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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?
The following four 4 equations are needed to be plotted as CONTOUR lines on the axes space (C, T) where C is the horizontal axis, and T is the vertical axis. Contour lines for
Your task is to implement the PHYSAT algorithm in Matlab to classify the phytoplankton species in the data you have selected. An algorithm demonstrating one solution is provided b
Micro-mouse is an engineering design competition created by IEEE in the late 1970s. Small robotic "mice" solve a 16x16 cell maze. The mice are completely autonomous and must find t
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Code and decode a 32 bit long data for a specific user in CDMA if there is only 1 user and no noise.
An environmental consulting firm is conducting a site investigation on an abandoned industrial site that is 200 m by 150 m in size (Fig. 1). A number of piezometers were installed
# how can I convert class of a number
Variable number of output arguments: The variable number of output arguments can also be identified. For illustration, the one input argument is passed to the below function t
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