ECE 564/645 - Digital Communication Systems (Spring 2014) Final Exam Friday, May 2nd, 8:00-10:00am, Marston 220

Size: px
Start display at page:

Download "ECE 564/645 - Digital Communication Systems (Spring 2014) Final Exam Friday, May 2nd, 8:00-10:00am, Marston 220"

Transcription

1 ECE 564/645 - Digital Commuicatio Systems (Sprig 014) Fial Exam Friday, May d, 8:00-10:00am, Marsto 0 Overview The exam cosists of four (or five) problems for 100 (or 10) poits. The poits for each part of each problem are give i brackets - you should sped your two hours accordigly. The exam is closed book, but you are allowed three page-sides of otes. Calculators are ot allowed. I will provide all ecessary blak paper. Testmaship Full credit will be give oly to fully justified aswers. Givig the steps alog the way to the aswer will ot oly ear full credit but also maximize the partial credit should you stumble or get stuck. If you get stuck, attempt to eatly defie your approach to the problem ad why you are stuck. If part of a problem depeds o a previous part that you are uable to solve, explai the method for doig the curret part, ad, if possible, give the aswer i terms of the quatities of the previous part that you are uable to obtai. Start each problem o a ew page. Not oly will this facilitate gradig but also make it easier for you to jump back ad forth betwee problems. If you get to the ed of the problem ad realize that your aswer must be wrog (e.g. a egative probability), be sure to write this must be wrog because... so that I will kow you recogized such a fact. Academic dishoesty will be dealt with harshly - the miimum pealty will be a F for the course.

2 Some potetially useful iformatio cos(θ) = 1 (e jθ + e jθ) si(θ) = 1 j (e jθ e jθ) si(a ± b) = si(a) cos(b) ± cos(a) si(b) cos(a ± b) = cos(a) cos(b) si(a) si(b) cos(a) cos(b) = 1 [cos(a b) + cos(a + b)] si(a) si(b) = 1 [cos(a b) cos(a + b)] si(a) cos(b) = 1 [si(a b) + si(a + b)] cos (a) = 1 [1 + cos(a)] si (a) = 1 [1 cos(a)] si(a) cos(a) = 1 si(a)

3 1. Huffma Codig: [1] (a) A source produces a idepedet ad idetically distributed (IID) sequece from X = {A, B, C}, with P (X i = A) = 0.4, P (X i = B) = 0.4, P (X i = C) = 0.. A optimal lossless source coder takes symbols N = at a time ad compresses this source at miimum rate. Fid such a source code ad its rate (i output bits per iput symbol). [8] (b) Suppose I have a source that produces a radom variable X from a alphabet X = {A, B, C, D, E, F }, ad cosider codig this sigle radom variable with a optimal lossless source coder (i.e. Huffma codig with N = 1). My fried kows the probabilities of each of the letters (which he does ot tell me) ad desigs such a Huffma code. He the tells me that P (X = A) = 0.3, but ot ay of the other probabilities, ad A may or may ot be the most likely symbol. Give upper ad lower bouds o the legth of the bit sequece assiged to A by the Huffma code based o this iformatio. Be sure to justify your aswer. (Hit: Aalyze the Huffma codig algorithm.). Cosider the waveform chael: s(t) r(t) (t) where (t) is additive white Gaussia oise with power spectral desity N 0, r(t) is the received waveform, ad s(t) = s i (t) whe message m i is to be set durig time t (0, 1). Suppose there are M = 4 possible equally likely messages ad the correspodig sigals are: [7] (a) Calculate the itegrals: 0 cos(πt)dt 0 si(πt)dt 0 si(πt) cos(πt)dt 0 cos (πt)dt s 1 (t) =, 0 t 1 s (t) = 1 + cos(πt), 0 t 1 s 3 (t) = 1 + si(πt), 0 t 1 s 4 (t) = 1 + si(πt), 0 t 1 [13] (b) Fid a orthoormal basis {φ i (t) : i = 1,,..., N} for this sigal set ad give the vector represetatio of each sigal i this basis. [5] (c) Specify the MAP receiver by: Drawig a (simple) block diagram showig a method of obtaiig r j (the compoet of r(t) alog the basis fuctio φ j (t)) from r(t). Idicatig how a sigal is chose based o (r = (r 1,..., r N ) T ), (You ca do this with a picture or a descriptio with equatios.) [10] (d) Fid the Uio Boud to the probability of error of the MAP receiver i terms of uits of your sigal space. Idetify the term i your sum that will domiate performace at moderate-to-high sigal-to-oise ratios. Covert this domiat summad oly to be i terms of E b ad N 0.

4 3. Cosider the followig (uusual) biary amplitude-shift keyig (ASK) system for trasmittig a bit b 0, equally likely to be 0 or 1, i t (0, 1). For b 0 = 0, we let s(t) = 1 4 p(t), ad for b 0 = 1, we let s(t) = 3 4p(t), where p(t) = { 1 0 t 1 0 otherwise The sigal s(t) is trasmitted across a chael modeled as the followig: s(t) (t) r(t) where (t) is additive white Gaussia oise with power spectral desity N 0 ad r(t) is the received waveform. [5] (a) Fid the receiver for processig r(t), t (0, 1) to obtai a estimate for the trasmitted bit that miimizes the probability of a bit error. [5] (b) Fid the probability of a bit error i terms of the average eergy per symbol E s ad N 0. [5] (c) How much better or worse (i db of Es N 0 ) is this system tha a biary phase-shift keyed (BPSK) system operatig o a AWGN chael? Suppose ow that chael codig is employed i the system; that is, the iformatio bits {I k } are iput to the ecoder to produce the bits {b k } for modulatio. The parity check matrix of the code is give by: [15] (d) Fid the followig: H = A geerator matrix G for the code The weight eumerator polyomial for the code: A(x) = i=0 A i x i, where A i is the umber of weight-i codewords. The rate r of the code. The sydromes ad the correspodig coset leaders. [7] (e) Now suppose the bits from the ecoder are trasmitted with the biary amplitude shift-keyed (ASK) system o a AWGN chael as described i the first part of the problem. Fid the exact probability of a codeword error i terms of E s ad N 0 whe hard-decisio decodig is doe at the receiver; that is, the demodulator outputs its best estimate of each bit b k ad the decoder uses six such bit decisios to decode the codeword. The, covert your aswer to be i terms of the eergy per iformatio bit (i.e. eergy per bit at the iput to the chael coder) E b ad N 0.

5 [8] (f) Now suppose the bits from the ecoder are trasmitted with the biary amplitude shift-keyed (ASK) system o a AWGN chael as described i the first part of the problem. Fid the Uio Boud to the probability of a codeword error i terms of E s ad N 0 whe optimal soft-decisio decodig is doe at the receiver; that is, the decoder chooses the most likely codeword trasmitted based o the received waveform over the correspodig six symbol periods. The, covert your aswer to be i terms of the eergy per iformatio bit (i.e. eergy per bit at the iput to the chael coder) E b ad N [645 oly] Whe decodig a liear block code, we ca employ the stadard array, ad we kow a error patter is correctible if ad oly if it is declared a coset leader. [10] (a) Cosider a t-error correctig (, k) liear block code. (Recall, that for a code to be t-error correctig, it must correct every patter of t or fewer errors.) Show that the followig equatio must be true: k log ( ) t where k =! k!( k)!. The, show how this implies a upper boud o the umber of codewords i a code for a give t ad. [10] (b) Without usig a repetitio code, fid a example of a liear block code (give G, H, or the codewords) with t 1 (you get to choose t as log as t 1) such that: k = log ( ) t

The Maximum-Likelihood Decoding Performance of Error-Correcting Codes

The Maximum-Likelihood Decoding Performance of Error-Correcting Codes The Maximum-Lielihood Decodig Performace of Error-Correctig Codes Hery D. Pfister ECE Departmet Texas A&M Uiversity August 27th, 2007 (rev. 0) November 2st, 203 (rev. ) Performace of Codes. Notatio X,

More information

Channel coding, linear block codes, Hamming and cyclic codes Lecture - 8

Channel coding, linear block codes, Hamming and cyclic codes Lecture - 8 Digital Commuicatio Chael codig, liear block codes, Hammig ad cyclic codes Lecture - 8 Ir. Muhamad Asial, MSc., PhD Ceter for Iformatio ad Commuicatio Egieerig Research (CICER) Electrical Egieerig Departmet

More information

Information Theory and Coding

Information Theory and Coding Sol. Iformatio Theory ad Codig. The capacity of a bad-limited additive white Gaussia (AWGN) chael is give by C = Wlog 2 ( + σ 2 W ) bits per secod(bps), where W is the chael badwidth, is the average power

More information

Entropies & Information Theory

Entropies & Information Theory Etropies & Iformatio Theory LECTURE I Nilajaa Datta Uiversity of Cambridge,U.K. For more details: see lecture otes (Lecture 1- Lecture 5) o http://www.qi.damtp.cam.ac.uk/ode/223 Quatum Iformatio Theory

More information

Fig. 2. Block Diagram of a DCS

Fig. 2. Block Diagram of a DCS Iformatio source Optioal Essetial From other sources Spread code ge. Format A/D Source ecode Ecrypt Auth. Chael ecode Pulse modu. Multiplex Badpass modu. Spread spectrum modu. X M m i Digital iput Digital

More information

Lecture 7: Channel coding theorem for discrete-time continuous memoryless channel

Lecture 7: Channel coding theorem for discrete-time continuous memoryless channel Lecture 7: Chael codig theorem for discrete-time cotiuous memoryless chael Lectured by Dr. Saif K. Mohammed Scribed by Mirsad Čirkić Iformatio Theory for Wireless Commuicatio ITWC Sprig 202 Let us first

More information

Shannon s noiseless coding theorem

Shannon s noiseless coding theorem 18.310 lecture otes May 4, 2015 Shao s oiseless codig theorem Lecturer: Michel Goemas I these otes we discuss Shao s oiseless codig theorem, which is oe of the foudig results of the field of iformatio

More information

PRACTICE PROBLEMS FOR THE FINAL

PRACTICE PROBLEMS FOR THE FINAL PRACTICE PROBLEMS FOR THE FINAL Math 36Q Fall 25 Professor Hoh Below is a list of practice questios for the Fial Exam. I would suggest also goig over the practice problems ad exams for Exam ad Exam 2 to

More information

Lecture 27. Capacity of additive Gaussian noise channel and the sphere packing bound

Lecture 27. Capacity of additive Gaussian noise channel and the sphere packing bound Lecture 7 Ageda for the lecture Gaussia chael with average power costraits Capacity of additive Gaussia oise chael ad the sphere packig boud 7. Additive Gaussia oise chael Up to this poit, we have bee

More information

Information Theory Tutorial Communication over Channels with memory. Chi Zhang Department of Electrical Engineering University of Notre Dame

Information Theory Tutorial Communication over Channels with memory. Chi Zhang Department of Electrical Engineering University of Notre Dame Iformatio Theory Tutorial Commuicatio over Chaels with memory Chi Zhag Departmet of Electrical Egieerig Uiversity of Notre Dame Abstract A geeral capacity formula C = sup I(; Y ), which is correct for

More information

Problems for ENEE Coding Theory

Problems for ENEE Coding Theory Problems for ENEE626-2006 Codig Theory Add problems o obiary cyclic codes: symbol field, locator field (see prob.2 i the fial of 2006); miimal polyomials over F q Last modified o 12/22/2006. Computers

More information

MASSACHUSETTS INSTITUTE OF TECHNOLOGY Department of Electrical Engineering and Computer Science. BACKGROUND EXAM September 30, 2004.

MASSACHUSETTS INSTITUTE OF TECHNOLOGY Department of Electrical Engineering and Computer Science. BACKGROUND EXAM September 30, 2004. MASSACHUSETTS INSTITUTE OF TECHNOLOGY Departmet of Electrical Egieerig ad Computer Sciece 6.34 Discrete Time Sigal Processig Fall 24 BACKGROUND EXAM September 3, 24. Full Name: Note: This exam is closed

More information

MTH 133 Solutions to Exam 2 November 16th, Without fully opening the exam, check that you have pages 1 through 12.

MTH 133 Solutions to Exam 2 November 16th, Without fully opening the exam, check that you have pages 1 through 12. Name: Sectio: Recitatio Istructor: INSTRUCTIONS Fill i your ame, etc. o this first page. Without fully opeig the exam, check that you have pages through. Show all your work o the stadard respose questios.

More information

6.003 Homework #3 Solutions

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

More information

Math 113, Calculus II Winter 2007 Final Exam Solutions

Math 113, Calculus II Winter 2007 Final Exam Solutions Math, Calculus II Witer 7 Fial Exam Solutios (5 poits) Use the limit defiitio of the defiite itegral ad the sum formulas to compute x x + dx The check your aswer usig the Evaluatio Theorem Solutio: I this

More information

ELEC1200: A System View of Communications: from Signals to Packets Lecture 3

ELEC1200: A System View of Communications: from Signals to Packets Lecture 3 ELEC2: A System View of Commuicatios: from Sigals to Packets Lecture 3 Commuicatio chaels Discrete time Chael Modelig the chael Liear Time Ivariat Systems Step Respose Respose to sigle bit Respose to geeral

More information

CS434a/541a: Pattern Recognition Prof. Olga Veksler. Lecture 5

CS434a/541a: Pattern Recognition Prof. Olga Veksler. Lecture 5 CS434a/54a: Patter Recogitio Prof. Olga Veksler Lecture 5 Today Itroductio to parameter estimatio Two methods for parameter estimatio Maimum Likelihood Estimatio Bayesia Estimatio Itroducto Bayesia Decisio

More information

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

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

More information

ECE 8527: Introduction to Machine Learning and Pattern Recognition Midterm # 1. Vaishali Amin Fall, 2015

ECE 8527: Introduction to Machine Learning and Pattern Recognition Midterm # 1. Vaishali Amin Fall, 2015 ECE 8527: Itroductio to Machie Learig ad Patter Recogitio Midterm # 1 Vaishali Ami Fall, 2015 tue39624@temple.edu Problem No. 1: Cosider a two-class discrete distributio problem: ω 1 :{[0,0], [2,0], [2,2],

More information

ECE 564/645 - Digital Communications, Spring 2018 Midterm Exam #1 March 22nd, 7:00-9:00pm Marston 220

ECE 564/645 - Digital Communications, Spring 2018 Midterm Exam #1 March 22nd, 7:00-9:00pm Marston 220 ECE 564/645 - Digital Communications, Spring 08 Midterm Exam # March nd, 7:00-9:00pm Marston 0 Overview The exam consists of four problems for 0 points (ECE 564) or 5 points (ECE 645). The points for each

More information

Signals & Systems Chapter3

Signals & Systems Chapter3 Sigals & Systems Chapter3 1.2 Discrete-Time (D-T) Sigals Electroic systems do most of the processig of a sigal usig a computer. A computer ca t directly process a C-T sigal but istead eeds a stream of

More information

Lecture 7: October 18, 2017

Lecture 7: October 18, 2017 Iformatio ad Codig Theory Autum 207 Lecturer: Madhur Tulsiai Lecture 7: October 8, 207 Biary hypothesis testig I this lecture, we apply the tools developed i the past few lectures to uderstad the problem

More information

MATH 10550, EXAM 3 SOLUTIONS

MATH 10550, EXAM 3 SOLUTIONS MATH 155, EXAM 3 SOLUTIONS 1. I fidig a approximate solutio to the equatio x 3 +x 4 = usig Newto s method with iitial approximatio x 1 = 1, what is x? Solutio. Recall that x +1 = x f(x ) f (x ). Hece,

More information

Introduction to Signals and Systems, Part V: Lecture Summary

Introduction to Signals and Systems, Part V: Lecture Summary EEL33: Discrete-Time Sigals ad Systems Itroductio to Sigals ad Systems, Part V: Lecture Summary Itroductio to Sigals ad Systems, Part V: Lecture Summary So far we have oly looked at examples of o-recursive

More information

The multiplicative structure of finite field and a construction of LRC

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

More information

MTH 133 Solutions to Exam 2 Nov. 18th 2015

MTH 133 Solutions to Exam 2 Nov. 18th 2015 Name: Sectio: Recitatio Istructor: READ THE FOLLOWING INSTRUCTIONS. Do ot ope your exam util told to do so. No calculators, cell phoes or ay other electroic devices ca be used o this exam. Clear your desk

More information

6.895 Essential Coding Theory October 20, Lecture 11. This lecture is focused in comparisons of the following properties/parameters of a code:

6.895 Essential Coding Theory October 20, Lecture 11. This lecture is focused in comparisons of the following properties/parameters of a code: 6.895 Essetial Codig Theory October 0, 004 Lecture 11 Lecturer: Madhu Suda Scribe: Aastasios Sidiropoulos 1 Overview This lecture is focused i comparisos of the followig properties/parameters of a code:

More information

4x 2. (n+1) x 3 n+1. = lim. 4x 2 n+1 n3 n. n 4x 2 = lim = 3

4x 2. (n+1) x 3 n+1. = lim. 4x 2 n+1 n3 n. n 4x 2 = lim = 3 Exam Problems (x. Give the series (, fid the values of x for which this power series coverges. Also =0 state clearly what the radius of covergece is. We start by settig up the Ratio Test: x ( x x ( x x

More information

Carleton College, Winter 2017 Math 121, Practice Final Prof. Jones. Note: the exam will have a section of true-false questions, like the one below.

Carleton College, Winter 2017 Math 121, Practice Final Prof. Jones. Note: the exam will have a section of true-false questions, like the one below. Carleto College, Witer 207 Math 2, Practice Fial Prof. Joes Note: the exam will have a sectio of true-false questios, like the oe below.. True or False. Briefly explai your aswer. A icorrectly justified

More information

Lecture 14: Graph Entropy

Lecture 14: Graph Entropy 15-859: Iformatio Theory ad Applicatios i TCS Sprig 2013 Lecture 14: Graph Etropy March 19, 2013 Lecturer: Mahdi Cheraghchi Scribe: Euiwoog Lee 1 Recap Bergma s boud o the permaet Shearer s Lemma Number

More information

EECS564 Estimation, Filtering, and Detection Hwk 2 Solns. Winter p θ (z) = (2θz + 1 θ), 0 z 1

EECS564 Estimation, Filtering, and Detection Hwk 2 Solns. Winter p θ (z) = (2θz + 1 θ), 0 z 1 EECS564 Estimatio, Filterig, ad Detectio Hwk 2 Sols. Witer 25 4. Let Z be a sigle observatio havig desity fuctio where. p (z) = (2z + ), z (a) Assumig that is a oradom parameter, fid ad plot the maximum

More information

Applications in Linear Algebra and Uses of Technology

Applications in Linear Algebra and Uses of Technology 1 TI-89: Let A 1 4 5 6 7 8 10 Applicatios i Liear Algebra ad Uses of Techology,adB 4 1 1 4 type i: [1,,;4,5,6;7,8,10] press: STO type i: A type i: [4,-1;-1,4] press: STO (1) Row Echelo Form: MATH/matrix

More information

Optimization Methods MIT 2.098/6.255/ Final exam

Optimization Methods MIT 2.098/6.255/ Final exam Optimizatio Methods MIT 2.098/6.255/15.093 Fial exam Date Give: December 19th, 2006 P1. [30 pts] Classify the followig statemets as true or false. All aswers must be well-justified, either through a short

More information

Lecture 11: Channel Coding Theorem: Converse Part

Lecture 11: Channel Coding Theorem: Converse Part EE376A/STATS376A Iformatio Theory Lecture - 02/3/208 Lecture : Chael Codig Theorem: Coverse Part Lecturer: Tsachy Weissma Scribe: Erdem Bıyık I this lecture, we will cotiue our discussio o chael codig

More information

Chapter 3 Soft Decision and Quantised Soft Decision Decoding

Chapter 3 Soft Decision and Quantised Soft Decision Decoding Chapter 3 Soft Decisio ad Quatised Soft Decisio Decodig 3.1 Itroductio The use of hard decisio decodig results i a decodig loss compared to soft decisio decodig. There are several refereces that have quatified

More information

Lecture 10: Universal coding and prediction

Lecture 10: Universal coding and prediction 0-704: Iformatio Processig ad Learig Sprig 0 Lecture 0: Uiversal codig ad predictio Lecturer: Aarti Sigh Scribes: Georg M. Goerg Disclaimer: These otes have ot bee subjected to the usual scrutiy reserved

More information

SOLUTIONS TO EXAM 3. Solution: Note that this defines two convergent geometric series with respective radii r 1 = 2/5 < 1 and r 2 = 1/5 < 1.

SOLUTIONS TO EXAM 3. Solution: Note that this defines two convergent geometric series with respective radii r 1 = 2/5 < 1 and r 2 = 1/5 < 1. SOLUTIONS TO EXAM 3 Problem Fid the sum of the followig series 2 + ( ) 5 5 2 5 3 25 2 2 This series diverges Solutio: Note that this defies two coverget geometric series with respective radii r 2/5 < ad

More information

GEORGIA INSTITUTE OF TECHNOLOGY SCHOOL OF ELECTRICAL AND COMPUTER ENGINEERING

GEORGIA INSTITUTE OF TECHNOLOGY SCHOOL OF ELECTRICAL AND COMPUTER ENGINEERING GEORGIA INSTITUTE OF TECHNOLOGY SCHOOL OF ELECTRICAL AND COMPUTER ENGINEERING ECE 06 Summer 07 Problem Set #5 Assiged: Jue 3, 07 Due Date: Jue 30, 07 Readig: Chapter 5 o FIR Filters. PROBLEM 5..* (The

More information

10-701/ Machine Learning Mid-term Exam Solution

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

More information

ELEG 4603/5173L Digital Signal Processing Ch. 1 Discrete-Time Signals and Systems

ELEG 4603/5173L Digital Signal Processing Ch. 1 Discrete-Time Signals and Systems Departmet of Electrical Egieerig Uiversity of Arasas ELEG 4603/5173L Digital Sigal Processig Ch. 1 Discrete-Time Sigals ad Systems Dr. Jigxia Wu wuj@uar.edu OUTLINE 2 Classificatios of discrete-time sigals

More information

Problem Set 4 Due Oct, 12

Problem Set 4 Due Oct, 12 EE226: Radom Processes i Systems Lecturer: Jea C. Walrad Problem Set 4 Due Oct, 12 Fall 06 GSI: Assae Gueye This problem set essetially reviews detectio theory ad hypothesis testig ad some basic otios

More information

SCORE. Exam 2. MA 114 Exam 2 Fall 2016

SCORE. Exam 2. MA 114 Exam 2 Fall 2016 Exam 2 Name: Sectio ad/or TA: Do ot remove this aswer page you will retur the whole exam. You will be allowed two hours to complete this test. No books or otes may be used. You may use a graphig calculator

More information

Fundamentals. Relative data redundancy of the set. C.E., NCU, Taiwan Angela Chih-Wei Tang,

Fundamentals. Relative data redundancy of the set. C.E., NCU, Taiwan Angela Chih-Wei Tang, Image Compressio Agela Chih-Wei Tag ( 唐之瑋 ) Departmet of Commuicatio Egieerig Natioal Cetral Uiversity JhogLi, Taiwa 2012 Sprig Fudametals Compressio ratio C R / 2, 1 : origial, 2 1 : compressed Relative

More information

Lecture 1: Basic problems of coding theory

Lecture 1: Basic problems of coding theory Lecture 1: Basic problems of codig theory Error-Correctig Codes (Sprig 016) Rutgers Uiversity Swastik Kopparty Scribes: Abhishek Bhrushudi & Aditya Potukuchi Admiistrivia was discussed at the begiig of

More information

SCORE. Exam 2. MA 114 Exam 2 Fall 2017

SCORE. Exam 2. MA 114 Exam 2 Fall 2017 Exam Name: Sectio ad/or TA: Do ot remove this aswer page you will retur the whole exam. You will be allowed two hours to complete this test. No books or otes may be used. You may use a graphig calculator

More information

First Year Quantitative Comp Exam Spring, Part I - 203A. f X (x) = 0 otherwise

First Year Quantitative Comp Exam Spring, Part I - 203A. f X (x) = 0 otherwise First Year Quatitative Comp Exam Sprig, 2012 Istructio: There are three parts. Aswer every questio i every part. Questio I-1 Part I - 203A A radom variable X is distributed with the margial desity: >

More information

Math 116 Final Exam December 12, 2014

Math 116 Final Exam December 12, 2014 Math 6 Fial Exam December 2, 24 Name: EXAM SOLUTIONS Istructor: Sectio:. Do ot ope this exam util you are told to do so. 2. This exam has 4 pages icludig this cover. There are 2 problems. Note that the

More information

Machine Learning Theory (CS 6783)

Machine Learning Theory (CS 6783) Machie Learig Theory (CS 6783) Lecture 2 : Learig Frameworks, Examples Settig up learig problems. X : istace space or iput space Examples: Computer Visio: Raw M N image vectorized X = 0, 255 M N, SIFT

More information

Lecture 2: Monte Carlo Simulation

Lecture 2: Monte Carlo Simulation STAT/Q SCI 43: Itroductio to Resamplig ethods Sprig 27 Istructor: Ye-Chi Che Lecture 2: ote Carlo Simulatio 2 ote Carlo Itegratio Assume we wat to evaluate the followig itegratio: e x3 dx What ca we do?

More information

Math 132, Fall 2009 Exam 2: Solutions

Math 132, Fall 2009 Exam 2: Solutions Math 3, Fall 009 Exam : Solutios () a) ( poits) Determie for which positive real umbers p, is the followig improper itegral coverget, ad for which it is diverget. Evaluate the itegral for each value of

More information

MA131 - Analysis 1. Workbook 2 Sequences I

MA131 - Analysis 1. Workbook 2 Sequences I MA3 - Aalysis Workbook 2 Sequeces I Autum 203 Cotets 2 Sequeces I 2. Itroductio.............................. 2.2 Icreasig ad Decreasig Sequeces................ 2 2.3 Bouded Sequeces..........................

More information

Consider the n-dimensional additive white Gaussian noise (AWGN) channel

Consider the n-dimensional additive white Gaussian noise (AWGN) channel 8. Lattice codes (by O. Ordetlich) Cosider the -dimesioal additive white Gaussia oise (AWGN) chael Y = X + Z where Z N (0, I ) is statistically idepedet of the iput X. Our goal is to commuicate reliably

More information

CHAPTER 10 INFINITE SEQUENCES AND SERIES

CHAPTER 10 INFINITE SEQUENCES AND SERIES CHAPTER 10 INFINITE SEQUENCES AND SERIES 10.1 Sequeces 10.2 Ifiite Series 10.3 The Itegral Tests 10.4 Compariso Tests 10.5 The Ratio ad Root Tests 10.6 Alteratig Series: Absolute ad Coditioal Covergece

More information

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

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

More information

MIDTERM 3 CALCULUS 2. Monday, December 3, :15 PM to 6:45 PM. Name PRACTICE EXAM SOLUTIONS

MIDTERM 3 CALCULUS 2. Monday, December 3, :15 PM to 6:45 PM. Name PRACTICE EXAM SOLUTIONS MIDTERM 3 CALCULUS MATH 300 FALL 08 Moday, December 3, 08 5:5 PM to 6:45 PM Name PRACTICE EXAM S Please aswer all of the questios, ad show your work. You must explai your aswers to get credit. You will

More information

Random Signals and Noise Winter Semester 2017 Problem Set 12 Wiener Filter Continuation

Random Signals and Noise Winter Semester 2017 Problem Set 12 Wiener Filter Continuation Radom Sigals ad Noise Witer Semester 7 Problem Set Wieer Filter Cotiuatio Problem (Sprig, Exam A) Give is the sigal W t, which is a Gaussia white oise with expectatio zero ad power spectral desity fuctio

More information

Section A assesses the Units Numerical Analysis 1 and 2 Section B assesses the Unit Mathematics for Applied Mathematics

Section A assesses the Units Numerical Analysis 1 and 2 Section B assesses the Unit Mathematics for Applied Mathematics X0/70 NATIONAL QUALIFICATIONS 005 MONDAY, MAY.00 PM 4.00 PM APPLIED MATHEMATICS ADVANCED HIGHER Numerical Aalysis Read carefully. Calculators may be used i this paper.. Cadidates should aswer all questios.

More information

PRACTICE PROBLEMS FOR THE FINAL

PRACTICE PROBLEMS FOR THE FINAL PRACTICE PROBLEMS FOR THE FINAL Math 36Q Sprig 25 Professor Hoh Below is a list of practice questios for the Fial Exam. I would suggest also goig over the practice problems ad exams for Exam ad Exam 2

More information

1 Hash tables. 1.1 Implementation

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

More information

1.0 Probability of Error for non-coherent BFSK

1.0 Probability of Error for non-coherent BFSK Probability of Error, Digital Sigalig o a Fadig Chael Ad Equalizatio Schemes for ISI Wireless Commuicatios echologies Sprig 5 Lectures & R Departmet of Electrical Egieerig, Rutgers Uiversity, Piscataway,

More information

Sequences I. Chapter Introduction

Sequences I. Chapter Introduction Chapter 2 Sequeces I 2. Itroductio A sequece is a list of umbers i a defiite order so that we kow which umber is i the first place, which umber is i the secod place ad, for ay atural umber, we kow which

More information

Time-Domain Representations of LTI Systems

Time-Domain Representations of LTI Systems 2.1 Itroductio Objectives: 1. Impulse resposes of LTI systems 2. Liear costat-coefficiets differetial or differece equatios of LTI systems 3. Bloc diagram represetatios of LTI systems 4. State-variable

More information

Definitions and Theorems. where x are the decision variables. c, b, and a are constant coefficients.

Definitions and Theorems. where x are the decision variables. c, b, and a are constant coefficients. Defiitios ad Theorems Remember the scalar form of the liear programmig problem, Miimize, Subject to, f(x) = c i x i a 1i x i = b 1 a mi x i = b m x i 0 i = 1,2,, where x are the decisio variables. c, b,

More information

Arithmetic Distribution Matching

Arithmetic Distribution Matching Arithmetic Distributio Matchig Sebastia Baur ad Georg Böcherer Istitute for Commuicatios Egieerig Techische Uiversität Müche, Germay Email: baursebastia@mytum.de,georg.boecherer@tum.de arxiv:48.393v [cs.it]

More information

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

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

More information

Some remarks for codes and lattices over imaginary quadratic

Some remarks for codes and lattices over imaginary quadratic Some remarks for codes ad lattices over imagiary quadratic fields Toy Shaska Oaklad Uiversity, Rochester, MI, USA. Caleb Shor Wester New Eglad Uiversity, Sprigfield, MA, USA. shaska@oaklad.edu Abstract

More information

DESCRIPTION OF THE SYSTEM

DESCRIPTION OF THE SYSTEM Sychroous-Serial Iterface for absolute Ecoders SSI 1060 BE 10 / 01 DESCRIPTION OF THE SYSTEM TWK-ELEKTRONIK GmbH D-001 Düsseldorf PB 1006 Heirichstr. Tel +9/11/6067 Fax +9/11/6770 e-mail: ifo@twk.de Page

More information

UC Berkeley CS 170: Efficient Algorithms and Intractable Problems Handout 17 Lecturer: David Wagner April 3, Notes 17 for CS 170

UC Berkeley CS 170: Efficient Algorithms and Intractable Problems Handout 17 Lecturer: David Wagner April 3, Notes 17 for CS 170 UC Berkeley CS 170: Efficiet Algorithms ad Itractable Problems Hadout 17 Lecturer: David Wager April 3, 2003 Notes 17 for CS 170 1 The Lempel-Ziv algorithm There is a sese i which the Huffma codig was

More information

ECE 901 Lecture 12: Complexity Regularization and the Squared Loss

ECE 901 Lecture 12: Complexity Regularization and the Squared Loss ECE 90 Lecture : Complexity Regularizatio ad the Squared Loss R. Nowak 5/7/009 I the previous lectures we made use of the Cheroff/Hoeffdig bouds for our aalysis of classifier errors. Hoeffdig s iequality

More information

Please do NOT write in this box. Multiple Choice. Total

Please do NOT write in this box. Multiple Choice. Total Istructor: Math 0560, Worksheet Alteratig Series Jauary, 3000 For realistic exam practice solve these problems without lookig at your book ad without usig a calculator. Multiple choice questios should

More information

Topic 5 [434 marks] (i) Find the range of values of n for which. (ii) Write down the value of x dx in terms of n, when it does exist.

Topic 5 [434 marks] (i) Find the range of values of n for which. (ii) Write down the value of x dx in terms of n, when it does exist. Topic 5 [44 marks] 1a (i) Fid the rage of values of for which eists 1 Write dow the value of i terms of 1, whe it does eist Fid the solutio to the differetial equatio 1b give that y = 1 whe = π (cos si

More information

, then cv V. Differential Equations Elements of Lineaer Algebra Name: Consider the differential equation. and y2 cos( kx)

, then cv V. Differential Equations Elements of Lineaer Algebra Name: Consider the differential equation. and y2 cos( kx) Cosider the differetial equatio y '' k y 0 has particular solutios y1 si( kx) ad y cos( kx) I geeral, ay liear combiatio of y1 ad y, cy 1 1 cy where c1, c is also a solutio to the equatio above The reaso

More information

Lecture 15: Strong, Conditional, & Joint Typicality

Lecture 15: Strong, Conditional, & Joint Typicality EE376A/STATS376A Iformatio Theory Lecture 15-02/27/2018 Lecture 15: Strog, Coditioal, & Joit Typicality Lecturer: Tsachy Weissma Scribe: Nimit Sohoi, William McCloskey, Halwest Mohammad I this lecture,

More information

Solutions for the Exam 9 January 2012

Solutions for the Exam 9 January 2012 Mastermath ad LNMB Course: Discrete Optimizatio Solutios for the Exam 9 Jauary 2012 Utrecht Uiversity, Educatorium, 15:15 18:15 The examiatio lasts 3 hours. Gradig will be doe before Jauary 23, 2012. Studets

More information

Problem Cosider the curve give parametrically as x = si t ad y = + cos t for» t» ß: (a) Describe the path this traverses: Where does it start (whe t =

Problem Cosider the curve give parametrically as x = si t ad y = + cos t for» t» ß: (a) Describe the path this traverses: Where does it start (whe t = Mathematics Summer Wilso Fial Exam August 8, ANSWERS Problem 1 (a) Fid the solutio to y +x y = e x x that satisfies y() = 5 : This is already i the form we used for a first order liear differetial equatio,

More information

Statistical Pattern Recognition

Statistical Pattern Recognition Statistical Patter Recogitio Classificatio: No-Parametric Modelig Hamid R. Rabiee Jafar Muhammadi Sprig 2014 http://ce.sharif.edu/courses/92-93/2/ce725-2/ Ageda Parametric Modelig No-Parametric Modelig

More information

Math 155 (Lecture 3)

Math 155 (Lecture 3) Math 55 (Lecture 3) September 8, I this lecture, we ll cosider the aswer to oe of the most basic coutig problems i combiatorics Questio How may ways are there to choose a -elemet subset of the set {,,,

More information

DISTRIBUTION LAW Okunev I.V.

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

More information

Mechatronics. Time Response & Frequency Response 2 nd -Order Dynamic System 2-Pole, Low-Pass, Active Filter

Mechatronics. Time Response & Frequency Response 2 nd -Order Dynamic System 2-Pole, Low-Pass, Active Filter Time Respose & Frequecy Respose d -Order Dyamic System -Pole, Low-Pass, Active Filter R 4 R 7 C 5 e i R 1 C R 3 - + R 6 - + e out Assigmet: Perform a Complete Dyamic System Ivestigatio of the Two-Pole,

More information

SUCCESSIVE INTERFERENCE CANCELLATION DECODING FOR THE K -USER CYCLIC INTERFERENCE CHANNEL

SUCCESSIVE INTERFERENCE CANCELLATION DECODING FOR THE K -USER CYCLIC INTERFERENCE CHANNEL Joural of Theoretical ad Applied Iformatio Techology 31 st December 212 Vol 46 No2 25-212 JATIT & LLS All rights reserved ISSN: 1992-8645 wwwatitorg E-ISSN: 1817-3195 SCCESSIVE INTERFERENCE CANCELLATION

More information

SCORE. Exam 2. MA 114 Exam 2 Fall 2016

SCORE. Exam 2. MA 114 Exam 2 Fall 2016 MA 4 Exam Fall 06 Exam Name: Sectio ad/or TA: Do ot remove this aswer page you will retur the whole exam. You will be allowed two hours to complete this test. No books or otes may be used. You may use

More information

Solutions. Number of Problems: 4. None. Use only the prepared sheets for your solutions. Additional paper is available from the supervisors.

Solutions. Number of Problems: 4. None. Use only the prepared sheets for your solutions. Additional paper is available from the supervisors. Quiz November 4th, 23 Sigals & Systems (5-575-) P. Reist & Prof. R. D Adrea Solutios Exam Duratio: 4 miutes Number of Problems: 4 Permitted aids: Noe. Use oly the prepared sheets for your solutios. Additioal

More information

Classification of problem & problem solving strategies. classification of time complexities (linear, logarithmic etc)

Classification of problem & problem solving strategies. classification of time complexities (linear, logarithmic etc) Classificatio of problem & problem solvig strategies classificatio of time complexities (liear, arithmic etc) Problem subdivisio Divide ad Coquer strategy. Asymptotic otatios, lower boud ad upper boud:

More information

Kurskod: TAMS11 Provkod: TENB 21 March 2015, 14:00-18:00. English Version (no Swedish Version)

Kurskod: TAMS11 Provkod: TENB 21 March 2015, 14:00-18:00. English Version (no Swedish Version) Kurskod: TAMS Provkod: TENB 2 March 205, 4:00-8:00 Examier: Xiagfeg Yag (Tel: 070 2234765). Please aswer i ENGLISH if you ca. a. You are allowed to use: a calculator; formel -och tabellsamlig i matematisk

More information

ADVANCED DIGITAL SIGNAL PROCESSING

ADVANCED DIGITAL SIGNAL PROCESSING ADVANCED DIGITAL SIGNAL PROCESSING PROF. S. C. CHAN (email : sccha@eee.hku.hk, Rm. CYC-702) DISCRETE-TIME SIGNALS AND SYSTEMS MULTI-DIMENSIONAL SIGNALS AND SYSTEMS RANDOM PROCESSES AND APPLICATIONS ADAPTIVE

More information

Chapter 6 Infinite Series

Chapter 6 Infinite Series Chapter 6 Ifiite Series I the previous chapter we cosidered itegrals which were improper i the sese that the iterval of itegratio was ubouded. I this chapter we are goig to discuss a topic which is somewhat

More information

( 1) n (4x + 1) n. n=0

( 1) n (4x + 1) n. n=0 Problem 1 (10.6, #). Fid the radius of covergece for the series: ( 1) (4x + 1). For what values of x does the series coverge absolutely, ad for what values of x does the series coverge coditioally? Solutio.

More information

PRELIMINARY EXAMINATION Department of Physics University of Florida Part A, January, 2016, 09:00 12:00. Instructions

PRELIMINARY EXAMINATION Department of Physics University of Florida Part A, January, 2016, 09:00 12:00. Instructions Studet ID Number: PRELIMINRY EXMINTION Part, Jauary, 6, 9: : Istructios. You may use a calculator ad CRC Math tables or equivalet. No other tables or aids are allowed or required. You may NOT use programmable

More information

BER results for a narrowband multiuser receiver based on successive subtraction for M-PSK modulated signals

BER results for a narrowband multiuser receiver based on successive subtraction for M-PSK modulated signals results for a arrowbad multiuser receiver based o successive subtractio for M-PSK modulated sigals Gerard J.M. Jasse Telecomm. ad Traffic-Cotrol Systems Group Dept. of Iformatio Techology ad Systems Delft

More information

PROBLEM SET 5 SOLUTIONS 126 = , 37 = , 15 = , 7 = 7 1.

PROBLEM SET 5 SOLUTIONS 126 = , 37 = , 15 = , 7 = 7 1. Math 7 Sprig 06 PROBLEM SET 5 SOLUTIONS Notatios. Give a real umber x, we will defie sequeces (a k ), (x k ), (p k ), (q k ) as i lecture.. (a) (5 pts) Fid the simple cotiued fractio represetatios of 6

More information

c. Explain the basic Newsvendor model. Why is it useful for SC models? e. What additional research do you believe will be helpful in this area?

c. Explain the basic Newsvendor model. Why is it useful for SC models? e. What additional research do you believe will be helpful in this area? 1. Research Methodology a. What is meat by the supply chai (SC) coordiatio problem ad does it apply to all types of SC s? Does the Bullwhip effect relate to all types of SC s? Also does it relate to SC

More information

CS161: Algorithm Design and Analysis Handout #10 Stanford University Wednesday, 10 February 2016

CS161: Algorithm Design and Analysis Handout #10 Stanford University Wednesday, 10 February 2016 CS161: Algorithm Desig ad Aalysis Hadout #10 Staford Uiversity Wedesday, 10 February 2016 Lecture #11: Wedesday, 10 February 2016 Topics: Example midterm problems ad solutios from a log time ago Sprig

More information

ECEN 655: Advanced Channel Coding Spring Lecture 7 02/04/14. Belief propagation is exact on tree-structured factor graphs.

ECEN 655: Advanced Channel Coding Spring Lecture 7 02/04/14. Belief propagation is exact on tree-structured factor graphs. ECEN 655: Advaced Chael Codig Sprig 014 Prof. Hery Pfister Lecture 7 0/04/14 Scribe: Megke Lia 1 4-Cycles i Gallager s Esemble What we already kow: Belief propagatio is exact o tree-structured factor graphs.

More information

Performance Analysis and Channel Capacity for Multiple-Pulse Position Modulation on Multipath Channels

Performance Analysis and Channel Capacity for Multiple-Pulse Position Modulation on Multipath Channels Performace Aalysis ad Chael Capacity for Multiple-Pulse Positio Modulatio o Multipath Chaels Hyucheol Park ad Joh R. Barry School of Electrical ad Computer Egieerig Georgia Istitute of Techology, Atlata,

More information

Practical Spectral Anaysis (continue) (from Boaz Porat s book) Frequency Measurement

Practical Spectral Anaysis (continue) (from Boaz Porat s book) Frequency Measurement Practical Spectral Aaysis (cotiue) (from Boaz Porat s book) Frequecy Measuremet Oe of the most importat applicatios of the DFT is the measuremet of frequecies of periodic sigals (eg., siusoidal sigals),

More information

Machine Learning Brett Bernstein

Machine Learning Brett Bernstein Machie Learig Brett Berstei Week 2 Lecture: Cocept Check Exercises Starred problems are optioal. Excess Risk Decompositio 1. Let X = Y = {1, 2,..., 10}, A = {1,..., 10, 11} ad suppose the data distributio

More information

Markov Decision Processes

Markov Decision Processes Markov Decisio Processes Defiitios; Statioary policies; Value improvemet algorithm, Policy improvemet algorithm, ad liear programmig for discouted cost ad average cost criteria. Markov Decisio Processes

More information

Machine Learning for Data Science (CS 4786)

Machine Learning for Data Science (CS 4786) Machie Learig for Data Sciece CS 4786) Lecture & 3: Pricipal Compoet Aalysis The text i black outlies high level ideas. The text i blue provides simple mathematical details to derive or get to the algorithm

More information

Diversity Combining Techniques

Diversity Combining Techniques Diversity Combiig Techiques Whe the required sigal is a combiatio of several waves (i.e, multipath), the total sigal amplitude may experiece deep fades (i.e, Rayleigh fadig), over time or space. The major

More information

EE / EEE SAMPLE STUDY MATERIAL. GATE, IES & PSUs Signal System. Electrical Engineering. Postal Correspondence Course

EE / EEE SAMPLE STUDY MATERIAL. GATE, IES & PSUs Signal System. Electrical Engineering. Postal Correspondence Course Sigal-EE Postal Correspodece Course 1 SAMPLE STUDY MATERIAL Electrical Egieerig EE / EEE Postal Correspodece Course GATE, IES & PSUs Sigal System Sigal-EE Postal Correspodece Course CONTENTS 1. SIGNAL

More information