Estimation in Phase-Shift and Forward Wireless Sensor Networks

We consider a network of single-antenna sensors that observe an unknown deterministic parameter. Each sensor applies a phase shift to the observation and the sensors simultaneously transmit the result to a multi-antenna fusion center (FC). Based on its knowledge of the wireless channel to the sensors, the FC calculates values for the phase factors that minimize the variance of the parameter estimate, and feeds this information back to the sensors. The use of a phase-shift-only transmission scheme provides a simplified analog implementation at the sensor, and also leads to a simpler algorithm design and performance analysis. We propose two algorithms for this problem, a numerical solution based on a relaxed semidefinite programming problem, and a closed-form solution based on the analytic constant modulus algorithm. Both approaches are shown to provide performance close to the theoretical bound. We derive asymptotic performance analyses for cases involving large numbers of sensors or large numbers of FC antennas, and we also study the impact of phase errors at the sensor transmitters. Finally, we consider the sensor selection problem, in which only a subset of the sensors is chosen to send their observations to the FC.Comment: 28 pages, 5 figures, accepted by IEEE Transactions on Signal Processing, Apr. 201

Hopping in Disordered Media: A Model Glass Former and A Hopping Model

Two models involving particles moving by ``hopping'' in disordered media are investigated: I) A model glass-forming liquid is investigated by molecular dynamics under (pseudo-) equilibrium conditions. ``Standard'' results such as mean square displacements, intermediate scattering functions, etc. are reported. At low temperatures hopping is present in the system as indicated by a secondary peak in the distribution of particle displacements during a time interval 't'. The dynamics of the model is analyzed in terms of its potential energy landscape (potential energy as function of the 3N particle coordinates), and we present direct numerical evidence for a 30 years old picture of the dynamics at sufficiently low temperatures. Transitions between local potential energy minima in configuration space are found to involve particles moving in a cooperative string-like manner. II) In the symmetric hopping model particles are moving on a lattice by doing thermally activated hopping over energy barriers connecting nearest neighbor sites. This model is analyzed in the extreme disorder limit (i.e. low temperatures) using the Velocity Auto Correlation (VAC) method. The VAC method is developed in this thesis and has the advantage over previous methods, that it can calculate a diffusive regime in finite samples using periodic boundary conditions. Numerical results using the VAC method are compared to three analytical approximations, including the Diffusion Cluster Approximation (DCA), which is found to give excellent agrement with the numerical results.Comment: Ph.D. thesis, 101 pages, 52 figure

L(sub 1) Adaptive Control Design for NASA AirSTAR Flight Test Vehicle

In this paper we present a new L(sub 1) adaptive control architecture that directly compensates for matched as well as unmatched system uncertainty. To evaluate the L(sub 1) adaptive controller, we take advantage of the flexible research environment with rapid prototyping and testing of control laws in the Airborne Subscale Transport Aircraft Research system at the NASA Langley Research Center. We apply the L(sub 1) adaptive control laws to the subscale turbine powered Generic Transport Model. The presented results are from a full nonlinear simulation of the Generic Transport Model and some preliminary pilot evaluations of the L(sub 1) adaptive control law

Grindability and combustion behavior of coal and torrefied biomass blends

Biomass samples (pine, black poplar and chestnut woodchips) were torrefied to improve their grindability before being combusted in blends with coal. Torrefaction temperatures between 240 and 300 °C and residence times between 11 and 43 min were studied. The grindability of the torrefied biomass, evaluated from the particle size distribution of the ground sample, significantly improved compared to raw biomass. Higher temperatures increased the proportion of smaller-sized particles after grinding. Torrefied chestnut woodchips (280 °C, 22 min) showed the best grinding properties. This sample was blended with coal (5–55 wt.% biomass). The addition of torrefied biomass to coal up to 15 wt.% did not significantly increase the proportion of large-sized particles after grinding. No relevant differences in the burnout value were detected between the coal and coal/torrefied biomass blends due to the high reactivity of the coal. NO and SO2 emissions decreased as the percentage of torrefied biomass in the blend with coal increased.This work was part of the subcontracted contribution of INCAR to a project carried out by EDP Spain with the financial support from the European Regional Development Fund (ERDF) and acting IDEPA (Economic Development Agency of the Principality of Asturias) as research funding agency (Ref.: IDE/2013/000233). The authors thank A. J. Martín, member of the PrEM group at INCAR-CSIC, for his contribution.Peer reviewe

Peaks Over Threshold Method for Structural Health Monitoring Detector Design

Structural Health Monitoring (SHM) system offers new approaches to interrogate the integrity of structures. The most critical step of such systems is the damage detection step since it is the first and because performances of the following steps (damage localization, severity estimation…) depend on it. Care has thus to be taken when designing the detector. The objective of this communication is to discuss issues related to the design of a detector for the structural health monitoring of composite structures. The structure under monitoring is a substructure of an aircraft nacelle. In the absence of damage, the detector principle is to statistically characterize the healthy behavior of the structure. This characterization is based on the availability of a decision statistics synthesized from a damage index. Airline business models rely on Probability of False Alarms (Pfa) as main performance criterion. In general, the requirement on Pfa is 10E-9 which is very small. To determine the decision threshold, the approach we consider, consists to model the tail of the decision statistics using the Peaks Over Threshold method extracted from the extreme value theory (EVT). This method has been applied for different configuration of learning sample and probability of false alarm. This approach of tail distribution estimation is interesting since it is not necessary to know the distribution of the decision statistic to develop a detector. However, its main drawback is that it is necessary to have very large databases to accurately estimate decision thresholds to then decide the presence or absence of damage