PROBE DATA COLLECTED VIA INFRARED BEACONS TO TRAFFIC MANEGEMENT
APPLICATION OF PROBE DATA COLLECTED VIA INFRARED BEACONS TO TRAFFIC MANEGEMENT Toshhko Oda (1), Kochro Iwaoka (2) (1), (2) Infrastructure Systems Busness Unt, Panasonc System Networks Co., Ltd. Saedocho
More informationPSYCHOLOGICAL RESEARCH (PYC 304C) Lecture 12
14 The Chsquared dstrbuton PSYCHOLOGICAL RESEARCH (PYC 304C) Lecture 1 If a normal varable X, havng mean µ and varance σ, s standardsed, the new varable Z has a mean 0 and varance 1. When ths standardsed
More informationThe Analysis of Covariance. ERSH 8310 Keppel and Wickens Chapter 15
The Analyss of Covarance ERSH 830 Keppel and Wckens Chapter 5 Today s Class Intal Consderatons Covarance and Lnear Regresson The Lnear Regresson Equaton TheAnalyss of Covarance Assumptons Underlyng the
More informationINVESTIGATION OF VEHICULAR USERS FAIRNESS IN CDMAHDR NETWORKS
21 22 September 2007, BULGARIA 119 Proceedngs of the Internatonal Conference on Informaton Technologes (InfoTech2007) 21 st 22 nd September 2007, Bulgara vol. 2 INVESTIGATION OF VEHICULAR USERS FAIRNESS
More informationAn interactive system for structurebased ASCII art creation
An nteractve system for structurebased ASCII art creaton Katsunor Myake Henry Johan Tomoyuk Nshta The Unversty of Tokyo Nanyang Technologcal Unversty Abstract NonPhotorealstc Renderng (NPR), whose am
More informationNonlinear data mapping by neural networks
Nonlnear data mappng by neural networks R.P.W. Dun Delft Unversty of Technology, Netherlands Abstract A revew s gven of the use of neural networks for nonlnear mappng of hgh dmensonal data on lower dmensonal
More informationDetecting Credit Card Fraud using Periodic Features
Detectng Credt Card Fraud usng Perodc Features Alejandro Correa Bahnsen, Djamla Aouada, Aleksandar Stojanovc and Björn Ottersten Interdscplnary Centre for Securty, Relablty and Trust Unversty of Luxembourg,
More informationVision Mouse. Saurabh Sarkar a* University of Cincinnati, Cincinnati, USA ABSTRACT 1. INTRODUCTION
Vson Mouse Saurabh Sarkar a* a Unversty of Cncnnat, Cncnnat, USA ABSTRACT The report dscusses a vson based approach towards trackng of eyes and fngers. The report descrbes the process of locatng the possble
More informationWhat is Candidate Sampling
What s Canddate Samplng Say we have a multclass or mult label problem where each tranng example ( x, T ) conssts of a context x a small (mult)set of target classes T out of a large unverse L of possble
More informationForecasting the Demand of Emergency Supplies: Based on the CBR Theory and BP Neural Network
700 Proceedngs of the 8th Internatonal Conference on Innovaton & Management Forecastng the Demand of Emergency Supples: Based on the CBR Theory and BP Neural Network Fu Deqang, Lu Yun, L Changbng School
More informationBayesian Network Based Causal Relationship Identification and Funding Success Prediction in P2P Lending
Proceedngs of 2012 4th Internatonal Conference on Machne Learnng and Computng IPCSIT vol. 25 (2012) (2012) IACSIT Press, Sngapore Bayesan Network Based Causal Relatonshp Identfcaton and Fundng Success
More informationCHOLESTEROL REFERENCE METHOD LABORATORY NETWORK. Sample Stability Protocol
CHOLESTEROL REFERENCE METHOD LABORATORY NETWORK Sample Stablty Protocol Background The Cholesterol Reference Method Laboratory Network (CRMLN) developed certfcaton protocols for total cholesterol, HDL
More informationSIX WAYS TO SOLVE A SIMPLE PROBLEM: FITTING A STRAIGHT LINE TO MEASUREMENT DATA
SIX WAYS TO SOLVE A SIMPLE PROBLEM: FITTING A STRAIGHT LINE TO MEASUREMENT DATA E. LAGENDIJK Department of Appled Physcs, Delft Unversty of Technology Lorentzweg 1, 68 CJ, The Netherlands Emal: e.lagendjk@tnw.tudelft.nl
More informationModule 2 LOSSLESS IMAGE COMPRESSION SYSTEMS. Version 2 ECE IIT, Kharagpur
Module LOSSLESS IMAGE COMPRESSION SYSTEMS Lesson 3 Lossless Compresson: Huffman Codng Instructonal Objectves At the end of ths lesson, the students should be able to:. Defne and measure source entropy..
More informationLecture 2: Single Layer Perceptrons Kevin Swingler
Lecture 2: Sngle Layer Perceptrons Kevn Sngler kms@cs.str.ac.uk Recap: McCullochPtts Neuron Ths vastly smplfed model of real neurons s also knon as a Threshold Logc Unt: W 2 A Y 3 n W n. A set of synapses
More informationCalculation of Sampling Weights
Perre Foy Statstcs Canada 4 Calculaton of Samplng Weghts 4.1 OVERVIEW The basc sample desgn used n TIMSS Populatons 1 and 2 was a twostage stratfed cluster desgn. 1 The frst stage conssted of a sample
More informationA DATA MINING APPLICATION IN A STUDENT DATABASE
JOURNAL OF AERONAUTICS AND SPACE TECHNOLOGIES JULY 005 VOLUME NUMBER (5357) A DATA MINING APPLICATION IN A STUDENT DATABASE Şenol Zafer ERDOĞAN Maltepe Ünversty Faculty of Engneerng BüyükbakkalköyIstanbul
More informationA Multimode Image Tracking System Based on Distributed Fusion
A Multmode Image Tracng System Based on Dstrbuted Fuson Ln zheng Chongzhao Han Dongguang Zuo Hongsen Yan School of Electroncs & nformaton engneerng, X an Jaotong Unversty X an, Shaanx, Chna Lnzheng@malst.xjtu.edu.cn
More informationVehicle Detection and Tracking in Video from Moving Airborne Platform
Journal of Computatonal Informaton Systems 10: 12 (2014) 4965 4972 Avalable at http://www.jofcs.com Vehcle Detecton and Trackng n Vdeo from Movng Arborne Platform Lye ZHANG 1,2,, Hua WANG 3, L LI 2 1 School
More informationMultiplePeriod Attribution: Residuals and Compounding
MultplePerod Attrbuton: Resduals and Compoundng Our revewer gave these authors full marks for dealng wth an ssue that performance measurers and vendors often regard as propretary nformaton. In 1994, Dens
More informationEfficient Project Portfolio as a tool for Enterprise Risk Management
Effcent Proect Portfolo as a tool for Enterprse Rsk Management Valentn O. Nkonov Ural State Techncal Unversty Growth Traectory Consultng Company January 5, 27 Effcent Proect Portfolo as a tool for Enterprse
More informationAn InterestOriented Network Evolution Mechanism for Online Communities
An InterestOrented Network Evoluton Mechansm for Onlne Communtes Cahong Sun and Xaopng Yang School of Informaton, Renmn Unversty of Chna, Bejng 100872, P.R. Chna {chsun,yang}@ruc.edu.cn Abstract. Onlne
More informationAn Enhanced SuperResolution System with Improved Image Registration, Automatic Image Selection, and Image Enhancement
An Enhanced SuperResoluton System wth Improved Image Regstraton, Automatc Image Selecton, and Image Enhancement YuChuan Kuo ( ), ChenYu Chen ( ), and ChouShann Fuh ( ) Department of Computer Scence
More informationTime Series Analysis in Studies of AGN Variability. Bradley M. Peterson The Ohio State University
Tme Seres Analyss n Studes of AGN Varablty Bradley M. Peterson The Oho State Unversty 1 Lnear Correlaton Degree to whch two parameters are lnearly correlated can be expressed n terms of the lnear correlaton
More informationLogistic Regression. Lecture 4: More classifiers and classes. Logistic regression. Adaboost. Optimization. Multiple class classification
Lecture 4: More classfers and classes C4B Machne Learnng Hlary 20 A. Zsserman Logstc regresson Loss functons revsted Adaboost Loss functons revsted Optmzaton Multple class classfcaton Logstc Regresson
More informationTesting CABIDS through Mutations: on the Identification of Network Scans
Testng CABIDS through Mutatons: on the Identfcaton of Network Scans Emlo Corchado, Álvaro Herrero, José Manuel Sáz Department of Cvl Engneerng, Unversty of Burgos, Span {escorchado, ahcoso, msaz}@ubu.es
More informationRESEARCH ON DUALSHAKER SINE VIBRATION CONTROL. Yaoqi FENG 1, Hanping QIU 1. China Academy of Space Technology (CAST) yaoqi.feng@yahoo.
ICSV4 Carns Australa 9 July, 007 RESEARCH ON DUALSHAKER SINE VIBRATION CONTROL Yaoq FENG, Hanpng QIU Dynamc Test Laboratory, BISEE Chna Academy of Space Technology (CAST) yaoq.feng@yahoo.com Abstract
More informationForensic Handwritten Document Retrieval System
Forensc Handwrtten Document Retreval System Sargur N SRIHARI and Zhxn SHI + Center of Excellence for Document Analyss and Recognton (CEDAR), Unversty at Buffalo, State Unversty of New York, Buffalo, USA
More informationThe Analysis of Outliers in Statistical Data
THALES Project No. xxxx The Analyss of Outlers n Statstcal Data Research Team Chrysses Caron, Assocate Professor (P.I.) Vaslk Karot, Doctoral canddate Polychrons Economou, Chrstna Perrakou, Postgraduate
More informationGraph Theory and Cayley s Formula
Graph Theory and Cayley s Formula Chad Casarotto August 10, 2006 Contents 1 Introducton 1 2 Bascs and Defntons 1 Cayley s Formula 4 4 Prüfer Encodng A Forest of Trees 7 1 Introducton In ths paper, I wll
More informationComplex Number Representation in RCBNS Form for Arithmetic Operations and Conversion of the Result into Standard Binary Form
Complex Number epresentaton n CBNS Form for Arthmetc Operatons and Converson of the esult nto Standard Bnary Form Hatm Zan and. G. Deshmukh Florda Insttute of Technology rgd@ee.ft.edu ABSTACT Ths paper
More informationDesign and Development of a Security Evaluation Platform Based on International Standards
Internatonal Journal of Informatcs Socety, VOL.5, NO.2 (203) 780 7 Desgn and Development of a Securty Evaluaton Platform Based on Internatonal Standards Yuj Takahash and Yoshm Teshgawara Graduate School
More informationRecurrence. 1 Definitions and main statements
Recurrence 1 Defntons and man statements Let X n, n = 0, 1, 2,... be a MC wth the state space S = (1, 2,...), transton probabltes p j = P {X n+1 = j X n = }, and the transton matrx P = (p j ),j S def.
More informationA Performance Analysis of View Maintenance Techniques for Data Warehouses
A Performance Analyss of Vew Mantenance Technques for Data Warehouses Xng Wang Dell Computer Corporaton Round Roc, Texas Le Gruenwald The nversty of Olahoma School of Computer Scence orman, OK 739 Guangtao
More informationIdentifying Workloads in Mixed Applications
, pp.395400 http://dx.do.org/0.4257/astl.203.29.8 Identfyng Workloads n Mxed Applcatons Jeong Seok Oh, Hyo Jung Bang, Yong Do Cho, Insttute of Gas Safety R&D, Korea Gas Safety Corporaton, ShghungSh,
More information9.1 The Cumulative Sum Control Chart
Learnng Objectves 9.1 The Cumulatve Sum Control Chart 9.1.1 Basc Prncples: Cusum Control Chart for Montorng the Process Mean If s the target for the process mean, then the cumulatve sum control chart s
More informationBERNSTEIN POLYNOMIALS
OnLne Geometrc Modelng Notes BERNSTEIN POLYNOMIALS Kenneth I. Joy Vsualzaton and Graphcs Research Group Department of Computer Scence Unversty of Calforna, Davs Overvew Polynomals are ncredbly useful
More informationMAPP. MERIS level 3 cloud and water vapour products. Issue: 1. Revision: 0. Date: 9.12.1998. Function Name Organisation Signature Date
Ttel: Project: Doc. No.: MERIS level 3 cloud and water vapour products MAPP MAPPATBDClWVL3 Issue: 1 Revson: 0 Date: 9.12.1998 Functon Name Organsaton Sgnature Date Author: Bennartz FUB Preusker FUB Schüller
More informationMultisensor Data Fusion for Cyber Security Situation Awareness
Avalable onlne at www.scencedrect.com Proceda Envronmental Scences 0 (20 ) 029 034 20 3rd Internatonal Conference on Envronmental 3rd Internatonal Conference on Envronmental Scence and Informaton Applcaton
More informationDocument image template matching based on component block list
Pattern Recognton Letters 22 2001) 1033±1042 www.elsever.nl/locate/patrec Document mage template matchng based on component block lst Hanchuan Peng a,b,c, *, Fuhu Long b, Zheru Ch b, WanCh Su b a Department
More informationbenefit is 2, paid if the policyholder dies within the year, and probability of death within the year is ).
REVIEW OF RISK MANAGEMENT CONCEPTS LOSS DISTRIBUTIONS AND INSURANCE Loss and nsurance: When someone s subject to the rsk of ncurrng a fnancal loss, the loss s generally modeled usng a random varable or
More informationLuby s Alg. for Maximal Independent Sets using Pairwise Independence
Lecture Notes for Randomzed Algorthms Luby s Alg. for Maxmal Independent Sets usng Parwse Independence Last Updated by Erc Vgoda on February, 006 8. Maxmal Independent Sets For a graph G = (V, E), an ndependent
More information320 The Internatonal Arab Journal of Informaton Technology, Vol. 5, No. 3, July 2008 Comparsons Between Data Clusterng Algorthms Osama Abu Abbas Computer Scence Department, Yarmouk Unversty, Jordan Abstract:
More informationConversion between the vector and raster data structures using Fuzzy Geographical Entities
Converson between the vector and raster data structures usng Fuzzy Geographcal Enttes Cdála Fonte Department of Mathematcs Faculty of Scences and Technology Unversty of Combra, Apartado 38, 3 454 Combra,
More informationFeature selection for intrusion detection. Slobodan Petrović NISlab, Gjøvik University College
Feature selecton for ntruson detecton Slobodan Petrovć NISlab, Gjøvk Unversty College Contents The feature selecton problem Intruson detecton Traffc features relevant for IDS The CFS measure The mrmr measure
More informationOnLine Fault Detection in Wind Turbine Transmission System using Adaptive Filter and Robust Statistical Features
OnLne Fault Detecton n Wnd Turbne Transmsson System usng Adaptve Flter and Robust Statstcal Features Ruoyu L Remote Dagnostcs Center SKF USA Inc. 3443 N. Sam Houston Pkwy., Houston TX 77086 Emal: ruoyu.l@skf.com
More informationTHE APPLICATION OF DATA MINING TECHNIQUES AND MULTIPLE CLASSIFIERS TO MARKETING DECISION
Internatonal Journal of Electronc Busness Management, Vol. 3, No. 4, pp. 3030 (2005) 30 THE APPLICATION OF DATA MINING TECHNIQUES AND MULTIPLE CLASSIFIERS TO MARKETING DECISION YuMn Chang *, YuCheh
More informationAutomated Mobile ph Reader on a Camera Phone
Automated Moble ph Reader on a Camera Phone B.Y. Loh, N.K. Vuong, S. Chan and C.. Lau AbstractA robust classfcaton algorthm that apples color scence and mage processng technques s developed to automatcally
More informationDescriptive Models. Cluster Analysis. Example. General Applications of Clustering. Examples of Clustering Applications
CMSC828G Prncples of Data Mnng Lecture #9 Today s Readng: HMS, chapter 9 Today s Lecture: Descrptve Modelng Clusterng Algorthms Descrptve Models model presents the man features of the data, a global summary
More informationMining Multiple Large Data Sources
The Internatonal Arab Journal of Informaton Technology, Vol. 7, No. 3, July 2 24 Mnng Multple Large Data Sources Anmesh Adhkar, Pralhad Ramachandrarao 2, Bhanu Prasad 3, and Jhml Adhkar 4 Department of
More informationA machine vision approach for detecting and inspecting circular parts
A machne vson approach for detectng and nspectng crcular parts DuMng Tsa Machne Vson Lab. Department of Industral Engneerng and Management YuanZe Unversty, ChungL, Tawan, R.O.C. Emal: edmtsa@saturn.yzu.edu.tw
More informationAutomated Network Performance Management and Monitoring via Oneclass Support Vector Machine
Automated Network Performance Management and Montorng va Oneclass Support Vector Machne R. Zhang, J. Jang, and S. Zhang Dgtal Meda & Systems Research Insttute, Unversty of Bradford, UK Abstract: In ths
More informationQuestions that we may have about the variables
Antono Olmos, 01 Multple Regresson Problem: we want to determne the effect of Desre for control, Famly support, Number of frends, and Score on the BDI test on Perceved Support of Latno women. Dependent
More informationCalculating the high frequency transmission line parameters of power cables
< ' Calculatng the hgh frequency transmsson lne parameters of power cables Authors: Dr. John Dcknson, Laboratory Servces Manager, N 0 RW E B Communcatons Mr. Peter J. Ncholson, Project Assgnment Manager,
More informationLinear Circuits Analysis. Superposition, Thevenin /Norton Equivalent circuits
Lnear Crcuts Analyss. Superposton, Theenn /Norton Equalent crcuts So far we hae explored tmendependent (resste) elements that are also lnear. A tmendependent elements s one for whch we can plot an / cure.
More informationA Simple Approach to Clustering in Excel
A Smple Approach to Clusterng n Excel Aravnd H Center for Computatonal Engneerng and Networng Amrta Vshwa Vdyapeetham, Combatore, Inda C Rajgopal Center for Computatonal Engneerng and Networng Amrta Vshwa
More informationDesign of Output Codes for Fast Covering Learning using Basic Decomposition Techniques
Journal of Computer Scence (7): 56557, 6 ISSN 5966 6 Scence Publcatons Desgn of Output Codes for Fast Coverng Learnng usng Basc Decomposton Technques Aruna Twar and Narendra S. Chaudhar, Faculty of Computer
More informationGaining Insights to the Tea Industry of Sri Lanka using Data Mining
Proceedngs of the Internatonal MultConference of Engneers and Computer Scentsts 2008 Vol I Ganng Insghts to the Tea Industry of Sr Lanka usng Data Mnng H.C. Fernando, W. M. R Tssera, and R. I. Athauda
More informationA Dynamic Load Balancing for Massive Multiplayer Online Game Server
A Dynamc Load Balancng for Massve Multplayer Onlne Game Server Jungyoul Lm, Jaeyong Chung, Jnryong Km and Kwanghyun Shm Dgtal Content Research Dvson Electroncs and Telecommuncatons Research Insttute Daejeon,
More informationStatistical Methods to Develop Rating Models
Statstcal Methods to Develop Ratng Models [Evelyn Hayden and Danel Porath, Österrechsche Natonalbank and Unversty of Appled Scences at Manz] Source: The Basel II Rsk Parameters Estmaton, Valdaton, and
More informationRELIABILITY, RISK AND AVAILABILITY ANLYSIS OF A CONTAINER GANTRY CRANE ABSTRACT
Kolowrock Krzysztof Joanna oszynska MODELLING ENVIRONMENT AND INFRATRUCTURE INFLUENCE ON RELIABILITY AND OPERATION RT&A # () (Vol.) March RELIABILITY RIK AND AVAILABILITY ANLYI OF A CONTAINER GANTRY CRANE
More informationA FEATURE SELECTION AGENTBASED IDS
A FEATURE SELECTION AGENTBASED IDS Emlo Corchado, Álvaro Herrero and José Manuel Sáz Department of Cvl Engneerng, Unversty of Burgos C/Francsco de Vtora s/n., 09006, Burgos, Span Phone: +34 947259395,
More informationSearch Efficient Representation of Healthcare Data based on the HL7 RIM
181 JOURNAL OF COMPUTERS, VOL. 5, NO. 12, DECEMBER 21 Search Effcent Representaton of Healthcare Data based on the HL7 RIM Razan Paul Department of Computer Scence and Engneerng, Bangladesh Unversty of
More informationAmit Choudhary* Deptt. of Comp. Sc. Maharaja Surajmal Institute, New Delhi, India. Rahul Rishi
Amt Choudhary et al/(ijcse) Internatonal Journal on Computer Scence and Engneerng, Optmal Feed Forward MLPArchtecture for OffLne Cursve Numeral Recognton Amt Choudhary* Deptt. of Comp. Sc. Maharaja Surajmal
More informationInstitute of Informatics, Faculty of Business and Management, Brno University of Technology,Czech Republic
Lagrange Multplers as Quanttatve Indcators n Economcs Ivan Mezník Insttute of Informatcs, Faculty of Busness and Management, Brno Unversty of TechnologCzech Republc Abstract The quanttatve role of Lagrange
More informationA COLLABORATIVE TRADING MODEL BY SUPPORT VECTOR REGRESSION AND TS FUZZY RULE FOR DAILY STOCK TURNING POINTS DETECTION
A COLLABORATIVE TRADING MODEL BY SUPPORT VECTOR REGRESSION AND TS FUZZY RULE FOR DAILY STOCK TURNING POINTS DETECTION JHENGLONG WU, PEICHANN CHANG, KAITING CHANG Department of Informaton Management,
More informationSIMPLE LINEAR CORRELATION
SIMPLE LINEAR CORRELATION Smple lnear correlaton s a measure of the degree to whch two varables vary together, or a measure of the ntensty of the assocaton between two varables. Correlaton often s abused.
More informationMachine Learning and Software Quality Prediction: As an Expert System
I.J. Informaton Engneerng and Electronc Busness, 2014, 2, 927 Publshed Onlne Aprl 2014 n MECS (http://www.mecspress.org/) DOI: 10.5815/jeeb.2014.02.02 Machne Learnng and Software Qualty Predcton: As
More informationA Crossplatform ECG Compression Library for Mobile HealthCare Services
A Crossplatform ECG Compresson Lbrary for Moble HealthCare Servces Alexander Borodn, Yulya Zavyalova Department of Computer Scence Petrozavodsk State Unversty Petrozavodsk, Russa {aborod, yzavyalo}@cs.petrsu.ru
More informationA Computer Technique for Solving LP Problems with Bounded Variables
Dhaka Unv. J. Sc. 60(2): 163168, 2012 (July) A Computer Technque for Solvng LP Problems wth Bounded Varables S. M. Atqur Rahman Chowdhury * and Sanwar Uddn Ahmad Department of Mathematcs; Unversty of
More informationAuditing Cloud Service Level Agreement on VM CPU Speed
Audtng Cloud Servce Level Agreement on VM CPU Speed Ryan Houlhan, aojang Du, Chu C. Tan, Je Wu Department of Computer and Informaton Scences Temple Unversty Phladelpha, PA 19122, USA Emal: {ryan.houlhan,
More informationA GENERIC HANDOVER DECISION MANAGEMENT FRAMEWORK FOR NEXT GENERATION NETWORKS
A GENERIC HANDOVER DECISION MANAGEMENT FRAMEWORK FOR NEXT GENERATION NETWORKS Shanthy Menezes 1 and S. Venkatesan 2 1 Department of Computer Scence, Unversty of Texas at Dallas, Rchardson, TX, USA 1 shanthy.menezes@student.utdallas.edu
More informationA GENERAL APPROACH FOR SECURITY MONITORING AND PREVENTIVE CONTROL OF NETWORKS WITH LARGE WIND POWER PRODUCTION
A GENERAL APPROACH FOR SECURITY MONITORING AND PREVENTIVE CONTROL OF NETWORKS WITH LARGE WIND POWER PRODUCTION Helena Vasconcelos INESC Porto hvasconcelos@nescportopt J N Fdalgo INESC Porto and FEUP jfdalgo@nescportopt
More information8 Algorithm for Binary Searching in Trees
8 Algorthm for Bnary Searchng n Trees In ths secton we present our algorthm for bnary searchng n trees. A crucal observaton employed by the algorthm s that ths problem can be effcently solved when the
More informationA MultiCamera System on PCCluster for Realtime 3D Tracking
The 23 rd Conference of the Mechancal Engneerng Network of Thaland November 4 7, 2009, Chang Ma A MultCamera System on PCCluster for Realtme 3D Trackng Vboon Sangveraphunsr*, Krtsana Uttamang, and
More informationgreatest common divisor
4. GCD 1 The greatest common dvsor of two ntegers a and b (not both zero) s the largest nteger whch s a common factor of both a and b. We denote ths number by gcd(a, b), or smply (a, b) when there s no
More informationDamage detection in composite laminates using cointap method
Damage detecton n composte lamnates usng contap method S.J. Km Korea Aerospace Research Insttute, 45 EoeunDong, YouseongGu, 35333 Daejeon, Republc of Korea yaeln@kar.re.kr 45 The contap test has the
More informationImproved SVM in Cloud Computing Information Mining
Internatonal Journal of Grd Dstrbuton Computng Vol.8, No.1 (015), pp.3340 http://dx.do.org/10.1457/jgdc.015.8.1.04 Improved n Cloud Computng Informaton Mnng Lvshuhong (ZhengDe polytechnc college JangSu
More informationv a 1 b 1 i, a 2 b 2 i,..., a n b n i.
SECTION 8.4 COMPLEX VECTOR SPACES AND INNER PRODUCTS 455 8.4 COMPLEX VECTOR SPACES AND INNER PRODUCTS All the vector spaces we have studed thus far n the text are real vector spaces snce the scalars are
More informationSOLVING CARDINALITY CONSTRAINED PORTFOLIO OPTIMIZATION PROBLEM BY BINARY PARTICLE SWARM OPTIMIZATION ALGORITHM
SOLVIG CARDIALITY COSTRAIED PORTFOLIO OPTIMIZATIO PROBLEM BY BIARY PARTICLE SWARM OPTIMIZATIO ALGORITHM Aleš Kresta Klíčová slova: optmalzace portfola, bnární algortmus rojení částc Key words: portfolo
More informationProject Networks With MixedTime Constraints
Project Networs Wth MxedTme Constrants L Caccetta and B Wattananon Western Australan Centre of Excellence n Industral Optmsaton (WACEIO) Curtn Unversty of Technology GPO Box U1987 Perth Western Australa
More informationPAS: A Packet Accounting System to Limit the Effects of DoS & DDoS. Debish Fesehaye & Klara Naherstedt University of IllinoisUrbana Champaign
PAS: A Packet Accountng System to Lmt the Effects of DoS & DDoS Debsh Fesehaye & Klara Naherstedt Unversty of IllnosUrbana Champagn DoS and DDoS DDoS attacks are ncreasng threats to our dgtal world. Exstng
More informationMining Feature Importance: Applying Evolutionary Algorithms within a Webbased Educational System
Mnng Feature Importance: Applyng Evolutonary Algorthms wthn a Webbased Educatonal System Behrouz MINAEIBIDGOLI 1, and Gerd KORTEMEYER 2, and Wllam F. PUNCH 1 1 Genetc Algorthms Research and Applcatons
More informationLaddered Multilevel DC/AC Inverters used in Solar Panel Energy Systems
Proceedngs of the nd Internatonal Conference on Computer Scence and Electroncs Engneerng (ICCSEE 03) Laddered Multlevel DC/AC Inverters used n Solar Panel Energy Systems Fang Ln Luo, Senor Member IEEE
More information