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—Many motion compensation algorithms are based on block matching. The quality of the block correlation depends on the validity of the brightness constancy assumption and the assumption of fixed translational motion within a block. These assumptions are invalid in areas with texture changes, noise, lighting changes, and rapid deformations. Smoothness priors(More)
Deep Neural Network (DNN) based acoustic models have shown significant improvement over their Gaussian Mixture Model (GMM) counterparts in the last few years. While several studies exist that evaluate the performance of GMM systems under noisy and channel degraded conditions, noise robustness studies on DNN systems have been far fewer. In this work we(More)
— Growing complexity of multiprocessor systems on chip (MP-SoC) requires future communication resources that can only be met by highly scalable architectures. Networks-on-Chip (NoCs) offer this scalability and other advantages like modularity, quality-of-service (QoS), possibly smaller area footprint and lower power dissipation. Although many papers(More)
Integral projections have been proposed as an efficient method to reduce the dimensionality of the search space in motion estimation (ME) algorithms. A number of papers describe methods extending direct (correlation based) block matching algorithms with projections , others derive optical flow methods based on the Radon transform. With a single exception(More)
• Chris Bartels and Jeff Bilmes. Creating non-minimal triangulations for use in inference in mixed stochastic / deterministic graphical models. the prepausal lengthening effect for speech recognition: A dynamic Bayesian network approach. • Chris Bartels and Jeff Bilmes. Using syllable nuclei locations to improve automatic speech recognition in the presence(More)