Self-describing and data propagation model for data distribution service

Abstract. To realize real-time information sharing in generic platforms, it is especially important to support dynamic message structure changes. For the case of IDL, it is necessary to rewrite applications to change data sample structures. In this paper, we propose a dynamic reconfiguration scheme of data sample structures for DDS. Instead of using IDL, which is the static data sample structure model of DDS, we use a self describing model using data sample schema, as a dynamic data sample structure model to support dynamic reconfiguration of data sample structures. We also propose a data propagation model to provide data persistency in distributed environments. We guarantee persistency by transferring data samples through relay nodes to the receiving nodes, which have not participated in the data distribution network at the data sample distribution time. The proposed schemes can be utilized to support data sample structure changes during operation time and to provide data persistency in various environments, such as real-time enterprise environments and connection-less internet environments

25th annual computational neuroscience meeting: CNS-2016

The same neuron may play different functional roles in the neural circuits to which it belongs. For example, neurons in the Tritonia pedal ganglia may participate in variable phases of the swim motor rhythms [1]. While such neuronal functional variability is likely to play a major role the delivery of the functionality of neural systems, it is difficult to study it in most nervous systems. We work on the pyloric rhythm network of the crustacean stomatogastric ganglion (STG) [2]. Typically network models of the STG treat neurons of the same functional type as a single model neuron (e.g. PD neurons), assuming the same conductance parameters for these neurons and implying their synchronous firing [3, 4]. However, simultaneous recording of PD neurons shows differences between the timings of spikes of these neurons. This may indicate functional variability of these neurons. Here we modelled separately the two PD neurons of the STG in a multi-neuron model of the pyloric network. Our neuron models comply with known correlations between conductance parameters of ionic currents. Our results reproduce the experimental finding of increasing spike time distance between spikes originating from the two model PD neurons during their synchronised burst phase. The PD neuron with the larger calcium conductance generates its spikes before the other PD neuron. Larger potassium conductance values in the follower neuron imply longer delays between spikes, see Fig. 17.Neuromodulators change the conductance parameters of neurons and maintain the ratios of these parameters [5]. Our results show that such changes may shift the individual contribution of two PD neurons to the PD-phase of the pyloric rhythm altering their functionality within this rhythm. Our work paves the way towards an accessible experimental and computational framework for the analysis of the mechanisms and impact of functional variability of neurons within the neural circuits to which they belong

Robust Speech Recognition in a Car Using a Microphone Array

121 p.Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2006.The proposed approach has two steps: speech enhancement as a preprocessor of noisy speech signals, followed by the phoneme restoration for robust speech recognition against nonstationary noises given knowledge of H S and HN.U of I OnlyRestricted to the U of I community idenfinitely during batch ingest of legacy ETD

Robust Speech Recognition in a Car Using a Microphone Array

Performance of automatic speech recognition relies on a vast amount of training speech data mostly recorded with little or no background noise. The performance degrades significantly with existence of background noise, which increases type mismatch between train and test environments. Speech enhancement techniques can reduce the amount of type mismatch by extracting reliable speech features from the background noise. At very low SNR with nonstationary noise, the enhanced speech may still contain significant noise either in noise-only segments or speech segments. The former masquerade as nonexistent speech features and the latter as distorted speech features. Both significantly degrade the performance of the automatic speech recognizer. This encourages the use of voice activity detection (VAD) algorithms to determine parts with speech present. To use only the reliable speech features, we need to further determine whether the features from the speech region are mainly from speech or from nonstationary noises masking the speech. For more robust speech recognition, this thesis proposes a three-hypothesis VAD consisting of H0: noise-only region; HS: speech-dominant speech region; and HN: noise-dominant speech region