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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: 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: 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
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: 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
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
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
School: UT Arlington
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
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: 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
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
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: Random Probabilities
EE 351K Probability and Random Processes Instructor: Haris Vikalo Homework 2 Solutions FALL 2010 hvikalo@ece.utexas.edu Problem 1 Bob, Carol, Ted and Alice take turns (in that order) tossing a coin with probability of tossing a Head, P (H ) = p, where 0 <
School: UT Arlington
Course: Semiconductor
Score distribution of EE 5368 Midterm spring 2011 Average =59.22 Mid=58.75 Variance =l-6.05 EE 5368 Wireless Commun tcation Systems Exarn#I Spring 201 1 Student name: 5, hhvn SN #: March23,20Il The lJniversity of Texas atArlington Problem Part (I) l. (15%
School: UT Arlington
Course: Semiconductor
Elements of Information Theory Second Edition Solutions to Problems Thomas M. Cover Joy A. Thomas September 22, 2006 1 COPYRIGHT 2006 Thomas Cover Joy Thomas All rights reserved 2 Contents 1 Introduction 2 Entropy, Relative Entropy and Mutual Information
School: UT Arlington
Course: 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
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
School: UT Arlington
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
School: UT Arlington
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
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
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
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
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
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
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: 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
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
School: UT Arlington
Course: Linear Systems Engineering
Lab 5 Nested functions & Local functions Nested functions are functions defined within functions. They are helpful when: - We wish to write small or temporary functions which do not merit creation of a new m-file - When we wish to share some information b
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
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
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
Course: Probability And Random Signals
Name: _ ID: _ University of Texas at Arlington Department of Electrical Engineering EE 3330 Probability and Random Signals Spring 2013 Instructor: Dr. Venkat Devarajan Test 1 6th March 2013 Duration: 1 hour 20 min Note: This is a closed book, closed notes
School: UT Arlington
Course: Probability And Random Signals
EE3330 - Random Signals and Noise Final Exam August 14th , 2012 1. Let X1 , X2 ,.,Xn be a sample from a distribution with pdf : f(x) = axeax 2 /2 , where x>0 Find the ML-estimator of a. 2. To investigate whether cafne affects cholesterol levels, ve patien
School: UT Arlington
Course: Probability And Random Signals
16 16 16 16 31.41 10.8508 12.684, 36.716
School: UT Arlington
Course: Fundamentals Of Power Systems
o Fundamentals f Power Systems F,F.3302 Quiz 1 Fall 2012 8-24-2012 . .S\rsfr N a m e : . .flnsde.r: . Mav-ID. f oP t 1. Supposehat a sinusoidalvoltageis given by c n v(t) - 1 50 os(200t - 3 0" ) Find the frequency in hertzothe periodothe peak value, and t
School: UT Arlington
Course: Digital Communications
No. of students U) c 3 3 o - N) o J a q8 o m m (Jl ('l) o) N) d o a 3 U o =. C' c * o f 3.ro,r*" loII Do* EVsZay K;"l1ery" S'lvliar, Ki' ol"o'iwqu tA 9. ,a/ fto^ uou scfw_,.* i'l . 9rv,.e lK- l :o 7 It* 1o H, . Cgrrfr.,^) ^) Xcvt: s chrr,nr-yo.^l + x Lt^)
School: UT Arlington
Course: Digital Communications
8E536 ?lAidfrrr^,., txo,r,^ (I ) tyt uvr. 2, b 3. f-l,.*'c,-, dv Lr" b S.W Gb ,+, L gc 1" ,v ro. c (i) .3 st/^h; P*bl*n, 'w z' OPsK OPSp nt*[rt*ttsr, 0r* on I *J. tl-" coN t ru*t .l as -tu.ro st[*r PopfL oh. 8- BPsr. t l- P*o.*tt)Jz ?*-*L): t- Pspss ts) =
School: UT Arlington
Course: Digital Communications
Student ID # Name: EE5362MIDTERM EXAM October 8 (Thursday), 2009 2:00pm - 3:20pm Closed book and lecture notes. Calculator is allowed. lnstructions: o o o Verify that your exam contains 13 pages (including the cover sheet). Some space is provided for you
School: UT Arlington
Course: Digital Image Processing
EE5356 Digital Image Processing Final Exam 5/13/04 Thursday 11:00 am1:00 pm 1), Closed books and closed notes. 2), Problems carry weights as indicated. 3), Please print your name and last four digits of your ID. 4), For problems 1-15, circle the correct a
School: UT Arlington
Course: Digital Image Processing
EE5356 Key to TEST 2 4/1/03 Tuesday Dr. K.R. Rao Problem 1 (a) u in = int B - n = int K1 2n -1 + K 2 2 n- 2 + L + K n 20 + L + K B 2 - B 2 = K1 2n-1 + K 2 2n -2 + L + K n u in-1 = int B- n +1 = int K1 2n - 2 + K 2 2n -3 + L + K n-1 2 0 + K n 2-1 + L + K
School: UT Arlington
Course: Digital Image Processing
EE5356 Digital Image Processing Instructor: Dr. K.R. Rao Spring 2010, Test 1 Tuesday, 23 February 2010 12:30 1:50 PM (1 hour and 20 minutes) (CLOSED BOOK, CLOSED NOTES) INSTRUCTIONS: 1. Closed books and closed notes. 2. Please show all the steps in your w
School: UT Arlington
Course: Digital Image Processing
EE5356DigitalImageProcessing ~Final Exam~ 5/13/2010Thursday 11;00am1:30pm ATTENTIONReadandfolloworriskpenalty: 1) Closedbooksandclosednotes. 2) PleaseprintyournameandlastfourdigitsofyourID. 3) FormultiplechoicewriteONEanswerinthespaceprovidednextto theque
School: UT Arlington
Course: Neural Networks
Exam # 1, EE5353, Fall 2011 1. Here we consider MLPs with binary-valued inputs (0 or 1). (a) If the MLP has N inputs, what is the maximum degree D of its PBF model? (b) If the MLP has N inputs, what is the maximum value of L' in its PBF model? (c) If the
School: UT Arlington
Course: Neural Networks
Exam # 1, EE5353, Fall 2012 1. Here we consider MLPs with binary-valued inputs (0 or 1). (a) If the MLP has N inputs, what is the maximum degree D of its PBF model? (b) If the MLP has N inputs, what is the maximum value of L' in its PBF model? (c) If the
School: UT Arlington
Course: Neural Networks
Exam # 2, EE5353, Fall 2011 1. There are Nv training patterns for a sigmoidal MLP network having N inputs and M outputs. Assume that we want the network to memorize the training patterns. (a) How many hidden units should the network have if it has 1 hidde
School: UT Arlington
Course: Neural Networks
Exam # 2, EE5353, Fall 2012 1. A sigmoidal MLP has 8 inputs, 12 units in the first hidden layer, 6 units in the second hidden layer, and 3 outputs. It is fully connected. As usual, thresholds in the hidden and output layers are handled by adding a 9th inp
School: UT Arlington
Course: Neural Networks
Exam # 3, EE5353, Fall 2011 1. In a PLN with K clusters, yp = Akxap where the kth cluster is closest to xp. We want to calculate derivatives of this mapping. (a) Let ak(m,n) denote an element of Ak and let g(i,n) denote the partial derivative of ypi with
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
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
School: UT Arlington
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
School: UT Arlington
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
School: UT Arlington
Course: Linear Systems Engineering
Lab 5 Nested functions & Local functions Nested functions are functions defined within functions. They are helpful when: - We wish to write small or temporary functions which do not merit creation of a new m-file - When we wish to share some information b
School: UT Arlington
Solutions Manual Semiconductor Device Fundamentals
School: UT Arlington
School: UT Arlington
School: UT Arlington
Course: ELECTRONICS I
Last Name: First Name ID:xxxx-xx_. University of Texas at Arlington EE 2403 Summer 2014 HW 3 Due July 8, 2014 PRINT YOUR NAME in CAPITAL LETTERS. Last NAME: ID # : XXXX-XX- First NAME: INSTRUCTIONS: Return the entire document with your solutions in the sp
School: UT Arlington
Epitaxial Growth of Silicon by CVD Outline Epitaxy CVD Process Considerations Deposition equipment Doping Auto-doping Film characterization Defects Advantages Applications Conclusion Epitaxy Epitaxial growth of Silicon The epitaxial growth of si
School: UT Arlington
Epitaxial growth of Silicon by CVD Technique Abstract: The epitaxial growth of silicon refers to the process of growing a thin layer of single crystal silicon over a single crystal silicon substrate. Here we use the chemical vapor deposition technique (CV
School: UT Arlington
University of Texas at Arlington EE 2403 Fall 2013 HW 2 Due September 5, 2013 PRINT YOUR NAME in CAPITAL LETTERS. Last NAME: ID # : XXXX-XX- First NAME: INSTRUCTIONS: Return the entire document with your solutions. Solutions submitted must be your work an
School: UT Arlington
Last Name: First Name ID:xxxx-xx_. University of Texas at Arlington EE 2347 Fall 2013 Homework 2 Due Oct. 2, 2013 PRINT YOUR NAME in CAPITAL LETTERS. Last NAME: ID # : XXXX-XX- First NAME: INSTRUCTIONS: COPY THIS DOCUMENT AND WRITE YOUR SOLUTIONS IN SPACE
School: UT Arlington
UT Arlington EE 2347 Fall 2012 Homework 3 Due on 10/3/2012 in Lab Section Name in CAPITAL LETTERS: _ _ LAST FIRST _ UTA ID # Print this document and STAPLE this page as a cover of your Homework submission. The following Problems are from Textbook: Advance
School: UT Arlington
Course: Digital Signal Processing
Homework 6 5.1 The periodic convolution of two periodic sequences, x[n] and h[n], of period N each, is dened by: N 1 x[r]h[n r] y [n] = (1) r=0 Show that y [n] is also periodic sequence of period N . Solution N y [n + kN ] = r=01 x[r]h[n + kN r] Since h[n
School: UT Arlington
Course: Digital Signal Processing
EE 5350 Homework 5 Solution 4.18 LHS: x3 [n] x2 [n] = 1 x3 [k ]x2 [n k ] = n= x3 [k ][n k ] = [n] [n 1] = [n] n=0 x3 [n] x2 [n] x1 [n] = [n] x1 [n] = x1 [n]. RHS: x3 [n] x1 [n] = 1 x3 [k ]A = A A = 0 x3 [k ]x1 [n k ] = n= n=0 x2 [n] x3 [n] x1 [n] = x2 [n]
School: UT Arlington
Course: Digital Signal Processing
Homework 4 solutions Solution for exercise 3.50 (a) 3 j0 G( e ) = g [n]e j0 g [n]ej 0 = 2 + 1 + 3 + 0 3 1 + 2 = 0 = n= n=3 (b) Because the sequence is an odd sequence, the corresponding DTFT is imaginary. arg (G(ej ) = pi 2 (c) 3 G(ej ) = g [n]ejn = n= g
School: UT Arlington
Course: Digital Signal Processing
EE 5350 Homework 3 Solution 3.18 (a)Y1 (ej ) = y1 [n]ejn = n= (b)Y2 (ej ) = y2 [n]ejn = n= N ejn = n=N N ejn = n=0 sin( (N + 1 ) ej(N +1) ejN 2 = ej 1 sin(/2) sin( (N + 1)/2) 1 ej(N +1) = ejN/2 1 ej sin(/2) (c)Let y [n] = 1, 0 n N 1 0, otherwise (ej )Y (e
School: UT Arlington
Course: Digital Signal Processing
EE 5350 Homework 1 solutions 2.2 (|x|1 )2 = ( |x[n]|)2 n n |x[n]|2 = |x|2 2 Because both |x|1 and |x|2 are non-negative, we have |x|1 |x|2 2.10 y [n] = x1 [n] x2 [n] = x1 [n k ]x2 [k ]. Now, k= v [n] = x1 [n N1 ] x2 [n N2 ] = x1 [n N1 k ]x2 [k N2 ]. Let
School: UT Arlington
Course: Fundamentals Of Power Systems
llo.*L -A t/1 L r^LL olz r 5 'l ProLV^ ? 1 tr-,r *ov t V, l ok'- *lWc=-1 l ' p t= 0 'lt I ^XX;') [ '!f = o 'iJ : L Q t ' * o " , t f - ' L = - Yxo;ts= - r r 5 ( / ' l Vt=L1ogvt Y= IVPY1 7'Yo 7, = J 5S t t )t= [ l'b' 4 ( z+o)' = 2'1[lJw o l, Y o , = v ,+ T
School: UT Arlington
Course: Fundamentals Of Power Systems
w tlN Foll > otL L 37c I -,-t)( L = 9 \ I ' ? : :0' = 1 l ) 5 t u ^ I -\ lew 7 t, r tr,- T , 7 aa/ 31"d r 7 v o )" L S + ' P 7 ' + 1 sw 1 17lt i u .zs ? 1 o^df + o-s io'ts = 2 1 ' 4f ?l.ol> 'Xt-Xl -rv [^) (t J / . ' J L= 7 p - ^= t s + l t l - t t r u , l
School: UT Arlington
Course: Fundamentals Of Power Systems
I lat[ )s I l+r- l Jo"ti"n: -t v, |f 1 v L lt, Y Dz, t/ ;trl (v lN | : ] ,[llrl ] y +*r) = 6 '11 \t V I - L W L \/ t * lf t t LL * \ ft v r - Vt= 't t/7 4 \ /= I +o( 9 v Arr^* ^ : z o - tt y ! A 6,t1t3t"? ,t v l Hwl ef 4D) >0t7 tt l = l f - d P: s - jr T*
School: UT Arlington
Course: Fundamentals Of Power Systems
TA , ' l^bb^ ? - l Do , = t L i n = Do, L ? , in. t l1 v0 ( - /rtu ltn t b a nrt-t, qzd L ?"t1 orL 5'lnli"1 H w3 c,*f C t'Al(= t - L x;, ) ' r #t , ( H ) ( ), ( #)*)nc#t) Lr,t = l . a tx , p - e H / n i + 6 l v t o ri l ft n T'6 o H , = 2 w (l L6- - s , t
School: UT Arlington
Course: Fundamentals Of Power Systems
7 F"l,t, Dl > H W6 IA eE-7loz l"b 5 '5 ToF r*, ^ 3 f h ^*f r w'lo( h-*(^) lr Ionwtfrtovt Y-A = lo tvtW , h,"0 $)\Lc) V1,.1 f Ll.b^t ( loXi'"t) Pf= o >f= o .d,F = k pr^a 7 .1trtu/ != l l-1ot' = t 'eotkv = )lU A Prft ."i -,4 t I V k cmJ*1 * ^* c rtt,dal 2 '
School: UT Arlington
Course: Fundamentals Of Power Systems
Y yubcfw_y,*.L, \./\-'\-z.:z.\-^ f = ?w ^a, , t W = z St 7 4y,1 i/;'41't l B,b rd,F nt purorr 1cfw_ I 'T n l/', - f ls$p , lv", r F t?.cfw_r1 N n pl t - 1 6"/, 0,1q l h I g*f f\r * "*rJ \ t* Llyz V v,t ?1* d * =V.(l'^r - - ^ l > b , ) Q ' / " r ' l l- l.
School: UT Arlington
Course: Linear Systems Engineering
EE5307 Assignment #1 Solution: (a) Free Body Diagram: & d&1 Consider M1: k2 (d 2 d1 ) k1d1 M1 & b3 (d 2 d1 ) & b1d1 & & & & M 1 d&1 = k 2 (d 2 d1 ) + b3 (d 2 d1 ) k1 d1 b1 d1 & d&2 Consider M2: k2 (d 2 d1 ) & b3 (d 2 d1 ) M2 f & b2 d 2 & & & & M 2 d&2 =
School: UT Arlington
Course: Digital Communications
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School: UT Arlington
Course: Digital Communications
EE 1' tft) soLrrton E*d ti'u # l s=Hr=r e Il;I o o tJIrorortln,l?l il;l toJ L'r T.hn4v^ g4 gawdr,l ettrt+t1oi'clntr tcdewlrnd, tn*rl h,*L+is t='1oos eto]t 0=r+0 :[1 otntt]tf [oorotglt ir r- =Ltaluollt g= f * s : I ro olrl*1 ,[roooo'r]ta Ilol,u l,lt f, &re
School: UT Arlington
Course: Digital Communications
Hrr., S. I Jolu*t*ru &tsJ[2 ff* t!Jo,Irl,)= Ffr cfw_x-Jrr) u) exf rl IA "r Jn c.l H'l = S*$* J*r'l;*'r ._-1 *xf rl L4 (x*Jee * r Ht Pcfw_fli r'*'cfw_'n ? hf I ) "t".-,r "*L ' PrH') ptHr) cfw_ r-,6)" +-*? zcfw_' * ( 6'-a-, )+Jl- ( J;- J; ;-f -\ r t-h 1 r4*
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: 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: 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: