The multilayer perceptron, when trained as a classifier using backpropagation, is shown to approximate the Bayes optimal discriminant function. The result is demonstrated for both the two-class problem and multiple classes. It is shown that the outputs of the multilayer perceptron approximate the a posteriori probability functions of the classes being trained. The proof applies to any number of layers and any type of unit activation function, linear or nonlinear.

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This paper presents the first physiologically motivated pulse coupled neural network (PCNN)-based image fusion network for object detection. Primate vision processing principles, such as expectation driven filtering, state dependent modulation, temporal synchronization, and multiple processing paths are applied to create a physiologically motivated image fusion network. PCNN's are used to fuse the results of several object detection techniques to improve object detection accuracy. Image processing techniques (wavelets, morphological, etc.) are used to extract target features and PCNN's are used to focus attention by segmenting and fusing the information. The object detection property of the resulting image fusion network is demonstrated on mammograms and Forward Looking Infrared Radar (FLIR) images. The network removed 94% of the false detections without removing any true detections in the FLIR images and removed 46% of the false detections while removing only 7% of the true detections in the mammograms. The model exceeded the accuracy obtained by any individual filtering methods or by logical ANDing the individual object detection technique results.

Previous work has demonstrated the viability of using RF-DNA fingerprinting to provide serial number discrimination of IEEE 802.11a WiFi devices as a means to augment conventional bit-level security. This was done using RF-DNA extracted from signal regions containing standard pre-defined responses (preamble, midamble, etc.). Using these responses, proof-of-concept demonstrations with RF-DNA fingerprinting have shown some effectiveness for providing serial number discrimination. The discrimination challenge increases considerably when pre-defined signal responses are not present. This challenge is addressed here using experimentally collected IEEE 802.16e WiMAX signals from Alvarion BreezeMAX Mobile Subscriber (MS) devices. Relative to previous Time Domain (TD) and Spectral Domain (SD) fingerprint features, joint time-frequency Gabor (GT) and Gabor-Wigner (GWT) Transform features are considered here as a means to extract greater device discriminating information. For comparison, RF-DNA is extracted from TD, SD, GT, and GWT responses and MDA/ML feature extraction and classification performed. Preliminary assessment shows that Gabor-based RF-DNA fingerprinting is much more effective than either TD or SD methods. GT RF-DNA fingerprinting achieves individual WiMAX MS device classification of 98.5% or better for SN R ≥ −3 dB.

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Aquifer cleanup efforts at contaminated sites frequently involve operation of a system of extraction wells. It has been found that contaminant load discharged by extraction wells typically declines with time, asymptotically approaching a residual level. Such behavior could be due to rate-limited desorption of an organic contaminant from aquifer solids. An analytical model is presented which accounts for rate-limited desorption of an organic solute during cleanup of a contaminated site. Model equations are presented which describe transport of a sorbing contaminant in a converging radial flow field, with sorption described by (I) equilibrium, (2) first-order rate, and {,3) Fickian diffusion expressions. The model equations are solved in the Laplace domain and numerically inverted to simulate contaminant concentrations at an extraction well. A Laplace domain solution for the total contaminant mass remaining in the aquifer is also derived. It is shown that rate-limited sorption can have a significant impact upon aquifer remediation. Approximate equivalence among the various rate-limited models is also demonstrated. INTRODUCTION It has been theoretically and experimentally shown that rate-limited sorption/desorption can have a profound effect upon the transport of sorbing organic contaminants [van Genuchten and Wierenga, 1976; Nkedi-Kizza et al., 1982; Goltz and Roberts, 1988; Ball, 1989: Brusseau and Rao, 1989]. The advection/dispersion equation, which has been traditionally used to model contaminant transport, uses a retardation factor to account for sorption, thereby implicitly assuming local equilibrium between contaminant in the sorbed and aqueous phases [Bear, 1972]. By making this assumption, the possibly large effects of rate-limited sorption/desorption are not considered. Aquifer cleanup efforts at contaminated sites frequently involve operation of a system of extraction wells [Valocchi, 1986; Keel.),, et al., 1987; Maci•ay and Cherr3.,, 1989; U.S. Environmental Protection Agency (EPA), 1989]. It has been found that contaminant load discharged by extraction wells typically declines with time, asymptotically approaching a residual level [Keely et al., 1987; Hall, 1989; Mackay and Cherr3.,, 1989; U.S. EPA, 1989]. Such behavior could be due to rate-limited desorption of an organic contaminant from aquifer solids [Keely et al., 1987; Hall, 1989; Mackay and Cherr3.,, 1989]. In this paper, an analytical model is presented which accounts for rate-limited desorption of an organic solute during cleanup of a contaminated site. By comparing model simulations with simulations of a model which assumes local e.quilibrium between contaminant in the sorbed and aqueous phases, the effects of rate-limited desorption upon aquifer cleanup (where cleanup is defined as the This paper is not subject to U.S. copyright. Published in 1991 by the American Geophysical Union. Paper number 90WR02760. attainment of a specified contaminant concentration) may be assessed. BACKGROUND Various workers have presented models describing diverging...

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