For signals containing discontinuities, the usual assumptions of Gauss-Markov distributed signal sources do not hold. To preserve edges, non-Gaussian prior models have been developed for use in Bayesian restoration. T...
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For signals containing discontinuities, the usual assumptions of Gauss-Markov distributed signal sources do not hold. To preserve edges, non-Gaussian prior models have been developed for use in Bayesian restoration. These models are generally dependent upon two parameters, one controlling the size of reconstructed discontinuities, and the other controlling data smoothing. The authors propose a maximum likelihood technique for automatically estimating these parameters, resulting in the optimization of an expression dependent upon the prior model partition function. An exact expression is derived for the 1D signal model partition function, while an approximation is proposed for the 2D image model partition function. Parameters estimated from degraded signals result in high quality restorations.< >
A novel approach to solving the problem of signal separation under model uncertainties and unknown source signal characteristics is proposed. The approach features the incorporation of blind identification with cluste...
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A novel approach to solving the problem of signal separation under model uncertainties and unknown source signal characteristics is proposed. The approach features the incorporation of blind identification with clustering techniques. The approach is capable of estimating source locations and source signals under various uncertain conditions including unknown sensor gains, unknown combinations of near-field and far-field sources, unknown combinations of wideband and narrowband sources, unknown source spectral characteristics (their spectra may be overlapping or non-overlapping), and unknown number of signals.< >
In this paper, the motivation for studying the simultaneous quadratic equations with unknown coefficients is first presented. Its indeterminacy and identifiability are considered. A main lemma and three theorems are t...
A set of simultaneous quadratic equations with unknown coefficients is considered. The motivation for studying the equations is presented. Indeterminacy and identifiability are considered. A main lemma and three theor...
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A set of simultaneous quadratic equations with unknown coefficients is considered. The motivation for studying the equations is presented. Indeterminacy and identifiability are considered. A main lemma and three theorems are presented. A comparison with the well-known simultaneous diagonalization problem is given.< >
The implementation of real-time image encoding requires a high and constant throughput rate not achievable by a SISD machine. With a reasonable size codebook and SIMD machine architecture. Vector Quantization algorith...
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The implementation of real-time image encoding requires a high and constant throughput rate not achievable by a SISD machine. With a reasonable size codebook and SIMD machine architecture. Vector Quantization algorithm can be implemented in real-time. But, single stage Vector Quantization requires fairly large-size codebook for good quality image encoding. Multi-Stage Vector Quantization with codebooks of moderate size at each stage has been shown to be an alternative viable approach. The bits per pixel rates for TV quality composite color image encoding using Multi-Stage Vector Quantization are reported. The VQ/MSVQ implementation requires two processors, inner product processor and comparator-address generator. The implementation details of the processors and their throughput rate are described.
Autoregressive(AR) or linear predictive(LP) modeling and Wigner time-frequency representations have been proposed for non-stationary signalanalysis and synthesis, owing to their specific advantages over the short-tim...
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Autoregressive(AR) or linear predictive(LP) modeling and Wigner time-frequency representations have been proposed for non-stationary signalanalysis and synthesis, owing to their specific advantages over the short-time Fourier transform, viz. reduced data set characterisation and improved frequency resolution of the former, and the improved time resolution and thence better non-stationary signal representation of the latter. However, the former is limited in time resolution and the latter in frequency resolution and size of characterising data set, depending on the size of the windows that need to be used. This paper investigates the potential combination of the two above methods, with the aim of exploiting their advantages simultaneously and addressing the window size-resolution dilemma. The concept shows good promise to this end, despite the problems of the cross-spectral components and computational complexity, that need to be addressed. Examples of simulated signals are presented to illustrate the advantages of this representation.
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