The world is witnessing the rapid evolution of its own nervous system by an unparalleled growth in communication technology. Like the evolution of the nervous systems in animals, this growth is being driven by a survival-of-the-fittest-mechanism. In telecommunications, the entities that fuel this growth are companies and nations who compete with each other. Companies with superior information systems can outrun and outsmart others because they serve their customers better.
On the threshold of an explosion in the variety, speed and usefulness of telecommunication networks, neural network researchers can make important contributions to this emerging new telecommunications infrastructure. The first International Workshop on Applications of Neural Networks to Telecommunications (IWANNT) was planned in response to the telecommunications industry's needs for new adaptive technologies. This workshop featured 50 talks and posters that were selected by an organizing committee of experts in both telecommunications and neural networks. These proceedings will also be available on-line in an electronic format providing multimedia figures, cross-referencing, and annotation.
Table of Contents
Contents: S.H. Bang, B.J. Sheu, J. Choi, Programmable VLSI Neural Network Processors for Equalization of Digital Communication Channels. A. Jayakumar, J. Alspector, An Analog Neural-Network Co- Processor System for Rapid Prototyping of Telecommunications Applications. J. Cid-Sueiro, A.R. Figueiras-Vidal, Improving Conventional Equalizers with Neural Networks. T.X. Brown, Neural Networks for Adaptive Equalization. R. Goodman, B. Ambrose, Applications of Learning Techniques to Network Management. M. Littman, J. Boyan, A Distributed Reinforcement Learning Scheme for Network Routing. M. Goudreau, C.L. Giles, Discovering the Structure of a Self-Routing Interconnection Network with a Recurrent Neural Network. G. Kechriotis, E. Manolakos, Implementing the Optimal CDMA Multiuser Detector with Hopfield Neural Networks. A. Jagota, Scheduling Problems in Radio Networks Using Hopfield Networks. E. Nordstrom, A Hybrid Admission Control Scheme for Broadband ATM Traffic. A. Tarraf, I. Habib, T. Saadawi, Neural Networks for ATM Multimedia Traffic Prediction. A. Chhabra, S. Chandran, R. Kasturi, Table Structure Interpretation & Neural Network Based Text Recognition for Conversion of Telephone Company Tabular Drawings. A. Amin, H. Al-Sadoun, Arabic Character Recognition System Using Artificial Neural Network. S. Amin, M. Gell, Constrained Optimization for Switching Using Neural Networks. Y-K. Park, V. Cherkassky, G. Lee, ATM Cell Scheduling for Broadband Switching Systems by Neural Network. F. Comellas, R. Roca, Using Genetic Algorithms to Design Constant Weight Codes. J. Connor, Bootstrap Methods in Neural Network Time Series Prediction. S. Haykin, L. Li, 16 kbps Adaptive Differential Pulse Code Modulation of Speech. A. Hasegawa, K. Shibata, K. Itoh, Y. Ichioka, K. Inamura, Adapting-Size Neural Network for Character Recognition on X-Ray Films. A. Holst, A. Lansner, Diagnosis of Technical Equipment Using a Bayesian Neural Network. N. Karunanithi, A Connectionist Approach for Incorporating Continuous Code Churn into Software Reliability Growth Models. S. Kwasny, B. Kalman, A.M. Engebretson, W. Wu, Real-Time Identification of Language from Raw Speech Waveforms. P. Leray, Y. Burnod, CUBICORT: A Simulation of a Multicolumn Model for 3D Image Analysis, Understanding & Compression for Digital TV, HDTV & Multimedia. H. Liu, D. Yun, Self-Organizing Finite State Vector Quantization for Image Coding. T. Martinez, G. Rudolph, A Learning Model for Adaptive Network Routing. M. Meyer, G. Pfeiffer, Multilayer Perceptron Based Equalizers Applied to Nonlinear Channels. A. Mikler, J. Wong, V. Honavar, Quo Vadis - A Framework for Adaptive Routing in Very Large Communication Networks. S. Neuhauser, Hopfield Optimization Techniques Applied to Routing in Computer Networks. J.E. Neves, L.B. Almeida, M.J. Leitão, ATM Call Control by Neural Networks. M.K. Sönmez, T. Adali, Channel Equalization by Distribution Learning: The Least Relative Entropy Algorithm. C-X. Zhang, Optimal Traffic Routing Using Self-Organization Principle. J. Connor, Prediction of Access Line Growth. B.P. Yuhas, Toll-Fraud Detection. T. John, Multistage Information Filtering Using Cascaded Neural Networks. L.R. Leerink, M.A. Jabri, Temporal Difference Learning Applied to Continuous Speech Recognition. T-D. Chiueh, T-T. Tang, L-G. Chen, Vector Quantization Using Tree-Structured Self-Organizing Feature Maps. N. Karunanithi, Identifying Fault-Prone Software Modules Using Connectionist Networks. S. Carter, R.J. Frank, D.S.W. Tansley, Clone Detection in Telecommunications Software Systems: A Neural Net Approach. L. Lewis, S. Sycamore, Learning Index Rules and Adaptation Functions for a Communications Network Fault Resolution System. T. Sone, Using Distributed Neural Networks to Identify Faults in Switching Systems. A. Chattell, J.B. Brook, A Neural Network Pre-Processor for a Fault Diagnosis Expert System.
"Even if you're not working in the area of telecommunications, neural network systems developers will find a wealth of engineering detail and application inspiration here."