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Digital Signal Processing Definition

The analysis and modification of digital signals through digital processing by devices such as computers for a variety of signal processing operations is called digital signal processing (DSP). It includes mathematical and computational algorithms that optimizes and improves efficiency or performance of both analog and digital signals. The primary use of DSP is to detect errors, and to enhance and compress analog signals in transit, however analog signals need to be digitized with a convertor first.

Sampling involves two stages- discretization and quantization. Discretization involves division of the signal into equal intervals of time, where each interval represents a single measurement of amplitude. Quantization refers to each measurement of amplitude being approximated by a value form a finite set. DSP is studied in one of the following domains- time, spatial, frequency, and wavelet.

DSP algorithms are being leveraged in general-purpose computers and purpose-built hardware such as application-specific integrated circuit. When real-time processing is not required, it can be done economically with general-purpose computers and the input or output data exists in data files.  This process is similar to conventional data processing, but involves DSP mathematical techniques to be used, and the sampled data is assumed to be uniformly sampled in time or space. Yet, when real-time processing is required, DSP is implemented using specialized microprocessors that process data using fixed-point and floating-point arithmetic. Since 2007, multicore implementations of DSP have emerged from companies such as Freescale and Stream Processors, Inc. In addition, DSP applications are being implemented increasingly on embedded systems utilizing powerful computers with multi-core processors.    

The increasing reliance on computers has had a reciprocal effect on the use of DSP. Some of the DSP applications are audio and speech processing, sonar, radar and other sensor array processing, spectral density estimation, statistical signal processing, digital image processing, signal processing for telecommunications, control systems, biomedical engineering, and seismology. 

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