Block-error performance of root-ldpc codes. Author(s): Andriyanova, Iryna; Boutros, Joseph J.; Biglieri, Ezio; Declercq, David

Size: px
Start display at page:

Download "Block-error performance of root-ldpc codes. Author(s): Andriyanova, Iryna; Boutros, Joseph J.; Biglieri, Ezio; Declercq, David"

Transcription

1 Research Collecton Conference Paper Bloc-error perforance of root-ldpc codes Authors: Andryanova, Iryna; Boutros, Joseph J.; Bgler, Ezo; Declercq, Davd Publcaton Date: 00 Peranent Ln: Rghts / Lcense: In Copyrght - Non-Coercal Use Pertted Ths page was generated autoatcally upon download fro the ETH Zurch Research Collecton. For ore nforaton please consult the Ters of use. ETH Lbrary

2 Int. Zurch Senar on Councatons IZS, March 3-5, 00 Bloc-Error Perforance of Root-LDPC Codes Iryna Andryanova ETIS group ENSEA/UCP/CNRS-UMR Cergy-Pontose, France Joseph J. Boutros Elec. Eng. Departent Texas A&M Unversty at Qatar 3874, Doha, Qatar Ezo Bgler WISER S.r.l. Va Fue Lvorno, Italy e.bgler@eee.org Davd Declercq ETIS group ENSEA/UCP/CNRS-UMR Cergy-Pontose, France declercq@ensea.fr Abstract Ths paper nvestgates the error rate of root-ldpc RLDPC codes. These codes were ntroduced n [], as a class of codes achevng full dversty D over a nonergodc blocfadng transsson channel, and hence wth an error probablty decreasng as SNR D at hgh sgnal-to-nose ratos. As for ther structure, root-ldpc codes can be vewed as a specal case of ultedge-type LDPC codes []. However, RLDPC code optzaton for nonergodc channels does not follow the sae crtera as those appled for standard ergodc erasure or Gaussan channels. Whle prevous analyses of RLDPC codes were based on ther asyptotc bt threshold for nforaton varables under teratve decodng, n ths wor we nvestgate asyptotc bloc threshold. A stablty condton s frst derved for a gven fadng channel realzaton. Then, n a slar way as for unstructured LDPC codes [3], wth the help of Bhattacharyya paraeter, we state a suffcent condton for a vanshng bloc-error probablty wth the nuber of decodng teratons. I. INTRODUCTION AND MOTIVATION OF OUR WORK When a bloc of encoded data s sent, after beng splt nto F subblocs, through F ndependent slow-fadng channels, the approprate channel odel s nonergodc. Ths odel ay correspond to a parallel MIMO systes or to a sequental HARQ protocols data-transsson schee. It turns out that specal desgn crtera are needed for codes to be used wth such a odel n partcular, full transt dversty s sought, whch guarantees that, at large sgnal-tonose ratos SNR, the error probablty of the transsson schee scales as /SNR D, wth D the axu dversty order achevable. It has been shown n [] that standard sparsegraph code ensebles allow one to obtan error probabltes decreasng only as /SNR, and hence they are not fulldversty ensebles. Even nfnte-length rando code ensebles cannot acheve full dversty, as shown va a dversty populaton evoluton technque n [4]. The ey dea for codes achevng full dversty s to ensure that each nforaton node s recevng ultple essages affected by ndependent fadng coeffcents. Ths dea has been pleented n RLDPC codes [] desgned for bloc-fadng channels wth F = by ntroducng the concept of root checnodes. A root checnode protects a essage receved fro the second subchannel when the varable node s receved fro the frst subchannel. RLDPC codes are full-dversty codes thus, they are also Maxu Dstance Separable n the Ths wor was supported by the European FP7 ICT-STREP DAVINCI proect under the contract No. INFSO-ICT-603. Sngleton-bound sense and can be devsed for any dversty order. In ths paper we focus on rate-/, dversty- RLDPC codes, and study ther stablty under teratve decodng. We also derve a suffcent condton for vanshng bloc-error probablty. As expected, snce root checnodes occupy a sngle edge n each nforaton varable, stablty and blocerror perforance of RLDPC codes depend on the fracton of varables wth degrees and 3. II. TRANSMISSION MODEL Under our assuptons, a bloc of encoded data a codeword s dvded nto two equal subblocs, each one beng transtted over an ndependent Raylegh fadng channel wth SNR= γ and fadng coeffcents α and α. Therefore, the observaton y correspondng to the bnary transtted sybol x = ± receved fro the -th channel s y = α x + z, α [0, +, and z N0,σ wth σ =/γ. III. RLDPC CODES: DEFINITION AND DENSITY EVOLUTION A. Defnton Gven an ntal λ, ρ LDPC enseble, one defnes a λ, ρ RLDPC enseble wth dversty through the ultnoals λ root μ,x and ρ root μ,x, wth μ μ,μ and x x,x,x 3,x 4,x 5,x 6 : λ root μ,x λ μ x + λ μ x + λ μ x 3 +λ μ x 4 + λ μ x 5 + λ μ x 6, ρ root μ,x ρ x fe x 4 g e x 5 +x 6 fe x 3ge x, the fractons f e and g e wll be defned n next subsecton. In words, the structure of the RLDPC enseble conssts of four types of varable nodes, p,, p, two sets of chec nodes c, c, and 6 dfferent edge classes see Fg.a. Perutatons of edges wthn edge classes are chosen unforly at rando. Varable nodes and p correspond to nforaton and redundancy bts, respectvely, n a codeword 94

3 Int. Zurch Senar on Councatons IZS, March 3-5, 00 sent through the frst fadng subchannel. Slarly, varable nodes and p correspond to bts sent through the second subchannel. Note that the nforaton varable nodes =, are connected to chec nodes of the sae type, c, through exactly one edge; all other edges are connected to chec nodes of the other type. Redundancy varable nodes are always connected to chec nodes of dfferent type. In -, μ and μ correspond to two fadng subchannels, and the varables x,x,...,x 6 to the followng edge classes: c, c, p c, p c, c, and c. We have thus obtaned a code enseble of rate /. As shown n [], such a constructon guarantees transt dversty, whch s the axu we can obtan wth two ndependent transsson subchannels. a b Fg.. Structure of a λ, ρ RLDPC code enseble of dversty. B. Densty Evoluton RLDPC codes are decoded, as standard LDPC codes, usng an teratve algorth. An asyptotc analyss of teratve decodng s provded n [], [4] and we shall suarze t here, after gvng soe notaton. We denote the probablty densty functons pdfs of channel LLR outputs fro the two transsson subchannels by μ x and μ x, respectvely. These are noral pdfs wth eans α/γ and α/γ and varances 4α/γ and 4α/γ, respectvely. Further, we denote by the operaton of convoluton of two pdfs. We also defne the followng operaton: Defnton : The R-convoluton of two pdfs αx and βx s α βx =fˆαx ˆβx, and ˆαx αth x βth x, ˆβx x = x fx =cosh ˆα ˆβx th ˆα ˆβx. Note that the R-convoluton of pdfs corresponds to the followng operaton over the correspondng rando varables A and B: th tha/ + thb/, whch s exactly the operaton perfored at the chec nodes. Let us denote the average pdfs for 6 edge sets by q x, f x, g x, g x,f x, and q x as shown n Fg.b. Then the evoluton of the pdfs at the teraton + can be descrbed by the followng recursons: q + x = μ x λ ρq x,f e f x+g e g x f + x = μ x λ ρq x,f e f x+g e g x ρf e f x+g e g x g + x = μ x λ ρq x,f e f x+g e g x g + x = μ x λ ρq x,f e f x+g e g x f + x = μ x λ ρq x,f e f x+g e g x ρf e f x+g e g x q + x = μ x λ ρq x,f e f x+g e g x we have borrowed fro [4] the followng notaton: λx ρx f e d b d b d c d c λ λ + = λx db Also, we defne ρq, x λ x ; ρ x ; λ x ; d c d c db / dc / d b d b ; g e f e ; ρx d c ρ q x 3. IV. STABILITY CONDITIONS λ /; ρ /; ρ x. We are nterested n defnng stablty condtons for RLDPC codes. The an dffculty here les n the fact that not all essages need be recovered exactly or, n LDPC argon, not all pdfs converge to δ. It s not hard to prove that only the pdfs responsble for the convergence of nforaton essages,.e., f and f, need to converge for exact recovery of the nforaton bts ths condton s also suffcent. The an concept of the proof s that f and f are strctly better than q and q. In ths secton we derve the stablty condton for RLDPC codes based on the recovery of nforaton bts only. Before startng our dervaton, let us frst apply the tradtonal stablty condton [] to RLDPC codes, assung that all the code bts should be recovered. In such case the RLDPC codes are sply vewed as a ult-edge code enseble, A. RLDPCs as Mult-Edge Codes The stablty condton for ult-edge codes conssts n ensurng that the spectral radus of a atrx M s <, M BμΛP, wth Bμ the vector of Bhattacharyya paraeters for all transsson channels, the Λ atrx correspondng to the varable node sde of the graph, and P correspondng to 95

4 Int. Zurch Senar on Councatons IZS, March 3-5, 00 the chec node sde. Applyng the expressons derved n [], we fnd that Bμ = Bμ Bμ Bμ Bμ Bμ Bμ T I Λ= db λ d b λ db d λ λ b λ d b P = T P P P P 3 P 4 P 3 d b λ Fnally, the approxaton of f lnear n ε s obtaned as f = μ x λ + λ ρq x,g eg x + λ ε f ef q x,g eg x ρg eg x + ε f ef g eg x + const δ = μ x [ λ + λ ρq x,g eg x] ρg eg x + ε f e[ λ + λ ρq x,g eg x] F g eg x + λ ε f ef q x,g eg x ρg eg x + c δ wth P d c g e d c f e 0 P 0 ρ f e ρ g e 0 0 ρ P 3 ρ 0 0 ρ f e ρ g e 0 P 4 0 d c f e d c g e Note that two egenvalues of M are already 0. B. RLDPCs as Full-Dversty Codes wth By loong at RLDPC as at full-dversty codes, we only as C 0 x [ λ + λ ρq x,g e g x] ρg e g x for the convergence of f and f to δ. To derve a stablty C x [ λ + condton for ths case, assue that, at teraton, λ ρq x,g e g x] F g e g x C x λ F q x,g e g x ρg e g x f = ε δ 0 + ε δ, f = ε δ 0 + ε δ. Therefore, we have the followng relaton: and fnd an approxaton of essages f and f at the next f ε teraton whch s lnear n ε. f = a + f e A, ε To do ths, let us frst fnd a lnear approxaton of ρf e fx+g e gx: wth μ x C a 0 x μ ρf efx+g egx = ρf eεδ 0 + f e εδ + g egx = gx x C 0 x = and ρ ge gx + f eε fe ge gx μ x C x μ x C x A =0 μ x C x μ x C x + c δ = ρg egx + εf ef g egx + c δ, Denote now by qf the Bhattacharyya paraeter related to c s a constant, and ρg e gx denotes the frst ter the pdf f, n the su, whle F g e gx denotes the second one. Over Bf e x/ fxdx. the bnary erasure channel, ρg e gx and F g e gx can R be coputed explctly, whle, n the general case, the two B s closely related to the bt error probablty P functons should be coputed by runnng the densty evoluton b correspondng to fx, and t has been shown n [5] that P b teratons. Also note that one can bound the pdf of gx by the ntal pdf correspondng to the channel estate. If the 0 Bf 0. Knowng ths, and tang nto account the transsson channel s bad, the bound wll be qute tght. propertes of convoluton and of R-convoluton, we obtan that Next, ρq x,f efx+g egx = ρ ge q x gx ρ f eε fe 3 ge q x gx =0 + c δ = ρq x,g egx + εf ef q x,g egx + c δ. Further calculatons yeld λ ρq x,f e fx+g e gx = = λ ρq x,f e εδ 0 + f e εδ + g e gx = λ + λ ρq x,g e gx + εf e F q x,g e gx+c δ. = μ x C 0 x+ε f ec x+ε f ec x + c δ C 0 x [ λ + λ ρq x,g e g x] ρg e g x C x λ F q x,g e g x ρg e g x C x [ λ + λ ρq x,g e g x] F g e g x Slarly, f = μ x C0 x+ε f e C x+ε f e C x + c δ C = Bf Bf [C + f e BA] ε ε,. 0 Bμ λge ρ g e Bμ λge ρ g e 0 Note that we splfed the expressons by boundng Bg Bq and Bg Bq, and further boundng C 0 x and C 0 x. Defne next D C + f e BA. Then the followng recurrence relaton can be obtaned: Bf Bf Bf D Bf, 96

5 Int. Zurch Senar on Councatons IZS, March 3-5, 00 and hence, f we perfor teratons of densty evoluton, we obtan that Bf Bf D Bf 0 Bf 0, we assue that the essages q and g for any teraton are bounded by q 0 and g 0. We are nterested n the case of Bf decreasng to 0. Tang all the above nto account, we have the followng suffcent stablty condton for full-dversty codes: Theore Suffcency part of the stablty condton: The bt error probablty P e for a full-dversty RLDPC enseble converges to 0 f all the absolute values of the egenvalues of D are <. Notce that the usual stablty condton entoned n Secton IV-A depends on λ, whle the stablty condton derved here depends on both λ and λ 3, hdden n λ and λ. V. BLOCK-ERROR RATE OF RLDPC CODES The an result of ths paper s the study of the bloc-error probablty P B of RLDPC codes. Usng the suffcent part of the stablty condton derved above, we can ln P B to the bt-error probablty P b, and show n whch cases P b 0 ples P B 0. Usng a unon bound at soe teraton, we obtan P B n 4 P l b+ n 4 P l b 4 ax M l 6+ε P l b+ 4 ax M l 6+ε P l b, n s the code length, and ax M ax M s the axu nuber of varable nodes n a coputaton tree of a varable node fro the set n the bpartte graph, after teratons. The second nequalty follows fro the sae reasonng used n [3, Secton II], to whch we refer the reader desrng a detaled proof. Now, to ensure that, as, PB decreases to 0 whle P b 0, one has to ensure that Pb decreases wth faster than the axu nuber of varable nodes n the coputaton tree. A. Case of λ = λ 3 =0 Let us consder the sple case of both λ and λ 3 beng 0. ths s slar to the case of standard LDPC codes wth λ =0. Repeatng the calculatons of [3, Secton VI.A], we obtan P B + v d ax c 6+ε+ [Bf 3/ + Bf 3/ ], 4 dax whch decreases to 0 as. B. General case Gven that for varable nodes and Boutput =Π Bnput Boutput Π Bnput for chec nodes, and snce Bq and Bg for any, q, and g are no greater than the correspondng Bμ, one can bound BC λ g e ρ g e Bμ ax{bμ,bμ } BC λ + λ ρg e g e Bμ B C λ + λ ρg e g e Bμ B C λ g e ρ g e Bμ ax{bμ,bμ } and obtan Bf Bμ w Bf +w Bf Bf Bμ w Bf +w Bf wth w f e λ ρ g e and w f e λ + λ ρg e + λ g e ρ g e. Thus, wth a lnear approxaton, Bf + Bμ w Bf +Bμ Bμ ww Bf Bf + Bμ w Bf +Bμ Bμ ww Bf. Consequently, the bloc error probablty P B + 4 dax v d ax c 6+ε+ [Bμ w Bf + Bμ w Bf +Bμ Bμ ww {Bf +Bf }], can be seen to decrease to 0, as, f the followng condtons are satsfed: Bμ w d ax v d ax c 3, Bμ w d ax v d ax c 3. VI. CONCLUSION In ths paper we have derved the condtons under whch the bloc-error rate of a RLDPC code enseble decreases to 0 as the bt-error rate does the sae. The nterest of our fndngs les n the fact that results exstng n the lterature deal wth errors related to all the of code bts, whle for RLDPC only errors affectng nforaton bts should be consdered. REFERENCES [] J. Boutros, A. G. Fabregas, E. Bgler, and G. Zeor, Low-densty party-chec codes for nonergodc bloc-fadng channels, 007, subtted to IEEE Trans. Infor. Theory. [] T. Rchardson and R. Urbane, Mult-edge LDPC codes, 004, subtted to IEEE Trans. Infor. Theory. [Onlne]. Avalable: vay/ultedge.pdf [3] H. Jn and T. Rchardson, Bloc error teratve decodng capacty for ldpc codes, n ISIT 05, Adelade, Australa, Septeber 005. [4] J. Boutros, Dversty and codng gan evoluton n graph codes, n ITA 09, San-Dego, USA, February 009. [5] T. Rchardson, A. Shorollah, and R. Urbane, Desgn of capactyapproachng rregular low-densty party-chec codes, IEEE Trans. Infor. Theory, vol. 47, no., pp , February

Applied Mathematics Letters

Applied Mathematics Letters Appled Matheatcs Letters 2 (2) 46 5 Contents lsts avalable at ScenceDrect Appled Matheatcs Letters journal hoepage: wwwelseverco/locate/al Calculaton of coeffcents of a cardnal B-splne Gradr V Mlovanovć

More information

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

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

More information

XII.3 The EM (Expectation-Maximization) Algorithm

XII.3 The EM (Expectation-Maximization) Algorithm XII.3 The EM (Expectaton-Maxzaton) Algorth Toshnor Munaata 3/7/06 The EM algorth s a technque to deal wth varous types of ncoplete data or hdden varables. It can be appled to a wde range of learnng probles

More information

On the Construction of Polar Codes

On the Construction of Polar Codes On the Constructon of Polar Codes Ratn Pedarsan School of Coputer and Councaton Systes, Lausanne, Swtzerland. ratn.pedarsan@epfl.ch S. Haed Hassan School of Coputer and Councaton Systes, Lausanne, Swtzerland.

More information

Least Squares Fitting of Data

Least Squares Fitting of Data Least Squares Fttng of Data Davd Eberly Geoetrc Tools, LLC http://www.geoetrctools.co/ Copyrght c 1998-2014. All Rghts Reserved. Created: July 15, 1999 Last Modfed: February 9, 2008 Contents 1 Lnear Fttng

More information

On the Construction of Polar Codes

On the Construction of Polar Codes On the Constructon of Polar Codes Ratn Pedarsan School of Coputer and Councaton Systes, Lausanne, Swtzerland. ratn.pedarsan@epfl.ch S. Haed Hassan School of Coputer and Councaton Systes, Lausanne, Swtzerland.

More information

Excess Error, Approximation Error, and Estimation Error

Excess Error, Approximation Error, and Estimation Error E0 370 Statstcal Learnng Theory Lecture 10 Sep 15, 011 Excess Error, Approxaton Error, and Estaton Error Lecturer: Shvan Agarwal Scrbe: Shvan Agarwal 1 Introducton So far, we have consdered the fnte saple

More information

Denote the function derivatives f(x) in given points. x a b. Using relationships (1.2), polynomials (1.1) are written in the form

Denote the function derivatives f(x) in given points. x a b. Using relationships (1.2), polynomials (1.1) are written in the form SET OF METHODS FO SOUTION THE AUHY POBEM FO STIFF SYSTEMS OF ODINAY DIFFEENTIA EUATIONS AF atypov and YuV Nulchev Insttute of Theoretcal and Appled Mechancs SB AS 639 Novosbrs ussa Introducton A constructon

More information

arxiv: v2 [math.co] 3 Sep 2017

arxiv: v2 [math.co] 3 Sep 2017 On the Approxate Asyptotc Statstcal Independence of the Peranents of 0- Matrces arxv:705.0868v2 ath.co 3 Sep 207 Paul Federbush Departent of Matheatcs Unversty of Mchgan Ann Arbor, MI, 4809-043 Septeber

More information

Slobodan Lakić. Communicated by R. Van Keer

Slobodan Lakić. Communicated by R. Van Keer Serdca Math. J. 21 (1995), 335-344 AN ITERATIVE METHOD FOR THE MATRIX PRINCIPAL n-th ROOT Slobodan Lakć Councated by R. Van Keer In ths paper we gve an teratve ethod to copute the prncpal n-th root and

More information

Error Probability for M Signals

Error Probability for M Signals Chapter 3 rror Probablty for M Sgnals In ths chapter we dscuss the error probablty n decdng whch of M sgnals was transmtted over an arbtrary channel. We assume the sgnals are represented by a set of orthonormal

More information

Computational and Statistical Learning theory Assignment 4

Computational and Statistical Learning theory Assignment 4 Coputatonal and Statstcal Learnng theory Assgnent 4 Due: March 2nd Eal solutons to : karthk at ttc dot edu Notatons/Defntons Recall the defnton of saple based Radeacher coplexty : [ ] R S F) := E ɛ {±}

More information

1 Review From Last Time

1 Review From Last Time COS 5: Foundatons of Machne Learnng Rob Schapre Lecture #8 Scrbe: Monrul I Sharf Aprl 0, 2003 Revew Fro Last Te Last te, we were talkng about how to odel dstrbutons, and we had ths setup: Gven - exaples

More information

Least Squares Fitting of Data

Least Squares Fitting of Data Least Squares Fttng of Data Davd Eberly Geoetrc Tools, LLC http://www.geoetrctools.co/ Copyrght c 1998-2015. All Rghts Reserved. Created: July 15, 1999 Last Modfed: January 5, 2015 Contents 1 Lnear Fttng

More information

System in Weibull Distribution

System in Weibull Distribution Internatonal Matheatcal Foru 4 9 no. 9 94-95 Relablty Equvalence Factors of a Seres-Parallel Syste n Webull Dstrbuton M. A. El-Dacese Matheatcs Departent Faculty of Scence Tanta Unversty Tanta Egypt eldacese@yahoo.co

More information

1 Definition of Rademacher Complexity

1 Definition of Rademacher Complexity COS 511: Theoretcal Machne Learnng Lecturer: Rob Schapre Lecture #9 Scrbe: Josh Chen March 5, 2013 We ve spent the past few classes provng bounds on the generalzaton error of PAClearnng algorths for the

More information

The Parity of the Number of Irreducible Factors for Some Pentanomials

The Parity of the Number of Irreducible Factors for Some Pentanomials The Party of the Nuber of Irreducble Factors for Soe Pentanoals Wolfra Koepf 1, Ryul K 1 Departent of Matheatcs Unversty of Kassel, Kassel, F. R. Gerany Faculty of Matheatcs and Mechancs K Il Sung Unversty,

More information

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

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

More information

COS 511: Theoretical Machine Learning

COS 511: Theoretical Machine Learning COS 5: Theoretcal Machne Learnng Lecturer: Rob Schapre Lecture #0 Scrbe: José Sões Ferrera March 06, 203 In the last lecture the concept of Radeacher coplexty was ntroduced, wth the goal of showng that

More information

On the number of regions in an m-dimensional space cut by n hyperplanes

On the number of regions in an m-dimensional space cut by n hyperplanes 6 On the nuber of regons n an -densonal space cut by n hyperplanes Chungwu Ho and Seth Zeran Abstract In ths note we provde a unfor approach for the nuber of bounded regons cut by n hyperplanes n general

More information

ITERATIVE ESTIMATION PROCEDURE FOR GEOSTATISTICAL REGRESSION AND GEOSTATISTICAL KRIGING

ITERATIVE ESTIMATION PROCEDURE FOR GEOSTATISTICAL REGRESSION AND GEOSTATISTICAL KRIGING ESE 5 ITERATIVE ESTIMATION PROCEDURE FOR GEOSTATISTICAL REGRESSION AND GEOSTATISTICAL KRIGING Gven a geostatstcal regresson odel: k Y () s x () s () s x () s () s, s R wth () unknown () E[ ( s)], s R ()

More information

Several generation methods of multinomial distributed random number Tian Lei 1, a,linxihe 1,b,Zhigang Zhang 1,c

Several generation methods of multinomial distributed random number Tian Lei 1, a,linxihe 1,b,Zhigang Zhang 1,c Internatonal Conference on Appled Scence and Engneerng Innovaton (ASEI 205) Several generaton ethods of ultnoal dstrbuted rando nuber Tan Le, a,lnhe,b,zhgang Zhang,c School of Matheatcs and Physcs, USTB,

More information

Composite Hypotheses testing

Composite Hypotheses testing Composte ypotheses testng In many hypothess testng problems there are many possble dstrbutons that can occur under each of the hypotheses. The output of the source s a set of parameters (ponts n a parameter

More information

BAYESIAN CURVE FITTING USING PIECEWISE POLYNOMIALS. Dariusz Biskup

BAYESIAN CURVE FITTING USING PIECEWISE POLYNOMIALS. Dariusz Biskup BAYESIAN CURVE FITTING USING PIECEWISE POLYNOMIALS Darusz Bskup 1. Introducton The paper presents a nonparaetrc procedure for estaton of an unknown functon f n the regresson odel y = f x + ε = N. (1) (

More information

Xiangwen Li. March 8th and March 13th, 2001

Xiangwen Li. March 8th and March 13th, 2001 CS49I Approxaton Algorths The Vertex-Cover Proble Lecture Notes Xangwen L March 8th and March 3th, 00 Absolute Approxaton Gven an optzaton proble P, an algorth A s an approxaton algorth for P f, for an

More information

Lecture 5 Decoding Binary BCH Codes

Lecture 5 Decoding Binary BCH Codes Lecture 5 Decodng Bnary BCH Codes In ths class, we wll ntroduce dfferent methods for decodng BCH codes 51 Decodng the [15, 7, 5] 2 -BCH Code Consder the [15, 7, 5] 2 -code C we ntroduced n the last lecture

More information

LECTURE :FACTOR ANALYSIS

LECTURE :FACTOR ANALYSIS LCUR :FACOR ANALYSIS Rta Osadchy Based on Lecture Notes by A. Ng Motvaton Dstrbuton coes fro MoG Have suffcent aount of data: >>n denson Use M to ft Mture of Gaussans nu. of tranng ponts If

More information

Revision: December 13, E Main Suite D Pullman, WA (509) Voice and Fax

Revision: December 13, E Main Suite D Pullman, WA (509) Voice and Fax .9.1: AC power analyss Reson: Deceber 13, 010 15 E Man Sute D Pullan, WA 99163 (509 334 6306 Voce and Fax Oerew n chapter.9.0, we ntroduced soe basc quanttes relate to delery of power usng snusodal sgnals.

More information

1. Statement of the problem

1. Statement of the problem Volue 14, 010 15 ON THE ITERATIVE SOUTION OF A SYSTEM OF DISCRETE TIMOSHENKO EQUATIONS Peradze J. and Tsklaur Z. I. Javakhshvl Tbls State Uversty,, Uversty St., Tbls 0186, Georga Georgan Techcal Uversty,

More information

On Syndrome Decoding of Punctured Reed-Solomon and Gabidulin Codes 1

On Syndrome Decoding of Punctured Reed-Solomon and Gabidulin Codes 1 Ffteenth Internatonal Workshop on Algebrac and Cobnatoral Codng Theory June 18-24, 2016, Albena, Bulgara pp. 35 40 On Syndroe Decodng of Punctured Reed-Soloon and Gabduln Codes 1 Hannes Bartz hannes.bartz@tu.de

More information

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

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

More information

Fermi-Dirac statistics

Fermi-Dirac statistics UCC/Physcs/MK/EM/October 8, 205 Fer-Drac statstcs Fer-Drac dstrbuton Matter partcles that are eleentary ostly have a type of angular oentu called spn. hese partcles are known to have a agnetc oent whch

More information

Solutions for Homework #9

Solutions for Homework #9 Solutons for Hoewor #9 PROBEM. (P. 3 on page 379 n the note) Consder a sprng ounted rgd bar of total ass and length, to whch an addtonal ass s luped at the rghtost end. he syste has no dapng. Fnd the natural

More information

Pulse Coded Modulation

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

More information

Lecture 10: May 6, 2013

Lecture 10: May 6, 2013 TTIC/CMSC 31150 Mathematcal Toolkt Sprng 013 Madhur Tulsan Lecture 10: May 6, 013 Scrbe: Wenje Luo In today s lecture, we manly talked about random walk on graphs and ntroduce the concept of graph expander,

More information

PROBABILITY AND STATISTICS Vol. III - Analysis of Variance and Analysis of Covariance - V. Nollau ANALYSIS OF VARIANCE AND ANALYSIS OF COVARIANCE

PROBABILITY AND STATISTICS Vol. III - Analysis of Variance and Analysis of Covariance - V. Nollau ANALYSIS OF VARIANCE AND ANALYSIS OF COVARIANCE ANALYSIS OF VARIANCE AND ANALYSIS OF COVARIANCE V. Nollau Insttute of Matheatcal Stochastcs, Techncal Unversty of Dresden, Gerany Keywords: Analyss of varance, least squares ethod, odels wth fxed effects,

More information

Lecture 3: Probability Distributions

Lecture 3: Probability Distributions Lecture 3: Probablty Dstrbutons Random Varables Let us begn by defnng a sample space as a set of outcomes from an experment. We denote ths by S. A random varable s a functon whch maps outcomes nto the

More information

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

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

More information

Structure and Drive Paul A. Jensen Copyright July 20, 2003

Structure and Drive Paul A. Jensen Copyright July 20, 2003 Structure and Drve Paul A. Jensen Copyrght July 20, 2003 A system s made up of several operatons wth flow passng between them. The structure of the system descrbes the flow paths from nputs to outputs.

More information

j) = 1 (note sigma notation) ii. Continuous random variable (e.g. Normal distribution) 1. density function: f ( x) 0 and f ( x) dx = 1

j) = 1 (note sigma notation) ii. Continuous random variable (e.g. Normal distribution) 1. density function: f ( x) 0 and f ( x) dx = 1 Random varables Measure of central tendences and varablty (means and varances) Jont densty functons and ndependence Measures of assocaton (covarance and correlaton) Interestng result Condtonal dstrbutons

More information

College of Computer & Information Science Fall 2009 Northeastern University 20 October 2009

College of Computer & Information Science Fall 2009 Northeastern University 20 October 2009 College of Computer & Informaton Scence Fall 2009 Northeastern Unversty 20 October 2009 CS7880: Algorthmc Power Tools Scrbe: Jan Wen and Laura Poplawsk Lecture Outlne: Prmal-dual schema Network Desgn:

More information

Chapter 7 Channel Capacity and Coding

Chapter 7 Channel Capacity and Coding Chapter 7 Channel Capacty and Codng Contents 7. Channel models and channel capacty 7.. Channel models Bnary symmetrc channel Dscrete memoryless channels Dscrete-nput, contnuous-output channel Waveform

More information

Modelli Clamfim Equazione del Calore Lezione ottobre 2014

Modelli Clamfim Equazione del Calore Lezione ottobre 2014 CLAMFIM Bologna Modell 1 @ Clamfm Equazone del Calore Lezone 17 15 ottobre 2014 professor Danele Rtell danele.rtell@unbo.t 1/24? Convoluton The convoluton of two functons g(t) and f(t) s the functon (g

More information

Convergence of random processes

Convergence of random processes DS-GA 12 Lecture notes 6 Fall 216 Convergence of random processes 1 Introducton In these notes we study convergence of dscrete random processes. Ths allows to characterze phenomena such as the law of large

More information

ON THE NUMBER OF PRIMITIVE PYTHAGOREAN QUINTUPLES

ON THE NUMBER OF PRIMITIVE PYTHAGOREAN QUINTUPLES Journal of Algebra, Nuber Theory: Advances and Applcatons Volue 3, Nuber, 05, Pages 3-8 ON THE NUMBER OF PRIMITIVE PYTHAGOREAN QUINTUPLES Feldstrasse 45 CH-8004, Zürch Swtzerland e-al: whurlann@bluewn.ch

More information

Lecture 3: Shannon s Theorem

Lecture 3: Shannon s Theorem CSE 533: Error-Correctng Codes (Autumn 006 Lecture 3: Shannon s Theorem October 9, 006 Lecturer: Venkatesan Guruswam Scrbe: Wdad Machmouch 1 Communcaton Model The communcaton model we are usng conssts

More information

More metrics on cartesian products

More metrics on cartesian products More metrcs on cartesan products If (X, d ) are metrc spaces for 1 n, then n Secton II4 of the lecture notes we defned three metrcs on X whose underlyng topologes are the product topology The purpose of

More information

Lectures - Week 4 Matrix norms, Conditioning, Vector Spaces, Linear Independence, Spanning sets and Basis, Null space and Range of a Matrix

Lectures - Week 4 Matrix norms, Conditioning, Vector Spaces, Linear Independence, Spanning sets and Basis, Null space and Range of a Matrix Lectures - Week 4 Matrx norms, Condtonng, Vector Spaces, Lnear Independence, Spannng sets and Bass, Null space and Range of a Matrx Matrx Norms Now we turn to assocatng a number to each matrx. We could

More information

Hidden Markov Models & The Multivariate Gaussian (10/26/04)

Hidden Markov Models & The Multivariate Gaussian (10/26/04) CS281A/Stat241A: Statstcal Learnng Theory Hdden Markov Models & The Multvarate Gaussan (10/26/04) Lecturer: Mchael I. Jordan Scrbes: Jonathan W. Hu 1 Hdden Markov Models As a bref revew, hdden Markov models

More information

C/CS/Phy191 Problem Set 3 Solutions Out: Oct 1, 2008., where ( 00. ), so the overall state of the system is ) ( ( ( ( 00 ± 11 ), Φ ± = 1

C/CS/Phy191 Problem Set 3 Solutions Out: Oct 1, 2008., where ( 00. ), so the overall state of the system is ) ( ( ( ( 00 ± 11 ), Φ ± = 1 C/CS/Phy9 Problem Set 3 Solutons Out: Oct, 8 Suppose you have two qubts n some arbtrary entangled state ψ You apply the teleportaton protocol to each of the qubts separately What s the resultng state obtaned

More information

Lecture 4: Universal Hash Functions/Streaming Cont d

Lecture 4: Universal Hash Functions/Streaming Cont d CSE 5: Desgn and Analyss of Algorthms I Sprng 06 Lecture 4: Unversal Hash Functons/Streamng Cont d Lecturer: Shayan Oves Gharan Aprl 6th Scrbe: Jacob Schreber Dsclamer: These notes have not been subjected

More information

Eigenvalues of Random Graphs

Eigenvalues of Random Graphs Spectral Graph Theory Lecture 2 Egenvalues of Random Graphs Danel A. Spelman November 4, 202 2. Introducton In ths lecture, we consder a random graph on n vertces n whch each edge s chosen to be n the

More information

Universal communication part II: channels with memory

Universal communication part II: channels with memory Unversal councaton part II: channels wth eory Yuval Lontz, Mer Feder Tel Avv Unversty, Dept. of EE-Systes Eal: {yuvall,er@eng.tau.ac.l arxv:202.047v2 [cs.it] 20 Mar 203 Abstract Consder councaton over

More information

Econ107 Applied Econometrics Topic 3: Classical Model (Studenmund, Chapter 4)

Econ107 Applied Econometrics Topic 3: Classical Model (Studenmund, Chapter 4) I. Classcal Assumptons Econ7 Appled Econometrcs Topc 3: Classcal Model (Studenmund, Chapter 4) We have defned OLS and studed some algebrac propertes of OLS. In ths topc we wll study statstcal propertes

More information

The Gaussian classifier. Nuno Vasconcelos ECE Department, UCSD

The Gaussian classifier. Nuno Vasconcelos ECE Department, UCSD he Gaussan classfer Nuno Vasconcelos ECE Department, UCSD Bayesan decson theory recall that we have state of the world X observatons g decson functon L[g,y] loss of predctng y wth g Bayes decson rule s

More information

ECE 534: Elements of Information Theory. Solutions to Midterm Exam (Spring 2006)

ECE 534: Elements of Information Theory. Solutions to Midterm Exam (Spring 2006) ECE 534: Elements of Informaton Theory Solutons to Mdterm Eam (Sprng 6) Problem [ pts.] A dscrete memoryless source has an alphabet of three letters,, =,, 3, wth probabltes.4,.4, and., respectvely. (a)

More information

Finding Dense Subgraphs in G(n, 1/2)

Finding Dense Subgraphs in G(n, 1/2) Fndng Dense Subgraphs n Gn, 1/ Atsh Das Sarma 1, Amt Deshpande, and Rav Kannan 1 Georga Insttute of Technology,atsh@cc.gatech.edu Mcrosoft Research-Bangalore,amtdesh,annan@mcrosoft.com Abstract. Fndng

More information

An Optimal Bound for Sum of Square Roots of Special Type of Integers

An Optimal Bound for Sum of Square Roots of Special Type of Integers The Sxth Internatonal Syposu on Operatons Research and Its Applcatons ISORA 06 Xnang, Chna, August 8 12, 2006 Copyrght 2006 ORSC & APORC pp. 206 211 An Optal Bound for Su of Square Roots of Specal Type

More information

Determination of the Confidence Level of PSD Estimation with Given D.O.F. Based on WELCH Algorithm

Determination of the Confidence Level of PSD Estimation with Given D.O.F. Based on WELCH Algorithm Internatonal Conference on Inforaton Technology and Manageent Innovaton (ICITMI 05) Deternaton of the Confdence Level of PSD Estaton wth Gven D.O.F. Based on WELCH Algorth Xue-wang Zhu, *, S-jan Zhang

More information

ECE559VV Project Report

ECE559VV Project Report ECE559VV Project Report (Supplementary Notes Loc Xuan Bu I. MAX SUM-RATE SCHEDULING: THE UPLINK CASE We have seen (n the presentaton that, for downlnk (broadcast channels, the strategy maxmzng the sum-rate

More information

Gradient Descent Learning and Backpropagation

Gradient Descent Learning and Backpropagation Artfcal Neural Networks (art 2) Chrstan Jacob Gradent Descent Learnng and Backpropagaton CSC 533 Wnter 200 Learnng by Gradent Descent Defnton of the Learnng roble Let us start wth the sple case of lnear

More information

Expected Value and Variance

Expected Value and Variance MATH 38 Expected Value and Varance Dr. Neal, WKU We now shall dscuss how to fnd the average and standard devaton of a random varable X. Expected Value Defnton. The expected value (or average value, or

More information

Lecture 12: Discrete Laplacian

Lecture 12: Discrete Laplacian Lecture 12: Dscrete Laplacan Scrbe: Tanye Lu Our goal s to come up wth a dscrete verson of Laplacan operator for trangulated surfaces, so that we can use t n practce to solve related problems We are mostly

More information

Asymptotics of the Solution of a Boundary Value. Problem for One-Characteristic Differential. Equation Degenerating into a Parabolic Equation

Asymptotics of the Solution of a Boundary Value. Problem for One-Characteristic Differential. Equation Degenerating into a Parabolic Equation Nonl. Analyss and Dfferental Equatons, ol., 4, no., 5 - HIKARI Ltd, www.m-har.com http://dx.do.org/.988/nade.4.456 Asymptotcs of the Soluton of a Boundary alue Problem for One-Characterstc Dfferental Equaton

More information

U.C. Berkeley CS294: Spectral Methods and Expanders Handout 8 Luca Trevisan February 17, 2016

U.C. Berkeley CS294: Spectral Methods and Expanders Handout 8 Luca Trevisan February 17, 2016 U.C. Berkeley CS94: Spectral Methods and Expanders Handout 8 Luca Trevsan February 7, 06 Lecture 8: Spectral Algorthms Wrap-up In whch we talk about even more generalzatons of Cheeger s nequaltes, and

More information

Series Expansion for L p Hardy Inequalities

Series Expansion for L p Hardy Inequalities Seres Expanson for L p Hardy Inequaltes G. BARBATIS, S.FILIPPAS, & A. TERTIKAS ABSTRACT. We consder a general class of sharp L p Hardy nequaltes n R N nvolvng dstance fro a surface of general codenson

More information

Source-Channel-Sink Some questions

Source-Channel-Sink Some questions Source-Channel-Snk Soe questons Source Channel Snk Aount of Inforaton avalable Source Entro Generall nos and a be te varng Introduces error and lts the rate at whch data can be transferred ow uch nforaton

More information

Two Conjectures About Recency Rank Encoding

Two Conjectures About Recency Rank Encoding Internatonal Journal of Matheatcs and Coputer Scence, 0(205, no. 2, 75 84 M CS Two Conjectures About Recency Rank Encodng Chrs Buhse, Peter Johnson, Wlla Lnz 2, Matthew Spson 3 Departent of Matheatcs and

More information

Edge Isoperimetric Inequalities

Edge Isoperimetric Inequalities November 7, 2005 Ross M. Rchardson Edge Isopermetrc Inequaltes 1 Four Questons Recall that n the last lecture we looked at the problem of sopermetrc nequaltes n the hypercube, Q n. Our noton of boundary

More information

Implicit Integration Henyey Method

Implicit Integration Henyey Method Implct Integraton Henyey Method In realstc stellar evoluton codes nstead of a drect ntegraton usng for example the Runge-Kutta method one employs an teratve mplct technque. Ths s because the structure

More information

Tornado and Luby Transform Codes. Ashish Khisti Presentation October 22, 2003

Tornado and Luby Transform Codes. Ashish Khisti Presentation October 22, 2003 Tornado and Luby Transform Codes Ashsh Khst 6.454 Presentaton October 22, 2003 Background: Erasure Channel Elas[956] studed the Erasure Channel β x x β β x 2 m x 2 k? Capacty of Noseless Erasure Channel

More information

CHAPTER 5: Lie Differentiation and Angular Momentum

CHAPTER 5: Lie Differentiation and Angular Momentum CHAPTER 5: Le Dfferentaton and Angular Momentum Jose G. Vargas 1 Le dfferentaton Kähler s theory of angular momentum s a specalzaton of hs approach to Le dfferentaton. We could deal wth the former drectly,

More information

On the Calderón-Zygmund lemma for Sobolev functions

On the Calderón-Zygmund lemma for Sobolev functions arxv:0810.5029v1 [ath.ca] 28 Oct 2008 On the Calderón-Zygund lea for Sobolev functons Pascal Auscher october 16, 2008 Abstract We correct an naccuracy n the proof of a result n [Aus1]. 2000 MSC: 42B20,

More information

A new Approach for Solving Linear Ordinary Differential Equations

A new Approach for Solving Linear Ordinary Differential Equations , ISSN 974-57X (Onlne), ISSN 974-5718 (Prnt), Vol. ; Issue No. 1; Year 14, Copyrght 13-14 by CESER PUBLICATIONS A new Approach for Solvng Lnear Ordnary Dfferental Equatons Fawz Abdelwahd Department of

More information

THE ARIMOTO-BLAHUT ALGORITHM FOR COMPUTATION OF CHANNEL CAPACITY. William A. Pearlman. References: S. Arimoto - IEEE Trans. Inform. Thy., Jan.

THE ARIMOTO-BLAHUT ALGORITHM FOR COMPUTATION OF CHANNEL CAPACITY. William A. Pearlman. References: S. Arimoto - IEEE Trans. Inform. Thy., Jan. THE ARIMOTO-BLAHUT ALGORITHM FOR COMPUTATION OF CHANNEL CAPACITY Wllam A. Pearlman 2002 References: S. Armoto - IEEE Trans. Inform. Thy., Jan. 1972 R. Blahut - IEEE Trans. Inform. Thy., July 1972 Recall

More information

2E Pattern Recognition Solutions to Introduction to Pattern Recognition, Chapter 2: Bayesian pattern classification

2E Pattern Recognition Solutions to Introduction to Pattern Recognition, Chapter 2: Bayesian pattern classification E395 - Pattern Recognton Solutons to Introducton to Pattern Recognton, Chapter : Bayesan pattern classfcaton Preface Ths document s a soluton manual for selected exercses from Introducton to Pattern Recognton

More information

Linear Regression Analysis: Terminology and Notation

Linear Regression Analysis: Terminology and Notation ECON 35* -- Secton : Basc Concepts of Regresson Analyss (Page ) Lnear Regresson Analyss: Termnology and Notaton Consder the generc verson of the smple (two-varable) lnear regresson model. It s represented

More information

Multipoint Analysis for Sibling Pairs. Biostatistics 666 Lecture 18

Multipoint Analysis for Sibling Pairs. Biostatistics 666 Lecture 18 Multpont Analyss for Sblng ars Bostatstcs 666 Lecture 8 revously Lnkage analyss wth pars of ndvduals Non-paraetrc BS Methods Maxu Lkelhood BD Based Method ossble Trangle Constrant AS Methods Covered So

More information

Chapter 7 Channel Capacity and Coding

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

More information

On the Transient and Steady-State Analysis of a Special Single Server Queuing System with HOL Priority Scheduling

On the Transient and Steady-State Analysis of a Special Single Server Queuing System with HOL Priority Scheduling 96 On the Transent and Steady-State Analyss of a Specal On the Transent and Steady-State Analyss of a Specal Sngle Server Queung Syste wth HOL Prorty Schedulng Faou Kaoun Duba Uversty College, College

More information

One-Shot Quantum Information Theory I: Entropic Quantities. Nilanjana Datta University of Cambridge,U.K.

One-Shot Quantum Information Theory I: Entropic Quantities. Nilanjana Datta University of Cambridge,U.K. One-Shot Quantu Inforaton Theory I: Entropc Quanttes Nlanjana Datta Unversty of Cabrdge,U.K. In Quantu nforaton theory, ntally one evaluated: optal rates of nfo-processng tasks, e.g., data copresson, transsson

More information

MATH 5707 HOMEWORK 4 SOLUTIONS 2. 2 i 2p i E(X i ) + E(Xi 2 ) ä i=1. i=1

MATH 5707 HOMEWORK 4 SOLUTIONS 2. 2 i 2p i E(X i ) + E(Xi 2 ) ä i=1. i=1 MATH 5707 HOMEWORK 4 SOLUTIONS CİHAN BAHRAN 1. Let v 1,..., v n R m, all lengths v are not larger than 1. Let p 1,..., p n [0, 1] be arbtrary and set w = p 1 v 1 + + p n v n. Then there exst ε 1,..., ε

More information

General viscosity iterative method for a sequence of quasi-nonexpansive mappings

General viscosity iterative method for a sequence of quasi-nonexpansive mappings Avalable onlne at www.tjnsa.com J. Nonlnear Sc. Appl. 9 (2016), 5672 5682 Research Artcle General vscosty teratve method for a sequence of quas-nonexpansve mappngs Cuje Zhang, Ynan Wang College of Scence,

More information

Quantum Particle Motion in Physical Space

Quantum Particle Motion in Physical Space Adv. Studes Theor. Phys., Vol. 8, 014, no. 1, 7-34 HIKARI Ltd, www.-hkar.co http://dx.do.org/10.1988/astp.014.311136 Quantu Partcle Moton n Physcal Space A. Yu. Saarn Dept. of Physcs, Saara State Techncal

More information

Performance Analysis of V-BLAST with Optimum Power Allocation

Performance Analysis of V-BLAST with Optimum Power Allocation Perforance Analyss of V-BLAST wth Optu Power Allocaton Vctora Kostna, Sergey Loyka School of Inforaton Technology and Engneerng, Unversty of Ottawa, 6 Lous Pasteur, Ottawa, Canada, KN 6N5 E-al: sergey.loyka@eee.org

More information

MATH 829: Introduction to Data Mining and Analysis The EM algorithm (part 2)

MATH 829: Introduction to Data Mining and Analysis The EM algorithm (part 2) 1/16 MATH 829: Introducton to Data Mnng and Analyss The EM algorthm (part 2) Domnque Gullot Departments of Mathematcal Scences Unversty of Delaware Aprl 20, 2016 Recall 2/16 We are gven ndependent observatons

More information

Discrete Memoryless Channels

Discrete Memoryless Channels Dscrete Meorless Channels Source Channel Snk Aount of Inforaton avalable Source Entro Generall nos, dstorted and a be te varng ow uch nforaton s receved? ow uch s lost? Introduces error and lts the rate

More information

APPENDIX A Some Linear Algebra

APPENDIX A Some Linear Algebra APPENDIX A Some Lnear Algebra The collecton of m, n matrces A.1 Matrces a 1,1,..., a 1,n A = a m,1,..., a m,n wth real elements a,j s denoted by R m,n. If n = 1 then A s called a column vector. Smlarly,

More information

Appendix B. Criterion of Riemann-Stieltjes Integrability

Appendix B. Criterion of Riemann-Stieltjes Integrability Appendx B. Crteron of Remann-Steltes Integrablty Ths note s complementary to [R, Ch. 6] and [T, Sec. 3.5]. The man result of ths note s Theorem B.3, whch provdes the necessary and suffcent condtons for

More information

Notes on Frequency Estimation in Data Streams

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

More information

EEE 241: Linear Systems

EEE 241: Linear Systems EEE : Lnear Systems Summary #: Backpropagaton BACKPROPAGATION The perceptron rule as well as the Wdrow Hoff learnng were desgned to tran sngle layer networks. They suffer from the same dsadvantage: they

More information

The Impact of the Earth s Movement through the Space on Measuring the Velocity of Light

The Impact of the Earth s Movement through the Space on Measuring the Velocity of Light Journal of Appled Matheatcs and Physcs, 6, 4, 68-78 Publshed Onlne June 6 n ScRes http://wwwscrporg/journal/jap http://dxdoorg/436/jap646 The Ipact of the Earth s Moeent through the Space on Measurng the

More information

How to Find Good Finite-Length Codes: From Art Towards Science

How to Find Good Finite-Length Codes: From Art Towards Science How to Fnd Good Fnte-Length Codes: From Art Towards Scence Abdelazz Amraou Andrea Montanar and Ruedger Urbanke arxv:cs.it/6764 v 3 Jul 26 Abstract We explan how to optmze fnte-length LDPC codes for transmsson

More information

Infinite Length MMSE Decision Feedback Equalization

Infinite Length MMSE Decision Feedback Equalization Infnte Lengt SE ecson Feedbac Equalzaton FE N * * Y F Z ' Z SS ˆ Y Q N b... Infnte-Lengt ecson Feedbac Equalzer as reoval ^ Y Z Feedforward Flter Feedbac Flter - Input to Slcer - Z Y Assung prevous decsons

More information

Stanford University CS359G: Graph Partitioning and Expanders Handout 4 Luca Trevisan January 13, 2011

Stanford University CS359G: Graph Partitioning and Expanders Handout 4 Luca Trevisan January 13, 2011 Stanford Unversty CS359G: Graph Parttonng and Expanders Handout 4 Luca Trevsan January 3, 0 Lecture 4 In whch we prove the dffcult drecton of Cheeger s nequalty. As n the past lectures, consder an undrected

More information

Designing Fuzzy Time Series Model Using Generalized Wang s Method and Its application to Forecasting Interest Rate of Bank Indonesia Certificate

Designing Fuzzy Time Series Model Using Generalized Wang s Method and Its application to Forecasting Interest Rate of Bank Indonesia Certificate The Frst Internatonal Senar on Scence and Technology, Islac Unversty of Indonesa, 4-5 January 009. Desgnng Fuzzy Te Seres odel Usng Generalzed Wang s ethod and Its applcaton to Forecastng Interest Rate

More information

Modified parallel multisplitting iterative methods for non-hermitian positive definite systems

Modified parallel multisplitting iterative methods for non-hermitian positive definite systems Adv Coput ath DOI 0.007/s0444-0-9262-8 odfed parallel ultsplttng teratve ethods for non-hertan postve defnte systes Chuan-Long Wang Guo-Yan eng Xue-Rong Yong Receved: Septeber 20 / Accepted: 4 Noveber

More information

A DISCONTINUOUS LEAST-SQUARES SPATIAL DISCRETIZATION FOR THE S N EQUATIONS. A Thesis LEI ZHU

A DISCONTINUOUS LEAST-SQUARES SPATIAL DISCRETIZATION FOR THE S N EQUATIONS. A Thesis LEI ZHU A DISCONTINUOUS LEAST-SQUARES SPATIAL DISCRETIZATION FOR THE S N EQUATIONS A Thess by LEI ZHU Subtted to the Offce of Graduate Studes of Texas A&M Unversty n partal fulfllent of the requreents for the

More information

ISSN: ISO 9001:2008 Certified International Journal of Engineering and Innovative Technology (IJEIT) Volume 3, Issue 1, July 2013

ISSN: ISO 9001:2008 Certified International Journal of Engineering and Innovative Technology (IJEIT) Volume 3, Issue 1, July 2013 ISSN: 2277-375 Constructon of Trend Free Run Orders for Orthogonal rrays Usng Codes bstract: Sometmes when the expermental runs are carred out n a tme order sequence, the response can depend on the run

More information

Final Exam Solutions, 1998

Final Exam Solutions, 1998 58.439 Fnal Exa Solutons, 1998 roble 1 art a: Equlbru eans that the therodynac potental of a consttuent s the sae everywhere n a syste. An exaple s the Nernst potental. If the potental across a ebrane

More information