The Experts below are selected from a list of 17934 Experts worldwide ranked by ideXlab platform
C.k. K Wong - One of the best experts on this subject based on the ideXlab platform.
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Urban traffic flow prediction using a fuzzy-neural approach
Transportation Research Part C: Emerging Technologies, 2002Co-Authors: Hongbin Yin, S.c. C Wong, Jianmin Xu, C.k. K WongAbstract:This paper develops a fuzzy-neural model (FNM) to predict the traffic flows in an urban street Network, which has long been considered a major element in the responsive urban traffic control systems. The FNM consists of two modules: a Gate Network (GN) and an expert Network (EN). The GN classifies the input data into a number of clusters using a fuzzy approach, and the EN specifies the input–output relationship as in a conventional neural Network approach. While the GN groups traffic patterns of similar characteristics into clusters, the EN models the specific relationship within each cluster. An online rolling training procedure is proposed to train the FNM, which enhances its predictive power through adaptive adjustments of the model coefficients in response to the real-time traffic conditions. Both simulation and real observation data are used to demonstrative the effectiveness of the method.
Juan Perezmercader - One of the best experts on this subject based on the ideXlab platform.
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from chemical soup to computing circuit transforming a contiguous chemical medium into a logic Gate Network by modulating its external conditions
arXiv: Chemical Physics, 2019Co-Authors: Matthew D Egbert, Jeansebastien Gagnon, Juan PerezmercaderAbstract:It has been shown that it is possible to transform a well-stirred chemical medium into a logic-Gate simply by varying the chemistry's external conditions (feed rates, lighting conditions, etc). We extend this work, showing that the same method can be generalized to spatially-extended systems. We vary the external conditions of a well-known chemical medium (a cubic autocatalytic reaction diffusion model), so that different regions of the simulated chemistry are operating under particular conditions at particular times. In so doing, we are able to transform the initially uniform chemistry, not just into a single logic Gate, but into a functionally integrated Network of diverse logic Gates that operate as a basic computational circuit known as a full-adder.
Perez-mercader Juan - One of the best experts on this subject based on the ideXlab platform.
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From chemical soup to computing circuit: Transforming a contiguous chemical medium into a logic Gate Network by modulating its external conditions
'The Royal Society', 2019Co-Authors: Egbert Matthew, Gagnon Jean-sebastien, Perez-mercader JuanAbstract:It has been shown that it is possible to transform a well-stirred chemical medium into a logic-Gate simply by varying the chemistry's external conditions (feed rates, lighting conditions, etc). We extend this work, showing that the same method can be generalized to spatially-extended systems. We vary the external conditions of a well-known chemical medium (a cubic autocatalytic reaction diffusion model), so that different regions of the simulated chemistry are operating under particular conditions at particular times. In so doing, we are able to transform the initially uniform chemistry, not just into a single logic Gate, but into a functionally integrated Network of diverse logic Gates that operate as a basic computational circuit known as a full-adder.Comment: 11 pages, 5 figure
L. D. Cheremisinova - One of the best experts on this subject based on the ideXlab platform.
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Extracting a Logic Gate Network from a Transistor-Level CMOS Circuit
Russian Microelectronics, 2019Co-Authors: D. I. Cheremisinov, L. D. CheremisinovaAbstract:In this paper, we address the problem of converting a flat CMOS circuit of transistors in the SPICE format into a hierarchical circuit of CMOS Gates in the same format. This problem arises in the process of layout versus schematic (LVS) verification, as well as when reengineering integrated circuits. A method for recognizing subcircuits (CMOS Gates) is described. The method is implemented as a C++ program; it recognizes subcircuits that are described by the same logic functions but are not isomorphic at the transistor level as different ones. This provides the isomorphism of the original and decompiled circuits.
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Extracting a Logic Gate Network from a Transistor-Level CMOS Circuit
Russian Microelectronics, 2019Co-Authors: D. I. Cheremisinov, L. D. CheremisinovaAbstract:In this paper, we address the problem of converting a flat CMOS circuit of transistors in the SPICE format into a hierarchical circuit of CMOS Gates in the same format. This problem arises in the process of layout versus schematic (LVS) verification, as well as when reengineering integrated circuits. A method for recognizing subcircuits (CMOS Gates) is described. The method is implemented as a C++ program; it recognizes subcircuits that are described by the same logic functions but are not isomorphic at the transistor level as different ones. This provides the isomorphism of the original and decompiled circuits.
Hongbin Yin - One of the best experts on this subject based on the ideXlab platform.
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Urban traffic flow prediction using a fuzzy-neural approach
Transportation Research Part C: Emerging Technologies, 2002Co-Authors: Hongbin Yin, S.c. C Wong, Jianmin Xu, C.k. K WongAbstract:This paper develops a fuzzy-neural model (FNM) to predict the traffic flows in an urban street Network, which has long been considered a major element in the responsive urban traffic control systems. The FNM consists of two modules: a Gate Network (GN) and an expert Network (EN). The GN classifies the input data into a number of clusters using a fuzzy approach, and the EN specifies the input–output relationship as in a conventional neural Network approach. While the GN groups traffic patterns of similar characteristics into clusters, the EN models the specific relationship within each cluster. An online rolling training procedure is proposed to train the FNM, which enhances its predictive power through adaptive adjustments of the model coefficients in response to the real-time traffic conditions. Both simulation and real observation data are used to demonstrative the effectiveness of the method.