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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
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
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 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: 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
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
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
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)
School: UT Arlington
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
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 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 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 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
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
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
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
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
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
School: UT Arlington
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
School: UT Arlington
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
School: UT Arlington
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
School: UT Arlington
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 (
School: UT Arlington
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
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
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
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
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 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: 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
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
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
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
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 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
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
School: UT Arlington
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
School: UT Arlington
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
School: UT Arlington
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 (
School: UT Arlington
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
School: UT Arlington
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
School: UT Arlington
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
School: UT Arlington
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
School: UT Arlington
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
School: UT Arlington
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
School: UT Arlington
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
School: UT Arlington
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
School: UT Arlington
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
School: UT Arlington
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)
School: UT Arlington
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
School: UT Arlington
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
School: UT Arlington
School: UT Arlington
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
School: UT Arlington
9.1111760: wax-Wm: n: m t 04%:3 (i2 I Cg ym %URE ;: U1 .CME 830% gaka "5 \f3 Nani-Q Wk _ VH3, 00$ K\/,L me. Sobgfacqg) an; :mf M1 (9 Va. : Z2, A . Me: sum wk \Om CZ Clack 3? ('2: m annA \Wook 2: WVWMQ U6, M Ma WWWA $V\OS\?4LKQS ) Wu U9! \ 3&Mk I Wm
School: UT Arlington
School: UT Arlington
School: UT Arlington
cfw_WWWMM jlg x in ' u I 4p ; A 4m a . . mm . rnrtnwzfnvvzrv 'r:MA.-6.-.tg .1 A, (14 x. 4amm5-rrr41. . . 4r. . (rrre - a.- an m ' (wr'h mlm F ma4;? r: .DA':(- my -Asunaumar=a a. < -_ Macaw! an. y #7 RQ,CDU\ em Wyiws cikcugshm a9 QHLV bank; (Dz Pm
School: UT Arlington
School: UT Arlington
School: UT Arlington
School: UT Arlington
School: UT Arlington
School: UT Arlington
School: UT Arlington
School: UT Arlington
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
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
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)
School: UT Arlington
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
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
School: UT Arlington
School: UT Arlington
School: UT Arlington
School: UT Arlington
School: UT Arlington
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
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
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.
School: UT Arlington
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
School: UT Arlington
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
School: UT Arlington
Course: Neural Networks
Neural Net Project 2: Linear Networks for Function Approximation (1) Download and unzip Map.zip and compile the c program. Familiarize yourself with the code. (a) Download the file Twod.tra from the webpage. This file has 8 inputs and 7 outputs. (a) Apply
School: UT Arlington
Semiconductors Crystals An Introduction Materials Crystal Structure Unit Cell Bravais Lattice Semiconductor Lattices Miller Indices References: Pierret book; J. Singh book; Kittel book 1 Semiconductor Crystals Weidong Zhou Materials Matter Solid Liquid A
School: UT Arlington
EE4329/5340 Final Review Questions: Fall 2014 (Weidong Zhou) 1. Semiconductor Crystals a. What is semiconductor crystal? b. What are lattice and unit cells? c. How to define the Miller indices? (100), (110), (111) planes? d. What is the bandgap, Ec, Ev, E
School: UT Arlington
School: UT Arlington
Course: ELECTRONICS I
Problem: For the FET drain characteristics in Figure 5.11 (Page 297 of the textbook) nd Vto , and K . Solution: The drain currunt iD is given by equation 5.11 (Page 294) iD = K(vGS Vto )2 (1 + vDS ) To calculate Vto , select two points in the saturation r
School: UT Arlington
Course: Automated Control
EE 362K Review Topics for the Final Exam Spring 2010 First and second order system behavior: dominant poles concept; step response overshoot, rise time, settling time, and steady state error. Effects of extra poles/zeroes. Block diagrams and block diagram
School: UT Arlington
EE 2446 CIRCUIT ANALYSIS II Introduction to Electrical Circuits 7th edition Dorf and Svoboda Chapter 11 EE 2446 1 Dr. Raymond Shoults Phasor Notation-Review Horizontal projection on real axis a (t ) = A cos(t + 0 ) Projection on vertical axis I
School: UT Arlington
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
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
Figure 5.1 n-Channel enhancement MOSFET showing channel length L and channel width W. 2000 Prentice Hall Inc. Figure 5.2 Circuit symbol for an enhancement-mode n-channel MOSFET. 2000 Prentice Hall Inc. Figure 5.3 For vGS < Vto the pn junction between dr
School: UT Arlington
Course: ELECTRONICS I
Figure 4.1 The npn BJT. 2000 Prentice Hall Inc. Figure 4.2 An npn transistor with variable biasing sources (common-emitter configuration). 2000 Prentice Hall Inc. Figure 4.3 Current flow for an $npn$ BJT in the active region. Most of the current is due
School: UT Arlington
Course: ELECTRONICS I
Figure 3.1 Semiconductor diode. 2000 Prentice Hall Inc. Figure 3.2 Volt-ampere characteristic for a typical small-signal silicon diode at a temperature of 300 K. Notice the changes of scale. 2000 Prentice Hall Inc. Figure 3.3 Zener diode symbol. 2000 P
School: UT Arlington
Course: ELECTRONICS I
Figure 2.1 Circuit symbol for the op amp. 2000 Prentice Hall Inc. Figure 2.2 Equivalent circuit for the ideal op amp. AOL is very large (approaching infinity). 2000 Prentice Hall Inc. Figure 2.3 Op-amp symbol showing power supplies. 2000 Prentice Hall
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
Figure 1.1 Block diagram of a simple electronic system: an AM radio. 2000 Prentice Hall Inc. Figure 1.2 Analog signals take a continuum of amplitude values. Digital signals take a few discrete amplitudes. 2000 Prentice Hall Inc. Figure 1.3 An analog sig
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 +
School: UT Arlington
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
School: UT Arlington
Course: ELECTRONICS I
Op Amp Integrator I UTA 1. Initial Condition: The switch is closed for a time long enough to discharge the capacitor and reset the Capacitor Voltage to 0 so that vc(0)=0 2. EE2403 Fall 2103 K. Alavi At t=0 switch is opened allowing capacitor to charge . f
School: UT Arlington
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
School: UT Arlington
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
School: UT Arlington
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
School: UT Arlington
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
School: UT Arlington
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
Course: ELECTRONICS I
Section B3: The Practical Diode OK, the ideal diode is an extraordinarily well-behaved creature that allows us to deal with its nonlinearities in unbelievably reasonable terms. But.and there's always a but. we have to look at the deviations from ideal tha
School: UT Arlington
Course: ELECTRONICS I
K. Alavi U Texas at Arlington Fall 2013 EE 2403 Hints for Solving Circuits with OpAmp Step 1. Assume ideal OpAmp (Rin=, Ro=0, A=) with power supplies VCC and VEE. Step 2. Write Constraints imposed by the ideal OpAmp: Step 3. Write Constraints imposed by e
School: UT Arlington
School: UT Arlington
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
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)
School: UT Arlington
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
School: UT Arlington
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
School: UT Arlington
School: UT Arlington
School: UT Arlington
School: UT Arlington
School: UT Arlington
School: UT Arlington
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
School: UT Arlington
Course: Wireless Communication
11/18/2014 Resistivity Measurement Resistivity 1 q ( n n p p ) Where n and p are the electron and hole mobilities and n and p are the electron and hole concentrations. 1 4-pt Probe Measurement 2-wire measurement Rp Rc Rs Rsp RT V 2 R p 2 Rc 2 Rsp Rs I c
School: UT Arlington
Course: Wireless Communication
Resistivity Measurement Resistivity 1 q(n p ) n p Where n and p are the electron and hole mobilities and n and p are the electron and hole concentrations. 1 4-pt Probe Measurement 2-wire measurement Rp Rs Rc R 2R p 2Rc 2Rsp RT V R I s Only want Rs to ac
School: UT Arlington
Course: Wireless Communication
9/5/2014 EE5381 Foundations in Semiconductors Chapter Ch t 2 Essentials of Quantum Mechanics (Continued) 1 Solutions of the TISE 3.Electron and a Potential Step U=Uo U(x) U 0 U=0 E 0 x Two Region Problem Assume electron is approaching the barrier from l
School: UT Arlington
Course: Wireless Communication
11/15/2014 EE5381 Foundations in Semiconductors Chapter Ch t 6 Applications of Drift-Diffusion Theory: PN Junctions 1 PN Junction Diodes Assumption: Assuming low-level injection where additional drift currents due to the applied voltage are small. x n-si
School: UT Arlington
Course: Wireless Communication
9/15/2014 EE5381 Foundations in Semiconductors Chapter Ch t 3 Energy Band Theory (Continued) 1 Bloch Parameter, k for a free particle <p> = k where k=the wavevector. Inside a crystal, k is a momentum related constant which incorporates the electron inte
School: UT Arlington
Course: Wireless Communication
EE5381 Foundations in Semiconductors Chapter 1 Basic Properties of Semiconductors 1 Electrical Classification of Materials Electrical Conductivity Metals ~ 107 S/m Semiconductors ~ 10-6 to 106 S/m Insulators < 10-6 S/m Location in periodic table 2 3 4
School: UT Arlington
Course: Wireless Communication
9/11/2014 EE5381 Foundations in Semiconductors Chapter Ch t 3 Energy Band Theory 1 Energy Band Theory Assumptions All defects and impurities can be treated as 2nd order perturbations. Ignore lattice vibrations (phonons) and many electron interactions. 2
School: UT Arlington
Course: Wireless Communication
EE5381 Foundations in Semiconductors Chapter 2 Essentials of Quantum Mechanics (Continued) 1 Solutions of the TISE 2. Electron in 1-D Infinite Potential Well In one dimension, inside the well U(x) U=0 U 0 U= U= E 0 a x 2 d 2 E k 2 2m dx d2 2mE m k 2 2
School: UT Arlington
Course: Wireless Communication
kB := 1.3806 10 mo := 9.1 10 23 joule 31 T := 300 K K eV := 1.602 10 19 joule 34 kg h := 6.626 10 gD := 2 joule sec mn := 1.182 mo 19 ND := 10 cm 3 phosphorous impurity atoms ED := 0.045 eV Ec := 0 eV n( EF) := ND c( EF) := EF ED 1 + gD exp kB
School: UT Arlington
Course: Wireless Communication
8/25/2014 EE5381 Foundations in Semiconductors Chapter Ch t 2 Essentials of Quantum Mechanics 1 Classical Devices -critical dimensions on the order of >0.5 m -transport equation Jn=qnn+qDnn where Jn is the electron current density, n is the electron conce
School: UT Arlington
Course: Wireless Communication
8/29/2014 EE5381 Foundations in Semiconductors Chapter Ch t 2 Essentials of Quantum Mechanics (Continued) 1 Basic Formalism of Quantum Mechanics Simultaneously, in 1926, Schrodinger proposed wave mechanics and Heisenberg proposed matrix mechanics. Both th
School: UT Arlington
Course: Neural Networks
Exam # 2, EE5353, Fall 2013 1. A sigmoidal MLP has 10 inputs, 8 units in the first hidden layer, 7 units in the second hidden layer, and 2 outputs. It is fully connected. As usual, thresholds in the hidden and output layers are handled by adding an 11th i
School: UT Arlington
Course: Neural Networks
Exam # 3, EE5353, Fall 2013 1. Sometimes continuous approximations are needed. Consider a smoothed PLN (SPLN) that uses a weighted squared Euclidean distance measure. In order to make the mapping continuous, we can calculate intermediate outputs ypk as Ak
School: UT Arlington
Course: Neural Networks
Exam # 1, EE5353, Fall 2013 1. A functional link net has N inputs, M outputs, and is degree D. The weights wik, which feed into output number i, are found by minimizing the error function, E(i) = 1 Nv L Nv [t p (i) - y p (i)] 2 y p (i) = p=1 w X p (m) im
School: UT Arlington
School: UT Arlington
2- -jl 5 z_, - f 2.-rf.-. 5 Jz_,+ cfw_ OS + L.Jo =0 + ~-~ ~ 0 1/cfw_ ctJ = :;1.' 7 r; - 0. 7~ = 2 Z ltL 0 / = d l/ z;t 2 ,J;-x(:1) ~ - _z.7> - t / o, 7 ) c-cr) c ,.75 -.: -~ - / - c1o Lf rzJ:d) ~ ;J1_SL. c:~- ~:~ Z I R. 7I C-=- o. z. F v :fV 0 tJ ~e- C- L
School: UT Arlington
Course: POWER SYSTEM MODELING AND ANALYSIS
Et glo| l,4td tan h"^ J Sutuh'ory tb) L*= Inl- Ih,= 6r,7+ - B ,oy f),gg = /-u),v: & Icfw_ A Vr",= Vt^t-^+ T q, ( 0 .5-+ = ,?(,Tf:cfw_Jlo V j a/) t S= ).Vqr. = ).(t?f.zt -s,t!:") g .tg/*.fu" f Iqf / ( = J tvs L -wn l.6? V+ vb L rFr y*) , T rubln Z ?e^ plt*
School: UT Arlington
Course: POWER SYSTEM MODELING AND ANALYSIS
M ;d-terq h a^ l t S ,0&q E E9 8 P*Ut*-rt I (4" Fh, $o*"eq u-durbry h# ),>Lr^)= o ,zfo. | o'rA2 dne t, une,fti Jo, D , ,h/b/k^ '-' D b,: Drr, \.dnu6 4 (f f r*) t 9 fv>fit) '(Affi I - , r c I . ) = o , r ! - t D u n wb/k^ (r W; t" Trt"l ft^r l;^kges bfurur
School: UT Arlington
Course: POWER SYSTEM MODELING AND ANALYSIS
I A,' nL - t- L l_ Nz f, S*+*tu A,ilz I E E E )oB Nz z f- d (terttpt rt.7o7 9"* Vs= tooff I,lnn,= tooLt toor2 f ZaJ * Z*u = >, ( Ef F >+(,|t"jJ) | 00 /9- V/ W " -gr tAr c z Vuod.= rbvf, ( F*jF):25/1t5'z l E L = w (v t t VW = > 5h ,vtr E*jF)= W ( Y) a + \/
School: UT Arlington
Course: POWER SYSTEM MODELING AND ANALYSIS
Soh-dtn E E e )08 O.*iz 2 l. F A) :tlr )+, W -[a'ng, d r - 2- - t,r6E 2 = Tino qnd,qctvrc t* , .t3)-J r h Pl* Dr.: r7. n = ) .>Z\t ) n, 2- Dufi= D"b Dt, D - : J z 6'26.j z n LA,n - = ) )./tzf ft = cfw_ )'ln )nt l^ ( bb / D r") -l) x )n. 3 .&9+ to ( "l,n )
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 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 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 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
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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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)+
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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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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
Course: Probability And Random Signals
UTA EE-3330 Prof. Venkat Devarajan Homework Set #4 Solution 4.4 4.7 4.18 4.20 4.42 4.55 4.61 4.62 4.63
School: UT Arlington
Course: Probability And Random Signals
3.9) n m p (1 p) nm m And from binomial theory p[ X m] Now for Y=0 m=n/2 and p=1/2 n 1 p[Y 0] n / 2 2 n For Y n p[Y 0] nk 2 n 1 2
School: UT Arlington
Course: Probability And Random Signals
UTA EE-3330 Prof. Venkat Devarajan Homework Set #2 Solution Let P(A) = 0.8, P(Bc) = 0.6, and P(AU B) = 0.8. Find (a) P(Ac|Bc). (b) P(Bc|A). 1) Soln a) b) 2) Soln Suppose that the evidence of an event B increases the probability of a criminals guilt; that
School: UT Arlington
Course: Probability And Random Signals
UTA EE-3330 Prof. Venkat Devarajan Homework Set #1 Solution 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 probabil
School: UT Arlington
EE5381/Butler Assigned: Mon., 22 Sept. 2014 Due: Fri., 3 Oct. 2014 1. Problem 3.1 from text 2. Problem 3.2 from text Homework #3 Fall 2014 3. Problem 3.4 from text 4. Problem 3.7 from text 5. Problem 4.3 from text
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EE5381/Butler Homework #4 Fall 2014 Assigned: Monday 6 Oct. 2014 Due: Monday 13 Oct. 2014 Midterm: Wednesday 22 Oct. 2014, two 8.5x11 inch pages (both sides) of notes allowed 1. What fraction of the holes in Ge lie in the heavy hole band? Use 4K effective
School: UT Arlington
EE5381/Butler Homework #2 Assigned: Monday, 8 September 2014 Due: Monday, 22 Sept. 2014 Fall 2014 1. Problem 2.1 from text 2. An electron is contained in the potential well shown below (a simplified model for the channel of an FET). a) Solve the Schrdinge
School: UT Arlington
EE5381/Butler Term Paper Fall 2014 Assigned: Wed. 10 Sept. 2014 Proposal Due: Wed. 8 Oct. 2014 Term Paper Due: Wed. 26 Nov. 2014 The term paper will make up 25% of the course grade. The term paper must be focused on some aspect of semiconductors. A succes
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EE5381/Butler Assigned: Mon. 25 Aug. 2014 Due: Mon. 8 Sept. 2014 Homework #1 Fall 2014 1. AlN crystallizes in the Wurtzite lattice with a=0.31115 nm and c=0.49798 nm at 300 K. Determine the number of nitrogen atoms in the unit cell and the mass density of
School: UT Arlington
Course: Neural Networks
Homework # 4, EE5353 1. For a Bayes-Gaussian classifier the mean vector for the ith class is mi and has elements mi(n), the covariance matrix for the ith class is Ci, and the elements of the inverse covariance matrix are a(i,m,n), where m is the row numbe
School: UT Arlington
Course: Neural Networks
Homework # 5, EE5353 1. In K-means clustering, means can be updated during the data pass or recalculated afterwards. (a) Under what conditions are the resulting clusters identical ? (b) If we update the means during the data pass, under what conditions wi
School: UT Arlington
Course: Neural Networks
Homework # 1, EE5353 1. An XOR network has two inputs, one hidden unit, and one output. It is fully connected. Give the network's weights if the output unit has a step activation and the hidden unit activation is (a) Also a step function (b) The square of
School: UT Arlington
Course: Linear Systems Engineering
Lab 4 Representing Polynomials A polynomial of nth degree looks like: n a n s +a n1 a n1 2 +.+a 2 s +a 1 s+ a0 The coefficients an, an-1, , a2, a1, a0 are the coefficients of decreasing powers of s. MATLAB has some powerful built-in functions to work with
School: UT Arlington
Course: Linear Systems Engineering
LAB 2 This lab will seem like a repetition of lab 1, but considering that most of the class is new to MATLAB, this is necessary. There will be a class roll at the beginning of the class, and a submission of the exercises from the class both of which will
School: UT Arlington
Course: Linear Systems Engineering
LAB 3 Matrix (and array/vector) operations We will treat a vector (mathematical name for array) of some length N as a matrix of size Nx1 (or 1xN if necessary). So any Matrix operations we describe below apply to vectors too. A = (2x3) 1 2 3 4 5 6 B = (3x2
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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
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
School: UT Arlington
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
School: UT Arlington
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
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
School: UT Arlington
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
School: UT Arlington
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
School: UT Arlington
School: UT Arlington
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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9.1111760: wax-Wm: n: m t 04%:3 (i2 I Cg ym %URE ;: U1 .CME 830% gaka "5 \f3 Nani-Q Wk _ VH3, 00$ K\/,L me. Sobgfacqg) an; :mf M1 (9 Va. : Z2, A . Me: sum wk \Om CZ Clack 3? ('2: m annA \Wook 2: WVWMQ U6, M Ma WWWA $V\OS\?4LKQS ) Wu U9! \ 3&Mk I Wm
School: UT Arlington
School: UT Arlington
School: UT Arlington
cfw_WWWMM jlg x in ' u I 4p ; A 4m a . . mm . rnrtnwzfnvvzrv 'r:MA.-6.-.tg .1 A, (14 x. 4amm5-rrr41. . . 4r. . (rrre - a.- an m ' (wr'h mlm F ma4;? r: .DA':(- my -Asunaumar=a a. < -_ Macaw! an. y #7 RQ,CDU\ em Wyiws cikcugshm a9 QHLV bank; (Dz Pm
School: UT Arlington
School: UT Arlington
School: UT Arlington
School: UT Arlington
School: UT Arlington
School: UT Arlington
School: UT Arlington
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
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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: