Titlebook: Variable-Structure Approaches; Analysis, Simulation Andreas Rauh,Luise Senkel Book 2016 Springer International Publishing Switzerland 2016

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Saif Siddique Butt,Hao Sun,Harald Aschemann speed can be validly achieved. It implies that this framework is a competent model for visual selective attention, which expands the way to implement a computational mechanism for top-down modulating the bottom-up process especially in the case of task-related attentional action in machine vision s

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Piotr Leśniewski,Andrzej Bartoszewiczizing procedure to increase the re-covered image information. With a training set of eighty images, the network is trained by means of mean square error (MSE). The proposed method utilizing peak signal to noise ratio (PSNR) and structural similarity (SSIM) index measures are used to assess the resto

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Andreas Rauh,Luise SenkelThis work mainly focusses on analyzing the performance of optical link with various prediction strategies (hard decision-FEC, soft decision-FEC and probabilistic shaping)) using forward error correcting codes (FEC). The symbol error rate, bit error rate and achievable information rates have been ana

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Luise Senkel,Andreas Rauh,Harald Aschemannrmance of Deep AR and GRU did not degrade when the amount of training data was reduced, suggesting that these models may not require a large amount of data to achieve consistent and reliable performance. The study demonstrates that incorporating deep learning approaches in a forecasting scenario sig

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Horst Schulte,Florian Pöschkementation results. Besides, we transform multi-class segmentation tasks into multiple binary sub-segmentation tasks. Experiments on the BraTS’2017 Challenge Dataset show that the proposed . framework is very suitable for organ tissue segmentation with nested anatomical structures. Here, our single-v