Evaluation, energy optimization, and spectrum analysis of an artificial noise technique to improve CWSN security

This paper presents the security evaluation, energy consumption optimization, and spectrum scarcity analysis of artificial noise techniques to increase physical-layer security in Cognitive Wireless Sensor Networks (CWSNs). These techniques introduce noise into the spectrum in order to hide real information. Nevertheless, they directly affect two important parameters in Cognitive Wireless Sensor Networks (CWSNs), energy consumption and spectrum utilization. Both are affected because the number of packets transmitted by the network and the active period of the nodes increase. Security evaluation demonstrates that these techniques are effective against eavesdropper attacks, but also optimization allows for the implementation of these approaches in low-resource networks such as Cognitive Wireless Sensor Networks. In this work, the scenario is formally modeled and the optimization according to the simulation results and the impact analysis over the frequency spectrum are presented.

Noise-induced attractor annihilation in the delayed feedback logistic map

We study dynamics of the bistable logistic map with delayed feedback, under the influence of white Gaussian noise and periodic modulation applied to the variable. This system may serve as a model to describe population dynamics under finite resources in noisy environment with seasonal fluctuations. While a very small amount of noise has no effect on the global structure of the coexisting attractors in phase space, an intermediate noise totally eliminates one of the attractors. Slow periodic modulation enhances the attractor annihilation.

Evaluation of noise environments during daily activities of university students

Noise conditions specifically in areas inside university facilities and its impact on the quality of life of university students aretopics that have received little attention. In this paper, a study of the noise conditions in which university students of various institutions in Madrid carry out their daily studies. A representative number of measurements were carried out using noise dosemeters and dataloggers in order to evaluate the levels of noise, noise dose and exposure to noise during study period and extracurricular activities. The results obtained in these measurements were compared with the recommendations given by current environmental noise regulations.This paper was complemented with a survey to get to know how students perceive the exposure to noise inuniversity environments.

Measurement of the intrinsic damping constant in individual nanodisks of Y3Fe5O12 and Y3Fe5O12|Pt

We report on an experimental study on the spin-waves relaxation rate in two series of nanodisks of diameter ϕ=300 , 500, and 700 nm, patterned out of two systems: a 20 nm thick yttrium iron garnet (YIG) film grown by pulsed laser deposition either bare or covered by 13 nm of Pt. Using a magnetic resonance force microscope, we measure precisely the ferromagnetic resonance linewidth of each individual YIG and YIG|Pt nanodisks. We find that the linewidth in the nanostructure is sensibly smaller than the one measured in the extended film. Analysis of the frequency dependence of the spectral linewidth indicates that the improvement is principally due to the suppression of the inhomogeneous part of the broadening due to geometrical confinement, suggesting that only the homogeneous broadening contributes to the linewidth of the nanostructure. For the bare YIG nano-disks, the broadening is associated to a damping constant α=4 × 10−4 . A threefold increase of the linewidth is observed for the series with Pt cap layer, attributed to the spin pumping effect. The measured enhancement allows to extract the spin mixing conductance found to be G↑↓=1.55 × 1014 Ω−1 m−2 for our YIG(20nm)|Pt interface, thus opening large opportunities for the design of YIG based nanostructures with optimized magnetic losses.

Aeroacoustic noise prediction in high speed trains due to attached and detached flow

This paper aims to set out the influence of the flow field around high speed trains in open field. To achieve this parametric analysis of the sound pressure inside the train was performed. Three vibroacoustic models of a characteristic train section are used to predict the noise inside the train in open field by using finite element method FEM, boundary element method (BEM) and statistical energy analysis (SEA) depending on the frequency range of analysis. The turbulent boundary layer excitation is implemented as the only airborne noise source, in order to focus on the study of the attached and detached flow in the surface of the train. The power spectral densities of the pressure fluctuation in the train surface proposed by [Cockburn and Roberson 1974, Rennison et al. 2009] are applied on the exterior surface of the structural subsystems in the vibroacoustic models. An increase in the sound pressure level up to10 dB can be appreciated due to the detachment of the flow around the train. These results highlight the importance to determine the detached regions prediction, making critical the airborne noise due to turbulent boundary layer.

A clustering technique for partial discharge and noise sources identification in power cables by means of waveform parameters

On-line partial discharge (PD) measurements have become a common technique for assessing the insulation condition of installed high voltage (HV) insulated cables. When on-line tests are performed in noisy environments, or when more than one source of pulse-shaped signals are present in a cable system, it is difficult to perform accurate diagnoses. In these cases, an adequate selection of the non-conventional measuring technique and the implementation of effective signal processing tools are essential for a correct evaluation of the insulation degradation. Once a specific noise rejection filter is applied, many signals can be identified as potential PD pulses, therefore, a classification tool to discriminate the PD sources involved is required. This paper proposes an efficient method for the classification of PD signals and pulse-type noise interferences measured in power cables with HFCT sensors. By using a signal feature generation algorithm, representative parameters associated to the waveform of each pulse acquired are calculated so that they can be separated in different clusters. The efficiency of the clustering technique proposed is demonstrated through an example with three different PD sources and several pulse-shaped interferences measured simultaneously in a cable system with a high frequency current transformer (HFCT).