Simulation Model for a Hardware Implementation of Modular Multiplication

Size: px
Start display at page:

Download "Simulation Model for a Hardware Implementation of Modular Multiplication"

Transcription

1 Smulato Model for a Hardware Implemetato of Modular Multplcato NADIA NEDJAH AND LUIZA DE MACEDO MOURELLE Departmet of de Systems Egeerg ad Computato, State Uversty of Ro de Jaero São Fracsco Xaver, 54, 5 O. Adar, Ro de Jaero, BRAZIL Abstract:- Modular multplcato s fudametal to several publc-key cryptography systems such as the RSA ecrypto system. It s also the most domat part of the computato performed such systems. The operato s tme cosumg for large operads. Ths paper exames the characterstcs of yet aother archtecture to mplemet modular multplcato. A expermetal modular multpler prototype s descrbed VHDL ad smulated. The smulato results are preseted. Key-Words:- Modular multplcato, smulato, cryptosystems. 1 Itroducto The modular expoetato s a commo operato for scramblg ad s used by several publc-key cryptosystems, such as the RSA ecrypto scheme [1]. It cossts of a repetto of modular multplcatos: C = T E mod M, where T s the pla text such that T < M ad C s the cpher text or vce-versa, E s ether the publc or the prvate key depedg o whether T s the pla or the cpher text, ad M s called the modulus. The decrypto ad ecrypto operatos are performed usg the same procedure,.e. usg the modular expoetato. The performace of such cryptosystems s prmarly determed by the mplemetato effcecy of the modular multplcato ad expoetato. As the operads (the pla text of a message or the cpher or possbly a partally cphered) text are usually large (.e. 14 bts or more), ad order to mprove tme requremets of the ecrypto/decrypto operatos, t s essetal to attempt to mmse the umber of modular multplcatos performed ad to reduce the tme requremet of a sgle modular multplcato. A RSA cryptosystem cossts of a set of three tems: a modulus M of aroud 14 bts ad two tegers d ad e called prvate ad publc keys that satsfy the property T de = T mod M. Pla text T obeyg T < M. Messages are ecrypted usg the publc key as C = T d mod M ad decrypted as T = C e mod M. So the same operato s used to perform both processes: ecrypto ad decrypto. Hardware mplemetatos of the RSA cryptosystem are wdely studed as [, 3, 4]. I the rest of ths paper, we start off by descrbg the algorthms used to mplemet the modular operato. The we preset the archtecture of the hardware modular multpler ad expla detals how t executes a sgle multplcato. The we commet the smulato results obtaed for such archtecture.. Multplcato algorthm Algorthms that formalse the operato of multplcato geerally cosst of two steps: oe geerates a partal product ad the other accumulates t wth the prevous partal products. The most basc algorthm for multplcato s based o the add-adshft method: the shft operato geerates the partal products whle the add step sums them up [5]. The straghtforward way to mplemet a multplcato s based o a teratve adderaccumulator for the geerated partal products. However, ths soluto s qute slow as the fal result s oly avalable after clock cycles, s the sze of the operads. A faster verso of the teratve multpler should add several partal products at oce. Ths could be acheved by ufoldg the teratve multpler ad yeldg a combatoral crcut that cossts of several partal product geerators together wth several adders that operate parallel. I ths paper, we use such a parallel multpler as descrbed Fgure 1. Now, we detal the algorthms used to compute the partal products ad to sum them up.

2 Fg. 1: Parallel multpler. Now, we cocetrate o the algorthm used to compute partal products as well as reducg the correspodg umber wthout deteroratg the space ad tme requremet of the multpler. Let X ad Y be the multplcad ad multplcator respectvely ad let ad m be ther respectve szes. So, we deote X ad Y as follows: X = = x X Y= = ad Y = x Y m = y Ispred by the above otato of X, Y ad that of XY, the add-ad-shft method [5] geerates partal products: x Y, <. Each partal product obtaed s shfted left or rght depedg o whether the startg bt was the less or the most sgfcat ad added up. The umber of partal products geerated s boud above by the sze (.e. umber of bts) of the multpler operad. I cryptosystems, operads are qute large as they represet blocks of text (.e. 14 bts). Aother otato of X ad Y allows to halve the umber of partal products wthout much crease space requremets. Cosder the followg otato of X ad XY: ( 1)/ 1 ~ x = = x x 1 x 1 X =, where ~ x X Y = ( 1)/ 1 = ~ x Y ad ~ x ~ x = ~ x = 1 = 1 The possble values of x~ wth the respectve values of x 1, x, ad x 1 are (1), 1 (11, 11), (, 111), 1 (1, 1) ad (11). Ths recodg wll geerate ( ) partal products. Ispred by the above otato, the modfed Booth algorthm [6, 7, 8] geerates the partal products x~ Y. These partal products ca be computed very effcetly due to the dgts of the ew represetato x~. The hardware mplemetato wll be detaled Secto 3. I the algorthm of Fgure 3, the terms 4 1 ad 3 1 are suppled to avod workg wth egatve umbers. The sum of these addtoal terms s cogruet to zero modulo ( 1) 1. So, oce the sum of the partal products s obtaed, the rest of ths sum the dvso by ( 1) 1 s fally the result of the multplcato XY. Algorthm ModMult(x -1,x,x 1, Y) { t product = t pp [ ( 1 )/ 1] pp[]= ( ~ x Y 4 1 ) for = to ( 1 )/ 1 { pp[] = ( x~ Y 3 1 ) product = product pp[] } retur product mod ( 1)/ 1 } Fg. : Multplcato algorthm. 3 Reducto algorthm A modular reducto s smply the computato of the remader of a teger dvso. It ca be deoted by: X X mod M = X M M However, a dvso s very expesve eve compared wth a multplcato. Usg Barrett s method [9, 1], we ca estmate the remader usg two smple multplcatos. The approxmato of the quotet s calculated as follows: X M = X 1 1 M 1 1 X 1 M 1 The equato above ca be calculated very effcetly as dvso by a power of two x are smply a trucato of the operad x-least sgfcat dgts. The term M depeds oly o the modulus M ad s costat for a gve modulus hece, ca be precomputed ad saved a extra regster. Hece the approxmato of the remader usg Barrett s

3 method [9, 1] s a postve teger smaller tha (M 1). So, oe or two subtractos of M mght be requred to yeld the exact remader. 4. Modular multpler archtecture I ths secto, we outle the archtecture of the multpler, whch s depcted Fgure 3. Later o ths secto ad for each of the ma parts of ths archtecture, we gve the detaled crcutry,.e. that of the partal product geerator, adder ad reducer. The multpler of Fgure 4.1 performs the modular multplcato XY mod M three ma steps: 1. Computg the product P = XY. Computg the estmate quotet Q = P/M Q P 1 M 3. Computg the product QM 4. Computg the fal result P QM. Durg the frst step, the modular multpler frst loads regster 1 ad regster wth X ad Y respectvely the wats for PPG to yeld the partal products ad fally wats for the ADDER to sum all of them. Durg the secod step, the modular multpler loads regster 1, regster ad regster 3 wth the obtaed product P, the pre-computed costat M ad P respectvely the wats for PPG to yeld the partal products ad fally wats for the ADDER to sum all of them. Durg the thrd step, the modular multpler frst loads regster 1 ad regster wth the obtaed product Q ad the modulus M respectvely the awats for PPG to geerate the partal products, the wats for the ADDER to provde the sum of these partal products ad fally wats for the REDUCER to calculate the fal result P QM, whch s subsequetly loaded the accumulator acc. Fg. 3: The modular multpler archtecture. Fg. 4: The partal product geerator terface. The terface of the Booth decoder s descrbed Fgure The multpler The multpler terface s show Fgure 4. It s composed of a partal product geerator ad a adder. The partal product geerator s tur composed of k Booth recoders [6, 7, 8] that commucate drectly wth k partal product selectors. Fg. 5: The Booth recoder terface. The Booth selecto logc crcutry used s very smple. The puts are the three bts formg the Booth dgt ad outputs are three bts: the frst oe

4 SelectY s set whe the partal product to be geerated s Y are Y, the secod oe SelectY s set whe the partal product to be geerated s Y are Y, the last bt s the smply the last bt of the Booth dgt gve as put. It allows us to complemet the bts of the partal products whe a egatve multple s eeded. The output sgal are yelded from the put oes as Fgure 6: SelectM <= lsb mdle SelectM <= ( (mdle msb)(lsb mdle)) Sg <= msb Fg. 6: Selecto logc of the Booth decoder. The requred partal products,.e. x~ Y are easy multple. They ca be obtaed by a smple shft. The egatve multples s complemet form, ca be obtaed form the postve correspodg umber usg a bt by bt complemet wth a 1 added at the least sgfcat bt of the partal product. The addtoal terms troduced the prevous secto ca be cluded to the partal product geerated as three/two/oe most sgfcat bts computed as follows, whereby, s the bts cocateato operato, A s the bary otato of teger A, s a ru of zeros ad B<:> s the selecto of the less sgfcat bts of the bary represetato B. pp = s s s ~ x Y s s pp = ( s x Y s s ) 1 ~ for 1 < k 1 ad for = k 1 = k, we have: pp ( s x Y s s ) = ~ pp = ~ x Y k k k [ : ] The terface of the partal product selector s gve Fgure whle the logc of the compoet s show Fgure 8 ad the correspodg logc Fgure 7. PP()<= (SelectM.M()) Sg PP(1)<= (SelectM.M()SelectM.M(1)) Sg... PP()<=(SelectM.M(-1)SelectM.M()) Sg... PP()<=(SelectM.M(-1)SelectM.M()) Sg PP(1)<=(SelectM.M()) Sg Fg. 7: The logc of the partal product selector. Fg. 8: The partal product selector terface. 4. The adder I order to mplemet the adder of the geerated partal products, we use a hybrd ew kd of adder. It cossts of a tal stage of carry save adders followed by a cascade of stages of delayed carry adders [11] ad a fal stage of full adder. The carry save adder (CSA) s smply a parallel esemble of f full adders wthout ay horzotal coecto. Its fucto s to add three f-bt tegers a, b ad c to yeld two ew tegers carry ad sum such that carry sum = a b c. The par (carry, sum) wll be called a delayed carry teger. The delayed carry adder (DCA) s a parallel esemble of f half adders. Its fucto s to add two delayed carry tegers (a 1, b 1 ) ad (a, b ) together wth a teger c to produce a delayed carry teger (sum, carry) such that sum carry = a 1 b 1 a b c. The geeral archtecture of the proposed adder s depcted Fgure 9, where the partal products PP, 15 are the put operads. Usg the carry save adder, the th bt of carry ad sum are defed as sum = a b c ad carry = a b a c b c respectvely. The archtecture of the delayed carry adder uses 5 half adders as descrbed Fgure The reducer The ma task of the reducer cossts of subtractg QM,.e. the product obtaed the thrd step of the modular multpler from P,.e. the product yelded the frst step of the modular multpler. A subtracto of a p-bts teger K s equvalet to the addto of p x. Hece the reducer smply performs the addto P ( m QM). The latter value s smply the two s complemet of QM. The addto s performed usg a carry look-ahead adder. It s based o computg the carry bts C pror to the actual summato. The adder takes advatage of a relatoshp betwee the carry bts C ad the put bts A ad B. C G ( G ( G ( G ( G C P) P) P ) P ) = whereby G = A B ad P = A B. The geeral structure of the used carry look-ahead adder s gve Fgure 1. 5 Smulato results The proect of the modular multpler descrbed throughout ths paper was specfed Very Hgh Speed Itegrated Crcut Descrpto Laguage - VHDL [1], ad smulated usg the MyVHDL Stato of MyCad Ic. [13].

5 Fg. 9: The ma cell of the proposed adder. Fg. 9: The structure of the carry delayed adder. I order to sychrose the work of the MULTIPLIER, ADDER ad REDUCER, we desged a module called the CONTROLLER that cossts of a smple state mache, that has 13 states defed as follows, where ext(s ) = S 1 ad ext(s 1 ) = S. S : talsato of the state mache S 1 : loads multplcator to regster 1 loads multplcad to regster S : wats for the MULTIPLIER S 3 : wats for the ADDER S 4 : wats for the SHIFTER S 5 : loads the product obtaed P to regster 1 loads the costat to regster loads P to regster 3 S 6 : wats for the MULTIPLIER S 7 : wats for the ADDER S 8 : loads the product obtaed Q to regster 1 loads the modulus M to regster S 9 : wats for the MULTIPLIER S 1 : wats for the ADDER S 11 : wats for the REDUCER S 1 : loads regster acc the fal result. I Fgure 1 ad 13, we show how the dfferet regsters of the modular multpler are loaded,.e. whch state, wth the put data X, Y, M ad Cte ad/or wth the results obtaed from the three-steps multplcatos,.e. P, Q, ModProd. 6 Cocluso Fg. 1: The structure of the carry look-ahead adder. I ths paper, a alteratve archtecture for computg modular multplcato based o Booth algorthm ad o Barrett s relaxed resduum method s descrbed.

6 Fg. 1: Cotrolg a 8-bts modular multpler - frst as secod multplcatos. The Booth algorthm s used to compute the product whle Barrett's method s used to calculate the remader. The archtecture was valdated through behavoural smulato results usg the.6µm CMOS-AMS stadard cell lbrary. The total executo tme s 357 aosecods for 14-bt operads. Oe of the advatages of ths modular multplcato mplemetato resdes the fact that t s easly scalable wth respect to the multpler ad modulus legths. 7 Refereces [1] R. Rvest, A. Shamr ad L. Adlema, A method for obtag dgtal sgature ad publc-key cryptosystems, Commucatos of the ACM, 1:1-16, [] E. F. Brckell, A survey of hardware mplemetato of RSA, I G. Brassard, ed., Advaces Cryptology, Proceedgs of CRYPTO'98, Lecture Notes Computer Scece 435:368-37, Sprger-Verlag, [3] C. D. Walter, Systolc modular multplcato, IEEE Trasactos o Computers, 4(3): , [4] S. E. Eldrdge ad C. D. Walter, Hardware mplemetato of Motgomery s Modular Multplcato Algorthm, IEEE Trasactos o Computers, 4(6):619-64, [5] J. Rabaey, Dgtal tegrated crcuts: A desg perspectve, Pretce-Hall, Fg. 13: Cotrolg a 8-bts modular multpler - thrd multplcato. [6] A. Booth, A sged bary multplcato techque, Quarterly Joural of Mechacs ad Appled Mathematcs, pp. 36-4, [7] O. MacSorley, Hgh-speed arthmetc bary computers, Proceedgs of the IRE, pp , [8] G. W. Bewck, Fast multplcato algorthms ad mplemetato, Ph. D. Thess, Departmet of Electrcal Egeerg, Staford Uversty, Uted States of Amerca, [9] P. Barrett, Implemetatg the Rvest, Shamr ad Adlema publc-key ecrypto algorthm o stadard dgtal sgal processor, Proceedgs of CRYPTO'86, Lecture Notes Computer Scece 63:311-33, Sprger-Verlag, [1] V. Shdler, Hgh-speed RSA hardware based o low-power ppled logc, Ph. D. Thess, Isttut für Agewadte Iformato-sverarbetug ud Kommukatos-techologe, Techshe Uverstät Graz, Jauary [11] C. D. Walter, A verfcato of Brckell sfast modular multplcato algorthm, Iteratoal Joural of Computer Mathematcs, 33:153:169. [1] Z. Navab, VHDL - Aalyss ad Modellg of Dgtal Systems, McGraw Hll, Secod Edto, [13] MyCad, Ic. ad Seodu Logc, Ic., MyVHDL Stato V 4. Tutoral, or

A Multiplier-Free Residue to Weighted Converter. for the Moduli Set {3 n 2, 3 n 1, 3 n }

A Multiplier-Free Residue to Weighted Converter. for the Moduli Set {3 n 2, 3 n 1, 3 n } Cotemporary Egeerg Sceces, Vol., 8, o., 7-8 A Multpler-Free Resdue to Weghted Coverter for the Modul Set {,, } Amr Sabbagh Molahosse ad Mehd Hossezadeh Islamc Azad Uversty, Scece ad Research Brach, Tehra,

More information

Polynomial Encryption Using The Subset Problem Based On Elgamal. Raipur, Chhattisgarh , India. Raipur, Chhattisgarh , India.

Polynomial Encryption Using The Subset Problem Based On Elgamal. Raipur, Chhattisgarh , India. Raipur, Chhattisgarh , India. Polyomal Ecrypto Usg The Subset Problem Based O Elgamal Khushboo Thakur 1, B. P. Trpath 2 1 School of Studes Mathematcs Pt. Ravshakar Shukla Uversty Rapur, Chhattsgarh 92001, Ida. 2 Departmet of Mathematcs,

More information

Hybrid RNS-to-Binary Converter for the Moduli Set {2 2n, 2 n -1, 2 n +1, 2 n+1-1}

Hybrid RNS-to-Binary Converter for the Moduli Set {2 2n, 2 n -1, 2 n +1, 2 n+1-1} Research Joural of Appled Sceces, Egeerg ad echology 6(): 07-0, 0 ISSN: 00-759; e-issn: 00-767 Mawell Scetfc Orgazato, 0 Submtted: November, 0 Accepted: Jauary 9, 0 ublshed: July 5, 0 Hybrd RNS-to-Bary

More information

A tighter lower bound on the circuit size of the hardest Boolean functions

A tighter lower bound on the circuit size of the hardest Boolean functions Electroc Colloquum o Computatoal Complexty, Report No. 86 2011) A tghter lower boud o the crcut sze of the hardest Boolea fuctos Masak Yamamoto Abstract I [IPL2005], Fradse ad Mlterse mproved bouds o the

More information

Evaluating Polynomials

Evaluating Polynomials Uverst of Nebraska - Lcol DgtalCommos@Uverst of Nebraska - Lcol MAT Exam Expostor Papers Math the Mddle Isttute Partershp 7-7 Evaluatg Polomals Thomas J. Harrgto Uverst of Nebraska-Lcol Follow ths ad addtoal

More information

Scaling Function Based on Chinese Remainder Theorem Applied to a Recursive Filter Design

Scaling Function Based on Chinese Remainder Theorem Applied to a Recursive Filter Design SERBIAN JOURNAL OF ELECTRICAL ENGINEERING Vol., No. 3, October 04, 365-377 UDC: 6.37.54:004.383.3]:5.64 DOI: 0.98/SJEE40306S Scalg Fucto Based o Chese Remader Theorem Appled to a Recursve Flter Desg Negova

More information

Analysis of Lagrange Interpolation Formula

Analysis of Lagrange Interpolation Formula P IJISET - Iteratoal Joural of Iovatve Scece, Egeerg & Techology, Vol. Issue, December 4. www.jset.com ISS 348 7968 Aalyss of Lagrage Iterpolato Formula Vjay Dahya PDepartmet of MathematcsMaharaja Surajmal

More information

Solving Constrained Flow-Shop Scheduling. Problems with Three Machines

Solving Constrained Flow-Shop Scheduling. Problems with Three Machines It J Cotemp Math Sceces, Vol 5, 2010, o 19, 921-929 Solvg Costraed Flow-Shop Schedulg Problems wth Three Maches P Pada ad P Rajedra Departmet of Mathematcs, School of Advaced Sceces, VIT Uversty, Vellore-632

More information

1. A real number x is represented approximately by , and we are told that the relative error is 0.1 %. What is x? Note: There are two answers.

1. A real number x is represented approximately by , and we are told that the relative error is 0.1 %. What is x? Note: There are two answers. PROBLEMS A real umber s represeted appromately by 63, ad we are told that the relatve error s % What s? Note: There are two aswers Ht : Recall that % relatve error s What s the relatve error volved roudg

More information

Estimation of Stress- Strength Reliability model using finite mixture of exponential distributions

Estimation of Stress- Strength Reliability model using finite mixture of exponential distributions Iteratoal Joural of Computatoal Egeerg Research Vol, 0 Issue, Estmato of Stress- Stregth Relablty model usg fte mxture of expoetal dstrbutos K.Sadhya, T.S.Umamaheswar Departmet of Mathematcs, Lal Bhadur

More information

Functions of Random Variables

Functions of Random Variables Fuctos of Radom Varables Chapter Fve Fuctos of Radom Varables 5. Itroducto A geeral egeerg aalyss model s show Fg. 5.. The model output (respose) cotas the performaces of a system or product, such as weght,

More information

New Arithmetic Residue to Binary Converters

New Arithmetic Residue to Binary Converters IJCSES Iteratoal Joural of Computer Sceces ad Egeerg Systems, Vol., No.4, October 007 CSES Iteratoal c007 ISSN 0973-4406 95 New Arthmetc Resdue to Bary Coerters Amr Sabbagh MOLAHOSSEINI ad Kea NAVI Departmet

More information

EECE 301 Signals & Systems

EECE 301 Signals & Systems EECE 01 Sgals & Systems Prof. Mark Fowler Note Set #9 Computg D-T Covoluto Readg Assgmet: Secto. of Kame ad Heck 1/ Course Flow Dagram The arrows here show coceptual flow betwee deas. Note the parallel

More information

CHAPTER 4 RADICAL EXPRESSIONS

CHAPTER 4 RADICAL EXPRESSIONS 6 CHAPTER RADICAL EXPRESSIONS. The th Root of a Real Number A real umber a s called the th root of a real umber b f Thus, for example: s a square root of sce. s also a square root of sce ( ). s a cube

More information

Non-uniform Turán-type problems

Non-uniform Turán-type problems Joural of Combatoral Theory, Seres A 111 2005 106 110 wwwelsevercomlocatecta No-uform Turá-type problems DhruvMubay 1, Y Zhao 2 Departmet of Mathematcs, Statstcs, ad Computer Scece, Uversty of Illos at

More information

Some Notes on the Probability Space of Statistical Surveys

Some Notes on the Probability Space of Statistical Surveys Metodološk zvezk, Vol. 7, No., 200, 7-2 ome Notes o the Probablty pace of tatstcal urveys George Petrakos Abstract Ths paper troduces a formal presetato of samplg process usg prcples ad cocepts from Probablty

More information

On Modified Interval Symmetric Single-Step Procedure ISS2-5D for the Simultaneous Inclusion of Polynomial Zeros

On Modified Interval Symmetric Single-Step Procedure ISS2-5D for the Simultaneous Inclusion of Polynomial Zeros It. Joural of Math. Aalyss, Vol. 7, 2013, o. 20, 983-988 HIKARI Ltd, www.m-hkar.com O Modfed Iterval Symmetrc Sgle-Step Procedure ISS2-5D for the Smultaeous Icluso of Polyomal Zeros 1 Nora Jamalud, 1 Masor

More information

Block-Based Compact Thermal Modeling of Semiconductor Integrated Circuits

Block-Based Compact Thermal Modeling of Semiconductor Integrated Circuits Block-Based Compact hermal Modelg of Semcoductor Itegrated Crcuts Master s hess Defese Caddate: Jg Ba Commttee Members: Dr. Mg-Cheg Cheg Dr. Daqg Hou Dr. Robert Schllg July 27, 2009 Outle Itroducto Backgroud

More information

Beam Warming Second-Order Upwind Method

Beam Warming Second-Order Upwind Method Beam Warmg Secod-Order Upwd Method Petr Valeta Jauary 6, 015 Ths documet s a part of the assessmet work for the subject 1DRP Dfferetal Equatos o Computer lectured o FNSPE CTU Prague. Abstract Ths documet

More information

{ }{ ( )} (, ) = ( ) ( ) ( ) Chapter 14 Exercises in Sampling Theory. Exercise 1 (Simple random sampling): Solution:

{ }{ ( )} (, ) = ( ) ( ) ( ) Chapter 14 Exercises in Sampling Theory. Exercise 1 (Simple random sampling): Solution: Chapter 4 Exercses Samplg Theory Exercse (Smple radom samplg: Let there be two correlated radom varables X ad A sample of sze s draw from a populato by smple radom samplg wthout replacemet The observed

More information

PTAS for Bin-Packing

PTAS for Bin-Packing CS 663: Patter Matchg Algorthms Scrbe: Che Jag /9/00. Itroducto PTAS for B-Packg The B-Packg problem s NP-hard. If we use approxmato algorthms, the B-Packg problem could be solved polyomal tme. For example,

More information

Bounds on the expected entropy and KL-divergence of sampled multinomial distributions. Brandon C. Roy

Bounds on the expected entropy and KL-divergence of sampled multinomial distributions. Brandon C. Roy Bouds o the expected etropy ad KL-dvergece of sampled multomal dstrbutos Brado C. Roy bcroy@meda.mt.edu Orgal: May 18, 2011 Revsed: Jue 6, 2011 Abstract Iformato theoretc quattes calculated from a sampled

More information

Introduction to local (nonparametric) density estimation. methods

Introduction to local (nonparametric) density estimation. methods Itroducto to local (oparametrc) desty estmato methods A slecture by Yu Lu for ECE 66 Sprg 014 1. Itroducto Ths slecture troduces two local desty estmato methods whch are Parze desty estmato ad k-earest

More information

Transforms that are commonly used are separable

Transforms that are commonly used are separable Trasforms s Trasforms that are commoly used are separable Eamples: Two-dmesoal DFT DCT DST adamard We ca the use -D trasforms computg the D separable trasforms: Take -D trasform of the rows > rows ( )

More information

Computations with large numbers

Computations with large numbers Comutatos wth large umbers Wehu Hog, Det. of Math, Clayto State Uversty, 2 Clayto State lvd, Morrow, G 326, Mgshe Wu, Det. of Mathematcs, Statstcs, ad Comuter Scece, Uversty of Wscos-Stout, Meomoe, WI

More information

Algorithms Theory, Solution for Assignment 2

Algorithms Theory, Solution for Assignment 2 Juor-Prof. Dr. Robert Elsässer, Marco Muñz, Phllp Hedegger WS 2009/200 Algorthms Theory, Soluto for Assgmet 2 http://lak.formatk.u-freburg.de/lak_teachg/ws09_0/algo090.php Exercse 2. - Fast Fourer Trasform

More information

2006 Jamie Trahan, Autar Kaw, Kevin Martin University of South Florida United States of America

2006 Jamie Trahan, Autar Kaw, Kevin Martin University of South Florida United States of America SOLUTION OF SYSTEMS OF SIMULTANEOUS LINEAR EQUATIONS Gauss-Sedel Method 006 Jame Traha, Autar Kaw, Kev Mart Uversty of South Florda Uted States of Amerca kaw@eg.usf.edu Itroducto Ths worksheet demostrates

More information

The internal structure of natural numbers, one method for the definition of large prime numbers, and a factorization test

The internal structure of natural numbers, one method for the definition of large prime numbers, and a factorization test Fal verso The teral structure of atural umbers oe method for the defto of large prme umbers ad a factorzato test Emmaul Maousos APM Isttute for the Advacemet of Physcs ad Mathematcs 3 Poulou str. 53 Athes

More information

Cubic Nonpolynomial Spline Approach to the Solution of a Second Order Two-Point Boundary Value Problem

Cubic Nonpolynomial Spline Approach to the Solution of a Second Order Two-Point Boundary Value Problem Joural of Amerca Scece ;6( Cubc Nopolyomal Sple Approach to the Soluto of a Secod Order Two-Pot Boudary Value Problem W.K. Zahra, F.A. Abd El-Salam, A.A. El-Sabbagh ad Z.A. ZAk * Departmet of Egeerg athematcs

More information

Numerical Simulations of the Complex Modied Korteweg-de Vries Equation. Thiab R. Taha. The University of Georgia. Abstract

Numerical Simulations of the Complex Modied Korteweg-de Vries Equation. Thiab R. Taha. The University of Georgia. Abstract Numercal Smulatos of the Complex Moded Korteweg-de Vres Equato Thab R. Taha Computer Scece Departmet The Uversty of Georga Athes, GA 002 USA Tel 0-542-2911 e-mal thab@cs.uga.edu Abstract I ths paper mplemetatos

More information

Lecture 07: Poles and Zeros

Lecture 07: Poles and Zeros Lecture 07: Poles ad Zeros Defto of poles ad zeros The trasfer fucto provdes a bass for determg mportat system respose characterstcs wthout solvg the complete dfferetal equato. As defed, the trasfer fucto

More information

NP!= P. By Liu Ran. Table of Contents. The P vs. NP problem is a major unsolved problem in computer

NP!= P. By Liu Ran. Table of Contents. The P vs. NP problem is a major unsolved problem in computer NP!= P By Lu Ra Table of Cotets. Itroduce 2. Strategy 3. Prelmary theorem 4. Proof 5. Expla 6. Cocluso. Itroduce The P vs. NP problem s a major usolved problem computer scece. Iformally, t asks whether

More information

Entropy ISSN by MDPI

Entropy ISSN by MDPI Etropy 2003, 5, 233-238 Etropy ISSN 1099-4300 2003 by MDPI www.mdp.org/etropy O the Measure Etropy of Addtve Cellular Automata Hasa Aı Arts ad Sceces Faculty, Departmet of Mathematcs, Harra Uversty; 63100,

More information

Mu Sequences/Series Solutions National Convention 2014

Mu Sequences/Series Solutions National Convention 2014 Mu Sequeces/Seres Solutos Natoal Coveto 04 C 6 E A 6C A 6 B B 7 A D 7 D C 7 A B 8 A B 8 A C 8 E 4 B 9 B 4 E 9 B 4 C 9 E C 0 A A 0 D B 0 C C Usg basc propertes of arthmetc sequeces, we fd a ad bm m We eed

More information

Unimodality Tests for Global Optimization of Single Variable Functions Using Statistical Methods

Unimodality Tests for Global Optimization of Single Variable Functions Using Statistical Methods Malaysa Umodalty Joural Tests of Mathematcal for Global Optmzato Sceces (): of 05 Sgle - 5 Varable (007) Fuctos Usg Statstcal Methods Umodalty Tests for Global Optmzato of Sgle Varable Fuctos Usg Statstcal

More information

AN UPPER BOUND FOR THE PERMANENT VERSUS DETERMINANT PROBLEM BRUNO GRENET

AN UPPER BOUND FOR THE PERMANENT VERSUS DETERMINANT PROBLEM BRUNO GRENET AN UPPER BOUND FOR THE PERMANENT VERSUS DETERMINANT PROBLEM BRUNO GRENET Abstract. The Permaet versus Determat problem s the followg: Gve a matrx X of determates over a feld of characterstc dfferet from

More information

Bayes Estimator for Exponential Distribution with Extension of Jeffery Prior Information

Bayes Estimator for Exponential Distribution with Extension of Jeffery Prior Information Malaysa Joural of Mathematcal Sceces (): 97- (9) Bayes Estmator for Expoetal Dstrbuto wth Exteso of Jeffery Pror Iformato Hadeel Salm Al-Kutub ad Noor Akma Ibrahm Isttute for Mathematcal Research, Uverst

More information

For combinatorial problems we might need to generate all permutations, combinations, or subsets of a set.

For combinatorial problems we might need to generate all permutations, combinations, or subsets of a set. Addtoal Decrease ad Coquer Algorthms For combatoral problems we mght eed to geerate all permutatos, combatos, or subsets of a set. Geeratg Permutatos If we have a set f elemets: { a 1, a 2, a 3, a } the

More information

Overview of the weighting constants and the points where we evaluate the function for The Gaussian quadrature Project two

Overview of the weighting constants and the points where we evaluate the function for The Gaussian quadrature Project two Overvew of the weghtg costats ad the pots where we evaluate the fucto for The Gaussa quadrature Project two By Ashraf Marzouk ChE 505 Fall 005 Departmet of Mechacal Egeerg Uversty of Teessee Koxvlle, TN

More information

NP!= P. By Liu Ran. Table of Contents. The P versus NP problem is a major unsolved problem in computer

NP!= P. By Liu Ran. Table of Contents. The P versus NP problem is a major unsolved problem in computer NP!= P By Lu Ra Table of Cotets. Itroduce 2. Prelmary theorem 3. Proof 4. Expla 5. Cocluso. Itroduce The P versus NP problem s a major usolved problem computer scece. Iformally, t asks whether a computer

More information

Low Power Modulo 2 n +1 Adder Based on Carry Save Diminished-One Number System

Low Power Modulo 2 n +1 Adder Based on Carry Save Diminished-One Number System Amerca Joural of Appled Sceces 5 (4: 3-39, 8 ISSN 546-939 8 Scece Publcatos Low Power Modulo + Adder Based o Carry Save Dmshed-Oe Number System Somayeh Tmarch, Omd Kavehe, ad Keva Nav Departmet of Electrcal

More information

Runtime analysis RLS on OneMax. Heuristic Optimization

Runtime analysis RLS on OneMax. Heuristic Optimization Lecture 6 Rutme aalyss RLS o OeMax trals of {,, },, l ( + ɛ) l ( ɛ)( ) l Algorthm Egeerg Group Hasso Platter Isttute, Uversty of Potsdam 9 May T, We wat to rgorously uderstad ths behavor 9 May / Rutme

More information

best estimate (mean) for X uncertainty or error in the measurement (systematic, random or statistical) best

best estimate (mean) for X uncertainty or error in the measurement (systematic, random or statistical) best Error Aalyss Preamble Wheever a measuremet s made, the result followg from that measuremet s always subject to ucertaty The ucertaty ca be reduced by makg several measuremets of the same quatty or by mprovg

More information

A note on An efficient certificateless aggregate signature with constant pairing computations

A note on An efficient certificateless aggregate signature with constant pairing computations A ote o A effcet certfcateless aggregate sgature wth costat parg computatos Debao He Maomao Ta Jahua Che School of Mathematcs ad Statstcs Wuha Uversty Wuha Cha School of Computer Scece ad Techology Uversty

More information

18.413: Error Correcting Codes Lab March 2, Lecture 8

18.413: Error Correcting Codes Lab March 2, Lecture 8 18.413: Error Correctg Codes Lab March 2, 2004 Lecturer: Dael A. Spelma Lecture 8 8.1 Vector Spaces A set C {0, 1} s a vector space f for x all C ad y C, x + y C, where we take addto to be compoet wse

More information

The Mathematical Appendix

The Mathematical Appendix The Mathematcal Appedx Defto A: If ( Λ, Ω, where ( λ λ λ whch the probablty dstrbutos,,..., Defto A. uppose that ( Λ,,..., s a expermet type, the σ-algebra o λ λ λ are defed s deoted by ( (,,...,, σ Ω.

More information

Feature Selection: Part 2. 1 Greedy Algorithms (continued from the last lecture)

Feature Selection: Part 2. 1 Greedy Algorithms (continued from the last lecture) CSE 546: Mache Learg Lecture 6 Feature Selecto: Part 2 Istructor: Sham Kakade Greedy Algorthms (cotued from the last lecture) There are varety of greedy algorthms ad umerous amg covetos for these algorthms.

More information

arxiv:math/ v1 [math.gm] 8 Dec 2005

arxiv:math/ v1 [math.gm] 8 Dec 2005 arxv:math/05272v [math.gm] 8 Dec 2005 A GENERALIZATION OF AN INEQUALITY FROM IMO 2005 NIKOLAI NIKOLOV The preset paper was spred by the thrd problem from the IMO 2005. A specal award was gve to Yure Boreko

More information

Efficient Algorithm in Projective Coordinates for EEC Over

Efficient Algorithm in Projective Coordinates for EEC Over Effcet Algorthm Projectve Coordates for EEC Over GF Effcet Algorthm Projectve Coordates for EEC Over GF Iqbal H. Jebrl a Rosl Salleh b ad Al-Shawabkeh M c. Faculty of Computer Scece ad Iformato Techology

More information

Ordinary Least Squares Regression. Simple Regression. Algebra and Assumptions.

Ordinary Least Squares Regression. Simple Regression. Algebra and Assumptions. Ordary Least Squares egresso. Smple egresso. Algebra ad Assumptos. I ths part of the course we are gog to study a techque for aalysg the lear relatoshp betwee two varables Y ad X. We have pars of observatos

More information

Part 4b Asymptotic Results for MRR2 using PRESS. Recall that the PRESS statistic is a special type of cross validation procedure (see Allen (1971))

Part 4b Asymptotic Results for MRR2 using PRESS. Recall that the PRESS statistic is a special type of cross validation procedure (see Allen (1971)) art 4b Asymptotc Results for MRR usg RESS Recall that the RESS statstc s a specal type of cross valdato procedure (see Alle (97)) partcular to the regresso problem ad volves fdg Y $,, the estmate at the

More information

Complex Numbers Primer

Complex Numbers Primer Complex Numbers Prmer Before I get started o ths let me frst make t clear that ths documet s ot teded to teach you everythg there s to kow about complex umbers. That s a subject that ca (ad does) take

More information

A New Family of Transformations for Lifetime Data

A New Family of Transformations for Lifetime Data Proceedgs of the World Cogress o Egeerg 4 Vol I, WCE 4, July - 4, 4, Lodo, U.K. A New Famly of Trasformatos for Lfetme Data Lakhaa Watthaacheewakul Abstract A famly of trasformatos s the oe of several

More information

Lecture 6: October 10, DES: Modes of Operation

Lecture 6: October 10, DES: Modes of Operation Lecture 6: October 1, 21 Revew: DES, Merkle s puzzles Oe-tme sgatures Publc key cryptography Proect proposals due ext Moday Homework : due ext Wedesday Aoymous commets gts@dr.com Sged PGP/GPG emal gts@dr.com

More information

The Primitive Idempotents in

The Primitive Idempotents in Iteratoal Joural of Algebra, Vol, 00, o 5, 3 - The Prmtve Idempotets FC - I Kulvr gh Departmet of Mathematcs, H College r Jwa Nagar (rsa)-5075, Ida kulvrsheora@yahoocom K Arora Departmet of Mathematcs,

More information

Analysis of Variance with Weibull Data

Analysis of Variance with Weibull Data Aalyss of Varace wth Webull Data Lahaa Watthaacheewaul Abstract I statstcal data aalyss by aalyss of varace, the usual basc assumptos are that the model s addtve ad the errors are radomly, depedetly, ad

More information

(b) By independence, the probability that the string 1011 is received correctly is

(b) By independence, the probability that the string 1011 is received correctly is Soluto to Problem 1.31. (a) Let A be the evet that a 0 s trasmtted. Usg the total probablty theorem, the desred probablty s P(A)(1 ɛ ( 0)+ 1 P(A) ) (1 ɛ 1)=p(1 ɛ 0)+(1 p)(1 ɛ 1). (b) By depedece, the probablty

More information

Homework 1: Solutions Sid Banerjee Problem 1: (Practice with Asymptotic Notation) ORIE 4520: Stochastics at Scale Fall 2015

Homework 1: Solutions Sid Banerjee Problem 1: (Practice with Asymptotic Notation) ORIE 4520: Stochastics at Scale Fall 2015 Fall 05 Homework : Solutos Problem : (Practce wth Asymptotc Notato) A essetal requremet for uderstadg scalg behavor s comfort wth asymptotc (or bg-o ) otato. I ths problem, you wll prove some basc facts

More information

Packing of graphs with small product of sizes

Packing of graphs with small product of sizes Joural of Combatoral Theory, Seres B 98 (008) 4 45 www.elsever.com/locate/jctb Note Packg of graphs wth small product of szes Alexadr V. Kostochka a,b,,gexyu c, a Departmet of Mathematcs, Uversty of Illos,

More information

hp calculators HP 30S Statistics Averages and Standard Deviations Average and Standard Deviation Practice Finding Averages and Standard Deviations

hp calculators HP 30S Statistics Averages and Standard Deviations Average and Standard Deviation Practice Finding Averages and Standard Deviations HP 30S Statstcs Averages ad Stadard Devatos Average ad Stadard Devato Practce Fdg Averages ad Stadard Devatos HP 30S Statstcs Averages ad Stadard Devatos Average ad stadard devato The HP 30S provdes several

More information

VLSI Implementation of High-Performance CORDIC-Based Vector Interpolator in Power-Aware 3-D Graphic Systems

VLSI Implementation of High-Performance CORDIC-Based Vector Interpolator in Power-Aware 3-D Graphic Systems Proceedgs of the 6th WSEAS Iteratoal Coferece o Istrumetato, Measuremet, Crcuts & Systems, Hagzhou, Cha, Aprl 5-7, 7 7 VLSI Implemetato of Hgh-Performace CORDIC-Based Vector Iterpolator Power-Aware 3-D

More information

This lecture and the next. Why Sorting? Sorting Algorithms so far. Why Sorting? (2) Selection Sort. Heap Sort. Heapsort

This lecture and the next. Why Sorting? Sorting Algorithms so far. Why Sorting? (2) Selection Sort. Heap Sort. Heapsort Ths lecture ad the ext Heapsort Heap data structure ad prorty queue ADT Qucksort a popular algorthm, very fast o average Why Sortg? Whe doubt, sort oe of the prcples of algorthm desg. Sortg used as a subroute

More information

Derivation of 3-Point Block Method Formula for Solving First Order Stiff Ordinary Differential Equations

Derivation of 3-Point Block Method Formula for Solving First Order Stiff Ordinary Differential Equations Dervato of -Pot Block Method Formula for Solvg Frst Order Stff Ordary Dfferetal Equatos Kharul Hamd Kharul Auar, Kharl Iskadar Othma, Zara Bb Ibrahm Abstract Dervato of pot block method formula wth costat

More information

Median as a Weighted Arithmetic Mean of All Sample Observations

Median as a Weighted Arithmetic Mean of All Sample Observations Meda as a Weghted Arthmetc Mea of All Sample Observatos SK Mshra Dept. of Ecoomcs NEHU, Shllog (Ida). Itroducto: Iumerably may textbooks Statstcs explctly meto that oe of the weakesses (or propertes) of

More information

UNIT 2 SOLUTION OF ALGEBRAIC AND TRANSCENDENTAL EQUATIONS

UNIT 2 SOLUTION OF ALGEBRAIC AND TRANSCENDENTAL EQUATIONS Numercal Computg -I UNIT SOLUTION OF ALGEBRAIC AND TRANSCENDENTAL EQUATIONS Structure Page Nos..0 Itroducto 6. Objectves 7. Ital Approxmato to a Root 7. Bsecto Method 8.. Error Aalyss 9.4 Regula Fals Method

More information

Comparing Different Estimators of three Parameters for Transmuted Weibull Distribution

Comparing Different Estimators of three Parameters for Transmuted Weibull Distribution Global Joural of Pure ad Appled Mathematcs. ISSN 0973-768 Volume 3, Number 9 (207), pp. 55-528 Research Ida Publcatos http://www.rpublcato.com Comparg Dfferet Estmators of three Parameters for Trasmuted

More information

1 Mixed Quantum State. 2 Density Matrix. CS Density Matrices, von Neumann Entropy 3/7/07 Spring 2007 Lecture 13. ψ = α x x. ρ = p i ψ i ψ i.

1 Mixed Quantum State. 2 Density Matrix. CS Density Matrices, von Neumann Entropy 3/7/07 Spring 2007 Lecture 13. ψ = α x x. ρ = p i ψ i ψ i. CS 94- Desty Matrces, vo Neuma Etropy 3/7/07 Sprg 007 Lecture 3 I ths lecture, we wll dscuss the bascs of quatum formato theory I partcular, we wll dscuss mxed quatum states, desty matrces, vo Neuma etropy

More information

Efficient Identification of Bad Signatures in RSA-Type Batch Signature

Efficient Identification of Bad Signatures in RSA-Type Batch Signature 74 PAPER Specal Secto o Cryptography ad Iformato Securty Effcet Idetfcato of Bad Sgatures RSA-Type Batch Sgature Seugwo LEE a, Nomember,SeogjeCHO b, Member, Jogmoo CHOI c, Nomember, ad Yooku CHO d, Member

More information

Chapter 9 Jordan Block Matrices

Chapter 9 Jordan Block Matrices Chapter 9 Jorda Block atrces I ths chapter we wll solve the followg problem. Gve a lear operator T fd a bass R of F such that the matrx R (T) s as smple as possble. f course smple s a matter of taste.

More information

EVALUATION OF FUNCTIONAL INTEGRALS BY MEANS OF A SERIES AND THE METHOD OF BOREL TRANSFORM

EVALUATION OF FUNCTIONAL INTEGRALS BY MEANS OF A SERIES AND THE METHOD OF BOREL TRANSFORM EVALUATION OF FUNCTIONAL INTEGRALS BY MEANS OF A SERIES AND THE METHOD OF BOREL TRANSFORM Jose Javer Garca Moreta Ph. D research studet at the UPV/EHU (Uversty of Basque coutry) Departmet of Theoretcal

More information

Analyzing Control Structures

Analyzing Control Structures Aalyzg Cotrol Strutures sequeg P, P : two fragmets of a algo. t, t : the tme they tae the tme requred to ompute P ;P s t t Θmaxt,t For loops for to m do P t: the tme requred to ompute P total tme requred

More information

Linear Approximating to Integer Addition

Linear Approximating to Integer Addition Lear Approxmatg to Iteger Addto L A-Pg Bejg 00085, P.R. Cha apl000@a.com Abtract The teger addto ofte appled cpher a a cryptographc mea. I th paper we wll preet ome reult about the lear approxmatg for

More information

MA/CSSE 473 Day 27. Dynamic programming

MA/CSSE 473 Day 27. Dynamic programming MA/CSSE 473 Day 7 Dyamc Programmg Bomal Coeffcets Warshall's algorthm (Optmal BSTs) Studet questos? Dyamc programmg Used for problems wth recursve solutos ad overlappg subproblems Typcally, we save (memoze)

More information

10.1 Approximation Algorithms

10.1 Approximation Algorithms 290 0. Approxmato Algorthms Let us exame a problem, where we are gve A groud set U wth m elemets A collecto of subsets of the groud set = {,, } s.t. t s a cover of U: = U The am s to fd a subcover, = U,

More information

Arithmetic Mean and Geometric Mean

Arithmetic Mean and Geometric Mean Acta Mathematca Ntresa Vol, No, p 43 48 ISSN 453-6083 Arthmetc Mea ad Geometrc Mea Mare Varga a * Peter Mchalča b a Departmet of Mathematcs, Faculty of Natural Sceces, Costate the Phlosopher Uversty Ntra,

More information

Some identities involving the partial sum of q-binomial coefficients

Some identities involving the partial sum of q-binomial coefficients Some dettes volvg the partal sum of -bomal coeffcets Bg He Departmet of Mathematcs, Shagha Key Laboratory of PMMP East Cha Normal Uversty 500 Dogchua Road, Shagha 20024, People s Republc of Cha yuhe00@foxmal.com

More information

ANALYSIS ON THE NATURE OF THE BASIC EQUATIONS IN SYNERGETIC INTER-REPRESENTATION NETWORK

ANALYSIS ON THE NATURE OF THE BASIC EQUATIONS IN SYNERGETIC INTER-REPRESENTATION NETWORK Far East Joural of Appled Mathematcs Volume, Number, 2008, Pages Ths paper s avalable ole at http://www.pphm.com 2008 Pushpa Publshg House ANALYSIS ON THE NATURE OF THE ASI EQUATIONS IN SYNERGETI INTER-REPRESENTATION

More information

Comparison of Dual to Ratio-Cum-Product Estimators of Population Mean

Comparison of Dual to Ratio-Cum-Product Estimators of Population Mean Research Joural of Mathematcal ad Statstcal Sceces ISS 30 6047 Vol. 1(), 5-1, ovember (013) Res. J. Mathematcal ad Statstcal Sc. Comparso of Dual to Rato-Cum-Product Estmators of Populato Mea Abstract

More information

Investigating Cellular Automata

Investigating Cellular Automata Researcher: Taylor Dupuy Advsor: Aaro Wootto Semester: Fall 4 Ivestgatg Cellular Automata A Overvew of Cellular Automata: Cellular Automata are smple computer programs that geerate rows of black ad whte

More information

Research Article A New Derivation and Recursive Algorithm Based on Wronskian Matrix for Vandermonde Inverse Matrix

Research Article A New Derivation and Recursive Algorithm Based on Wronskian Matrix for Vandermonde Inverse Matrix Mathematcal Problems Egeerg Volume 05 Artcle ID 94757 7 pages http://ddoorg/055/05/94757 Research Artcle A New Dervato ad Recursve Algorthm Based o Wroska Matr for Vadermode Iverse Matr Qu Zhou Xja Zhag

More information

Logistic regression (continued)

Logistic regression (continued) STAT562 page 138 Logstc regresso (cotued) Suppose we ow cosder more complex models to descrbe the relatoshp betwee a categorcal respose varable (Y) that takes o two (2) possble outcomes ad a set of p explaatory

More information

Introduction to Probability

Introduction to Probability Itroducto to Probablty Nader H Bshouty Departmet of Computer Scece Techo 32000 Israel e-mal: bshouty@cstechoacl 1 Combatorcs 11 Smple Rules I Combatorcs The rule of sum says that the umber of ways to choose

More information

Lecture 9: Tolerant Testing

Lecture 9: Tolerant Testing Lecture 9: Tolerat Testg Dael Kae Scrbe: Sakeerth Rao Aprl 4, 07 Abstract I ths lecture we prove a quas lear lower boud o the umber of samples eeded to do tolerat testg for L dstace. Tolerat Testg We have

More information

Lecture 2 - What are component and system reliability and how it can be improved?

Lecture 2 - What are component and system reliability and how it can be improved? Lecture 2 - What are compoet ad system relablty ad how t ca be mproved? Relablty s a measure of the qualty of the product over the log ru. The cocept of relablty s a exteded tme perod over whch the expected

More information

Hard Core Predicates: How to encrypt? Recap

Hard Core Predicates: How to encrypt? Recap Hard Core Predcates: How to ecrypt? Debdeep Mukhopadhyay IIT Kharagpur Recap A ecrypto scheme s secured f for every probablstc adversary A carryg out some specfed kd of attack ad for every polyomal p(.),

More information

Department of Agricultural Economics. PhD Qualifier Examination. August 2011

Department of Agricultural Economics. PhD Qualifier Examination. August 2011 Departmet of Agrcultural Ecoomcs PhD Qualfer Examato August 0 Istructos: The exam cossts of sx questos You must aswer all questos If you eed a assumpto to complete a questo, state the assumpto clearly

More information

Lecture 7. Confidence Intervals and Hypothesis Tests in the Simple CLR Model

Lecture 7. Confidence Intervals and Hypothesis Tests in the Simple CLR Model Lecture 7. Cofdece Itervals ad Hypothess Tests the Smple CLR Model I lecture 6 we troduced the Classcal Lear Regresso (CLR) model that s the radom expermet of whch the data Y,,, K, are the outcomes. The

More information

Chapter Business Statistics: A First Course Fifth Edition. Learning Objectives. Correlation vs. Regression. In this chapter, you learn:

Chapter Business Statistics: A First Course Fifth Edition. Learning Objectives. Correlation vs. Regression. In this chapter, you learn: Chapter 3 3- Busess Statstcs: A Frst Course Ffth Edto Chapter 2 Correlato ad Smple Lear Regresso Busess Statstcs: A Frst Course, 5e 29 Pretce-Hall, Ic. Chap 2- Learg Objectves I ths chapter, you lear:

More information

A Collocation Method for Solving Abel s Integral Equations of First and Second Kinds

A Collocation Method for Solving Abel s Integral Equations of First and Second Kinds A Collocato Method for Solvg Abel s Itegral Equatos of Frst ad Secod Kds Abbas Saadatmad a ad Mehd Dehgha b a Departmet of Mathematcs, Uversty of Kasha, Kasha, Ira b Departmet of Appled Mathematcs, Faculty

More information

A Novel Low Complexity Combinational RNS Multiplier Using Parallel Prefix Adder

A Novel Low Complexity Combinational RNS Multiplier Using Parallel Prefix Adder IJCSI Iteratoal Joural o Computer Scece Issues, Vol. 0, Issue, No 3, March 03 ISSN (Prt): 694-084 ISSN (Ole): 694-0784 www.ijcsi.org 430 A Novel Low Complexty Combatoal RNS Multpler Usg Parallel Prex Adder

More information

1 Onto functions and bijections Applications to Counting

1 Onto functions and bijections Applications to Counting 1 Oto fuctos ad bectos Applcatos to Coutg Now we move o to a ew topc. Defto 1.1 (Surecto. A fucto f : A B s sad to be surectve or oto f for each b B there s some a A so that f(a B. What are examples of

More information

A New Method for Solving Fuzzy Linear. Programming by Solving Linear Programming

A New Method for Solving Fuzzy Linear. Programming by Solving Linear Programming ppled Matheatcal Sceces Vol 008 o 50 7-80 New Method for Solvg Fuzzy Lear Prograg by Solvg Lear Prograg S H Nasser a Departet of Matheatcs Faculty of Basc Sceces Mazadara Uversty Babolsar Ira b The Research

More information

Johns Hopkins University Department of Biostatistics Math Review for Introductory Courses

Johns Hopkins University Department of Biostatistics Math Review for Introductory Courses Johs Hopks Uverst Departmet of Bostatstcs Math Revew for Itroductor Courses Ratoale Bostatstcs courses wll rel o some fudametal mathematcal relatoshps, fuctos ad otato. The purpose of ths Math Revew s

More information

Kernel-based Methods and Support Vector Machines

Kernel-based Methods and Support Vector Machines Kerel-based Methods ad Support Vector Maches Larr Holder CptS 570 Mache Learg School of Electrcal Egeerg ad Computer Scece Washgto State Uverst Refereces Muller et al. A Itroducto to Kerel-Based Learg

More information

Strong Convergence of Weighted Averaged Approximants of Asymptotically Nonexpansive Mappings in Banach Spaces without Uniform Convexity

Strong Convergence of Weighted Averaged Approximants of Asymptotically Nonexpansive Mappings in Banach Spaces without Uniform Convexity BULLETIN of the MALAYSIAN MATHEMATICAL SCIENCES SOCIETY Bull. Malays. Math. Sc. Soc. () 7 (004), 5 35 Strog Covergece of Weghted Averaged Appromats of Asymptotcally Noepasve Mappgs Baach Spaces wthout

More information

Chapter 5. Curve fitting

Chapter 5. Curve fitting Chapter 5 Curve ttg Assgmet please use ecell Gve the data elow use least squares regresso to t a a straght le a power equato c a saturato-growthrate equato ad d a paraola. Fd the r value ad justy whch

More information

QR Factorization and Singular Value Decomposition COS 323

QR Factorization and Singular Value Decomposition COS 323 QR Factorzato ad Sgular Value Decomposto COS 33 Why Yet Aother Method? How do we solve least-squares wthout currg codto-squarg effect of ormal equatos (A T A A T b) whe A s sgular, fat, or otherwse poorly-specfed?

More information

Chapter 8: Statistical Analysis of Simulated Data

Chapter 8: Statistical Analysis of Simulated Data Marquette Uversty MSCS600 Chapter 8: Statstcal Aalyss of Smulated Data Dael B. Rowe, Ph.D. Departmet of Mathematcs, Statstcs, ad Computer Scece Copyrght 08 by Marquette Uversty MSCS600 Ageda 8. The Sample

More information

A Study on Generalized Generalized Quasi hyperbolic Kac Moody algebra QHGGH of rank 10

A Study on Generalized Generalized Quasi hyperbolic Kac Moody algebra QHGGH of rank 10 Global Joural of Mathematcal Sceces: Theory ad Practcal. ISSN 974-3 Volume 9, Number 3 (7), pp. 43-4 Iteratoal Research Publcato House http://www.rphouse.com A Study o Geeralzed Geeralzed Quas (9) hyperbolc

More information

Multiple Regression. More than 2 variables! Grade on Final. Multiple Regression 11/21/2012. Exam 2 Grades. Exam 2 Re-grades

Multiple Regression. More than 2 variables! Grade on Final. Multiple Regression 11/21/2012. Exam 2 Grades. Exam 2 Re-grades STAT 101 Dr. Kar Lock Morga 11/20/12 Exam 2 Grades Multple Regresso SECTIONS 9.2, 10.1, 10.2 Multple explaatory varables (10.1) Parttog varablty R 2, ANOVA (9.2) Codtos resdual plot (10.2) Trasformatos

More information