Radio frequency identification (RFID) is a technology where a reader device can "sense" the presence of a close by object by reading a tag device attached to the object. To guarantee the coverage quality, mu...
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This paper presents a method to detect vehicles from a moving camera. The detection component involves a cascade of modules. First, motion estimation of singular points in video sequences is used to detect moving vehi...
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A remote debugging system for OpenMP parallel program is presented in this paper. The system consists of two parts, namely, an integrated debugging environment running on the clent-side and a background daemon running...
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ISBN:
(纸本)9781457717000
A remote debugging system for OpenMP parallel program is presented in this paper. The system consists of two parts, namely, an integrated debugging environment running on the clent-side and a background daemon running on the server-side. Information exchange between the two sides is accomplished through the socket-based network communication technology. The remote debugging function is realized by the automatic instrumentation technology at code level based on syntax tree. Instance tests show that the system can provide remote users with correctness checking, performance analysis and other debugging functions for OpenMP parallel programs. The user interface is simple and very easy to use.
The present study used electroencephalography to examine mu rhythm suppression （a putative index of human mirror neuron system activation） at frontal sites （F3, Fz and F4）, central sites （C3, Cz and C4）, parieta...
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The present study used electroencephalography to examine mu rhythm suppression （a putative index of human mirror neuron system activation） at frontal sites （F3, Fz and F4）, central sites （C3, Cz and C4）, parietal sites （P3, Pz and P4） and occipital sites （O1 and O2）, while subjects observed real hand motion （real hand motion condition） and illustrative depictions of hand motion （drawn hand motion condition）. Experimental data revealed that mu rhythm suppression was exhibited in the mirror neuron system when subjects observed both real and drawn hand motion. Moreover, the mu rhythm recorded at the F3, Fz, F4, and Pz poles was significantly suppressed while observing both stimulus types, but no obvious mu suppression occurred at the O1, 02 and 03 poles. These results suggest that the observation of drawings of human hand actions can activate the human mirror neuron system. This evidence supports the hypothesis that the mirror neuron system may be involved in intransitively abstract action understanding.
Arithmetic coding is a widely applied compression tool with superior coding efficiency to other entropy coding methodsHowever, it suffers from the error resilience and complexityIn this paper, the integer implementati...
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Arithmetic coding is a widely applied compression tool with superior coding efficiency to other entropy coding methodsHowever, it suffers from the error resilience and complexityIn this paper, the integer implementation of binary arithmetic coding with forbidden symbol for error resilience is studiedCoding redundancies for employing different quantization coefficients in probability representation and cost effective backtracking distance in bits for maximum a posteriori(MAP) decoding are studied in depthWe observe that the optimal quantization coefficients are independent of forbidden symbol and the probabilities of source and the cost effective backtracking distance is related to the source entropy and the given forbidden symbol probabilitiesSuch observations are also demonstrated by extensive experiments.
This paper introduces an automatic Web service composition method based on logical inference of Horn clauses and Petri nets. The Web service composition problem is transformed into the logical inference problem of Hor...
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A new book-lending system is designed and analyzed based on logic Petri nets in this paper. The batch processing function and indeterminacy of readers are included in the system. Its logic Petri net model is establish...
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In order to apply our high efficiency fibre-channel token-routing network(shortened as FC-TR network) to the field of materials simulation research, a new MPI parallel computing environment is proposed and designed, a...
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In order to apply our high efficiency fibre-channel token-routing network(shortened as FC-TR network) to the field of materials simulation research, a new MPI parallel computing environment is proposed and designed, and independently developed a parallel programming environment FC-TR-MPI based on FC-TR network. In FC-TR-MPI, a new method was applied to point-to-point communication that the network communications between processes in the same computing node were changed into memory operations;moreover, according to the underlying software and hardware features of FC-TR network, new algorithms were proposed to optimize the communication performance of some collective communications. Experimental results show that, compared with Sca MPI parallel programming environment, FC-TR-MPI has a higher parallel efficiency and speedup.
Protein subcellular localization aims at predicting the location of a protein within a cell using computational methods. Knowledge of subcellular localization of proteins indicates protein functions and helps in ident...
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Protein subcellular localization aims at predicting the location of a protein within a cell using computational methods. Knowledge of subcellular localization of proteins indicates protein functions and helps in identifying drug targets. Prediction of protein subcellular localization is an important but challenging problem, particularly when proteins may simultaneously exist at, or move between, two or more different subcellular location sites. Most of the existing protein subcellular localization methods are only used to deal with the single-location proteins. To better reflect the characteristics of multiplex proteins, we formulate prediction of subcellular localization of multiplex proteins as a multi-label learning problem. We present and compare two multi-label learning approaches, which exploit correlations between labels and leverage label-specific features, respectively, to induce a high quality prediction model. Experimental results on six protein data sets under various organisms show that our described methods achieve significantly higher performance than any of the existing methods. Among the different multi-label learning methods, we find that methods exploiting label correlations performs better than those leveraging label-specific features.
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