Haris M. Stellakis

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The authors describe how systematic mapping methodologies can be used to derive special-purpose processor arrays for the estimation of the bispectrum via the third-order moments. A novel design that is optimal in terms of total execution time for multiple pipelined data blocks is proposed, and it is shown how formal verification of the design can be(More)
Fine granularity parallel architectures for the eecient estimation of Higher Order Statistics are systematically derived in this paper. A uniied methodology for constructing Locally Recursive Algorithms and Space-Time linear mapping operators that lead to highly pipelined architectures consisting of multiple, tightly coupled array stages is discussed rst.(More)
In signal processing applications that require new estimates of the fourth and lower order moments every time a new data sample is received, it is necessary to design algorithms that adaptively update these terms. In addition, if real-time performance is necessary we should transform these algorithms so that their parallel processing and pipelining(More)
In order to estimate higher-order statistics in real time it is necessary to use parallel processing and pipelining. We present a triangular VLSI architecture that can provide in real time estimates of all the nonnegative lags of all the moments, up to the fourth order. The array computes the moments of a one-dimensional data sequence in an sorder-recursive(More)
The Higher Order Statistics, such as the Higher Order Moments, Cumulants and Polyspectra, have been recognized as important tools in modern time series analysis since they overcome well known limitations of the autocorrelation/power spectrum second order methods. The systematic synthesis of parallel algorithms and architectures for the real-time estimation(More)
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