EE 350. Continuous-Time Linear Systems. Recitation 2. 1

Size: px
Start display at page:

Download "EE 350. Continuous-Time Linear Systems. Recitation 2. 1"

Transcription

1 EE 350 Continuous-Time Linear Systems Recitation 2 Recitation 2. 1

2 Recitation 2 Topics MATLAB Programming Vector Manipulation Built-in Housekeeping Functions Solved Problems Classification of Signals Basic Signal Operations Recitation 2. 2

3 Vector Manipulation Elements are accessed by specifying an index ranging from 1 to the number of elements in the vector Display elements 2 through 3 of vector >> x = [2, 4, 6, 8]; >> x(2:3) ans = 4 6 Delete elements 2 through 3 >> x = [2, 4, 6, 8]; >> x(2:3) = [] ans = 2 8 Recitation 2. 3

4 Vector Manipulation Display the last two elements of the vector x >> x = [2, 4, 6, 8]; >> x(end-1 : end) ans = 6 8 Display every other element starting with the first element of x >> x = [2, 4, 6, 8]; >> x(1 : 2 : end) ans = 2 6 Recitation 2. 4

5 Vector Functions in MATLAB Operation length of vector x; returns the number of elements Function length(x) size of vector x; returns the number of rows and columns size(x) transpose of x x largest element in x max(x) smallest element in x min(x) sum of elements in vector x sum(x) indices of nonzero elements find(x) indices of elements in a specified range find(x>3) first n indices corresponding to the nonzero elements in x find(x, n) Last n indices corresponding to the nonzero elements in x find(x, n, last ) Recitation 2. 5

6 Problem 1 For the vector x = -4 : 2 : 10, use MATLAB to determine 1. length(x) 2. size(x) 3. x 4. size(x ) 5. min(x) 6. max(x) 7. sum(x) 8. find(x) 9. find(x < 0) 10. find(x,3) 11. find(x,3, last ) Recitation 2. 6

7 Housekeeping Functions Operation lists variables currently in the workspace lists variables currently in the workspace with their size clear all variables from the workspace clear command window, cursor moves to the top clear the current figure window close the current figure window close all the figure windows Function who whos clear clc clf close close all Recitation 2. 7

8 Problem 2 The response of system to a unit-step input is often characterized by the rise-time, which is the time for the output to rise from 10% to 90% of its final value Write an m-file that determines the rise-time of t yt () 1 e t 0 Recitation 2. 8

9 Problem 2 The m-file must Clear the workspace and command window, and close open figures Generate a time vector t of 1000 points uniformly spaced between 0 and 10 Generate a vector y whose elements represent the values of the response at the time instants in t Numerically determine the rise-time using find Plot y versus t, and in the legend, specify the rise-time use the strcat and num2str functions Recitation 2. 9

10 Problem 2 Recitation 2. 10

11 Problem 2 Recitation 2. 11

12 Problem 3 Consider the signal t f() t eu( t) 1. Sketch f(t) 2. Classify f(t) as a causal or noncausal signal 3. Is f(t) either an even or odd function? 4. Is f(t) periodic or aperiodic? 5. Is f(t) an energy signal? If so, determine the energy metric E f 6. What is P f, the power of the signal f(t)? Recitation 2. 12

13 Problem 3 Solution Recitation 2. 13

14 Problem 3 Solution Recitation 2. 14

15 Problem 3 Solution Recitation 2. 15

16 Problem 4 Consider the signal n f( t) 5 u( t 10n 2) u(t 10n 2) n 1. Sketch f(t) 2. Classify f(t) as a causal or noncausal signal 3. Is f(t) either an even or odd function? 4. Is f(t) periodic or aperiodic? If periodic, what is the fundamental period? 5. Is f(t) an energy signal? If so, determine the energy metric E f 6. Is f(t) a power signal? If so, determine the power metric P f Recitation 2. 16

17 Problem 4 Solution Recitation 2. 17

18 Problem 4 Solution Recitation 2. 18

19 Problem 4 Solution Recitation 2. 19

20 Problem 5 Express the signal f(t) as the sum of even and odd functions f t e u t f t f t -t () () e() o() where f() t f( t) f() t f( t) fe( t) and fo( t) Verify, for an arbitrary f(t), that the functions f e (t) and f o (t) are even and odd functions, respectively 2. Determine expressions for f e (t) and f o (t) for the given f(t) 3. Sketch f(t), f e (t) and f o (t) Recitation 2. 20

21 Problem 5 Solution Recitation 2. 21

22 Problem 5 Solution Recitation 2. 22

23 Problem 5 Solution Recitation 2. 23

24 Problem 6 Consider the signal shown below f () t t 1. Find an expression for f(t) 2. Sketch the following signals: f(t-1), 2f(t+1), f(t/3), f(-t), f(1-t) Recitation 2. 24

25 Problem 6 Solution Recitation 2. 25

26 Problem 6 Solution Recitation 2. 26

27 Problem 6 Solution Recitation 2. 27

28 EE 350 Continuous-Time Linear Systems Recitation 2 Recitation 2. 1

29 Recitation 2 Topics MATLAB Programming Vector Manipulation Built-in Housekeeping Functions Solved Problems Classification of Signals Basic Signal Operations Recitation 2. 2

30 Vector Manipulation Elements are accessed by specifying an index ranging from 1 to the number of elements in the vector Display elements 2 through 3 of vector Delete elements 2 through 3 Recitation 2. 3

31 Vector Manipulation Display the last two elements of the vector x Display every other element starting with the first element of x Recitation 2. 4

32 Vector Functions in MATLAB Operation length of vector x; returns the number of elements Function length(x) size of vector x; returns the number of rows and columns size(x) transpose of x x largest element in x max(x) smallest element in x min(x) sum of elements in vector x sum(x) indices of nonzero elements find(x) indices of elements in a specified range find(x>3) first n indices corresponding to the nonzero elements in x find(x, n) Last n indices corresponding to the nonzero elements in x find(x, n, last ) Recitation 2. 5

33 Problem 1 For the vector x = -4 : 2 : 10, use MATLAB to determine 1. length(x) 2. size(x) 3. x 4. size(x ) 5. min(x) 6. max(x) 7. sum(x) 8. find(x) 9. find(x < 0) 10. find(x,3) 11. find(x,3, last ) Recitation 2. 6

34 Housekeeping Functions Operation lists variables currently in the workspace lists variables currently in the workspace with their size clear all variables from the workspace clear command window, cursor moves to the top clear the current figure window close the current figure window close all the figure windows Function who whos clear clc clf close close all Recitation 2. 7

35 Problem 2 The response of system to a unit-step input is often characterized by the rise-time, which is the time for the output to rise from 10% to 90% of its final value Write an m-file that determines the rise-time of Recitation 2. 8

36 Problem 2 The m-file must Clear the workspace and command window, and close open figures Generate a time vector t of 1000 points uniformly spaced between 0 and 10 Generate a vector y whose elements represent the values of the response at the time instants in t Numerically determine the rise-time using find Plot y versus t, and in the legend, specify the rise-time use the strcat and num2str functions Recitation 2. 9

37 Problem 2 Recitation 2. 10

38 Problem 2 Recitation 2. 11

39 Problem 3 Consider the signal 1. Sketch f(t) 2. Classify f(t) as a causal or noncausal signal 3. Is f(t) either an even or odd function? 4. Is f(t) periodic or aperiodic? 5. Is f(t) an energy signal? If so, determine the energy metric E f 6. What is P f, the power of the signal f(t)? Recitation 2. 12

40 Problem 3 Solution Recitation 2. 13

41 Problem 3 Solution Recitation 2. 14

42 Problem 3 Solution Recitation 2. 15

43 Problem 4 Consider the signal 1. Sketch f(t) 2. Classify f(t) as a causal or noncausal signal 3. Is f(t) either an even or odd function? 4. Is f(t) periodic or aperiodic? If periodic, what is the fundamental period? 5. Is f(t) an energy signal? If so, determine the energy metric E f 6. Is f(t) a power signal? If so, determine the power metric P f Recitation 2. 16

44 Problem 4 Solution Recitation 2. 17

45 Problem 4 Solution Recitation 2. 18

46 Problem 4 Solution Recitation 2. 19

47 Problem 5 Express the signal f(t) as the sum of even and odd functions 1. Verify, for an arbitrary f(t), that the functions f e (t) and f o (t) are even and odd functions, respectively 2. Determine expressions for f e (t) and f o (t) for the given f(t) 3. Sketch f(t), f e (t) and f o (t) Recitation 2. 20

48 Problem 5 Solution Recitation 2. 21

49 Problem 5 Solution Recitation 2. 22

50 Problem 5 Solution Recitation 2. 23

51 Problem 6 Consider the signal shown below 1. Find an expression for f(t) 2. Sketch the following signals: f(t-1), 2f(t+1), f(t/3), f(-t), f(1-t) Recitation 2. 24

52 Problem 6 Solution Recitation 2. 25

53 Problem 6 Solution Recitation 2. 26

54 Problem 6 Solution Recitation 2. 27

Problem Weight Score Total 100

Problem Weight Score Total 100 EE 350 Exam # 1 25 September 2014 Last Name (Print): First Name (Print): ID number (Last 4 digits): Section: DO NOT TURN THIS PAGE UNTIL YOU ARE TOLD TO DO SO Problem Weight Score 1 25 2 25 3 25 4 25 Total

More information

Handout 1 - Introduction to plots in Matlab 7

Handout 1 - Introduction to plots in Matlab 7 SPHSC 53 Speech Signal Processing UW Summer 6 Handout - Introduction to plots in Matlab 7 Signal analysis is an important part of signal processing. And signal analysis is not complete without signal visualization.

More information

Common Spatial Patterns 3 class BCI V Copyright 2012 g.tec medical engineering GmbH

Common Spatial Patterns 3 class BCI V Copyright 2012 g.tec medical engineering GmbH g.tec medical engineering GmbH Sierningstrasse 14, A-4521 Schiedlberg Austria - Europe Tel.: (43)-7251-22240-0 Fax: (43)-7251-22240-39 office@gtec.at, http://www.gtec.at Common Spatial Patterns 3 class

More information

Lab P-6: Synthesis of Sinusoidal Signals A Music Illusion. A k cos.! k t C k / (1)

Lab P-6: Synthesis of Sinusoidal Signals A Music Illusion. A k cos.! k t C k / (1) DSP First, 2e Signal Processing First Lab P-6: Synthesis of Sinusoidal Signals A Music Illusion Pre-Lab: Read the Pre-Lab and do all the exercises in the Pre-Lab section prior to attending lab. Verification:

More information

Common Spatial Patterns 2 class BCI V Copyright 2012 g.tec medical engineering GmbH

Common Spatial Patterns 2 class BCI V Copyright 2012 g.tec medical engineering GmbH g.tec medical engineering GmbH Sierningstrasse 14, A-4521 Schiedlberg Austria - Europe Tel.: (43)-7251-22240-0 Fax: (43)-7251-22240-39 office@gtec.at, http://www.gtec.at Common Spatial Patterns 2 class

More information

Problem Weight Total 100

Problem Weight Total 100 EE 350 Problem Set 4 Cover Sheet Fall 2016 Last Name (Print): First Name (Print): ID number (Last 4 digits): Section: Submission deadlines: Turn in the written solutions by 4:00 pm on Tuesday October 4

More information

Graphical Displays of Univariate Data

Graphical Displays of Univariate Data . Chapter 1 Graphical Displays of Univariate Data Topic 2 covers sorting data and constructing Stemplots and Dotplots, Topic 3 Histograms, and Topic 4 Frequency Plots. (Note: Boxplots are a graphical display

More information

ECE438 - Laboratory 1: Discrete and Continuous-Time Signals

ECE438 - Laboratory 1: Discrete and Continuous-Time Signals Purdue University: ECE438 - Digital Signal Processing with Applications 1 ECE438 - Laboratory 1: Discrete and Continuous-Time Signals By Prof. Charles Bouman and Prof. Mireille Boutin Fall 2015 1 Introduction

More information

Normalization Methods for Two-Color Microarray Data

Normalization Methods for Two-Color Microarray Data Normalization Methods for Two-Color Microarray Data 1/13/2009 Copyright 2009 Dan Nettleton What is Normalization? Normalization describes the process of removing (or minimizing) non-biological variation

More information

Proceedings of the Third International DERIVE/TI-92 Conference

Proceedings of the Third International DERIVE/TI-92 Conference Description of the TI-92 Plus Module Doing Advanced Mathematics with the TI-92 Plus Module Carl Leinbach Gettysburg College Bert Waits Ohio State University leinbach@cs.gettysburg.edu waitsb@math.ohio-state.edu

More information

UNIVERSITY OF BAHRAIN COLLEGE OF ENGINEERING DEPARTMENT OF ELECTRICAL AND ELECTRONIC ENGINEERING

UNIVERSITY OF BAHRAIN COLLEGE OF ENGINEERING DEPARTMENT OF ELECTRICAL AND ELECTRONIC ENGINEERING UNIVERSITY OF BAHRAIN COLLEGE OF ENGINEERING DEPARTMENT OF ELECTRICAL AND ELECTRONIC ENGINEERING EENG 373: DIGITAL COMMUNICATIONS EXPERIMENT NO. 3 BASEBAND DIGITAL TRANSMISSION Objective This experiment

More information

Laboratory Assignment 3. Digital Music Synthesis: Beethoven s Fifth Symphony Using MATLAB

Laboratory Assignment 3. Digital Music Synthesis: Beethoven s Fifth Symphony Using MATLAB Laboratory Assignment 3 Digital Music Synthesis: Beethoven s Fifth Symphony Using MATLAB PURPOSE In this laboratory assignment, you will use MATLAB to synthesize the audio tones that make up a well-known

More information

Problem Set #1 Problem Set Due: Friday, April 12

Problem Set #1 Problem Set Due: Friday, April 12 1 EE102B Pring 2018-19 Signal Processing and Linear Systems II Pauly Problem Set #1 Problem Set Due: Friday, April 12 In the following problems, assume that δ T (t) = δ(t nt ) n = is an infinite array

More information

Outline. Why do we classify? Audio Classification

Outline. Why do we classify? Audio Classification Outline Introduction Music Information Retrieval Classification Process Steps Pitch Histograms Multiple Pitch Detection Algorithm Musical Genre Classification Implementation Future Work Why do we classify

More information

Stimulus presentation using Matlab and Visage

Stimulus presentation using Matlab and Visage Stimulus presentation using Matlab and Visage Cambridge Research Systems Visual Stimulus Generator ViSaGe Programmable hardware and software system to present calibrated stimuli using a PC running Windows

More information

EE373B Project Report Can we predict general public s response by studying published sales data? A Statistical and adaptive approach

EE373B Project Report Can we predict general public s response by studying published sales data? A Statistical and adaptive approach EE373B Project Report Can we predict general public s response by studying published sales data? A Statistical and adaptive approach Song Hui Chon Stanford University Everyone has different musical taste,

More information

Course Web site:

Course Web site: The University of Texas at Austin Spring 2018 EE 445S Real- Time Digital Signal Processing Laboratory Prof. Evans Solutions for Homework #1 on Sinusoids, Transforms and Transfer Functions 1. Transfer Functions.

More information

Audio Processing Exercise

Audio Processing Exercise Name: Date : Audio Processing Exercise In this exercise you will learn to load, playback, modify, and plot audio files. Commands for loading and characterizing an audio file To load an audio file (.wav)

More information

Using Multiple DMs for Increased Spatial Frequency Response

Using Multiple DMs for Increased Spatial Frequency Response AN: Multiple DMs for Increased Spatial Frequency Response Using Multiple DMs for Increased Spatial Frequency Response AN Author: Justin Mansell Revision: // Abstract Some researchers have come to us suggesting

More information

Department of CSIT. Class: B.SC Semester: II Year: 2013 Paper Title: Introduction to logics of Computer Max Marks: 30

Department of CSIT. Class: B.SC Semester: II Year: 2013 Paper Title: Introduction to logics of Computer Max Marks: 30 Department of CSIT Class: B.SC Semester: II Year: 2013 Paper Title: Introduction to logics of Computer Max Marks: 30 Section A: (All 10 questions compulsory) 10X1=10 Very Short Answer Questions: Write

More information

E X P E R I M E N T 1

E X P E R I M E N T 1 E X P E R I M E N T 1 Getting to Know Data Studio Produced by the Physics Staff at Collin College Copyright Collin College Physics Department. All Rights Reserved. University Physics, Exp 1: Getting to

More information

CSE 166: Image Processing. Overview. Representing an image. What is an image? History. What is image processing? Today. Image Processing CSE 166

CSE 166: Image Processing. Overview. Representing an image. What is an image? History. What is image processing? Today. Image Processing CSE 166 CSE 166: Image Processing Overview Image Processing CSE 166 Today Course overview Logistics Some mathematics MATLAB Lectures will be boardwork and slides Take written notes or take pictures of the board

More information

MATH& 146 Lesson 11. Section 1.6 Categorical Data

MATH& 146 Lesson 11. Section 1.6 Categorical Data MATH& 146 Lesson 11 Section 1.6 Categorical Data 1 Frequency The first step to organizing categorical data is to count the number of data values there are in each category of interest. We can organize

More information

Digital Image and Fourier Transform

Digital Image and Fourier Transform Lab 5 Numerical Methods TNCG17 Digital Image and Fourier Transform Sasan Gooran (Autumn 2009) Before starting this lab you are supposed to do the preparation assignments of this lab. All functions and

More information

ECE 45 Homework 2. t x(τ)dτ. Problem 2.2 Find the Bode plot (magnitude and phase) and label all critical points of the transfer function

ECE 45 Homework 2. t x(τ)dτ. Problem 2.2 Find the Bode plot (magnitude and phase) and label all critical points of the transfer function UC San Diego Spring 2018 ECE 45 Homework 2 Problem 2.1 Are the following systems linear? Are they time invariant? (a) x(t) [ System (a)] 2x(t 3) (b) x(t) [ System (b)] x(t)+t (c) x(t) [ System (c)] (x(t)+1)

More information

Discrete-time equivalent systems example from matlab: the c2d command

Discrete-time equivalent systems example from matlab: the c2d command Discrete-time equivalent systems example from matlab: the cd command Create the -time system using the tf command and then generate the discrete-time equivalents using the cd command. We use this command

More information

Preface 11 Key Concept 1: Know your machine from a programmer s viewpoint 17

Preface 11 Key Concept 1: Know your machine from a programmer s viewpoint 17 Table of contents Preface 11 Prerequisites 11 Basic machining practice experience 11 Math 12 Motivation 12 Controls covered 12 What about conversational controls? 13 Controls other than Fanuc 13 Limitations

More information

MC9211 Computer Organization

MC9211 Computer Organization MC9211 Computer Organization Unit 2 : Combinational and Sequential Circuits Lesson2 : Sequential Circuits (KSB) (MCA) (2009-12/ODD) (2009-10/1 A&B) Coverage Lesson2 Outlines the formal procedures for the

More information

COGS 119/219 MATLAB for Experimental Research. Fall 2017 Image Processing in Matlab

COGS 119/219 MATLAB for Experimental Research. Fall 2017 Image Processing in Matlab COGS 119/219 MATLAB for Experimental Research Fall 2017 Image Processing in Matlab What is an image? An image is an array, or a matrix of square pixels (picture elements) arranged in rows and columns.

More information

AN INTEGRATED MATLAB SUITE FOR INTRODUCTORY DSP EDUCATION. Richard Radke and Sanjeev Kulkarni

AN INTEGRATED MATLAB SUITE FOR INTRODUCTORY DSP EDUCATION. Richard Radke and Sanjeev Kulkarni SPE Workshop October 15 18, 2000 AN INTEGRATED MATLAB SUITE FOR INTRODUCTORY DSP EDUCATION Richard Radke and Sanjeev Kulkarni Department of Electrical Engineering Princeton University Princeton, NJ 08540

More information

1 Lesson 11: Antiderivatives of Elementary Functions

1 Lesson 11: Antiderivatives of Elementary Functions 1 Lesson 11: Antiderivatives of Elementary Functions Chapter 6 Material: pages 237-252 in the textbook: The material in this lesson covers The definition of the antiderivative of a function of one variable.

More information

Fast Ethernet Consortium Clause 25 PMD-EEE Conformance Test Suite v1.1 Report

Fast Ethernet Consortium Clause 25 PMD-EEE Conformance Test Suite v1.1 Report Fast Ethernet Consortium Clause 25 PMD-EEE Conformance Test Suite v1.1 Report UNH-IOL 121 Technology Drive, Suite 2 Durham, NH 03824 +1-603-862-0090 Consortium Manager: Peter Scruton pjs@iol.unh.edu +1-603-862-4534

More information

Section 6.8 Synthesis of Sequential Logic Page 1 of 8

Section 6.8 Synthesis of Sequential Logic Page 1 of 8 Section 6.8 Synthesis of Sequential Logic Page of 8 6.8 Synthesis of Sequential Logic Steps:. Given a description (usually in words), develop the state diagram. 2. Convert the state diagram to a next-state

More information

Display Lists in R. Paul Murrell and Ross Ihaka. February The University of Auckland

Display Lists in R. Paul Murrell and Ross Ihaka. February The University of Auckland Paul Murrell and Ross Ihaka The University of Auckland February 16 2007 What is a Display List? A display list is a record of the graphics commands used to create a drawing. It is not a backing store.

More information

Bite Size Brownies. Designed by: Jonathan Thompson George Mason University, COMPLETE Math

Bite Size Brownies. Designed by: Jonathan Thompson George Mason University, COMPLETE Math Bite Size Brownies Designed by: Jonathan Thompson George Mason University, COMPLETE Math The Task Mr. Brown E. Pan recently opened a new business making brownies called The Brown E. Pan. On his first day

More information

Algebra I Module 2 Lessons 1 19

Algebra I Module 2 Lessons 1 19 Eureka Math 2015 2016 Algebra I Module 2 Lessons 1 19 Eureka Math, Published by the non-profit Great Minds. Copyright 2015 Great Minds. No part of this work may be reproduced, distributed, modified, sold,

More information

Music BCI ( )

Music BCI ( ) Music BCI (006-2015) Matthias Treder, Benjamin Blankertz Technische Universität Berlin, Berlin, Germany September 5, 2016 1 Introduction We investigated the suitability of musical stimuli for use in a

More information

From Fourier Series to Analysis of Non-stationary Signals - X

From Fourier Series to Analysis of Non-stationary Signals - X From Fourier Series to Analysis of Non-stationary Signals - X prof. Miroslav Vlcek December 12, 217 Contents 1 Nonstationary Signals and Analysis 2 Introduction to Wavelets 3 A note to your compositions

More information

SIPROTEC Fault Record Analysis SIGRA

SIPROTEC Fault Record Analysis SIGRA Preface SIPROTEC Fault Record Analysis SIGRA V4.58 Manual Contents System Overview 1 Operating Functions 2 Fault Records 3 SIPROTEC Records 4 Views / Diagrams / Signals / Tables 5 Calculations/Definitions

More information

Tempo Estimation and Manipulation

Tempo Estimation and Manipulation Hanchel Cheng Sevy Harris I. Introduction Tempo Estimation and Manipulation This project was inspired by the idea of a smart conducting baton which could change the sound of audio in real time using gestures,

More information

Topic: Instructional David G. Thomas December 23, 2015

Topic: Instructional David G. Thomas December 23, 2015 Procedure to Setup a 3ɸ Linear Motor This is a guide to configure a 3ɸ linear motor using either analog or digital encoder feedback with an Elmo Gold Line drive. Topic: Instructional David G. Thomas December

More information

CITATION METRICS WORKSHOP (WEB of SCIENCE)

CITATION METRICS WORKSHOP (WEB of SCIENCE) CITATION METRICS WORKSHOP (WEB of SCIENCE) BASIC LEVEL: Searching Indexed Works Only Prepared by Bibliometric Team, NUS Libraries, Apr 2018 Section Description Pages I Citation Searching of Indexed Works

More information

CHAPTER 4: Logic Circuits

CHAPTER 4: Logic Circuits CHAPTER 4: Logic Circuits II. Sequential Circuits Combinational circuits o The outputs depend only on the current input values o It uses only logic gates, decoders, multiplexers, ALUs Sequential circuits

More information

CPSC 121: Models of Computation. Module 1: Propositional Logic

CPSC 121: Models of Computation. Module 1: Propositional Logic CPSC 121: Models of Computation Module 1: Propositional Logic Module 1: Propositional Logic By the start of the class, you should be able to: Translate back and forth between simple natural language statements

More information

Chapter 1. Introduction to Digital Signal Processing

Chapter 1. Introduction to Digital Signal Processing Chapter 1 Introduction to Digital Signal Processing 1. Introduction Signal processing is a discipline concerned with the acquisition, representation, manipulation, and transformation of signals required

More information

ENGIN 100: Music Signal Processing. PROJECT #1: Tone Synthesizer/Transcriber

ENGIN 100: Music Signal Processing. PROJECT #1: Tone Synthesizer/Transcriber ENGIN 100: Music Signal Processing 1 PROJECT #1: Tone Synthesizer/Transcriber Professor Andrew E. Yagle Dept. of EECS, The University of Michigan, Ann Arbor, MI 48109-2122 I. ABSTRACT This project teaches

More information

Elasticity Imaging with Ultrasound JEE 4980 Final Report. George Michaels and Mary Watts

Elasticity Imaging with Ultrasound JEE 4980 Final Report. George Michaels and Mary Watts Elasticity Imaging with Ultrasound JEE 4980 Final Report George Michaels and Mary Watts University of Missouri, St. Louis Washington University Joint Engineering Undergraduate Program St. Louis, Missouri

More information

Lab 5 Linear Predictive Coding

Lab 5 Linear Predictive Coding Lab 5 Linear Predictive Coding 1 of 1 Idea When plain speech audio is recorded and needs to be transmitted over a channel with limited bandwidth it is often necessary to either compress or encode the audio

More information

Interactive Methods in Multiobjective Optimization 1: An Overview

Interactive Methods in Multiobjective Optimization 1: An Overview Interactive Methods in Multiobjective Optimization 1: An Overview Department of Mathematical Information Technology, University of Jyväskylä, Finland Table of Contents 1 General Properties of Interactive

More information

EE 200 Problem Set 3 Cover Sheet Fall 2015

EE 200 Problem Set 3 Cover Sheet Fall 2015 EE 200 Problem Set 3 Cover Sheet Fall 2015 Last Name (Print): First Name (Print): PSU User ID (e.g. xyz1234): Section: Submission deadline: All work is due by Monday 21 September at 4 pm. Written work

More information

Agilent PN Time-Capture Capabilities of the Agilent Series Vector Signal Analyzers Product Note

Agilent PN Time-Capture Capabilities of the Agilent Series Vector Signal Analyzers Product Note Agilent PN 89400-10 Time-Capture Capabilities of the Agilent 89400 Series Vector Signal Analyzers Product Note Figure 1. Simplified block diagram showing basic signal flow in the Agilent 89400 Series VSAs

More information

Frequencies. Chapter 2. Descriptive statistics and charts

Frequencies. Chapter 2. Descriptive statistics and charts An analyst usually does not concentrate on each individual data values but would like to have a whole picture of how the variables distributed. In this chapter, we will introduce some tools to tabulate

More information

Release Year Prediction for Songs

Release Year Prediction for Songs Release Year Prediction for Songs [CSE 258 Assignment 2] Ruyu Tan University of California San Diego PID: A53099216 rut003@ucsd.edu Jiaying Liu University of California San Diego PID: A53107720 jil672@ucsd.edu

More information

Word 2003 Class Project. Page numbering and page breaking

Word 2003 Class Project. Page numbering and page breaking Word 2003 Class Project Page numbering and page breaking 1. Open document c:\data\word 2003 Student Start Document. 2. Page 1 Title Page a. Increase the size and boldness of the following title of the

More information

1. Track Ball, Paddle and Count How Many Times Ball Hits Paddle in Video Game

1. Track Ball, Paddle and Count How Many Times Ball Hits Paddle in Video Game 1. Track Ball, Paddle and Count How Many Times Ball Hits Paddle in Video Game Your program needs to track the ball (or balls) and the paddle in each of the video game frame sequences below. The program

More information

4.4 The FFT and MATLAB

4.4 The FFT and MATLAB 4.4. THE FFT AND MATLAB 69 4.4 The FFT and MATLAB 4.4.1 The FFT and MATLAB MATLAB implements the Fourier transform with the following functions: fft, ifft, fftshift, ifftshift, fft2, ifft2. We describe

More information

Fundamentals of DSP Chap. 1: Introduction

Fundamentals of DSP Chap. 1: Introduction Fundamentals of DSP Chap. 1: Introduction Chia-Wen Lin Dept. CSIE, National Chung Cheng Univ. Chiayi, Taiwan Office: 511 Phone: #33120 Digital Signal Processing Signal Processing is to study how to represent,

More information

GS Bloch Equations Simulator 1. GS Introduction to Medical Physics IV Exercise 1: Discrete Subjects

GS Bloch Equations Simulator 1. GS Introduction to Medical Physics IV Exercise 1: Discrete Subjects GS02-1193 Bloch Equations Simulator 1 GS02-1193 Introduction to Medical Physics IV Exercise 1: Discrete Subjects Once SpinWright is running, select the Subject tab. The GUI display toward the top of the

More information

The XYZ Colour Space. 26 January 2011 WHITE PAPER. IMAGE PROCESSING TECHNIQUES

The XYZ Colour Space. 26 January 2011 WHITE PAPER.   IMAGE PROCESSING TECHNIQUES www.omnitek.tv IMAE POESSIN TEHNIQUES The olour Space The colour space has the unique property of being able to express every colour that the human eye can see which in turn means that it can express every

More information

Dot Plots and Distributions

Dot Plots and Distributions EXTENSION Dot Plots and Distributions A dot plot is a data representation that uses a number line and x s, dots, or other symbols to show frequency. Dot plots are sometimes called line plots. E X A M P

More information

Brain-Computer Interface (BCI)

Brain-Computer Interface (BCI) Brain-Computer Interface (BCI) Christoph Guger, Günter Edlinger, g.tec Guger Technologies OEG Herbersteinstr. 60, 8020 Graz, Austria, guger@gtec.at This tutorial shows HOW-TO find and extract proper signal

More information

[FILE] INDEX BY NUMBER MANUALS

[FILE] INDEX BY NUMBER MANUALS 05 April, 2018 [FILE] INDEX BY NUMBER MANUALS Document Filetype: PDF 499.62 KB 0 [FILE] INDEX BY NUMBER MANUALS Answer : An index number is a statistical device that is used to measure the changes in the

More information

White Paper. Uniform Luminance Technology. What s inside? What is non-uniformity and noise in LCDs? Why is it a problem? How is it solved?

White Paper. Uniform Luminance Technology. What s inside? What is non-uniformity and noise in LCDs? Why is it a problem? How is it solved? White Paper Uniform Luminance Technology What s inside? What is non-uniformity and noise in LCDs? Why is it a problem? How is it solved? Tom Kimpe Manager Technology & Innovation Group Barco Medical Imaging

More information

1 Overview. 1.1 Digital Images GEORGIA INSTITUTE OF TECHNOLOGY. ECE 2026 Summer 2016 Lab #6: Sampling: A/D and D/A & Aliasing

1 Overview. 1.1 Digital Images GEORGIA INSTITUTE OF TECHNOLOGY. ECE 2026 Summer 2016 Lab #6: Sampling: A/D and D/A & Aliasing GEORGIA INSTITUTE OF TECHNOLOGY SCHOOL of ELECTRICAL and COMPUTER ENGINEERING ECE 2026 Summer 2016 Lab #6: Sampling: A/D and D/A & Aliasing Date: 30 June 2016 Pre-Lab: You should read the Pre-Lab section

More information

Digital Signal Processing Lecture One Introduction to Digital Signal Processing Third Stage Prepared by: Marwah Kareem

Digital Signal Processing Lecture One Introduction to Digital Signal Processing Third Stage Prepared by: Marwah Kareem Lecture One Introduction to Digital Signal Processing Third Stage Prepared by: Marwah Kareem Digital Signal Processing Digital signal processing (DSP) technology and its advancements have dramatically

More information

Optical Technologies Micro Motion Absolute, Technology Overview & Programming

Optical Technologies Micro Motion Absolute, Technology Overview & Programming Optical Technologies Micro Motion Absolute, Technology Overview & Programming TN-1003 REV 180531 THE CHALLENGE When an incremental encoder is turned on, the device needs to report accurate location information

More information

2. Document setup: The full physical page size including all margins will be 148mm x 210mm The five sets of margins

2. Document setup: The full physical page size including all margins will be 148mm x 210mm The five sets of margins Submission Guidelines Please use this section as a guideline for preparing your manuscript. This set of guidelines (updated November 2007) replaces all previously issued guidelines. Please ensure that

More information

VISSIM Tutorial. Starting VISSIM and Opening a File CE 474 8/31/06

VISSIM Tutorial. Starting VISSIM and Opening a File CE 474 8/31/06 VISSIM Tutorial Starting VISSIM and Opening a File Click on the Windows START button, go to the All Programs menu and find the PTV_Vision directory. Start VISSIM by selecting the executable file. The following

More information

MODFLOW - Grid Approach

MODFLOW - Grid Approach GMS 7.0 TUTORIALS MODFLOW - Grid Approach 1 Introduction Two approaches can be used to construct a MODFLOW simulation in GMS: the grid approach and the conceptual model approach. The grid approach involves

More information

MATLAB Basics 6 plotting

MATLAB Basics 6 plotting 1 MATLAB Basics 6 plotting Anthony Rossiter University of Sheffield For a neat organisation of all videos and resources http://controleducation.group.shef.ac.uk/indexwebbook.html Introduction 2 1. The

More information

2018 ncode User Group Meeting

2018 ncode User Group Meeting 2018 ncode User Group Meeting February 28 March 1, 2018 Novi, MI USA What s New in ncode 2018 (GlyphWorks and VibeSys) Fred Kihm Product Manager ncode ncode Product Range 4 ncode Product Range 5 ncode

More information

Lecture 11 Practice III

Lecture 11 Practice III Lecture 11 Practice III Example 1 Write a program that reads an array of temperature values in Celsius and compute the corresponding temperatures in Fahrenheit and Kelvin. The program shows the results

More information

An Efficient Low Bit-Rate Video-Coding Algorithm Focusing on Moving Regions

An Efficient Low Bit-Rate Video-Coding Algorithm Focusing on Moving Regions 1128 IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, VOL. 11, NO. 10, OCTOBER 2001 An Efficient Low Bit-Rate Video-Coding Algorithm Focusing on Moving Regions Kwok-Wai Wong, Kin-Man Lam,

More information

Google Scholar and ISI WoS Author metrics within Earth Sciences subjects. Susanne Mikki Bergen University Library

Google Scholar and ISI WoS Author metrics within Earth Sciences subjects. Susanne Mikki Bergen University Library Google Scholar and ISI WoS Author metrics within Earth Sciences subjects Susanne Mikki Bergen University Library My first steps within bibliometry Research question How well is Google Scholar performing

More information

Melody Extraction from Generic Audio Clips Thaminda Edirisooriya, Hansohl Kim, Connie Zeng

Melody Extraction from Generic Audio Clips Thaminda Edirisooriya, Hansohl Kim, Connie Zeng Melody Extraction from Generic Audio Clips Thaminda Edirisooriya, Hansohl Kim, Connie Zeng Introduction In this project we were interested in extracting the melody from generic audio files. Due to the

More information

v. 8.0 GMS 8.0 Tutorial MODFLOW Grid Approach Build a MODFLOW model on a 3D grid Prerequisite Tutorials None Time minutes

v. 8.0 GMS 8.0 Tutorial MODFLOW Grid Approach Build a MODFLOW model on a 3D grid Prerequisite Tutorials None Time minutes v. 8.0 GMS 8.0 Tutorial Build a MODFLOW model on a 3D grid Objectives The grid approach to MODFLOW pre-processing is described in this tutorial. In most cases, the conceptual model approach is more powerful

More information

Lab experience 1: Introduction to LabView

Lab experience 1: Introduction to LabView Lab experience 1: Introduction to LabView LabView is software for the real-time acquisition, processing and visualization of measured data. A LabView program is called a Virtual Instrument (VI) because

More information

CURIE Day 3: Frequency Domain Images

CURIE Day 3: Frequency Domain Images CURIE Day 3: Frequency Domain Images Curie Academy, July 15, 2015 NAME: NAME: TA SIGN-OFFS Exercise 7 Exercise 13 Exercise 17 Making 8x8 pictures Compressing a grayscale image Satellite image debanding

More information

Transform Coding of Still Images

Transform Coding of Still Images Transform Coding of Still Images February 2012 1 Introduction 1.1 Overview A transform coder consists of three distinct parts: The transform, the quantizer and the source coder. In this laboration you

More information

EE 367 Lab Part 1: Sequential Logic

EE 367 Lab Part 1: Sequential Logic EE367: Introduction to Microprocessors Section 1.0 EE 367 Lab Part 1: Sequential Logic Contents 1 Preface 1 1.1 Things you need to do before arriving in the Laboratory............... 2 1.2 Summary of material

More information

Environmental Controls Laboratory

Environmental Controls Laboratory (Electro-Oculography Application) Introduction Spinal cord injury, cerebral palsy, and stroke are some examples of clinical problems which can have a large effect on upper extremity motor control for afflicted

More information

DSP First Lab 04: Synthesis of Sinusoidal Signals - Music Synthesis

DSP First Lab 04: Synthesis of Sinusoidal Signals - Music Synthesis DSP First Lab 04: Synthesis of Sinusoidal Signals - Music Synthesis Pre-Lab and Warm-Up: You should read at least the Pre-Lab and Warm-up sections of this lab assignment and go over all exercises in the

More information

NENS 230 Assignment #2 Data Import, Manipulation, and Basic Plotting

NENS 230 Assignment #2 Data Import, Manipulation, and Basic Plotting NENS 230 Assignment #2 Data Import, Manipulation, and Basic Plotting Compound Action Potential Due: Tuesday, October 6th, 2015 Goals Become comfortable reading data into Matlab from several common formats

More information

Figure 1: Feature Vector Sequence Generator block diagram.

Figure 1: Feature Vector Sequence Generator block diagram. 1 Introduction Figure 1: Feature Vector Sequence Generator block diagram. We propose designing a simple isolated word speech recognition system in Verilog. Our design is naturally divided into two modules.

More information

Signals And Systems Roberts 2ed Solution Manual

Signals And Systems Roberts 2ed Solution Manual We have made it easy for you to find a PDF Ebooks without any digging. And by having access to our ebooks online or by storing it on your computer, you have convenient answers with signals and systems

More information

Simple applications of neural nets. Character recognition. CIS 412 Artificial Intelligence, Dr. Iren Valova, UMASS Dartmouth

Simple applications of neural nets. Character recognition. CIS 412 Artificial Intelligence, Dr. Iren Valova, UMASS Dartmouth Simple applications of neural nets 1 Character recognition 2 Character recognition 3 Backpropagation issues 4 Backpropagation issues 5 Demonstration - classification of crabs 6 In this demo, we will train

More information

How To Remove Page Number From First Two Pages In Word 2007

How To Remove Page Number From First Two Pages In Word 2007 How To Remove Page Number From First Two Pages In Word 2007 How to Number Pages in Your Thesis with Word - YouTube 5. Word: Insert Page Word 2007. I need to number page 1 to 7 in roman numerals (starting

More information

Package schoenberg. June 26, 2018

Package schoenberg. June 26, 2018 Type Package Title Tools for 12-Tone Musical Composition Version 2.0.2 Date 2018-06-26 Author Jeffrey A. Dahlke Package schoenberg June 26, 2018 Maintainer Jeffrey A. Dahlke

More information

EE292: Fundamentals of ECE

EE292: Fundamentals of ECE EE292: Fundamentals of ECE Fall 2012 TTh 10:00-11:15 SEB 1242 Lecture 23 121120 http://www.ee.unlv.edu/~b1morris/ee292/ 2 Outline Review Combinatorial Logic Sequential Logic 3 Combinatorial Logic Circuits

More information

Hidden Markov Model based dance recognition

Hidden Markov Model based dance recognition Hidden Markov Model based dance recognition Dragutin Hrenek, Nenad Mikša, Robert Perica, Pavle Prentašić and Boris Trubić University of Zagreb, Faculty of Electrical Engineering and Computing Unska 3,

More information

CHAPTER 4: Logic Circuits

CHAPTER 4: Logic Circuits CHAPTER 4: Logic Circuits II. Sequential Circuits Combinational circuits o The outputs depend only on the current input values o It uses only logic gates, decoders, multiplexers, ALUs Sequential circuits

More information

Cluster Analysis of Internet Users Based on Hourly Traffic Utilization

Cluster Analysis of Internet Users Based on Hourly Traffic Utilization Cluster Analysis of Internet Users Based on Hourly Traffic Utilization M. Rosário de Oliveira, Rui Valadas, António Pacheco, Paulo Salvador Instituto Superior Técnico - UTL Department of Mathematics and

More information

NH 67, Karur Trichy Highways, Puliyur C.F, Karur District UNIT-III SEQUENTIAL CIRCUITS

NH 67, Karur Trichy Highways, Puliyur C.F, Karur District UNIT-III SEQUENTIAL CIRCUITS NH 67, Karur Trichy Highways, Puliyur C.F, 639 114 Karur District DEPARTMENT OF ELETRONICS AND COMMUNICATION ENGINEERING COURSE NOTES SUBJECT: DIGITAL ELECTRONICS CLASS: II YEAR ECE SUBJECT CODE: EC2203

More information

North Carolina Standard Course of Study - Mathematics

North Carolina Standard Course of Study - Mathematics A Correlation of To the North Carolina Standard Course of Study - Mathematics Grade 4 A Correlation of, Grade 4 Units Unit 1 - Arrays, Factors, and Multiplicative Comparison Unit 2 - Generating and Representing

More information

Acoustic Echo Canceling: Echo Equality Index

Acoustic Echo Canceling: Echo Equality Index Acoustic Echo Canceling: Echo Equality Index Mengran Du, University of Maryalnd Dr. Bogdan Kosanovic, Texas Instruments Industry Sponsored Projects In Research and Engineering (INSPIRE) Maryland Engineering

More information

For an alphabet, we can make do with just { s, 0, 1 }, in which for typographic simplicity, s stands for the blank space.

For an alphabet, we can make do with just { s, 0, 1 }, in which for typographic simplicity, s stands for the blank space. Problem 1 (A&B 1.1): =================== We get to specify a few things here that are left unstated to begin with. I assume that numbers refers to nonnegative integers. I assume that the input is guaranteed

More information

CSE 101. Algorithm Design and Analysis Miles Jones Office 4208 CSE Building Lecture 9: Greedy

CSE 101. Algorithm Design and Analysis Miles Jones Office 4208 CSE Building Lecture 9: Greedy CSE 101 Algorithm Design and Analysis Miles Jones mej016@eng.ucsd.edu Office 4208 CSE Building Lecture 9: Greedy GENERAL PROBLEM SOLVING In general, when you try to solve a problem, you are trying to find

More information

Trial version. Analogue to Digital Conversion in Distance Measurement

Trial version. Analogue to Digital Conversion in Distance Measurement Analogue to Digital Conversion in Distance Measurement How is an analogue to digital conversion of a distance measurement made and how accurate is it? Analogue to Digital Conversion in Distance Measurement

More information

Moving on from MSTAT. March The University of Reading Statistical Services Centre Biometrics Advisory and Support Service to DFID

Moving on from MSTAT. March The University of Reading Statistical Services Centre Biometrics Advisory and Support Service to DFID Moving on from MSTAT March 2000 The University of Reading Statistical Services Centre Biometrics Advisory and Support Service to DFID Contents 1. Introduction 3 2. Moving from MSTAT to Genstat 4 2.1 Analysis

More information

Adaptive Key Frame Selection for Efficient Video Coding

Adaptive Key Frame Selection for Efficient Video Coding Adaptive Key Frame Selection for Efficient Video Coding Jaebum Jun, Sunyoung Lee, Zanming He, Myungjung Lee, and Euee S. Jang Digital Media Lab., Hanyang University 17 Haengdang-dong, Seongdong-gu, Seoul,

More information