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An adaptive implementation of the filter was shown to converge relatively quickly for mild interference.
Multiplicative gain variations are utilized to describe the phenomenon of parameter variations in actual implementation of the filter.
The classical approach requires the selection of the filter structure, the calculation of parameters, and, finally, the implementation of the filter in a programming language.
The value of Δ max time ( f ) is less critical, in particular because of the inter-frames smoothing in the temporal implementation of the filter.
A digital implementation of the filter requires utilisation of filter design tools in order to evaluate the parameters of the filter.
One adder is saved for each negative coefficient implementation and this reduces the hardware cost compared to the convectional implementation of the filter with CSD coefficients.
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It is derived as an efficient implementation of the filter-bank summation method and performs time-domain filtering with coefficients adapted in the uniform or non-uniform frequency-domain.
Note that the filtering procedure conducted here is a direct implementation of the filtering model suggested by [15].
The implementation of the filtering mechanism in each component avoids added complexity to the messaging server and cloud controller.
This facilitates the implementation of the filtering algorithm in high dimensional settings using a remarkably small number of particles.
The time-varying parameters belong to a polytope with known vertices, affect all the system matrices and are assumed to be available online for implementation of the filters.
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