We propose a new interprocessor communication network, named the Segmented Bus, for multiprocessor message passing computer architectures executing groups of processes with localized communication patterns and time va...
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An effective method for visual pattern recognition using morphological techniques is presented. It is shown that it can be successfully used for the recognition of deformed letters. The method extracts morphological i...
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The effectiveness of the ellipsoid representation for geometrical reasoning is demonstrated in the context of robotics. Specifically, a robot collision-detection problem that consists of computing a quantity that refl...
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The effectiveness of the ellipsoid representation for geometrical reasoning is demonstrated in the context of robotics. Specifically, a robot collision-detection problem that consists of computing a quantity that reflects, as a function of the geometrical data, the amount of clearance between the robot and its environment is discussed. The method consists of two algorithms. The first computes the optimal ellipsoid surrounding a convex polyhedron. The second computes an analytic formula for the free margin about one ellipsoid with respect to another, as a standard eigenvalue problem. An efficient incremental version of the latter algorithm is proposed. This system has been implemented, and preliminary simulation results are provided.< >
The design of binary hypothesis tests in the absence of any statistical information, based only on a set of available observations is studied. A Structured Adaptive Network (SAN) configuration for the design of such t...
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The design of binary hypothesis tests in the absence of any statistical information, based only on a set of available observations is studied. A Structured Adaptive Network (SAN) configuration for the design of such tests based on several criteria of optimality, is presented and certain key asymptotic properties of these criteria are established.
A method for model reference adaptive control (MRAC) with improved transient performance is introduced. It is shown that with this method the zero-state output error can be made arbitrarily small. For the linear syste...
A method for model reference adaptive control (MRAC) with improved transient performance is introduced. It is shown that with this method the zero-state output error can be made arbitrarily small. For the linear system case, the value of the high frequency gain, k/sub p/, of the plant does not have to be known a priori, i.e., some uncertainty on k/sub p/ is allowed. The structure of the proposed controller allows for existing convergence results such as exponential convergence of output and parameter errors in the presence of sufficiently rich reference inputs to remain valid. It is also shown that the proposed controller demonstrates improved robustness in the presence of bounded disturbances and/or modeled dynamics, as well as in the case in which adaptation is switched off.< >
The authors present a general approach to determining the number of sinusoids present in measurements corrupted by additive white Gaussian and nonGaussian noise. The approach involves the simultaneous application of m...
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The authors present a general approach to determining the number of sinusoids present in measurements corrupted by additive white Gaussian and nonGaussian noise. The approach involves the simultaneous application of maximum a posteriori detection and nonlinear estimation using either the extended Kalman filter when the noise is Gaussian, or the extended high order filter when the noise is nonGaussian. The problem is formulated as a multiple hypothesis testing problem with assumed known a priori probabilities for each hypothesis. The advantage of the approach lies in the potential to accommodate time-varying as well as time-invariant parameters in the measurement model. Experimental evaluation of the approach demonstrates excellent performance in selecting the correct model order and estimating the system parameters even for signal-to-noise ratios as low as -5 dB.< >
A nonlinear adaptive detector/estimator is introduced for single and multiple radar data processing. The problem of target detection from returns of monostatic radar(s) is formulated as a nonlinear joint detection/est...
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A nonlinear adaptive detector/estimator is introduced for single and multiple radar data processing. The problem of target detection from returns of monostatic radar(s) is formulated as a nonlinear joint detection/estimation problem on the unknown parameters in the signal return. The problems of detecting the target and estimating its parameters are considered jointly. A bank of spatially and temporally localized nonlinear filters is used to estimate the a posteriori likelihood of the existence of the target in a given space-time resolution cell. Within a given cell, the localized filters are used to produce refined spatial estimates of the target parameters. A decision logic is used to decide on the existence of a target within any given resolution cell based on the a posteriori estimates obtained from the likelihood functions. Simulation results show excellent detection capabilities and excellent resolution in target parameter estimation for both single and multiple sensor data.< >
The authors address the problem of high-resolution parameter estimation of superimposed sinusoids using nonlinear filtering techniques. Six separate nonlinear filters are evaluated for the estimation of the parameters...
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The authors address the problem of high-resolution parameter estimation of superimposed sinusoids using nonlinear filtering techniques. Six separate nonlinear filters are evaluated for the estimation of the parameters of sinusoids in white and colored Gaussian noise. Experimental evaluation demonstrates that the nonlinear filters perform very satisfactorily (close to the Cramer-Rao bound) for reasonable values of the initial estimation error. A major advantage of using nonlinear filtering methods for harmonic retrieval is that the filters can be applied to time-varying process models as well.< >
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