369 research outputs found

    Robust Distributed Estimation over Multiple Access Channels with Constant Modulus Signaling

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    A distributed estimation scheme where the sensors transmit with constant modulus signals over a multiple access channel is considered. The proposed estimator is shown to be strongly consistent for any sensing noise distribution in the i.i.d. case both for a per-sensor power constraint, and a total power constraint. When the distributions of the sensing noise are not identical, a bound on the variances is shown to establish strong consistency. The estimator is shown to be asymptotically normal with a variance (AsV) that depends on the characteristic function of the sensing noise. Optimization of the AsV is considered with respect to a transmission phase parameter for a variety of noise distributions exhibiting differing levels of impulsive behavior. The robustness of the estimator to impulsive sensing noise distributions such as those with positive excess kurtosis, or those that do not have finite moments is shown. The proposed estimator is favorably compared with the amplify and forward scheme under an impulsive noise scenario. The effect of fading is shown to not affect the consistency of the estimator, but to scale the asymptotic variance by a constant fading penalty depending on the fading statistics. Simulations corroborate our analytical results.Comment: 28 pages, 10 figures, submitted to IEEE Transactions on Signal Processing for consideratio

    Fuzzy Associative Memory in Conceptual Design

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    Examines the feasibility of utilizing fuzzy associative memory (FAM) in conceptual design and illustrates the concept by applying it to the idea generation phase of a design activity. The mapping of fuzzy functional requirements to physical design is examined. Trigger and conflict-resolution strategies are proposed to solve the vectors association problem in a multi-FAM environment. Experiments show an extremely high rate of accuracy in retrieving fuzzy information. The simplicity of its practical implementation makes this paradigm computationally attractive for this application

    A Hierarchial Neural Network Implementation for Forecasting

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    In this paper, a hierarchical neural network architecture for forecasting time series is presented. The architecture is composed of two hierarchical levels using a maximum likelihood competitive learning algorithm. The first level of the system has three experts each using backpropagation and a gating network to partition the input space in order to map the input vectors to the output vectors. The second level of the hierarchical network has an expert using fuzzy ART for producing the correct trend coming from the first level. The experiments show that the resulting network is capable of forecasting the changes in the input and identifying the trends correctl

    Generalization of Weierstrassian Elliptic Functions to Rn{\bf R}^{n}

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    The Weierstrassian ,ζ\wp, \zeta and σ\sigma functions are generalized to Rn{\bf R}^{n}. The n=3n=3 and n=4n=4 cases have already been used in gravitational and Yang-Mills instanton solutions which may be interpreted as explicit realizations of spacetime foam and the monopole condensate, respectively. The new functions satisfy higher dimensional versions of the periodicity properties and Legendre's relations obeyed by their familiar complex counterparts. For n=4n=4, the construction reproduces functions found earlier by Fueter using quaternionic methods. Integrating over lattice points along all directions but two, one recovers the original Weierstrassian elliptic functions.Comment: pp. 9, Late

    Type-tunable amplified spontaneous emission from core-seeded CdSe/CdS nanorods controlled by exciton-exciton interaction

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    Cataloged from PDF version of article.Type-tunable optical gain performance of core-seeded CdSe/CdS nanorods is studied via two-photon optical pumping. Controlling the exciton-exciton interaction by varying the core and shell size, blue-shifted and red-shifted modes of amplified spontaneous emission are systematically demonstrated and their type attributions are verified by time-resolved emission kinetics

    Solutions of the Einstein-Dirac and Seiberg-Witten Monopole Equations

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    We present unique solutions of the Seiberg-Witten Monopole Equations in which the U(1) curvature is covariantly constant, the monopole Weyl spinor consists of a single constant component, and the 4-manifold is a product of two Riemann surfaces of genuses p_1 and p_2. There are p_1 -1 magnetic vortices on one surface and p_2 - 1 electric ones on the other, with p_1 + p_2 \geq 2 p_1 = p_2= 1 being excluded). When p_1 = p_2, the electromagnetic fields are self-dual and one also has a solution of the coupled euclidean Einstein-Maxwell-Dirac equations, with the monopole condensate serving as cosmological constant. The metric is decomposable and the electromagnetic fields are covariantly constant as in the Bertotti-Robinson solution. The Einstein metric can also be derived from a K\"{a}hler potential satisfying the Monge-Amp\`{e}re equations.Comment: 22 pages. Rep. no: FGI-99-
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