EE3TP4_20_CT_IdealFilters_Lecture 28

EE3TP4_20_CT_IdealFilters_Lecture 28 - 5.3 Ideal Filters...

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5.3 Ideal Filters Often we have a scenario where we have a “good” signal, x g ( t ), corrupted by a “bad” signal, x b ( t ), and we want to use an LTI system to remove (or filter out) the bad signal, leaving only the good signal. How do we do this? What H ( ϖ ) do we want? h ( t ) H ( ) x t = x g t  x b t y t = x g t “Filter” Desired Output These overheads were originally developed by Mark Fowler at Binghamton University, State University of New York.
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X g ω ∣ − ω X b ω ∣ Case #1: is a low-frequency signal x g t is a high-frequency signal x b t X ω ∣ Spectrum of the Input Signal In this case, we want a filter like this: H ω ∣ H ω ∣= { 1, − ω  0, otherwise Mathematically: “Passband” “Stopband” ω − − ω
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( ϖ ) X ω Y ω = X ω H ω Then: Y ω ∣=∣ H ω
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This note was uploaded on 02/01/2011 for the course ECE 3TP4 taught by Professor Drterrencetodd during the Fall '10 term at McMaster University.

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EE3TP4_20_CT_IdealFilters_Lecture 28 - 5.3 Ideal Filters...

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