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Use the MATLAB randn function to generate 1000 points for x. Generate the output of the unknown system with the ?lter function and b=[1232 1] and a=[1]. Normalise the ?lter output so that its variance is unity, i.e. y = y./sqrt((sum(b.*b)); call the randn function again to generate 1000 points for the measurement noise, scale the values by 0.1 and add them to [ ], and calculate the Signal-to-Noise Ratio (SNR) in dB for y[k] (The power of zero mean white noise is 2 ; when a noise signal is scaled its standard deviation, i.e , gets scaled by the same factor).
- Use the xcorr function to estimate the cross-correlation and autocor relation elements to form Rxx and Pzx.
- Solve for the optimum Wiener ?lter. Is it close to that of the unknown system?
- Repeat the experiment by varying the scaling applied to the additive noise to 1.0 and 10.0, re-calculate the SNR for each case. What is the effect upon the Wiener solution? What happens if w is assumed to be greater than 4?
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Illustration of script: For illustration, we will now generate a script known as script1.m which calculates area of the circle. It assigns a value for the radius, and then com
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Write a script to determine the volume of a pyramid, which is 1/3 * base * height, where the base is length * width. On time the user to enter values for the length, width, and th
An FIR filter has coefficients b = [ 1.0000 -0.6387 1.0214 0.8210 -0.7470 1.0920 ] (a) Find H(z) for the filter and plot its frequency response (magnitude and phase
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Hello sir, I am Bharat Prajapati.I am working on Copy-move forgery detection using 2D-DWT transform.How to code in matlab to detect copy-move forgery in the given image.Please help
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