Showing papers by "Nirwan Ansari published in 1994"

A genetic algorithm for multiprocessor scheduling

Abstract: The problem of multiprocessor scheduling can be stated as finding a schedule for a general task graph to be executed on a multiprocessor system so that the schedule length can be minimized. This scheduling problem is known to be NP-hard, and methods based on heuristic search have been proposed to obtain optimal and suboptimal solutions. Genetic algorithms have recently received much attention as a class of robust stochastic search algorithms for various optimization problems. In this paper, an efficient method based on genetic algorithms is developed to solve the multiprocessor scheduling problem. The representation of the search node is based on the order of the tasks being executed in each individual processor. The genetic operator proposed is based on the precedence relations between the tasks in the task graph. Simulation results comparing the proposed genetic algorithm, the list scheduling algorithm, and the optimal schedule using random task graphs, and a robot inverse dynamics computational task graph are presented. >

Mobile manipulator path planning by a genetic algorithm

Abstract: This article addresses the path-planning problem for a mobile manipulator system that is used to perform a sequence of tasks specified by locations and minimum oriented force capabilities. The problem is to find an optimal sequence of base positions and manipulator configurations for performing a sequence of tasks given a series of task specifications. The formulation of the problem is nonlinear. The feasible regions for the problem are nonconvex and unconnected. Genetic algorithms applied to such problems appear to be very promising while traditional optimization methods cause difficulties. Computer simulations are carried out on a three-degrees-of-freedom manipulator mounted on a two-degrees-of-freedom mobile base to search for the near optimal path-planning solution for performing the sequence of tasks. © 1994 John Wiley & Sons, Inc.

Neural Networks in Telecommunications

Abstract: Neural Networks in Telecommunications consists of a carefully edited collection of chapters that provides an overview of a wide range of telecommunications tasks being addressed with neural networks. These tasks range from the design and control of the underlying transport network to the filtering, interpretation and manipulation of the transported media. The chapters focus on specific applications, describe specific solutions and demonstrate the benefits that neural networks can provide. By doing this, the authors demonstrate that neural networks should be another tool in the telecommunications engineer's toolbox. Neural networks offer the computational power of nonlinear techniques, while providing a natural path to efficient massively-parallel hardware implementations. In addition, the ability of neural networks to learn allows them to be used on problems where straightforward heuristic or rule-based solutions do not exist. Together these capabilities mean that neural networks offer unique solutions to problems in telecommunications. For engineers and managers in telecommunications, Neural Networks in Telecommunications provides a single point of access to the work being done by leading researchers in this field, and furnishes an in-depth description of neural network applications.

Evolutionary programming for fast and robust point pattern matching

Abstract: Matching model point patterns to observed point patterns is of important concern in machine vision. Conventional search algorithms not only fail to arrive at the optimal match, but are computationally expensive, time consuming, and search the solution space sequentially. This paper presents a fast, inexpensive, algorithmically and operationally parallel evolutionary program (EP) for optimal point pattern matching based on a stochastic and heuristic optimisation framework. Novel, knowledge-based, genetic operators are defined and are dynamically controlled to achieve "fast fine tuning" and an optimal global search by efficiently combining the elements of "gradient descent" and "random search". The developed EP algorithm outperforms existing techniques and is robust as it achieves a fast, optimal pattern match even in the presence of high noise and incomplete data sets, with insignificant degradation. >

A neural network approach to broadcast scheduling in multi-hop radio networks

Abstract: The problem of scheduling interference-free transmissions with maximum throughput in a multi-hop radio network is NP-complete. The computational complexity becomes intractable as the network size increases. In this paper, the scheduling is formulated as a combinatorial optimization problem. An efficient neural network approach, namely, mean field annealing, is applied to obtain optimal transmission schedules. Numerical examples show that this method is capable of finding an interference-free schedule with (almost) optimal throughput. >

Maximizing data throughput in an integrated TDMA communication system using mean field annealing

Abstract: In an integrated TDMA communication system, voice and data are multiplexed in time to share a common transmission link. The system operates in a frame format in which time is divided into slots. A certain number of time slots in a frame are allocated to voice and the rest are used to transmit data, Maximum data throughput can be achieved by searching for the optimal configuration of relative positions of voice and data transmission in a frame. This is a NP-complete constrained optimisation problem. In the paper, mean field annealing is used to solve this problem. The determination of the Lagrange parameters, estimation of critical temperature and convergence criterion are addressed. Simulation results show that this approach is a good tradeoff between performance and computational complexity.

Traffic management of a satellite communication network using mean field annealing

Abstract: The performance of non-hierarchical circuit switched networks at moderate load conditions is improved when alternate routes are made available. However, alternate routes introduce instability under heavy and overloaded conditions, and under these load conditions network performance is found to deteriorate. To alleviate this problem, a control mechanism that reserves a traction of the capacity of each link for direct routed calls is used. In this paper, a traffic management scheme is developed to enhance the performance of a mesh-connected, circuit-switched satellite communication network. The network load is measured and the network is continually adapted by reconfiguring the map to suit the current traffic conditions. The reconfiguration of the network is done by properly allocating the capacity of each link and placing an optimal reservation on each link. The optimization is done by using a neural network-based optimization technique called mean field annealing. The simulation results show that this method of traffic management performs better than pure dynamic routing with a fixed configuration. >

Convergence and stability analysis of a synchronous adaptive CDMA-based PCS receiver

Abstract: A thorough investigation on the convergence and stability of an adaptive synchronous CDMA receiver is presented. The receiver consists of a decorrelator at the first stage and an adaptive interference canceler at the second stage. By using a steepest descent algorithm to adaptively control the weights, the knowledge of the users' received powers is no longer required. It is shown that the system has good "near-far" resistance, and can approach optimum performance when the interfering users SNRs are high. Sufficient conditions for the receiver to achieve convergence are derived, and their properties are analyzed. >

A Neurocomputing Approach to Optimizing the Performance of a Satellite Communication Network

Abstract: Satellite communications plays an important role in both the military and the civilian sector. The properties that are intrinsic to satellite communications, such as broadcasting and geographical flexibility, explain the use of this system mainly in Wide Area Networks. With the commercial sector expanding, especially in terms of number of locations and distances between them becoming more widespread, there is potential for growth in satellite communication networks to take advantage of the relatively constant cost [1].