Automatic control of traffic signals in many cities are often based on a constant green-to-red cycle. The time period for green light (or red light) to be on is fixed, and is determined based on a stochastic model. In some situations, a human operator, or a fixed timer, may change the time period dyring time intervals where there is heavy traffic--typically during commuting times. Although in today's traffic controllers, the time period for green light can be changed in certain circumstances, in general they are not able to adjust dynamically to traffic changes. This paper presents the design of a controller that considers the intersection of two two-way streets and is able to adjust to environmental changes. Simulation of the proposed controller has produced results more in line with what we expected. In many cases, the proposed system has produced better results than systems that are based on a constant green-to-red cycle.
The paper proposes a neural network model for two layer channel routing. We hope this research will lead to a better understanding of the capability and limitations of neural networks as a general design methodology and in particular when it is applied to the routing problem in the design of VLSI chips. In our model the neurons form a two dimensional array where the value of element N represents the " chances" of net i is positioned at track j. The strength of connections between neurons are defined such that to prevent horizontal conflict between nets and also optimize a number of important routing metrics such as: minimum routing area and minimum wire length. We ran several examples for most of the small size examples ( less than 1 5 nets ) we were able to obtain good solutions. However for larger size examples apart from the known problems of long simulation times the router was not able to find a solution in many cases. 1.
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