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I've defined the following function in R State 0 indicates that one is located within a right-angle region State 1 indicates that one is located within a Hilbert curve State 2 indicates that one is located within a wedge region rot <- function(n, x, y, rx, ry) { if (ry == 1) { if (rx == 0) { x <<- n-1 - x y <<- n-1 - y ...


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In one level DWT, each output of the low-pass or a high-pass can indeed be considered as signals. Thus each of those signals are subsampled by a factor of 2, and the same two-filter-subsampling is iterated on the low-pass output, several times (wavelet decomposition) at $L$ levels. Each final output of the different branches could still individually be ...


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After upsampling with zero-insert, the higher frequency images need to be filtered out, which will serve the purpose of growing the zero insert values to the correct interpolated value in between samples. Once this is done, subsequent decimation operations can be properly done since every sample will then properly represent the signal. For a signal that ...


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Here is one answer, if someone can improve on this I will select it as the "right" answer (also comments very welcome on obvious flaws with this approach): Given Cauchy's argument principle, the number of zeros outside the unit circle is given by the number of encirclements of the origin for the frequency response of the filter as plotted on a complex plane....


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