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Recent developments in image coding tend to promote schemes consisting of a great variety of coding algorithms applied to different parts of the image to code. This results in an improved rate-distortion behavior of the global system. The selection of the optimal coding method for a given region of an image is still a computationally intensive task, as most of the current schemes need to compute the result for all algorithms and to chose the most suited one. This paper investigates a method to
[[abstract]]This paper presents VLSI circuits with continuous-valued probabilistic behaviour realized by injecting noise into each computing unit(neuron). Interconnecting the noisy neurons forms a Continuous Restricted Boltzmann Machine (CRBM), which has shown promising performance in modelling and classifying noisy biomedical data. The Minimising-Contrastive-Divergence learning algorithm for CRBM is also implemented in mixed-mode VLSI, to adapt the noisy neurons ’ parameters on-chip. 1
Scattered light spectrum of thermal sound waves used to provide velocity and absorption data about hypersonic waves in several liquids
Dispersion and absorption of hypersonic waves analyzed by examining Brillouin scattering as function of scattering angle
[[abstract]]We have mapped the contrastive divergence learning scheme of the product of experts (PoE) onto electrical circuits. The issues raised during that hardware translation are discussed in this paper and some circuits presenting our solutions are described. The entire learning rule is implemented in mixed-signal VLSI on a 0.6 μm CMOS process. Chips results validating our approach and methodology are also presented
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