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Write a MATLAB function called pgm which calculates the periodogram. The function inputs should be the length input vector x and its output should be the length N periodogram estimate Pxx. Generate a length N discrete random signal with the randn function and use the pgm to estimate the PSD for N = 128, 256, and 512. Inspect and comment upon the results. The theoretical autocorrelation function for this discrete Gaussian random signal equals σ2δ (i.e. there is zero similarity between the signal and a shifted version of it), where is the standard deviation of the random signal; for the above random signal (see help randn), is equal to 1. Therefore its true PSD is a constant, unity, for all frequencies. Such a signal is referred to as white noise because it has a constant spectrum independent of frequency, as an, albeit approximate, analogy with white light. The difference between this ideal PSD and those estimated with the datasets is due, in part, to their ?nite lengths. One method to improve these estimates is to apply frequency domain smoothing.
- Employ the ?lt?lt command within MATLAB to smooth the PSD estimates with a zero phase FIR ?lter with impulse response sequence 0.2*[1 1 1 1 1]. Does this improve the apparent PSD estimate?
- Generate a length 1024 discrete random signal with the randn function and sub-divide the signal into eight separate 128 point signals. Estimate the PSD for each length 128 signal and display on two plots the eight results (Note: Break each window into 2× 2 sections. Read help ?gure on how to obtain the second window). Notice the variation of the estimates.
- These eight results can be averaged to yield a new PSD estimator called the averaged periodogram which has less variation than the individual PSDs. Display this result.
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Problem 1. Use Matlab to solve the following system of linear equations: 2x + y + 3z = 1 2x + 6y + 8z = 3 6x + 8y + 18z = 5 Capture Matlab code and the result in a text fi
Hold and legend function: hold: is a toggle which freezes the present graph in the figure window, so that the new plots will be superimposed on the present one. Just hold
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Functions with Local Variables: The functions we have seen faraway have been very easy. Though, in many situations the computations in a function are more complex, and may nee
i want to save the values of a particular equation in a for loop.. for ex: for i=1:100 et=(x+yz); end thus how can i store the values of et at each iteration
Variable number of input arguments: For illustration, the below function areafori has a variable number of input arguments, either the 1 or 2. The name of the function stands
Background Protein therapeutics are a major component of the biotechnology industry, with sales estimated in the range of ~$99bn annually (2011) and steady market growth. Many phar
hi i have this programm function [IRN,number ] = randnumbers( IRN ) IRN=int32(IRN) ITOTAL=(IRN*330)+100 ITOTAL=int32(ITOTAL); IQUOTIENT=ITOTAL/2303 IQUOTIENT=int32(IQUOTIENT);
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 a
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