Lecture 14 Digital Filtering of Analog Signals

Slides:



Advertisements
Similar presentations
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.
Advertisements

CEN352, Dr. Ghulam Muhammad King Saud University
Systems: Definition Filter
8/27/2015 © 2003, JH McClellan & RW Schafer 1 Signal Processing First Lecture 8 Sampling & Aliasing.
EE513 Audio Signals and Systems Digital Signal Processing (Systems) Kevin D. Donohue Electrical and Computer Engineering University of Kentucky.
Prof. Nizamettin AYDIN Digital Signal Processing 1.
(Lecture #08)1 Digital Signal Processing Lecture# 8 Chapter 5.
Prof. Nizamettin AYDIN Digital Signal Processing 1.
1 Prof. Nizamettin AYDIN Digital Signal Processing.
Signals & Systems Lecture 13: Chapter 3 Spectrum Representation.
Fundamentals of Digital Signal Processing. Fourier Transform of continuous time signals with t in sec and F in Hz (1/sec). Examples:
INC 341PT & BP INC341 Frequency Response Method Lecture 11.
1 Today's lecture −Cascade Systems −Frequency Response −Zeros of H(z) −Significance of zeros of H(z) −Poles of H(z) −Nulling Filters.
1 Prof. Nizamettin AYDIN Digital Signal Processing.
1 Prof. Nizamettin AYDIN Digital Signal Processing.
DISP 2003 Lecture 5 – Part 1 Digital Filters 1 Frequency Response Difference Equations FIR versus IIR FIR Filters Properties and Design Philippe Baudrenghien,
DSP First, 2/e Lecture 11 FIR Filtering Intro. May 2016 © , JH McClellan & RW Schafer 2 License Info for DSPFirst Slides  This work released.
DSP First, 2/e Lecture 7 Fourier Series Analysis.
DSP First, 2/e Lecture 5 Spectrum Representation.
DSP First, 2/e Lecture 6 Periodic Signals, Harmonics & Time-Varying Sinusoids.
DSP First, 2/e Lecture 18 DFS: Discrete Fourier Series, and Windowing.
DSP-First, 2/e LECTURE #3 Complex Exponentials and Complex Numbers.
DSP First, 2/e LECTURE #1 Sinusoids Aug © , JH McClellan & RW Schafer.
DSP First, 2/e Lecture 16 DTFT Properties. June 2016 © , JH McClellan & RW Schafer 2 License Info for DSPFirst Slides  This work released under.
Prof. Brian L. Evans Dept. of Electrical and Computer Engineering The University of Texas at Austin EE445S Real-Time Digital Signal Processing Lab Spring.
Lecture 19 Spectrogram: Spectral Analysis via DFT & DTFT
Signals & systems Ch.3 Fourier Transform of Signals and LTI System
Lecture 6 Periodic Signals, Harmonics & Time-Varying Sinusoids
Fourier Series Prof. Brian L. Evans
Sampling and Aliasing Prof. Brian L. Evans
Signal Processing First
Example 1: Voltage on a capacitor as a function of time.
Digital Signal Processing
Lecture 11 FIR Filtering Intro
EEE422 Signals and Systems Laboratory
Periodic Signals Prof. Brian L. Evans
LECTURE #3 Complex Exponentials & Complex Numbers
Digital Signal Processing Lecture 4 DTFT
Signal Processing First
Lecture 12 Linearity & Time-Invariance Convolution
Lecture 5 Spectrum Representation
Recap: Chapters 1-7: Signals and Systems
Sampling and Reconstruction
Signals and Systems Lecture 20
EE Audio Signals and Systems
Sinusoids: continuous time
Analogue filtering UNIVERSITY of MANCHESTER School of Computer Science
Lecture 10 Digital to Analog (D-to-A) Conversion
Lecture 9 Sampling & Aliasing
Lecture 8 Fourier Series & Spectrum
LECTURE #2 Phase & Time-Shift Delay & Attenuation
Advanced Digital Signal Processing
Lecture 16a FIR Filter Design via Windowing
Lecture 13 Frequency Response of FIR Filters
Lecture 18 DFS: Discrete Fourier Series, and Windowing
Lecture 17 DFT: Discrete Fourier Transform
Lecture 15 DTFT: Discrete-Time Fourier Transform
LECTURE #4 Phasor Addition Theorem
Signal Processing First
Lecture 7C Fourier Series Examples: Common Periodic Signals
Lecture 20 Z Transforms: Introduction
Signal Processing First
CEN352, Dr. Ghulam Muhammad King Saud University
Chapter 9 Advanced Topics in DSP
Lecture 22 IIR Filters: Feedback and H(z)
Lecture 5A: Operations on the Spectrum
Today's lecture System Implementation Discrete Time signals generation
Signal Processing First
Lecture 24 Time-Domain Response for IIR Systems
Lecture 21 Zeros of H(z) and the Frequency Domain
Presentation transcript:

Lecture 14 Digital Filtering of Analog Signals DSP First, 2/e Lecture 14 Digital Filtering of Analog Signals

© 2003-2016, JH McClellan & RW Schafer READING ASSIGNMENTS This Lecture: Chapter 6, Sections 6-6, 6-7 & 6-8 Next Lecture: Chapter 7 (DTFT) Aug 2016 © 2003-2016, JH McClellan & RW Schafer

© 2003-2016, JH McClellan & RW Schafer LECTURE OBJECTIVES Two Domains: Time & Frequency Track the spectrum of x[n] thru an FIR Filter: Sinusoid-IN gives Sinusoid-OUT UNIFICATION: How does the Frequency Response affect x(t) to produce y(t) ? D-to-A A-to-D x(t) y(t) y[n] x[n] FIR Aug 2016 © 2003-2016, JH McClellan & RW Schafer

© 2003-2016, JH McClellan & RW Schafer TIME & FREQUENCY FIR DIFFERENCE EQUATION is the TIME-DOMAIN Aug 2016 © 2003-2016, JH McClellan & RW Schafer

FIRST DIFFERENCE SYSTEM y[n] x[n] y[n] x[n] Aug 2016 © 2003-2016, JH McClellan & RW Schafer

© 2003-2016, JH McClellan & RW Schafer Ex: DELAY by 2 SYSTEM y[n] x[n] y[n] x[n] Aug 2016 © 2003-2016, JH McClellan & RW Schafer

© 2003-2016, JH McClellan & RW Schafer DELAY by 2 SYSTEM x[n] y[n] k = 2 ONLY y[n] x[n] Aug 2016 © 2003-2016, JH McClellan & RW Schafer

GENERAL DELAY PROPERTY ONLY ONE non-ZERO TERM for k at k = nd Aug 2016 © 2003-2016, JH McClellan & RW Schafer

© 2003-2016, JH McClellan & RW Schafer FREQ DOMAIN  TIME ?? START with y[n] x[n] y[n] x[n] Aug 2016 © 2003-2016, JH McClellan & RW Schafer

FREQ DOMAIN --> TIME EULER’s Formula Aug 2016 © 2003-2016, JH McClellan & RW Schafer

PREVIOUS LECTURE REVIEW SINUSOIDAL INPUT SIGNAL OUTPUT has SAME FREQUENCY DIFFERENT Amplitude and Phase FREQUENCY RESPONSE of FIR MAGNITUDE vs. Frequency PHASE vs. Freq PLOTTING MAG PHASE Aug 2016 © 2003-2016, JH McClellan & RW Schafer

© 2003-2016, JH McClellan & RW Schafer FREQ. RESPONSE PLOTS DENSE GRID (ww) from -p to +p ww = -pi:(pi/100):pi; HH = freqz(bb,1,ww) VECTOR bb contains Filter Coefficients SP-First: HH = freekz(bb,1,ww) Aug 2016 © 2003-2016, JH McClellan & RW Schafer

© 2003-2016, JH McClellan & RW Schafer PLOT of FREQ RESPONSE Aug 2016 © 2003-2016, JH McClellan & RW Schafer

© 2003-2016, JH McClellan & RW Schafer EXAMPLE 6.2 y[n] x[n] Aug 2016 © 2003-2016, JH McClellan & RW Schafer

© 2003-2016, JH McClellan & RW Schafer PLOT of FREQ RESPONSE RESPONSE at p/3 Aug 2016 © 2003-2016, JH McClellan & RW Schafer

© 2003-2016, JH McClellan & RW Schafer EXAMPLE 6.2 (answer) Aug 2016 © 2003-2016, JH McClellan & RW Schafer

© 2003-2016, JH McClellan & RW Schafer EXAMPLE: COSINE INPUT y[n] x[n] Aug 2016 © 2003-2016, JH McClellan & RW Schafer

EX: COSINE INPUT (ans-1) Aug 2016 © 2003-2016, JH McClellan & RW Schafer

EX: COSINE INPUT (ans-2) Aug 2016 © 2003-2016, JH McClellan & RW Schafer

© 2003-2016, JH McClellan & RW Schafer SINUSOID thru FIR IF Multiply the Magnitudes Add the Phases When bk’s are real valued Aug 2016 © 2003-2016, JH McClellan & RW Schafer

DLTI Demo with Sinusoids = y[n] FILTER x[n] Aug 2016 © 2003-2016, JH McClellan & RW Schafer

© 2003-2016, JH McClellan & RW Schafer DIGITAL “FILTERING” D-to-A A-to-D x(t) y(t) y[n] x[n] SPECTRUM of x(t) (SUM of SINUSOIDS) SPECTRUM of x[n] (ALIASING a PROBLEM?) SPECTRUM y[n] (FIR Gain or Nulls) Then, OUTPUT y(t) = SUM of SINUSOIDS Question: how to characterize the effects of a digital filter on the analog signals? Answer: Frequency Scaling Aug 2016 © 2003-2016, JH McClellan & RW Schafer

© 2003-2016, JH McClellan & RW Schafer FREQUENCY SCALING D-to-A A-to-D x(t) y(t) y[n] x[n] TIME SAMPLING: IF NO ALIASING: FREQUENCY SCALING Aug 2016 © 2003-2016, JH McClellan & RW Schafer

11-pt Running Sum Example D-to-A A-to-D x(t) y(t) y[n] x[n] 250 Hz 25 Hz ? Aug 2016 © 2003-2016, JH McClellan & RW Schafer

© 2003-2016, JH McClellan & RW Schafer D-A FREQUENCY SCALING D-to-A A-to-D x(t) y(t) y[n] x[n] TIME SAMPLING: RECONSTRUCT up to 0.5fs FREQUENCY SCALING Aug 2016 © 2003-2016, JH McClellan & RW Schafer

© 2003-2016, JH McClellan & RW Schafer TRACK the FREQUENCIES D-to-A A-to-D x(t) y(t) y[n] x[n] 0.5p .05p 0.5p .05p 250 Hz 25 Hz 250 Hz 25 Hz Fs = 1000 Hz NO new freqs Aug 2016 © 2003-2016, JH McClellan & RW Schafer

© 2003-2016, JH McClellan & RW Schafer 11-pt Running Sum NULLS or ZEROS Aug 2016 © 2003-2016, JH McClellan & RW Schafer

EVALUATE Freq. Response Aug 2016 © 2003-2016, JH McClellan & RW Schafer

EVALUATE Freq. Response MAG SCALE fs = 1000 PHASE CHANGE Aug 2016 © 2003-2016, JH McClellan & RW Schafer

© 2003-2016, JH McClellan & RW Schafer DIGITAL FILTER EFFECTIVE RESPONSE LOW-PASS FILTER Aug 2016 © 2003-2016, JH McClellan & RW Schafer

© 2003-2016, JH McClellan & RW Schafer FILTER TYPES LOW-PASS FILTER (LPF) BLURRING ATTENUATES HIGH FREQUENCIES HIGH-PASS FILTER (HPF) SHARPENING for IMAGES BOOSTS THE HIGHS REMOVES DC BAND-PASS FILTER (BPF) Aug 2016 © 2003-2016, JH McClellan & RW Schafer

© 2003-2016, JH McClellan & RW Schafer B & W IMAGE Aug 2016 © 2003-2016, JH McClellan & RW Schafer

© 2003-2016, JH McClellan & RW Schafer ROW of B&W IMAGE BLACK = 255 WHITE = 0 Aug 2016 © 2003-2016, JH McClellan & RW Schafer

© 2003-2016, JH McClellan & RW Schafer FILTERED ROW of IMAGE Original is gray Filtered is orange DELAY adjusted by 5 samples Aug 2016 © 2003-2016, JH McClellan & RW Schafer

IMAGE with COSINE ADDED FILTERED with 11-pt running average across horizontal dimension  yields horizontal blur Aug 2016 © 2003-2016, JH McClellan & RW Schafer

© 2003-2016, JH McClellan & RW Schafer FILTERED B&W IMAGE Horizonal Blur only Aug 2016 © 2003-2016, JH McClellan & RW Schafer

© 2003-2016, JH McClellan & RW Schafer FILTER ROWS & COLUMNS Blur in both dimensions Aug 2016 © 2003-2016, JH McClellan & RW Schafer