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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 ...
In the context of digital signal processing (DSP), a digital signal is a discrete time, quantized amplitude signal. In other words, it is a sampled signal consisting of samples that take on values from a discrete set (a countable set that can be mapped one-to-one to a subset of integers).
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.
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.
Consider an informal example in the following figure. A system includes three sub-function units (F 0, F 1 and F 2). Assume that there are three independent tasks (T 0, T 1 and T 2) being performed by these three function units. The time for each function unit to complete a task is the same and will occupy a slot in the schedule.
The Blackman–Tukey transformation (or Blackman–Tukey method) is a digital signal processing method to transform data from the time domain to the frequency domain.It was originally programmed around 1953 by James Cooley for John Tukey at John von Neumann's Institute for Advanced Study as a way to get "good smoothed statistical estimates of power spectra without requiring large Fourier ...
Convolution is a frequently used operation in DSP. To compute the 2-D convolution of two m × m signals, it requires m 2 multiplications and m × (m – 1) additions for an output element. That is, the overall time complexity is Θ(n 4) for the entire output signal.
In digital signal processing (DSP), a normalized frequency is a ratio of a variable frequency and a constant frequency associated with a system (such as a sampling rate, ). Some software applications require normalized inputs and produce normalized outputs, which can be re-scaled to physical units when necessary.