Signal Processing with Missing Data: Reconstruction Techniques in MATLAB
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Signal Processing with Missing Data: Reconstruction Techniques in MATLAB
Welcome to this quick MATLAB Signal Processing example: Processing a Signal with Missing Samples. We're looking at a person's daily weight data from the leap year 2012. With 366 days, 27 readings are missing—shown as NaN values. First, we convert pounds to kilograms and count the gaps: 27 missing out of 366. To fix this, we interpolate the ...
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This simulation demonstrates how varying the sampling rate impacts the fidelity of signal reconstruction in digital systems. At the top, a sine wave is sampled and reconstructed using different ADC (Analog-to-Digital Converter) and DAC (Digital-to-Analog Converter) clock rates. As the sampling rate decreases, the reconstructed signal becomes less accurate, showing clear distortion. This highlights the importance of choosing an appropriate sampling rate—typically governed by the Nyquist theorem—t
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This simulation demonstrates how varying the sampling rate impacts
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