Neural Network Solutions to Problems in 'Early Taction'.

In this paper we examine the application of artificial neural networks to low level processing of tactile sensory data. In analogy to the term early msion, we call the first level of processing required in tactile sensing early taction. Associated with almost all existing realizations of tactile sensors, are fundamental inverse problems which must be solved. Solutions to these inverse problems are computationally demanding. Among such inverse problems, is the problem of 'deblurring' or deconvolution of data provided by any array of tactile sensors which is also assumed to be corrupted by noise. We note that this inverse problem is ill-posed and that the technique of regularization may be used to obtain solutions. The theory of nonlinear electrical networks is utilized to describe ene~y functions for a ~lass of nonlinear networks and to show that the equilibrium states of the proposed network correspond to ~r~d solutions of the delurring problem. An entropy regularizer is incorporated into the energy function of the network for the recovery of normal stress distributions. It is demonstrated by means of both computer simulations and hardware prototypes that neural networks provide an elegant solution to the need for fast, local computation in tactile sensing. An integrated circuit prototype of the proposed network which has been designed and fabricated is discussed as well

Cascaded Neural-Analog Networks for Real Time Decomposition of Superposed Radar Signals in the Presence of Noise.

Among the numerous problems which arise in the context of radar signal processing is the problem of extraction of information from a noise corrupted signal. In this application the signal is assumed to be the superposition of outputs from multiple radar emitters. Associated with the output of each emitter is a unique set of parameters which are in general unknown. Significant parameters associated with each emitter are (i) the pulse repetition frequencies, (ii) the pulse durations (widths) associated with pulse trains and (iii) the pulse amplitudes: A superposition of the outputs of multiple emitters together with additive noise is observed at the receiver. In this study we consider the problem of decomposing such a noise corrupted linear combination of emitter outputs into an underlying set of basis signals while also identifying the parameters associated with each of the emitters involved. Foremost among our objectives is to design a system capable of performing this decomposition/classification in a demanding realtime environment. We present here a system composed of three cascaded neural-analog networks which, in simulation, has demonstrated an ability to nominally perform the task of decomposition and classification of superposed radar signals under extremely high noise conditions