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For a statistically stationary environment it would be advantageous to use gear shifting, that is to reduce the adaptation gain with time. To illustrate this, try using a varying adaptation gain and observe the results. Also observe the error. If the error is high, what should the adaptation gain be to reduce it? Try incorporating into your program a subroutine which calculates a suitable adaptation gain for the (k+1) coeffcent update based on the kth error.
Note: to compare different methods for choosing the adaptation gain, always initialise the seed before creating a new vector of random numbers, and assume that the maximum permissible overshoot for each coeffcient is 20% of its true value; then compare the different methods, by considering the rise time in each case. The best method will give the shortest rise time.
The circuit diagram of an active filter is shown below. (a) Determine an expression for the transfer function of the filter. (b) Hence find an expression for the frequency res
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matlab code for path and adjancy martix between pixels
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