EENG580 Game 2: Verbal Representation of Course Content

Similar documents
Theory and Problems of Signals and Systems

E : Lecture 1 Introduction

DISCRETE-TIME SIGNAL PROCESSING

Course Name: Digital Signal Processing Course Code: EE 605A Credit: 3

Communication Engineering Prof. Surendra Prasad Department of Electrical Engineering Indian Institute of Technology, Delhi

! Introduction. ! Discrete Time Signals & Systems. ! Z-Transform. ! Inverse Z-Transform. ! Sampling of Continuous Time Signals

DIGITAL SIGNAL PROCESSING UNIT III INFINITE IMPULSE RESPONSE DIGITAL FILTERS. 3.6 Design of Digital Filter using Digital to Digital

DHANALAKSHMI COLLEGE OF ENGINEERING DEPARTMENT OF ELECTRICAL AND ELECTRONICS ENGINEERING EC2314- DIGITAL SIGNAL PROCESSING UNIT I INTRODUCTION PART A

UNIT 1. SIGNALS AND SYSTEM

LAB 6: FIR Filter Design Summer 2011

ENT 315 Medical Signal Processing CHAPTER 2 DISCRETE FOURIER TRANSFORM. Dr. Lim Chee Chin

Signals and Systems Laboratory with MATLAB

Let H(z) = P(z)/Q(z) be the system function of a rational form. Let us represent both P(z) and Q(z) as polynomials of z (not z -1 )

Responses of Digital Filters Chapter Intended Learning Outcomes:

Spring 2014 ECEN Signals and Systems

3. Lecture. Fourier Transformation Sampling

Lecture 3: Linear Filters

MITOCW MITRES_6-007S11lec09_300k.mp4

Ch.11 The Discrete-Time Fourier Transform (DTFT)

ELECTRONOTES APPLICATION NOTE NO Hanshaw Road Ithaca, NY Mar 6, 2015

Lecture 3: Linear Filters

The Continuous-time Fourier

(i) Represent discrete-time signals using transform. (ii) Understand the relationship between transform and discrete-time Fourier transform

Digital Signal Processing Prof. T. K. Basu Department of Electrical Engineering Indian Institute of Technology, Kharagpur

Review of Discrete-Time System

Digital Signal Processing Lecture 10 - Discrete Fourier Transform

ECE 301 Division 1 Final Exam Solutions, 12/12/2011, 3:20-5:20pm in PHYS 114.

Roll No. :... Invigilator s Signature :.. CS/B.Tech (EE-N)/SEM-6/EC-611/ DIGITAL SIGNAL PROCESSING. Time Allotted : 3 Hours Full Marks : 70

Review of Analog Signal Analysis

ECE Digital Signal Processing Spring Semester 2010 Classroom: 219 Riggs Hall Meeting Times: T,TH 3:30-4:45 pm

Implementation of Discrete-Time Systems

Simon Fraser University School of Engineering Science ENSC Linear Systems Spring Instructor Jim Cavers ASB

How to manipulate Frequencies in Discrete-time Domain? Two Main Approaches

SIGNALS AND SYSTEMS I. RAVI KUMAR

George Mason University ECE 201: Introduction to Signal Analysis Spring 2017

Analog vs. discrete signals

EE482: Digital Signal Processing Applications

EE 224 Signals and Systems I Review 1/10

Chapter Intended Learning Outcomes: (i) Understanding the relationship between transform and the Fourier transform for discrete-time signals

ECG782: Multidimensional Digital Signal Processing

Cast of Characters. Some Symbols, Functions, and Variables Used in the Book

Convolution. Define a mathematical operation on discrete-time signals called convolution, represented by *. Given two discrete-time signals x 1, x 2,

(i) Understanding the characteristics and properties of DTFT

Department of Electrical and Telecommunications Engineering Technology TEL (718) FAX: (718) Courses Description:

Novel Waveform Design and Scheduling For High-Resolution Radar and Interleaving

Chap 2. Discrete-Time Signals and Systems

Discrete-Time Systems

The Discrete-time Fourier Transform

Modeling and Analysis of Systems Lecture #8 - Transfer Function. Guillaume Drion Academic year

/ (2π) X(e jω ) dω. 4. An 8 point sequence is given by x(n) = {2,2,2,2,1,1,1,1}. Compute 8 point DFT of x(n) by

A system that is both linear and time-invariant is called linear time-invariant (LTI).


Continuous and Discrete Time Signals and Systems

Computer Vision. Filtering in the Frequency Domain

ELEN E4810: Digital Signal Processing Topic 11: Continuous Signals. 1. Sampling and Reconstruction 2. Quantization

Multidimensional digital signal processing

IT DIGITAL SIGNAL PROCESSING (2013 regulation) UNIT-1 SIGNALS AND SYSTEMS PART-A

Discrete-time signals and systems

E2.5 Signals & Linear Systems. Tutorial Sheet 1 Introduction to Signals & Systems (Lectures 1 & 2)

VALLIAMMAI ENGINEERING COLLEGE. SRM Nagar, Kattankulathur DEPARTMENT OF INFORMATION TECHNOLOGY. Academic Year

Discrete-Time Fourier Transform

SOUTHWEST TENNESSEE COMMUNITY COLLEGE COURSE SYLLABUS FOR Pre-Calculus II

Biomedical Signal Processing and Signal Modeling

Lecture Schedule: Week Date Lecture Title

Discrete-Time David Johns and Ken Martin University of Toronto

1 1.27z z 2. 1 z H 2

The Discrete Fourier Transform

GEORGIA INSTITUTE OF TECHNOLOGY SCHOOL of ELECTRICAL & COMPUTER ENGINEERING FINAL EXAM. COURSE: ECE 3084A (Prof. Michaels)

QUESTION BANK SIGNALS AND SYSTEMS (4 th SEM ECE)

Linear Convolution Using FFT

GRADE 12 MATHEMATICS Outcomes Comparison for Principles of Mathematics 12 and Applications of Mathematics 12: Analysis of Curriculum Changes

Lecture 27 Frequency Response 2

Fourier Series Representation of

Examples. 2-input, 1-output discrete-time systems: 1-input, 1-output discrete-time systems:

LTI H. the system H when xn [ ] is the input.

Lab 4: Quantization, Oversampling, and Noise Shaping

Review: Continuous Fourier Transform

Digital Filters Ying Sun

ESE 531: Digital Signal Processing

Fourier Transform and Frequency Domain

IB Paper 6: Signal and Data Analysis

Digital Signal Processing

Syllabus for IMGS-616 Fourier Methods in Imaging (RIT #11857) Week 1: 8/26, 8/28 Week 2: 9/2, 9/4

Fourier Transform and Frequency Domain

Information and Communications Security: Encryption and Information Hiding

PART 1. Review of DSP. f (t)e iωt dt. F(ω) = f (t) = 1 2π. F(ω)e iωt dω. f (t) F (ω) The Fourier Transform. Fourier Transform.

! Circular Convolution. " Linear convolution with circular convolution. ! Discrete Fourier Transform. " Linear convolution through circular

Index. p, lip, 78 8 function, 107 v, 7-8 w, 7-8 i,7-8 sine, 43 Bo,94-96

Poles and Zeros in z-plane

Sampling. Alejandro Ribeiro. February 8, 2018

R13 SET - 1

GEORGIA INSTITUTE OF TECHNOLOGY SCHOOL OF ELECTRICAL AND COMPUTER ENGINEERING

Signal Processing and Linear Systems1 Lecture 4: Characterizing Systems

Contents. Signals as functions (1D, 2D)

Contents. Signals as functions (1D, 2D)


Discrete Time Systems

Chirp Transform for FFT

DSP First. Laboratory Exercise #10. The z, n, and ˆω Domains

w n = c k v n k (1.226) w n = c k v n k + d k w n k (1.227) Clearly non-recursive filters are a special case of recursive filters where M=0.

Transcription:

Intro to DSP Game 2 Verbal Representation of Course Content EENG580 Game 2: Verbal Representation of Course Content Activity summary Overview: Word game similar to Taboo, Catch Phrase, Unspeakable, Battle of Words, and Word Charades. Setting: in class Curricular elements: gaming Prerequisites: completion of a majority of the course Topics/concepts covered: everything in the course to date Learning outcomes: After completing this activity, students should be able to: Know the definitions of most of the major concepts covered in the course Explain course concepts using alternative phrasing than the standard definitions Expected time to complete: one 1-hour class session Required hardware/materials: A whiteboard (for scoring), dry-erase markers, and a timer. The instructor should also print this handout on heavy card stock and use a paper cutter to separate the cards. Required instructor interaction: The instructor serves as scorekeeper and moderator Common mistakes/pitfalls: Some students forget the rules or don t pay attention to them. Students might miss the point of the activity if it is not reinforced; it is recommended to periodically encourage the students to prepare for this game throughout the course, so they spend time trying to understand how to explain each concept without repeating the definition verbatim rather than attempting to figure it out on the fly. Method of assessment: Scored by instructor during game play. The winning team can be offered bonus points or non-grade-related perks. Contributors: Richard Martin and Andrew Klein The views expressed in this document are those of the authors, and do not reflect the official policy or position of the United States Air Force, United States Navy, Department of Defense, or the U.S. Government. This document has been approved for public release; distribution unlimited. This work was supported by ONR grant N000141512442, and is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. http://creativecommons.org/licenses/by-sa/4.0/ c 2016 by A.G. Klein & R.K. Martin. Contact: andy.klein@wwu.edu and richard.martin@afit.edu. 1

EENG580 Game 2: verbal representation of course content This is a word game similar to Taboo, Catch Phrase, Unspeakable, Battle of Words, and Word Charades. Overview The object of the game is for one person to draw on the whiteboard and get their teammates to guess as many words as possible, without speaking. Gesturing is acceptable, charades are not. The point is for you to try to understand the meaning of the terms rather than just the verbatim definitions. Keep this in mind as you progress through the course material. Setting Up Divide the players into two teams. Team A chooses one of their players to be their first Clue-giver, and a second player as a wingman. A player from Team B sits beside Team A s Clue-giver to observe the cards and check for rule violations. The wingman can also see the cards and assists the Clue-giver by discretely offering definitions and suggestions, but otherwise cannot interact with Team A. After Team A has completed a turn, the teams swap roles. Playing the Game The Clue-giver draws a card. The word at the top is the Password that the Clue-giver is trying to get his or her teammates to say. The words below the Password are the forbidden words the Clue-giver can t say when giving clues. As soon as a card is drawn, the Clue-giver starts the timer and starts giving clues that will make teammates say the Password. The clues can t break any of the rules listed below. Go through as many words as you can within the time limit. You may pass, but you will lose 1 point per pass. Rules for Clues No form or part of any word on the card may be given as a clue. No gestures, sound effects or noises may be made, such as Doppler effect sounds ( neeeeeeeoooww ) or engine noises. If you make engine noises while playing, I will record it and post it on the school s social media accounts. You cannot say the Password sounds like or rhymes with another word. No initials or abbreviations can be given if the words they represent are included on the card (or vice versa). There is no penalty for wrong guesses, but please don t simply run through guesses of all the course-related words each turn (i.e. regardless of what the Clue-giver is saying). That defeats the pedagogical purpose of the game. Scoring/Losing Points Each time a teammate shouts out the correct Password, the Clue-giver s team scores a point. Getting buzzed (-1 point): During the Clue-giver s turn, the members of the opposing team watch the Clue-giver and the cards. If any of the rules are broken, the buzzer is sounded by the opposing team. The Clue-giver removes the card, draws a new card, and continues playing. Passing on a card (-1 point): You may choose to pass on a card at any time during your turn.

Samples Domain Axis When Linear Invariant Period Wavelength Repeat Pitch Property Superposition Additive Scaling Property Delay Input Stable Causal Memoryless Property Bounded BIBO Finite Property Past Future Property Past Future Store Continuous Sample Index Integer Fourier Nyquist Unit Step Fourier Coordinates Z Laplace He Mathematician Inventor French Sampling Theorem Rate Aliasing Bandwidth Zero One Function Accumulator Impulse Response Response Parseval Symmetric LTI system Delta Kronecker Dirac LTI system Eigenfunction Impulse Energy Conservation Same Real Imaginary Even Odd Mirror

Complex Exponential Euler Sine Cosine Imaginary Unit Circle Spectrum Sampling Reconstruction Fourier Wave Prism Band Nyquist Digital Impulse Dr. Dre Sampling Sinc Nyquist Aliasing Bandlimited Upsampling Downsampling Interpolation Periodic Rate Change Increase Integer Zeros Rate Change Decrease Integer Aliasing Sinc Low pass filter Zero padding Linear Between Repeat Cycle Same Again Table Aliasing Accumulator Difference Eqn Band-Limited Sampling Nyquist Bandlimited Ghost Add Sum Filter Feedback Feedback Input Block Nyquist Finite Sampling Spectrum Mellin Linear Phase Convolution Delta Function Scale Linear Phase Integral Stretch Speech Shift Symmetric Delay Scale Summation Integral Star Asterisk Impulse One Kronecker Dirac Sorority

Conjugate Real Imaginary Complex Negative Sign Circular Conv. Filter Analog Multiplication DFT Periodic Wrap Repeat Input FIR IIR Continuous Digital Old Quantized A/D Converter D/A converter Duality Left-sided Analog Ditigal Continuous Block Analog Ditigal Continuous Block Same Properties Pairs Anti-causal Right-sided Two-sided Region Z DTFT CTFT DFT FFT Fourier Infinite Sampled Fourier Continuous Infinite Laplace Fourier Finite Sampled Fourier Sampled Fast Sinc Magnitude Amplitude Phase Rectangle Boxcar Interpolation Sine Function Amplitude Phase Absolute Real Pop pop Amplitude Phase Absolute Imaginary Negative Magnitude Amplitude Real Imaginary Complex

Signal Processing Digital Course Computer Matlab Analyze Kronecker Dirac Acoustic Delta Stem Unit Impulse Delta Continuous Arrow Unit Impulse Sound Impulse Response Echo Reverberate FIR Filter IIR filter Feedback Notch Filter Finite Impulse Short Feedback Length Infinite Impulse Forever Feedback Length Signal Input Infinite IIR Band Cut Stop Narrow Interference Stem Plot Matlab Low Pass Band Stop Matlab Lollypop Circle Signal Compute Program Language Function Script Filter Rectangle Average Signal Rectangle Filter Edge Detection Image Registration Accumulator Poles High Pass Filter Sharp Segment Boundary Translation Align Shift Change Phase Summation Feedback Unstable Add Unit Step Z Unstable Convergence Finite Zeros

Real Even Symmetric Imaginary Complex Number Imaginary Matrix Vector Odd Symmetric Real Complex Number Vector Matlab Array Number Elements Matrix Matlab Row Column Number Summation Dot Product Omega Inverse Series Add Plus Accumulate Sigma Inner Vector Summation Row Column Greek Letter Hertz Radians One Over Cancel Identity s Hertz Seconds Radians freqz Seconds Omega Unit Hertz Axis Unit Omega Axis Angle Unit Matlab Command Magnitude Plot Digital Continuous Even Odd Sample Analog Watch Fingers Sample Analog Connected Break Symmetry Real Mirror Flip Two Symmetry Imaginary Flip Even Dr. Martin

Rectangle Boxcar Function Sinc Square Filter Cosine Absolute Block Exponential Wave Periodic Trigonometry Loan Magnitude Negative Phase Value Length Diagram Input Function Box LTI (or LSI) Anti-Causal Variant Fourier Series Linear Invariant Property Convolution Past Future Backwards Opposite Change Property Delay Periodic Finite Z BIBO Zeros Eigenfunction IIR Filter Poles Zeros Region Stable Bounded Finite Baggins Poles Z Convergence Roots Null Input Same LTI Bandwidth Harmonic Region of Convergence Lookup Table Spectrum Data Nyquist Amount Multiple Series Higher Wave Z Poles Left-sided Roots Ring Inverse FPGA Register Store Textbook