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School: UT Arlington
Course: Digital Signal Processing
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
Course: Digital Signal Processing
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
Course: Digital Signal Processing
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
Course: Digital Signal Processing
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
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
Course: Digital Image Processing
All these figures are from A K.Jain, Fundamentals of Digital Image Processing, Prentice Hall,1989 for EE 5356 Instructional Use Only
School: UT Arlington
Course: Digital Image Processing
“’52 E195?“ ‘2“ 1.4, _\~ EE 5354 L; gut/Pink; 9% A‘M‘je "Cg": wwiﬁ’mm’ fégukcoﬂw;n ’ 154‘“? we“! ‘5 9*“ ‘.‘”hV‘¢£‘~e C Lawn 14":qu [ix/aces: , I, [J ZEQ . V 1‘ TLese awe i8. W‘wa’g 498mb“: Wrgwj 7,6»? [2%140’“ a t; \P. “ oV'W‘cst-am'. 1”" - ;4§ m (7») Ex
School: UT Arlington
Course: Digital Image Processing
EE4328, Section 005 Introduction to Digital Image Processing Image Segmentation Zhou Wang Dept. of Electrical Engineering The Univ. of Texas at Arlington Fall 2006 Concepts and Approaches What is Image Segmentation? Partition an image into regions, each
School: UT Arlington
Course: Digital Image Processing
Y' :5. (UL) 5": fupu. V 3/ o Prob [ggvj = Pros [a my] = Rob [u< Fu “(WJ F «210) Pu. (“>01”- =a<ﬂwu .‘V \H Py (w) Prob [v = vk] Bob [v = a ma] ROEEF;1(V)= XJ Prob. [x =ij 120 .— —_— £/_.(,v,‘j _ = Pu (xx) NoZ‘e : F;‘<v) = xx 'z/ a (xx) 2v and I Fa (Xk_1)
School: UT Arlington
Hecht; 10/12/2010; 8-1 Chapter 8. Polarization 8.1 The Nature of Polarized Light A. Linear Polarization Sum of two waves E ( z , t ) = xEox cos (kz t ) + yEoy cos (kz t + ) For = 0, 2 . , E = ( xE ox + yE oy ) cos (kz t ) For = . , E = ( xE ox yE oy ) cos
School: UT Arlington
Polarization Optics, Eugene Hecht, Chpt. 8 Linear polarization E-field magnitude oscillates Direction fixed Arbitrary polarization angle superposition of x and y polarized waves real numbers Example 45 linear polarization Time evolution Circular pola
School: UT Arlington
Lenses Optics, Eugene Hecht, Chpt. 5 Lenses for imaging Object produces many spherical waves scattering centers Want to project to different location Lens designed to project and reproduce scattering centers Object is collection of scattering centers D
School: UT Arlington
Reflection and refraction Optics, Eugene Hecht, Chpt. 4 Notation Start with propagating waves: E = E0 cos(kx - wt) and B = B0 cos(kx - wt) Use complex amplitudes (as in ac circuits): E0 cos(kx - wt) = (1/2) (E0 exp[i(kx - wt)] + c.c.) drop (1/2) and
School: UT Arlington
' ' ,IAV—bc 11>? ‘f’ 1‘: MO 2 N“? f" ‘ . he» :aaym‘ . ,V / j 5) £1; . T7”— 3143142 alpha/a Cc mavfdﬂzw Cf £565,” > go "' ibich i/ﬁiiﬁf‘C a . V , , a I = ‘ a “I W» Lth’IL/W ’ z 4.2wa w) , W ﬁﬂﬁgwm ‘5 If“ A PKE’MW @3ng EMA A I ‘2‘- _;/L If, - . 5 . ‘ "
School: UT Arlington
Refractive index dispersion and Drude model Optics, Eugene Hecht, Chpt. 3 Dielectrics Electric field is reduced inside dielectric Space charge partly cancels E / Ev = e / e0 Index of refraction: n Also possible for magnetic fields but usually B = Bv
School: UT Arlington
Course: Wireless Communication Systems
EE 5368 Wireless Communication Systems Final Exam Spring 2012 Student name: Sulrx^ SN #: Apri|25,2012 The University of Texas at Arlington Problem l. (20%) I: Part Answer the following questions. (I) Space-Time Transmit-Diversity Case: Ant. #1 Y -.-ho l)r
School: UT Arlington
Course: Wireless Communication Systems
EE 5368 Wireless Communication Systems Final Exam Spring 2012 Student name: _ SN #: _ April 25, 2012 The University of Texas at Arlington 1 Problem 1. (20%) Part I: Answer the following questions. (I) Space-Time Transmit-Diversity Case: Ant. #1 h0 h1 Ant.
School: UT Arlington
Course: Wireless Communication Systems
EE 5368 Wireless Communication Systems Final Exam Spring 201 1 Student name: ID #2 "‘e May 04, 2011 The University of Texas at Arlington Problem 1. (20%) Two-ray model is show below. d r P A 2 d . 2 here ——’—=G -G - ~—-—— - 1+ -ex — A W Pt , r(47rd)!
School: UT Arlington
Course: Wireless Communication Systems
EE 5368 Wireless Communication Systems Final Exam Fall 2011 Student name: _ SN #: _ December 07, 2011 The University of Texas at Arlington 1 Problem 1. (20%) Part (I) Cellular systems GSM IS-95 IS-136 Analog or Digital System Multiple Access Method FDD or
School: UT Arlington
Course: Wireless Communication Systems
EE 5368 Wireless Communication Systems Final Exam Spring 2010 Student name: _ ID #: _ May 06, 2010 The University of Texas at Arlington 1 Problem 1. (25%) In order to increase the bit rate, 3GPP LTE (Long Term Evolution) Radio Access Network uses many tec
School: UT Arlington
Course: Wireless Communication Systems
EE 5368 Wireless Communication Systems Final Exam Fall 2009 Student name: _ SN #: _ December 02, 2009 The University of Texas at Arlington 1 Problem 1. (20%) Four received power measurements were taken at distances of 100m, 200m, 1km, and 2 km from a tran
School: UT Arlington
Course: Digital Signal Processing
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
Course: Digital Signal Processing
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
Course: Digital Signal Processing
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
Course: Digital Signal Processing
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
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
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
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: Wireless Communication Systems
I LONG TERM EVOLIJTION (LTE) 3.1 MULTIPLEACCESS Mulliple access techniques allou,resources to be shared between a group ofusers The downlink ofLTE uses Orthogonal Frequency Division Multiple Access (OFDMA). This is in contrast to Frequency, Time or Code D
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
Principles of Photonics and Optical Engineering 2015 Kambiz Alavi -UNIFORM PLANE WAVES I: Uniform Plane wave with angular frequency w propagating in +z direction in a "Simple" medium (Linear, Isotropic, Homogeneous, Time Invariant medium, no net free char
School: UT Arlington
Course: Wireless Communication Systems
SYLLABUS EE5368 Wireless Communication Systems Fall 2014 Wed. 6:00pm - 8:50pm Room NH 105 Instructor: Peter (Shu-Shaw) Wang Office: Office Hours: By appointment Phone: (817) 795-4421 Mailbox: Electrical Engineering, Box 19016, UTA, Arlington TX 76019 Emai
School: UT Arlington
Course: POWER SYSTEM MODELING AND ANALYSIS
The UT Arlington Syllabus Template for 2015-16 Frequently Asked Questions Whats new for 2015-16? The most important revision to this years template is the new section titled Emergency Exit Procedures. See below for important details. We have also updated
School: UT Arlington
Course: Wireless Communication Systems
SYLLABUS EE5368 Wireless Communication Systems Spring 2015 Wed. 5:30pm - 8:20pm Room NH 105 Instructor: Peter Wang Office: Office Hours: By appointment Phone: (817) 565-3226 Mailbox: Electrical Engineering, Box 19016, UTA, Arlington TX 76019 Email: peter.
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
Course: Digital Signal Processing
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
Course: Digital Signal Processing
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
Course: Digital Signal Processing
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
Course: Digital Signal Processing
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
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
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: 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 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: 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
Hecht; 10/12/2010; 8-1 Chapter 8. Polarization 8.1 The Nature of Polarized Light A. Linear Polarization Sum of two waves E ( z , t ) = xEox cos (kz t ) + yEoy cos (kz t + ) For = 0, 2 . , E = ( xE ox + yE oy ) cos (kz t ) For = . , E = ( xE ox yE oy ) cos
School: UT Arlington
Polarization Optics, Eugene Hecht, Chpt. 8 Linear polarization E-field magnitude oscillates Direction fixed Arbitrary polarization angle superposition of x and y polarized waves real numbers Example 45 linear polarization Time evolution Circular pola
School: UT Arlington
Lenses Optics, Eugene Hecht, Chpt. 5 Lenses for imaging Object produces many spherical waves scattering centers Want to project to different location Lens designed to project and reproduce scattering centers Object is collection of scattering centers D
School: UT Arlington
Reflection and refraction Optics, Eugene Hecht, Chpt. 4 Notation Start with propagating waves: E = E0 cos(kx - wt) and B = B0 cos(kx - wt) Use complex amplitudes (as in ac circuits): E0 cos(kx - wt) = (1/2) (E0 exp[i(kx - wt)] + c.c.) drop (1/2) and
School: UT Arlington
Principles of Photonics and Optical Engineering 2015 Kambiz Alavi -UNIFORM PLANE WAVES I: Uniform Plane wave with angular frequency w propagating in +z direction in a "Simple" medium (Linear, Isotropic, Homogeneous, Time Invariant medium, no net free char
School: UT Arlington
' ' ,IAV—bc 11>? ‘f’ 1‘: MO 2 N“? f" ‘ . he» :aaym‘ . ,V / j 5) £1; . T7”— 3143142 alpha/a Cc mavfdﬂzw Cf £565,” > go "' ibich i/ﬁiiﬁf‘C a . V , , a I = ‘ a “I W» Lth’IL/W ’ z 4.2wa w) , W ﬁﬂﬁgwm ‘5 If“ A PKE’MW @3ng EMA A I ‘2‘- _;/L If, - . 5 . ‘ "
School: UT Arlington
Refractive index dispersion and Drude model Optics, Eugene Hecht, Chpt. 3 Dielectrics Electric field is reduced inside dielectric Space charge partly cancels E / Ev = e / e0 Index of refraction: n Also possible for magnetic fields but usually B = Bv
School: UT Arlington
Laser basics Optics, Eugene Hecht, Chpt. 13; Optical resonator tutorial: http:/www.dewtronics.com/tutorials/lasers/leot/ Laser oscillation Laser is oscillator Like servo with positive feedback Greater than unity gain Laser gain and losses Laser turn-on
School: UT Arlington
Principles of Photonics and Optical Engineering 2015 Kambiz Alavi -POLARIZATION STATE OF UNIFORM PLANE WAVES Uniform Plane wave with angular frequency propagating in + z direction in a "Simple" medium (Linear, Isotropic, Homogeneous, Time Invariant medium
School: UT Arlington
Ray tracing and ABCD matrix Optics, Eugene Hecht, Chpt. 6 Basics of ray tracing Consider 2D projection Ray uniquely defined by position and angle Make components of vector Paraxial approximation - express angle as slope = y f=y y z Example: Propagate
School: UT Arlington
Relation between r, r and t, t at ar at ar a a air glass r glass air Proof: algebraic from the Fresnel coefficients or using the property of preservation of the field properties upon time reversal r r 2 tt 1 Stokes relationships MIT 2.71/2.710 Optics 10/2
School: UT Arlington
Principles of Photonics and Optical Engineering 2015 Kambiz Alavi -ELLIPTICAL POLARIZATION of UNIFORM PLANE WAVES Uniform Plane wave with angular frequency propagating in + z direction in a "Simple" medium (Linear, Isotropic, Homogeneous, Time Invariant m
School: UT Arlington
Optics, by Eugene Hecht, 4th Edition, Pearson Addison Wesley Errata, by Chris Mack, chris@lithoguru.com While teaching out of this book at the University of Texas at Austin, Spring 2008, I discovered the following errors: p. 112, equation (4.16), top line
School: UT Arlington
Course: Digital Image Processing
All these figures are from A K.Jain, Fundamentals of Digital Image Processing, Prentice Hall,1989 for EE 5356 Instructional Use Only
School: UT Arlington
Course: Digital Image Processing
“’52 E195?“ ‘2“ 1.4, _\~ EE 5354 L; gut/Pink; 9% A‘M‘je "Cg": wwiﬁ’mm’ fégukcoﬂw;n ’ 154‘“? we“! ‘5 9*“ ‘.‘”hV‘¢£‘~e C Lawn 14":qu [ix/aces: , I, [J ZEQ . V 1‘ TLese awe i8. W‘wa’g 498mb“: Wrgwj 7,6»? [2%140’“ a t; \P. “ oV'W‘cst-am'. 1”" - ;4§ m (7») Ex
School: UT Arlington
Course: Digital Image Processing
EE4328, Section 005 Introduction to Digital Image Processing Image Segmentation Zhou Wang Dept. of Electrical Engineering The Univ. of Texas at Arlington Fall 2006 Concepts and Approaches What is Image Segmentation? Partition an image into regions, each
School: UT Arlington
Course: Digital Image Processing
Y' :5. (UL) 5": fupu. V 3/ o Prob [ggvj = Pros [a my] = Rob [u< Fu “(WJ F «210) Pu. (“>01”- =a<ﬂwu .‘V \H Py (w) Prob [v = vk] Bob [v = a ma] ROEEF;1(V)= XJ Prob. [x =ij 120 .— —_— £/_.(,v,‘j _ = Pu (xx) NoZ‘e : F;‘<v) = xx 'z/ a (xx) 2v and I Fa (Xk_1)
School: UT Arlington
Course: Digital Image Processing
All these figures are from A K.Jain, Fundamentals of Digital Image Processing, Prentice Hall,1989 for EE 5356 Instructional Use Only
School: UT Arlington
Course: Digital Image Processing
“’52 E195?“ ‘2“ 1.4, _\~ EE 5354 L; gut/Pink; 9% A‘M‘je "Cg": wwiﬁ’mm’ fégukcoﬂw;n ’ 154‘“? we“! ‘5 9*“ ‘.‘”hV‘¢£‘~e C Lawn 14":qu [ix/aces: , I, [J ZEQ . V 1‘ TLese awe i8. W‘wa’g 498mb“: Wrgwj 7,6»? [2%140’“ a t; \P. “ oV'W‘cst-am'. 1”" - ;4§ m (7») Ex
School: UT Arlington
Course: Digital Image Processing
EE4328, Section 005 Introduction to Digital Image Processing Image Segmentation Zhou Wang Dept. of Electrical Engineering The Univ. of Texas at Arlington Fall 2006 Concepts and Approaches What is Image Segmentation? Partition an image into regions, each
School: UT Arlington
Course: Digital Image Processing
Y' :5. (UL) 5": fupu. V 3/ o Prob [ggvj = Pros [a my] = Rob [u< Fu “(WJ F «210) Pu. (“>01”- =a<ﬂwu .‘V \H Py (w) Prob [v = vk] Bob [v = a ma] ROEEF;1(V)= XJ Prob. [x =ij 120 .— —_— £/_.(,v,‘j _ = Pu (xx) NoZ‘e : F;‘<v) = xx 'z/ a (xx) 2v and I Fa (Xk_1)
School: UT Arlington
Course: Digital Image Processing
EX >3.ﬁ-.1_ #1121 {CW/(LA?) LEI BNIT? {F}. nF—F— ULE a {Hﬂgﬁxﬂmhf (“133%th L _-'-" 3 f" ' ﬁg.) 2 J x A K 5.4 Unlfﬁl‘m GUI! Bar 215 I. 2: w . +' 1904'“ .x J ‘33? — {(6.1% H Lani—wig: “1 I. I. Immlxu-unﬂﬂunnunllurm ﬁulﬂl‘ll
School: UT Arlington
Course: Digital Image Processing
[I’ve I’ve) ‘ ‘ / ca _ ~ M6 M 62¢ god X‘S’Q Cavw I‘dgm/f‘ / C966 x» -*é~4ﬁ1'7) / @535] / .“A i ’ my «x Mk 1% :7 w it _ J j J “L J‘i‘i Caz/M M Elk/Wm J‘ I ‘3” \ ' “‘3 Q’- ﬂ / ’m m (4‘43? (< f I ii l /< «1- ’ > ‘ + “(L * ‘ 1/1131") :2. — L1 z (“3 ' C k“
School: UT Arlington
Course: Digital Image Processing
) ])A (ABAr) = [ .2? L:.I J:.I , N N ~:.I L = : N _d.- (., ~ or: da.~ a.i~ ," b. "J ~. ~.J ] L. "' .L. J:/ ~. J ~ b~ ~ t.,=~ l. ) ~=~ n 2. ~;.I tJ Q. -" ~" =Y"I I J;' = 0 0 the.y /.oJ ise. = = [ 2: ~J. b . "J A6T + AB j=-1 Q. N Q. ~ a. b. ~" 0 <' (:) 0<.
School: UT Arlington
Course: POWER SYSTEM MODELING AND ANALYSIS
w3zhang Digitally signed by w3zhang DN: cn=w3zhang, c=CA Reason: I am approving this document Date: 2006.09.27 19:45:25 -04'00'
School: UT Arlington
Course: POWER SYSTEM MODELING AND ANALYSIS
Phasor, Transformer, and Transmission Lines Wei-Jen Lee, Ph.D., P.E. Professor and Director Energy Systems Research Center The University of Texas at Arlington 1 Text Book: J. Duncan Glover, Mulukutla S. Sarma, and Thomas J. Overbye, Power Systems Analysi
School: UT Arlington
Course: POWER SYSTEM MODELING AND ANALYSIS
The Power Flow Equations 1.0 The Admittance Matrix Current injections at a bus are analogous to power injections. The student may have already been introduced to them in the form of current sources at a node. Current injections may be either positive (int
School: UT Arlington
Course: POWER SYSTEM MODELING AND ANALYSIS
Due: Lesson 6 ECE 422 Homework 1 Power System Basics 1. (20 points) Problems 2.30a and 2.31a 2.30. Figure 2.26 shows three loads connected in parallel across a 1000 Volt rms, 60 Hertz single phase source. Load 1: Inductive load, 125kVA, 0.28pf lagging Loa
School: UT Arlington
Course: POWER SYSTEM MODELING AND ANALYSIS
Whites, EE 481/581 Lecture 14 Page 1 of 8 Lecture 14: Impedance and Admittance Matrices. As in low frequency electrical circuits, a matrix description for portions of microwave circuits can prove useful in simulations and for understanding the behavior of
School: UT Arlington
Course: POWER SYSTEM MODELING AND ANALYSIS
ECE 404 Introduction to Power Systems HW #4 2.24, 2.39, 2.44, & 2.45 Solutions ORIGIN := 1 Define some commonly used variables. j := 1 VA := W kVA := kW kVAR := kW VAR := W 2.24 MVA := MW MVAR := MW A source supplies power to the following three loads con
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EE5321 OPTIMAL CONTROL Homework #2 Sai Prasanna Gundu 1001151387 1a) b) 1 u 1 s xdotdot Integrator xdot 1 s Integrator1 x Position 1 x Matlab Code: t=0:0.1:10; u=ones(length(t),1); u(51:end)=-1; sim('example1',t',[],[t' u]) lb = ones(101,1)*(-100); ub = o
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Course: Wireless Communication Systems
IN BULLETS 355 LONG TERM EVOLUTION (LTE) 23.1 PLMN SELECTIOIcfw_ * The UE is responsible for selecting a Public Land Mobile Network (PLMN) for subsequent cell selection. A PLMN is identified by its PLMN identity broadcast within System Information Block I
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Course: Wireless Communication Systems
Propsim User Reference r rrr 6im 13 Wireless propagation environment Understanding the radio channel behavior is a key factor in performing meaningful emulations with the Propsim. The mechanisms of electromagnetic wave propagation are complex and diverse.
School: UT Arlington
Course: Wireless Communication Systems
PERFORMANCE OF SPATIAL MULTIPLEXING IN THE PRESENCE OF POLARIZATION DIVERSITY Helmut Bolcskei'), Rohit U. Nubar'), V. Erceg'), D. Gesbert'), and Arogyaswami J . Paulrajl) '1 Information Systems Laboratory, Stanford University Packard 223, 350 Serra Mall,
School: UT Arlington
Course: Wireless Communication Systems
LONG TERM EVOLUTION (LTE) 5.2.5 CSI REFERENCE SIGNALS * Channel State Information (CSI) Reference Signals were introduced within the release IO version of the 3GPP speciﬁcations * CSI Reference Signals are intended to improve the performance of link ada
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Course: Wireless Communication Systems
IN BULLETS * Figure 7'? illustrates the timing of the PSS and SSS for TDD. The example assumes the normal cyclic preﬁx, uplink-downlink subframe conﬁguration 0 and special subframe conﬁguration 0. The extended cyclic preﬁx follows a similar pattern exc
School: UT Arlington
Course: Wireless Communication Systems
This page intentionally left blank Mobile Wireless Communications Wireless communication has become a ubiquitous part of modern life, from global cellular telephone systems to local and even personal-area networks. This book provides a tutorial introducti
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Hecht; 10/12/2010; 8-1 Chapter 8. Polarization 8.1 The Nature of Polarized Light A. Linear Polarization Sum of two waves E ( z , t ) = xEox cos (kz t ) + yEoy cos (kz t + ) For = 0, 2 . , E = ( xE ox + yE oy ) cos (kz t ) For = . , E = ( xE ox yE oy ) cos
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Polarization Optics, Eugene Hecht, Chpt. 8 Linear polarization E-field magnitude oscillates Direction fixed Arbitrary polarization angle superposition of x and y polarized waves real numbers Example 45 linear polarization Time evolution Circular pola
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Lenses Optics, Eugene Hecht, Chpt. 5 Lenses for imaging Object produces many spherical waves scattering centers Want to project to different location Lens designed to project and reproduce scattering centers Object is collection of scattering centers D
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Reflection and refraction Optics, Eugene Hecht, Chpt. 4 Notation Start with propagating waves: E = E0 cos(kx - wt) and B = B0 cos(kx - wt) Use complex amplitudes (as in ac circuits): E0 cos(kx - wt) = (1/2) (E0 exp[i(kx - wt)] + c.c.) drop (1/2) and
School: UT Arlington
' ' ,IAV—bc 11>? ‘f’ 1‘: MO 2 N“? f" ‘ . he» :aaym‘ . ,V / j 5) £1; . T7”— 3143142 alpha/a Cc mavfdﬂzw Cf £565,” > go "' ibich i/ﬁiiﬁf‘C a . V , , a I = ‘ a “I W» Lth’IL/W ’ z 4.2wa w) , W ﬁﬂﬁgwm ‘5 If“ A PKE’MW @3ng EMA A I ‘2‘- _;/L If, - . 5 . ‘ "
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Refractive index dispersion and Drude model Optics, Eugene Hecht, Chpt. 3 Dielectrics Electric field is reduced inside dielectric Space charge partly cancels E / Ev = e / e0 Index of refraction: n Also possible for magnetic fields but usually B = Bv
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Laser basics Optics, Eugene Hecht, Chpt. 13; Optical resonator tutorial: http:/www.dewtronics.com/tutorials/lasers/leot/ Laser oscillation Laser is oscillator Like servo with positive feedback Greater than unity gain Laser gain and losses Laser turn-on
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Principles of Photonics and Optical Engineering 2015 Kambiz Alavi -POLARIZATION STATE OF UNIFORM PLANE WAVES Uniform Plane wave with angular frequency propagating in + z direction in a "Simple" medium (Linear, Isotropic, Homogeneous, Time Invariant medium
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Ray tracing and ABCD matrix Optics, Eugene Hecht, Chpt. 6 Basics of ray tracing Consider 2D projection Ray uniquely defined by position and angle Make components of vector Paraxial approximation - express angle as slope = y f=y y z Example: Propagate
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Relation between r, r and t, t at ar at ar a a air glass r glass air Proof: algebraic from the Fresnel coefficients or using the property of preservation of the field properties upon time reversal r r 2 tt 1 Stokes relationships MIT 2.71/2.710 Optics 10/2
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Principles of Photonics and Optical Engineering 2015 Kambiz Alavi -ELLIPTICAL POLARIZATION of UNIFORM PLANE WAVES Uniform Plane wave with angular frequency propagating in + z direction in a "Simple" medium (Linear, Isotropic, Homogeneous, Time Invariant m
School: UT Arlington
Optics, by Eugene Hecht, 4th Edition, Pearson Addison Wesley Errata, by Chris Mack, chris@lithoguru.com While teaching out of this book at the University of Texas at Austin, Spring 2008, I discovered the following errors: p. 112, equation (4.16), top line
School: UT Arlington
Course: Wireless Communication
1:79 LTE Meir—mark Basic; KI/S ) u) ()5 pool“ ‘ium ON 9) we W315 5W em midi/a”: I?ng W5 Lt “Was. ‘ '1 (g :4; Wm select H12 MM uni He ﬂay-ﬁg gw ~ . (2) HE 6/wéroncgd.‘wyrocess unfit The «Md MJWK eNB. Keep em paws-1138 Java in Frame“?— ‘Lma alD’MM. (‘2 (“Ha
School: UT Arlington
Course: Wireless Communication
Wireless Fundamentals Peter Wang January 16,2011 1 Wireless fundamentals_04022010 OUTLINE Wireless channels Far-field average power Long-term fading channel Short-term fading channel Signal transmission through a system Nonlinear system Linear system L
School: UT Arlington
Course: Wireless Communication
LTE(LongTermEvolution) Let us begin our journey to understand the LTE(Long Term Evolution). Here, we will be concentrating on the LTE expecting us to have a fair bit of idea about wireless technologies. Our main focus will be to explain you the LTE in the
School: UT Arlington
Course: Wireless Communication
SYLLABUS EE5368 Wireless Communication Systems Fall 2015 Wed. 6:00pm - 8:50pm Room NH 105 Instructor: Peter (Shu-Shaw) Wang Office: Office Hours: By appointment Phone: (817) 565-3226 Mailbox: Electrical Engineering, Box 19016, UTA, Arlington TX 76019 Emai
School: UT Arlington
Course: Digital Signal Processing
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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Course: Digital Signal Processing
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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Course: Digital Signal Processing
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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Course: Digital Signal Processing
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
Course: Digital Signal Processing
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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Course: Digital Signal Processing
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
Course: Digital Signal Processing
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
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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: Wireless Communication Systems
EE 5368 Wireless Communication Systems Final Exam Spring 2012 Student name: Sulrx^ SN #: Apri|25,2012 The University of Texas at Arlington Problem l. (20%) I: Part Answer the following questions. (I) Space-Time Transmit-Diversity Case: Ant. #1 Y -.-ho l)r
School: UT Arlington
Course: Wireless Communication Systems
EE 5368 Wireless Communication Systems Final Exam Spring 2012 Student name: _ SN #: _ April 25, 2012 The University of Texas at Arlington 1 Problem 1. (20%) Part I: Answer the following questions. (I) Space-Time Transmit-Diversity Case: Ant. #1 h0 h1 Ant.
School: UT Arlington
Course: Wireless Communication Systems
EE 5368 Wireless Communication Systems Final Exam Spring 201 1 Student name: ID #2 "‘e May 04, 2011 The University of Texas at Arlington Problem 1. (20%) Two-ray model is show below. d r P A 2 d . 2 here ——’—=G -G - ~—-—— - 1+ -ex — A W Pt , r(47rd)!
School: UT Arlington
Course: Wireless Communication Systems
EE 5368 Wireless Communication Systems Final Exam Fall 2011 Student name: _ SN #: _ December 07, 2011 The University of Texas at Arlington 1 Problem 1. (20%) Part (I) Cellular systems GSM IS-95 IS-136 Analog or Digital System Multiple Access Method FDD or
School: UT Arlington
Course: Wireless Communication Systems
EE 5368 Wireless Communication Systems Final Exam Spring 2010 Student name: _ ID #: _ May 06, 2010 The University of Texas at Arlington 1 Problem 1. (25%) In order to increase the bit rate, 3GPP LTE (Long Term Evolution) Radio Access Network uses many tec
School: UT Arlington
Course: Wireless Communication Systems
EE 5368 Wireless Communication Systems Final Exam Fall 2009 Student name: _ SN #: _ December 02, 2009 The University of Texas at Arlington 1 Problem 1. (20%) Four received power measurements were taken at distances of 100m, 200m, 1km, and 2 km from a tran
School: UT Arlington
Course: Wireless Communication Systems
EE 5368 Wireless Communication Systems Final Exam Fall 2008 Student name: _ SN #: _ December 03, 2008 The University of Texas at Arlington 1 Problem 1. (20%) Four received power measurements were taken at distances of 100 m, 200 m, 1 Km, and 3 Km from a t
School: UT Arlington
Course: Wireless Communication Systems
EE 5368 Wireless Communication Systems Final Exam Fall 2007 Student name: _ SN #: _ December 05, 2007 The University of Texas at Arlington 1 Problem 1. (20%) For each of the three scenarios below, decide if the receive signal is best described as undergoi
School: UT Arlington
Course: Wireless Communication Systems
mffi $3ss S/i r*lsss Cnmrnmnicati*n Systerns thwmrzt FmI* ffi ncfw_p #2 2*14 tudtuA-"twv- e- Er-rmt*,f, feyqr qde-w&blr ffi S&d*-/;x y*ff *heir sl;d-& f7,w tht 6\ @ N* r,&esfs&rte4 trt<*yw#2 drn* fm'*8:nPfcfw_4 $tudent nff.nls: sN #: N*vcrnir *r 19. 2Al4
School: UT Arlington
Course: Wireless Communication Systems
Score distribution of Exam 2, Spring 2013 4 3.5 3 2.5 2 1.5 1 0.5 0 60 65 70 75 80 85 90 Mean=84.65, Variance=10.17, median=86 95 100
School: UT Arlington
Course: Wireless Communication Systems
EE 5368 Wireless Communication Systems Exam #2 Sprtng2012 studentname: 9l +,*, ID #: April 11,2012 The University of Texas at Arlinglon 3 2.5 2 1.5 1 0.5 0 30 40 50 60 70 Average: 78.9565 Mid: 83 Variance: 17.2718 80 90 100 ' Problem l. (20%) ,-.I Two-ray
School: UT Arlington
Course: Wireless Communication Systems
EE 5368 exam 2 score distribution, Fall2011 Mean-J 7 .69 I 4893 6 17 02120 Median-79 Variance- I 0 .0 47 47 0 583 429 659 EE 5368 Wireless Communication Systems Exam#2 Fall 2011 Student name: SN #: November 16,2011 The University of Texas at Arlington ' P
School: UT Arlington
Course: Wireless Communication Systems
EE 5368 Wireless Communication Systems Exam #1 Fall 2014 Student name: 3 0 Z thm/k/ SN #: October 01, 2014 The University of Texas at Arlington Problem 1. (20%) Signal transmission through a system (1) Nonlinear System A nonlinear system y(t) = x(t) + 0.0
School: UT Arlington
Course: Wireless Communication Systems
EE 5368 Wireless Communication Systems Exam #1 Spring 2012 Student name: _ SN #: _ Jan. 25, 2012 The University of Texas at Arlington 1 Problem 1. (25%) Part (I) Wireless Fundamentals (1) Wireless -> Mobility -> Channel complexity List three kinds of radi
School: UT Arlington
Course: Digital Signal Processing
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
Course: Digital Signal Processing
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
Course: Digital Signal Processing
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
Course: Digital Signal Processing
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
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
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
Mixed Discrete/Continuous Time Optimal Control Discrete Time Optimal Control Pros Simpler to implement Designed for the digital world Sometimes the only way to get it right Cons Physical domain is usually continuous Often not intuitive Mixed Discrete/Cont
School: UT Arlington
PROGRAM ASSIGNMENT #1 Sai Prasanna Gundu Std.ID: 1001151387 Question1 Program: % QUESTION 1 % Reading and loading the data A = textread('data.txt'); B = load('data.txt'); % Copying the data into arrays using for loop for i=1:441 X(i)=A(i,1); end for i=1:4
School: UT Arlington
EE5321, Spring 2015 Homework 2: Continuous Time Optimal Control Due Feb 19 Problem 1) 30 points 10 Given the following performance index = 0 1 2 and the state constraints 1 = 2 2 and 2 = , with 1 (0) = 0 and 2 (0) = 0. a) Implement the requirements 1 (10)
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EE 5321 OPTIMAL CONTROL TAKE HOME TEST- II On my honor I have neither given nor received aid for this examination Signature: Sai Prasanna Gundu Submitted by Sai Prasanna Gundu 1001151387 Question 1: Question 2: Implement the system and setup described in
School: UT Arlington
EE5321, Spring 2015 Take Home Test 1: Fixed Final Time Optimal Control Due 3/12/2015 Bahare Kiumarsi Khomartash 1000913583 Pledge of honor: On my honor I have neither given nor received aid on this examination Signature: Bahare Kiumarsi 1 Problem 1: Analy
School: UT Arlington
Course: Wireless Communication Systems
PERFORMANCE EVALUATION OF PROPORTIONAL FAIRNESS SCHEDULING IN LTE TOPICS 1.Introduction 2.Scheduling techniques 3.System model 4.Simulation results 5.Conclusion INTRODUCTION In 3GPP LTE network architecture, the eNodeB is the only node between the UE and
School: UT Arlington
Course: Wireless Communication Systems
MIMO PROJECT SAI PRASANNA-1001151387 Department of Electrical Engineering, University of Texas, Arlington EE 5368, Spring 2015 Abstract: The objective of this project is to simulate MIMO performance using dual-polarized antennas in wireless system. In thi
School: UT Arlington
Course: Digital Signal Processing
( on 0 .erH x r .Azyr q“! (-51,“ H|r~=1n73< 0 A 5— I 7* 1’ d \ r3 lHq'Jm ' . 0.8MK -~ ~v—-—-———, ’. j.“ w—L—m—w»~—~—— w, . a ‘ _ W I O'2U‘I'rd 0‘3R/WJ M/TA>. L Lb‘ [bumwmm }«,q;}r0mt m W'4OWC ? . 3 Hit (50.2»?!ng '-' , (_0.r{’q¢?t) .‘ ’2 ,2 I , ‘Hc (’;°*
School: UT Arlington
Course: Digital Signal Processing
Program Assignment # 1, EE5350 1. Write a function called "CONV" which: (a) Has input parameters x, Nx, h, Nh, where x and h are arrays of sizes Nx and Nh. (b) Calculates output parameter Ny and convolves the two signals as Nh y(n) = h(k ) x(n k +1) k =1
School: UT Arlington
Course: Digital Signal Processing
Homework Assignments Homework 1; (See Handout) Homework 2; 2:1-5,12,22,29,30,31,39,40,51,67 Homework 3; 2:6,13,52,53,54,56,58,61,78,84,85 Homework 4; 4:5,6,9,19,30,55 Homework 5; 3:1,4,6,7,11,27,34,36,50 Homework 6; 8:5,30,32,37,45,50,51,58,60,63 Homework
School: UT Arlington
Course: Digital Signal Processing
PROGRAM ASSIGNMENT #1 EE 5350 Digital Signal Processing Sai Prasanna Gundu 1001151387 Question1 Program: % QUESTION 1 % Reading and loading the data A = textread('data.txt'); B = load('data.txt'); % Copying the data into arrays using for loop for i=1:441
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
Course: Digital Signal Processing
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
Course: Digital Signal Processing
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
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
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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
School: UT Arlington
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.
School: UT Arlington
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
School: UT Arlington
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
School: UT Arlington
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
Course: Wireless Communication Systems
I LONG TERM EVOLIJTION (LTE) 3.1 MULTIPLEACCESS Mulliple access techniques allou,resources to be shared between a group ofusers The downlink ofLTE uses Orthogonal Frequency Division Multiple Access (OFDMA). This is in contrast to Frequency, Time or Code D
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
Course: Digital Signal Processing
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
Course: Digital Signal Processing
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
Course: Digital Signal Processing
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
Course: Digital Signal Processing
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: Digital Signal Processing
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
Course: Digital Signal Processing
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
Course: Digital Signal Processing
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
Course: Digital Signal Processing
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
Course: Digital Signal Processing
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
Course: Digital Signal Processing
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
Course: Digital Signal Processing
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
Course: Digital Signal Processing
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
Course: Digital Signal Processing
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
Course: Digital Signal Processing
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
Course: Digital Signal Processing
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
Course: Digital Signal Processing
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
Course: Digital Signal Processing
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
Principles of Photonics and Optical Engineering 2015 Kambiz Alavi -UNIFORM PLANE WAVES I: Uniform Plane wave with angular frequency w propagating in +z direction in a "Simple" medium (Linear, Isotropic, Homogeneous, Time Invariant medium, no net free char
School: UT Arlington
Course: Wireless Communication Systems
SYLLABUS EE5368 Wireless Communication Systems Fall 2014 Wed. 6:00pm - 8:50pm Room NH 105 Instructor: Peter (Shu-Shaw) Wang Office: Office Hours: By appointment Phone: (817) 795-4421 Mailbox: Electrical Engineering, Box 19016, UTA, Arlington TX 76019 Emai
School: UT Arlington
Course: POWER SYSTEM MODELING AND ANALYSIS
The UT Arlington Syllabus Template for 2015-16 Frequently Asked Questions Whats new for 2015-16? The most important revision to this years template is the new section titled Emergency Exit Procedures. See below for important details. We have also updated
School: UT Arlington
Course: Wireless Communication Systems
SYLLABUS EE5368 Wireless Communication Systems Spring 2015 Wed. 5:30pm - 8:20pm Room NH 105 Instructor: Peter Wang Office: Office Hours: By appointment Phone: (817) 565-3226 Mailbox: Electrical Engineering, Box 19016, UTA, Arlington TX 76019 Email: peter.
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
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
School: UT Arlington
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
School: UT Arlington
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
School: UT Arlington
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
School: UT Arlington
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: