174,274 research outputs found

    Recent advances on recursive filtering and sliding mode design for networked nonlinear stochastic systems: A survey

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    Copyright © 2013 Jun Hu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.Some recent advances on the recursive filtering and sliding mode design problems for nonlinear stochastic systems with network-induced phenomena are surveyed. The network-induced phenomena under consideration mainly include missing measurements, fading measurements, signal quantization, probabilistic sensor delays, sensor saturations, randomly occurring nonlinearities, and randomly occurring uncertainties. With respect to these network-induced phenomena, the developments on filtering and sliding mode design problems are systematically reviewed. In particular, concerning the network-induced phenomena, some recent results on the recursive filtering for time-varying nonlinear stochastic systems and sliding mode design for time-invariant nonlinear stochastic systems are given, respectively. Finally, conclusions are proposed and some potential future research works are pointed out.This work was supported in part by the National Natural Science Foundation of China under Grant nos. 61134009, 61329301, 61333012, 61374127 and 11301118, the Engineering and Physical Sciences Research Council (EPSRC) of the UK under Grant no. GR/S27658/01, the Royal Society of the UK, and the Alexander von Humboldt Foundation of Germany

    Visual selective behavior can be triggered by a feed-forward process

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    The ventral visual pathway implements object recognition and categorization in a hierarchy of processing areas with neuronal selectivities of increasing complexity. The presence of massive feedback connections within this hierarchy raises the possibility that normal visual processing relies on the use of computational loops. It is not known, however, whether object recognition can be performed at all without such loops (i.e., in a purely feed-forward mode). By analyzing the time course of reaction times in a masked natural scene categorization paradigm, we show that the human visual system can generate selective motor responses based on a single feed-forward pass. We confirm these results using a more constrained letter discrimination task, in which the rapid succession of a target and mask is actually perceived as a distractor. We show that a masked stimulus presented for only 26 msec—and often not consciously perceived—can fully determine the earliest selective motor responses: The neural representations of the stimulus and mask are thus kept separated during a short period corresponding to the feed-forward "sweep." Therefore, feedback loops do not appear to be "mandatory" for visual processing. Rather, we found that such loops allow the masked stimulus to reverberate in the visual system and affect behavior for nearly 150 msec after the feed-forward sweep

    True zero-training brain-computer interfacing: an online study

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    Despite several approaches to realize subject-to-subject transfer of pre-trained classifiers, the full performance of a Brain-Computer Interface (BCI) for a novel user can only be reached by presenting the BCI system with data from the novel user. In typical state-of-the-art BCI systems with a supervised classifier, the labeled data is collected during a calibration recording, in which the user is asked to perform a specific task. Based on the known labels of this recording, the BCI's classifier can learn to decode the individual's brain signals. Unfortunately, this calibration recording consumes valuable time. Furthermore, it is unproductive with respect to the final BCI application, e.g. text entry. Therefore, the calibration period must be reduced to a minimum, which is especially important for patients with a limited concentration ability. The main contribution of this manuscript is an online study on unsupervised learning in an auditory event-related potential (ERP) paradigm. Our results demonstrate that the calibration recording can be bypassed by utilizing an unsupervised trained classifier, that is initialized randomly and updated during usage. Initially, the unsupervised classifier tends to make decoding mistakes, as the classifier might not have seen enough data to build a reliable model. Using a constant re-analysis of the previously spelled symbols, these initially misspelled symbols can be rectified posthoc when the classifier has learned to decode the signals. We compare the spelling performance of our unsupervised approach and of the unsupervised posthoc approach to the standard supervised calibration-based dogma for n = 10 healthy users. To assess the learning behavior of our approach, it is unsupervised trained from scratch three times per user. Even with the relatively low SNR of an auditory ERP paradigm, the results show that after a limited number of trials (30 trials), the unsupervised approach performs comparably to a classic supervised model

    Quantum cryptography: key distribution and beyond

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    Uniquely among the sciences, quantum cryptography has driven both foundational research as well as practical real-life applications. We review the progress of quantum cryptography in the last decade, covering quantum key distribution and other applications.Comment: It's a review on quantum cryptography and it is not restricted to QK
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