Seishi Nishikawa

A three-stage heuristic combined neural-network algorithm for channel assignment in cellular mobile systems

A three-stage algorithm of combining sequential heuristic methods into a parallel neural network is presented for the channel assignment problem in cellular mobile communication systems in this paper. The goal of this NP-complete problem is to find a channel assignment to requested calls with the minimum number of channels subject to interference constraints between channels. The three-stage algorithm consists of: (1) the regular interval assignment stage; (2) the greedy assignment stage; and (3) the neural-network assignment stage. In the first stage, the calls in a cell determining the lower bound on the total number of channels are assigned channels at regular intervals. In the second stage, the calls in a cell with the largest degree and its adjacent cells are assigned channels by a greedy heuristic method. In the third stage, the calls in the remaining cells are assigned channels by a binary neural network. The performance is verified through solving well-known benchmark problems. Especially for Sivarajans benchmark problems, our three-stage algorithm first achieves the lower bound solutions in all of the 13 instances, while the computation time is comparable with existing algorithms.

A gradual neural-network approach for frequency assignment in satellite communication systems

A novel neural-network approach called gradual neural network (GNN) is presented for a class of combinatorial optimization problems of requiring the constraint satisfaction and the goal function optimization simultaneously. The frequency assignment problem in the satellite communication system is efficiently solved by GNN as the typical problem of this class. The goal of this NP-complete problem is to minimize the cochannel interference between satellite communication systems by rearranging the frequency assignment so that they can accommodate the increasing demands. The GNN consists of NxM binary neurons for the N-carrier-M-segment system with the gradual expansion scheme of activated neurons. The binary neural network achieves the constrain satisfaction with the help of heuristic methods, whereas the gradual expansion scheme seeks the cost optimization. The capability of GNN is demonstrated through solving 15 instances in practical size systems, where GNN can find far better solutions than the existing algorithm.

A binary Hopfield neural-network approach for satellite broadcast scheduling problems

This paper presents a binary Hopfield neural network approach for finding a broadcasting schedule in a low-altitude satellite system. Our neural network is composed of simple binary neurons on the synchronous parallel computation, which is greatly suitable for implementation on a digital machine. With the help of heuristic methods, the neural network of a maximum of 200000 neurons can always find near-optimum solutions on a conventional workstation in our simulations.

A MAXIMUM NEURAL NETWORK APPROACH FOR N-QUEENS PROBLEMS

Abstract. A novel neural network approach using the maximum neuron model is presented for N-queens problems. The goal of the N-queens problem is to find a set of locations of N queens on an N×N chessboard such that no pair of queens commands each other. The maximum neuron model proposed by Takefuji et al. has been applied to two optimization problems where the optimization of objective functions is requested without constraints. This paper demonstrates the effectiveness of the maximum neuron model for constraint satisfaction problems through the N-queens problem. The performance is verified through simulations in up to 500-queens problems on the sequential mode, the N-parallel mode, and the N2-parallel mode, where our maximum neural network shows the far better performance than the existing neural networks.

A gradual neural network approach for FPGA segmented channel routing problems

A novel neural network approach called gradual neural network (GNN) is presented for segmented channel routing in field programmable gate arrays (FPGAs). FPGAs contain predefined segmented channels for net routing, where adjacent segments in a track can be interconnected through programmable switches for longer segments. The goal of the FPGA segmented channel routing problem, known to be NP-complete, is to find a conflict-free net routing with the minimum routing cost. The GNN for the N-net-M-track problem consists of a neural network of NxM binary neurons and a gradual expansion scheme. The neural network satisfies the constraints of the problem, while the gradual expansion scheme seeks the cost minimization by gradually increasing activated neurons. The energy function and the motion equation are newly defined with heuristic methods. The performance is verified through solving 30 instances, where GNN finds better solutions than existing algorithms within a constant number of iteration steps.

A neural network model for multilayer topological via minimization in a switchbox

This paper presents a new approach using a neural network model for the multilayer topological via minimization problem in a switchbox. Our algorithm consists of three steps: 1) dividing multiterminal nets into two-terminal nets, 2) finding a layer-assignment of the two-terminal nets by a neural network model so as to minimize the number of unassigned nets, and 3) embedding one via for each unassigned net by Marek-Sadowskas algorithm. The neural network model is composed of N/spl times/M processing elements to assign N two-terminal nets in an M-layer switchbox. The performance of our algorithm is verified by 15 benchmark problems where it can find optimum or near-optimum solutions. In the two-layer Bursteins switchbox, our algorithm finds a 15-via solution while the best known solution requires 20 vias.

Early B Cell Differentiation from Hematopoietic Stem Cells in the Presence of Stromal Cells and Interleukin-7 (IL-7)

The early stage of the pathway by which lymphocytes differentiate from hematopoietic stem cells was studied at a clonal level, using a stromal cell line (ST-2). IL-3-induced blast colonies, shown to be capable of differentiation into a variety of hematopoietic cells, were used as a source of enriched hematopoietic progenitor cells. These cells were Thy-1+ and B220−. In 7 of 211 wells receiving each blast colony, lymphoid cell and myeloid cell growth was observed on ST-2 layer. The cells were proved to be B lymphocytes by phenotype and immunoglobulin gene rearrangement analysis and by demonstration of surface expression of IgM. The clonal origin of B lymphoid and myeloid lineage cells was confirmed by the generation of both B lymphoid and myeloid cells in the same well following a fluorescence-activate cell sorter (FACS) clone sorting of IL-3-induced blast cells. These results provide evidence that cells of B lymphoid and myeloid lineage can originate from single primitive hematopoieitc stem cells.

A maximum neural network algorithm for route selection problems in multihop radio networks

In a multihop radio network, packets are transmitted from course nodes to destination nodes by activating several links between nodes. Each node can either send a packet to, or receive a packet from, at most one of its adjacent nodes simultaneously. To minimize the transmission time for given requests, the problems must be solved by selecting a transmission route for each request (the routing problem) and by finding a link activation schedule (the link activation problem). The routing problem is decomposed into two subproblems: the candidate extraction problem and the route selection problem. In this paper, we propose a neural network algorithm using the maximum neuron model for the route selection problem. We verify through simulations that our algorithm finds better solutions in a shorter time than the existing algorithms. We also probe the NP-hardness of this problem.

A three-stage heuristic and neural network algorithm for channel assignment in cellular radio networks

A three stage algorithm of heuristic search methods and a neural network is presented for the channel assignment problem in cellular mobile communication systems. This NP complete problem requires us to find a channel assignment to requested calls with the minimum number of channels subject to interference constraints between channels. The proposed algorithm consists of: (1) the regular interval assignment stage, (2) the greedy assignment stage, and (3) the neural network assignment stage. The performance is evaluated through benchmark problems, where our algorithm finds the optimum or near optimum solutions in all the instances.

A binary neural network approach for one-shot scheduling problems in multicast packet switching systems

A multicast packet switching system can replicate a packet in the window of each input port to send out the copies from different output ports simultaneously. In order to maximize the throughput, a combinatorial optimization problem must be solved in real time of finding a switching configuration which does not only satisfy the constraints on the system, but also maximizes the number of copies under transmission demands. In this paper, we focus on the one-shot scheduling problem where all the copies of selected packets must be sent out simultaneously. We propose the neural network composed of W/spl times/N binary neurons for the problem in the W-window-N-input-port system. The motion equation is newly defined with three heuristic methods. We verify the performance through simulations in up to 3-window-1000-input-port systems, where our binary neural network provides the better performance than the existing methods so as to reduce the delay time under practical situations.