235 resultados para Statistical parameters


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For the quasi-static, Rayleigh-fading multiple-input multiple-output (MIMO) channel with n(t) transmit and n(r) receive antennas, Zheng and Tse showed that there exists a fundamental tradeoff between diversity and spatial-multiplexing gains, referred to as the diversity-multiplexing gain (D-MG) tradeoff. Subsequently, El Gamal, Caire, and Damen considered signaling across the same channel using an L-round automatic retransmission request (ARQ) protocol that assumes the presence of a noiseless feedback channel capable of conveying one bit of information per use of the feedback channel. They showed that given a fixed number L of ARQ rounds and no power control, there is a tradeoff between diversity and multiplexing gains, termed the diversity-multiplexing-delay (DMD) tradeoff. This tradeoff indicates that the diversity gain under the ARQ scheme for a particular information rate is considerably larger than that obtainable in the absence of feedback. In this paper, a set of sufficient conditions under which a space-time (ST) code will achieve the DMD tradeoff is presented. This is followed by two classes of explicit constructions of ST codes which meet these conditions. Constructions belonging to the first class achieve minimum delay and apply to a broad class of fading channels whenever n(r) >= n(t) and either L/n(t) or n(t)kslashL. The second class of constructions do not achieve minimum delay, but do achieve the DMD tradeoff of the fading channel for all statistical descriptions of the channel and for all values of the parameters n(r,) n(t,) L.

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The problem of learning correct decision rules to minimize the probability of misclassification is a long-standing problem of supervised learning in pattern recognition. The problem of learning such optimal discriminant functions is considered for the class of problems where the statistical properties of the pattern classes are completely unknown. The problem is posed as a game with common payoff played by a team of mutually cooperating learning automata. This essentially results in a probabilistic search through the space of classifiers. The approach is inherently capable of learning discriminant functions that are nonlinear in their parameters also. A learning algorithm is presented for the team and convergence is established. It is proved that the team can obtain the optimal classifier to an arbitrary approximation. Simulation results with a few examples are presented where the team learns the optimal classifier.

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Differential scanning calorimetry (DSC) has been used to obtain kinetic and nucleation parameters for polymer crystallization under a non-isothermal mode of operation. The available isothermal nucleation growth-rate equation has been modified for non-isothermal kinetic analysis. The values of the nucleation constant (K g ) and surface free energies (sgr, sgr e ) have been obtained for i-polybutene-1, i-polypropylene, poly(L-lactic acid), and polyoxymethylene and are compared with those obtained from isothermal kinetic analysis; a good agreement in both is seen.

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The effect of some experimental parameters, namely sample weight, particle size and its distribution, heating rate and flow rate of inert gas, on the fractional decomposition of calcium carbonate samples have been studied both experimentally and theoretical. The general conclusions obtained from theoretical analysis are corroborated qualitatively by the experimental data. The analysis indicates that the kinetic compensating effect may be partly due to the variations in experimental parameters for different experiments.

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We analyze recent experimental results of Sundström and Gillbro by using the theory of Bagchi, Fleming and Oxtoby. The experimental results are in good agreement with this theory, but not with the earlier theory of Förster and Hoffmann. By fitting the new experimental results to the theory, we obtain approximate estimates of the frequency of the excited surface (assumed harmonic) and the width of the sink function.

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In the present investigation, experiments were conducted on a tribological couple-copper pin against steel plate-using an inclined pin-on-plate sliding tester to understand the role of surface texture and roughness parameters of the plate on the coefficient friction and transfer layer formation. Two surface characteristics of the steel plates-roughness and texture-were varied in the tests. It was observed that the transfer layer formation and the coefficient of friction along with its two components, namely, the adhesion and plowing, are controlled by the surface texture of the plate. The plowing component of friction was highest for the surface texture that promotes plane strain conditions while it was lowest for the texture that favors plane stress conditions at the interface. Dimensionless quantifiable roughness parameters were formulated to describe the degree of plowing and hence the plane strain/stress type deformations taking place at the asperity level.

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We present an introductory overview of several challenging problems in the statistical characterization of turbulence. We provide examples from fluid turbulence in three and two dimensions, from the turbulent advection of passive scalars, turbulence in the one-dimensional Burgers equation, and fluid turbulence in the presence of polymer additives.

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A method is developed for demonstrating how solitons with some internal periodic motion may emerge as elementary excitations in the statistical mechanics of field systems. The procedure is demonstrated in the context of complex scalar fields which can, for appropriate choices of the Lagrangian, yield charge-carrying solitons with such internal motion. The derivation uses the techniques of the steepest-descent method for functional integrals. It is shown that, despite the constraint of some fixed total charge, a gaslike excitation of such charged solitons does emerge.

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The simultaneous state and parameter estimation problem for a linear discrete-time system with unknown noise statistics is treated as a large-scale optimization problem. The a posterioriprobability density function is maximized directly with respect to the states and parameters subject to the constraint of the system dynamics. The resulting optimization problem is too large for any of the standard non-linear programming techniques and hence an hierarchical optimization approach is proposed. It turns out that the states can be computed at the first levelfor given noise and system parameters. These, in turn, are to be modified at the second level.The states are to be computed from a large system of linear equations and two solution methods are considered for solving these equations, limiting the horizon to a suitable length. The resulting algorithm is a filter-smoother, suitable for off-line as well as on-line state estimation for given noise and system parameters. The second level problem is split up into two, one for modifying the noise statistics and the other for modifying the system parameters. An adaptive relaxation technique is proposed for modifying the noise statistics and a modified Gauss-Newton technique is used to adjust the system parameters.

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A very general and numerically quite robust algorithm has been proposed by Sastry and Gauvrit (1980) for system identification. The present paper takes it up and examines its performance on a real test example. The example considered is the lateral dynamics of an aircraft. This is used as a vehicle for demonstrating the performance of various aspects of the algorithm in several possible modes.

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Abstract is not available.

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Sequential firings with fixed time delays are frequently observed in simultaneous recordings from multiple neurons. Such temporal patterns are potentially indicative of underlying microcircuits and it is important to know when a repeatedly occurring pattern is statistically significant. These sequences are typically identified through correlation counts. In this paper we present a method for assessing the significance of such correlations. We specify the null hypothesis in terms of a bound on the conditional probabilities that characterize the influence of one neuron on another. This method of testing significance is more general than the currently available methods since under our null hypothesis we do not assume that the spiking processes of different neurons are independent. The structure of our null hypothesis also allows us to rank order the detected patterns. We demonstrate our method on simulated spike trains.

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Abstract is not available.

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A simple method for evaluating dielectric relaxation parameters ie given whioh can be used for analyeing the arelaxation times of a liquid into two absorptions.

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By using the same current-time (I-t) curves, electrochemical kinetic parameters are determined by two methods, (a) using the ratio of current at a given potential to the diffusion-controlled limiting current and (b) curve fitting method, for the reduction of Cu(II)–CyDTA complex. The analysis by the method (a) shows that the rate determining step involves only one electron although the overall reduction of the complex involves two electrons suggesting thereby the stepwise reduction of the complex. The nature of I-t curves suggests the adsorption of intermediate species at the electrode surface. Under these circumstances more reliable kinetic parameters can be obtained by the method (a) compared to that of (b). Similar observations are found in the case of reduction of Cu(II)–EDTA complex.