Statistical model of an undermoded reverberation chamber

Weibull distribution is adopted to model the electric field component of a Reverberation Chamber (RC). Its first property is to include the asymptotic laws, such as Rayleigh and exponential, and its main advantage lies in the fact that the Weibull shape parameter enables a model of the departure from overmoded to undermoded RC regime. Applications are given, such as an RC modal finite element modeling and a Monte Carlo simulation: they prove that the Weibull two-parameter distribution correctly models the quality factor influence. Moreover, the relevance of the use of this extreme value distribution is illustrated.     

Nonequilibrium nonstoichiometry in ionic compounds

Ionic substances under normal conditions are stoichiometric compounds with phase diagrams featuring no homogeneity regions. Crystals grown under nonequilibrium conditions, in particular, under significant centrifugal acceleration, are characterized by nonstoichiometric compositions (e.g., K_2−x Br_1.07). In this way, it is possible to obtain nonequilibrium nonstoichiometric ionic compounds possessing certain homogeneity regions. Nonstoichiometric crystals of KCl, KBr, and KI grown under various accelerations ranging within (1.3–100.0)×10³ g exhibit insignificant variations in the lattice constants and a significant increase in micro-hardness with acceleration.     

Fractal vortex structure in a superconductor lattice model

The vortex structure is considered within the framework of a superconductor lattice model with a kinetic term of the Harper type. The problem is reduced to the analysis of discrete maps typical of the theory of fractal structures.     

Development of an end-point detector for parylene deposition process

Parylene is an emerging material for MEMS. It is an organic material that is grown by using the chemical vapor deposition method at room temperature. The deposition thickness is commonly controlled by the amount of solid-phase dimer loaded in a sublimation chamber. In a conventional deposition machine, the end point of the process is designated by the moment the dimer is exhausted. However, this end-of-process criterion does not offer precise, repeatable control of film thickness. We present the results of the development of an in situ end-point detector for a Parylene chemical vapor deposition process. The detector is based on the thermal transfer principle and can be implemented on commercial parylene deposition systems with minimal system modification. Such a sensor enables a user to stop the deposition when a targeted thickness is reached. The end point detector is very simple to implement on existing parylene deposition systems. A series of such sensors with different target deposition thickness would allow extraction of the actual deposition rate within a deposition run.     

On the possibility of determining internal wave characteristics from the ray arrival time distribution in an underwater sound channel under conditions of Ray Chaos

We describe the simplest mechanism of cluster structure formation in the timefront of an acoustic signal propagating in a submerged channel under ray chaos conditions. It is shown that this cluster structure is related to the phase space structure. This relation allows, in principle, the characteristics of an inhomogeneous medium to be determined from the timefront shape parameters. As an example, the period of inhomogeneity is calculated for a periodic perturbation in the sound velocity caused by an internal wave propagation.     

A digital architecture for support vector machines: theory, algorithm, and FPGA implementation

In this paper, we propose a digital architecture for support vector machine (SVM) learning and discuss its implementation on a field programmable gate array (FPGA). We analyze briefly the quantization effects on the performance of the SVM in classification problems to show its robustness, in the feedforward phase, respect to fixed-point math implementations; then, we address the problem of SVM learning. The architecture described here makes use of a new algorithm for SVM learning which is less sensitive to quantization errors respect to the solution appeared so far in the literature. The algorithm is composed of two parts: the first one exploits a recurrent network for finding the parameters of the SVM; the second one uses a bisection process for computing the threshold. The architecture implementing the algorithm is described in detail and mapped on a real current-generation FPGA (Xilinx Virtex II). Its effectiveness is then tested on a channel equalization problem, where real-time performances are of paramount importance.     

Memory of Distributed Computer Systems: Distinctive Features of Their Architectural-Structural Organization and Use

Distinctive features of architectural-structural organization and use of memory networks are considered with the aim of choosing an optimal configuration in designing distributed computer systems.