PYTHON IMPLEMENTATION OF VISUAL SECRET SHARING SCHEMES

Size: px
Start display at page:

Download "PYTHON IMPLEMENTATION OF VISUAL SECRET SHARING SCHEMES"

Transcription

1 PYTHON IMPLEMENTATION OF VISUAL SECRET SHARING SCHEMES Ruxandra Olmd Faculty of Mathematcs and Computer Scence, Unversty of Bucharest Emal: Abstract Vsual secret sharng schemes (VSS) represent an mportant concept of vsual cryptography. They permt the sharng of a secret mage between multple partcpants so that only authorzed groups can recover the secret. Ths paper consders the software mplementaton of some black-and-whte secret mages VSS n Python programmng language. PIL (Python Imagng Lbrary) provdes strong mage processng capabltes, makng the lbrary sutable for ths knd of mplementaton. We present samples of the results obtaned from the software computaton and draw some conclusons. Keywords: vsual secret sharng, vsual cryptography, Python, PIL (Python Imagng Lbrary). Introducton A secret sharng scheme permts the sharng of a secret between multple partcpants so that only authorzed groups can recover the secret. Even by puttng ther s together, the members of an unauthorzed group are not able to reveal the secret or sgnfcant nformaton about t. Vsual secret sharng schemes (VSS) represent the partcular case for whch the secret and therefore the s are mages. Naor and Shamr were the frst to ntroduce them as a part of vsual cryptography [NAOR94]. Snce then, multple models of VSS were defned, for blackand-whte, grayscale or color pctures. Examples from the lterature ncludes [ATEN96] and [ITO99]. Ths paper exemplfes and analyses the mplementaton of some of the exstng VSS usng Python s Image Lbrary (PIL). Secton 2 defnes the VSS that were consdered for the mplementaton. Secton 3 ntroduces the resources and technques used durng development. Secton 4 exposes computatonal samples and data analyss. Fnally, we conclude. 2. Vsual Secret Sharng Schemes Ths secton ntroduces the VSS that were consdered for the software mplementaton. We restrct our nterest to black-and-whte secret mages, but a smlar approach can be consdered for color pctures as well. In most cases, the type of the s copes the type of the secret, n the sense that for a black-and-whte secret, the s are also black-and-whte mages. However, Ito and al. ntroduced a way to a black-and-whte secret mage nto colored mages components [ITO99]. A smlar, but smpler secret scheme that follows the same dea wll be consdered [OLIM]. Each mage (secret or ) s consdered to be a matrx of pxels. By conventon, n black-and-whte representaton, a whte pxel s represented by and a black pxel s

2 represented by. For color s, only R (red), G (green) and B (blue) pxels are used (no other combnaton of RGB colors can appear n a ). For the rest of the paper, P = { P,..., P n } denotes the set of partcpants and therefore n denotes ther number. Defnton. The collecton of all authorzed sets of partcpants to reconstruct the secret s called the access structure ( A ). The collecton of all forbdden sets of partcpants to access the secret s denoted by NA. Defnton 2. The collecton of the maxmal forbdden sets NA max s defned as NA max = { B NA B' NA \ B, B B'} Defnton 3. A secret sharng scheme s unanmous (or (n,n) secret sharng scheme) f all n s are needed n order to reconstruct the secret. In case of a unanmous secret sharng scheme, the access structure contans only set, the set of all partcpants. Ths represents a partcular type of access structure. If no restrctons are requred, then the access structure s called general access structure. The paper consders 3 types of black-and-whte secret mage VSS: ) unanmous vsual secret sharng scheme; 2) general access structure secret sharng scheme; 3) color s VSS. 2.. Naor-Shamr Unanmous VSS Naor and Shamr were the frst to ntroduce a vsual secret sharng scheme [NAOR94]. We wll restrct to ther unanmous scheme, the partcular case n whch all partcpants must agree to reconstructon. If at least one partcpant does not agree to ts component, the secret mage s perfectly secured (the others fnd no nformaton about the secret). ) Computng s Consder W = { e, e2,..., en} where n s the number of partcpants and e s the n- element vector wth on poston and otherwse. Let: π, π 2,..., π be the even cardnalty subsets of W ; 2 n σ σ 2,..., σ be the odd cardnalty subsets of W. n Each set defnes a, 2 2 n n matrx, S = ( ) and respectvely S = ( S ) : S n S = e π, =.. n, =..2 ; n S = e σ, =.. n, =..2 ; Consder: C, the set of matrces obtaned by permutng the columns of C, the set of matrces obtaned by permutng the columns of S ; S.

3 To each pxel n the secret mage wll correspond pxels n each : f the pxel s whte, an element from C s randomly chosen. The correspondng pxels n are gven by row of the selected matrx; f the pxel s black, an element from C s randomly chosen. The correspondng pxels n are gven by row of the selected matrx. 2) Reconstructon of the secret mage All s are added pxel by pxel (or the correspondng bts are OR -ed ). Theorem. The above scheme s a unanmous scheme wth n partcpants, where k = 2 n n s the number of pxels n each that correspond to a pxel n the secret; α = / 2 s the contrast parameter; r = 2 n! s the cardnal of C and C [NAOR94]. In order to obtan the exact orgnal secret mage, addtonal transformatons are needed. The 2 n pxel groups should be transformed n a black pxel, f the number of s s greater than the contrast parameter, or whte, otherwse. For the rest of the paper, we wll not consder ths mprovement. Example. Naor-Shamr unanmous VSS for n = 2 partcpants. From the constructon algorthm, the values of S and S are: S = ; S = The possble correspondng s of a pxel are summarzed n Table. 2 n Whte pxel Frst Second Result Black pxel Frst Second Result Table. All possble s n Naor-Shamr unanmous VSS wth 2 partcpants Example 2: Naor-Shamr unanmous VSS for n = 3 partcpants. From the constructon algorthm, the values of S and S are: S = ; S =

4 Some of the possble correspondng s of a pxel are dsplayed n Table 2. Whte pxel Frst Second Thrd Black pxel Table 2. Some possble s n Naor-Shamr unanmous VSS wth 3 partcpants Frst Result Frst Second Thrd Result Whte pxel Second Result Frst Black pxel Second Result Table 3. All possble s n Naor-Shamr unanmous VSS wth 2 partcpants that mantans mage rato constant Example 3: Naor-Shamr unanmous VSS for n = 2 partcpants that mantans the rato of the secret mage constant In prevous examples, n order to represent a pxel, one dmenson ncreases k tmes. To avod the dstorton of the mage, both dmensons can be ncreased by the same amount. Nevertheless, the dmenson of a becomes larger (Table 3).

5 2.2. General VSS The prevous schemes present the specal property of unanmty. Atenese and al. ntroduced a model of VSS that permts general access structures [ATEN96]. Defnton 4. Be P = { P,..., P n } the set of all partcpants and NA max = { B,..., B r } the collecton of the maxmal forbdden sets. The cumulatve array of the access structure A s defned as a n r matrx C (, where: A = b ) n, r b, P B =, P B The cumulatve array specfes f a partcpant P belongs to an unauthorzed maxmal set B, by settng the approprate element to. A scheme that allows a general access structure can be defned starng from the Naor- Shamr ( n, n) VSS: ) Computng s Let P = { P,..., P n } be the set of partcpants, A the access structure, NAmax the collecton of maxmal forbdden sets, t the cardnal of NAmax andc A the cumulatve array. S and S are defned as the matrxes from the unanmous Naor-Shamr scheme for t partcpants. For each fxed ( n) consder J = { C A (, ) = } and defne: S as the matrx wth row, of matrx S ; J S as the matrx wth row, of matrx S. J row equal to the result of applyng OR to all row equal to the result of applyng OR to all Consder: C, the set of matrces obtaned by permutng the columns of C, the set of matrces obtaned by permutng the columns of 2 n S ; To each pxel n the secret mage wll correspond pxels n each : f the pxel s whte, an element from C s randomly chosen. The correspondng pxels n are gven by row of the selected matrx; f the pxel s black, an element from C s randomly chosen. The correspondng pxels n are gven by row of the selected matrx. 2) Reconstructon of the secret mage All s are added pxel by pxel (or the correspondng bts are OR -ed ). S.

6 Example 4. A general VSS for n = 4 partcpant, and the collecton of the maxmal forbdden sets NA max = {{ P, P2 },{ P2, P3 },{ P3, P4 }}. From constructon: C A = As the cardnal of s 3, then the correspondng matrxes S and NAmax startng from S, respectvely S defned n Example 2: S = ; S = An example of sharng a whte, respectvely a black pxel s shown n Table 4. S are buld Whte pxel Frst Second Thrd Forth Black pxel Frst Second Thrd Forth Table 4. A possble sharng for a whte and a black pxel for the VSS n Example Black-and-whte secret mage VSS wth RGB s Ito, Kuwakado and Tanaka used color components n order to a black-and-whte secret mage [ITO99]. We wll consder here a scheme that uses the same dea, but the pxels color (red, green or blue) n the components s (almost) randomly chosen from R, G and B [OLIM]. The usage of randomness elmnates the need of the fxed matrces, dea nherted from Naor-Shamr VSS. The scheme s avalable n the addtve RGB model, n whch whte results by addng red, green and blue together at maxmum ntensty, under the assumpton that addng any other color to whte t remans whte. Black s consdered to be the absence of any color. ) Computng s The pxels of the secret s are randomly choose from {R, G, B} so that: f the pxel of the secret mage s whte, then there must exst a component wth the correspondng pxel R, another component wth the correspondng pxel G and a thrd component, dfferent from the prevous two, wth the correspondng pxel B;

7 f the pxel of the secret mage s black, then all the correspondng pxels of the s are randomly choose from {R, G}, from {R, B} or from {G, B} so that there exst at least 2 pxels of dfferent colors. 2) Reconstructon of the secret mage k users try to reconstruct the secret by overlappng ther correspondent s (the s are added together, pxel by pxel); each colored computed pxel n the resultng mage s transformed nto a black pxel. Whte pxels reman unchanged. 4 examples of sharng are gven for a whte and respectvely a black pxel n Table 5. Please notce that for each whte pxel exst at least a red, a green and a blue, whle for a black pxel, the correspondng s have only 2 colors (R&G or R&B or B&G). 4 whte pxels Frst RR RR Second GR RB Thrd BB BG Forth RG GG Ffth RB GB Reconstructed color mage WW WW 4 black pxels Frst RR BB Second GB BB Thrd RR GB Forth RR GG Ffth RR GG Reconstructed color mage YM CC Table 5. A possble sharng for 4 whte and 4 black pxels n a scheme wth 5 partcpants (R = Red, G = Green, B=Blue, Y = Yellow, M = Magenta, C = Cyan, W=Whte) 3. Development envronment, mplementaton methods and technques The prevously mentoned VSS algorthms were mplemented n Python programmng language usng IDLE (Python s Integrated DeveLopment Envronment 2.6.6) as the development envronment [PYTH]. Python s a powerful dynamc programmng language, avalable for all maor operatng systems and avalable under an open source lcense. Besdes other benefts, Python was manly chosen due to the fact that t provdes a powerful and easy to use mage lbrary, named PIL (Python Image Lbrary), also avalable n a free verson. PIL provdes mage processng capabltes, avalable for dfferent knd of fle formats. A fully documentaton can be found at [PIL]. Some of the specal features that PIL provdes and were used durng the mplementaton nclude: Dfferent mage modes, defnng the type and depth of a pxel n the mage. Even though t supports multple standard modes, for the current mplementatons were used: o (-bt pxels, black and whte, stored as 8-bt pxels); o RGB (3x8-bt pxels, true color). The possblty to consder the mage as a matrx of pxels, permttng the modfcaton at pxel level, settng the pxel value, etc.;

8 Fgure. Graphcal nterfaces for 2 mplemented VSS Defnng or readng the sze of an mage as a 2-tuple consstng n the horzontal and vertcal sze n pxels (<mage>.sze); Workng wth bands of colors (R, G and B bands for color mages); Usng mage functons as: openng an exstng mage (mage.open(<mage>, <mode>)), creatng a new mage (mage.new(<mode>,<sze>)), creatng an mage based on multple bands of color (mage.merge(<mode>, (<band>, <band2>, <band3>))). Python also provdes other functons that were mandatory for the mplementaton of the VSS as: ntertools module, that standardzes effcent and useful tools for combnatorc generators as permutatons (ntertools.permutatons(<p>)) or combnatons (tertools.combnatons(<n>,<k>)); random module, that mplements pseudo-random generators: random.randrange([<start>], <stop>, <step>) returns a randomly selected element n the specfed range. All the prevously mentoned schemes were mplemented wthn graphcal nterfaces (Fgure ) that permt: to receve the secret mage as the nput fle; to fll n all the necessary parameters, dependng on the selected VSS (for example the number of partcpants for Naor-Shamr unanmous VSS or the number of partcpants and the collecton of maxmal forbdden sets for general access structure VSS); to compute the s accordngly to the flled n nputs; to reconstruct the mage based on the selected s (the selecton of dfferent set of s leads to dfferent reconstructed mages, dependng on the authorzaton of the group). Fgure 2. Input test mage

9 4. Software computaton and data analyss The mage n Fgure 2 was gven as the secret mage nput for all the mplemented VSS models. The other nputs are specfed below: Naor-Shamr unanmous VSS was tested for n = 3 partcpants (Fgure 3); Naor-Shamr unanmous VSS that mantans mage rato constant was tested for n = 2 partcpants (Fgure 4); The cumulatve VSS was tested as n Example 4, wth n = 4 partcpants and NA max = {{ P, P2 },{ P2, P3 },{ P3, P4 }} (Fgure 5); Black-and-whte secret mage VSS wth RGB s was tested for n = 4 partcpants (Fgure 6). Share Fgure 3.a. The s n the Naor-Shamr unanmous VSS (n = 3) Combned s Computed result Share Share Share Fgure 3.b. Computed mages by usng all possble sets of s n the Naor-Shamr unanmous VSS (n = 3) It s easy to remark that the practcal mplementaton supports the theory results. In case of Naor-Shamr unanmous VSS: The secret mage s reconstructed only by combnng together all the s. No nformaton s leaked when fewer partcpants cooperate; The reconstructed mage ncreases the length of the orgnal mage; The contrast of the reconstructed mage s decreasng (whte becomes gray);

10 Share Share 2 Fgure 4.a. The s n Naor-Shamr unanmous VSS (n = 2) that mantans the mage rato Combned s Computed result Share Fgure 4.b. The reconstructed mage from the s n the Naor-Shamr unanmous VSS (n = 2) that mantans the mage rato constant Share Share 4 Fgure 5.a. The s n the cumulatve VSS (n = 4) Combned s Computed result Share Share Share Share 4 Fgure 5.b. Sample of computed mages by usng dfferent sets of s n the cumulatve VSS (n = 4)

11 Share Share 3 Share 2 Share 4 Fgure 6.a. Shares n black-and-whte secret mage VSS wth RGB s (n = 4) Combned s Share Share Share Share 4 Computed result (color mage) Computed result (blackand-whte mage) Fgure 6.b. Sample of computed mages by usng dfferent sets of s n black-andwhte secret mage VSS wth RGB s (n = 4) In case of Naor-Shamr unanmous VSS that mantans the mage rato constant: The mage rato s mantaned constant; All the other propertes of the Naor-Shamr unanmous VSS are preserved: contrast s smaller than n the orgnal mage and the scheme s perfect n the sense that t provdes no nformaton about the secret when the number of cooperatng partcpants s less than n. In the case of the cumulatve general access structure VSS: The contrast of the reconstructed mage s decreasng; { P, P2 } s a maxmal forbdden group, so they are not able to reconstruct the secret mage;

12 By onng any other partcpant to a maxmal forbdden group, they become able to reconstruct the secret: for example { P, P2, P3 } can recover the secret mage (Fgure 5.b); Because P, P, P, } extends the maxmal forbdden set P, } (or { 2 3 P4 { P, P 3 4 { P2 P, }, or } ), t also represents an authorzed group of partcpants. But { P 2 3 even though the number of s s larger than n case of the set { P, P2, P3 }, the reconstructed mage s less smlar than the orgnal. In the case of the black-and-whte secret mage VSS wth RGB s: The dmenson of the s s equal to the dmenson of the secret, but more nformaton s needed for storage because of the color nformaton; The reconstructed mage mantans the rato of the ntal secret mage; Any 2 s provde no nformaton about the secret mage (the reconstructed mage s totally black); More than 2 s provdes enough nformaton (all black pxels are correctly reconstructed); All s lead to the perfect reconstructon of the ntal secret mage. 5. Conclusons The paper presents a possble mplementaton of some vsual secret schemes (VSS) under the Python programmng language, usng the PIL lbrary. Data nputs, computatonal results and a succnct analyss are performed for each consdered scheme. References: [ATEN96] Guseppe Atenese, Carlo Blundo, Alfred De Sants, Douglas R. Stnson, Vsual Cryptography for General Access Structures, Electronc Colloquum on Computatonal Complexty (ECCC), vol.3, no.2, 996. [GHOD98] Hossen Ghodos, Josef Peprzyk, Re Safar-Navn, Huaxong Wang, On constructon of Cumulatve Secret Sharng Schemes, LNCS, 998, pg [ITO99] Ryo Ito, Hdenor Kuwakado, Hatsukazu Tanaka, Image Sze Invarant Vsual Cryptography, IEICE Trans. On Funda. Of Elect., Comm. And Comp.Sc., Vol.E82- A, No.(999), pg [NAOR94] Mon Naor, Ad Shamr, Vsual Cryptography, Advanced n Cryptology EUROCRYPT 94, LNCS95, Sprnger-Verlag(995), pg.-2. [OLIM] Ruxandra Olmd, About a Vsual Secret Sharng Scheme, Proceedngs of the 3 rd Internatonal Conference on Securty for Informaton Tehnology and Communcatons, Bucharest, 2, pg [PIL] Python Image Lbrary (PIL) [PYTH] Python Programmng Language Offcal Webste

A Binarization Algorithm specialized on Document Images and Photos

A Binarization Algorithm specialized on Document Images and Photos A Bnarzaton Algorthm specalzed on Document mages and Photos Ergna Kavalleratou Dept. of nformaton and Communcaton Systems Engneerng Unversty of the Aegean kavalleratou@aegean.gr Abstract n ths paper, a

More information

Related-Mode Attacks on CTR Encryption Mode

Related-Mode Attacks on CTR Encryption Mode Internatonal Journal of Network Securty, Vol.4, No.3, PP.282 287, May 2007 282 Related-Mode Attacks on CTR Encrypton Mode Dayn Wang, Dongda Ln, and Wenlng Wu (Correspondng author: Dayn Wang) Key Laboratory

More information

Enhanced Watermarking Technique for Color Images using Visual Cryptography

Enhanced Watermarking Technique for Color Images using Visual Cryptography Informaton Assurance and Securty Letters 1 (2010) 024-028 Enhanced Watermarkng Technque for Color Images usng Vsual Cryptography Enas F. Al rawashdeh 1, Rawan I.Zaghloul 2 1 Balqa Appled Unversty, MIS

More information

An Optimal Algorithm for Prufer Codes *

An Optimal Algorithm for Prufer Codes * J. Software Engneerng & Applcatons, 2009, 2: 111-115 do:10.4236/jsea.2009.22016 Publshed Onlne July 2009 (www.scrp.org/journal/jsea) An Optmal Algorthm for Prufer Codes * Xaodong Wang 1, 2, Le Wang 3,

More information

Parallelism for Nested Loops with Non-uniform and Flow Dependences

Parallelism for Nested Loops with Non-uniform and Flow Dependences Parallelsm for Nested Loops wth Non-unform and Flow Dependences Sam-Jn Jeong Dept. of Informaton & Communcaton Engneerng, Cheonan Unversty, 5, Anseo-dong, Cheonan, Chungnam, 330-80, Korea. seong@cheonan.ac.kr

More information

SLAM Summer School 2006 Practical 2: SLAM using Monocular Vision

SLAM Summer School 2006 Practical 2: SLAM using Monocular Vision SLAM Summer School 2006 Practcal 2: SLAM usng Monocular Vson Javer Cvera, Unversty of Zaragoza Andrew J. Davson, Imperal College London J.M.M Montel, Unversty of Zaragoza. josemar@unzar.es, jcvera@unzar.es,

More information

Load Balancing for Hex-Cell Interconnection Network

Load Balancing for Hex-Cell Interconnection Network Int. J. Communcatons, Network and System Scences,,, - Publshed Onlne Aprl n ScRes. http://www.scrp.org/journal/jcns http://dx.do.org/./jcns.. Load Balancng for Hex-Cell Interconnecton Network Saher Manaseer,

More information

Content Based Image Retrieval Using 2-D Discrete Wavelet with Texture Feature with Different Classifiers

Content Based Image Retrieval Using 2-D Discrete Wavelet with Texture Feature with Different Classifiers IOSR Journal of Electroncs and Communcaton Engneerng (IOSR-JECE) e-issn: 78-834,p- ISSN: 78-8735.Volume 9, Issue, Ver. IV (Mar - Apr. 04), PP 0-07 Content Based Image Retreval Usng -D Dscrete Wavelet wth

More information

Course Introduction. Algorithm 8/31/2017. COSC 320 Advanced Data Structures and Algorithms. COSC 320 Advanced Data Structures and Algorithms

Course Introduction. Algorithm 8/31/2017. COSC 320 Advanced Data Structures and Algorithms. COSC 320 Advanced Data Structures and Algorithms Course Introducton Course Topcs Exams, abs, Proects A quc loo at a few algorthms 1 Advanced Data Structures and Algorthms Descrpton: We are gong to dscuss algorthm complexty analyss, algorthm desgn technques

More information

3D vector computer graphics

3D vector computer graphics 3D vector computer graphcs Paolo Varagnolo: freelance engneer Padova Aprl 2016 Prvate Practce ----------------------------------- 1. Introducton Vector 3D model representaton n computer graphcs requres

More information

Problem Definitions and Evaluation Criteria for Computational Expensive Optimization

Problem Definitions and Evaluation Criteria for Computational Expensive Optimization Problem efntons and Evaluaton Crtera for Computatonal Expensve Optmzaton B. Lu 1, Q. Chen and Q. Zhang 3, J. J. Lang 4, P. N. Suganthan, B. Y. Qu 6 1 epartment of Computng, Glyndwr Unversty, UK Faclty

More information

Cluster Analysis of Electrical Behavior

Cluster Analysis of Electrical Behavior Journal of Computer and Communcatons, 205, 3, 88-93 Publshed Onlne May 205 n ScRes. http://www.scrp.org/ournal/cc http://dx.do.org/0.4236/cc.205.350 Cluster Analyss of Electrcal Behavor Ln Lu Ln Lu, School

More information

On Some Entertaining Applications of the Concept of Set in Computer Science Course

On Some Entertaining Applications of the Concept of Set in Computer Science Course On Some Entertanng Applcatons of the Concept of Set n Computer Scence Course Krasmr Yordzhev *, Hrstna Kostadnova ** * Assocate Professor Krasmr Yordzhev, Ph.D., Faculty of Mathematcs and Natural Scences,

More information

TN348: Openlab Module - Colocalization

TN348: Openlab Module - Colocalization TN348: Openlab Module - Colocalzaton Topc The Colocalzaton module provdes the faclty to vsualze and quantfy colocalzaton between pars of mages. The Colocalzaton wndow contans a prevew of the two mages

More information

User Authentication Based On Behavioral Mouse Dynamics Biometrics

User Authentication Based On Behavioral Mouse Dynamics Biometrics User Authentcaton Based On Behavoral Mouse Dynamcs Bometrcs Chee-Hyung Yoon Danel Donghyun Km Department of Computer Scence Department of Computer Scence Stanford Unversty Stanford Unversty Stanford, CA

More information

A Fast Visual Tracking Algorithm Based on Circle Pixels Matching

A Fast Visual Tracking Algorithm Based on Circle Pixels Matching A Fast Vsual Trackng Algorthm Based on Crcle Pxels Matchng Zhqang Hou hou_zhq@sohu.com Chongzhao Han czhan@mal.xjtu.edu.cn Ln Zheng Abstract: A fast vsual trackng algorthm based on crcle pxels matchng

More information

A Fast Content-Based Multimedia Retrieval Technique Using Compressed Data

A Fast Content-Based Multimedia Retrieval Technique Using Compressed Data A Fast Content-Based Multmeda Retreval Technque Usng Compressed Data Borko Furht and Pornvt Saksobhavvat NSF Multmeda Laboratory Florda Atlantc Unversty, Boca Raton, Florda 3343 ABSTRACT In ths paper,

More information

ON SOME ENTERTAINING APPLICATIONS OF THE CONCEPT OF SET IN COMPUTER SCIENCE COURSE

ON SOME ENTERTAINING APPLICATIONS OF THE CONCEPT OF SET IN COMPUTER SCIENCE COURSE Yordzhev K., Kostadnova H. Інформаційні технології в освіті ON SOME ENTERTAINING APPLICATIONS OF THE CONCEPT OF SET IN COMPUTER SCIENCE COURSE Yordzhev K., Kostadnova H. Some aspects of programmng educaton

More information

Constructing Minimum Connected Dominating Set: Algorithmic approach

Constructing Minimum Connected Dominating Set: Algorithmic approach Constructng Mnmum Connected Domnatng Set: Algorthmc approach G.N. Puroht and Usha Sharma Centre for Mathematcal Scences, Banasthal Unversty, Rajasthan 304022 usha.sharma94@yahoo.com Abstract: Connected

More information

A mathematical programming approach to the analysis, design and scheduling of offshore oilfields

A mathematical programming approach to the analysis, design and scheduling of offshore oilfields 17 th European Symposum on Computer Aded Process Engneerng ESCAPE17 V. Plesu and P.S. Agach (Edtors) 2007 Elsever B.V. All rghts reserved. 1 A mathematcal programmng approach to the analyss, desgn and

More information

Meta-heuristics for Multidimensional Knapsack Problems

Meta-heuristics for Multidimensional Knapsack Problems 2012 4th Internatonal Conference on Computer Research and Development IPCSIT vol.39 (2012) (2012) IACSIT Press, Sngapore Meta-heurstcs for Multdmensonal Knapsack Problems Zhbao Man + Computer Scence Department,

More information

Compiler Design. Spring Register Allocation. Sample Exercises and Solutions. Prof. Pedro C. Diniz

Compiler Design. Spring Register Allocation. Sample Exercises and Solutions. Prof. Pedro C. Diniz Compler Desgn Sprng 2014 Regster Allocaton Sample Exercses and Solutons Prof. Pedro C. Dnz USC / Informaton Scences Insttute 4676 Admralty Way, Sute 1001 Marna del Rey, Calforna 90292 pedro@s.edu Regster

More information

Feature Reduction and Selection

Feature Reduction and Selection Feature Reducton and Selecton Dr. Shuang LIANG School of Software Engneerng TongJ Unversty Fall, 2012 Today s Topcs Introducton Problems of Dmensonalty Feature Reducton Statstc methods Prncpal Components

More information

Problem Set 3 Solutions

Problem Set 3 Solutions Introducton to Algorthms October 4, 2002 Massachusetts Insttute of Technology 6046J/18410J Professors Erk Demane and Shaf Goldwasser Handout 14 Problem Set 3 Solutons (Exercses were not to be turned n,

More information

Simulation Based Analysis of FAST TCP using OMNET++

Simulation Based Analysis of FAST TCP using OMNET++ Smulaton Based Analyss of FAST TCP usng OMNET++ Umar ul Hassan 04030038@lums.edu.pk Md Term Report CS678 Topcs n Internet Research Sprng, 2006 Introducton Internet traffc s doublng roughly every 3 months

More information

Assignment # 2. Farrukh Jabeen Algorithms 510 Assignment #2 Due Date: June 15, 2009.

Assignment # 2. Farrukh Jabeen Algorithms 510 Assignment #2 Due Date: June 15, 2009. Farrukh Jabeen Algorthms 51 Assgnment #2 Due Date: June 15, 29. Assgnment # 2 Chapter 3 Dscrete Fourer Transforms Implement the FFT for the DFT. Descrbed n sectons 3.1 and 3.2. Delverables: 1. Concse descrpton

More information

CHAPTER 2 DECOMPOSITION OF GRAPHS

CHAPTER 2 DECOMPOSITION OF GRAPHS CHAPTER DECOMPOSITION OF GRAPHS. INTRODUCTION A graph H s called a Supersubdvson of a graph G f H s obtaned from G by replacng every edge uv of G by a bpartte graph,m (m may vary for each edge by dentfyng

More information

Lecture 5: Multilayer Perceptrons

Lecture 5: Multilayer Perceptrons Lecture 5: Multlayer Perceptrons Roger Grosse 1 Introducton So far, we ve only talked about lnear models: lnear regresson and lnear bnary classfers. We noted that there are functons that can t be represented

More information

Hybrid Non-Blind Color Image Watermarking

Hybrid Non-Blind Color Image Watermarking Hybrd Non-Blnd Color Image Watermarkng Ms C.N.Sujatha 1, Dr. P. Satyanarayana 2 1 Assocate Professor, Dept. of ECE, SNIST, Yamnampet, Ghatkesar Hyderabad-501301, Telangana 2 Professor, Dept. of ECE, AITS,

More information

Loop Transformations for Parallelism & Locality. Review. Scalar Expansion. Scalar Expansion: Motivation

Loop Transformations for Parallelism & Locality. Review. Scalar Expansion. Scalar Expansion: Motivation Loop Transformatons for Parallelsm & Localty Last week Data dependences and loops Loop transformatons Parallelzaton Loop nterchange Today Scalar expanson for removng false dependences Loop nterchange Loop

More information

Module Management Tool in Software Development Organizations

Module Management Tool in Software Development Organizations Journal of Computer Scence (5): 8-, 7 ISSN 59-66 7 Scence Publcatons Management Tool n Software Development Organzatons Ahmad A. Al-Rababah and Mohammad A. Al-Rababah Faculty of IT, Al-Ahlyyah Amman Unversty,

More information

An Application of the Dulmage-Mendelsohn Decomposition to Sparse Null Space Bases of Full Row Rank Matrices

An Application of the Dulmage-Mendelsohn Decomposition to Sparse Null Space Bases of Full Row Rank Matrices Internatonal Mathematcal Forum, Vol 7, 2012, no 52, 2549-2554 An Applcaton of the Dulmage-Mendelsohn Decomposton to Sparse Null Space Bases of Full Row Rank Matrces Mostafa Khorramzadeh Department of Mathematcal

More information

Steps for Computing the Dissimilarity, Entropy, Herfindahl-Hirschman and. Accessibility (Gravity with Competition) Indices

Steps for Computing the Dissimilarity, Entropy, Herfindahl-Hirschman and. Accessibility (Gravity with Competition) Indices Steps for Computng the Dssmlarty, Entropy, Herfndahl-Hrschman and Accessblty (Gravty wth Competton) Indces I. Dssmlarty Index Measurement: The followng formula can be used to measure the evenness between

More information

The Codesign Challenge

The Codesign Challenge ECE 4530 Codesgn Challenge Fall 2007 Hardware/Software Codesgn The Codesgn Challenge Objectves In the codesgn challenge, your task s to accelerate a gven software reference mplementaton as fast as possble.

More information

Private Information Retrieval (PIR)

Private Information Retrieval (PIR) 2 Levente Buttyán Problem formulaton Alce wants to obtan nformaton from a database, but she does not want the database to learn whch nformaton she wanted e.g., Alce s an nvestor queryng a stock-market

More information

Range images. Range image registration. Examples of sampling patterns. Range images and range surfaces

Range images. Range image registration. Examples of sampling patterns. Range images and range surfaces Range mages For many structured lght scanners, the range data forms a hghly regular pattern known as a range mage. he samplng pattern s determned by the specfc scanner. Range mage regstraton 1 Examples

More information

Proper Choice of Data Used for the Estimation of Datum Transformation Parameters

Proper Choice of Data Used for the Estimation of Datum Transformation Parameters Proper Choce of Data Used for the Estmaton of Datum Transformaton Parameters Hakan S. KUTOGLU, Turkey Key words: Coordnate systems; transformaton; estmaton, relablty. SUMMARY Advances n technologes and

More information

Accounting for the Use of Different Length Scale Factors in x, y and z Directions

Accounting for the Use of Different Length Scale Factors in x, y and z Directions 1 Accountng for the Use of Dfferent Length Scale Factors n x, y and z Drectons Taha Soch (taha.soch@kcl.ac.uk) Imagng Scences & Bomedcal Engneerng, Kng s College London, The Rayne Insttute, St Thomas Hosptal,

More information

The Greedy Method. Outline and Reading. Change Money Problem. Greedy Algorithms. Applications of the Greedy Strategy. The Greedy Method Technique

The Greedy Method. Outline and Reading. Change Money Problem. Greedy Algorithms. Applications of the Greedy Strategy. The Greedy Method Technique //00 :0 AM Outlne and Readng The Greedy Method The Greedy Method Technque (secton.) Fractonal Knapsack Problem (secton..) Task Schedulng (secton..) Mnmum Spannng Trees (secton.) Change Money Problem Greedy

More information

Shape-adaptive DCT and Its Application in Region-based Image Coding

Shape-adaptive DCT and Its Application in Region-based Image Coding Internatonal Journal of Sgnal Processng, Image Processng and Pattern Recognton, pp.99-108 http://dx.do.org/10.14257/sp.2014.7.1.10 Shape-adaptve DCT and Its Applcaton n Regon-based Image Codng Yamn Zheng,

More information

Loop Permutation. Loop Transformations for Parallelism & Locality. Legality of Loop Interchange. Loop Interchange (cont)

Loop Permutation. Loop Transformations for Parallelism & Locality. Legality of Loop Interchange. Loop Interchange (cont) Loop Transformatons for Parallelsm & Localty Prevously Data dependences and loops Loop transformatons Parallelzaton Loop nterchange Today Loop nterchange Loop transformatons and transformaton frameworks

More information

An Image Fusion Approach Based on Segmentation Region

An Image Fusion Approach Based on Segmentation Region Rong Wang, L-Qun Gao, Shu Yang, Yu-Hua Cha, and Yan-Chun Lu An Image Fuson Approach Based On Segmentaton Regon An Image Fuson Approach Based on Segmentaton Regon Rong Wang, L-Qun Gao, Shu Yang 3, Yu-Hua

More information

Support Vector Machines

Support Vector Machines /9/207 MIST.6060 Busness Intellgence and Data Mnng What are Support Vector Machnes? Support Vector Machnes Support Vector Machnes (SVMs) are supervsed learnng technques that analyze data and recognze patterns.

More information

Report on On-line Graph Coloring

Report on On-line Graph Coloring 2003 Fall Semester Comp 670K Onlne Algorthm Report on LO Yuet Me (00086365) cndylo@ust.hk Abstract Onlne algorthm deals wth data that has no future nformaton. Lots of examples demonstrate that onlne algorthm

More information

Real-time Motion Capture System Using One Video Camera Based on Color and Edge Distribution

Real-time Motion Capture System Using One Video Camera Based on Color and Edge Distribution Real-tme Moton Capture System Usng One Vdeo Camera Based on Color and Edge Dstrbuton YOSHIAKI AKAZAWA, YOSHIHIRO OKADA, AND KOICHI NIIJIMA Graduate School of Informaton Scence and Electrcal Engneerng,

More information

Research and Application of Fingerprint Recognition Based on MATLAB

Research and Application of Fingerprint Recognition Based on MATLAB Send Orders for Reprnts to reprnts@benthamscence.ae The Open Automaton and Control Systems Journal, 205, 7, 07-07 Open Access Research and Applcaton of Fngerprnt Recognton Based on MATLAB Nng Lu* Department

More information

An Efficient Garbage Collection for Flash Memory-Based Virtual Memory Systems

An Efficient Garbage Collection for Flash Memory-Based Virtual Memory Systems S. J and D. Shn: An Effcent Garbage Collecton for Flash Memory-Based Vrtual Memory Systems 2355 An Effcent Garbage Collecton for Flash Memory-Based Vrtual Memory Systems Seunggu J and Dongkun Shn, Member,

More information

Edge Detection in Noisy Images Using the Support Vector Machines

Edge Detection in Noisy Images Using the Support Vector Machines Edge Detecton n Nosy Images Usng the Support Vector Machnes Hlaro Gómez-Moreno, Saturnno Maldonado-Bascón, Francsco López-Ferreras Sgnal Theory and Communcatons Department. Unversty of Alcalá Crta. Madrd-Barcelona

More information

A Unified Framework for Semantics and Feature Based Relevance Feedback in Image Retrieval Systems

A Unified Framework for Semantics and Feature Based Relevance Feedback in Image Retrieval Systems A Unfed Framework for Semantcs and Feature Based Relevance Feedback n Image Retreval Systems Ye Lu *, Chunhu Hu 2, Xngquan Zhu 3*, HongJang Zhang 2, Qang Yang * School of Computng Scence Smon Fraser Unversty

More information

Query Clustering Using a Hybrid Query Similarity Measure

Query Clustering Using a Hybrid Query Similarity Measure Query clusterng usng a hybrd query smlarty measure Fu. L., Goh, D.H., & Foo, S. (2004). WSEAS Transacton on Computers, 3(3), 700-705. Query Clusterng Usng a Hybrd Query Smlarty Measure Ln Fu, Don Hoe-Lan

More information

Available online at Available online at Advanced in Control Engineering and Information Science

Available online at   Available online at   Advanced in Control Engineering and Information Science Avalable onlne at wwwscencedrectcom Avalable onlne at wwwscencedrectcom Proceda Proceda Engneerng Engneerng 00 (2011) 15000 000 (2011) 1642 1646 Proceda Engneerng wwwelsevercom/locate/proceda Advanced

More information

An Iterative Solution Approach to Process Plant Layout using Mixed Integer Optimisation

An Iterative Solution Approach to Process Plant Layout using Mixed Integer Optimisation 17 th European Symposum on Computer Aded Process Engneerng ESCAPE17 V. Plesu and P.S. Agach (Edtors) 2007 Elsever B.V. All rghts reserved. 1 An Iteratve Soluton Approach to Process Plant Layout usng Mxed

More information

A Modified Median Filter for the Removal of Impulse Noise Based on the Support Vector Machines

A Modified Median Filter for the Removal of Impulse Noise Based on the Support Vector Machines A Modfed Medan Flter for the Removal of Impulse Nose Based on the Support Vector Machnes H. GOMEZ-MORENO, S. MALDONADO-BASCON, F. LOPEZ-FERRERAS, M. UTRILLA- MANSO AND P. GIL-JIMENEZ Departamento de Teoría

More information

Complex Numbers. Now we also saw that if a and b were both positive then ab = a b. For a second let s forget that restriction and do the following.

Complex Numbers. Now we also saw that if a and b were both positive then ab = a b. For a second let s forget that restriction and do the following. Complex Numbers The last topc n ths secton s not really related to most of what we ve done n ths chapter, although t s somewhat related to the radcals secton as we wll see. We also won t need the materal

More information

Efficient Content Distribution in Wireless P2P Networks

Efficient Content Distribution in Wireless P2P Networks Effcent Content Dstrbuton n Wreless P2P Networs Qong Sun, Vctor O. K. L, and Ka-Cheong Leung Department of Electrcal and Electronc Engneerng The Unversty of Hong Kong Pofulam Road, Hong Kong, Chna {oansun,

More information

Hermite Splines in Lie Groups as Products of Geodesics

Hermite Splines in Lie Groups as Products of Geodesics Hermte Splnes n Le Groups as Products of Geodescs Ethan Eade Updated May 28, 2017 1 Introducton 1.1 Goal Ths document defnes a curve n the Le group G parametrzed by tme and by structural parameters n the

More information

A fast algorithm for color image segmentation

A fast algorithm for color image segmentation Unersty of Wollongong Research Onlne Faculty of Informatcs - Papers (Arche) Faculty of Engneerng and Informaton Scences 006 A fast algorthm for color mage segmentaton L. Dong Unersty of Wollongong, lju@uow.edu.au

More information

Improvement of Spatial Resolution Using BlockMatching Based Motion Estimation and Frame. Integration

Improvement of Spatial Resolution Using BlockMatching Based Motion Estimation and Frame. Integration Improvement of Spatal Resoluton Usng BlockMatchng Based Moton Estmaton and Frame Integraton Danya Suga and Takayuk Hamamoto Graduate School of Engneerng, Tokyo Unversty of Scence, 6-3-1, Nuku, Katsuska-ku,

More information

An Approach in Coloring Semi-Regular Tilings on the Hyperbolic Plane

An Approach in Coloring Semi-Regular Tilings on the Hyperbolic Plane An Approach n Colorng Sem-Regular Tlngs on the Hyperbolc Plane Ma Louse Antonette N De Las Peñas, mlp@mathscmathadmueduph Glenn R Lago, glago@yahoocom Math Department, Ateneo de Manla Unversty, Loyola

More information

Analysis on the Workspace of Six-degrees-of-freedom Industrial Robot Based on AutoCAD

Analysis on the Workspace of Six-degrees-of-freedom Industrial Robot Based on AutoCAD Analyss on the Workspace of Sx-degrees-of-freedom Industral Robot Based on AutoCAD Jn-quan L 1, Ru Zhang 1,a, Fang Cu 1, Q Guan 1 and Yang Zhang 1 1 School of Automaton, Bejng Unversty of Posts and Telecommuncatons,

More information

Solving two-person zero-sum game by Matlab

Solving two-person zero-sum game by Matlab Appled Mechancs and Materals Onlne: 2011-02-02 ISSN: 1662-7482, Vols. 50-51, pp 262-265 do:10.4028/www.scentfc.net/amm.50-51.262 2011 Trans Tech Publcatons, Swtzerland Solvng two-person zero-sum game by

More information

Circuit Analysis I (ENGR 2405) Chapter 3 Method of Analysis Nodal(KCL) and Mesh(KVL)

Circuit Analysis I (ENGR 2405) Chapter 3 Method of Analysis Nodal(KCL) and Mesh(KVL) Crcut Analyss I (ENG 405) Chapter Method of Analyss Nodal(KCL) and Mesh(KVL) Nodal Analyss If nstead of focusng on the oltages of the crcut elements, one looks at the oltages at the nodes of the crcut,

More information

Performance Evaluation of Information Retrieval Systems

Performance Evaluation of Information Retrieval Systems Why System Evaluaton? Performance Evaluaton of Informaton Retreval Systems Many sldes n ths secton are adapted from Prof. Joydeep Ghosh (UT ECE) who n turn adapted them from Prof. Dk Lee (Unv. of Scence

More information

Parallel matrix-vector multiplication

Parallel matrix-vector multiplication Appendx A Parallel matrx-vector multplcaton The reduced transton matrx of the three-dmensonal cage model for gel electrophoress, descrbed n secton 3.2, becomes excessvely large for polymer lengths more

More information

Empirical Distributions of Parameter Estimates. in Binary Logistic Regression Using Bootstrap

Empirical Distributions of Parameter Estimates. in Binary Logistic Regression Using Bootstrap Int. Journal of Math. Analyss, Vol. 8, 4, no. 5, 7-7 HIKARI Ltd, www.m-hkar.com http://dx.do.org/.988/jma.4.494 Emprcal Dstrbutons of Parameter Estmates n Bnary Logstc Regresson Usng Bootstrap Anwar Ftranto*

More information

Kent State University CS 4/ Design and Analysis of Algorithms. Dept. of Math & Computer Science LECT-16. Dynamic Programming

Kent State University CS 4/ Design and Analysis of Algorithms. Dept. of Math & Computer Science LECT-16. Dynamic Programming CS 4/560 Desgn and Analyss of Algorthms Kent State Unversty Dept. of Math & Computer Scence LECT-6 Dynamc Programmng 2 Dynamc Programmng Dynamc Programmng, lke the dvde-and-conquer method, solves problems

More information

UB at GeoCLEF Department of Geography Abstract

UB at GeoCLEF Department of Geography   Abstract UB at GeoCLEF 2006 Mguel E. Ruz (1), Stuart Shapro (2), June Abbas (1), Slva B. Southwck (1) and Davd Mark (3) State Unversty of New York at Buffalo (1) Department of Lbrary and Informaton Studes (2) Department

More information

X- Chart Using ANOM Approach

X- Chart Using ANOM Approach ISSN 1684-8403 Journal of Statstcs Volume 17, 010, pp. 3-3 Abstract X- Chart Usng ANOM Approach Gullapall Chakravarth 1 and Chaluvad Venkateswara Rao Control lmts for ndvdual measurements (X) chart are

More information

Learning the Kernel Parameters in Kernel Minimum Distance Classifier

Learning the Kernel Parameters in Kernel Minimum Distance Classifier Learnng the Kernel Parameters n Kernel Mnmum Dstance Classfer Daoqang Zhang 1,, Songcan Chen and Zh-Hua Zhou 1* 1 Natonal Laboratory for Novel Software Technology Nanjng Unversty, Nanjng 193, Chna Department

More information

2x x l. Module 3: Element Properties Lecture 4: Lagrange and Serendipity Elements

2x x l. Module 3: Element Properties Lecture 4: Lagrange and Serendipity Elements Module 3: Element Propertes Lecture : Lagrange and Serendpty Elements 5 In last lecture note, the nterpolaton functons are derved on the bass of assumed polynomal from Pascal s trangle for the fled varable.

More information

MULTISPECTRAL IMAGES CLASSIFICATION BASED ON KLT AND ATR AUTOMATIC TARGET RECOGNITION

MULTISPECTRAL IMAGES CLASSIFICATION BASED ON KLT AND ATR AUTOMATIC TARGET RECOGNITION MULTISPECTRAL IMAGES CLASSIFICATION BASED ON KLT AND ATR AUTOMATIC TARGET RECOGNITION Paulo Quntlano 1 & Antono Santa-Rosa 1 Federal Polce Department, Brasla, Brazl. E-mals: quntlano.pqs@dpf.gov.br and

More information

Classifier Selection Based on Data Complexity Measures *

Classifier Selection Based on Data Complexity Measures * Classfer Selecton Based on Data Complexty Measures * Edth Hernández-Reyes, J.A. Carrasco-Ochoa, and J.Fco. Martínez-Trndad Natonal Insttute for Astrophyscs, Optcs and Electroncs, Lus Enrque Erro No.1 Sta.

More information

SENSITIVITY ANALYSIS IN LINEAR PROGRAMMING USING A CALCULATOR

SENSITIVITY ANALYSIS IN LINEAR PROGRAMMING USING A CALCULATOR SENSITIVITY ANALYSIS IN LINEAR PROGRAMMING USING A CALCULATOR Judth Aronow Rchard Jarvnen Independent Consultant Dept of Math/Stat 559 Frost Wnona State Unversty Beaumont, TX 7776 Wnona, MN 55987 aronowju@hal.lamar.edu

More information

Analysis of Continuous Beams in General

Analysis of Continuous Beams in General Analyss of Contnuous Beams n General Contnuous beams consdered here are prsmatc, rgdly connected to each beam segment and supported at varous ponts along the beam. onts are selected at ponts of support,

More information

Remote display of large raster images using JPEG2000 and the rectangular FishEye-View

Remote display of large raster images using JPEG2000 and the rectangular FishEye-View Remote dsplay of large raster mages usng JPEG2000 and the rectangular FshEye-Vew René Rosenbaum* Insttute of Computer Graphcs Unversty of Rostock 18059 Rostock Germany rrosen@nformatk.un-rostock.de Davd

More information

Pictures at an Exhibition

Pictures at an Exhibition 1 Pctures at an Exhbton Stephane Kwan and Karen Zhu Department of Electrcal Engneerng Stanford Unversty, Stanford, CA 9405 Emal: {skwan1, kyzhu}@stanford.edu Abstract An mage processng algorthm s desgned

More information

Data Representation in Digital Design, a Single Conversion Equation and a Formal Languages Approach

Data Representation in Digital Design, a Single Conversion Equation and a Formal Languages Approach Data Representaton n Dgtal Desgn, a Sngle Converson Equaton and a Formal Languages Approach Hassan Farhat Unversty of Nebraska at Omaha Abstract- In the study of data representaton n dgtal desgn and computer

More information

VRT012 User s guide V0.1. Address: Žirmūnų g. 27, Vilnius LT-09105, Phone: (370-5) , Fax: (370-5) ,

VRT012 User s guide V0.1. Address: Žirmūnų g. 27, Vilnius LT-09105, Phone: (370-5) , Fax: (370-5) , VRT012 User s gude V0.1 Thank you for purchasng our product. We hope ths user-frendly devce wll be helpful n realsng your deas and brngng comfort to your lfe. Please take few mnutes to read ths manual

More information

Machine Learning: Algorithms and Applications

Machine Learning: Algorithms and Applications 14/05/1 Machne Learnng: Algorthms and Applcatons Florano Zn Free Unversty of Bozen-Bolzano Faculty of Computer Scence Academc Year 011-01 Lecture 10: 14 May 01 Unsupervsed Learnng cont Sldes courtesy of

More information

An Improved Image Segmentation Algorithm Based on the Otsu Method

An Improved Image Segmentation Algorithm Based on the Otsu Method 3th ACIS Internatonal Conference on Software Engneerng, Artfcal Intellgence, Networkng arallel/dstrbuted Computng An Improved Image Segmentaton Algorthm Based on the Otsu Method Mengxng Huang, enjao Yu,

More information

Term Weighting Classification System Using the Chi-square Statistic for the Classification Subtask at NTCIR-6 Patent Retrieval Task

Term Weighting Classification System Using the Chi-square Statistic for the Classification Subtask at NTCIR-6 Patent Retrieval Task Proceedngs of NTCIR-6 Workshop Meetng, May 15-18, 2007, Tokyo, Japan Term Weghtng Classfcaton System Usng the Ch-square Statstc for the Classfcaton Subtask at NTCIR-6 Patent Retreval Task Kotaro Hashmoto

More information

Type-2 Fuzzy Non-uniform Rational B-spline Model with Type-2 Fuzzy Data

Type-2 Fuzzy Non-uniform Rational B-spline Model with Type-2 Fuzzy Data Malaysan Journal of Mathematcal Scences 11(S) Aprl : 35 46 (2017) Specal Issue: The 2nd Internatonal Conference and Workshop on Mathematcal Analyss (ICWOMA 2016) MALAYSIAN JOURNAL OF MATHEMATICAL SCIENCES

More information

Concurrent Apriori Data Mining Algorithms

Concurrent Apriori Data Mining Algorithms Concurrent Apror Data Mnng Algorthms Vassl Halatchev Department of Electrcal Engneerng and Computer Scence York Unversty, Toronto October 8, 2015 Outlne Why t s mportant Introducton to Assocaton Rule Mnng

More information

Programming in Fortran 90 : 2017/2018

Programming in Fortran 90 : 2017/2018 Programmng n Fortran 90 : 2017/2018 Programmng n Fortran 90 : 2017/2018 Exercse 1 : Evaluaton of functon dependng on nput Wrte a program who evaluate the functon f (x,y) for any two user specfed values

More information

Corner-Based Image Alignment using Pyramid Structure with Gradient Vector Similarity

Corner-Based Image Alignment using Pyramid Structure with Gradient Vector Similarity Journal of Sgnal and Informaton Processng, 013, 4, 114-119 do:10.436/jsp.013.43b00 Publshed Onlne August 013 (http://www.scrp.org/journal/jsp) Corner-Based Image Algnment usng Pyramd Structure wth Gradent

More information

New Region Incrementing Visual Cryptography Scheme

New Region Incrementing Visual Cryptography Scheme New Regon Incrementng Vsual Cryptography Scheme Chng-Nung Yang Hsang-Wen Shh Yu-Yng Chu Len Harn CSIE Dept Natonal Dong Hwa Unversty Hualen Tawan CSEE Dept Unversty of Mssour-Kansas Cty Kansas Mssour USA

More information

A Secured Method for Image Steganography Based On Pixel Values

A Secured Method for Image Steganography Based On Pixel Values A Secured Method for Image Steganography Based On Pxel Values Tarun Gulat #, Sanskrt Gupta * # Assocate Professor, Electroncs and Communcaton Engneerng Department, MMEC, M.M.U., Mullana, Ambala, Haryana,

More information

mquest Quickstart Version 11.0

mquest Quickstart Version 11.0 mquest Quckstart Verson 11.0 cluetec GmbH Emmy-Noether-Straße 17 76131 Karlsruhe Germany www.cluetec.de www.mquest.nfo cluetec GmbH Karlsruhe, 2016 Document verson 5 27.04.2016 16:59 > Propretary notce

More information

NOVEL CONSTRUCTION OF SHORT LENGTH LDPC CODES FOR SIMPLE DECODING

NOVEL CONSTRUCTION OF SHORT LENGTH LDPC CODES FOR SIMPLE DECODING Journal of Theoretcal and Appled Informaton Technology 27 JATIT. All rghts reserved. www.jatt.org NOVEL CONSTRUCTION OF SHORT LENGTH LDPC CODES FOR SIMPLE DECODING Fatma A. Newagy, Yasmne A. Fahmy, and

More information

The stream cipher MICKEY-128 (version 1) Algorithm specification issue 1.0

The stream cipher MICKEY-128 (version 1) Algorithm specification issue 1.0 The stream cpher MICKEY-128 (verson 1 Algorthm specfcaton ssue 1. Steve Babbage Vodafone Group R&D, Newbury, UK steve.babbage@vodafone.com Matthew Dodd Independent consultant matthew@mdodd.net www.mdodd.net

More information

R s s f. m y s. SPH3UW Unit 7.3 Spherical Concave Mirrors Page 1 of 12. Notes

R s s f. m y s. SPH3UW Unit 7.3 Spherical Concave Mirrors Page 1 of 12. Notes SPH3UW Unt 7.3 Sphercal Concave Mrrors Page 1 of 1 Notes Physcs Tool box Concave Mrror If the reflectng surface takes place on the nner surface of the sphercal shape so that the centre of the mrror bulges

More information

The Shortest Path of Touring Lines given in the Plane

The Shortest Path of Touring Lines given in the Plane Send Orders for Reprnts to reprnts@benthamscence.ae 262 The Open Cybernetcs & Systemcs Journal, 2015, 9, 262-267 The Shortest Path of Tourng Lnes gven n the Plane Open Access Ljuan Wang 1,2, Dandan He

More information

Sum of Linear and Fractional Multiobjective Programming Problem under Fuzzy Rules Constraints

Sum of Linear and Fractional Multiobjective Programming Problem under Fuzzy Rules Constraints Australan Journal of Basc and Appled Scences, 2(4): 1204-1208, 2008 ISSN 1991-8178 Sum of Lnear and Fractonal Multobjectve Programmng Problem under Fuzzy Rules Constrants 1 2 Sanjay Jan and Kalash Lachhwan

More information

ELEC 377 Operating Systems. Week 6 Class 3

ELEC 377 Operating Systems. Week 6 Class 3 ELEC 377 Operatng Systems Week 6 Class 3 Last Class Memory Management Memory Pagng Pagng Structure ELEC 377 Operatng Systems Today Pagng Szes Vrtual Memory Concept Demand Pagng ELEC 377 Operatng Systems

More information

Pattern Recognition 43 (2010) Contents lists available at ScienceDirect. Pattern Recognition. journal homepage:

Pattern Recognition 43 (2010) Contents lists available at ScienceDirect. Pattern Recognition. journal homepage: Pattern Recognton 43 (2010) 397 -- 404 Contents lsts avalable at ScenceDrect Pattern Recognton ournal homepage: www.elsever.com/locate/pr Secret mage sharng based on cellular automata and steganography

More information

A SYSTOLIC APPROACH TO LOOP PARTITIONING AND MAPPING INTO FIXED SIZE DISTRIBUTED MEMORY ARCHITECTURES

A SYSTOLIC APPROACH TO LOOP PARTITIONING AND MAPPING INTO FIXED SIZE DISTRIBUTED MEMORY ARCHITECTURES A SYSOLIC APPROACH O LOOP PARIIONING AND MAPPING INO FIXED SIZE DISRIBUED MEMORY ARCHIECURES Ioanns Drosts, Nektaros Kozrs, George Papakonstantnou and Panayots sanakas Natonal echncal Unversty of Athens

More information

Estimating Costs of Path Expression Evaluation in Distributed Object Databases

Estimating Costs of Path Expression Evaluation in Distributed Object Databases Estmatng Costs of Path Expresson Evaluaton n Dstrbuted Obect Databases Gabrela Ruberg, Fernanda Baão, and Marta Mattoso Department of Computer Scence COPPE/UFRJ P.O.Box 685, Ro de Janero, RJ, 2945-970

More information

Cordial and 3-Equitable Labeling for Some Star Related Graphs

Cordial and 3-Equitable Labeling for Some Star Related Graphs Internatonal Mathematcal Forum, 4, 009, no. 31, 1543-1553 Cordal and 3-Equtable Labelng for Some Star Related Graphs S. K. Vadya Department of Mathematcs, Saurashtra Unversty Rajkot - 360005, Gujarat,

More information

Fast Computation of Shortest Path for Visiting Segments in the Plane

Fast Computation of Shortest Path for Visiting Segments in the Plane Send Orders for Reprnts to reprnts@benthamscence.ae 4 The Open Cybernetcs & Systemcs Journal, 04, 8, 4-9 Open Access Fast Computaton of Shortest Path for Vstng Segments n the Plane Ljuan Wang,, Bo Jang

More information

Analysis of 3D Cracks in an Arbitrary Geometry with Weld Residual Stress

Analysis of 3D Cracks in an Arbitrary Geometry with Weld Residual Stress Analyss of 3D Cracks n an Arbtrary Geometry wth Weld Resdual Stress Greg Thorwald, Ph.D. Ted L. Anderson, Ph.D. Structural Relablty Technology, Boulder, CO Abstract Materals contanng flaws lke nclusons

More information