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School: UT Arlington
ProgrammingAssignment03 Signal.m function s = Signal(w, nx) s = zeros(nx,1)'; % Making an array of zeros for n = 1:nx+1 % Between 0 and Nx (the same as between 1 and Nx + 1) s(n) = (n-1)*exp(-(n-1)/6)*cos(w*(n-1); % The equation of the given signal end AM
School: UT Arlington
Lab 4: Sump Pump System Monitor The assignment for this lab is to design, build, and demonstrate the logic to monitor storm water management system. A holding tank collects water during rain storms. Although it will drain itself under normal conditions, w
School: UT Arlington
ProgrammingAssignment06 DSINE2.m function [LP_Num LP_Den HP_Num HP_Den BP_Num BP_Den]=DSINE2(w1,w2) %T=2sec % wc1 and wc2 are analog filter cuttoff frequencies (rad/sec) wc1=2*atan(w1); wc2=2*atan(w2); % Low Pass Filter A=2.63*w2; B=3.414*(w2^2); C=2.613*
School: UT Arlington
Last Name: First Name ID:xxxx-xx_. University of Texas at Arlington EE 2347 Fall 2013 Homework 1 Due Sept. 18, 2013 PRINT YOUR NAME in CAPITAL LETTERS. Last NAME: ID # : XXXX-XX- First NAME: INSTRUCTIONS: COPY THIS DOCUMENT AND WRITE YOUR SOLUTIONS IN SPA
School: UT Arlington
Course: Random Probabilities
EE 351K Probability and Random Processes Instructor: Haris Vikalo Homework 3 Solutions FALL 2010 hvikalo@ece.utexas.edu Problem 1 There are three dice in a bag. One has one red face, another has two red faces, and the third has three red faces. One of the
School: UT Arlington
Course: Random Probabilities
EE 351K Probability and Random Processes Instructor: Haris Vikalo Homework 4 FALL 2010 hvikalo@ece.utexas.edu Due on : Tuesday 09/28/10 Problem 1 There are n multiple-choice questions in an exam, each with 5 choices. The student knows the correct answer t
School: UT Arlington
1 I. Introduction A. Relationship of DSP to other Fields. Common topics to all these fields: transfer function and impulse response, Fourierrelated transforms, convolution theorem. 2 y(t) = x(T) h(t-T)dT, Y(f) = H(f)X(f), Y1(s) = H1(s) X1(s) etc. B. Class
School: UT Arlington
1 VII. Discrete Fourier Transform (DFT) Chapter-8 A. Modulo Arithmetic (n)N is n modulo N, n is an integer variable. (n)N = n mN 0 n mN N-1, pick m Ex. (k)4 WN = e-j2/N 2 Note that WNk = 0 WNmk = but N if m is a multiple of N but 0 , elsewhere Theorem :
School: UT Arlington
1 IV Discrete Time Fourier transform DTFT. A. Basic Definitions The discrete-time Fourier transform (DTFT) of x(n) is X(ejw) = x(n) e-jwn where w is in radians. X(ejw) is periodic with period 2, since a function of a periodic function is periodic, and has
School: UT Arlington
Complete time-domain response of a 2nd-order RLC network Howard T. Russell, Jr. July 29, 2011 R L 2K 10H iL(t) Eg(t) = Egu(t) C vC(t) (Eg = 2V) Figure 1 RLC network with initial conditions Descriptive equation for t 0+: LC d2 d t2 vC ( t ) + RC d vC ( t )
School: UT Arlington
Time-domain response of a 1st-order RL network Howard T. Russell, Jr. July 18, 2011 vR(t) iL(t) R 27 5Vsin(t) Eg(t) L 680H (f = 6KHz) iL(0) Figure 1 RL network with sinusoidal input Descriptive equation for t 0+: L d iL ( t ) dt E g = 5V + RiL ( t ) = E g
School: UT Arlington
System response characteristics of a 2nd-order RLC network Howard T. Russell, Jr. August 3, 2011 R L 2K 100H iL(t) C Eg(t) = Egu(t) vC(t) 6.25pF (Eg = 10V) Figure 1 RLC network with zero initial conditions Descriptive network and system equation for t 0+:
School: UT Arlington
1 VIII. Digital Filter Design Techniques 11/16/2011 General procedure for Design of Frequency selective filters. (1) Specify or determine the digital filter desired frequency response in radians. (a) This could be directly specified in radians, or (b) Giv
School: UT Arlington
1 VII. Discrete Fourier Transform (DFT) Chapter-8 A. Modulo Arithmetic (n)N is n modulo N, n is an integer variable. (n)N = n mN 0 n mN N-1, pick m Ex. (k)4 WN = e-j2/N 2 Note that WNk = 0 WNmk = but N if m is a multiple of N but 0 , elsewhere Theorem :
School: UT Arlington
1 III. Time Domain Analysis of systems Here, we adapt properties of continuous time systems to discrete time systems Section 2.2-2.5, pp 17-39 System Notation y(n) = T[ x(n) ] A. Types of Systems Memoryless (instantaneous) Systems y(n) = function of x(n),
School: UT Arlington
1 IV Discrete Time Fourier transform DTFT. A. Basic Definitions The discrete-time Fourier transform (DTFT) of x(n) is X(ejw) = x(n) e-jwn where w is in radians. X(ejw) is periodic with period 2, since a function of a periodic function is periodic, and has
School: UT Arlington
1 I. Introduction A. Relationship of DSP to other Fields. Common topics to all these fields: transfer function and impulse response, Fourierrelated transforms, convolution theorem. 2 y(t) = x(T) h(t-T)dT, Y(f) = H(f)X(f), Y1(s) = H1(s) X1(s) etc. B. Class
School: UT Arlington
1 VI. Z Transform Ch 24 Use: Analysis of systems, simple convolution, shorthand for ejw, stability. A. Definition: X(z) = x(n) z z - transforms Motivation easier to write Or X(z) = Z cfw_x(n) Note if z = e+jw, X(z) | = x(n) e-jwn = F cfw_ x(n) Can thin
School: UT Arlington
School: UT Arlington
August 7, 2013 OPALtx, 2013 HTR, Jr. Table 1 Phasor elements Element Time-domain model Time-domain VCR/CVR Frequency-domain VCR/CVR Frequency-domain model vR(t) vR ( t ) = RiR ( t ) VR ( j ) = RI R ( j ) VR ( j ) Resistor iR(t) R vG(t) Conductor iG(t) G
School: UT Arlington
School: UT Arlington
Exam 2, EE5350, Fall 2009 1. Find z-transforms of the following in closed form. For sequences containing x(), find the transforms in terms of X(), using real coefficients only. (a) nu(n) (b) cnu(n) (c) (n+8) + (n-8) (d) cos(won)u(n) 2. An IIR digital filt
School: UT Arlington
Exam 1, EE5350, Fall 2008 1. Convolve h(n) and x(n) to get y(n) below. Put y(n) in closed form when possible. (a) h(n) = a-nu(-n) and x(n) = b-nu(-n). (b) h(n) = anu(n) and x(n) = u(n). (c) h(n) = u(n-5) and x(n) = r(n) where r(n) = nu(n). Express y(n) in
School: UT Arlington
Final Exam, EE5350, Summer 2006 1. Convolve h(n) and x(n) to get y(n) below. Put y(n) in closed form when possible. (a) h(n) = a-nu(-n) and x(n) = b-nu(-n). (b) h(n) = anu(n) and x(n) = u(n). (c) h(n) = u(n-5) and x(n) = r(n) where r(n) = nu(n). (d) h(n)
School: UT Arlington
ProgrammingAssignment03 Signal.m function s = Signal(w, nx) s = zeros(nx,1)'; % Making an array of zeros for n = 1:nx+1 % Between 0 and Nx (the same as between 1 and Nx + 1) s(n) = (n-1)*exp(-(n-1)/6)*cos(w*(n-1); % The equation of the given signal end AM
School: UT Arlington
ProgrammingAssignment06 DSINE2.m function [LP_Num LP_Den HP_Num HP_Den BP_Num BP_Den]=DSINE2(w1,w2) %T=2sec % wc1 and wc2 are analog filter cuttoff frequencies (rad/sec) wc1=2*atan(w1); wc2=2*atan(w2); % Low Pass Filter A=2.63*w2; B=3.414*(w2^2); C=2.613*
School: UT Arlington
Last Name: First Name ID:xxxx-xx_. University of Texas at Arlington EE 2347 Fall 2013 Homework 1 Due Sept. 18, 2013 PRINT YOUR NAME in CAPITAL LETTERS. Last NAME: ID # : XXXX-XX- First NAME: INSTRUCTIONS: COPY THIS DOCUMENT AND WRITE YOUR SOLUTIONS IN SPA
School: UT Arlington
Course: Random Probabilities
EE 351K Probability and Random Processes Instructor: Haris Vikalo Homework 3 Solutions FALL 2010 hvikalo@ece.utexas.edu Problem 1 There are three dice in a bag. One has one red face, another has two red faces, and the third has three red faces. One of the
School: UT Arlington
Course: Random Probabilities
EE 351K Probability and Random Processes Instructor: Haris Vikalo Homework 4 FALL 2010 hvikalo@ece.utexas.edu Due on : Tuesday 09/28/10 Problem 1 There are n multiple-choice questions in an exam, each with 5 choices. The student knows the correct answer t
School: UT Arlington
Course: Random Probabilities
EE 351K Probability and Random Processes Instructor: Haris Vikalo Homework 2 Solutions FALL 2010 hvikalo@ece.utexas.edu Problem 1 Bob, Carol, Ted and Alice take turns (in that order) tossing a coin with probability of tossing a Head, P (H ) = p, where 0 <
School: UT Arlington
Lab 4: Sump Pump System Monitor The assignment for this lab is to design, build, and demonstrate the logic to monitor storm water management system. A holding tank collects water during rain storms. Although it will drain itself under normal conditions, w
School: UT Arlington
EE 3446 Circuits II Lab Rules V1.0 August 23, 2014 HTR, Jr. 1. Regardless of the lab section, student attendance in all EE 3446 labs is mandatory and not an option. You must attend each and every lab meeting for the entire time the lab is scheduled to be
School: UT Arlington
General Instructions for EE Labs, Lab Reports, and Course Homework Howard T. Russell, Jr., PhD September 16, 2012 I. Introduction As any student knows, lab reports and homework assignments are integral parts of most if not all electrical engineering cours
School: UT Arlington
Lab Experiment No. 6 FM Transmitter Design I. Introduction The object of this lab experiment is to gain familiarity with the design cycle of analog circuits. The network used in this project is a simple RF transmitter that must oscillate with a frequency
School: UT Arlington
Mesh and Nodal Matrix Equations of Linear Resistive Networks Lab Experiment No. 5 I. Introduction Matrix analysis methods are very powerful tools for calculating the branch voltages and currents of a linear network. The purpose of this experiment is to ap
School: UT Arlington
Lab Experiment No. 9 Amplifier Networks I. Introduction The purpose of this lab session is to gain familiarity with several well-known amplifier circuits built with standard operational amplifiers. The theory and derivations associated with each of the ci
School: UT Arlington
Course: DSP
COPYRIGHT 2011 IJCIT, ISSN 2078-5828 (PRINT), ISSN 2218-5224 (ONLINE), VOLUME 01, ISSUE 02, MANUSCRIPT CODE: 110125 Performance Comparison of AWGN, Flat Fading and Frequency Selective Fading Channel for Wireless Communication System using 4QPSK Md. Sipon
School: UT Arlington
Course: DSP
Antenna Configurations for MIMO In-Building Distributed Antenna Systems Notebooks Infrastructure In-Building DAS Mobile Phones Outdoor DAS Telemetry Home & Enterprise Networking antennas.galtronics.com Galtronics Innovation Center INTRODUCTION The followi
School: UT Arlington
Course: DSP
White Paper FADING BASICS Narrow Band, Wide Band, and Spatial Channels White Paper 101 Rev. X mm/08 SPIRENT 1325 Borregas Avenue Sunnyvale, CA 94089 USA Email: sales@spirent.com Web: http:/www.spirent.com AMERICAS 1-800-SPIRENT +1-818-676-2683 sales@s
School: UT Arlington
Course: DSP
Synchronization for OFDM systems EIT 140, tom<AT>eit.lth.se Synchronization: oset/errors and their cause Symbol (timing) oset (OFDM and DMT) transmitter and receiver do not have a common time reference receiver needs to nd symbol boundaries to avoid ISI/I
School: UT Arlington
Course: DSP
Orthogonal Frequency Division Multiplex (OFDM) Tutorial 1 Intuitive Guide to Principles of Communications www.complextoreal.com Orthogonal Frequency Division Multiplexing (OFDM) Modulation - a mapping of the information on changes in the carrier phase, fr
School: UT Arlington
Course: DSP
Amplifier Terms Defined (AN-60-038) 1 dB compression point defines the output level at which the amplifier's gain is 1 dB less than the small signal gain, or is compressed by 1 dB (P1dB). P1dB (Output Power at 1 dB Compression) Saturated Output Power (PSA
School: UT Arlington
Course: ELECTRONICS I
EE 2403-001 and 2403-101- Electronics I (Spring 2014) Syllabus Instructor: Professor Kambiz Alavi, alavi@uta.edu , 524 Nedderman Hall, (office hours: 1:00 PM to 3:00 PM, Tues and Thu; other times by appointment), 817/2725633, fax 817/272-2253 Course Learn
School: UT Arlington
Course: Semiconductor
UTA EE5368 Wireless Communication Systems Fall 2010 Instructor: Tracy Jing Liang, PhD, Adjunct Assistant Professor Electrical Engineering NH205 Phone: 817-272-3488 Fax: 817-272-2253 E-mail: jliang@uta.edu Lecture: MoWe 2:30PM - 3:50PM, WH308 Office Hours:
School: UT Arlington
EE 5350 Digital Signal Processing (Section 001) Fall 2003, TR/ 2:00 - 3:20pm Nedderman Hall, Room 106 http:/www-ee.uta.edu/Online/Oraintara/ee5350 INSTRUCTOR: Soontorn Oraintara, Assistant Professor, EE Department. Office: 539 Nedderman Hall, Phone:
School: UT Arlington
EE 5360 - Spring 2008 Data Communication Engineering Course Syllabus & Course Information Instructor: Iyad Al Falujah NH 254 Phone: 817-272-5433 Fax: 817-272-2253 Email: alfalujah@uta.edu Office Hours: W 10:0011:00 am, F 10:0011:00 am GTA:TBA Class
School: UT Arlington
EE 5380 Principles of Photonics and Optical Engineering Fall Semester 2005 Monday/Wednesday 4:00-5:20 pm, NH Room 106 Instructor: Office Hours: Instructor Website: E-mail: Michael Vasilyev, Asst. Prof. Office: Monday 5:30 pm 6:30 pm Phone: http:/www
School: UT Arlington
Medical Imaging BME 5300 / EE 5359 Spring 2005 Tuesday and Thursday 11:00 am - 12:20 pm or 2:00 pm 3:20 pm Instructors: Phone: Office Hours: Mailbox: E-mail: TAs' Names: Hanli Liu, Ph.D. (817) 272-2054 upon discussion 19138 hanli@uta.edu Kambiz A
School: UT Arlington
ProgrammingAssignment03 Signal.m function s = Signal(w, nx) s = zeros(nx,1)'; % Making an array of zeros for n = 1:nx+1 % Between 0 and Nx (the same as between 1 and Nx + 1) s(n) = (n-1)*exp(-(n-1)/6)*cos(w*(n-1); % The equation of the given signal end AM
School: UT Arlington
Lab 4: Sump Pump System Monitor The assignment for this lab is to design, build, and demonstrate the logic to monitor storm water management system. A holding tank collects water during rain storms. Although it will drain itself under normal conditions, w
School: UT Arlington
ProgrammingAssignment06 DSINE2.m function [LP_Num LP_Den HP_Num HP_Den BP_Num BP_Den]=DSINE2(w1,w2) %T=2sec % wc1 and wc2 are analog filter cuttoff frequencies (rad/sec) wc1=2*atan(w1); wc2=2*atan(w2); % Low Pass Filter A=2.63*w2; B=3.414*(w2^2); C=2.613*
School: UT Arlington
Last Name: First Name ID:xxxx-xx_. University of Texas at Arlington EE 2347 Fall 2013 Homework 1 Due Sept. 18, 2013 PRINT YOUR NAME in CAPITAL LETTERS. Last NAME: ID # : XXXX-XX- First NAME: INSTRUCTIONS: COPY THIS DOCUMENT AND WRITE YOUR SOLUTIONS IN SPA
School: UT Arlington
Course: Random Probabilities
EE 351K Probability and Random Processes Instructor: Haris Vikalo Homework 3 Solutions FALL 2010 hvikalo@ece.utexas.edu Problem 1 There are three dice in a bag. One has one red face, another has two red faces, and the third has three red faces. One of the
School: UT Arlington
Course: Random Probabilities
EE 351K Probability and Random Processes Instructor: Haris Vikalo Homework 4 FALL 2010 hvikalo@ece.utexas.edu Due on : Tuesday 09/28/10 Problem 1 There are n multiple-choice questions in an exam, each with 5 choices. The student knows the correct answer t
School: UT Arlington
Course: Linear Systems
EE5307 EXAM I October 11, 2007 Name (Print): _ (Last) (First) I.D.: _ Solve ALL THREE problems. Time: 1 hr. 30 min. Maximum Score: 36 points. Problem 1 (a) Set up the state-variable description for the following circuit with input u, output y and state va
School: UT Arlington
Course: Random Probabilities
EE 351K Probability and Random Processes Instructor: Haris Vikalo Homework 2 Solutions FALL 2010 hvikalo@ece.utexas.edu Problem 1 Bob, Carol, Ted and Alice take turns (in that order) tossing a coin with probability of tossing a Head, P (H ) = p, where 0 <
School: UT Arlington
Course: Semiconductor
Score distribution of EE 5368 Midterm spring 2011 Average =59.22 Mid=58.75 Variance =l-6.05 EE 5368 Wireless Commun tcation Systems Exarn#I Spring 201 1 Student name: 5, hhvn SN #: March23,20Il The lJniversity of Texas atArlington Problem Part (I) l. (15%
School: UT Arlington
Course: Semiconductor
Elements of Information Theory Second Edition Solutions to Problems Thomas M. Cover Joy A. Thomas September 22, 2006 1 COPYRIGHT 2006 Thomas Cover Joy Thomas All rights reserved 2 Contents 1 Introduction 2 Entropy, Relative Entropy and Mutual Information
School: UT Arlington
Course: Probability And Random Signals
mu = [0 10]; sigma = [1 0.7; 0.7 1]; r = mvnrnd(mu,sigma,10000); plot(r(:,1),r(:,2),'+') binranges = -15:15; [bincounts] = histc(r,binranges); bar(binranges,bincounts,'histc')
School: UT Arlington
Course: Probability And Random Signals
mu = [0 10]; sigma = [1 0.1; 0.1 1]; r = mvnrnd(mu,sigma,10000); plot(r(:,1),r(:,2),'+') binranges = -15:15; [bincounts] = histc(r,binranges); bar(binranges,bincounts,'histc')
School: UT Arlington
Course: Probability And Random Signals
mu = [0 10]; sigma = [1 0.3; 0.3 1]; r = mvnrnd(mu,sigma,10000); plot(r(:,1),r(:,2),'+') binranges = -15:15; [bincounts] = histc(r,binranges); bar(binranges,bincounts,'histc')
School: UT Arlington
School: UT Arlington
August 7, 2013 OPALtx, 2013 HTR, Jr. Table 1 Phasor elements Element Time-domain model Time-domain VCR/CVR Frequency-domain VCR/CVR Frequency-domain model vR(t) vR ( t ) = RiR ( t ) VR ( j ) = RI R ( j ) VR ( j ) Resistor iR(t) R vG(t) Conductor iG(t) G
School: UT Arlington
EE 3446 Circuits II Lab Rules V1.0 August 23, 2014 HTR, Jr. 1. Regardless of the lab section, student attendance in all EE 3446 labs is mandatory and not an option. You must attend each and every lab meeting for the entire time the lab is scheduled to be
School: UT Arlington
General Instructions for EE Labs, Lab Reports, and Course Homework Howard T. Russell, Jr., PhD September 16, 2012 I. Introduction As any student knows, lab reports and homework assignments are integral parts of most if not all electrical engineering cours
School: UT Arlington
Course: DSP
COPYRIGHT 2011 IJCIT, ISSN 2078-5828 (PRINT), ISSN 2218-5224 (ONLINE), VOLUME 01, ISSUE 02, MANUSCRIPT CODE: 110125 Performance Comparison of AWGN, Flat Fading and Frequency Selective Fading Channel for Wireless Communication System using 4QPSK Md. Sipon
School: UT Arlington
Course: DSP
Antenna Configurations for MIMO In-Building Distributed Antenna Systems Notebooks Infrastructure In-Building DAS Mobile Phones Outdoor DAS Telemetry Home & Enterprise Networking antennas.galtronics.com Galtronics Innovation Center INTRODUCTION The followi
School: UT Arlington
Course: DSP
White Paper FADING BASICS Narrow Band, Wide Band, and Spatial Channels White Paper 101 Rev. X mm/08 SPIRENT 1325 Borregas Avenue Sunnyvale, CA 94089 USA Email: sales@spirent.com Web: http:/www.spirent.com AMERICAS 1-800-SPIRENT +1-818-676-2683 sales@s
School: UT Arlington
Course: DSP
Synchronization for OFDM systems EIT 140, tom<AT>eit.lth.se Synchronization: oset/errors and their cause Symbol (timing) oset (OFDM and DMT) transmitter and receiver do not have a common time reference receiver needs to nd symbol boundaries to avoid ISI/I
School: UT Arlington
Course: DSP
Orthogonal Frequency Division Multiplex (OFDM) Tutorial 1 Intuitive Guide to Principles of Communications www.complextoreal.com Orthogonal Frequency Division Multiplexing (OFDM) Modulation - a mapping of the information on changes in the carrier phase, fr
School: UT Arlington
Course: DSP
Amplifier Terms Defined (AN-60-038) 1 dB compression point defines the output level at which the amplifier's gain is 1 dB less than the small signal gain, or is compressed by 1 dB (P1dB). P1dB (Output Power at 1 dB Compression) Saturated Output Power (PSA
School: UT Arlington
Course: DSP
Sheet 1 of 26 Oscillator Basics Tutorial J P Silver E-mail: john@rfic.co.uk ABSTRACT This paper discusses the basics of oscillator design including the parameters effecting oscillator performance, with special emphasis on the causes of phase noise. Theory
School: UT Arlington
ProgrammingAssignment05 AMP.m function [Am w] = AMP(x, Nm) Nx = length(x)-1; w = zeros(Nm,1)'; Am = zeros(Nm,1)'; n = 0:Nx; for k=1:Nm+1 w(k) = pi*k/Nm; Z = exp(1i*w(k); Am(k) = abs(x*(Z.^n)'); end end conv.m function [Ny y] = conv(x, Nx, h, Nh) Ny = Nh +
School: UT Arlington
ProgrammingAssignment04 RECON.m function x = RECON(x, N1, N2) % has parameters x, Nx, N1 and N2 Nx = length(x); % an input array with Nx samples for n=1:Nx; if mod(n,N1)~=0 N3 = floor(n/N1); x(n)=0; for k=(N3-N2+1):(N3+N2) if (k>0) & (k*N1<=Nx) x(n)=x(n)+
School: UT Arlington
ProgrammingAssignment02 conv.m function [Ny y] = conv(x, Nx, h, Nh) Ny = Nh + Nx; y = zeros(Ny+1,1)'; % Has input parameters x, Nx, h, Nh, where x and h are arrays of sizes % (1+Nx) and (1+Nh) for n = 0:Ny for k = 0:Nh m = n-k; if m>=0 & m<=Nx y(n+1) = y(
School: UT Arlington
ProgrammingAssignmentI Listingofconv.m function [Ny y] = conv(x, Nx, h, Nh) % Has input parameters x, Nx, h, Nh where x and h are arrays of sizes % (1 + Nx) and (1 + Nh) Ny = Nh + Nx; y = zeros(Ny+1,1)'; % From the definition of discrete convolution hold
School: UT Arlington
1 VIII. Digital Filter Design Techniques 11/16/2011 General procedure for Design of Frequency selective filters. (1) Specify or determine the digital filter desired frequency response in radians. (a) This could be directly specified in radians, or (b) Giv
School: UT Arlington
1 VII. Discrete Fourier Transform (DFT) Chapter-8 A. Modulo Arithmetic (n)N is n modulo N, n is an integer variable. (n)N = n mN 0 n mN N-1, pick m Ex. (k)4 WN = e-j2/N 2 Note that WNk = 0 WNmk = but N if m is a multiple of N but 0 , elsewhere Theorem :
School: UT Arlington
1 I. Introduction A. Relationship of DSP to other Fields. Common topics to all these fields: transfer function and impulse response, Fourierrelated transforms, convolution theorem. 2 y(t) = x(T) h(t-T)dT, Y(f) = H(f)X(f), Y1(s) = H1(s) X1(s) etc. B. Class
School: UT Arlington
1 VII. Discrete Fourier Transform (DFT) Chapter-8 A. Modulo Arithmetic (n)N is n modulo N, n is an integer variable. (n)N = n mN 0 n mN N-1, pick m Ex. (k)4 WN = e-j2/N 2 Note that WNk = 0 WNmk = but N if m is a multiple of N but 0 , elsewhere Theorem :
School: UT Arlington
1 IV Discrete Time Fourier transform DTFT. A. Basic Definitions The discrete-time Fourier transform (DTFT) of x(n) is X(ejw) = x(n) e-jwn where w is in radians. X(ejw) is periodic with period 2, since a function of a periodic function is periodic, and has
School: UT Arlington
Complete time-domain response of a 2nd-order RLC network Howard T. Russell, Jr. July 29, 2011 R L 2K 10H iL(t) Eg(t) = Egu(t) C vC(t) (Eg = 2V) Figure 1 RLC network with initial conditions Descriptive equation for t 0+: LC d2 d t2 vC ( t ) + RC d vC ( t )
School: UT Arlington
Time-domain response of a 1st-order RL network Howard T. Russell, Jr. July 18, 2011 vR(t) iL(t) R 27 5Vsin(t) Eg(t) L 680H (f = 6KHz) iL(0) Figure 1 RL network with sinusoidal input Descriptive equation for t 0+: L d iL ( t ) dt E g = 5V + RiL ( t ) = E g
School: UT Arlington
System response characteristics of a 2nd-order RLC network Howard T. Russell, Jr. August 3, 2011 R L 2K 100H iL(t) C Eg(t) = Egu(t) vC(t) 6.25pF (Eg = 10V) Figure 1 RLC network with zero initial conditions Descriptive network and system equation for t 0+:
School: UT Arlington
Natural time-domain response of a 2nd-order RLC network Howard T. Russell, Jr. July 27, 2008 3.3H L C iL(0+) = 1.65mA R 330pF vC(0+) = 5V vC(t) Figure 1 RLC network with initial conditions Descriptive equation for t 0+: LC d2 d vC t + RC vC t + vC t = 0 d
School: UT Arlington
School: UT Arlington
PROPERTIES OF GENERAL TWO-PORT NETWORKS Howard T. Russell, Jr. April 17, 1998 The twoport networks discussed here are assumed to contain physically realizable RLCM components, dependent controlled sources, and no independent voltage or current sources. Fu
School: UT Arlington
Response Characteristics of Linear Networks . 1/ w v MW e, w Howard T. Russell, J12, PhD OPAL Engineering, Inc. June 12, 1998 The frequency and time-domain characteristics of linear networks are determined from responses to known excitations. Network ex
School: UT Arlington
An/ Algorithm for the Fast Generation of the Nodal-Analysis Matrix Equation of a Linear Network Howard T. Russell, Jr., PhD OPAL Engineering, Inc. June 28, 1995 Nodevoltage equations can be quite effective in the analysis and design of linear circuits.
School: UT Arlington
School: UT Arlington
School: UT Arlington
O ()41U/tM/QC'Jé1é/ém Ve/f)2 [jg/y a? C 47 re \mde- i1 vC/t) +/é>iu¢e)dy ZLC) dC/tj :[gtg/w d9 » dzr _ w fm Jamar 0/- 57(@» Ina/W . 1 CL w, t» M1. 3/1 UL/tJ ~K [29%)Jr 7V [ ) % w z 1%.: UWiawt/LML WW E2 5 f [$43) zl/QV/Vvé/e/ 436.» _ <1 ~ I I I I (Co
School: UT Arlington
January 24, 2010 OPALtx, 2010 HTR, Jr. Table 1 Transformed network elements Element Time-domain model Time-domain VCR/CVR s-domain VCR/CVR s-domain model vR(t) vR ( t ) = RiR ( t ) VR ( s ) = RI R ( s ) VR(s) Resistor iR(t) R iR ( t ) = 1 vR ( t ) R IR (
School: UT Arlington
_ Derivatibn of the Mesh- . Analysis I'Matrixy, g Equation (MAME) I . -An Algorithm for the Fast, Generation a 9 - ~ ' Mesh-Analysis Matrix Equation "of a Linear Network: ' ' - Howard T. Russell, IJr.,yPhD> OPA
School: UT Arlington
Wednesday, September 07, 2011 EE 3446 HTR, Jr. Resistive MAME Examples Example 1. The oriented resistive network N1 shown in Figure 1(a) is driven by a constant current source (Jg1) and voltage source (Eg2). Figure 1(b) is the schematic of the network aft
School: UT Arlington
School: UT Arlington
School: UT Arlington
Biasing Methods 1. DC feedback avoid - a non-inverting dc path OR app 3; - an inverting dc path +VCC app 1 - a non-inverting do path AND avoid - an inverting dc path KEEN1. 2. Dual-supply biasing Figure 3 Typical values VCC = VEE RG1, = M t
School: UT Arlington
An Algorithm for the Fast Generation of the Node-Analysis Matrix Equation for Linear Resistive Networks Howard T. Russell, Jr., PhD OPALtx (V1.0 June 11, 2010) Node-voltage equations are quite effective in the analysis and design of linear networks. Thes
School: UT Arlington
An Algorithm for the Fast Generation of the Mesh-Analysis Matrix Equation for Linear Resistive Networks Howard T. Russell, Jr., PhD OPALtx (V1.0 May 23, 2008) (V1.1 January 21, 2010) Mesh-current equations are very useful in the analysis and design of li
School: UT Arlington
School: UT Arlington
School: UT Arlington
Design second- and third-order Sallen-Key filters with one op amp Christopher Paul, Motorola - January 31, 2011 RP Sallen and EL Key of the Massachusetts Institute of Technologys Lincoln Laboratory in 1955 introduced the Sallen-Key analog filter topology.
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Course: Neural Networks
I. Introduction A. Approximating Functions of One Variable, Review 1. Functions of Time Goal: Review approximation techniques for functions of time a. Example Applications (1) Approximating message signals in communications (2) Finding local approximation
School: UT Arlington
Course: Neural Networks
Neural Net Project 5: Simple Nonlinear Networks for Function Approximation and Classification In this project, we begin the task of producing multilayer perceptron (MLP) training software for 3-layer networks having floating point inputs and outputs. The
School: UT Arlington
Course: Neural Networks
Neural Net Project 1: Small Linear Networks for Function Approximation 1. Read the Reference Material below. 2. Using the data specified in part C, implement the linear equation solution of part D, printing out r, c, and w. Implement the steepest descent
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Course: Neural Networks
Neural Net Project 4: 11/15/2012 Comparing One- and Two-step MLP Training Algorithms In this project, we train 2 data files using BP3, cg, and MOLF, all of which have been somewhat discussed in class. 1. The Random10-2 datafile has 10 zero-mean, unit vari
School: UT Arlington
Course: Neural Networks
Neural Net Project 3: Functional Link Net (Volterra Filter) Design Using Regression In this project, we upgrade our software from project 2 to design a 2nd degree polynomial network ( called a functional link net or Volterra Filter) for function approxima
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1 VIII. Digital Filter Design Techniques 11/16/2011 General procedure for Design of Frequency selective filters. (1) Specify or determine the digital filter desired frequency response in radians. (a) This could be directly specified in radians, or (b) Giv
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1 VII. Discrete Fourier Transform (DFT) Chapter-8 A. Modulo Arithmetic (n)N is n modulo N, n is an integer variable. (n)N = n mN 0 n mN N-1, pick m Ex. (k)4 WN = e-j2/N 2 Note that WNk = 0 WNmk = but N if m is a multiple of N but 0 , elsewhere Theorem :
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1 III. Time Domain Analysis of systems Here, we adapt properties of continuous time systems to discrete time systems Section 2.2-2.5, pp 17-39 System Notation y(n) = T[ x(n) ] A. Types of Systems Memoryless (instantaneous) Systems y(n) = function of x(n),
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1 IV Discrete Time Fourier transform DTFT. A. Basic Definitions The discrete-time Fourier transform (DTFT) of x(n) is X(ejw) = x(n) e-jwn where w is in radians. X(ejw) is periodic with period 2, since a function of a periodic function is periodic, and has
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1 I. Introduction A. Relationship of DSP to other Fields. Common topics to all these fields: transfer function and impulse response, Fourierrelated transforms, convolution theorem. 2 y(t) = x(T) h(t-T)dT, Y(f) = H(f)X(f), Y1(s) = H1(s) X1(s) etc. B. Class
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1 VI. Z Transform Ch 24 Use: Analysis of systems, simple convolution, shorthand for ejw, stability. A. Definition: X(z) = x(n) z z - transforms Motivation easier to write Or X(z) = Z cfw_x(n) Note if z = e+jw, X(z) | = x(n) e-jwn = F cfw_ x(n) Can thin
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1 V. Ideal Sampling and Reconstruction of Analog Signals A. Continuous Time Fourier Transform F cfw_ x(t) = X( j ) = x(t) e-jt dt x(t) = F-1cfw_X(j) = 1/2 X(j)ejtd Similar to inverse discrete time Fourier transform 1/2 X(ejw) ejwn dw 2 B. Important Series
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Quantum Mechanics Introduction To describe or model the action of electrons in crystalline solid: From Classical Newtonian Mechanics (Continuum) to Quantum Mechanics (Quantization): Blackbody Radiation The Bohr Atom Wave-Particle Duality Schrodinger Equat
School: UT Arlington
Course: ELECTRONICS I
Last Name: Problem 2 (20 points) First Name ID:xxxx-xx Consider the BJT circuit shown below. Do all your calculations and derivations in detail on the next Dacle and enter your final answers in the boxes provided below: A. Express I'C and IE in terms of i
School: UT Arlington
Course: ELECTRONICS I
Formula Sheet FOUR-RESISTOR BIASING OF IDEAL BJT MODEL OPERATING IN FORWARD ACTIVE REGION AND ASSUMING CONSTANT VOLTAGE DROP COMMON EMITTER AMPLIFIER N-CHANNEL ENHANCEMENT TYPE MOSFET
School: UT Arlington
Course: ELECTRONICS I
Section C2: BJT Structure and Operational Modes Recall that the semiconductor diode is simply a pn junction. Depending on how the junction is biased, current may easily flow between the diode terminals (forward bias, vD > VON) or the current is essentiall
School: UT Arlington
Course: ELECTRONICS I
Section C5: Single-Stage BJT Amplifier Configurations In the previous discussions, we've talked about modes, models and curves of the BJT. So. pretty cool, but so what? Actually, this background was necessary. What we really care about for these devices i
School: UT Arlington
Course: ELECTRONICS I
Section C3: BJT Equivalent Circuit Models OK, we've got the terminal currents defined in terms of our gain constants and each other. Now. we've got to come up with a model for the entire device that we can put in an electrical circuit for design and analy
School: UT Arlington
Course: ELECTRONICS I
!"# $% & ' ) * " * # & $ * $ ( VBB = VTH = R1VCC R1 + R2 R1 R2 R1 + R2 . 0 $/ ' !#1 2 0 RB = RTH = R1 R2 = + ,& I BQ = 3 45 VBB - VBE RB + RE $ 3 '5 6" I CQ = 7 VBB - V BE VBB - VBE , I EQ = RB ( + 1) + RE RB + RE 6" / . ) ' . !#8 ,& IC = VCC - VCE RC +
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Course: ELECTRONICS I
ELECTRONICS-I PN junction (diode) Nihan Kosku Perkgz pn junction physics PN Junctions - DIODES - Semiconductor materials and their properties - - Charge carriers Doping Transport of carriers PN-junction diodes - - Structure Reverse and forward bias condit
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Course: ELECTRONICS I
School: UT Arlington
Course: ELECTRONICS I
Section B8: Clippers And Clampers We've been talking about one application of the humble diode rectification. These simple devices are also powerful tools in other applications. Specifically, this section of our studies looks at signal modification in ter
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Course: ELECTRONICS I
Section B9: Zener Diodes When we first talked about practical diodes, it was mentioned that a parameter associated with the diode in the reverse bias region was the breakdown voltage, VBR, also known as the peak-inverse voltage (PIV). This was a bad thing
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Course: ELECTRONICS I
Section B7: Filtering As mentioned at the end of the previous section, simple rectification results in a pulsating dc voltage at the output, also known as output ripple. These deviations from the desired dc may be reduced by the process of filtering. The
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Course: ELECTRONICS I
Section B6: Rectification Using Semiconductor Diodes Practically, we live in an ac world. However, many times a dc signal is required and we have to have a way to convert between ac and dc. This requires restricting the original ac signal that may alterna
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Course: ELECTRONICS I
Section B5: Diode Circuit Analysis We've spent a lot of time discussing the physical characteristics of a diode and the material/operational properties that are important. I'll just say one more time. getting familiar with these concepts now will help imm
School: UT Arlington
Course: ELECTRONICS I
Section B4: Diode Equivalent Circuit Models If we keep the diode operation away from the breakdown region, the curve of Figure 3.18 may be approximated as piecewise linear and we can model the diode as a simple circuit element or combination of standard c
School: UT Arlington
School: UT Arlington
August 7, 2013 OPALtx, 2013 HTR, Jr. Table 1 Phasor elements Element Time-domain model Time-domain VCR/CVR Frequency-domain VCR/CVR Frequency-domain model vR(t) vR ( t ) = RiR ( t ) VR ( j ) = RI R ( j ) VR ( j ) Resistor iR(t) R vG(t) Conductor iG(t) G
School: UT Arlington
School: UT Arlington
Exam 2, EE5350, Fall 2009 1. Find z-transforms of the following in closed form. For sequences containing x(), find the transforms in terms of X(), using real coefficients only. (a) nu(n) (b) cnu(n) (c) (n+8) + (n-8) (d) cos(won)u(n) 2. An IIR digital filt
School: UT Arlington
Exam 1, EE5350, Fall 2008 1. Convolve h(n) and x(n) to get y(n) below. Put y(n) in closed form when possible. (a) h(n) = a-nu(-n) and x(n) = b-nu(-n). (b) h(n) = anu(n) and x(n) = u(n). (c) h(n) = u(n-5) and x(n) = r(n) where r(n) = nu(n). Express y(n) in
School: UT Arlington
Final Exam, EE5350, Summer 2006 1. Convolve h(n) and x(n) to get y(n) below. Put y(n) in closed form when possible. (a) h(n) = a-nu(-n) and x(n) = b-nu(-n). (b) h(n) = anu(n) and x(n) = u(n). (c) h(n) = u(n-5) and x(n) = r(n) where r(n) = nu(n). (d) h(n)
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Final Exam, EE5350, Summer 2007 1. Convolve h(n) and x(n) to get y(n) below. Put y(n) in closed form when possible. (a) h(n) = anu(n) and x(n) = bnu(n). (b) h(n) = anu(n) and x(n) = b-nu(-n). (c) h(n) = u(n-8) and x(n) = r(n) where r(n) = nu(n). (d) h(n)
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Final Exam, EE5350, Summer 2008 1. A linear time invariant) system is described by the recursive difference equation y(n) = 2x(n) - x(n - 1) + 7 1 y(n - 1) y(n - 2) 12 12 (a) Find H(ejw) in closed form. (b) Find the homogeneous solution. (c) Find h(0) and
School: UT Arlington
Final Exam, EE5350, Spring 2005 1. A system is described by the recursive difference equation y(n) = 8 1 y(n - 1) y(n - 2) + x(n) 15 15 (a) Find H(ejw) in closed form. (b) Find the homogeneous solution for y(n). (c) Find the impulse response h(n). (d) Sta
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Final Exam, EE5350, Spring 2003 1. Here we derive Fcfw_x(2n). (a) First, set up this Fourier transform as a sum over n, with no simplifications. (b) Next, what do we substitute for n so that the sum is over even values of the variable m ? (n = f(m). what
School: UT Arlington
Final Exam, EE5350, Spring 2006 1. Convolve h(n) and x(n) to get y(n) below. Put y(n) in closed form when possible. (a) h(n) = anu(n) and x(n) = b-nu(-n). (b) h(n) = cos(wcn)u(n) and x(n) = u(n). (c) h(n) = u(n-5) and x(n) = r(n) where r(n) = nu(n). (d) h
School: UT Arlington
Final Exam, EE5350, Fall 2006 1. Convolve h(n) and x(n) to get y(n) below. Put y(n) in closed form when possible. (a) h(n) = a-nu(-n) and x(n) = b-nu(-n). (b) h(n) = u(n) and x(n) = r(n) where r(n) = nu(n). (c) h(n) = u(n+3)-u(n-3), x(n) = u(n-2)-u(n-8).
School: UT Arlington
Final Exam, EE5350, Fall 2005 1. Convolve h(n) and x(n) to get y(n) below. Put y(n) in closed form when possible. (a) h(n) = anu(n) and x(n) = ejwnu(n). Is x(n) an eigenfunction of the system ? (b) h(n) = anu(n) and x(n) = ejwn. Is x(n) an eigenfunction o
School: UT Arlington
Final Exam, EE5350, Spring 2002 1. Convolve h(n) and x(n) to get y(n) below. Put y(n) in closed form when possible. (a) h(n) = anu(n) and x(n) = bnu(n). (b) h(n) = anu(n) and x(n) = u(n). (c) h(n) = u(n) and x(n) = r(n) where r(n) = n u(n). (d) h(n) = u(n
School: UT Arlington
Final Exam, EE5350, Fall 2003 1. A linear time invariant) system is described by the recursive difference equation y(n) = 2x(n) - x(n - 1) + 7 1 y(n - 1) y(n - 2) 12 12 (a) Find H(ejw) in closed form. (b) Find the homogeneous solution. (c) Find h(0) and h
School: UT Arlington
Final Exam, EE5350, Fall 2002 1. Convolve h(n) and x(n) to get y(n) below. Put y(n) in closed form when possible. (a) h(n) = anu(n) and x(n) = bnu(n). (b) h(n) = anu(n) and x(n) = u(n). (c) h(n) = cos(wcn)u(n) and x(n) = u(n-5). (d) h(n) = u(n)-u(n-7), x(
School: UT Arlington
Final Exam, EE5350, Fall 2004 1. An LSI system is described by the recursive difference equation y(n) = a x(n) - b y(n - 2) (a) Find H(ejw) in closed form. (b) Find the impulse response h(n). (c) State whether or not the given difference equation is causa
School: UT Arlington
Exam 2, EE5350 and EE4318, Fall 2010 1. Find z-transforms of the following in closed form, and their regions of convergence. (a) u(-n) (b) anu(n-5) (c) nu(-n) (d) cos(w5n)u(n) 2. An IIR digital filter has the transfer function 2 7 z 1 H(z) = 1 7 z 1 + 10
School: UT Arlington
School: UT Arlington
The University of Texas at Arlington Department of Electrical Engineering EB 3446 Dr. H.T. Russell, Jr. Circuit Analysis II Fall 2012 November 16, 2012 MIDTERM EXAMINATION N0. 2 (Open-book, one hour, three problems) INSTRUCTIONS: . Write your last name an
School: UT Arlington
The University of Texas at Arlington Department of Electrical Engineering EE 3446 Dr. H.T. Russell, Jr. Circuit Analysis II ' Spring 2012 April 16, 2012 MID-TERM EXAMINATION No. 2 (Openbook, one hour, three problems) INSTRUCTIONS: Write your last name and
School: UT Arlington
School: UT Arlington
School: UT Arlington
School: UT Arlington
U I ' The University of Texas at Arlington Department of Electrical Engineering Dr. H.T. Russell, Jr. EE 3446 a Circuit Analysis II Spring 2012 March 2, 2012 MIDTERM EXAMINATION No. 1 (Open-book, one hour, three problems) INSTRUCTIONS; Write your last nam
School: UT Arlington
[I The University of Texas at Arlington Department of Electrical Engineering EE 3446 ' Dr. H.T. Russell, Jr. Circuit Analysis II Spring 2013 May 6, 2013 FINAL EXAMINATION (Open-book, two hours thirty minutes, four problems) INSTRUCTIONS: Write your last n
School: UT Arlington
EE 3310 Test 2 Fall 2014 Name: ID#: 1000 Test 2 will contribute 20% toward your final grade. You will have from 11:00 a.m. until 12:15 a.m. to work on the test. Show all work in order
School: UT Arlington
Course: Wireless Communication
EE5381 Midterm Exam Topics Fall 2014 Midterm Wed. Oct. 22, two 8.5 x 11 in. pages of notes (both sides) allowed Review Session: in class on Monday Oct. 20 1. Crystal Structure: unit cells, Bravais lattices, symmetry properties, crystal systems, semiconduc
School: UT Arlington
Course: Wireless Communication
11/24/2014 Longitudinal and Transverse Transport Processes Longitudinal Effects A) B) C) D) E) Electrical El t i l conductivity d ti it Thermal conductivity Thomson Effect Peltier Effect Seebeck Effect Jx Ex Qx Gx Jx + Qx simultaneously Transverse Effects
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ProgrammingAssignment03 Signal.m function s = Signal(w, nx) s = zeros(nx,1)'; % Making an array of zeros for n = 1:nx+1 % Between 0 and Nx (the same as between 1 and Nx + 1) s(n) = (n-1)*exp(-(n-1)/6)*cos(w*(n-1); % The equation of the given signal end AM
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ProgrammingAssignment06 DSINE2.m function [LP_Num LP_Den HP_Num HP_Den BP_Num BP_Den]=DSINE2(w1,w2) %T=2sec % wc1 and wc2 are analog filter cuttoff frequencies (rad/sec) wc1=2*atan(w1); wc2=2*atan(w2); % Low Pass Filter A=2.63*w2; B=3.414*(w2^2); C=2.613*
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Last Name: First Name ID:xxxx-xx_. University of Texas at Arlington EE 2347 Fall 2013 Homework 1 Due Sept. 18, 2013 PRINT YOUR NAME in CAPITAL LETTERS. Last NAME: ID # : XXXX-XX- First NAME: INSTRUCTIONS: COPY THIS DOCUMENT AND WRITE YOUR SOLUTIONS IN SPA
School: UT Arlington
Course: Random Probabilities
EE 351K Probability and Random Processes Instructor: Haris Vikalo Homework 3 Solutions FALL 2010 hvikalo@ece.utexas.edu Problem 1 There are three dice in a bag. One has one red face, another has two red faces, and the third has three red faces. One of the
School: UT Arlington
Course: Random Probabilities
EE 351K Probability and Random Processes Instructor: Haris Vikalo Homework 4 FALL 2010 hvikalo@ece.utexas.edu Due on : Tuesday 09/28/10 Problem 1 There are n multiple-choice questions in an exam, each with 5 choices. The student knows the correct answer t
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Course: Random Probabilities
EE 351K Probability and Random Processes Instructor: Haris Vikalo Homework 2 Solutions FALL 2010 hvikalo@ece.utexas.edu Problem 1 Bob, Carol, Ted and Alice take turns (in that order) tossing a coin with probability of tossing a Head, P (H ) = p, where 0 <
School: UT Arlington
Course: Probability And Random Signals
mu = [0 10]; sigma = [1 0.7; 0.7 1]; r = mvnrnd(mu,sigma,10000); plot(r(:,1),r(:,2),'+') binranges = -15:15; [bincounts] = histc(r,binranges); bar(binranges,bincounts,'histc')
School: UT Arlington
Course: Probability And Random Signals
mu = [0 10]; sigma = [1 0.1; 0.1 1]; r = mvnrnd(mu,sigma,10000); plot(r(:,1),r(:,2),'+') binranges = -15:15; [bincounts] = histc(r,binranges); bar(binranges,bincounts,'histc')
School: UT Arlington
Course: Probability And Random Signals
mu = [0 10]; sigma = [1 0.3; 0.3 1]; r = mvnrnd(mu,sigma,10000); plot(r(:,1),r(:,2),'+') binranges = -15:15; [bincounts] = histc(r,binranges); bar(binranges,bincounts,'histc')
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ProgrammingAssignment05 AMP.m function [Am w] = AMP(x, Nm) Nx = length(x)-1; w = zeros(Nm,1)'; Am = zeros(Nm,1)'; n = 0:Nx; for k=1:Nm+1 w(k) = pi*k/Nm; Z = exp(1i*w(k); Am(k) = abs(x*(Z.^n)'); end end conv.m function [Ny y] = conv(x, Nx, h, Nh) Ny = Nh +
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ProgrammingAssignment04 RECON.m function x = RECON(x, N1, N2) % has parameters x, Nx, N1 and N2 Nx = length(x); % an input array with Nx samples for n=1:Nx; if mod(n,N1)~=0 N3 = floor(n/N1); x(n)=0; for k=(N3-N2+1):(N3+N2) if (k>0) & (k*N1<=Nx) x(n)=x(n)+
School: UT Arlington
ProgrammingAssignment02 conv.m function [Ny y] = conv(x, Nx, h, Nh) Ny = Nh + Nx; y = zeros(Ny+1,1)'; % Has input parameters x, Nx, h, Nh, where x and h are arrays of sizes % (1+Nx) and (1+Nh) for n = 0:Ny for k = 0:Nh m = n-k; if m>=0 & m<=Nx y(n+1) = y(
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ProgrammingAssignmentI Listingofconv.m function [Ny y] = conv(x, Nx, h, Nh) % Has input parameters x, Nx, h, Nh where x and h are arrays of sizes % (1 + Nx) and (1 + Nh) Ny = Nh + Nx; y = zeros(Ny+1,1)'; % From the definition of discrete convolution hold
School: UT Arlington
Course: Probability And Random Signals
UTA EE-3330 Prof. Venkat Devarajan Homework Set #2 1) Let P(A) = 0.8, P(Bc) = 0.6, and P(AU B) = 0.8. Find (a) P(Ac|Bc). (b) P(Bc|A). 2) Suppose that the evidence of an event B increases the probability of a criminals guilt; that is, if A is the event tha
School: UT Arlington
Course: Probability And Random Signals
UTA EE-3330 Prof. Venkat Devarajan Homework Set #1 1) A number X is selected at random in the interval [2, 2]. Let the events A = cfw_X < 0, B = cfw_|X 0.5| < 1, and C = cfw_X > 0.75. a) Find the probabilities of B,A B, and A C. b) Find the probabilities
School: UT Arlington
Lab 4: Sump Pump System Monitor The assignment for this lab is to design, build, and demonstrate the logic to monitor storm water management system. A holding tank collects water during rain storms. Although it will drain itself under normal conditions, w
School: UT Arlington
EE 3446 Circuits II Lab Rules V1.0 August 23, 2014 HTR, Jr. 1. Regardless of the lab section, student attendance in all EE 3446 labs is mandatory and not an option. You must attend each and every lab meeting for the entire time the lab is scheduled to be
School: UT Arlington
General Instructions for EE Labs, Lab Reports, and Course Homework Howard T. Russell, Jr., PhD September 16, 2012 I. Introduction As any student knows, lab reports and homework assignments are integral parts of most if not all electrical engineering cours
School: UT Arlington
Lab Experiment No. 6 FM Transmitter Design I. Introduction The object of this lab experiment is to gain familiarity with the design cycle of analog circuits. The network used in this project is a simple RF transmitter that must oscillate with a frequency
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Mesh and Nodal Matrix Equations of Linear Resistive Networks Lab Experiment No. 5 I. Introduction Matrix analysis methods are very powerful tools for calculating the branch voltages and currents of a linear network. The purpose of this experiment is to ap
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Lab Experiment No. 9 Amplifier Networks I. Introduction The purpose of this lab session is to gain familiarity with several well-known amplifier circuits built with standard operational amplifiers. The theory and derivations associated with each of the ci
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Time-Domain Characteristics of 2nd Order Linear Networks Lab Experiment No. 3 I. Introduction This lab experiment in an extension of the experiments performed on first-order RC and RL networks in Lab 1. The experiments introduced in this lab exercise are
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Lab Experiment No. 2 Relaxation Oscillator I. Introduction This lab experiment involves the analysis and design of a class of waveform generators that produce signals for timing and control applications. These circuits are free-running oscillators called
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Time-Domain Characteristics of 1st Order Linear Networks Lab Experiment No. 1 I. Introduction The purpose of this lab is to investigate the behavior and characteristics of linear networks in the time-domain. The networks used here are simple first-order R
School: UT Arlington
Course: ELECTRONICS I
University of Texas at Arlington EE 2403 Summer 14 K. Alavi Design/Analysis/Simulation Project #1 6/26/14 Due 7/3/2014. 10:30 AM You must choose a partner to do this assignment. Make sure each partner makes significant contribution to the solution. Submit
School: UT Arlington
Lab 1: Familiarization Introduction: Hello students and welcome to EE 2441 or whatever new number they have assigned it. This is the documentation for lab 1 which will help guide you for all of the labs to come. At the end of this lab you should: 1. 2. 3.
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Lab 7: PIC12F609 Familiarization The purpose of this lab is to introduce the PIC12F609, an 8-bit microcontroller from Microchip. There are three parts to this lab. 1. You will identify some key parameters regarding the PIC12F609. 2. You will build a circu
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EE2441-Lab 5 Read Only Memory Basic Read Only Memory contains a decoder and memory array to generate the required m bit words at the output as shown in figure 1. Figure 1 Basic ROM circuit The goal of this session is to design and test a simple ROM. The m
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Lab 6: Shift Registers The purpose of this lab is to experiment with Flip Flops and Shift Registers. At the end of this lab you will understand how a D Flip-Flop works, and how to convert a D Flip-Flop to a Shift Register capable of performing Shift Left
School: UT Arlington
Lab 4: Sump Pump System Monitor The assignment for this lab is to design, build, and demonstrate the logic to monitor a storm water management system. A holding tank collects water during rain storms. Although it will drain itself under normal conditions,
School: UT Arlington
Lab 2: DeMorgans Laws For this assignment you will design, build, and test circuits that demonstrate the validity of DeMorgans Laws. The purpose of this lab is to design and explain an experiment that demonstrates DeMorgans Laws, Putting logic chips toget
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EE2441-Lab 3 Two bit multiplier Prelab activities: The goal of this session is to design a circuit which will yield the product of two binary numbers, n and m, Where (00)2 n, m (11)2 . For example, if n = (10)2 and m =(11)2, then the product is n*m = 102
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Page 1 of 3 Laboratory 11 Active Filters Introduction In this laboratory you will obtain practice with active lowpass and bandpass filters. A bandpass filter design is provided below; you will build and characterize the performance of this filter. T
School: UT Arlington
Course: DSP
COPYRIGHT 2011 IJCIT, ISSN 2078-5828 (PRINT), ISSN 2218-5224 (ONLINE), VOLUME 01, ISSUE 02, MANUSCRIPT CODE: 110125 Performance Comparison of AWGN, Flat Fading and Frequency Selective Fading Channel for Wireless Communication System using 4QPSK Md. Sipon
School: UT Arlington
Course: DSP
Antenna Configurations for MIMO In-Building Distributed Antenna Systems Notebooks Infrastructure In-Building DAS Mobile Phones Outdoor DAS Telemetry Home & Enterprise Networking antennas.galtronics.com Galtronics Innovation Center INTRODUCTION The followi
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Course: DSP
White Paper FADING BASICS Narrow Band, Wide Band, and Spatial Channels White Paper 101 Rev. X mm/08 SPIRENT 1325 Borregas Avenue Sunnyvale, CA 94089 USA Email: sales@spirent.com Web: http:/www.spirent.com AMERICAS 1-800-SPIRENT +1-818-676-2683 sales@s
School: UT Arlington
Course: DSP
Synchronization for OFDM systems EIT 140, tom<AT>eit.lth.se Synchronization: oset/errors and their cause Symbol (timing) oset (OFDM and DMT) transmitter and receiver do not have a common time reference receiver needs to nd symbol boundaries to avoid ISI/I
School: UT Arlington
Course: DSP
Orthogonal Frequency Division Multiplex (OFDM) Tutorial 1 Intuitive Guide to Principles of Communications www.complextoreal.com Orthogonal Frequency Division Multiplexing (OFDM) Modulation - a mapping of the information on changes in the carrier phase, fr
School: UT Arlington
Course: DSP
Amplifier Terms Defined (AN-60-038) 1 dB compression point defines the output level at which the amplifier's gain is 1 dB less than the small signal gain, or is compressed by 1 dB (P1dB). P1dB (Output Power at 1 dB Compression) Saturated Output Power (PSA
School: UT Arlington
Course: DSP
Sheet 1 of 26 Oscillator Basics Tutorial J P Silver E-mail: john@rfic.co.uk ABSTRACT This paper discusses the basics of oscillator design including the parameters effecting oscillator performance, with special emphasis on the causes of phase noise. Theory
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School: UT Arlington
School: UT Arlington
The z -transform We introduced the z -transform before as h[k ]z k H (z ) = k= where z is a complex number. When H (z ) exists (the sum converges), it can be interpreted as the response of an LSI system with impulse response h[n] to the input of z n . The
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Fourier Representations Throughout the course we have been alluding to various Fourier representations. We rst recall the appropriate transforms: Fourier Series (CTFS) x(t): continuous-time, nite/periodic on [, ] 1 X [k ] = 2 x(t)ejkt dt 1 x(t) = 2 X [k
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The DTFT as an eigenbasis We saw Parseval/Plancherel in the context of orthonormal basis expansions. This begs the question, do F and F 1 just take signals and compute their representation in another basis? Lets look at F 1 : L2 [, ] 2 (Z) rst: 1 F 1 (X (
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Stability, causality, and the z -transform In going from N m ak y [n k ] = k=0 bk x [ n k ] k=0 to H (z ) = Y (z ) X (z ) we did not specify an ROC. If we factor H (z ), we can plot the poles and zeros in the z -plane as below. Im[z ] Re[z ] Several ROCs
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Poles and zeros Suppose that X (z ) is a rational function, i.e., X (z ) = P (z ) Q(z ) where P (z ) and Q(z ) are both polynomials in z . The roots of P (z ) and Q(z ) are very important. Denition 1. A zero of X (z ) is a value of z for which X (z ) = 0
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Discrete-time systems We begin with the simplest of discrete-time systems, where X = CN and Y = CM . In this case a linear operator is just an M N matrix. We can generalize this concept by letting M and N go to , in which case we can think of a linear ope
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Approximation in p norms So far, our approximation problem has been posed in an inner product space, and we have thus measured our approximation error using norms that are induced by an inner product such as the L2 / 2 norms (or weighted L2 / 2 norms). So
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I II. Representation and Analysis of Systems Linear systems In this course we will focus much of our attention on linear systems. When our input and output signals are vectors, then the system is a linear operator. Suppose that L : X Y is a linear operato
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Examples: 1 3 Suppose V = cfw_piecewise constant functions on [0, 1 ), [ 1 , 2 ), [ 1 , 4 ), [ 3 , 1]. 4 4 2 4 An example of such a function is illustrated below. f (t) 1 1 4 1 2 1 3 4 t 1 Consider the set v2 (t) v1 (t) 1 1 1 4 1 2 3 4 1 t 1 4 1 1 2 3 4
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Orthobasis Expansions N Suppose that the cfw_vj j =1 are a nite-dimensional orthobasis. In this case we have N x= x, vj vj . j =1 But what if x span(cfw_vj ) = V already? Then we simply have N x= x, vj vj j =1 for all x V . This is often called the reprod
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Linear Operators Denition 1. Def: A transformation (mapping) L : X Y from a vector space X to a vector space Y (with the same scalar eld K ) is a linear transformation if: 1. L(x) = L(x) x X , K 2. L(x1 + x2 ) = L(x1 ) + L(x2 ) x1 , x2 X . We call such tr
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Hilbert Spaces in Signal Processing What makes Hilbert spaces so useful in signal processing? In modern signal processing, we often represent a signal as a point in high-dimensional space. Hilbert spaces are spaces in which our geometry intuition from R3
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I I. Signal Representations in Vector Spaces We will view signals as elements of certain mathematical spaces. The spaces have a common structure, so it will be useful to think of them in the abstract. Metric Spaces Denition 1. A set is a (possibly innite)
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Vector Spaces Metric spaces impose no requirements on the structure of the set M . We will now consider more structured M , beginning by generalizing the familiar concept of a vector. Denition 1. Let K be a eld of scalars, i.e., K = R or C. Let V be a set
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Inner Product Spaces Where normed vector spaces incorporate the concept of length into a vector space, inner product spaces incorporate the concept of angle. Denition 1. Let V be a vector space over K . An inner product is a function , : V V K such that f
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I. Introduction Information, Signals and Systems Signal processing concerns primarily with signals and systems that operate on signals to extract useful information. In this course our concept of a signal will be very broad, encompassing virtually any dat
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School: UT Arlington
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Course: ELECTRONICS I
EE 2403-001 and 2403-101- Electronics I (Spring 2014) Syllabus Instructor: Professor Kambiz Alavi, alavi@uta.edu , 524 Nedderman Hall, (office hours: 1:00 PM to 3:00 PM, Tues and Thu; other times by appointment), 817/2725633, fax 817/272-2253 Course Learn
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Course: Semiconductor
UTA EE5368 Wireless Communication Systems Fall 2010 Instructor: Tracy Jing Liang, PhD, Adjunct Assistant Professor Electrical Engineering NH205 Phone: 817-272-3488 Fax: 817-272-2253 E-mail: jliang@uta.edu Lecture: MoWe 2:30PM - 3:50PM, WH308 Office Hours:
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EE 5350 Digital Signal Processing (Section 001) Fall 2003, TR/ 2:00 - 3:20pm Nedderman Hall, Room 106 http:/www-ee.uta.edu/Online/Oraintara/ee5350 INSTRUCTOR: Soontorn Oraintara, Assistant Professor, EE Department. Office: 539 Nedderman Hall, Phone:
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EE 5360 - Spring 2008 Data Communication Engineering Course Syllabus & Course Information Instructor: Iyad Al Falujah NH 254 Phone: 817-272-5433 Fax: 817-272-2253 Email: alfalujah@uta.edu Office Hours: W 10:0011:00 am, F 10:0011:00 am GTA:TBA Class
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EE 5380 Principles of Photonics and Optical Engineering Fall Semester 2005 Monday/Wednesday 4:00-5:20 pm, NH Room 106 Instructor: Office Hours: Instructor Website: E-mail: Michael Vasilyev, Asst. Prof. Office: Monday 5:30 pm 6:30 pm Phone: http:/www
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Medical Imaging BME 5300 / EE 5359 Spring 2005 Tuesday and Thursday 11:00 am - 12:20 pm or 2:00 pm 3:20 pm Instructors: Phone: Office Hours: Mailbox: E-mail: TAs' Names: Hanli Liu, Ph.D. (817) 272-2054 upon discussion 19138 hanli@uta.edu Kambiz A
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EE 3318 Discrete Signals and Systems Summer 2008, TR/ 1:00 - 2:50pm Classroom: 108 NH http:/www-ee.uta.edu/Online/Oraintara/ee3318 INSTRUCTOR: Soontorn Oraintara, Associate Professor, EE Department. Office: 539 Nedderman Hall, Phone: 272-3482, Email
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EE 5350 Digital Signal Processing (Sections 001/002) Spring 2008, MW/ 10:30 - 11:50 AM Room 112 NH http:/www-ee.uta.edu/Online/Oraintara/ee5350 INSTRUCTOR: Soontorn Oraintara, Associate Professor, EE Department. Office: 539 Nedderman Hall, Phone: 272
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EE 5343 IC Fabrication Technology Spring 2007 F 9:00-11:30am 110 NH Instructor: Dr. Wiley Kirk/Dr. Weidong Zhou Office Location: NanoFAB Lab Hours: T/Th 12:30-3:20pm Phone: (817) 272-5632/1227 Email: kirk@nanfab.uta.edu; wzhou@uta.edu Required Textbo
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SYLLABUS EE 5321 Optimal Control (Sections 001 & 002) Spring 2008 MW 4:00-5:20pm Room 105 NH Instructor: Office: Office Hours: Phone: Mailbox: Email: Kai S. Yeung Room 507 NH M W 10:30am-12:00 noon (817) 272 3467 Electrical Engineering, Box 19016, UT
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Syllabus for Power System Modeling and Analysis EE 5308 Section 001 Fall 2007 11:00 a.m. - 12:20 p.m., Tuesday and Thursday Room 109 NH Instructor: Dr. Rasool Kenarangui OFFICE: 531 NH MAILBOX: Box 19048 EMAIL: kenarang@uta.edu INSTRUCTOR WEB SITE: