tailieunhanh - Signal Processing Matlab Dsp Toolbox P2

Two of the most important functions for signal processing are not in the Signal Processing Toolbox at all, but are built-in MATLAB functions: • filter applies a digital filter to a data sequence. • fft calculates the discrete Fourier transform of a sequence. The operations these functions perform are the main computational workhorses of classical signal processing. Both are described in this chapter. The Signal Processing Toolbox uses many other standard MATLAB functions and language features, including polynomial root finding, complex arithmetic, matrix inversion and manipulation, and graphics tools. Signals and Systems The basic. | Signal Processing Toolbox Central Features Signal Processing Toolbox Central Features The Signal Processing Toolbox functions are algorithms expressed mostly in M-files that implement a variety of signal processing tasks. These toolbox functions are a specialized extension of the MATLAB computational and graphical environment. Filtering and FFTs Two of the most important functions for signal processing are not in the Signal Processing Toolbox at all but are built-in MATLAB functions filter applies a digital filter to a data sequence. fft calculates the discrete Fourier transform of a sequence. The operations these functions perform are the main computational workhorses of classical signal processing. Both are described in this chapter. The Signal Processing Toolbox uses many other standard MATLAB functions and language features including polynomial root finding complex arithmetic matrix inversion and manipulation and graphics tools. Signals and Systems The basic entities that toolbox functions work with are signals and systems. The functions emphasize digital or discrete signals and filters as opposed to analog or continuous signals. The principal filter type the toolbox supports is the linear time-invariant digital filter with a single input and a single output. You can represent linear time-invariant systems using one of several models such as transfer function state-space zero-pole-gain and second-order section and convert between representations. Key Areas Filter Design and Spectral Analysis In addition to its core functions the toolbox provides rich customizable support for the key areas of filter design and spectral analysis. It is easy to implement a design technique that suits your application design digital filters directly or create analog prototypes and discretize them. Toolbox functions also estimate power spectral density and cross spectral density using either parametric or nonparametric techniques. Filter Design on page 2-1 and Statistical Signal .

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