論文

A parallel algorithm for solving meeting schedule problems is presented in this paper where the problem is NP-complete. The proposed system is composed of two maximum neural networks which interact with each other. One is an M x S neural network to assign meetings to available time slots on a timetable where M and S are the number of meetings and the number of time slots, respectively. The other is an M x P neural network to assign persons to the meetings where P is the number of persons. The simulation results show that the state of the system always converges to one of the solutions. Our empirical study shows that the solution quality of the proposed algorithm does not degrade with the problem size.

Microcode optimization is an NP-complete combinatorial optimization problem. This paper proposes a new method based on the Hopfield neural network for optimizing the wordwidth in the control memory of a microprogrammed digital computer. We present two methodologies, viz., the maximum clique approach, and a cost function based method to minimize an objective function. The maximum clique approach albeit being near O(1) in complexity, is limited in its use for small problem sizes, since it only partitions the data based on the compatibility between the microoperations, and does not minimize the cost function. We thereby use this approach to condition the data initially (to form compatibility classes), and then use the proposed second method to optimize on the cost function. The latter method is then able to discover better solutions than other schemes for the benchmark data set.

A novel approach to the one-dimensional gate assignment problem is presented in this paper where the problem is NP-hard and one of the most fundamental layout problems in VLSI design. The proposed system is composed of n×n processing elements called the artificial two-dimensional maximum neurons for (n+2)-gate assignment problems. We have discovered the improved solutions in the benchmark problems over the best existing algorithms. The proposed parallel algorithm is also applicable to other VLSI layout problems.

A near-optimum parallel algorithm for solving the one-dimensional gate assignment problem is presented in this paper, where the problem is NP-hard and one of the most fundamental layout problems in VLSI design. The proposed system is composed of n×n processing elements based on the artificial two-dimensional maximum neural network for (n+2)-gate assignment problems. Our algorithm has discovered improved solutions in the benchmark problems compared with the best existing algorithms. The proposed approach is applicable to other VLSI layout problems such as the PLA (Programmable Logic Array) folding problem.

In this article we examine our remote lecture experiments using ATM and UNII wireless devices. We discussed which functions should be improved in the current distance-learning system. UNII/SUPERNet wireless devices are introduced for achieving economical distance-learning systems. Two experiments of remote lectures are detailed: a wireless experiment between two buildings in the Philippines and an ATM experiment between Japan and the United States.