2. Grundlegende Verfahren zur Übertragung digitaler Signale (Zusammenfassung) Informationstechnik Universität Ulm

Size: px
Start display at page:

Download "2. Grundlegende Verfahren zur Übertragung digitaler Signale (Zusammenfassung) Informationstechnik Universität Ulm"

Transcription

1 . Grundlgnd Vrfahrn zur Übrtragung dgtalr Sgnal (Zusammnfassung) wt Dc. 5

2 Transmsson of Dgtal Sourc Sgnals Sourc COD SC COD MOD MOD CC dg RF s rado transmsson mdum Snk DC SC DC CC DM dg DM RF g physcal channl analog sgnal modm channl analog sgnal dscrt channl, sourc-snk dgtal sgnals dscrt channl, sourc codng dgtal sgnals dscrt channl, channl codng dgtal sgnals Sourc COD COD MOD MOD SC CC dg RF dgtal sourc sourc codng channl codng modulaton, dgtal transmsson modulaton, rf transmttr Snk DC DC DM DM SD CD dg RF dgtal snk sourc dcodng channal dcodng dmodulaton, dgtal transmsson dmodulaton, RF rcvr wt Dc. 5

3 Transmsson wth M Basc Wavforms: Transmttr xampl: 4PSK (M4) slcton by nformaton sourc t t s ntrfrnc transmsson channl g SLCTION t 3 t M 4 df frnt wavforms wt Dc. 5 3

4 Transmsson wth M Basc Wavforms: Rcvr xampl: 4PSK (M4) procssng: comparson wth dcson varabls y dcson DCISION g wth wth y y max. ndx of transmttd wavform ( bt) wth 3 y 3 wt Dc. 5 4

5 Bnary Transmsson wt Dc. 5 5

6 Bnary Transmsson ovr an AWGN Channl slcton by nformaton sourc compar wth ( t ) dcson WGN n, N P s + g P max. No. of most probably transmttd wavform transmttr AWGN channl compar wth ( t ) rcvr wt Dc. 5 6

7 Optmum Dtctor () Startng-pont: Probablty thory Sgnal spac rprsntaton of sgnals Knowldg at th dtctor: Rcvd sgnal g( t) (vnt ) ( t) + n( t) Statstcs of th addtv nos procss (wht Gaussan nos, WGN) Hypothss: : ( t ) has bn transmttd : ( t ) has bn transmttd Condtonal probablts: P Prob{ ( t) g( t) } { ( t) g( )} P Prob t wt Dc. 5 7

8 Optmum Dtctor () Maxmum A Postror (MAP) dcson rul: Dcd n favor of that hypothss or whch s th most probabl caus for vnt Usng Bays rul w obtan for th most probabl caus for vnt f and ar mutually xclusv vnts: k arg { [ ]} max Prob( ( t)) Prob(g ( t) Maxmum Lklhood (ML) dcson rul: ( ) Most lkly caus for vnt : k arg { [ ]} max Prob(g ( t) ( ) wt Dc. 5 8

9 Bnary Tx: Rprsntaton of Condtonal PDF () Sgnal vctor spac rprsntaton: 3D Plot D Gaussan dstrbutons Basc wavforms, rx sgnal g, and nos sampl functon n ar rprsntd by D vctors, g,and n p g ( x ) p g ( x ) p g ( x ) p n ( x ) N x xp π ( N f ) ( N f g ) g N-dmnsonal condtonal jont probablty dnsty functon wt Dc. 5 9

10 Bnary Tx: Rprsntaton of Condtonal PDF () D Contour Plot Dcson Boundary Mnmum Dstanc Rul (drvd from ML-prncpl): Calculat dstanc btwn rx sgnal g and th possbl tx sgnals and. Dcd n favor of mnmum dstanc. wt Dc. 5

11 Mnmum Dstanc Dcson Rul Calculaton of dstancs n sgnal vctor spac: g, ( g g ) ( g, g) + (, ) ( g, ) (, g) R{ ( )} g g, Modfd mnmum dstanc dcson rul : k arg max R {( g, )} basc wavform k has most lkly bn tx wt Dc. 5

12 Calculaton of th Dcson Varabls Intgrat and dump : ( g, ) g( t) ( t) dt Corrlaton fltr or Matchd Fltr (MF) ( g, ) ϕ () ϕ () g( t) ( t) g g wt Dc. 5

13 D Probablty Dnsty Functon at Output of MF transmttd transmttd Dcson boundary wt Dc. 5 3

14 wt Dc. 5 4 Informatonstchnk Unvrstät Ulm Bt rror Probablts for Bnary Transmsson () () 8 ) ( rfc ) ( N y y c P P y smlar: P P Avrag total symbol rror rat P s : Avrag total bt rror rat (BR) P b for bnary tx: 8 ) ( rfc N y y P P s b.5.5 s ) ( Prob( ) ( ( Prob P t P t P +

15 Optmum Rx for Bnary Tx: Spcal Cass () Antpodal transmsson t ( t) ( t) ( t) ( t) g (-t) k or k Unpolar transmsson t ( t) ( t) ( t) g (-t) dcson k or k thrshold : nrgy of wt Dc. 5 5

16 Optmum Rx for Bnary Tx: Spcal Cass () Orthogonal transmsson (M) ( t) ( t) t g Δ ( t ) k or k Δ( t) ( t) ( t) dcson as for ant-podal transmsson wt Dc. 5 6

17 Sgnal-to-Nos Ratos and Bt rror Probablty antpodal on-off orthogonal SNR a N N N b N N N N rfc N rfc 8 N rfc 4 N P b rfc SNR a rfc 4 SNRa rfc SNRa rfc N b rfc 4 N b rfc 4 N b wt Dc. 5 7

18 Bt rror Probablts for Bnary Transmsson unpolar & orthogonal on-off kyng orthogonal antpodal bpolar wt Dc. 5 8

19 Contnuous Transmsson () δ -samplr basc wavform matchd fltr samplr dcson kts (-t) x (k) s g y x~ (k) x(k) kts squnc of tx symbols tx sgnal rx sgnal squnc of stmatd symbols squnc of dtctd symbols unpolar & antpodal transmsson wt Dc. 5 9

20 Contnuous Transmsson () Symbol ntrval: Symbol rat: T s r s T s In gnral not dntcal wth duraton T of basc wavform! Transmt sgnal: wth: s ( t) x( k) ( t kt s ) x( k) x( k) k {, + } for antpodal {, + } for unpolar tx tx Sgnal at output of matchd fltr (n): y( t) n t) [ x( ) ( t T )] ( ) ( s t ϕ ( t T ) s wt Dc. 5

21 Contnuous Transmsson (3) Dcson varabl (squnc of stmatd symbols): ~ x ( k) y( kts ) n( t) x( k) ϕ() + x( ) ( kts T ) k n( t) ϕ s usfull trm ntrsymbol ntrfrnc (ISI). Nyqust crtron for unpolar and antpodal transmsson: ( s ϕ kt ) for k for k ~ x ( k) n( t ) x( k) - ISI avodd - sam dcson varabl as for sngl tx wt Dc. 5

22 Frst Nyqust Crtron: xampl y pattrn: - ISI, but - y stll opn nos margn tolranc wth rspct to dvatons from th optmum samplng tm wt Dc. 5

23 M-ary Transmsson wt Dc. 5 3

24 M-Orthogonal Transmsson: Optmum Rcvr Matchd fltr bank Samplng t Dcson vctor Dcson (-t) g y y t for,..., M (-t) M- (-t) y y M- t y y M- max No. of most probably transmttd wavform wt Dc. 5 4

25 M-Orthogonal Transmsson: Basc Wavforms wal(,t) c wal(,t) c t xampl for M-orthogonal tx wth M4 basc wavforms (Walsh-Hadamard-Cods) t wal(,t) c t wal(3,t) c t T wt Dc. 5 5

26 M-Orth. Tx: Jont Probablty Dnsty Functons wt Dc. 5 6

27 M-Orthogonal Tx: rror Probablts Avrag symbol rror probablty: P πn xp ( a ) N rfc a N M da no closd form soluton for ntgral Avrag bt rror probablty: P M ( M ) b P s mappng bts->symbols has no nflunc on P b wt Dc. 5 7

28 M-Orthogonal Tx: Bt rror Probablty () Shannonlmt lmt for M powr ffcnt transmsson 64 M ln wt Dc. 5 8

29 Gray Codng xampl: Borthogonal Tx wth M4 Gray codng: bt combnaton of nghbourng symbols dffr n only on poston - -.Bt.Bt wt Dc. 5 9

30 Contnuous M-Orthogonal Transmsson Transmt sgnal: s { ( t), ( t),..., ( t) } ( t) ( k )( t kts ) wth ( k )( t) M k Sgnal at output of matchd fltr m : y m Gnralzd frst Nyqust crtron: ( l Ts ) ϕ () + ϕ ( l Ts k Ts ) m m ( k ) m k l usfull trm ntrsymbol ntrfrnc (ISI) ϕ l ( kt ) δ ( k) δ ( l) for, l,,..., M s wt Dc. 5 3

31 Gnralzd Frst Nyqust Crtron Gnralzd frst Nyqust crtron n frquncy doman T s ( f ) * l ( f ) δ ( l) pr Ts l xampl: l ( f ) ( f ) : l T S. "Nyqust slops", must b oddsymmtrc wth rgard to cntr f wt Dc. 5 3

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

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

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

Outlier-tolerant parameter estimation

Outlier-tolerant parameter estimation Outlr-tolrant paramtr stmaton Baysan thods n physcs statstcs machn larnng and sgnal procssng (SS 003 Frdrch Fraundorfr fraunfr@cg.tu-graz.ac.at Computr Graphcs and Vson Graz Unvrsty of Tchnology Outln

More information

Soft k-means Clustering. Comp 135 Machine Learning Computer Science Tufts University. Mixture Models. Mixture of Normals in 1D

Soft k-means Clustering. Comp 135 Machine Learning Computer Science Tufts University. Mixture Models. Mixture of Normals in 1D Comp 35 Machn Larnng Computr Scnc Tufts Unvrsty Fall 207 Ron Khardon Th EM Algorthm Mxtur Modls Sm-Suprvsd Larnng Soft k-mans Clustrng ck k clustr cntrs : Assocat xampls wth cntrs p,j ~~ smlarty b/w cntr

More information

Consider the following passband digital communication system model. c t. modulator. t r a n s m i t t e r. signal decoder.

Consider the following passband digital communication system model. c t. modulator. t r a n s m i t t e r. signal decoder. PASSBAND DIGITAL MODULATION TECHNIQUES Consder the followng passband dgtal communcaton system model. cos( ω + φ ) c t message source m sgnal encoder s modulator s () t communcaton xt () channel t r a n

More information

Grand Canonical Ensemble

Grand Canonical Ensemble Th nsmbl of systms mmrsd n a partcl-hat rsrvor at constant tmpratur T, prssur P, and chmcal potntal. Consdr an nsmbl of M dntcal systms (M =,, 3,...M).. Thy ar mutually sharng th total numbr of partcls

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

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

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

More information

The Fourier Transform

The Fourier Transform /9/ Th ourr Transform Jan Baptst Josph ourr 768-83 Effcnt Data Rprsntaton Data can b rprsntd n many ways. Advantag usng an approprat rprsntaton. Eampls: osy ponts along a ln Color spac rd/grn/blu v.s.

More information

Logistic Regression I. HRP 261 2/10/ am

Logistic Regression I. HRP 261 2/10/ am Logstc Rgrsson I HRP 26 2/0/03 0- am Outln Introducton/rvw Th smplst logstc rgrsson from a 2x2 tabl llustrats how th math works Stp-by-stp xampls to b contnud nxt tm Dummy varabls Confoundng and ntracton

More information

10/7/14. Mixture Models. Comp 135 Introduction to Machine Learning and Data Mining. Maximum likelihood estimation. Mixture of Normals in 1D

10/7/14. Mixture Models. Comp 135 Introduction to Machine Learning and Data Mining. Maximum likelihood estimation. Mixture of Normals in 1D Comp 35 Introducton to Machn Larnng and Data Mnng Fall 204 rofssor: Ron Khardon Mxtur Modls Motvatd by soft k-mans w dvlopd a gnratv modl for clustrng. Assum thr ar k clustrs Clustrs ar not rqurd to hav

More information

??? Dynamic Causal Modelling for M/EEG. Electroencephalography (EEG) Dynamic Causal Modelling. M/EEG analysis at sensor level. time.

??? Dynamic Causal Modelling for M/EEG. Electroencephalography (EEG) Dynamic Causal Modelling. M/EEG analysis at sensor level. time. Elctroncphalography EEG Dynamc Causal Modllng for M/EEG ampltud μv tm ms tral typ 1 tm channls channls tral typ 2 C. Phllps, Cntr d Rchrchs du Cyclotron, ULg, Blgum Basd on slds from: S. Kbl M/EEG analyss

More information

Analyzing Frequencies

Analyzing Frequencies Frquncy (# ndvduals) Frquncy (# ndvduals) /3/16 H o : No dffrnc n obsrvd sz frquncs and that prdctd by growth modl How would you analyz ths data? 15 Obsrvd Numbr 15 Expctd Numbr from growth modl 1 1 5

More information

EGR 544 Communication Theory

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

More information

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

An Overview of Markov Random Field and Application to Texture Segmentation

An Overview of Markov Random Field and Application to Texture Segmentation An Ovrvw o Markov Random Fld and Applcaton to Txtur Sgmntaton Song-Wook Joo Octobr 003. What s MRF? MRF s an xtnson o Markov Procss MP (D squnc o r.v. s unlatral (causal: p(x t x,

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

Code Design for the Low SNR Noncoherent MIMO Block Rayleigh Fading Channel

Code Design for the Low SNR Noncoherent MIMO Block Rayleigh Fading Channel Cod Dsgn for th Low SNR Noncohrnt MIMO Block Raylgh Fadng Channl Shvratna Gr Srnvasan and Mahsh K. Varanas -mal: {srnvsg, varanas}@dsp.colorado.du Elctrcal & Computr Engnrng Dpartmnt Unvrsty of Colorado,

More information

Assuming that the transmission delay is negligible, we have

Assuming that the transmission delay is negligible, we have Baseband Transmsson of Bnary Sgnals Let g(t), =,, be a sgnal transmtted over an AWG channel. Consder the followng recever g (t) + + Σ x(t) LTI flter h(t) y(t) t = nt y(nt) threshold comparator Decson ˆ

More information

Review - Probabilistic Classification

Review - Probabilistic Classification Mmoral Unvrsty of wfoundland Pattrn Rcognton Lctur 8 May 5, 6 http://www.ngr.mun.ca/~charlsr Offc Hours: Tusdays Thursdays 8:3-9:3 PM E- (untl furthr notc) Gvn lablld sampls { ɛc,,,..., } {. Estmat Rvw

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

Digital Modems. Lecture 2

Digital Modems. Lecture 2 Dgtal Modems Lecture Revew We have shown that both Bayes and eyman/pearson crtera are based on the Lkelhood Rato Test (LRT) Λ ( r ) < > η Λ r s called observaton transformaton or suffcent statstc The crtera

More information

by log b, the natural logarithm by ln. The Kronecker product of two matrices is denoted by Ω.

by log b, the natural logarithm by ln. The Kronecker product of two matrices is denoted by Ω. 00 Confrnc on Informaton Scncs and Systms, Th Johns Hopkns Unvrsty, March, 00 Cohrnt Multusr Spac-Tm Communcatons: Optmum Rcvrs and Sgnal Dsgn Matthas Brhlr and Mahsh K. Varanas -mal: fbrhlr, varanasg@dsp.colorado.du

More information

September 27, Introduction to Ordinary Differential Equations. ME 501A Seminar in Engineering Analysis Page 1. Outline

September 27, Introduction to Ordinary Differential Equations. ME 501A Seminar in Engineering Analysis Page 1. Outline Introucton to Ornar Dffrntal Equatons Sptmbr 7, 7 Introucton to Ornar Dffrntal Equatons Larr artto Mchancal Engnrng AB Smnar n Engnrng Analss Sptmbr 7, 7 Outln Rvw numrcal solutons Bascs of ffrntal quatons

More information

Diversity and Spatial Multiplexing of MIMO Amplitude Detection Receivers

Diversity and Spatial Multiplexing of MIMO Amplitude Detection Receivers Dvrst and Spatal Multplxng of MIMO mpltud Dtcton Rcvrs Gorgos K. Psaltopoulos and rmn Wttnbn Communcaton Tchnolog Laborator, ETH Zurch, CH-809 Zurch, Swtzrland Emal: {psaltopoulos, wttnbn}@nar..thz.ch

More information

Maximum Likelihood Estimation of Binary Dependent Variables Models: Probit and Logit. 1. General Formulation of Binary Dependent Variables Models

Maximum Likelihood Estimation of Binary Dependent Variables Models: Probit and Logit. 1. General Formulation of Binary Dependent Variables Models ECO 452 -- OE 4: Probt and Logt Models ECO 452 -- OE 4 Maxmum Lkelhood Estmaton of Bnary Dependent Varables Models: Probt and Logt hs note demonstrates how to formulate bnary dependent varables models

More information

SER/BER in a Fading Channel

SER/BER in a Fading Channel SER/BER in a Fading Channl Major points for a fading channl: * SNR is a R.V. or R.P. * SER(BER) dpnds on th SNR conditional SER(BER). * Two prformanc masurs: outag probability and avrag SER(BER). * Ovrall,

More information

ST2352. Working backwards with conditional probability. ST2352 Week 8 1

ST2352. Working backwards with conditional probability. ST2352 Week 8 1 ST35 Workng backwards wth condtonal probablty ST35 Week 8 Roll two reg dce. One s 6; Pr(other s 6)? AR smulaton gves Y t = 3. Dst of Y t-? Y t = = Y t- + t ; t ~ N(0,) =? =0.5 ST35 Week 8 Sally Clarke

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

8-node quadrilateral element. Numerical integration

8-node quadrilateral element. Numerical integration Fnt Elmnt Mthod lctur nots _nod quadrlatral lmnt Pag of 0 -nod quadrlatral lmnt. Numrcal ntgraton h tchnqu usd for th formulaton of th lnar trangl can b formall tndd to construct quadrlatral lmnts as wll

More information

Maximum Likelihood Estimation of Binary Dependent Variables Models: Probit and Logit. 1. General Formulation of Binary Dependent Variables Models

Maximum Likelihood Estimation of Binary Dependent Variables Models: Probit and Logit. 1. General Formulation of Binary Dependent Variables Models ECO 452 -- OE 4: Probt and Logt Models ECO 452 -- OE 4 Mamum Lkelhood Estmaton of Bnary Dependent Varables Models: Probt and Logt hs note demonstrates how to formulate bnary dependent varables models for

More information

Physics 256: Lecture 2. Physics

Physics 256: Lecture 2. Physics Physcs 56: Lctur Intro to Quantum Physcs Agnda for Today Complx Numbrs Intrfrnc of lght Intrfrnc Two slt ntrfrnc Dffracton Sngl slt dffracton Physcs 01: Lctur 1, Pg 1 Constructv Intrfrnc Ths wll occur

More information

A Self-adaptive open loop architecture for weak GNSS signal tracking

A Self-adaptive open loop architecture for weak GNSS signal tracking NTERNATONAL JOURNAL OF CRCUTS, SYSTEMS AND SGNAL PROCESSNG Volum 8, 014 A Slf-adaptv opn loop archtctur for wa GNSS sgnal tracng Ao Png, Gang Ou, Janghong Sh Abstract An FFT-basd opn loop carrr tracng

More information

Goodness of fit and Wilks theorem

Goodness of fit and Wilks theorem DRAFT 0.0 Glen Cowan 3 June, 2013 Goodness of ft and Wlks theorem Suppose we model data y wth a lkelhood L(µ) that depends on a set of N parameters µ = (µ 1,...,µ N ). Defne the statstc t µ ln L(µ) L(ˆµ),

More information

Signal space Review on vector space Linear independence Metric space and norm Inner product

Signal space Review on vector space Linear independence Metric space and norm Inner product Sgnal space.... Revew on vector space.... Lnear ndependence... 3.3 Metrc space and norm... 4.4 Inner product... 5.5 Orthonormal bass... 7.6 Waveform communcaton system... 9.7 Some examples... 6 Sgnal space

More information

Homework Assignment 3 Due in class, Thursday October 15

Homework Assignment 3 Due in class, Thursday October 15 Homework Assgnment 3 Due n class, Thursday October 15 SDS 383C Statstcal Modelng I 1 Rdge regresson and Lasso 1. Get the Prostrate cancer data from http://statweb.stanford.edu/~tbs/elemstatlearn/ datasets/prostate.data.

More information

An Application of Fuzzy Hypotheses Testing in Radar Detection

An Application of Fuzzy Hypotheses Testing in Radar Detection Proceedngs of the th WSES Internatonal Conference on FUZZY SYSEMS n pplcaton of Fuy Hypotheses estng n Radar Detecton.K.ELSHERIF, F.M.BBDY, G.M.BDELHMID Department of Mathematcs Mltary echncal Collage

More information

Internet Engineering. Jacek Mazurkiewicz, PhD Softcomputing. Part 3: Recurrent Artificial Neural Networks Self-Organising Artificial Neural Networks

Internet Engineering. Jacek Mazurkiewicz, PhD Softcomputing. Part 3: Recurrent Artificial Neural Networks Self-Organising Artificial Neural Networks Internet Engneerng Jacek Mazurkewcz, PhD Softcomputng Part 3: Recurrent Artfcal Neural Networks Self-Organsng Artfcal Neural Networks Recurrent Artfcal Neural Networks Feedback sgnals between neurons Dynamc

More information

ÉCOLE POLYTECHNIQUE FÉDÉRALE DE LAUSANNE

ÉCOLE POLYTECHNIQUE FÉDÉRALE DE LAUSANNE ÉCOLE POLYTECHNIQUE FÉDÉRALE DE LAUSANNE School of Computer and Communcaton Scences Handout 0 Prncples of Dgtal Communcatons Solutons to Problem Set 4 Mar. 6, 08 Soluton. If H = 0, we have Y = Z Z = Y

More information

CIS526: Machine Learning Lecture 3 (Sept 16, 2003) Linear Regression. Preparation help: Xiaoying Huang. x 1 θ 1 output... θ M x M

CIS526: Machine Learning Lecture 3 (Sept 16, 2003) Linear Regression. Preparation help: Xiaoying Huang. x 1 θ 1 output... θ M x M CIS56: achne Learnng Lecture 3 (Sept 6, 003) Preparaton help: Xaoyng Huang Lnear Regresson Lnear regresson can be represented by a functonal form: f(; θ) = θ 0 0 +θ + + θ = θ = 0 ote: 0 s a dummy attrbute

More information

MACHINE APPLIED MACHINE LEARNING LEARNING. Gaussian Mixture Regression

MACHINE APPLIED MACHINE LEARNING LEARNING. Gaussian Mixture Regression 11 MACHINE APPLIED MACHINE LEARNING LEARNING MACHINE LEARNING Gaussan Mture Regresson 22 MACHINE APPLIED MACHINE LEARNING LEARNING Bref summary of last week s lecture 33 MACHINE APPLIED MACHINE LEARNING

More information

The Hyperelastic material is examined in this section.

The Hyperelastic material is examined in this section. 4. Hyprlastcty h Hyprlastc matral s xad n ths scton. 4..1 Consttutv Equatons h rat of chang of ntrnal nrgy W pr unt rfrnc volum s gvn by th strss powr, whch can b xprssd n a numbr of dffrnt ways (s 3.7.6):

More information

Basic Statistical Analysis and Yield Calculations

Basic Statistical Analysis and Yield Calculations October 17, 007 Basc Statstcal Analyss and Yeld Calculatons Dr. José Ernesto Rayas Sánchez 1 Outlne Sources of desgn-performance uncertanty Desgn and development processes Desgn for manufacturablty A general

More information

Module 2. Random Processes. Version 2 ECE IIT, Kharagpur

Module 2. Random Processes. Version 2 ECE IIT, Kharagpur Module Random Processes Lesson 6 Functons of Random Varables After readng ths lesson, ou wll learn about cdf of functon of a random varable. Formula for determnng the pdf of a random varable. Let, X be

More information

FUZZY FINITE ELEMENT METHOD

FUZZY FINITE ELEMENT METHOD FUZZY FINITE ELEMENT METHOD RELIABILITY TRUCTURE ANALYI UING PROBABILITY 3.. Maxmum Normal tress Internal force s the shear force, V has a magntude equal to the load P and bendng moment, M. Bendng moments

More information

Transform Coding. Transform Coding Principle

Transform Coding. Transform Coding Principle Transform Codng Prncple of block-wse transform codng Propertes of orthonormal transforms Dscrete cosne transform (DCT) Bt allocaton for transform coeffcents Entropy codng of transform coeffcents Typcal

More information

Logistic Regression. CAP 5610: Machine Learning Instructor: Guo-Jun QI

Logistic Regression. CAP 5610: Machine Learning Instructor: Guo-Jun QI Logstc Regresson CAP 561: achne Learnng Instructor: Guo-Jun QI Bayes Classfer: A Generatve model odel the posteror dstrbuton P(Y X) Estmate class-condtonal dstrbuton P(X Y) for each Y Estmate pror dstrbuton

More information

Quantum and Classical Information Theory with Disentropy

Quantum and Classical Information Theory with Disentropy Quantum and Classcal Informaton Theory wth Dsentropy R V Ramos rubensramos@ufcbr Lab of Quantum Informaton Technology, Department of Telenformatc Engneerng Federal Unversty of Ceara - DETI/UFC, CP 6007

More information

Predictive Analytics : QM901.1x Prof U Dinesh Kumar, IIMB. All Rights Reserved, Indian Institute of Management Bangalore

Predictive Analytics : QM901.1x Prof U Dinesh Kumar, IIMB. All Rights Reserved, Indian Institute of Management Bangalore Sesson Outlne Introducton to classfcaton problems and dscrete choce models. Introducton to Logstcs Regresson. Logstc functon and Logt functon. Maxmum Lkelhood Estmator (MLE) for estmaton of LR parameters.

More information

Chapter 1. Probability

Chapter 1. Probability Chapter. Probablty Mcroscopc propertes of matter: quantum mechancs, atomc and molecular propertes Macroscopc propertes of matter: thermodynamcs, E, H, C V, C p, S, A, G How do we relate these two propertes?

More information

Chapter 6 Student Lecture Notes 6-1

Chapter 6 Student Lecture Notes 6-1 Chaptr 6 Studnt Lctur Nots 6-1 Chaptr Goals QM353: Busnss Statstcs Chaptr 6 Goodnss-of-Ft Tsts and Contngncy Analyss Aftr compltng ths chaptr, you should b abl to: Us th ch-squar goodnss-of-ft tst to dtrmn

More information

Bayesian decision theory. Nuno Vasconcelos ECE Department, UCSD

Bayesian decision theory. Nuno Vasconcelos ECE Department, UCSD Bayesan decson theory Nuno Vasconcelos ECE Department, UCSD Bayesan decson theory recall that we have state of the world observatons decson functon L[,y] loss of predctn y wth the epected value of the

More information

Communication with AWGN Interference

Communication with AWGN Interference Communcaton wth AWG Interference m {m } {p(m } Modulator s {s } r=s+n Recever ˆm AWG n m s a dscrete random varable(rv whch takes m wth probablty p(m. Modulator maps each m nto a waveform sgnal s m=m

More information

OFDM is a good candidate for wireless multimedia communication

OFDM is a good candidate for wireless multimedia communication Detecton of OFDM-CPM Sgnals over Multpath Channels Imran A. Tasadduq and Raveendra K. Rao Department of Electrcal & Computer Engneerng Elborn College, 11 Western Road The Unversty of Western Ontaro London,

More information

Mathematical Models for Information Sources A Logarithmic i Measure of Information

Mathematical Models for Information Sources A Logarithmic i Measure of Information Introducton to Informaton Theory Wreless Informaton Transmsson System Lab. Insttute of Communcatons Engneerng g Natonal Sun Yat-sen Unversty Table of Contents Mathematcal Models for Informaton Sources

More information

Problem Set #2 Due: Friday April 20, 2018 at 5 PM.

Problem Set #2 Due: Friday April 20, 2018 at 5 PM. 1 EE102B Spring 2018 Signal Procssing and Linar Systms II Goldsmith Problm St #2 Du: Friday April 20, 2018 at 5 PM. 1. Non-idal sampling and rcovry of idal sampls by discrt-tim filtring 30 pts) Considr

More information

xp(x µ) = 0 p(x = 0 µ) + 1 p(x = 1 µ) = µ

xp(x µ) = 0 p(x = 0 µ) + 1 p(x = 1 µ) = µ CSE 455/555 Sprng 2013 Homework 7: Parametrc Technques Jason J. Corso Computer Scence and Engneerng SUY at Buffalo jcorso@buffalo.edu Solutons by Yngbo Zhou Ths assgnment does not need to be submtted and

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

Lossy Compression. Compromise accuracy of reconstruction for increased compression.

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

More information

A Hybrid Variational Iteration Method for Blasius Equation

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

More information

CHAPTER 4. The First Law of Thermodynamics for Control Volumes

CHAPTER 4. The First Law of Thermodynamics for Control Volumes CHAPTER 4 T Frst Law of Trodynacs for Control olus CONSERATION OF MASS Consrvaton of ass: Mass, lk nrgy, s a consrvd proprty, and t cannot b cratd or dstroyd durng a procss. Closd systs: T ass of t syst

More information

Linear Approximation with Regularization and Moving Least Squares

Linear Approximation with Regularization and Moving Least Squares Lnear Approxmaton wth Regularzaton and Movng Least Squares Igor Grešovn May 007 Revson 4.6 (Revson : March 004). 5 4 3 0.5 3 3.5 4 Contents: Lnear Fttng...4. Weghted Least Squares n Functon Approxmaton...

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

Strong Markov property: Same assertion holds for stopping times τ.

Strong Markov property: Same assertion holds for stopping times τ. Brownan moton Let X ={X t : t R + } be a real-valued stochastc process: a famlty of real random varables all defned on the same probablty space. Defne F t = nformaton avalable by observng the process up

More information

Polynomial Regression Models

Polynomial Regression Models LINEAR REGRESSION ANALYSIS MODULE XII Lecture - 6 Polynomal Regresson Models Dr. Shalabh Department of Mathematcs and Statstcs Indan Insttute of Technology Kanpur Test of sgnfcance To test the sgnfcance

More information

Maximum Likelihood Estimation

Maximum Likelihood Estimation Maxmum Lkelhood Estmaton INFO-2301: Quanttatve Reasonng 2 Mchael Paul and Jordan Boyd-Graber MARCH 7, 2017 INFO-2301: Quanttatve Reasonng 2 Paul and Boyd-Graber Maxmum Lkelhood Estmaton 1 of 9 Why MLE?

More information

Classification as a Regression Problem

Classification as a Regression Problem Target varable y C C, C,, ; Classfcaton as a Regresson Problem { }, 3 L C K To treat classfcaton as a regresson problem we should transform the target y nto numercal values; The choce of numercal class

More information

A Comparison between Weight Spectrum of Different Convolutional Code Types

A Comparison between Weight Spectrum of Different Convolutional Code Types A Comparson between Weght Spectrum of fferent Convolutonal Code Types Baltă Hora, Kovac Mara Abstract: In ths paper we present the non-recursve systematc, recursve systematc and non-recursve non-systematc

More information

Limited Dependent Variables

Limited Dependent Variables Lmted Dependent Varables. What f the left-hand sde varable s not a contnuous thng spread from mnus nfnty to plus nfnty? That s, gven a model = f (, β, ε, where a. s bounded below at zero, such as wages

More information

THE ROYAL STATISTICAL SOCIETY 2006 EXAMINATIONS SOLUTIONS HIGHER CERTIFICATE

THE ROYAL STATISTICAL SOCIETY 2006 EXAMINATIONS SOLUTIONS HIGHER CERTIFICATE THE ROYAL STATISTICAL SOCIETY 6 EXAMINATIONS SOLUTIONS HIGHER CERTIFICATE PAPER I STATISTICAL THEORY The Socety provdes these solutons to assst canddates preparng for the eamnatons n future years and for

More information

Laboratory 1c: Method of Least Squares

Laboratory 1c: Method of Least Squares Lab 1c, Least Squares Laboratory 1c: Method of Least Squares Introducton Consder the graph of expermental data n Fgure 1. In ths experment x s the ndependent varable and y the dependent varable. Clearly

More information

IRO0140 Advanced space time-frequency signal processing

IRO0140 Advanced space time-frequency signal processing IRO4 Advanced space tme-frequency sgnal processng Lecture Toomas Ruuben Takng nto account propertes of the sgnals, we can group these as followng: Regular and random sgnals (are all sgnal parameters determned

More information

Statistics and Probability Theory in Civil, Surveying and Environmental Engineering

Statistics and Probability Theory in Civil, Surveying and Environmental Engineering Statstcs and Probablty Theory n Cvl, Surveyng and Envronmental Engneerng Pro. Dr. Mchael Havbro Faber ETH Zurch, Swtzerland Contents o Todays Lecture Overvew o Uncertanty Modelng Random Varables - propertes

More information

Level crossing rate of relay system over LOS multipath fading channel with one and two clusters in the presence of co-channel interference

Level crossing rate of relay system over LOS multipath fading channel with one and two clusters in the presence of co-channel interference INFOTEHJAHORINA Vol 5, March 6 Lvl crossng rat of rla sstm ovr LOS multath fadng channl th on and to clustrs n th rsnc of cochannl ntrfrnc Goran Ptovć, Zorca Nolć Facult of Elctronc Engnrng Unvrst of Nš

More information

P R. Lecture 4. Theory and Applications of Pattern Recognition. Dept. of Electrical and Computer Engineering /

P R. Lecture 4. Theory and Applications of Pattern Recognition. Dept. of Electrical and Computer Engineering / Theory and Applcatons of Pattern Recognton 003, Rob Polkar, Rowan Unversty, Glassboro, NJ Lecture 4 Bayes Classfcaton Rule Dept. of Electrcal and Computer Engneerng 0909.40.0 / 0909.504.04 Theory & Applcatons

More information

COMPLEX NUMBER PAIRWISE COMPARISON AND COMPLEX NUMBER AHP

COMPLEX NUMBER PAIRWISE COMPARISON AND COMPLEX NUMBER AHP ISAHP 00, Bal, Indonsa, August -9, 00 COMPLEX NUMBER PAIRWISE COMPARISON AND COMPLEX NUMBER AHP Chkako MIYAKE, Kkch OHSAWA, Masahro KITO, and Masaak SHINOHARA Dpartmnt of Mathmatcal Informaton Engnrng

More information

Entropy Coding. A complete entropy codec, which is an encoder/decoder. pair, consists of the process of encoding or

Entropy Coding. A complete entropy codec, which is an encoder/decoder. pair, consists of the process of encoding or Sgnal Compresson Sgnal Compresson Entropy Codng Entropy codng s also known as zero-error codng, data compresson or lossless compresson. Entropy codng s wdely used n vrtually all popular nternatonal multmeda

More information

A Probabilistic Characterization of Simulation Model Uncertainties

A Probabilistic Characterization of Simulation Model Uncertainties A Proalstc Charactrzaton of Sulaton Modl Uncrtants Vctor Ontvros Mohaad Modarrs Cntr for Rsk and Rlalty Unvrsty of Maryland 1 Introducton Thr s uncrtanty n odl prdctons as wll as uncrtanty n xprnts Th

More information

1- Summary of Kinetic Theory of Gases

1- Summary of Kinetic Theory of Gases Dr. Kasra Etmad Octobr 5, 011 1- Summary of Kntc Thory of Gass - Radaton 3- E4 4- Plasma Proprts f(v f ( v m 4 ( kt 3/ v xp( mv kt V v v m v 1 rms V kt v m ( m 1/ v 8kT m 3kT v rms ( m 1/ E3: Prcntag of

More information

Differential Phase Shift Keying (DPSK)

Differential Phase Shift Keying (DPSK) Dfferental Phase Shft Keyng (DPSK) BPSK need to synchronze the carrer. DPSK no such need. Key dea: transmt the dfference between adjacent messages, not messages themselves. Implementaton: b = b m m = 1

More information

EM and Structure Learning

EM and Structure Learning EM and Structure Learnng Le Song Machne Learnng II: Advanced Topcs CSE 8803ML, Sprng 2012 Partally observed graphcal models Mxture Models N(μ 1, Σ 1 ) Z X N N(μ 2, Σ 2 ) 2 Gaussan mxture model Consder

More information

ESCI 341 Atmospheric Thermodynamics Lesson 10 The Physical Meaning of Entropy

ESCI 341 Atmospheric Thermodynamics Lesson 10 The Physical Meaning of Entropy ESCI 341 Atmospherc Thermodynamcs Lesson 10 The Physcal Meanng of Entropy References: An Introducton to Statstcal Thermodynamcs, T.L. Hll An Introducton to Thermodynamcs and Thermostatstcs, H.B. Callen

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

Math 656 March 10, 2011 Midterm Examination Solutions

Math 656 March 10, 2011 Midterm Examination Solutions Math 656 March 0, 0 Mdtrm Eamnaton Soltons (4pts Dr th prsson for snh (arcsnh sng th dfnton of snh w n trms of ponntals, and s t to fnd all als of snh (. Plot ths als as ponts n th compl plan. Mak sr or

More information

Introduction to Antennas & Arrays

Introduction to Antennas & Arrays Introducton to Antennas & Arrays Antenna transton regon (structure) between guded eaves (.e. coaxal cable) and free space waves. On transmsson, antenna accepts energy from TL and radates t nto space. J.D.

More information

Bayesian decision theory. Nuno Vasconcelos ECE Department, UCSD

Bayesian decision theory. Nuno Vasconcelos ECE Department, UCSD Bayesan decson theory Nuno Vasconcelos ECE Department UCSD Notaton the notaton n DHS s qute sloppy e.. show that error error z z dz really not clear what ths means we wll use the follown notaton subscrpts

More information

Statistical analysis using matlab. HY 439 Presented by: George Fortetsanakis

Statistical analysis using matlab. HY 439 Presented by: George Fortetsanakis Statstcal analyss usng matlab HY 439 Presented by: George Fortetsanaks Roadmap Probablty dstrbutons Statstcal estmaton Fttng data to probablty dstrbutons Contnuous dstrbutons Contnuous random varable X

More information

Laboratory 3: Method of Least Squares

Laboratory 3: Method of Least Squares Laboratory 3: Method of Least Squares Introducton Consder the graph of expermental data n Fgure 1. In ths experment x s the ndependent varable and y the dependent varable. Clearly they are correlated wth

More information

ST 524 NCSU - Fall 2008 One way Analysis of variance Variances not homogeneous

ST 524 NCSU - Fall 2008 One way Analysis of variance Variances not homogeneous ST 54 NCSU - Fall 008 On way Analyss of varanc Varancs not homognous On way Analyss of varanc Exampl (Yandll, 997) A plant scntst masurd th concntraton of a partcular vrus n plant sap usng ELISA (nzym-lnkd

More information

Multi-dimensional Central Limit Theorem

Multi-dimensional Central Limit Theorem Mult-dmensonal Central Lmt heorem Outlne ( ( ( t as ( + ( + + ( ( ( Consder a sequence of ndependent random proceses t, t, dentcal to some ( t. Assume t = 0. Defne the sum process t t t t = ( t = (; t

More information

Low Complexity Soft-Input Soft-Output Hamming Decoder

Low Complexity Soft-Input Soft-Output Hamming Decoder Low Complexty Soft-Input Soft-Output Hammng Der Benjamn Müller, Martn Holters, Udo Zölzer Helmut Schmdt Unversty Unversty of the Federal Armed Forces Department of Sgnal Processng and Communcatons Holstenhofweg

More information

VQ widely used in coding speech, image, and video

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

More information

Assignment 2. Tyler Shendruk February 19, 2010

Assignment 2. Tyler Shendruk February 19, 2010 Assgnment yler Shendruk February 9, 00 Kadar Ch. Problem 8 We have an N N symmetrc matrx, M. he symmetry means M M and we ll say the elements of the matrx are m j. he elements are pulled from a probablty

More information

Decentralized Power Control for Random Access with Iterative Multi-User Detection

Decentralized Power Control for Random Access with Iterative Multi-User Detection Dcntralzd Powr Control for Random Accss wth Itratv Mult-Usr Dtcton Chongbn Xu, Png Wang, Sammy Chan, and L Png Dpartmnt of Elctronc Engnrng, Cty Unvrsty of Hong ong, Hong ong SAR Emal: xcb5@mals.tsnghua.du.cn,

More information

Economics 600: August, 2007 Dynamic Part: Problem Set 5. Problems on Differential Equations and Continuous Time Optimization

Economics 600: August, 2007 Dynamic Part: Problem Set 5. Problems on Differential Equations and Continuous Time Optimization THE UNIVERSITY OF MARYLAND COLLEGE PARK, MARYLAND Economcs 600: August, 007 Dynamc Part: Problm St 5 Problms on Dffrntal Equatons and Contnuous Tm Optmzaton Quston Solv th followng two dffrntal quatons.

More information

Simulated Power of the Discrete Cramér-von Mises Goodness-of-Fit Tests

Simulated Power of the Discrete Cramér-von Mises Goodness-of-Fit Tests Smulated of the Cramér-von Mses Goodness-of-Ft Tests Steele, M., Chaselng, J. and 3 Hurst, C. School of Mathematcal and Physcal Scences, James Cook Unversty, Australan School of Envronmental Studes, Grffth

More information

Engineering Risk Benefit Analysis

Engineering Risk Benefit Analysis Engneerng Rsk Beneft Analyss.55, 2.943, 3.577, 6.938, 0.86, 3.62, 6.862, 22.82, ESD.72, ESD.72 RPRA 2. Elements of Probablty Theory George E. Apostolaks Massachusetts Insttute of Technology Sprng 2007

More information

Heisenberg Model. Sayed Mohammad Mahdi Sadrnezhaad. Supervisor: Prof. Abdollah Langari

Heisenberg Model. Sayed Mohammad Mahdi Sadrnezhaad. Supervisor: Prof. Abdollah Langari snbrg Modl Sad Mohammad Mahd Sadrnhaad Survsor: Prof. bdollah Langar bstract: n ths rsarch w tr to calculat analtcall gnvalus and gnvctors of fnt chan wth ½-sn artcls snbrg modl. W drov gnfuctons for closd

More information

From Structural Analysis to FEM. Dhiman Basu

From Structural Analysis to FEM. Dhiman Basu From Structural Analyss to FEM Dhman Basu Acknowldgmnt Followng txt books wr consultd whl prparng ths lctur nots: Znkwcz, OC O.C. andtaylor Taylor, R.L. (000). Th FntElmnt Mthod, Vol. : Th Bass, Ffth dton,

More information