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102_1_lecture6

102_1_lecture6 - UCLA Fall 2010 Systems and Signals Lecture...

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UCLA Fall 2010 Systems and Signals Lecture 6: Convolution October 13, 2010 EE102: Systems and Signals; Fall 2010, Jin Hyung Lee 1
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Agenda Homework 2 due in class at 10:00 am. During grace period (after class and before 5:00 pm): drop in the box in Engi IV 64-144 (TA Office Hour room) Midterm date changed to: November 1, in class, 2 hours EE102: Systems and Signals; Fall 2010, Jin Hyung Lee 2
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Convolution Evaluation and Properties Today’s topics: Review: Convolution Representation of convolution Graphical interpretation Examples Properties of convolution EE102: Systems and Signals; Fall 2010, Jin Hyung Lee 3
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Response of LTI System A linear system is completely characterized by its impulse response h ( t, τ ) . If the system is also time invariant, the impulse response is only a function of t , and is written h ( t ) . For a linear system with an input signal x ( t ) , the output is given by the superposition integral y ( t ) = Z -∞ x ( τ ) h ( t, τ ) If the system is also time invariant, the superposition integral simplifies to y ( t ) = Z -∞ x ( τ ) h ( t - τ ) = Z -∞ x ( t - τ ) h ( τ ) which is in the form of a convolution integral . EE102: Systems and Signals; Fall 2010, Jin Hyung Lee 4
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Convolution Integral The convolution of an input signal x ( t ) with and impulse response h ( t ) is y ( t ) = Z -∞ x ( τ ) h ( t - τ ) = ( x * h )( t ) or y = x * h. This is also often written as y ( t ) = x ( t ) * h ( t ) EE102: Systems and Signals; Fall 2010, Jin Hyung Lee 5
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Convolution Integral for Causal Systems For a causal system h ( t ) = 0 for t < 0 , y ( t ) = Z -∞ x ( τ ) h ( t - τ ) | {z } =0 if t - τ< 0 = y ( t ) = Z t -∞ x ( τ ) h ( t - τ ) Only past and present values of x ( τ ) contribute to y ( t ) . EE102: Systems and Signals; Fall 2010, Jin Hyung Lee 6
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