In an Energy Harvesting system (EHS) the gamma process is used to model the electromagnetic energy received from radiofrequency (RF) radiation. The stochastic characterization of the harvested energy as a continuous-time stochastic process, namely, gamma process, is obtained from the Nakagami-m fading model, which describes the signal reception in a large amount of types of radiofrequency channels. Using the gamma process, some performance measures of the EHS system are obtained. Also, a transmission policy subject to different fading conditions is considered.
In recent years, invisibility has become a research area of increasing interest due to the advances in material engineering. It may be possible to achieve invisibility through cloaking devices by coating the body using one or more layers of materials with the proper electromagnetic properties. By using techniques associated to plasmonic cloaking it is maybe possible to obtain also invisibility for small objects with several layers of homogeneous materials working from inside the object. We demonstrate numerically that it is, therefore, possible to achieve invisibility through an inner system based on scattering cancellation techniques.
Electromagnetic applications of periodic materials have become popular in many modern optical and RF applications. The accurate computation of the electromagnetic response of large structures requires solving problems with high number of unknowns. Fast methods are useful to deal with such big problems, but, in general they do not take advantage of the periodicity properties. Based on the behaviour of impedance matrices involved in the solution of the surface integral equations with the Method of Moments, an accelerated solution based on the FFT is implemented. The presented approach slots the original impedance matrix and it applies the FFT to calculate the exact solution of the matrix vector product in an iterative process. The proposed solution achieves a linear memory cost proportional to đȘ(N) and a computing time of đȘ(N log N), where N is the problem number of unknowns. Also, in this paper, the advantages of this technique are shown in the developed applications.
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