Department of Mathematics, Shantou University, Shantou, Guangdong, , China.

Size: px
Start display at page:

Download "Department of Mathematics, Shantou University, Shantou, Guangdong, , China."

Transcription

1 205 Internatonal Conference on Computer Scence and Communcaton Engneerng (CSCE 205) ISN: A Novel Color Image Encrypton Scheme ased on Permutaton-substtuton Archtecture Ru-Song Ye,a, Mng Ye, Xao-Yun Sh, Wen-Hao Ye, Ya-Fang L Department of Mathematcs, Shantou Unversty, Shantou, Guangdong, 55063, Chna a rsye@stu.edu.cn Keywords: Skew tent map, Permutaton, Substtuton, Image encrypton. Abstract. A novel color mage encrypton scheme wth permutaton-substtuton archtecture s proposed. One round of permutaton and one round of substtuton acheve desrable results. he color plan-mage szed H W s converted to 2D matrx wth sze H 3W and then dvded nto two equal parts P, P 2. he 2D skew tent map s appled to generate pseudo-random sequences for the permutaton and substtuton processes mplemented row-by-row/column-by-column nstead of pxel-by-pxel to ncrease the encrypton rate. he permutaton s performed between P, P 2 and the substtuton s executed wthn the permuted mage. he securty and performance of the proposed scheme have been analyzed as well. All the expermental results show that the proposed scheme s secure and effectve for practcal applcaton. Introducton hanks to the fantastc features of chaotc systems, such as hgh senstvty to ntal condtons and control parameters, ergodcty, pseudo-randomness etc., chaos-based mage encrypton schemes are extensvely studed and developed recently. On one hand, tradtonal encrypton algorthms, such as DES, AES, are typcally desgned for textual nformaton and are not sutable for mage encrypton due to the ntrnsc natures of mages lke hgh redundancy and hgh correlaton among pxels []. On the other hand, the good chaotc natures agree wth the fundamental requrements such as confuson and dffuson n cryptography, and therefore chaotc systems provde a potental canddate for constructng cryptosystems [2-4]. Snce Frdrch frstly presented the fundamental permutaton-dffuson archtecture of chaos-based mage encrypton n 998, a great number of chaos-based mage encrypton algorthms wth such permutaton-dffuson archtecture have been proposed [2-6]. However Wang et al. found that the typcal permutaton-dffuson archtecture wth fxed parameters has one fatal drawback, that s, the two processes wll become ndependent f the plan-mage s a homogeneous one wth dentcal pxel gray value [7]. As a matter of fact, some mage encrypton algorthms wth permutaton-dffuson archtecture have been broken by chosen-plantext or known-plantext attacks [8,9]. In ths paper, we present an mage encrypton scheme wth permutaton-substtuton mechansm nstead of permutaton-dffuson mechansm. Permutaton-substtuton mechansm has been shown to be one effectve mechansm for constructng cphers [6]. he mage encrypton scheme proposed here conssts of two stages: one permutaton and one substtuton. hey are performed between the RG,, components of color mage. he color plan-mage szed H W s converted to a 2D matrx wth sze H 3W and then s dvded nto two parts 22

2 P, P 2 wth the same sze H.5W. o acheve desrable key senstvty and plantext senstvty, the permutaton stage s desgned to be dependent on plan-mage. As a result, the proposed mage scheme owns good resstance to known-plantext and chosen-plantext attacks. he substtuton stage s performed row by row and column by column to mprove the encrypton rate. he substtuton acheves good dffuson effect and shows good resstance aganst dfferental analyss as well. he securty and performance analyss of the proposed mage encrypton are carred out thoroughly. All the expermental results show that the proposed mage encrypton scheme s hghly secure and demonstrates excellent performance. he Proposed Image Encrypton Scheme Read a color plan-mage PI expressed by a 3D matrx wth sze HW 3. We assume that the three color components are denoted by 2D matrx RGrespectvely.,, hen convert PI to a 2D matrx P wth sze H 3W by the way P [ R, G, ]. P s then dvded nto two parts P, P 2, whose szes are the same H.5W. For the sake of smplcty, we assume that W s even and let W.5W, max( H, W). he converson and dvson s shown n Fg.. Fg.. he converson and dvson of color plan-mage. he proposed mage encrypton scheme s composed of one permutaton process and one substtuton process. he entre mage encrypton scheme s outlned as follows. Step. Generaton of pseudo-random gray value vectors IVR, IVC. Wth cpher keys x0, y 0, aband, N, we terate the 2D skew tent map for N tmes and reect the transent ponts{( xk, yk) : k 0,,, N }. he values of ( xn, y N) are saved and the 2D skew tent map wth new ntal values ( xn, yn) to yeld IVR, IVC. We stll wrte ( xn, yn) as ( x0, y 0). x x / p, f x [0, p], IVR floor x*256, IVC floor y*256,,,, y ( x ) / ( p), f x ( p,], where floor( x) returns the largest nteger not larger than x. runcate the frst H elements of IVC and transpose t to get one column vector IVC wth H elements. runcate the frst W elements of IVR to get one row vector IVR wth W elements. Step 2. Generaton of pseudo-random gray value vectors SVR, SVC. We stll denote ( x, y ) as ( x0, y 0) for smplcty. x x / p, f x [0, p], SVR floor x *256, SVC floor y *256,,,. y ( x ) / ( p), f x ( p,], Step 3. Perform the permutaton stage. Calculate the number of teratons to skp before startng the permutaton by N P P W P PH W mod Startng,, 3 2,,

3 wth the ntal condtons ( xn, y N) obtaned n Step, we terate the 2D skew tent map for N tmes and save the new values ( xn, y ) N as ( x 0, y 0). For to, do the followng loop x x / p, f x [0, p], PR floor x* H, PC floor y* W. y ( x ) / ( p), f x ( p,], he vectors PR, PC are then employed to perform the permutaton between P and P 2 row-by-row and column-by-column va the followng loop and get one permuted mage G. For to H, exchange the PR( ) -th row of P 2 wth the -th row of P ; For to W, exchange the PC( ) -th column of P 2 wth the -th column of P. Step 4. Substtute the 2D matrx G row-by-row and column-by-column. he executon for the substtuton s defned by G(,:) G(,:) IVRSVR(); G(,:) G(,:) G(,:) SVR( ), 2,, H, G(:,) G(:,) IVCSVC(); G(:, ) G(:, ) G(:, ) SVC( ), 2,, W, where represents the btwse XOR operaton, and G(,:), G(:, ) denote the th row and th column of G. he resulted cpher-mage for plan-mage Lena s shown n Fg. 2(b). (a) (b) (c) (d) (e) (f) (g) (h) Note: (a) plan-mage Lena; (b) cpher-mage; (c),(d),(e): hstograms for RGcomponents,, of Lena; (f),(g),(h): hstograms for RGcomponents,, of cpher-mage. Fg. 2. he encrypted results. Performance Analyss Hstogram analyss Hstogram analyss s a vsual test whch shows the pxel dstrbuton over the avalable ntensty levels. For a 24-bt color mage, three hstograms can be drawn for each 8-bt red, green and blue channel. Encrypt the color mage Lena one round wth cpher keys ( x, y, a, b, N ) to be (0.20, 0.44, 0.23, 0.57, 933), and then plot the hstograms of plan-mage

4 and cpher-mage as shown n Fg. 2. One can conclude from the hstograms of the cpher-mage that they are farly unform and sgnfcantly dfferent from the correspondng hstograms of the plan-mage. Correlaton coeffcent analyss It s common sense that for one nature mage wth defnte vsual contents, each pxel s hghly correlated wth ts adacent pxels ether n horzontal, vertcal or dagonal drecton. An deal encrypton cryptosystem should produce cpher-mages wth less correlaton n the adacent pxels. We select 6000 pars of two adacent pxels randomly from an mage cov( x y) and then calculate the correlaton coeffcent of the selected pars by Cr, ( ) ( ( ))( ( )) cov xy x E x y E y 2 where, Ex ( ) x Dx ( ) ( x Ex ( )) D( x) D( y) x y form the -th par of horzontally, vertcally or dagonally adacent pxels. he correlaton coeffcents of horzontally, vertcally, dagonally adacent pxels for plan-mage Lena and ts cpher-mage are gven n able. It s clear from able that the proposed mage encrypton scheme sgnfcantly reduces the correlaton between the adacent pxels of the plan-mage. Informaton entropy analyss Informaton entropy measures the dsorder and randomness of nformaton sequence [0]. Regardng mage, t can be used to measure the unformty of mage hstograms. he entropy L H ( m ) of a message source m can be calculated by Hm ( ) pm ( )log( pm ( )) (bts), where L s the total number of symbols m, pm ( ) represents the probablty of occurrence of symbol m and log denotes the base 2 logarthm so that the entropy s expressed n bts. For a 24-bt color mage, the nformaton entropy for each color channel (Red, Green and lue) s gven as RG / / RG / / H ( m) P ( RI )log 2 (bts). RG / / P ( RI ) 0 ab.. Correlaton coeffcents between adacent pxels of plan and cpher mage. Correlaton between adacent pxels Red Green lue Horzontal Plan-mage Cpher-mage Vertcal Plan-mage Cpher-mage Dagonal Plan-mage Cpher-mage We have calculated the nformaton entropy for plan- mage Lena and ts cpher mage. he results are shown n able 2. he value of nformaton entropy for the cpher-mage s very-very close to the expected value of truly random mage,.e., 8bts. Hence the proposed encrypton scheme s extremely robust aganst entropy attacks. 25

5 ab. 2. Informaton entropy analyss. Red Green lue Plan-mage Lena Cpher-mage Dfferental attack analyss Dfferental cryptanalyss s the study of how dfferences n a plantext can affect the resultant dfferences n the cphertext wth the same cpher key. If one slght dfference n the plan-mage wll cause sgnfcant, random and unpredctable changes n the cpher-mage, then the encrypton scheme wll resst dfferental analyss attack effcently. wo most common measures NPCR (number of pxel change rate) and UACI (unfed average changng ntensty) are used to test the robustness of mage cryptosystems aganst the dfferental R/ G/ R/ G/ cryptanalyss. If C and C represent the R, G, channels for two cpher-mages, then NPCR for each color channel s defned by H W RG / / D, R/ G/ R/ G/ f / / / /, C RG RG, D, R/ G/ R/ G/ W H f, C, NPCR 00%, 0, C,, C. H W RG / / RG / / C, C, UACI 00%. L W H 2 RG RG / / UACI s calculated by / / We have performed the dfferental analyss by calculatng NPCR and UACI on plan-mage Lena. he analyss has been done by randomly choosng 500 pxels n plan-mage, and changng all three color ntensty values by one unt at the selectve pxel. he averages of 500 NPCR values and 500 UACI values thus obtaned for all three color components are gven n able 3. It s clear that the NPCR and UACI values are very close to the expected values, thus the proposed mage encrypton technque shows good senstvty to plantext and hence nvulnerable to dfferental attacks. ab. 3. Dfference analyss of plan-mage Lena. Average NPCR (%) Average UACI (%) Red Green lue Red Green lue Lena Key senstvty analyss A good mage encrypton scheme should be extremely senstve to cpher keys, whch s an essental feature for any good cryptosystem n the sense that t can effectvely prevent nvaders decryptng orgnal data. If one uses two slghtly dfferent keys to encrypt the same plan-mage, then two cpher-mages should possess neglgble correlaton. he plan-mage s respectvely encrypted wth one master cpher key and fve other cpher keys whch have only a mnor dfference n any one of fve parts of master cpher key. Sx cpher keys are used to encrypt mage Lena. Master cpher key s MKEY(0.20,0.44,0.23,0.57,933) ; fve slghtly dfferent 26

6 4 4 keys are SKEY(0.20-0, 0.44, 0.23, 0.57, 933), SKEY2(0.20, , 0.23, 0.57, 933), SKEY3(0.20, , , 0.657, 933), SKEY4(0.20, 0.44, 0.23, , 933), SKEY5(0.20,0.44,0.23,0.57,933+). We then calculate the 2D correlaton coeffcents between the varous color channels of the cpher-mage by MKEY and fve other cpher-mages by SKEY,,SKEY5. he results are provded n able 4. All the correlaton coeffcents are very small or practcally zero ndcatng that all the cpher-mages are hghly dfferent. ab. 4. Key senstvty tests. Correlaton coeffcents between the cpher-mages obtaned usng MKEY and SKEY SKEY2 SKEY3 SKEY4 SKEY5 Crr Crg Crb Cgr Cgg Cgb Cbr Cbg Cbb Acknowledgement hs research s supported by the Challenge Promoton Plan of Shantou Unversty. References []D.R. Stnson, Cryptography: heory and Practce. oca Raton: CRC Press, 995. [2]J. Frdrch, Symmetrc cphers based on two-dmensonal chaotc maps, Internatonal Journal of furcaton and Chaos, 8 (998), [3]R. Ye, A novel chaos-based mage encrypton scheme wth an effcent permutaton-dffuson mechansm, Opt. Commun., 284 (20), [4]L. Kocarev, Chaos-based cryptography: a bref overvew, IEEE Crcuts and Systems Magazne, (200), 6-2. [5]R. Ye, A novel mage encrypton scheme based on generalzed mult-sawtooth maps, Fundamenta Informatcae, 33 (204), [6]Vnod Patdar, N.K. Pareek, G. Puroht, K.K. Sud, A robust and secure chaotc standard map based pseudorandom permutaton-substtuton scheme for mage encrypton, Optcs Commun., 284 (20), [7]Y. Wang, K.W. Wong, X.F. Lao,. Xang, G.R. Chen, A chaos-based mage encrypton algorthm wth varable control parameters. Chaos, Soltons and Fractals, 4 (2009),

7 [8]S.J. L, C.Q. L, G.R. Chen, N.G. ourbaks, K.. Lo, A general quanttatve cryptanalyss of permutaton-only multmeda cphers aganst plan-mage attacks. Sgnal Process. Image Commun., 23 (2009), [9]X. Wang, G. He, Cryptanalyss on a novel mage encrypton method based on total shufflng scheme. Opt. Commun., 284 (20), [0]C.E. Shannon, Communcaton theory of secrecy system. ell Syst. ech. J, 28 (949),

An Image Encryption Scheme Based on Hybrid Orbit of Hyper-chaotic Systems

An Image Encryption Scheme Based on Hybrid Orbit of Hyper-chaotic Systems I. J. Computer Network and Informaton Securty 5 5 5-33 Publshed Onlne Aprl 5 n MECS (http://www.mecs-press.org/) DOI:.585/jcns.5.5.4 An Image Encrypton Scheme Based on Hybrd Orbt of Hyper-chaotc Systems

More information

Image Encryption Using Chaotic Signal and Max Heap Tree

Image Encryption Using Chaotic Signal and Max Heap Tree Image Encrypton Usng Chaotc Sgnal and Max Heap Tree Farborz Mahmoud 1, Rasul Enayatfar 2, and Mohsen Mrzashaer 1 1 Electrcal and Computer Engneerng Department, Islamc Azad Unversty, Qazvn Branch, Iran

More information

Kayhan CELİK. Erol KURT. x ' ay ax. y ' xz rx y. z ' xy bz

Kayhan CELİK. Erol KURT. x ' ay ax. y ' xz rx y. z ' xy bz ECAI 216 - Internatonal Conference 8th Edton Electroncs, Computers and Artfcal Intellgence 3 June -2 July, 216, Bucharest, ROMÂIA A ew Image Encrypton Algorthm Based on Lorenz System Kayhan CELİK Gaz Unversty,

More information

A New Scrambling Evaluation Scheme based on Spatial Distribution Entropy and Centroid Difference of Bit-plane

A New Scrambling Evaluation Scheme based on Spatial Distribution Entropy and Centroid Difference of Bit-plane A New Scramblng Evaluaton Scheme based on Spatal Dstrbuton Entropy and Centrod Dfference of Bt-plane Lang Zhao *, Avshek Adhkar Kouch Sakura * * Graduate School of Informaton Scence and Electrcal Engneerng,

More information

Faster Image Encryption: A Semi-Tensor Product Approach

Faster Image Encryption: A Semi-Tensor Product Approach 207 Internatonal Conference on Computer Scence and Applcaton Engneerng (CSAE 207) ISBN: 978--60595-505-6 Faster Image Encrypton: A Sem-Tensor Product Approach Shpng Ye, Jnmng Wang *, Zhenyu Xu and Chaoxang

More information

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

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

More information

arxiv: v2 [cs.cr] 29 Sep 2016

arxiv: v2 [cs.cr] 29 Sep 2016 Internatonal Journal of Bfurcaton and Chaos c World Scentfc Publshng Company Breakng a chaotc mage encrypton algorthm based on modulo addton and XOR operaton arxv:107.6536v [cs.cr] 9 Sep 016 Chengqng L

More information

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

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

More information

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

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

More information

COMPARISON OF SOME RELIABILITY CHARACTERISTICS BETWEEN REDUNDANT SYSTEMS REQUIRING SUPPORTING UNITS FOR THEIR OPERATIONS

COMPARISON OF SOME RELIABILITY CHARACTERISTICS BETWEEN REDUNDANT SYSTEMS REQUIRING SUPPORTING UNITS FOR THEIR OPERATIONS Avalable onlne at http://sck.org J. Math. Comput. Sc. 3 (3), No., 6-3 ISSN: 97-537 COMPARISON OF SOME RELIABILITY CHARACTERISTICS BETWEEN REDUNDANT SYSTEMS REQUIRING SUPPORTING UNITS FOR THEIR OPERATIONS

More information

Cryptanalysis of pairing-free certificateless authenticated key agreement protocol

Cryptanalysis of pairing-free certificateless authenticated key agreement protocol Cryptanalyss of parng-free certfcateless authentcated key agreement protocol Zhan Zhu Chna Shp Development Desgn Center CSDDC Wuhan Chna Emal: zhuzhan0@gmal.com bstract: Recently He et al. [D. He J. Chen

More information

Big Data Fast Encryption Algorithm in the Cloud Environment Based on the Constraints of the Shared Resources

Big Data Fast Encryption Algorithm in the Cloud Environment Based on the Constraints of the Shared Resources Revsta de la Facultad de Ingenería U.C.V., Vol. 32, N 0, pp. 796-805, 207 Bg Data Fast Encrypton Algorthm n the Cloud Envronment Based on the Constrants of the Shared Resources Yng Wang Informaton Engneerng

More information

A Hybrid Variational Iteration Method for Blasius Equation

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

More information

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

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

More information

A Robust Method for Calculating the Correlation Coefficient

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

More information

VQ widely used in coding speech, image, and video

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

More information

Appendix B: Resampling Algorithms

Appendix B: Resampling Algorithms 407 Appendx B: Resamplng Algorthms A common problem of all partcle flters s the degeneracy of weghts, whch conssts of the unbounded ncrease of the varance of the mportance weghts ω [ ] of the partcles

More information

The stream cipher MICKEY

The stream cipher MICKEY The stream cpher MICKEY-128 2.0 Steve Babbage Vodafone Group R&D, Newbury, UK steve.babbage@vodafone.com Matthew Dodd Independent consultant matthew@mdodd.net www.mdodd.net 30 th June 2006 Abstract: We

More information

BOOTSTRAP METHOD FOR TESTING OF EQUALITY OF SEVERAL MEANS. M. Krishna Reddy, B. Naveen Kumar and Y. Ramu

BOOTSTRAP METHOD FOR TESTING OF EQUALITY OF SEVERAL MEANS. M. Krishna Reddy, B. Naveen Kumar and Y. Ramu BOOTSTRAP METHOD FOR TESTING OF EQUALITY OF SEVERAL MEANS M. Krshna Reddy, B. Naveen Kumar and Y. Ramu Department of Statstcs, Osmana Unversty, Hyderabad -500 007, Inda. nanbyrozu@gmal.com, ramu0@gmal.com

More information

Markov Chain Monte Carlo Lecture 6

Markov Chain Monte Carlo Lecture 6 where (x 1,..., x N ) X N, N s called the populaton sze, f(x) f (x) for at least one {1, 2,..., N}, and those dfferent from f(x) are called the tral dstrbutons n terms of mportance samplng. Dfferent ways

More information

EGR 544 Communication Theory

EGR 544 Communication Theory EGR 544 Communcaton Theory. Informaton Sources Z. Alyazcoglu Electrcal and Computer Engneerng Department Cal Poly Pomona Introducton Informaton Source x n Informaton sources Analog sources Dscrete sources

More information

Negative Binomial Regression

Negative Binomial Regression STATGRAPHICS Rev. 9/16/2013 Negatve Bnomal Regresson Summary... 1 Data Input... 3 Statstcal Model... 3 Analyss Summary... 4 Analyss Optons... 7 Plot of Ftted Model... 8 Observed Versus Predcted... 10 Predctons...

More information

Power law and dimension of the maximum value for belief distribution with the max Deng entropy

Power law and dimension of the maximum value for belief distribution with the max Deng entropy Power law and dmenson of the maxmum value for belef dstrbuton wth the max Deng entropy Bngy Kang a, a College of Informaton Engneerng, Northwest A&F Unversty, Yanglng, Shaanx, 712100, Chna. Abstract Deng

More information

Application of Nonbinary LDPC Codes for Communication over Fading Channels Using Higher Order Modulations

Application of Nonbinary LDPC Codes for Communication over Fading Channels Using Higher Order Modulations Applcaton of Nonbnary LDPC Codes for Communcaton over Fadng Channels Usng Hgher Order Modulatons Rong-Hu Peng and Rong-Rong Chen Department of Electrcal and Computer Engneerng Unversty of Utah Ths work

More information

Cryptography System for Information Security Using Chaos Arnold's Cat Map Function

Cryptography System for Information Security Using Chaos Arnold's Cat Map Function 4 th ICRIEMS Proceedngs Publshed by The Faculty Of Mathematcs And Natural Scences Yogyakarta State Unversty, ISBN 978-602-74529-2-3 Cryptography System for Informaton Securty Usng Chaos Arnold's Cat Map

More information

Robust Image Encryption Based on Balanced Cellular Automaton and Pixel Separation

Robust Image Encryption Based on Balanced Cellular Automaton and Pixel Separation 548 D. TRALIC, S. GRGIC, ROBUST IMAGE ENCRYPTION BASED ON BALANCED CA AND PIXEL SEPARATION Robust Image Encrypton Based on Balanced Cellular Automaton and Pxel Separaton Djana TRALIC, Sonja GRGIC Dept.

More information

Improved Integral Cryptanalysis of FOX Block Cipher 1

Improved Integral Cryptanalysis of FOX Block Cipher 1 Improved Integral Cryptanalyss of FOX Block Cpher 1 Wu Wenlng, Zhang Wentao, and Feng Dengguo State Key Laboratory of Informaton Securty, Insttute of Software, Chnese Academy of Scences, Bejng 100080,

More information

The Study of Teaching-learning-based Optimization Algorithm

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

More information

Comments on a secure dynamic ID-based remote user authentication scheme for multiserver environment using smart cards

Comments on a secure dynamic ID-based remote user authentication scheme for multiserver environment using smart cards Comments on a secure dynamc ID-based remote user authentcaton scheme for multserver envronment usng smart cards Debao He chool of Mathematcs tatstcs Wuhan nversty Wuhan People s Republc of Chna Emal: hedebao@63com

More information

Regularized Discriminant Analysis for Face Recognition

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

More information

An Improved multiple fractal algorithm

An Improved multiple fractal algorithm Advanced Scence and Technology Letters Vol.31 (MulGraB 213), pp.184-188 http://dx.do.org/1.1427/astl.213.31.41 An Improved multple fractal algorthm Yun Ln, Xaochu Xu, Jnfeng Pang College of Informaton

More information

Scroll Generation with Inductorless Chua s Circuit and Wien Bridge Oscillator

Scroll Generation with Inductorless Chua s Circuit and Wien Bridge Oscillator Latest Trends on Crcuts, Systems and Sgnals Scroll Generaton wth Inductorless Chua s Crcut and Wen Brdge Oscllator Watcharn Jantanate, Peter A. Chayasena, and Sarawut Sutorn * Abstract An nductorless Chua

More information

Impossible differential attacks on 4-round DES-like ciphers

Impossible differential attacks on 4-round DES-like ciphers INENAIONA JOUNA OF COMPUES AND COMMUNICAIONS Volume 9, 2015 Impossble dfferental attacks on 4-round DES-lke cphers Pavol Zajac Abstract Data Encrypton Standard was a man publc encrypton standard for more

More information

Pulse Coded Modulation

Pulse Coded Modulation Pulse Coded Modulaton PCM (Pulse Coded Modulaton) s a voce codng technque defned by the ITU-T G.711 standard and t s used n dgtal telephony to encode the voce sgnal. The frst step n the analog to dgtal

More information

Improvement of Histogram Equalization for Minimum Mean Brightness Error

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

More information

Dr. Shalabh Department of Mathematics and Statistics Indian Institute of Technology Kanpur

Dr. Shalabh Department of Mathematics and Statistics Indian Institute of Technology Kanpur Analyss of Varance and Desgn of Experment-I MODULE VII LECTURE - 3 ANALYSIS OF COVARIANCE Dr Shalabh Department of Mathematcs and Statstcs Indan Insttute of Technology Kanpur Any scentfc experment s performed

More information

Lecture 5, October 8. DES System (Modification)

Lecture 5, October 8. DES System (Modification) Lecture 5, October 8. 10/10/01 Gene Tsudk, ICS 268 Fall 2001 1 Encrypton Process 64 Bt Plantext Intal Permutaton 32 Bt L0 32 Bt R0 + F(R0,K1) DES System (Modfcaton) Festel Network Buldng Block Key Schedule

More information

The Geometry of Logit and Probit

The Geometry of Logit and Probit The Geometry of Logt and Probt Ths short note s meant as a supplement to Chapters and 3 of Spatal Models of Parlamentary Votng and the notaton and reference to fgures n the text below s to those two chapters.

More information

Grover s Algorithm + Quantum Zeno Effect + Vaidman

Grover s Algorithm + Quantum Zeno Effect + Vaidman Grover s Algorthm + Quantum Zeno Effect + Vadman CS 294-2 Bomb 10/12/04 Fall 2004 Lecture 11 Grover s algorthm Recall that Grover s algorthm for searchng over a space of sze wors as follows: consder the

More information

Odd/Even Scroll Generation with Inductorless Chua s and Wien Bridge Oscillator Circuits

Odd/Even Scroll Generation with Inductorless Chua s and Wien Bridge Oscillator Circuits Watcharn Jantanate, Peter A. Chayasena, Sarawut Sutorn Odd/Even Scroll Generaton wth Inductorless Chua s and Wen Brdge Oscllator Crcuts Watcharn Jantanate, Peter A. Chayasena, and Sarawut Sutorn * School

More information

An efficient algorithm for multivariate Maclaurin Newton transformation

An efficient algorithm for multivariate Maclaurin Newton transformation Annales UMCS Informatca AI VIII, 2 2008) 5 14 DOI: 10.2478/v10065-008-0020-6 An effcent algorthm for multvarate Maclaurn Newton transformaton Joanna Kapusta Insttute of Mathematcs and Computer Scence,

More information

CONTRAST ENHANCEMENT FOR MIMIMUM MEAN BRIGHTNESS ERROR FROM HISTOGRAM PARTITIONING INTRODUCTION

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

More information

STATISTICS QUESTIONS. Step by Step Solutions.

STATISTICS QUESTIONS. Step by Step Solutions. STATISTICS QUESTIONS Step by Step Solutons www.mathcracker.com 9//016 Problem 1: A researcher s nterested n the effects of famly sze on delnquency for a group of offenders and examnes famles wth one to

More information

Chapter 7 Channel Capacity and Coding

Chapter 7 Channel Capacity and Coding Wreless Informaton Transmsson System Lab. Chapter 7 Channel Capacty and Codng Insttute of Communcatons Engneerng atonal Sun Yat-sen Unversty Contents 7. Channel models and channel capacty 7.. Channel models

More information

Analytical Chemistry Calibration Curve Handout

Analytical Chemistry Calibration Curve Handout I. Quck-and Drty Excel Tutoral Analytcal Chemstry Calbraton Curve Handout For those of you wth lttle experence wth Excel, I ve provded some key technques that should help you use the program both for problem

More information

} Often, when learning, we deal with uncertainty:

} Often, when learning, we deal with uncertainty: Uncertanty and Learnng } Often, when learnng, we deal wth uncertanty: } Incomplete data sets, wth mssng nformaton } Nosy data sets, wth unrelable nformaton } Stochastcty: causes and effects related non-determnstcally

More information

Unified Subspace Analysis for Face Recognition

Unified Subspace Analysis for Face Recognition Unfed Subspace Analyss for Face Recognton Xaogang Wang and Xaoou Tang Department of Informaton Engneerng The Chnese Unversty of Hong Kong Shatn, Hong Kong {xgwang, xtang}@e.cuhk.edu.hk Abstract PCA, LDA

More information

1 GSW Iterative Techniques for y = Ax

1 GSW Iterative Techniques for y = Ax 1 for y = A I m gong to cheat here. here are a lot of teratve technques that can be used to solve the general case of a set of smultaneous equatons (wrtten n the matr form as y = A), but ths chapter sn

More information

3.1 Expectation of Functions of Several Random Variables. )' be a k-dimensional discrete or continuous random vector, with joint PMF p (, E X E X1 E X

3.1 Expectation of Functions of Several Random Variables. )' be a k-dimensional discrete or continuous random vector, with joint PMF p (, E X E X1 E X Statstcs 1: Probablty Theory II 37 3 EPECTATION OF SEVERAL RANDOM VARIABLES As n Probablty Theory I, the nterest n most stuatons les not on the actual dstrbuton of a random vector, but rather on a number

More information

Chapter 3 Describing Data Using Numerical Measures

Chapter 3 Describing Data Using Numerical Measures Chapter 3 Student Lecture Notes 3-1 Chapter 3 Descrbng Data Usng Numercal Measures Fall 2006 Fundamentals of Busness Statstcs 1 Chapter Goals To establsh the usefulness of summary measures of data. The

More information

Recover plaintext attack to block ciphers

Recover plaintext attack to block ciphers Recover plantext attac to bloc cphers L An-Png Bejng 100085, P.R.Chna apl0001@sna.com Abstract In ths paper, we wll present an estmaton for the upper-bound of the amount of 16-bytes plantexts for Englsh

More information

Speeding up Computation of Scalar Multiplication in Elliptic Curve Cryptosystem

Speeding up Computation of Scalar Multiplication in Elliptic Curve Cryptosystem H.K. Pathak et. al. / (IJCSE) Internatonal Journal on Computer Scence and Engneerng Speedng up Computaton of Scalar Multplcaton n Ellptc Curve Cryptosystem H. K. Pathak Manju Sangh S.o.S n Computer scence

More information

Multigradient for Neural Networks for Equalizers 1

Multigradient for Neural Networks for Equalizers 1 Multgradent for Neural Netorks for Equalzers 1 Chulhee ee, Jnook Go and Heeyoung Km Department of Electrcal and Electronc Engneerng Yonse Unversty 134 Shnchon-Dong, Seodaemun-Ku, Seoul 1-749, Korea ABSTRACT

More information

Research Article Green s Theorem for Sign Data

Research Article Green s Theorem for Sign Data Internatonal Scholarly Research Network ISRN Appled Mathematcs Volume 2012, Artcle ID 539359, 10 pages do:10.5402/2012/539359 Research Artcle Green s Theorem for Sgn Data Lous M. Houston The Unversty of

More information

/ n ) are compared. The logic is: if the two

/ n ) are compared. The logic is: if the two STAT C141, Sprng 2005 Lecture 13 Two sample tests One sample tests: examples of goodness of ft tests, where we are testng whether our data supports predctons. Two sample tests: called as tests of ndependence

More information

What would be a reasonable choice of the quantization step Δ?

What would be a reasonable choice of the quantization step Δ? CE 108 HOMEWORK 4 EXERCISE 1. Suppose you are samplng the output of a sensor at 10 KHz and quantze t wth a unform quantzer at 10 ts per sample. Assume that the margnal pdf of the sgnal s Gaussan wth mean

More information

Lecture 3. Ax x i a i. i i

Lecture 3. Ax x i a i. i i 18.409 The Behavor of Algorthms n Practce 2/14/2 Lecturer: Dan Spelman Lecture 3 Scrbe: Arvnd Sankar 1 Largest sngular value In order to bound the condton number, we need an upper bound on the largest

More information

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

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

More information

Microwave Diversity Imaging Compression Using Bioinspired

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

More information

Lecture Space-Bounded Derandomization

Lecture Space-Bounded Derandomization Notes on Complexty Theory Last updated: October, 2008 Jonathan Katz Lecture Space-Bounded Derandomzaton 1 Space-Bounded Derandomzaton We now dscuss derandomzaton of space-bounded algorthms. Here non-trval

More information

COMPOSITE BEAM WITH WEAK SHEAR CONNECTION SUBJECTED TO THERMAL LOAD

COMPOSITE BEAM WITH WEAK SHEAR CONNECTION SUBJECTED TO THERMAL LOAD COMPOSITE BEAM WITH WEAK SHEAR CONNECTION SUBJECTED TO THERMAL LOAD Ákos Jósef Lengyel, István Ecsed Assstant Lecturer, Professor of Mechancs, Insttute of Appled Mechancs, Unversty of Mskolc, Mskolc-Egyetemváros,

More information

High resolution entropy stable scheme for shallow water equations

High resolution entropy stable scheme for shallow water equations Internatonal Symposum on Computers & Informatcs (ISCI 05) Hgh resoluton entropy stable scheme for shallow water equatons Xaohan Cheng,a, Yufeng Ne,b, Department of Appled Mathematcs, Northwestern Polytechncal

More information

Attacks on RSA The Rabin Cryptosystem Semantic Security of RSA Cryptology, Tuesday, February 27th, 2007 Nils Andersen. Complexity Theoretic Reduction

Attacks on RSA The Rabin Cryptosystem Semantic Security of RSA Cryptology, Tuesday, February 27th, 2007 Nils Andersen. Complexity Theoretic Reduction Attacks on RSA The Rabn Cryptosystem Semantc Securty of RSA Cryptology, Tuesday, February 27th, 2007 Nls Andersen Square Roots modulo n Complexty Theoretc Reducton Factorng Algorthms Pollard s p 1 Pollard

More information

Cryptanalysis of a Public-key Cryptosystem Using Lattice Basis Reduction Algorithm

Cryptanalysis of a Public-key Cryptosystem Using Lattice Basis Reduction Algorithm www.ijcsi.org 110 Cryptanalyss of a Publc-key Cryptosystem Usng Lattce Bass Reducton Algorthm Roohallah Rastagh 1, Hamd R. Dall Oskoue 2 1,2 Department of Electrcal Engneerng, Aeronautcal Unversty of Snce

More information

CME 302: NUMERICAL LINEAR ALGEBRA FALL 2005/06 LECTURE 13

CME 302: NUMERICAL LINEAR ALGEBRA FALL 2005/06 LECTURE 13 CME 30: NUMERICAL LINEAR ALGEBRA FALL 005/06 LECTURE 13 GENE H GOLUB 1 Iteratve Methods Very large problems (naturally sparse, from applcatons): teratve methods Structured matrces (even sometmes dense,

More information

Problem Set 9 Solutions

Problem Set 9 Solutions Desgn and Analyss of Algorthms May 4, 2015 Massachusetts Insttute of Technology 6.046J/18.410J Profs. Erk Demane, Srn Devadas, and Nancy Lynch Problem Set 9 Solutons Problem Set 9 Solutons Ths problem

More information

Lecture Notes on Linear Regression

Lecture Notes on Linear Regression Lecture Notes on Lnear Regresson Feng L fl@sdueducn Shandong Unversty, Chna Lnear Regresson Problem In regresson problem, we am at predct a contnuous target value gven an nput feature vector We assume

More information

Errors for Linear Systems

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

More information

Chapter 13: Multiple Regression

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

More information

Computation of Higher Order Moments from Two Multinomial Overdispersion Likelihood Models

Computation of Higher Order Moments from Two Multinomial Overdispersion Likelihood Models Computaton of Hgher Order Moments from Two Multnomal Overdsperson Lkelhood Models BY J. T. NEWCOMER, N. K. NEERCHAL Department of Mathematcs and Statstcs, Unversty of Maryland, Baltmore County, Baltmore,

More information

MMA and GCMMA two methods for nonlinear optimization

MMA and GCMMA two methods for nonlinear optimization MMA and GCMMA two methods for nonlnear optmzaton Krster Svanberg Optmzaton and Systems Theory, KTH, Stockholm, Sweden. krlle@math.kth.se Ths note descrbes the algorthms used n the author s 2007 mplementatons

More information

Hiding data in images by simple LSB substitution

Hiding data in images by simple LSB substitution Pattern Recognton 37 (004) 469 474 www.elsever.com/locate/patcog Hdng data n mages by smple LSB substtuton Ch-Kwong Chan, L.M. Cheng Department of Computer Engneerng and Informaton Technology, Cty Unversty

More information

PART 8. Partial Differential Equations PDEs

PART 8. Partial Differential Equations PDEs he Islamc Unverst of Gaza Facult of Engneerng Cvl Engneerng Department Numercal Analss ECIV 3306 PAR 8 Partal Dfferental Equatons PDEs Chapter 9; Fnte Dfference: Ellptc Equatons Assocate Prof. Mazen Abualtaef

More information

3) Surrogate Responses

3) Surrogate Responses 1) Introducton Vsual neurophysology has benefted greatly for many years through the use of smple, controlled stmul lke bars and gratngs. One common characterzaton of the responses elcted by these stmul

More information

Min Cut, Fast Cut, Polynomial Identities

Min Cut, Fast Cut, Polynomial Identities Randomzed Algorthms, Summer 016 Mn Cut, Fast Cut, Polynomal Identtes Instructor: Thomas Kesselhem and Kurt Mehlhorn 1 Mn Cuts n Graphs Lecture (5 pages) Throughout ths secton, G = (V, E) s a mult-graph.

More information

A Fast Computer Aided Design Method for Filters

A Fast Computer Aided Design Method for Filters 2017 Asa-Pacfc Engneerng and Technology Conference (APETC 2017) ISBN: 978-1-60595-443-1 A Fast Computer Aded Desgn Method for Flters Gang L ABSTRACT *Ths paper presents a fast computer aded desgn method

More information

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

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

More information

FTCS Solution to the Heat Equation

FTCS Solution to the Heat Equation FTCS Soluton to the Heat Equaton ME 448/548 Notes Gerald Recktenwald Portland State Unversty Department of Mechancal Engneerng gerry@pdx.edu ME 448/548: FTCS Soluton to the Heat Equaton Overvew 1. Use

More information

Motion Perception Under Uncertainty. Hongjing Lu Department of Psychology University of Hong Kong

Motion Perception Under Uncertainty. Hongjing Lu Department of Psychology University of Hong Kong Moton Percepton Under Uncertanty Hongjng Lu Department of Psychology Unversty of Hong Kong Outlne Uncertanty n moton stmulus Correspondence problem Qualtatve fttng usng deal observer models Based on sgnal

More information

Psychology 282 Lecture #24 Outline Regression Diagnostics: Outliers

Psychology 282 Lecture #24 Outline Regression Diagnostics: Outliers Psychology 282 Lecture #24 Outlne Regresson Dagnostcs: Outlers In an earler lecture we studed the statstcal assumptons underlyng the regresson model, ncludng the followng ponts: Formal statement of assumptons.

More information

Cryptanalysis of Some Double-Block-Length Hash Modes of Block Ciphers with n-bit Block and n-bit Key

Cryptanalysis of Some Double-Block-Length Hash Modes of Block Ciphers with n-bit Block and n-bit Key Cryptanalyss of Some Double-Block-Length Hash Modes of Block Cphers wth n-bt Block and n-bt Key Deukjo Hong and Daesung Kwon Abstract In ths paper, we make attacks on DBL (Double-Block-Length) hash modes

More information

THE EFFECT OF TORSIONAL RIGIDITY BETWEEN ELEMENTS ON FREE VIBRATIONS OF A TELESCOPIC HYDRAULIC CYLINDER SUBJECTED TO EULER S LOAD

THE EFFECT OF TORSIONAL RIGIDITY BETWEEN ELEMENTS ON FREE VIBRATIONS OF A TELESCOPIC HYDRAULIC CYLINDER SUBJECTED TO EULER S LOAD Journal of Appled Mathematcs and Computatonal Mechancs 7, 6(3), 7- www.amcm.pcz.pl p-issn 99-9965 DOI:.75/jamcm.7.3. e-issn 353-588 THE EFFECT OF TORSIONAL RIGIDITY BETWEEN ELEMENTS ON FREE VIBRATIONS

More information

Improvement of hill's classical method in image cryptography

Improvement of hill's classical method in image cryptography 27; 2(3): 37-43 Taza Morocco ISSN: 2456-452 Maths 27; 2(3): 37-43 27 Stats & Maths www.mathsjournal.com Receved: 25-3-27 Accepted: 26-4-27 Abdellatf Jarjar Moulay Rachd Hgh School Taza Morocco Improvement

More information

Hash functions : MAC / HMAC

Hash functions : MAC / HMAC Hash functons : MAC / HMAC Outlne Message Authentcaton Codes Keyed hash famly Uncondtonally Secure MACs Ref: D Stnson: Cryprography Theory and Practce (3 rd ed), Chap 4. Unversal hash famly Notatons: X

More information

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

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

More information

Lossy Compression. Compromise accuracy of reconstruction for increased compression.

Lossy Compression. Compromise accuracy of reconstruction for increased compression. Lossy Compresson Compromse accuracy of reconstructon for ncreased compresson. The reconstructon s usually vsbly ndstngushable from the orgnal mage. Typcally, one can get up to 0:1 compresson wth almost

More information

Notes on Frequency Estimation in Data Streams

Notes on Frequency Estimation in Data Streams Notes on Frequency Estmaton n Data Streams In (one of) the data streamng model(s), the data s a sequence of arrvals a 1, a 2,..., a m of the form a j = (, v) where s the dentty of the tem and belongs to

More information

Difference Equations

Difference Equations Dfference Equatons c Jan Vrbk 1 Bascs Suppose a sequence of numbers, say a 0,a 1,a,a 3,... s defned by a certan general relatonshp between, say, three consecutve values of the sequence, e.g. a + +3a +1

More information

2010 Black Engineering Building, Department of Mechanical Engineering. Iowa State University, Ames, IA, 50011

2010 Black Engineering Building, Department of Mechanical Engineering. Iowa State University, Ames, IA, 50011 Interface Energy Couplng between -tungsten Nanoflm and Few-layered Graphene Meng Han a, Pengyu Yuan a, Jng Lu a, Shuyao S b, Xaolong Zhao b, Yanan Yue c, Xnwe Wang a,*, Xangheng Xao b,* a 2010 Black Engneerng

More information

GEMINI GEneric Multimedia INdexIng

GEMINI GEneric Multimedia INdexIng GEMINI GEnerc Multmeda INdexIng Last lecture, LSH http://www.mt.edu/~andon/lsh/ Is there another possble soluton? Do we need to perform ANN? 1 GEnerc Multmeda INdexIng dstance measure Sub-pattern Match

More information

ANSWERS. Problem 1. and the moment generating function (mgf) by. defined for any real t. Use this to show that E( U) var( U)

ANSWERS. Problem 1. and the moment generating function (mgf) by. defined for any real t. Use this to show that E( U) var( U) Econ 413 Exam 13 H ANSWERS Settet er nndelt 9 deloppgaver, A,B,C, som alle anbefales å telle lkt for å gøre det ltt lettere å stå. Svar er gtt . Unfortunately, there s a prntng error n the hnt of

More information

Quadratic speedup for unstructured search - Grover s Al-

Quadratic speedup for unstructured search - Grover s Al- Quadratc speedup for unstructured search - Grover s Al- CS 94- gorthm /8/07 Sprng 007 Lecture 11 001 Unstructured Search Here s the problem: You are gven a boolean functon f : {1,,} {0,1}, and are promsed

More information

A New Grey Relational Fusion Algorithm Based on Approximate Antropy

A New Grey Relational Fusion Algorithm Based on Approximate Antropy Journal of Computatonal Informaton Systems 9: 20 (2013) 8045 8052 Avalable at http://www.jofcs.com A New Grey Relatonal Fuson Algorthm Based on Approxmate Antropy Yun LIN, Jnfeng PANG, Ybng LI College

More information

Convexity preserving interpolation by splines of arbitrary degree

Convexity preserving interpolation by splines of arbitrary degree Computer Scence Journal of Moldova, vol.18, no.1(52), 2010 Convexty preservng nterpolaton by splnes of arbtrary degree Igor Verlan Abstract In the present paper an algorthm of C 2 nterpolaton of dscrete

More information

U-Pb Geochronology Practical: Background

U-Pb Geochronology Practical: Background U-Pb Geochronology Practcal: Background Basc Concepts: accuracy: measure of the dfference between an expermental measurement and the true value precson: measure of the reproducblty of the expermental result

More information

DEMO #8 - GAUSSIAN ELIMINATION USING MATHEMATICA. 1. Matrices in Mathematica

DEMO #8 - GAUSSIAN ELIMINATION USING MATHEMATICA. 1. Matrices in Mathematica demo8.nb 1 DEMO #8 - GAUSSIAN ELIMINATION USING MATHEMATICA Obectves: - defne matrces n Mathematca - format the output of matrces - appl lnear algebra to solve a real problem - Use Mathematca to perform

More information

NP-Completeness : Proofs

NP-Completeness : Proofs NP-Completeness : Proofs Proof Methods A method to show a decson problem Π NP-complete s as follows. (1) Show Π NP. (2) Choose an NP-complete problem Π. (3) Show Π Π. A method to show an optmzaton problem

More information

Chapter 12 Analysis of Covariance

Chapter 12 Analysis of Covariance Chapter Analyss of Covarance Any scentfc experment s performed to know somethng that s unknown about a group of treatments and to test certan hypothess about the correspondng treatment effect When varablty

More information

Provable Security Signatures

Provable Security Signatures Provable Securty Sgnatures UCL - Louvan-la-Neuve Wednesday, July 10th, 2002 LIENS-CNRS Ecole normale supéreure Summary Introducton Sgnature FD PSS Forkng Lemma Generc Model Concluson Provable Securty -

More information

One-sided finite-difference approximations suitable for use with Richardson extrapolation

One-sided finite-difference approximations suitable for use with Richardson extrapolation Journal of Computatonal Physcs 219 (2006) 13 20 Short note One-sded fnte-dfference approxmatons sutable for use wth Rchardson extrapolaton Kumar Rahul, S.N. Bhattacharyya * Department of Mechancal Engneerng,

More information