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How are the convolution of signals represented?
Explanation: Convolution is considered in case of both continuous time and discrete time systems. In continuous time it is represented by x(t)*h(t) and in discrete time as x[n]*h[n], x is input and h is the response in both cases. This is in case of both continuous and discrete time signals.
How do you find convolution signals?
Steps for convolution
- Take signal x1t and put t = p there so that it will be x1p.
- Take the signal x2t and do the step 1 and make it x2p.
- Make the folding of the signal i.e. x2−p.
- Do the time shifting of the above signal x2[-p−t]
- Then do the multiplication of both the signals. i.e. x1(p). x2[−(p−t)]
What is the shape of the convolution of two rectangular pulses?
As a simple graphical illustration of the defining integral, they considered the following two rectangular pulses: This figure is redrawn from [1, p. 59]. The shaded areas are the overlap areas as a function of the shift, t, and the resulting convolution has a trapezoidal shape.
How is the rectangular function of the impulse response?
As the rectangular function of the impulse response has only two levels as does the input. How can the system ever produce a value that is between these levels let alone a ramp of values between these levels. It’s obvious I am missing something but I cant quite put my finger on what that is.
How is the convolution integral of X1 and x2 defined?
A convolution integral is an overlap integral, i.e., for any given shift of the two aperiodic functions being convolved, the convolution integral is simply the overlap area. McGillem and Cooper [1, p. 58] defined the convolution integral of x1 and x2 as
Is the convolution integral the overlap integral?
It’s obvious I am missing something but I cant quite put my finger on what that is. A convolution integral is an overlap integral, i.e., for any given shift of the two aperiodic functions being convolved, the convolution integral is simply the overlap area.