By Andre Quinquis
This e-book makes use of MATLAB as a computing device to discover conventional DSP themes and resolve difficulties. This enormously expands the diversity and complexity of difficulties that scholars can successfully research in sign processing classes. quite a few labored examples, computing device simulations and functions are supplied, in addition to theoretical points which are crucial so one can achieve an exceptional knowing of the most subject matters. training engineers can also locate it worthwhile as an introductory textual content at the topic.
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Extra resources for Digital Signal Processing Using Matlab
The main drawback of this type of filter is the residual aliasing due to the stopband ripple. The higher the order of the anti-aliasing filter, the closer the sampling frequency υs can be to 2υm. There is therefore a trade-off to find between the sampling frequency decrease toward the theoretical limit 2υm and the required anti-aliasing filter complexity. 5. The goal of this exercise is to analyze the properties of some basic discrete-time signals. First generate a sinusoidal signal on 1,000 points and represent it on 200 samples.
The discrete-time signal obtained by sampling the continuous-time one will then account for all its variations. It is always necessary to use an anti-aliasing filter before the sampling stage in order to avoid any spectral aliasing risk and to set an appropriate sampling frequency. In practice, a causal approximation of this ideal filter is used. Thus, depending on the chosen filter synthesis method, some imperfections are introduced, such as a passband amplitude ripple, a transition band and a stopband finite attenuation.
The MATLAB code below is aimed at comparing the spectral representation of a 1D discrete-time signal to that obtained when it is periodized. 14. Discrete-time signals and associated frequency spectra Notice that when a discrete-time signal is periodized, its spectrum is sampled. Run the same code again to perform this comparative analysis for the following signals: triangular, sawtooth and exponential. 12. 15. Magnitude of the Fourier series coefficients Verify the following main properties of the DTFS coefficients corresponding to a periodical real discrete-time signal: c(1) = 1 N N −1 ¦ x [ n + 1] , c( N 2) = n =0 1 N N −1 ¦ x [ n + 1] (−1)n , cN −k = ck∗ n =0 48 Digital Signal Processing using MATLAB Write a new code to calculate the DTFS coefficients of a 2D discrete-time signal.
Digital Signal Processing Using Matlab by Andre Quinquis