EE213 Lab 7: Convolution [PDF]

The objective of this laboratory is to first investigate MATLAB techniques to evaluate convolutions both numerically and

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EE213 Lab 7: Convolution 2014-03-13/14

1

Objective

The objective of this laboratory is to first investigate MATLAB techniques to evaluate convolutions both numerically and when appropriate, symbolically. Properties of convolution are then investigated using MATLAB: these properties are linearity, time invariance, and association. Finally the results of convolution with special signals such as impulses, unit steps, and complex exponentials are found.

2

Prelab

Use convolution to directly evaluate the following:

a(t) = e

−3t

4t

u(t) b(t) = e u(−t) c(t)=

Yt 4

d(t) = t

Yt 2

(1)

w(t) = a(t) ∗ b(t)

(2)

x(t) = a(t) ∗ c(t)

(3)

y(t) = b(t) ∗ d(t)

(4)

z(t) = b(t) ∗ c(t)

(5)

After plotting your solutions, discuss your results; pay particular attention to the smoothness of signals, the regions where signals are non-zero, and the widths of your solutions.

3 3.1

Experiment Numerical Convolution

The MATLAB script below will approximate and plot the signal c(t) that is the convolution of a signal a(t) with the signal b(t). The signal a(t) is non zero only for times in the interval from goa to stopa; the values of a(t) in this interval every dt time units are first stored in the vector a. Similarly, the signal b(t) is non zero only for times in the interval from gob to stopb; the values of a(t) in this interval every dt time units are first stored in the vector b. The convolution is non zero only for times between goa + gob and stopa + stopb; the values every dt time units are stored in the vector c. 1

dt=.01; goa=0; stopa=1/2; gob=0; stopb=2; ta=goa:dt:stopa; a=exp(-ta); tb=gob:dt:stopb; b=exp(-tb); tc=goa+gob:dt:stopa+stopb; c=dt*conv(a,b); subplot(311),plot(ta,a),xlabel(’t’),ylabel(’a(t)’), subplot(312),plot(tb,b),xlabel(’t’),ylabel(’b(t)’), subplot(313),plot(tc,c),xlabel(’t’),ylabel(’conv(a(t),b(t)’), The MATLAB plots that result from the above script are shown below.

3.1.1

Exercise

Use a variation of this script to plot the solutions to each of the pre-lab problems. Investigate and then discuss the effects of the choice of the sampling interval, dt, as well as the start and stop times for signals that are not zero outside a finite interval. Use a variation of this script to plot each of the outputs of an RC low-pass filter with unity time constant to an input equal to each of the pre-lab signals.

3.2

Symbolic Convolution

The MATLAB Symbolic Toolbox does not contain a convolution command; however the laplace() and ilaplace() commands can be used to evaluate a convolution when each of the signals to be convolved are one-sided. A one-sided signal is zero for all t < 0. The MATLAB symbolic script below convolves the two one sided exponentials of the numeric example when the stop times are infinite. syms t 2

a=exp(-t)*heaviside(t); A=laplace(a); b=ilaplace(A*A)*heaviside(t) The MATLAB output is b = (t*heaviside(t))/exp(t) 3.2.1

Exercise

Use a variation of the script above to verify the solutions of each of the pre-lab problems. Use a variation of the script to find the response of the RC high-pass filter with unity time constant to an input equal to each of the pre-lab signals. For those signals that cannot be evaluated with Laplace transforms, use the int() command to evaluate the convolutions.

3.3

Properties of Convolution

The three MATLAB properties to be considered are association, linearity and time invariance. The formal statements are: • Commutativity: a(t) ∗ b(t) = b(t) ∗ a(t) • Linearity: (a(t) + βb(t)) ∗ c(t) = a(t) ∗ c(t) + βb(t) ∗ c(t) • Time Invariance: if c(t) = a(t) ∗ b(t), then c(t − τ ) = a(t − τ ) ∗ b(t) 3.3.1

Exercise

Verify each of these properties numerically for at least three cases; then verify each of these properties symbolically for at least two cases. Discuss how time invariance can be used to compute symbolic convolutions for signals that are not one sided but are zero for large negative times. Illustrate your procedure with at least two examples. 3.3.2

Special Signals

Three special signals: the impulse, the unit step, and the complex exponential, are easy to convolve. The convolutions are: f (t) ∗ δ(t) = f (t) Zt f (t) ∗ u(t) = f (x)dx

(6) (7)

−∞ st

f (t) ∗ Xe = Xe

st

Z∞

−∞

3

e−sx f (x)dx

(8)

3.3.3

Exercise

If possible verify, numerically and symbolically, each of the convolutions with the three special sgnals for each of the special signals of the prelab.

4

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