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Reports on the use of the collective computational behavior of a compact neural network to realize the optimal multiuser detectors (OMD's) for code division multiple access (CDMA) communications in the presence of MAI and Gaussian noise. Popularity of the CDMA scheme; How compact realization of high precision detectors is possible.

Describes a GmC-style state constrained neuron (SCN) model for the design of processors in analog recurrent neural networks such as Hopfield neural networks. State-constrained neural network for optimization; Circuit implementation; Optimization theory based on GmC SCN; Comparison between constrained and unconstrained networks.

Presents the detailed design of a current-mode cellular neural network for optimization and image processing. Design issues; Snapshots of the energy landscape for increasing neuron gain from 1.0 to 0.8; Hardware annealing; Transistor size of the analog multiplier.

Presents an architecture of densely connected compact neural networks for the maximum-likelihood sequence estimation (MLSE) of signals in digital communications. Combinatorial minimization of detection cost; Optimization of concave Laypunov function associated with the network.

Reports on a study which looked at the compact neural network algorithm used in the partial-response maximum-likelihood sequence detection. Definition of compact neural networks; Details on cellular neural networks; Methodology used to conduct the study; Results of the study.