Chapter 2 System Models – Time Domain

Slides:



Advertisements
Similar presentations
Transformations of Continuous-Time Signals Continuous time signal: Time is a continuous variable The signal itself need not be continuous. Time Reversal.
Advertisements

Digital Filters. A/DComputerD/A x(t)x[n]y[n]y(t) Example:
ECE 8443 – Pattern Recognition ECE 3163 – Signals and Systems Objectives: Review Resources: Wiki: State Variables YMZ: State Variable Technique Wiki: Controllability.
Review of Frequency Domain
Lecture 6: Linear Systems and Convolution
Discrete-Time Convolution Linear Systems and Signals Lecture 8 Spring 2008.
Chapter 4 The Fourier Transform EE 207 Dr. Adil Balghonaim.
Finite Impuse Response Filters. Filters A filter is a system that processes a signal in some desired fashion. –A continuous-time signal or continuous.
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
Digital Signals and Systems
Discrete-Time and System (A Review)
ECE 8443 – Pattern Recognition EE 3512 – Signals: Continuous and Discrete Objectives: First-Order Second-Order N th -Order Computation of the Output Signal.
1 Signals & Systems Spring 2009 Week 3 Instructor: Mariam Shafqat UET Taxila.
Time-Domain Representations of LTI Systems
Time Domain Representation of Linear Time Invariant (LTI).
Chapter 3 Convolution Representation
Time-Domain Representations of LTI Systems
ECE 8443 – Pattern Recognition ECE 3163 – Signals and Systems Objectives: First-Order Second-Order N th -Order Computation of the Output Signal Transfer.
Discrete-time Systems Prof. Siripong Potisuk. Input-output Description A DT system transforms DT inputs into DT outputs.
1 Z-Transform. CHAPTER 5 School of Electrical System Engineering, UniMAP School of Electrical System Engineering, UniMAP NORSHAFINASH BT SAUDIN
Introduction to System Hany Ferdinando Dept. of Electrical Engineering Petra Christian University.
Fourier Analysis of Discrete-Time Systems
BYST CPE200 - W2003: LTI System 79 CPE200 Signals and Systems Chapter 2: Linear Time-Invariant Systems.
Linear Time-Invariant Systems
Course Outline (Tentative) Fundamental Concepts of Signals and Systems Signals Systems Linear Time-Invariant (LTI) Systems Convolution integral and sum.
1 Lecture 1: February 20, 2007 Topic: 1. Discrete-Time Signals and Systems.
Ch.10 Design of Digital Filters and Controllers Discretization The sampled signal x s (t) = x(t) p(t) where p(t) is the sampling pulse signal, with.
3.0 Fourier Series Representation of Periodic Signals 3.1 Exponential/Sinusoidal Signals as Building Blocks for Many Signals.
Chapter 2 Time Domain Analysis of CT System Basil Hamed
Motivation Thus far we have dealt primarily with the input/output characteristics of linear systems. State variable, or state space, representations describe.
Input Function x(t) Output Function y(t) T[ ]. Consider The following Input/Output relations.
ECE 8443 – Pattern Recognition EE 3512 – Signals: Continuous and Discrete Objectives: Causality Linearity Time Invariance Temporal Models Response to Periodic.
Lecture 5: Transfer Functions and Block Diagrams
ES97H Biomedical Signal Processing
1 Time-Domain Representations of LTI Systems CHAPTER 2.11 Characteristics of Systems Described by Differential and Difference Equations and Difference.
Signal & Linear system Chapter 3 Time Domain Analysis of DT System Basil Hamed.
Course Outline (Tentative) Fundamental Concepts of Signals and Systems Signals Systems Linear Time-Invariant (LTI) Systems Convolution integral and sum.
EE 207 Dr. Adil Balghonaim Chapter 4 The Fourier Transform.
Time Domain Representation of Linear Time Invariant (LTI).
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.
Linear Constant-Coefficient Difference Equations
Ch. 2 Time-Domain Models of Systems Kamen and Heck.
Signals and Systems Lecture #6 EE3010_Lecture6Al-Dhaifallah_Term3321.
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.
What is filter ? A filter is a circuit that passes certain frequencies and rejects all others. The passband is the range of frequencies allowed through.
Eeng360 1 Chapter 2 Linear Systems Topics:  Review of Linear Systems Linear Time-Invariant Systems Impulse Response Transfer Functions Distortionless.
Description and Analysis of Systems Chapter 3. 03/06/06M. J. Roberts - All Rights Reserved2 Systems Systems have inputs and outputs Systems accept excitation.
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.
Time Domain Representations of Linear Time-Invariant Systems
Linear Constant-Coefficient Difference Equations
Time Domain Representation of Linear Time Invariant (LTI).
Signal Processing First
Lecture 11 FIR Filtering Intro
Properties of LTI Systems
Discrete-time Systems
EE 309 Signal and Linear System Analysis
Signal Processing First
Lecture 12 Linearity & Time-Invariance Convolution
Laplace and Z transforms
Description and Analysis of Systems
UNIT V Linear Time Invariant Discrete-Time Systems
Chapter 2 Systems Defined by Differential or Difference Equations
Signal Processing First
Lecture 22 IIR Filters: Feedback and H(z)
Concept of frequency in Discrete Signals & Introduction to LTI Systems
SIGNALS & SYSTEMS (ENT 281)
Presentation transcript:

Chapter 2 System Models – Time Domain ELEC 2120 Fri. May 17, 2013 Roppel Chapter 2 System Models – Time Domain

Model of a System Mathematical equation that describes the relationship between input x(t) or x[n] and the output y(t) or y[n]. Drawing or diagram that illustrates the input – output relationship Words that define the inputs, outputs, and their relationship.

Time Domain Voltage “Hello” Time

Frequency Domain Voltage “Hello” Frequency

Chapter 2 2.1 – 2.3: Discrete time 2.4 – 2.6: Continuous time

Convolution is Most Important Model Discrete Time: 3 Models Input / Output Convolution Difference Equation Convolution is Most Important Model

Why Convolution is Important Leads most directly to frequency domain. Most signal analysis is performed in the frequency domain. Most system analysis is performed in the frequency domain.

INPUT/OUTPUT REPRESENTATION OF DISCRETE-TIME SYSTEMS N-point moving average filter:

INPUT/OUTPUT REPRESENTATION OF DISCRETE-TIME SYSTEMS Generalization: The weights, wi , define the system.

INPUT/OUTPUT REPRESENTATION OF DISCRETE-TIME SYSTEMS Further Generalization: Any causal linear time-invariant discrete-time system with the input x[n] equal to zero for all n < 0 can be expressed in this form!

UNIT-PULSE RESPONSE Denote by h[n] Set x[n] = δ[n]

UNIT-PULSE RESPONSE The unit-pulse response lets you “see” everything inside the system. It’s like kicking the tires and slamming the doors on a used car to see what rattles.

CONVOLUTION REPRESENTATION Rewriting the I/O equation using h[n] yields CONVOLUTION

CONVOLUTION EXAMPLE More about computing convolution later… >> x=[1 0 0 0]; % input unit pulse >> h=[1 2 3 2 1]; % unit pulse response >> y=conv(x,h); % output >> y y = 1 2 3 2 1 0 0 0 >> More about computing convolution later…

DIFFERENCE EQUATION MODEL (Sect. 2.3) Nth-Order Input/Output Difference Equation: Present output, y[n], depends on previous inputs and outputs, and present input, x[n]. Solve recursively, or find a closed-form solution.

DIFFERENTIAL EQUATION MODELS (Sect. 2.4 / 2.5) R-C circuit example (1st order) Mass-spring-damper example (2nd order) …solve using standard techniques from diff. eq.