Digital Signal Processing Lecture 3 LTI System

Slides:



Advertisements
Similar presentations
Discrete-Time Linear Time-Invariant Systems Sections
Advertisements

MM3FC Mathematical Modeling 3 LECTURE 3
EE-2027 SaS, L11 1/13 Lecture 11: Discrete Fourier Transform 4 Sampling Discrete-time systems (2 lectures): Sampling theorem, discrete Fourier transform.
Discrete-Time Convolution Linear Systems and Signals Lecture 8 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
Discrete-Time and System (A Review)
Time-Domain Representations of LTI Systems
Time Domain Representation of Linear Time Invariant (LTI).
DISCRETE-TIME SIGNALS and SYSTEMS
Time-Domain Representations of LTI Systems
Prof. Nizamettin AYDIN Digital Signal Processing 1.
CHAPTER 6 Digital Filter Structures
Discrete-time Systems Prof. Siripong Potisuk. Input-output Description A DT system transforms DT inputs into DT outputs.
Fourier Analysis of Discrete-Time Systems
Linear Time-Invariant Systems
1 Lecture 1: February 20, 2007 Topic: 1. Discrete-Time Signals and Systems.
Department of Electrical and Computer Engineering Brian M. McCarthy Department of Electrical & Computer Engineering Villanova University ECE8231 Digital.
Linear Time-Invariant Systems Quote of the Day The longer mathematics lives the more abstract – and therefore, possibly also the more practical – it becomes.
EEE 503 Digital Signal Processing Lecture #2 : EEE 503 Digital Signal Processing Lecture #2 : Discrete-Time Signals & Systems Dr. Panuthat Boonpramuk Department.
ES97H Biomedical Signal Processing
Course Outline (Tentative) Fundamental Concepts of Signals and Systems Signals Systems Linear Time-Invariant (LTI) Systems Convolution integral and sum.
Time Domain Representation of Linear Time Invariant (LTI).
Technological Educational Institute Of Crete Department Of Applied Informatics and Multimedia Neural Networks Laboratory Slide 1 DISCRETE SIGNALS AND SYSTEMS.
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.
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.
Signals and Systems Analysis NET 351 Instructor: Dr. Amer El-Khairy د. عامر الخيري.
Signals and Systems Lecture #6 EE3010_Lecture6Al-Dhaifallah_Term3321.
Description and Analysis of Systems Chapter 3. 03/06/06M. J. Roberts - All Rights Reserved2 Systems Systems have inputs and outputs Systems accept excitation.
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.
Finite Impuse Response Filters. Filters A filter is a system that processes a signal in some desired fashion. –A continuous-time signal or continuous.
Digital Signal Processing
Prepared by:D K Rout DSP-Chapter 2 Prepared by  Deepak Kumar Rout.
Chapter 2 The z-transform and Fourier Transforms The Z Transform The Inverse of Z Transform The Prosperity of Z Transform System Function System Function.
In summary If x[n] is a finite-length sequence (n  0 only when |n|
EENG 420 Digital Signal Processing Lecture 2.
Chapter 4 Structures for Discrete-Time System Introduction The block diagram representation of the difference equation Basic structures for IIR system.
Digital Signal Processing Lecture 9 Review of LTI systems
Structures for Discrete-Time Systems
Digital Signal Processing
Properties of LTI Systems
Lect2 Time Domain Analysis
CE Digital Signal Processing Fall Discrete-time Fourier Transform
CEN352 Dr. Nassim Ammour King Saud University
Discrete-time Systems
Digital Signal Processing Lecture 4 DTFT
Lecture 12 Linearity & Time-Invariance Convolution
Discrete-Time Structure
Laplace and Z transforms
Lecture 4: Discrete-Time Systems
UNIT V Linear Time Invariant Discrete-Time Systems
Lecture 13 Frequency Response of FIR Filters
CS3291: "Interrogation Surprise" on Section /10/04
2. Linear Time-Invariant Systems
Digital Signal Processing
Signals & Systems (CNET - 221) Chapter-2 Introduction to Systems
Signal Processing First
Zhongguo Liu Biomedical Engineering
LECTURE 05: CONVOLUTION OF DISCRETE-TIME SIGNALS
Lecture 22 IIR Filters: Feedback and H(z)
Concept of frequency in Discrete Signals & Introduction to LTI Systems
Extra Credit ECE Signals and Systems Exam II By: Joseph Cunningham
Today's lecture LTI Systems characteristics Cascade LTI Systems
Signals and Systems Lecture 18: FIR Filters.
Digital Signal Processing
Lecture 3 Discrete time systems
Presentation transcript:

Digital Signal Processing Lecture 3 LTI System add slide for format types , d , f, c etc Dr. Shoab Khan

Applications

Convolution in the time domain: y[n] = 2 –3 3 3 –6 0 1 0 0

Convolution

Useful Summation

Convolution

Stability

Causality

Causality & Stability- Example

Difference Equation For all computationally realizable LTI systems, the input and output satisfy a difference equation of the form This leads to the recurrence formula which can be used to compute the “present” output from the present and M past values of the input and N past values of the output

Linear Constant-Coefficient Difference(LCCD) Equations

Linear Constant-Coefficient Difference (LCCD) Equations…( Continued)

Linear Constant-Coefficient Difference (LCCD) Equations….( Continued)

First-Order Example Consider the difference equation y[n] =ay[n−1] +x[n] We can represent this system by the following block diagram:

Exponential Impulse Response With initial rest conditions, the difference Equation has impulse response y[n] =ay[n−1] +x[n] h[n] =anu[n]

Linear Constant-Coefficient Difference (LCCD) Equations….( Continued)

Digital Filter Y = FILTER(B,A,X) filters the data in vector X with the filter described by vectors A and B to create the filtered data Y. The filter is a "Direct Form II Transposed" implementation of the standard difference equation: a(1)*y(n) = b(1)*x(n) + b(2)*x(n-1) + ... + b(nb+1)*x(n-nb) - a(2)*y(n-1) - ... - a(na+1)*y(n-na) [Y,Zf] = FILTER(B,A,X,Zi) gives access to initial and final conditions, Zi and Zf, of the delays.

LTI summary