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Top 12 Matlab projects from matlabsolutions.com

Looking for inspiring MATLAB projects to sharpen your skills or impress in your next assignment? At MATLABSolutions.com , we’ve curated the top 12 MATLAB projects that showcase the power of MATLAB in signal processing, image analysis, machine learning, and more. These hands-on examples, complete with code and explanations, are perfect for beginners and advanced users alike. Dive in and explore the best MATLAB projects to elevate your expertise! Signal Smoothing with Moving Average Filter Master signal processing by smoothing noisy data using MATLAB’s movmean function. This project cleans a synthetic sine wave, teaching you noise reduction basics. Ideal for audio or sensor data analysis. Get the code at MATLABSolutions Projects Image Edge Detection Using Canny Filter Explore image processing with MATLAB’s Canny edge detection algorithm. This project highlights edges in any photo, perfect for computer vision applications. Download the script and try it on your own images! Bitcoin Price ...

Finding Accurate Amplitude Using FFT

 I have been working on code to use the FFT to find the amplitude of a signal. However, despite my best efforts to window I seem to get incorrect amplitudes. When I manually find Amplitude by inspecting the signal I get A = 63.7. When I use the following code I get A = 54.8. This is causing significant errors in my Bode plot.

 
I tried using a flat top window to maximize amplitude accuracy. My signal does not seem significantly noisy. I expected an amplitude much closer to the true value.
 
function Amplitude = Gaindb(x)

 if size(x, 2) > 1
    x = x';
end

 x = x - mean(x);

 N = length(x);
 win = flattopwin(N);

 x = fft(x.*flattopwin(N));

 Amplitude = 2*max(abs(x))/N;
  end



ANSWER



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When I run your code I get 14.5 for the answer, so I am not sure how you are getting 54.8. But the 14.5 is consistent with the Matlab definition of the flattop window. From the documentation, the flattop is a sum of cosines with certain coefficients, along with a constant coefficient a0 = .216. a0, the area of the window, is basically the amplitude reduction factor for the signal.
Without any windowing of x, the fft comes up with a signal amplitude of 67.1, and 67.1 x .216 = 14.5 which is consistent.
The Matlab window peaks out at a value of 1.

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Top 12 Matlab projects from matlabsolutions.com

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