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The zero-order hold (ZOH) is a mathematical model of the practical signal reconstruction done by a conventional digital-to-analog converter (DAC). [1] That is, it describes the effect of converting a discrete-time signal to a continuous-time signal by holding each sample value for one sample interval. It has several applications in electrical ...
Choosing an interpolation method is a trade-off between implementation complexity and conversion quality (according to application requirements). Commonly used are: zero-order hold (for film/video frames), cubic (for image processing) and windowed sinc function (for audio).
Jitter, noise, and quantization are often analyzed by modeling them as random errors added to the sample values. Integration and zero-order hold effects can be analyzed as a form of low-pass filtering. The non-linearities of either ADC or DAC are analyzed by replacing the ideal linear function mapping with a proposed nonlinear function.
A mathematical model such as FOH (or, more commonly, the zero-order hold) is necessary because, in the sampling and reconstruction theorem, a sequence of Dirac impulses, x s (t), representing the discrete samples, x(nT), is low-pass filtered to recover the original signal that was sampled, x(t). However, outputting a sequence of Dirac impulses ...
where v and w are continuous zero-mean white noise sources with power spectral densities (,) (,) can be discretized, assuming zero-order hold for the input u and continuous integration for the noise v, to
Interpolation: Smooth out the discontinuities using a lowpass filter, which replaces the zeros. In this application, the filter is called an interpolation filter , and its design is discussed below. When the interpolation filter is an FIR type, its efficiency can be improved, because the zeros contribute nothing to its dot product calculations.
The Zero-order hold (ZOH) is a mathematical model of the practical reconstruction of sampled signals done by conventional digital-to-analog converters (DAC). When a signal, x(t), is sampled at intervals of length T, we are left with just the discrete sequence : x(nT), for integer values of n.
Nearest-neighbor interpolation (also known as proximal interpolation or, in some contexts, point sampling) is a simple method of multivariate interpolation in one or more dimensions. Interpolation is the problem of approximating the value of a function for a non-given point in some space when given the value of that function in points around ...