Volume 3, Number 2, 2017 Pages Jordan Journal of Electrical Engineering ISSN (Print): , ISSN (Online):

Size: px
Start display at page:

Download "Volume 3, Number 2, 2017 Pages Jordan Journal of Electrical Engineering ISSN (Print): , ISSN (Online):"

Transcription

1 JJEE Volume 3, Number, 07 Pages Jorda Joural of Electrical Egieerig ISSN (Prit: , ISSN (Olie: Liftig Based S-Box for Scalable Bloc Cipher Desig Based o Filter Bas Saleh S. Saraireh Departmet of Commuicatio ad Electroics Egieerig, Philadelphia Uiversity, Amma, Jorda ssaraireh@philadelphia.edu.o Received: Jauary 30, 07 Accepted: April 7, 07 Abstract The security of data exchage is cosidered a sigificat problem. It requires the use of various cryptographic algorithms, such as stream cipher ad bloc cipher. The implemetatio of a secure cryptographic bloc cipher algorithm requires the geeratio of strog substitutio ad permutatio layers. These layers should satisfy the priciples of security (diffusio ad cofusio. The proposed liftig scheme substitutio box (s-box, which ca be used to implemet the substitutio layer i a filter ba bloc cipher structure to support the scalability ad security of the cipher. The cryptographic properties of the proposed s-box are studied, evaluated ad compared with Ridael s-box for the avalache criteria, strict avalache criterio (SAC, bit idepedet criterio (BIC, XOR table distributio, ad liear approximatio table (LAT. The results obtaied cofirm the security ad scalability of the proposed s-box. Keywords Avalache, Bloc cipher, Cryptography, Filter ba, Liftig, S-Box, Scalability. I. INTRODUCTION A Novel scalable bloc cipher structure based o filter bas over a fiite field was proposed by the authors i []. The substitutio layer combies the aalysis filter ba ad ovel liftig scheme s-box to address the scalability limitatios i existig bloc ciphers [] without icreasig complexity. This is achieved by exploitig the scalability ad high diffusio properties of the filter ba structures; the scalable cofusio via a udicious liftig scheme [] eables the security versus complexity versus performace trade-off to be made for a particular applicatio. Such trade-off is becomig icreasigly importat i emergig commuicatios systems. The liftig scheme beig reversible by structure reduces the boudary existece; also, predictio ad update liftig steps ca be either liear or oliear based o the applicatio. Hece, it allows oliearity to be itroduced i a regular, extedable ad simple form. These advatages of the liftig scheme mae it suitable to implemet a strog scalable s-box. To examie the stregth of the s-box, it is ecessary to study its cryptographic properties. I [3], parity chec bits were embedded i the output of the s-box of the modified DES cipher; the embedded process was applied to icrease the resistat of the cipher agaist liear cryptaalysis. The obtaied results showed that the embedded process did ot ehace the security of the modified DES cipher. The security of the modified DES cipher s-box was examied i [4]; each s-box of the modified DES was precoded separately ito eve weight codes of legth 4. The results were compared with the origial DES usig the same umber of rouds. I [5], the security of the s-boxes for RIJNDAEL, Expoetiatio K Safer-64 ad Logarithm K Safer-64 were evaluated. The evaluatio process used differet criteria, such as avalache, strict avalache ad bit idepedece. The results showed the domiace of the Ridael over the others. The s-boxes of AES, MARS, Sipac, Serpet ad Twofish ciphers were aalyzed based o two layers, amely, white box layer ad blac box layer [6]. The outcome Correspodig author's ssaraireh@philadelphia.edu.o

2 07 Jorda Joural of Electrical Egieerig. All rights reserved - Volume 3, Number 5 of the aalysis showed that AES s-box is the most secure amog all s-boxes followed by MARS s-box ad Sipac s-box, respectively. I this paper, the diffusio ad cofusio properties of the proposed s-box are aalyzed i terms of the avalache, strict avalache, ad bit idepedet criteria for the diffusio aspects. Cofusio aspects are aalyzed i terms of the XOR table distributio ad liear approximatio table. It is show that the proposed s-box satisfies the cryptographic security properties above. The liftig scheme s-box is show i Fig.. Note that the symbol S i Fig. is deotig the iverse fuctio with affie trasform over GF(8, which is a oliear fuctio. The liftig scheme over GF(8 as show i Fig. ca be represeted mathematically by the followig equatios: a a y y x ( x S( a ( a S( a a S( x S( y ( ( (3 (4 where is a exclusive or (XOR operatio. The liftig scheme is used i the ecryptio side. Thus, it is ecessary to satisfy perfect recostructio i the decryptio side. This ca be carried out by a perfect recostructio liftig scheme, which eeds a small process arragemet as show i Fig.. The recostructio show i Fig. ca be represeted mathematically as follows: b y y b x y S( S( b b S( b x b S( x (5 (6 (7 (8 Fig.. Liftig scheme Fig.. Perfect recostructio liftig scheme

3 5 07 Jorda Joural of Electrical Egieerig. All rights reserved - Volume 3, Number II. BACKGROUND A. Avalache Criteria Avalache criterio was defied by Feistel [7]. It is cosidered oe of the most importat cryptographic properties of s-box. It is ecessary to certify that a small chage betwee two plaitexts gives a radom differece (avalache chage betwee two correspodig cipher texts. I order to satisfy avalache criteria, flippig a sigle bit of the iput value will result i half of the output bit values chage. For a cryptographic fuctio f x : Z Z plaitexts P ad P i be differet oly i bit i Pi P e (, there are differet iputs. Suppose that ( i, where e i is a vector with -bits ad a i positio i [8], the the outputs of P ad P i are f (P ad f ( P i ; the differece vector e i D is called the avalache vector which ca be computed as i (9. Its elemets are called the avalache variables [5]: e D i e e e e e f ( p f ( p [ i, d i, d i,......, d i ], d i i d Z 3 (9 The overall chage of the th avalache variable over the whole iput size ca be doe by taig ito accout all iput pairs P ad Pi which differ i the i th bit [5]: e i W ( d d PZ ei The cryptographic fuctio is said to satisfy the avalache criterio if for all i,,..., [5]: (0 AVAL ( i W ( a ei ( Normally, the AVAL (i taes values i the rage [0, ]. Accordig to (, AVAL (i is the calculatio of the probability of chage of the overall output bits whe oly the i th bit i the iput is altered. If AVAL (i is very close to oe half, the cryptographic fuctio satisfies the avalache criterio; otherwise it does ot. B. Strict Avalache Criterio (SAC Completeess ad avalache were oied ito a strict avalache criterio (SAC by Webster ad Tavares [9]. A cryptographic fuctio ad,,..., f x : Z Z ( satisfies SAC if for all i,,..., ; iput bit i chages output bit with a probability of exactly 0.5. I such a case, it is ecessary to separately satisfy each term of the summatio of (. This meas that for each i ad to satisfy, SAC should satisfy the followig equatio [5]: W ( a e i ( The SAC parameter ( SAC ( i, ca be defied as follows [5]:

4 07 Jorda Joural of Electrical Egieerig. All rights reserved - Volume 3, Number 53 SAC ei ( i, W ( a (3 It should be oted that SAC (i taes the values i the rage [0, ]; ad is cosidered as the calculatio of the probability of chage of the th output bit oce the i th bit i the iput is altered. The cryptographic fuctio satisfies the strict avalache criterio, if SAC (i is very close to 0.5; otherwise it does ot. If a cryptographic fuctio satisfies the SAC, the it also satisfies the completeess ad avalache criterio. However, if the cryptographic fuctio satisfies the completeess ad the avalache criterio, it does ot mea it should satisfy the SAC. C. Bit Idepedet Criterio (BIC Bit idepedet criterio was defied by Webster ad Tavares [9]. A cryptographic fuctio f x : Z Z ( satisfies bit idepedet criterio if for all i,,,,..., with ; chagig the iput bit i maes the output bits ad chage idepedetly [7]. I order to determie the bit idepedet criterio of a cryptographic fuctio, it eeds to calculate the correlatio coefficiet betwee the th ad th compoets of the avalache vector. The bit idepedece parameter is related to the result of chagig the iput bit i to the output bits ad of the avalache vector. It ca be calculated mathematically by [7]: BIC ei ( d, d max corr ( d i, d (4 The the BIC ca be calculated as follows [7]: BIC ( f max i,,, BIC ( d, d (5 The BIC taes a value i the rage [0, ]; the best value of BIC is equal to 0. While avalache variables are idepedet, BIC is i the worst case. The avalache variables are correlated. D. XOR Table Distributio I order to ivestigate the security of the bloc cipher agaist differetial cryptaalysis, which exploits the high values of the XOR table of s-boxes used by a bloc cipher, it is essetial to determie the XOR table distributio (differece distributio table of the s-box. The values i the XOR table should be as small as possible to avoid differetial cryptaalysis. The dimesio of the XOR table of a colums idices 0,,,..., s-box is a matrix [0], with rows ad. I order to implemet the XOR table distributio, assume that the iput vector is P ad chaged by P ; the the output differece is C ad give by: C f ( P f ( P P (6 where P Z ad m C Z. Now the XOR table distributio is give by [5]: XOR f ( P, C # { P f ( P f ( P P C } (7

5 54 07 Jorda Joural of Electrical Egieerig. All rights reserved - Volume 3, Number The etries of the XOR table always tae a eve value; the sum of all values i the row equals. I order to desig a secure ad strog bloc cipher, it is ecessary to have a secure s-box that satisfies the cofusio property, ad has small etries i its XOR table distributio. E. Liear Approximatio Table (LAT I order to evaluate the security of the bloc cipher agaist liear cryptaalysis, it is ecessary to determie the liear approximatio table which gives iformatio about the security of the s-box agaist liear cryptaalysis. Therefore, it is cosidered as a measure of resistace for the s-boxes agaist liear cryptaalysis. To avoid liear cryptaalysis, the values of the liear approximatio table should be as small as possible [4]. The dimesios of the liear approximatio table are the same as the XOR table distributio. It is matrix for a s-box; ad its rows ad colums idices are 0,,,..., To implemet the liear approximatio table, it is assumed that X is a iput of a s-box (S; Y is the output of the s-box Y S(X ; ad the liear approximatio table has its etry sittig at the X ' th row ad the Y' th colum, defied as [4]:. LAT ( X ', Y' #{ X Y' S( X X ' X} (8 where ( idicates the scalar or products of the vectors X ad X '. Basically, liear cryptaalysis exploits wea elemets of the liear approximatio table, whereas differetial cryptaalysis exploits the wea compoets of the XOR distributio table. For both tables, the wea elemet is the highest magitude elemet i the correspodig table. III. SIMULATION RESULTS AND DISCUSSION A. Avalache Criterio Geerally, if s-box ( is ot matched exactly, there will be some margial error, which is called a relative error iterval. This error should be very small ad the value of AVAL (i should be very close to 0.5; otherwise, the s-box does ot satisfy the avalache criteria. Cosequetly, the diffusio property is ot satisfied. The avalache criterio withi a error rage for all i is defied as follows [5]: ( i AVAL (9 The overall relative absolute error of s-box is calculated by [5]: AVAL AVAL max i AVAL ( i (0 For the proposed liftig s-box, the avalache criterio i ( is evaluated ad foud; ad the maximum relative absolute error is obtaied usig (0, where its value is compared with for the s-box of Ridael [5]. Fig. 3 depicts the relative absolute error correspodig to the bit positio of the avalache vector for liftig scheme s-box.

6 07 Jorda Joural of Electrical Egieerig. All rights reserved - Volume 3, Number 55 Fig. 3. Relative absolute error versus bit positio of the avalache liftig scheme s-box B. Strict Avalache Criterio (SAC As metioed earlier, the satisfactio of completeess ad avalache criterio does ot mea there will be satisfactio of strict avalache criterio, but the reverse is true. Therefore, it is ecessary to ru the strict avalache criterio test to ivestigate the diffusio of the proposed liftig scheme s-boxes. The strict avalache criterio is cosidered a special case of the avalache criterio, as represeted i (. Thus, the error margi for the strict avalache criterio is more tha the error margi for the avalache criterio. Normally, the strict avalache criterio does ot satisfy (3 with exactly oe half, but there is some error margi. However, this error margi should be very small. The relative absolute error of the strict avalache criterio ( SAC is defied as follows [5]: SAC max i, SAC ( i, ( By applyig (3 ad (, the maximum relative absolute error of SAC for the liftig scheme s-box is foud to be 0.039, while it is 0.50 for Ridael [5]. The result deotes that the liftig scheme s-box satisfies both the SAC with a very small error margi ad the diffusio property. Fig. 4 shows 6 curves correspodig to iput differeces e, e, e3, e6. Fig. 4. Relative absolute error for SAC versus bit positio of the avalache vector for liftig scheme s-box

7 56 07 Jorda Joural of Electrical Egieerig. All rights reserved - Volume 3, Number C. Bit Idepedet Criterio (BIC The calculatio of BIC is differet from the calculatios of the avalache criterio ad SAC. The calculatio of BIC is based o calculatio of (4 to fid BIC ( f for the s-box, which is cosidered the maximum correlatio value betwee ay two avalache values. Therefore, the relative absolute error ( BIC is defied as follows [5]: BIC BIC( f ( It is foud to be compared with 0.34 for the s-box of Ridael [5]. Fig. 5 correspods to the maximum relative absolute error for BIC of the liftig scheme s-box accordig to the avalache bit positio. Table summarizes the maximum relative errors for the liftig s-box ad Ridael s-box. The relative error values obtaied for the liftig s-box are very small; reflectig the high diffusio rate that the proposed s-box exhibits. Also, the relative error values for the proposed s-box are very small compared with the relative error values for the s-box of Ridael. This meas that the proposed s-box has a higher diffusio rate; as a result, more security ca be exhibited. Fig. 5. Relative absolute error for BIC versus bit positio of the avalache vector for the liftig scheme s-box TABLE MAXIMUM RELATIVE ERROR FOR THE LIFTING SCHEME S-BOX AND RIJNDAEL S-BOX AVAL SAC BIC Liftig Ridael [5] Liftig Ridael [5] Liftig Ridael [5] D. XOR Table Distributio To ivestigate the cofusio of the proposed liftig scheme s-box, determiatio of the XOR table distributio is required sice the s-box is the oly oliear compoet of a bloc cipher. The XOR table distributio evaluates the security of the bloc cipher agaist differetial cryptaalysis; ad is cosidered the first step towards examiig the stregth of the bloc

8 07 Jorda Joural of Electrical Egieerig. All rights reserved - Volume 3, Number 57 cipher agaist differetial cryptaalysis. From the XOR table distributio, desigers ca decide if the iteded s-box is suitable for their bloc ciphers or ot. The dimesio of the XOR distributio table of the s-box depeds o its iput; its etries are calculated usig (7. It should be oted that for the liftig scheme s-boxes, the dimesio of 6 6 the XOR distributio table depeds o the umber of iput bits for each brach. It is i the case of a 6 6 liftig scheme s-box. All the etries of the XOR distributio table should be as small as possible because differetial attacs exploit large values i the XOR distributio table i order to brea the cipher. It should be oted that whe the iput XOR equals zero, the output XOR will be zero for all pairs. Because it is impossible to have ay ozero value, the etry of the XOR table will be 6 i the case of 6 6 s-box. Also, all the values i the table are eve values; ad the sum of each lie equals whe the dimesio of the XOR distributio table is. This meas that the summatio of each lie i the XOR table distributio depeds o the size of its s-box. For the proposed liftig scheme s-box, the maximum value i its XOR distributio table is by applyig (7. This value is very low ideed compared to the s-box size (6x6, resultig i a very small maximum differetial probability. E. Liear Approximatio Table (LAT The other table to be produced to examie the cofusio property of the proposed s-box is the liear approximatio table. The liear approximatio table evaluates the security of a bloc cipher agaist liear cryptaalysis. As metioed earlier, desigers ca use the XOR table distributio to decide o use of suitable s-boxes to couter differetial cryptaalysis. The liear approximatio table ca, however, be used by cryptographers to decide o use of suitable s-boxes to couter liear cryptaalysis. The dimesios of the liear approximatio table deped o the size of the s-box ad it is 6 6 for 6 6 liftig scheme s-box. The etries of the liear approximatio table are calculated by usig (8, the etries should be as small as possible to avoid liear cryptaalysis. If the iput subset is X ' =0; ad the output subset is Y ' 0, the etry to LAT cotais 3768 for a 6 6 liftig scheme s-box. Etries are zeros for all other output subsets. The maximum value obtaied i the liear approximatio table ( LAT is 90. This value is very low compared to the s-box size (6x6, resultig i a very small maximum liear probability. Maximum values i the XOR table distributio ad the liear approximatio table are very small compared to the s-box size; therefore, the proposed liftig scheme satisfies the cofusio property. Cosequetly, it is immue to liear ad differetial cryptaalysis. max IV. CONCLUSIONS The cryptographic properties of the liftig scheme s-box were evaluated ad compared with Ridael s-box. The results showed that the liftig s-box is domiat over Ridael s-box. It is cosidered as a strog s-box as it obeys the avalache criterio, SAC ad BIC with very small margial error. The maximum values i the XOR table distributio ad the liear approximatio table are also very small values compared with the s-box size. Therefore, the liftig scheme s-box supports the security ad scalability of the scalable filter ba bloc cipher.

9 58 07 Jorda Joural of Electrical Egieerig. All rights reserved - Volume 3, Number REFERENCES [] S. Saraireh ad M. Beaissa, "A scalable bloc cipher desig usig filter bas ad liftig over fiite fields," Proceedigs of IEEE Iteratioal Coferece o Commuicatios, pp. -5, 009. [] J. Daeme ad V. Rime, "AES proposal: Ridael, AES algorithm submissio," Produced by NIST Computer Security Resource Ceter, pp. -45, 999, ridael/ridael-ammeded.pdf [3] Y. Borissov, P. Boyvaleov, ad R. Tseov, "Liear cryptaalysis ad modified DES with embedded parity chec i the s-boxes," Cryptography ad Iformatio Security, Lecture Notes i Computer Sciece, vol. 6, o. 9540, pp , 06. [4] Y. Borissov, P. Boyvaleov, ad R. Tseov, "O a liear cryptaalysis of a family of modified DES ciphers with eve weight s-boxes," Cyberetics ad Iformatio Techologies, vol. 6, o 4, pp. 3-, 06. [5] S. Kavut ad M. Yücel, "O some cryptographic properties of Ridael," Lecture Notes i Computer Sciece: Iformatio Assurace i Computer Networs, Methods, Models ad Architectures for Networ Security, vol. 0, o. 05, pp , 00. [6] A. Ahmad ad M. Farooq, "S-box scope: a meta s-box stregth evaluatio framewor for heterogeeous cofusio boxes," Proceedigs of Hawaii Iteratioal Coferece o System Scieces, pp , 06. [7] I. Vergili ad D. Mele, "Avalache ad bit idepedece properties for the esembles of radomly chose x s-boxes," Turish Joural of Electrical Egieerig ad Computer Scieces, vol. 9, o., pp , 00. [8] K. Kim, T. Matsumoto, ad H. Imai, "A recursive costructio method of s-boxes satisfyig strict avalache criterio," Advaces i Cryptology, Lecture Notes i Computer Sciece, vol. 90, o. 537, pp , 990. [9] A. Webster ad S. Tavares, "O the desig of s-boxes," Advaces i Cryptology, vol. 85, o. 8, pp , 986. [0] E. Biham ad A. Shamir, "Differetial cryptaalysis of DES-lie cryptosystems," Joural. of Cryptology, vol. 4, o., pp. 3-7, 99.

A Block Cipher Using Linear Congruences

A Block Cipher Using Linear Congruences Joural of Computer Sciece 3 (7): 556-560, 2007 ISSN 1549-3636 2007 Sciece Publicatios A Block Cipher Usig Liear Cogrueces 1 V.U.K. Sastry ad 2 V. Jaaki 1 Academic Affairs, Sreeidhi Istitute of Sciece &

More information

7. Modern Techniques. Data Encryption Standard (DES)

7. Modern Techniques. Data Encryption Standard (DES) 7. Moder Techiques. Data Ecryptio Stadard (DES) The objective of this chapter is to illustrate the priciples of moder covetioal ecryptio. For this purpose, we focus o the most widely used covetioal ecryptio

More information

DIFFERENTIAL CRYPTANALYSIS FOR A 3-ROUND SPN

DIFFERENTIAL CRYPTANALYSIS FOR A 3-ROUND SPN IRNTIL RYPTNLYSIS OR -ROUN SPN M. Tolga Sakallı rca uluş daç Şahi atma üyüksaraçoğlu e-mail: tolga@trakya.edu.tr. e-mail: ercab@trakya.edu.tr e-mail: adacs@trakya.edu.tr e-mail: fbuyuksaracoglu@trakya.edu.tr

More information

DISTRIBUTION LAW Okunev I.V.

DISTRIBUTION LAW Okunev I.V. 1 DISTRIBUTION LAW Okuev I.V. Distributio law belogs to a umber of the most complicated theoretical laws of mathematics. But it is also a very importat practical law. Nothig ca help uderstad complicated

More information

Run-length & Entropy Coding. Redundancy Removal. Sampling. Quantization. Perform inverse operations at the receiver EEE

Run-length & Entropy Coding. Redundancy Removal. Sampling. Quantization. Perform inverse operations at the receiver EEE Geeral e Image Coder Structure Motio Video (s 1,s 2,t) or (s 1,s 2 ) Natural Image Samplig A form of data compressio; usually lossless, but ca be lossy Redudacy Removal Lossless compressio: predictive

More information

The multiplicative structure of finite field and a construction of LRC

The multiplicative structure of finite field and a construction of LRC IERG6120 Codig for Distributed Storage Systems Lecture 8-06/10/2016 The multiplicative structure of fiite field ad a costructio of LRC Lecturer: Keeth Shum Scribe: Zhouyi Hu Notatios: We use the otatio

More information

On Cryptographic Properties of Random Boolean Functions

On Cryptographic Properties of Random Boolean Functions O Cryptographic Properties of Radom Boolea Fuctios Daiel Olejár Departmet of Computer Sciece Comeius Uiversity olejar@dcs.fmph.uiba.s Marti Stae Departmet of Computer Sciece Comeius Uiversity stae@dcs.fmph.uiba.s

More information

Discrete-Time Systems, LTI Systems, and Discrete-Time Convolution

Discrete-Time Systems, LTI Systems, and Discrete-Time Convolution EEL5: Discrete-Time Sigals ad Systems. Itroductio I this set of otes, we begi our mathematical treatmet of discrete-time s. As show i Figure, a discrete-time operates or trasforms some iput sequece x [

More information

Algorithm of Superposition of Boolean Functions Given with Truth Vectors

Algorithm of Superposition of Boolean Functions Given with Truth Vectors IJCSI Iteratioal Joural of Computer Sciece Issues, Vol 9, Issue 4, No, July ISSN (Olie: 694-84 wwwijcsiorg 9 Algorithm of Superpositio of Boolea Fuctios Give with Truth Vectors Aatoly Plotikov, Aleader

More information

A statistical method to determine sample size to estimate characteristic value of soil parameters

A statistical method to determine sample size to estimate characteristic value of soil parameters A statistical method to determie sample size to estimate characteristic value of soil parameters Y. Hojo, B. Setiawa 2 ad M. Suzuki 3 Abstract Sample size is a importat factor to be cosidered i determiig

More information

LINEAR REGRESSION ANALYSIS. MODULE IX Lecture Multicollinearity

LINEAR REGRESSION ANALYSIS. MODULE IX Lecture Multicollinearity LINEAR REGRESSION ANALYSIS MODULE IX Lecture - 9 Multicolliearity Dr Shalabh Departmet of Mathematics ad Statistics Idia Istitute of Techology Kapur Multicolliearity diagostics A importat questio that

More information

62. Power series Definition 16. (Power series) Given a sequence {c n }, the series. c n x n = c 0 + c 1 x + c 2 x 2 + c 3 x 3 +

62. Power series Definition 16. (Power series) Given a sequence {c n }, the series. c n x n = c 0 + c 1 x + c 2 x 2 + c 3 x 3 + 62. Power series Defiitio 16. (Power series) Give a sequece {c }, the series c x = c 0 + c 1 x + c 2 x 2 + c 3 x 3 + is called a power series i the variable x. The umbers c are called the coefficiets of

More information

Module 5 EMBEDDED WAVELET CODING. Version 2 ECE IIT, Kharagpur

Module 5 EMBEDDED WAVELET CODING. Version 2 ECE IIT, Kharagpur Module 5 EMBEDDED WAVELET CODING Versio ECE IIT, Kharagpur Lesso 4 SPIHT algorithm Versio ECE IIT, Kharagpur Istructioal Objectives At the ed of this lesso, the studets should be able to:. State the limitatios

More information

Problem Set 2 Solutions

Problem Set 2 Solutions CS271 Radomess & Computatio, Sprig 2018 Problem Set 2 Solutios Poit totals are i the margi; the maximum total umber of poits was 52. 1. Probabilistic method for domiatig sets 6pts Pick a radom subset S

More information

11 Correlation and Regression

11 Correlation and Regression 11 Correlatio Regressio 11.1 Multivariate Data Ofte we look at data where several variables are recorded for the same idividuals or samplig uits. For example, at a coastal weather statio, we might record

More information

CS284A: Representations and Algorithms in Molecular Biology

CS284A: Representations and Algorithms in Molecular Biology CS284A: Represetatios ad Algorithms i Molecular Biology Scribe Notes o Lectures 3 & 4: Motif Discovery via Eumeratio & Motif Represetatio Usig Positio Weight Matrix Joshua Gervi Based o presetatios by

More information

The target reliability and design working life

The target reliability and design working life Safety ad Security Egieerig IV 161 The target reliability ad desig workig life M. Holický Kloker Istitute, CTU i Prague, Czech Republic Abstract Desig workig life ad target reliability levels recommeded

More information

Geometry of LS. LECTURE 3 GEOMETRY OF LS, PROPERTIES OF σ 2, PARTITIONED REGRESSION, GOODNESS OF FIT

Geometry of LS. LECTURE 3 GEOMETRY OF LS, PROPERTIES OF σ 2, PARTITIONED REGRESSION, GOODNESS OF FIT OCTOBER 7, 2016 LECTURE 3 GEOMETRY OF LS, PROPERTIES OF σ 2, PARTITIONED REGRESSION, GOODNESS OF FIT Geometry of LS We ca thik of y ad the colums of X as members of the -dimesioal Euclidea space R Oe ca

More information

Oblivious Transfer using Elliptic Curves

Oblivious Transfer using Elliptic Curves Oblivious Trasfer usig Elliptic Curves bhishek Parakh Louisiaa State Uiversity, ato Rouge, L May 4, 006 bstract: This paper proposes a algorithm for oblivious trasfer usig elliptic curves lso, we preset

More information

Algebra of Least Squares

Algebra of Least Squares October 19, 2018 Algebra of Least Squares Geometry of Least Squares Recall that out data is like a table [Y X] where Y collects observatios o the depedet variable Y ad X collects observatios o the k-dimesioal

More information

THE ASYMPTOTIC COMPLEXITY OF MATRIX REDUCTION OVER FINITE FIELDS

THE ASYMPTOTIC COMPLEXITY OF MATRIX REDUCTION OVER FINITE FIELDS THE ASYMPTOTIC COMPLEXITY OF MATRIX REDUCTION OVER FINITE FIELDS DEMETRES CHRISTOFIDES Abstract. Cosider a ivertible matrix over some field. The Gauss-Jorda elimiatio reduces this matrix to the idetity

More information

10-701/ Machine Learning Mid-term Exam Solution

10-701/ Machine Learning Mid-term Exam Solution 0-70/5-78 Machie Learig Mid-term Exam Solutio Your Name: Your Adrew ID: True or False (Give oe setece explaatio) (20%). (F) For a cotiuous radom variable x ad its probability distributio fuctio p(x), it

More information

1 Inferential Methods for Correlation and Regression Analysis

1 Inferential Methods for Correlation and Regression Analysis 1 Iferetial Methods for Correlatio ad Regressio Aalysis I the chapter o Correlatio ad Regressio Aalysis tools for describig bivariate cotiuous data were itroduced. The sample Pearso Correlatio Coefficiet

More information

DES-LIKE CIPHERS, DIFFERENTIAL ATTACKS AND APN FUNCTIONS

DES-LIKE CIPHERS, DIFFERENTIAL ATTACKS AND APN FUNCTIONS Joural of Mathematical Scieces: Advaces ad Applicatios Volume 49, 018, Pages 9-50 Available at http://scietificadvaces.co.i DOI: http://dx.doi.org/10.1864/jmsaa_710011860 DES-IKE CIPHES, DIFFEENTIA ATTACKS

More information

Lecture 19: Convergence

Lecture 19: Convergence Lecture 19: Covergece Asymptotic approach I statistical aalysis or iferece, a key to the success of fidig a good procedure is beig able to fid some momets ad/or distributios of various statistics. I may

More information

Apply change-of-basis formula to rewrite x as a linear combination of eigenvectors v j.

Apply change-of-basis formula to rewrite x as a linear combination of eigenvectors v j. Eigevalue-Eigevector Istructor: Nam Su Wag eigemcd Ay vector i real Euclidea space of dimesio ca be uiquely epressed as a liear combiatio of liearly idepedet vectors (ie, basis) g j, j,,, α g α g α g α

More information

Chapter Vectors

Chapter Vectors Chapter 4. Vectors fter readig this chapter you should be able to:. defie a vector. add ad subtract vectors. fid liear combiatios of vectors ad their relatioship to a set of equatios 4. explai what it

More information

Frequency Domain Filtering

Frequency Domain Filtering Frequecy Domai Filterig Raga Rodrigo October 19, 2010 Outlie Cotets 1 Itroductio 1 2 Fourier Represetatio of Fiite-Duratio Sequeces: The Discrete Fourier Trasform 1 3 The 2-D Discrete Fourier Trasform

More information

PAijpam.eu ON TENSOR PRODUCT DECOMPOSITION

PAijpam.eu ON TENSOR PRODUCT DECOMPOSITION Iteratioal Joural of Pure ad Applied Mathematics Volume 103 No 3 2015, 537-545 ISSN: 1311-8080 (prited versio); ISSN: 1314-3395 (o-lie versio) url: http://wwwijpameu doi: http://dxdoiorg/1012732/ijpamv103i314

More information

Warped, Chirp Z-Transform: Radar Signal Processing

Warped, Chirp Z-Transform: Radar Signal Processing arped, Chirp Z-Trasform: Radar Sigal Processig by Garimella Ramamurthy Report o: IIIT/TR// Cetre for Commuicatios Iteratioal Istitute of Iformatio Techology Hyderabad - 5 3, IDIA Jauary ARPED, CHIRP Z

More information

Determinants of order 2 and 3 were defined in Chapter 2 by the formulae (5.1)

Determinants of order 2 and 3 were defined in Chapter 2 by the formulae (5.1) 5. Determiats 5.. Itroductio 5.2. Motivatio for the Choice of Axioms for a Determiat Fuctios 5.3. A Set of Axioms for a Determiat Fuctio 5.4. The Determiat of a Diagoal Matrix 5.5. The Determiat of a Upper

More information

Filter banks. Separately, the lowpass and highpass filters are not invertible. removes the highest frequency 1/ 2and

Filter banks. Separately, the lowpass and highpass filters are not invertible. removes the highest frequency 1/ 2and Filter bas Separately, the lowpass ad highpass filters are ot ivertible T removes the highest frequecy / ad removes the lowest frequecy Together these filters separate the sigal ito low-frequecy ad high-frequecy

More information

Chaos-Based Image Encryption Using an Improved Quadratic Chaotic Map

Chaos-Based Image Encryption Using an Improved Quadratic Chaotic Map America Joural of Sigal Processig 26, 6(): -3 DOI:.5923/j.ajsp.266. Chaos-Based Image Ecryptio Usig a Improved Quadratic Chaotic Map Noha Ramada,*, Hossam Eldi H. Ahmed, Said E. Elkhamy 2, Fathi E. Abd

More information

Olli Simula T / Chapter 1 3. Olli Simula T / Chapter 1 5

Olli Simula T / Chapter 1 3. Olli Simula T / Chapter 1 5 Sigals ad Systems Sigals ad Systems Sigals are variables that carry iformatio Systemstake sigals as iputs ad produce sigals as outputs The course deals with the passage of sigals through systems T-6.4

More information

Simon Blackburn. Sean Murphy. Jacques Stern. Laboratoire d'informatique, Ecole Normale Superieure, Abstract

Simon Blackburn. Sean Murphy. Jacques Stern. Laboratoire d'informatique, Ecole Normale Superieure, Abstract The Cryptaalysis of a Public Key Implemetatio of Fiite Group Mappigs Simo Blackbur Sea Murphy Iformatio Security Group, Royal Holloway ad Bedford New College, Uiversity of Lodo, Egham, Surrey TW20 0EX,

More information

The Random Walk For Dummies

The Random Walk For Dummies The Radom Walk For Dummies Richard A Mote Abstract We look at the priciples goverig the oe-dimesioal discrete radom walk First we review five basic cocepts of probability theory The we cosider the Beroulli

More information

OBJECTIVES. Chapter 1 INTRODUCTION TO INSTRUMENTATION FUNCTION AND ADVANTAGES INTRODUCTION. At the end of this chapter, students should be able to:

OBJECTIVES. Chapter 1 INTRODUCTION TO INSTRUMENTATION FUNCTION AND ADVANTAGES INTRODUCTION. At the end of this chapter, students should be able to: OBJECTIVES Chapter 1 INTRODUCTION TO INSTRUMENTATION At the ed of this chapter, studets should be able to: 1. Explai the static ad dyamic characteristics of a istrumet. 2. Calculate ad aalyze the measuremet

More information

CEE 522 Autumn Uncertainty Concepts for Geotechnical Engineering

CEE 522 Autumn Uncertainty Concepts for Geotechnical Engineering CEE 5 Autum 005 Ucertaity Cocepts for Geotechical Egieerig Basic Termiology Set A set is a collectio of (mutually exclusive) objects or evets. The sample space is the (collectively exhaustive) collectio

More information

Optimum LMSE Discrete Transform

Optimum LMSE Discrete Transform Image Trasformatio Two-dimesioal image trasforms are extremely importat areas of study i image processig. The image output i the trasformed space may be aalyzed, iterpreted, ad further processed for implemetig

More information

Probability, Expectation Value and Uncertainty

Probability, Expectation Value and Uncertainty Chapter 1 Probability, Expectatio Value ad Ucertaity We have see that the physically observable properties of a quatum system are represeted by Hermitea operators (also referred to as observables ) such

More information

CALCULATION OF FIBONACCI VECTORS

CALCULATION OF FIBONACCI VECTORS CALCULATION OF FIBONACCI VECTORS Stuart D. Aderso Departmet of Physics, Ithaca College 953 Daby Road, Ithaca NY 14850, USA email: saderso@ithaca.edu ad Dai Novak Departmet of Mathematics, Ithaca College

More information

End-of-Year Contest. ERHS Math Club. May 5, 2009

End-of-Year Contest. ERHS Math Club. May 5, 2009 Ed-of-Year Cotest ERHS Math Club May 5, 009 Problem 1: There are 9 cois. Oe is fake ad weighs a little less tha the others. Fid the fake coi by weighigs. Solutio: Separate the 9 cois ito 3 groups (A, B,

More information

Design and Analysis of Algorithms

Design and Analysis of Algorithms Desig ad Aalysis of Algorithms Probabilistic aalysis ad Radomized algorithms Referece: CLRS Chapter 5 Topics: Hirig problem Idicatio radom variables Radomized algorithms Huo Hogwei 1 The hirig problem

More information

6.867 Machine learning, lecture 7 (Jaakkola) 1

6.867 Machine learning, lecture 7 (Jaakkola) 1 6.867 Machie learig, lecture 7 (Jaakkola) 1 Lecture topics: Kerel form of liear regressio Kerels, examples, costructio, properties Liear regressio ad kerels Cosider a slightly simpler model where we omit

More information

FIR Filter Design: Part II

FIR Filter Design: Part II EEL335: Discrete-Time Sigals ad Systems. Itroductio I this set of otes, we cosider how we might go about desigig FIR filters with arbitrary frequecy resposes, through compositio of multiple sigle-peak

More information

On Random Line Segments in the Unit Square

On Random Line Segments in the Unit Square O Radom Lie Segmets i the Uit Square Thomas A. Courtade Departmet of Electrical Egieerig Uiversity of Califoria Los Ageles, Califoria 90095 Email: tacourta@ee.ucla.edu I. INTRODUCTION Let Q = [0, 1] [0,

More information

Statistical Properties of the Square Map Modulo a Power of Two

Statistical Properties of the Square Map Modulo a Power of Two Statistical Properties of the Square Map Modulo a Power of Two S. M. Dehavi, A. Mahmoodi Rishakai, M. R. Mirzee Shamsabad 3, Hamidreza Maimai, Eiollah Pasha Kharazmi Uiversity, Faculty of Mathematical

More information

Lecture 12: November 13, 2018

Lecture 12: November 13, 2018 Mathematical Toolkit Autum 2018 Lecturer: Madhur Tulsiai Lecture 12: November 13, 2018 1 Radomized polyomial idetity testig We will use our kowledge of coditioal probability to prove the followig lemma,

More information

1 of 7 7/16/2009 6:06 AM Virtual Laboratories > 6. Radom Samples > 1 2 3 4 5 6 7 6. Order Statistics Defiitios Suppose agai that we have a basic radom experimet, ad that X is a real-valued radom variable

More information

a for a 1 1 matrix. a b a b 2 2 matrix: We define det ad bc 3 3 matrix: We define a a a a a a a a a a a a a a a a a a

a for a 1 1 matrix. a b a b 2 2 matrix: We define det ad bc 3 3 matrix: We define a a a a a a a a a a a a a a a a a a Math S-b Lecture # Notes This wee is all about determiats We ll discuss how to defie them, how to calculate them, lear the allimportat property ow as multiliearity, ad show that a square matrix A is ivertible

More information

15.083J/6.859J Integer Optimization. Lecture 3: Methods to enhance formulations

15.083J/6.859J Integer Optimization. Lecture 3: Methods to enhance formulations 15.083J/6.859J Iteger Optimizatio Lecture 3: Methods to ehace formulatios 1 Outlie Polyhedral review Slide 1 Methods to geerate valid iequalities Methods to geerate facet defiig iequalities Polyhedral

More information

Optimally Sparse SVMs

Optimally Sparse SVMs A. Proof of Lemma 3. We here prove a lower boud o the umber of support vectors to achieve geeralizatio bouds of the form which we cosider. Importatly, this result holds ot oly for liear classifiers, but

More information

6.003 Homework #3 Solutions

6.003 Homework #3 Solutions 6.00 Homework # Solutios Problems. Complex umbers a. Evaluate the real ad imagiary parts of j j. π/ Real part = Imagiary part = 0 e Euler s formula says that j = e jπ/, so jπ/ j π/ j j = e = e. Thus the

More information

Research Article Health Monitoring for a Structure Using Its Nonstationary Vibration

Research Article Health Monitoring for a Structure Using Its Nonstationary Vibration Advaces i Acoustics ad Vibratio Volume 2, Article ID 69652, 5 pages doi:.55/2/69652 Research Article Health Moitorig for a Structure Usig Its Nostatioary Vibratio Yoshimutsu Hirata, Mikio Tohyama, Mitsuo

More information

Best Optimal Stable Matching

Best Optimal Stable Matching Applied Mathematical Scieces, Vol., 0, o. 7, 7-7 Best Optimal Stable Matchig T. Ramachadra Departmet of Mathematics Govermet Arts College(Autoomous) Karur-6900, Tamiladu, Idia yasrams@gmail.com K. Velusamy

More information

Chapter 2 The Solution of Numerical Algebraic and Transcendental Equations

Chapter 2 The Solution of Numerical Algebraic and Transcendental Equations Chapter The Solutio of Numerical Algebraic ad Trascedetal Equatios Itroductio I this chapter we shall discuss some umerical methods for solvig algebraic ad trascedetal equatios. The equatio f( is said

More information

A Note on the Symmetric Powers of the Standard Representation of S n

A Note on the Symmetric Powers of the Standard Representation of S n A Note o the Symmetric Powers of the Stadard Represetatio of S David Savitt 1 Departmet of Mathematics, Harvard Uiversity Cambridge, MA 0138, USA dsavitt@mathharvardedu Richard P Staley Departmet of Mathematics,

More information

CS / MCS 401 Homework 3 grader solutions

CS / MCS 401 Homework 3 grader solutions CS / MCS 401 Homework 3 grader solutios assigmet due July 6, 016 writte by Jāis Lazovskis maximum poits: 33 Some questios from CLRS. Questios marked with a asterisk were ot graded. 1 Use the defiitio of

More information

Evapotranspiration Estimation Using Support Vector Machines and Hargreaves-Samani Equation for St. Johns, FL, USA

Evapotranspiration Estimation Using Support Vector Machines and Hargreaves-Samani Equation for St. Johns, FL, USA Evirometal Egieerig 0th Iteratioal Coferece eissn 2029-7092 / eisbn 978-609-476-044-0 Vilius Gedimias Techical Uiversity Lithuaia, 27 28 April 207 Article ID: eviro.207.094 http://eviro.vgtu.lt DOI: https://doi.org/0.3846/eviro.207.094

More information

Research Article Some E-J Generalized Hausdorff Matrices Not of Type M

Research Article Some E-J Generalized Hausdorff Matrices Not of Type M Abstract ad Applied Aalysis Volume 2011, Article ID 527360, 5 pages doi:10.1155/2011/527360 Research Article Some E-J Geeralized Hausdorff Matrices Not of Type M T. Selmaogullari, 1 E. Savaş, 2 ad B. E.

More information

Roger Apéry's proof that zeta(3) is irrational

Roger Apéry's proof that zeta(3) is irrational Cliff Bott cliffbott@hotmail.com 11 October 2011 Roger Apéry's proof that zeta(3) is irratioal Roger Apéry developed a method for searchig for cotiued fractio represetatios of umbers that have a form such

More information

1 Hash tables. 1.1 Implementation

1 Hash tables. 1.1 Implementation Lecture 8 Hash Tables, Uiversal Hash Fuctios, Balls ad Bis Scribes: Luke Johsto, Moses Charikar, G. Valiat Date: Oct 18, 2017 Adapted From Virgiia Williams lecture otes 1 Hash tables A hash table is a

More information

The Choquet Integral with Respect to Fuzzy-Valued Set Functions

The Choquet Integral with Respect to Fuzzy-Valued Set Functions The Choquet Itegral with Respect to Fuzzy-Valued Set Fuctios Weiwei Zhag Abstract The Choquet itegral with respect to real-valued oadditive set fuctios, such as siged efficiecy measures, has bee used i

More information

Lecture 11: Pseudorandom functions

Lecture 11: Pseudorandom functions COM S 6830 Cryptography Oct 1, 2009 Istructor: Rafael Pass 1 Recap Lecture 11: Pseudoradom fuctios Scribe: Stefao Ermo Defiitio 1 (Ge, Ec, Dec) is a sigle message secure ecryptio scheme if for all uppt

More information

Chapter 9: Numerical Differentiation

Chapter 9: Numerical Differentiation 178 Chapter 9: Numerical Differetiatio Numerical Differetiatio Formulatio of equatios for physical problems ofte ivolve derivatives (rate-of-chage quatities, such as velocity ad acceleratio). Numerical

More information

ADVANCED SOFTWARE ENGINEERING

ADVANCED SOFTWARE ENGINEERING ADVANCED SOFTWARE ENGINEERING COMP 3705 Exercise Usage-based Testig ad Reliability Versio 1.0-040406 Departmet of Computer Ssciece Sada Narayaappa, Aeliese Adrews Versio 1.1-050405 Departmet of Commuicatio

More information

Complex Stochastic Boolean Systems: Generating and Counting the Binary n-tuples Intrinsically Less or Greater than u

Complex Stochastic Boolean Systems: Generating and Counting the Binary n-tuples Intrinsically Less or Greater than u Proceedigs of the World Cogress o Egieerig ad Computer Sciece 29 Vol I WCECS 29, October 2-22, 29, Sa Fracisco, USA Complex Stochastic Boolea Systems: Geeratig ad Coutig the Biary -Tuples Itrisically Less

More information

Invariability of Remainder Based Reversible Watermarking

Invariability of Remainder Based Reversible Watermarking Joural of Network Itelligece c 16 ISSN 21-8105 (Olie) Taiwa Ubiquitous Iformatio Volume 1, Number 1, February 16 Ivariability of Remaider Based Reversible Watermarkig Shao-Wei Weg School of Iformatio Egieerig

More information

ECE-S352 Introduction to Digital Signal Processing Lecture 3A Direct Solution of Difference Equations

ECE-S352 Introduction to Digital Signal Processing Lecture 3A Direct Solution of Difference Equations ECE-S352 Itroductio to Digital Sigal Processig Lecture 3A Direct Solutio of Differece Equatios Discrete Time Systems Described by Differece Equatios Uit impulse (sample) respose h() of a DT system allows

More information

The Growth of Functions. Theoretical Supplement

The Growth of Functions. Theoretical Supplement The Growth of Fuctios Theoretical Supplemet The Triagle Iequality The triagle iequality is a algebraic tool that is ofte useful i maipulatig absolute values of fuctios. The triagle iequality says that

More information

Mathematical Methods for Physics and Engineering

Mathematical Methods for Physics and Engineering Mathematical Methods for Physics ad Egieerig Lecture otes Sergei V. Shabaov Departmet of Mathematics, Uiversity of Florida, Gaiesville, FL 326 USA CHAPTER The theory of covergece. Numerical sequeces..

More information

Topic 9: Sampling Distributions of Estimators

Topic 9: Sampling Distributions of Estimators Topic 9: Samplig Distributios of Estimators Course 003, 2016 Page 0 Samplig distributios of estimators Sice our estimators are statistics (particular fuctios of radom variables), their distributio ca be

More information

Sequences, Mathematical Induction, and Recursion. CSE 2353 Discrete Computational Structures Spring 2018

Sequences, Mathematical Induction, and Recursion. CSE 2353 Discrete Computational Structures Spring 2018 CSE 353 Discrete Computatioal Structures Sprig 08 Sequeces, Mathematical Iductio, ad Recursio (Chapter 5, Epp) Note: some course slides adopted from publisher-provided material Overview May mathematical

More information

Similarity Solutions to Unsteady Pseudoplastic. Flow Near a Moving Wall

Similarity Solutions to Unsteady Pseudoplastic. Flow Near a Moving Wall Iteratioal Mathematical Forum, Vol. 9, 04, o. 3, 465-475 HIKARI Ltd, www.m-hikari.com http://dx.doi.org/0.988/imf.04.48 Similarity Solutios to Usteady Pseudoplastic Flow Near a Movig Wall W. Robi Egieerig

More information

Finally, we show how to determine the moments of an impulse response based on the example of the dispersion model.

Finally, we show how to determine the moments of an impulse response based on the example of the dispersion model. 5.3 Determiatio of Momets Fially, we show how to determie the momets of a impulse respose based o the example of the dispersio model. For the dispersio model we have that E θ (θ ) curve is give by eq (4).

More information

w (1) ˆx w (1) x (1) /ρ and w (2) ˆx w (2) x (2) /ρ.

w (1) ˆx w (1) x (1) /ρ and w (2) ˆx w (2) x (2) /ρ. 2 5. Weighted umber of late jobs 5.1. Release dates ad due dates: maximimizig the weight of o-time jobs Oce we add release dates, miimizig the umber of late jobs becomes a sigificatly harder problem. For

More information

1 Summary: Binary and Logic

1 Summary: Binary and Logic 1 Summary: Biary ad Logic Biary Usiged Represetatio : each 1-bit is a power of two, the right-most is for 2 0 : 0110101 2 = 2 5 + 2 4 + 2 2 + 2 0 = 32 + 16 + 4 + 1 = 53 10 Usiged Rage o bits is [0...2

More information

Modified Logistic Maps for Cryptographic Application

Modified Logistic Maps for Cryptographic Application Applied Mathematics, 25, 6, 773-782 Published Olie May 25 i SciRes. http://www.scirp.org/joural/am http://dx.doi.org/.4236/am.25.6573 Modified Logistic Maps for Cryptographic Applicatio Shahram Etemadi

More information

It is always the case that unions, intersections, complements, and set differences are preserved by the inverse image of a function.

It is always the case that unions, intersections, complements, and set differences are preserved by the inverse image of a function. MATH 532 Measurable Fuctios Dr. Neal, WKU Throughout, let ( X, F, µ) be a measure space ad let (!, F, P ) deote the special case of a probability space. We shall ow begi to study real-valued fuctios defied

More information

Algorithm Analysis. Chapter 3

Algorithm Analysis. Chapter 3 Data Structures Dr Ahmed Rafat Abas Computer Sciece Dept, Faculty of Computer ad Iformatio, Zagazig Uiversity arabas@zu.edu.eg http://www.arsaliem.faculty.zu.edu.eg/ Algorithm Aalysis Chapter 3 3. Itroductio

More information

Soo King Lim Figure 1: Figure 2: Figure 3: Figure 4: Figure 5: Figure 6: Figure 7:

Soo King Lim Figure 1: Figure 2: Figure 3: Figure 4: Figure 5: Figure 6: Figure 7: 0 Multivariate Cotrol Chart 3 Multivariate Normal Distributio 5 Estimatio of the Mea ad Covariace Matrix 6 Hotellig s Cotrol Chart 6 Hotellig s Square 8 Average Value of k Subgroups 0 Example 3 3 Value

More information

EE260: Digital Design, Spring n Binary Addition. n Complement forms. n Subtraction. n Multiplication. n Inputs: A 0, B 0. n Boolean equations:

EE260: Digital Design, Spring n Binary Addition. n Complement forms. n Subtraction. n Multiplication. n Inputs: A 0, B 0. n Boolean equations: EE260: Digital Desig, Sprig 2018 EE 260: Itroductio to Digital Desig Arithmetic Biary Additio Complemet forms Subtractio Multiplicatio Overview Yao Zheg Departmet of Electrical Egieerig Uiversity of Hawaiʻi

More information

EE260: Digital Design, Spring n MUX Gate n Rudimentary functions n Binary Decoders. n Binary Encoders n Priority Encoders

EE260: Digital Design, Spring n MUX Gate n Rudimentary functions n Binary Decoders. n Binary Encoders n Priority Encoders EE260: Digital Desig, Sprig 2018 EE 260: Itroductio to Digital Desig MUXs, Ecoders, Decoders Yao Zheg Departmet of Electrical Egieerig Uiversity of Hawaiʻi at Māoa Overview of Ecoder ad Decoder MUX Gate

More information

Skip lists: A randomized dictionary

Skip lists: A randomized dictionary Discrete Math for Bioiformatics WS 11/12:, by A. Bocmayr/K. Reiert, 31. Otober 2011, 09:53 3001 Sip lists: A radomized dictioary The expositio is based o the followig sources, which are all recommeded

More information

An Introduction to Randomized Algorithms

An Introduction to Randomized Algorithms A Itroductio to Radomized Algorithms The focus of this lecture is to study a radomized algorithm for quick sort, aalyze it usig probabilistic recurrece relatios, ad also provide more geeral tools for aalysis

More information

Chapter 9 - CD companion 1. A Generic Implementation; The Common-Merge Amplifier. 1 τ is. ω ch. τ io

Chapter 9 - CD companion 1. A Generic Implementation; The Common-Merge Amplifier. 1 τ is. ω ch. τ io Chapter 9 - CD compaio CHAPTER NINE CD-9.2 CD-9.2. Stages With Voltage ad Curret Gai A Geeric Implemetatio; The Commo-Merge Amplifier The advaced method preseted i the text for approximatig cutoff frequecies

More information

1 Review of Probability & Statistics

1 Review of Probability & Statistics 1 Review of Probability & Statistics a. I a group of 000 people, it has bee reported that there are: 61 smokers 670 over 5 960 people who imbibe (drik alcohol) 86 smokers who imbibe 90 imbibers over 5

More information

FACULTY OF MATHEMATICAL STUDIES MATHEMATICS FOR PART I ENGINEERING. Lectures

FACULTY OF MATHEMATICAL STUDIES MATHEMATICS FOR PART I ENGINEERING. Lectures FACULTY OF MATHEMATICAL STUDIES MATHEMATICS FOR PART I ENGINEERING Lectures MODULE 5 STATISTICS II. Mea ad stadard error of sample data. Biomial distributio. Normal distributio 4. Samplig 5. Cofidece itervals

More information

Riesz-Fischer Sequences and Lower Frame Bounds

Riesz-Fischer Sequences and Lower Frame Bounds Zeitschrift für Aalysis ud ihre Aweduge Joural for Aalysis ad its Applicatios Volume 1 (00), No., 305 314 Riesz-Fischer Sequeces ad Lower Frame Bouds P. Casazza, O. Christese, S. Li ad A. Lider Abstract.

More information

Linear Regression Models

Linear Regression Models Liear Regressio Models Dr. Joh Mellor-Crummey Departmet of Computer Sciece Rice Uiversity johmc@cs.rice.edu COMP 528 Lecture 9 15 February 2005 Goals for Today Uderstad how to Use scatter diagrams to ispect

More information

The picture in figure 1.1 helps us to see that the area represents the distance traveled. Figure 1: Area represents distance travelled

The picture in figure 1.1 helps us to see that the area represents the distance traveled. Figure 1: Area represents distance travelled 1 Lecture : Area Area ad distace traveled Approximatig area by rectagles Summatio The area uder a parabola 1.1 Area ad distace Suppose we have the followig iformatio about the velocity of a particle, how

More information

ANALYSIS OF EXPERIMENTAL ERRORS

ANALYSIS OF EXPERIMENTAL ERRORS ANALYSIS OF EXPERIMENTAL ERRORS All physical measuremets ecoutered i the verificatio of physics theories ad cocepts are subject to ucertaities that deped o the measurig istrumets used ad the coditios uder

More information

Lecture 8: Solving the Heat, Laplace and Wave equations using finite difference methods

Lecture 8: Solving the Heat, Laplace and Wave equations using finite difference methods Itroductory lecture otes o Partial Differetial Equatios - c Athoy Peirce. Not to be copied, used, or revised without explicit writte permissio from the copyright ower. 1 Lecture 8: Solvig the Heat, Laplace

More information

a for a 1 1 matrix. a b a b 2 2 matrix: We define det ad bc 3 3 matrix: We define a a a a a a a a a a a a a a a a a a

a for a 1 1 matrix. a b a b 2 2 matrix: We define det ad bc 3 3 matrix: We define a a a a a a a a a a a a a a a a a a Math E-2b Lecture #8 Notes This week is all about determiats. We ll discuss how to defie them, how to calculate them, lear the allimportat property kow as multiliearity, ad show that a square matrix A

More information

Research Article A Unified Weight Formula for Calculating the Sample Variance from Weighted Successive Differences

Research Article A Unified Weight Formula for Calculating the Sample Variance from Weighted Successive Differences Discrete Dyamics i Nature ad Society Article ID 210761 4 pages http://dxdoiorg/101155/2014/210761 Research Article A Uified Weight Formula for Calculatig the Sample Variace from Weighted Successive Differeces

More information

2D DSP Basics: 2D Systems

2D DSP Basics: 2D Systems - Digital Image Processig ad Compressio D DSP Basics: D Systems D Systems T[ ] y = T [ ] Liearity Additivity: If T y = T [ ] The + T y = y + y Homogeeity: If The T y = T [ ] a T y = ay = at [ ] Liearity

More information

Sensitivity Analysis of Daubechies 4 Wavelet Coefficients for Reduction of Reconstructed Image Error

Sensitivity Analysis of Daubechies 4 Wavelet Coefficients for Reduction of Reconstructed Image Error Proceedigs of the 6th WSEAS Iteratioal Coferece o SIGNAL PROCESSING, Dallas, Texas, USA, March -4, 7 67 Sesitivity Aalysis of Daubechies 4 Wavelet Coefficiets for Reductio of Recostructed Image Error DEVINDER

More information

Chimica Inorganica 3

Chimica Inorganica 3 himica Iorgaica Irreducible Represetatios ad haracter Tables Rather tha usig geometrical operatios, it is ofte much more coveiet to employ a ew set of group elemets which are matrices ad to make the rule

More information

FFTs in Graphics and Vision. The Fast Fourier Transform

FFTs in Graphics and Vision. The Fast Fourier Transform FFTs i Graphics ad Visio The Fast Fourier Trasform 1 Outlie The FFT Algorithm Applicatios i 1D Multi-Dimesioal FFTs More Applicatios Real FFTs 2 Computatioal Complexity To compute the movig dot-product

More information

CHAPTER 4 BIVARIATE DISTRIBUTION EXTENSION

CHAPTER 4 BIVARIATE DISTRIBUTION EXTENSION CHAPTER 4 BIVARIATE DISTRIBUTION EXTENSION 4. Itroductio Numerous bivariate discrete distributios have bee defied ad studied (see Mardia, 97 ad Kocherlakota ad Kocherlakota, 99) based o various methods

More information