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Multi-dimensional raw data collections introduce noise and artefacts, which need to be recovered from degradations by an automated filtering system before, further machine analysis. The need for automating wide-ranged filtering applications necessitates the design of generic filtering architectures, together with the development of multidimensional and extensive convolution operators. Consequently, the aim of this book is to vet the problem of automated construction of a generic parallel filtering system. Serving this goal, performance-efficient FPGA implementation architectures are developed to realize parallel multi-dimensional filtering algorithms. These generic architectures provide a mechanism for fast FPGA prototyping of high performance computations to obtain efficiently implemented performance indices of area, speed, dynamic power, throughput and computation rates, as a complete package. These algorithms and their architectures tackle the major bottlenecks and limitationsof existing multiprocessor systems in wordlength, boundary conditions as well as interprocessor communications, in order to support high data throughput real-time applications of low-power architectures.