LECTURE OBJECTIVES
Signal Processing First
INTRODUCE the Z-TRANSFORM
Give Mathematical Definition
Show how the H(z) POLYNOMIAL simplifies
analysis
Lecture 14
Z Transforms: Introduction
CONVOLUTION is SIMPLIFIED !
Z-Transform can be applied to
FIR Filter:
LECTURE OBJECTIVES
Signal Processing First
INTRODUCE FILTERING IDEA
Weighted Average
Running Average
Lecture 10
FIR Filtering Intro
FINITE IMPULSE RESPONSE FILTERS
FIR
FIR
Filters
Show how to compute the output y[n] from
the input signal, x[n]
12/11/2003
LECTURE OBJECTIVES
Signal Processing First
Two Domains: Time & Frequency
Track the spectrum of x[n] thru an FIR
Filter: Sinusoid-IN gives Sinusoid-OUT
UNIFICATION: How does Frequency
Response affect x(t) to produce y(t) ?
Lecture 13
Digital Filtering
of A
LECTURE OBJECTIVES
Signal Processing First
ZEROS and POLES
Relate H(z) to FREQUENCY RESPONSE
H ( e j ) = H ( z ) z =e j
Lecture 15
Zeros of H(z) and the
Frequency Domain
1/15/2004
THREE DOMAINS:
Show Relationship for FIR:
h[n ] H ( z ) H ( e j )
1/15/2004
LECTURE OBJECTIVES
Signal Processing First
SECOND-ORDER IIR FILTERS
TWO FEEDBACK TERMS
2
y[n] = a1 y[n 1] + a2 y[n 2] + bk x[ n k ]
Lecture 18
3-Domains for IIR
k =0
H(z) can have COMPLEX POLES & ZEROS
THREE-DOMAIN APPROACH
BPFs have POLES NEAR THE UNIT C
ROSE-HULMAN INSTITUTE OF TECHNOLOGY
Department of Electrical and Computer Engineering
ECE 380 Discrete Time Signals and Systems
Sections 01-02
Winter 2015-16
Yong Jin Daniel Kim
Problem Set 6
Due: Jan. 28, 2016
1. Suppose you landed a job at Google (Hoora
ROSE-HULMAN INSTITUTE OF TECHNOLOGY
Department of Electrical and Computer Engineering
ECE 380 Discrete Time Signals and Systems
Sections 01-02
Winter 2015-16
Yong Jin Daniel Kim
Problem Set 7
Due: Feb. 4, 2016
1. Consider a discrete-time LTI system for wh