Electromagnetic Shielding Properties of Nano Carbon Filled Silicone Rubber Composites

Electromagnetic shielding has become important for various electrical systems because of the electromagnetic pollution caused by the large scale use of electronic devices operating at different frequencies and power levels. Traditionally used metallic shields lack flexibility and hence may not be the right choice for certain applications. In such situations, filled polymer composites provide a good alternative for electromagnetic shielding applications. Being polymer based, they are easy to manufacture and can be molded into the required geometry and shape. In this study, the shielding properties of multiwalled carbon nanotubes and carbon nanofibers filled silicone rubber are studied. The conductivity and the shielding effectiveness of the composites were measured at different filler loadings. Both the fillers are able to make the base polymer conducting even at very low filler loadings. The conductivity and the shielding effectiveness improved when the filler loading was above the percolation threshold.

Propagation Properties of Electromagnetic Wave in a Plasma Slab

In this paper, the calculated results about the propagation properties of electromagnetic wave in a plasma slab are described. The relationship of the propagation properties with frequencies of electromagnetic wave, and parameters of plasma (electron temperature, electron density, dimensionless collision frequency and the size of the plasma slab) is analyzed.

Uniform stability of a particle approximation of the optimal filter derivative

Sequential Monte Carlo methods, also known as particle methods, are a widely used set of computational tools for inference in non-linear non-Gaussian state-space models. In many applications it may be necessary to compute the sensitivity, or derivative, of the optimal filter with respect to the static parameters of the state-space model; for instance, in order to obtain maximum likelihood model parameters of interest, or to compute the optimal controller in an optimal control problem. In Poyiadjis et al. [2011] an original particle algorithm to compute the filter derivative was proposed and it was shown using numerical examples that the particle estimate was numerically stable in the sense that it did not deteriorate over time. In this paper we substantiate this claim with a detailed theoretical study. Lp bounds and a central limit theorem for this particle approximation of the filter derivative are presented. It is further shown that under mixing conditions these Lp bounds and the asymptotic variance characterized by the central limit theorem are uniformly bounded with respect to the time index. We demon- strate the performance predicted by theory with several numerical examples. We also use the particle approximation of the filter derivative to perform online maximum likelihood parameter estimation for a stochastic volatility model.

An investigation of electromagnetic wave propagation in plasma by shock tube

This paper presents the electromagnetic wave propagation characteristics in plasma and the attenuation coefficients of the microwave in terms of the parameters n(e), v, w, L, w(b). The phi800 mm high temperature shock tube has been used to produce a uniform plasma. In order to get the attenuation of the electromagnetic wave through the plasma behind a shock wave, the microwave transmission has been used to measure the relative change of the wave power. The working frequency is f = (2 similar to 35) GHz (w = 2pif, wave length lambda = 15 cm similar to 8 mm). The electron density in the plasma is n(e) = (3 x 10(10) similar to 1 x 10(14)) cm(-3). The collision frequency v = (1 x 10(8) similar to 6 x 10(10)) Hz. The thickness of the plasma layer L = (2 similar to 80) cm. The electron circular frequency w(b) = eB(0)/m(e), magnetic flux density B-0 = (0 similar to 0.84) T. The experimental results show that when the plasma layer is thick (such as L/lambda greater than or equal to 10), the correlation between the attenuation coefficients of the electromagnetic waves and the parameters n(e), v, w, L determined from the measurements are in good agreement with the theoretical predictions of electromagnetic wave propagations in the uniform infinite plasma. When the plasma layer is thin (such as when both L and lambda are of the same order), the theoretical results are only in a qualitative agreement with the experimental observations in the present parameter range, but the formula of the electromagnetic wave propagation theory in an uniform infinite plasma can not be used for quantitative computations of the correlation between the attenuation coefficients and the parameters n(e), v, w, L. In fact, if w < w(p), v(2) much less than w(2), the power attenuations K of the electromagnetic waves obtained from the measurements in the thin-layer plasma are much smaller than those of the theoretical predictions. On the other hand, if w > w(p), v(2) much less than w(2) (just v approximate to f), the measurements are much larger than the theoretical results. Also, we have measured the electromagnetic wave power attenuation value under the magnetic field and without a magnetic field. The result indicates that the value measured under the magnetic field shows a distinct improvement.

Convergence of the auxiliary particle implementation of the PHD filter

Optimal Bayesian multi-target filtering is in general computationally impractical owing to the high dimensionality of the multi-target state. The Probability Hypothesis Density (PHD) filter propagates the first moment of the multi-target posterior distribution. While this reduces the dimensionality of the problem, the PHD filter still involves intractable integrals in many cases of interest. Several authors have proposed Sequential Monte Carlo (SMC) implementations of the PHD filter. However, these implementations are the equivalent of the Bootstrap Particle Filter, and the latter is well known to be inefficient. Drawing on ideas from the Auxiliary Particle Filter (APF), a SMC implementation of the PHD filter which employs auxiliary variables to enhance its efficiency was proposed by Whiteley et. al. Numerical examples were presented for two scenarios, including a challenging nonlinear observation model, to support the claim. This paper studies the theoretical properties of this auxiliary particle implementation. $\mathbb{L}_p$ error bounds are established from which almost sure convergence follows.

Auxiliary particle implementation of the probability hypothesis density filter

Optimal Bayesian multi-target filtering is, in general, computationally impractical owing to the high dimensionality of the multi-target state. The Probability Hypothesis Density (PHD) filter propagates the first moment of the multi-target posterior distribution. While this reduces the dimensionality of the problem, the PHD filter still involves intractable integrals in many cases of interest. Several authors have proposed Sequential Monte Carlo (SMC) implementations of the PHD filter. However, these implementations are the equivalent of the Bootstrap Particle Filter, and the latter is well known to be inefficient. Drawing on ideas from the Auxiliary Particle Filter (APF), we present a SMC implementation of the PHD filter which employs auxiliary variables to enhance its efficiency. Numerical examples are presented for two scenarios, including a challenging nonlinear observation model.