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Digital signal processing (DSP) algorithms typically require a large number of mathematical operations to be performed quickly and repeatedly on a series of data samples. Signals (perhaps from audio or video sensors) are constantly converted from analog to digital, manipulated digitally, and then converted back to analog form.
Digital signal processing (DSP) is the use of digital processing, such as by computers or more specialized digital signal processors, to perform a wide variety of signal processing operations. The digital signals processed in this manner are a sequence of numbers that represent samples of a continuous variable in a domain such as time, space ...
Instrumentation and control engineering is a vital field of study offered at many universities worldwide at both the graduate and postgraduate levels. This discipline integrates principles from various branches of engineering, providing a comprehensive understanding of the design, analysis, and management of automated systems.
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Multidimensional Digital Signal Processing (MDSP) refers to the extension of Digital signal processing (DSP) techniques to signals that vary in more than one dimension. . While conventional DSP typically deals with one-dimensional data, such as time-varying audio signals, MDSP involves processing signals in two or more dimens
mD-DSP algorithms exhibit a large amount of complexity, as described in the previous section, which makes efficient implementation difficult in regard to run-time and power consumption. This article primarily addresses basic parallel concepts used to alleviate run-time of common mD-DSP applications.
In digital signal processing (DSP), parallel processing is a technique duplicating function units to operate different tasks (signals) simultaneously. [1] Accordingly, we can perform the same processing for different signals on the corresponding duplicated function units.
Stream processing is essentially a compromise, driven by a data-centric model that works very well for traditional DSP or GPU-type applications (such as image, video and digital signal processing) but less so for general purpose processing with more randomized data access (such as databases). By sacrificing some flexibility in the model, the ...