![]() Signal Processing BasicsĪlso useful to have would be some knowledge of signal processing, such as a basic grasp of the Fourier Transform and the Nyquist rate (see Intro). * (for a refresher check out Matlab's own refresher page). It's important to know the difference between ' and. Since we will be using Matlab, linear algebra is always useful, particularly the way matrices are treated. Here are a few areas of which prior knowledge may be useful: Linear Algebra/Matrix Algebra I am writing this article in order to not only enlighten others on CS but to build my own understanding of it. When I first heard about "compressed sensing" I found it very interesting but definitely didn't have a solid grasp. The purpose of compressed sensing is to allow us to obtain fewer than the previously required amount of samples while still perfectly (or nearly perfectly) recovering the signal. In order to capture all of the relevant information, it must be sampled at or above 100 Hz. Consider a signal that is the sum of several signals (perhaps simple sinusoids), where the highest frequency sinusoid is 50 Hz. It works for sparse signals and has a few restrictions which we will get into.įor those familiar with the Nyquist rate, it states that in order to obtain all relevant information in a signal, the sampling rate must be at least 2 times the bandwidth of the signal. Compressed sensing (CS) is a relatively new technique in the signal processing field which allows acquiring signals while taking few samples.
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