Extra Credit ECE Signals and Systems Exam II By: Joseph Cunningham

Slides:

Advertisements

Similar presentations

Digital Filters. A/DComputerD/A x(t)x[n]y[n]y(t) Example:

Advertisements

Block Convolution: overlap-save method  Input Signal x[n]: arbitrary length  Impulse response of the filter h[n]: lenght P  Block Size: N  we take.

Discrete-Time Linear Time-Invariant Systems Sections

EECS 20 Chapter 5 Part 21 Linear Infinite State Systems Last time we Looked at systems with an infinite number of states (Reals N ) Examined property of.

Lecture 4: Linear Systems and Convolution

EE-2027 SaS, L11 1/13 Lecture 11: Discrete Fourier Transform 4 Sampling Discrete-time systems (2 lectures): Sampling theorem, discrete Fourier transform.

Signals and Systems Lecture #5

Discrete-Time Convolution Linear Systems and Signals Lecture 8 Spring 2008.

Difference Equations and Stability Linear Systems and Signals Lecture 10 Spring 2008.

Difference Equations Linear Systems and Signals Lecture 9 Spring 2008.

Discrete-time Systems Prof. Siripong Potisuk. Input-output Description A DT system transforms DT inputs into DT outputs.

Analysis of Discrete Linear Time Invariant Systems

1 Signals & Systems Spring 2009 Week 3 Instructor: Mariam Shafqat UET Taxila.

Time Domain Representation of Linear Time Invariant (LTI).

Chapter 3 Convolution Representation

EE313 Linear Systems and Signals Fall 2010 Initial conversion of content to PowerPoint by Dr. Wade C. Schwartzkopf Prof. Brian L. Evans Dept. of Electrical.

Lecture 6: DFT XILIANG LUO 2014/10. Periodic Sequence  Discrete Fourier Series For a sequence with period N, we only need N DFS coefs.

Fourier Analysis of Signals and Systems

Linear Time-Invariant Systems Quote of the Day The longer mathematics lives the more abstract – and therefore, possibly also the more practical – it becomes.

Technological Educational Institute Of Crete Department Of Applied Informatics and Multimedia Neural Networks Laboratory Slide 1 DISCRETE SIGNALS AND SYSTEMS.

Dr. Engr. Sami ur Rahman Assistant Professor Department of Computer Science University of Malakand Visualization in Medicine Lecture: Convolution.

Signals and Systems Lecture 19: FIR Filters. 2 Today's lecture −System Properties:  Linearity  Time-invariance −How to convolve the signals −LTI Systems.

Signal and System I The representation of discrete-time signals in terms of impulse Example.

1 LTI Systems; Convolution September 4, 2000 EE 64, Section 1 ©Michael R. Gustafson II Pratt School of Engineering.

3/18/20161 Linear Time Invariant Systems Definitions A linear system may be defined as one which obeys the Principle of Superposition, which may be stated.

Signal and systems LTI Systems. Linear Time-Invariant Systems.

Analysis of Linear Time Invariant (LTI) Systems

1 Computing the output response of LTI Systems. By breaking or decomposing and representing the input signal to the LTI system into terms of a linear combination.

Chapter 2 System Models – Time Domain

Digital Signal Processing Lecture 3 LTI System

Digital Signal Processing

Homework 3 1. Suppose we have two four-point sequences x[n] and h[n] as follows: (a) Calculate the four-point DFT X[k]. (b) Calculate the four-point DFT.

Signal Processing First

Digital Signal Processing

Lecture 11 FIR Filtering Intro

Properties of LTI Systems

Lect2 Time Domain Analysis

Discrete-time Systems

Linear Time Invariant Systems

Lecture 12 Linearity & Time-Invariance Convolution

Laplace and Z transforms

EE Audio Signals and Systems

Outline Linear Shift-invariant system Linear filters

Neural Networks & MPIC.

Lecture 4: Discrete-Time Systems

UNIT V Linear Time Invariant Discrete-Time Systems

Neural Networks & MPIC.

Reconstruction of Bandlimited Signal From Samples

CS3291: "Interrogation Surprise" on Section /10/04

2 Linear Time-Invariant Systems --Analysis of Signals and Systems in time-domain An arbitrary signal can be represented as the supposition of scaled.

2. Linear Time-Invariant Systems

Design Specification Document

ВОМР Подмярка 19.2 Възможности за финансиране

Споразумение за партньорство

FLIPPED CLASSROOM on Discrete time signal processing - II

Signals & Systems (CNET - 221) Chapter-2 Introduction to Systems

Signal Processing First

Discrete Convolution of Two Signals

Signals & Systems (CNET - 221) Chapter-2

Signals & Systems (CNET - 221) Chapter-3 Linear Time Invariant System

Lecture 22 IIR Filters: Feedback and H(z)

Concept of frequency in Discrete Signals & Introduction to LTI Systems

Convolution sum.

Today's lecture LTI Systems characteristics Cascade LTI Systems

Signals and Systems Lecture 18: FIR Filters.

Lecture 4: Linear Systems and Convolution

copyright Robert J. Marks II

Convolution sum & Integral

Lecture 3 Discrete time systems

Presentation transcript:

Extra Credit ECE-3163 Signals and Systems Exam II By: Joseph Cunningham

Problem 5.45(a)‏ y[n+1] + 0.9 y[n] = 1.9x[n+1] h[n+1] + 0.9h[n] = 1.9x[n+1] (1.9)(-0.9)^(n+1)u[n+1] + (1.9)(-0.9)^(n)(.9)u[n] = 1.9(-0.9)^(n+1)(u[n+1] – u[n])‏ (1.9)d(n+1)‏

Problem 5.45(c)‏ x[n] = 1 + sin(pi(n)/4) + sin(pi(n)/2)‏ y[n] = h[n] + h[n-1] + h[n-2] = 1.9((-0.9)^(n)u[n] +(-0.9)^(n-1)u[n+1]+ (-0.9)^(n-2)u[n-2])‏

Explanation I still have not grasp this subject effective enough to theoretically break down this problem, but I will explain what I can follow. An output response y[n] in a discrete-time system is equal to the convolution of the input with the impulse response h[n]. y[n] = x[n] * h[n] Y(W) = X(W)H(W)‏