Defects induced enhancement of Eu3+Emission in yttria (Y2O3:Eu3+)

Bixbyite type Y2O3:Eu3+ apart from being the efficient red phosphor extensively used in trichromatic fluorescent lamps, it is a typical system one can apply Jorgensen's refined electron spin pairing theory. This can be used to explain the enhancement in Eu3+ emission intensity observed with the aliovalent substitution in the yttria host matrix. Results based on these are explained qualitatively by considering a simple configurational coordinate model. Futhermore, an insight into the different types of defects induced with the aliovalent substitution in the yttria lattice has become possible with EPR probe.

Spectral Sensing of Adaptive Frequency Hopping Signal for Cognitive Radio

The issue of dynamic spectrum scene analysis in any cognitive radio network becomes extremely complex when low probability of intercept, spread spectrum systems are present in environment. The detection and estimation become more complex if frequency hopping spread spectrum is adaptive in nature. In this paper, we propose two phase approach for detection and estimation of frequency hoping signals. Polyphase filter bank has been proposed as the architecture of choice for detection phase to efficiently detect the presence of frequency hopping signal. Based on the modeling of frequency hopping signal it can be shown that parametric methods of line spectral analysis are well suited for estimation of frequency hopping signals if the issues of order estimation and time localization are resolved. An algorithm using line spectra parameter estimation and wavelet based transient detection has been proposed which resolves above issues in computationally efficient manner suitable for implementation in cognitive radio. The simulations show promising results proving that adaptive frequency hopping signals can be detected and demodulated in a non cooperative context, even at a very low signal to noise ratio in real time.

On generating optimal signal probabilities for random tests: A genetic approach

Genetic Algorithms are robust search and optimization techniques. A Genetic Algorithm based approach for determining the optimal input distributions for generating random test vectors is proposed in the paper. A cost function based on the COP testability measure for determining the efficacy of the input distributions is discussed, A brief overview of Genetic Algorithms (GAs) and the specific details of our implementation are described. Experimental results based on ISCAS-85 benchmark circuits are presented. The performance pf our GA-based approach is compared with previous results. While the GA generates more efficient input distributions than the previous methods which are based on gradient descent search, the overheads of the GA in computing the input distributions are larger. To account for the relatively quick convergence of the gradient descent methods, we analyze the landscape of the COP-based cost function. We prove that the cost function is unimodal in the search space. This feature makes the cost function amenable to optimization by gradient-descent techniques as compared to random search methods such as Genetic Algorithms.

Shear-induced enhancement of self-diffusion in interacting colloidal suspensions

We set up the generalized Langevin equations describing coupled single-particle and collective motion in a suspension of interacting colloidal particles in a shear how and use these to show that the measured self-diffusion coefficients in these systems should be strongly dependent on shear rate epsilon. Three regimes are found: (i) an initial const+epsilon(.2), followed by (ii) a large regime of epsilon(.1/2) behavior, crossing over to an asymptotic power-law approach (iii) D-o - const x epsilon(.-1/2) to the Stokes-Einstein value D-o. The shear dependence is isotropic up to very large shear rates and increases with the interparticle interaction strength. Our results provide a straightforward explanation of recent experiments and simulations on sheared colloids.

Resonance de-enhancement in the 2(1)A(g) state of trans-azobenzene

We analyze the origin of de-enhancement for a number of vibrational modes in the 2(1)A(g) excited state of trans-azobenzene. We have used the time-dependent wave packet analysis of the RR intensities by including the multimode damping effects in the calculation. This avoids the use of unrealistically large values for the damping parameter. It is concluded that the de-enhancement is caused by the interference between the two uncoupled electronic states, and that the intensities observed under the so-called symmetry forbidden 2(1)A(g) <-- 1(1)A(g) transition are purely due to resonance excitation. It is also observed that the use of the time-dependent approach to study the de-enhancement effects caused by multiple electronic states on the RR intensities is not necessarily useful if one is interested in the structural dynamics.

Enhancement of uniaxial magnetic anisotropy in Fe thin films grown on GaAs(001) with an MgO underlayer

The understanding and control of anisotropy in Fe films grown on cubic systems such as GaAs and MgO has been of interest from the point of view of applications in devices. We report magnetic anisotropy studies on Fe/GaAs(001) and Fe/MgO/GaAs(001) prepared by pulsed laser deposition. In Fe/GaAs(001), magneto optical Kerr effect (MOKE) measurements revealed a dominant uniaxial anisotropy for Fe thickness less than 20 monolayers (ML) and this was confirmed by ferromagnetic resonance (FMR) studies. Multiple steps in the hysteresis loops were observed for Fe films of thickness 20 and 25 ML. Whereas, in Fe/MgO/GaAs(001), even at 25 ML of Fe, the uniaxial anisotropy remained dominant. The anisotropy constants obtained from FMR spectra have shown that the relative strength of uniaxial anisotropy is higher as compared to the cubic anisotropy constant in the case of Fe/MgO/GaAs(001). (C) 2011 American Institute of Physics. doi:10.1063/1.3556941]

Reinforcement Learning With Function Approximation for Traffic Signal Control

We propose, for the first time, a reinforcement learning (RL) algorithm with function approximation for traffic signal control. Our algorithm incorporates state-action features and is easily implementable in high-dimensional settings. Prior work, e. g., the work of Abdulhai et al., on the application of RL to traffic signal control requires full-state representations and cannot be implemented, even in moderate-sized road networks, because the computational complexity exponentially grows in the numbers of lanes and junctions. We tackle this problem of the curse of dimensionality by effectively using feature-based state representations that use a broad characterization of the level of congestion as low, medium, or high. One advantage of our algorithm is that, unlike prior work based on RL, it does not require precise information on queue lengths and elapsed times at each lane but instead works with the aforementioned described features. The number of features that our algorithm requires is linear to the number of signaled lanes, thereby leading to several orders of magnitude reduction in the computational complexity. We perform implementations of our algorithm on various settings and show performance comparisons with other algorithms in the literature, including the works of Abdulhai et al. and Cools et al., as well as the fixed-timing and the longest queue algorithms. For comparison, we also develop an RL algorithm that uses full-state representation and incorporates prioritization of traffic, unlike the work of Abdulhai et al. We observe that our algorithm outperforms all the other algorithms on all the road network settings that we consider.

Signal compression through spatial frequency-based motion estimation

The interest in low bit rate video coding has increased considerably. Despite rapid progress in storage density and digital communication system performance, demand for data-transmission bandwidth and storage capacity continue to exceed the capabilities of available technologies. The growth of data-intensive digital audio, video applications and the increased use of bandwidth-limited media such as video conferencing and full motion video have not only sustained the need for efficient ways to encode analog signals, but made signal compression central to digital communication and data-storage technology. In this paper we explore techniques for compression of image sequences in a manner that optimizes the results for the human receiver. We propose a new motion estimator using two novel block match algorithms which are based on human perception. Simulations with image sequences have shown an improved bit rate while maintaining ''image quality'' when compared to conventional motion estimation techniques using the MAD block match criteria.

Distributed intrusion detection in the presence of correlated sensor readings: Signal-space and communication-complexity view-point

The problem of sensor-network-based distributed intrusion detection in the presence of clutter is considered. It is argued that sensing is best regarded as a local phenomenon in that only sensors in the immediate vicinity of an intruder are triggered. In such a setting, lack of knowledge of intruder location gives rise to correlated sensor readings. A signal-space view-point is introduced in which the noise-free sensor readings associated to intruder and clutter appear as surfaces f(s) and f(g) and the problem reduces to one of determining in distributed fashion, whether the current noisy sensor reading is best classified as intruder or clutter. Two approaches to distributed detection are pursued. In the first, a decision surface separating f(s) and f(g) is identified using Neyman-Pearson criteria. Thereafter, the individual sensor nodes interactively exchange bits to determine whether the sensor readings are on one side or the other of the decision surface. Bounds on the number of bits needed to be exchanged are derived, based on communication-complexity (CC) theory. A lower bound derived for the two-party average case CC of general functions is compared against the performance of a greedy algorithm. Extensions to the multi-party case is straightforward and is briefly discussed. The average case CC of the relevant greaterthan (CT) function is characterized within two bits. Under the second approach, each sensor node broadcasts a single bit arising from appropriate two-level quantization of its own sensor reading, keeping in mind the fusion rule to be subsequently applied at a local fusion center. The optimality of a threshold test as a quantization rule is proved under simplifying assumptions. Finally, results from a QualNet simulation of the algorithms are presented that include intruder tracking using a naive polynomial-regression algorithm. 2010 Elsevier B.V. All rights reserved.

GNSS Signal Detection Under Noise Uncertainty

High sensitivity detection techniques are required for indoor navigation using Global Navigation Satellite System (GNSS) receivers, and typically, a combination of coherent and non- coherent integration is used as the test statistic for detection. The coherent integration exploits the deterministic part of the signal and is limited due to the residual frequency error, navigation data bits and user dynamics, which are not known apriori. So, non- coherent integration, which involves squaring of the coherent integration output, is used to improve the detection sensitivity. Due to this squaring, it is robust against the artifacts introduced due to data bits and/or frequency error. However, it is susceptible to uncertainty in the noise variance, and this can lead to fundamental sensitivity limits in detecting weak signals. In this work, the performance of the conventional non-coherent integration-based GNSS signal detection is studied in the presence of noise uncertainty. It is shown that the performance of the current state of the art GNSS receivers is close to the theoretical SNR limit for reliable detection at moderate levels of noise uncertainty. Alternate robust post-coherent detectors are also analyzed, and are shown to alleviate the noise uncertainty problem. Monte-Carlo simulations are used to confirm the theoretical predictions.

Electrochemical processing of ocean manganese nodules with microbial enhancement to recover valuable metals

In this paper, electroleaching and electrobioleaching of ocean manganese nodules are discussed along with the role of galvanic interactions in bioleaching. Polarization studies using a manganese nodule slurry electrode system indicated that the maximum dissolution of iron and manganese due to electrochemical reduction occurred at negative DC potentials of -600 and -1,400 mV(SCE). Electroleaching and electrobioleaching of ocean manganese nodules in the presence of Acidithiobacillus ferrooxidans and Acidithiobacillus thiooxidans at the above negative applied DC potentials resulted insignificant dissolution of copper, nickel and cobalt in 1 M H2SO4 and in sulfuric acid solution at pH 0.5 and 2.0. Mechanisms involved in electrobioleaching of ocean manganese nodules are discussed. Galvanic leaching of ocean manganese nodules in the presence of externally added pyrite and pyrolusite for enhancement of dissolution was also studied. Various electrochemical and biochemical parameters were optimized, and the electroleaching and galvanic processes thus developed are shown to yield almost complete dissolution of all metal values. This electrobioleaching process developed in the laboratory may be cost effective, energy efficient and environmentally friendly.

Role of interface states associated with transitional nanophase precipitates in the photoluminescence enhancement of SrTiO3 : Pr3+,Al3+

SrTiO3:Pr3+,Al3+ phosphor samples with varying ratios of Sr/Ti/Al were prepared by the gel-carbonate method and the mechanism of enhancement of the red photoluminescence intensity therein was investigated. The photoluminescence (PL) spectra of SrTiO3:Pr3+ show both D-1(2) --> H-3(4) and P-3(0) --> H-3(4) emission in the red and blue spectral regions, respectively, with comparable intensity. The emission intensity of D-1(2) --> H-3(4) is drastically enhanced by the incorporation of Al3+ and excess Ti4+ in the compositional range Sr(Ti,Al-y)(O3+3y/2):Pr3+ (0.2 less than or equal to y less than or equal to 0.4) and SrTi1+xAlyO3+z:Pr3+ (0.2 less than or equal to x less than or equal to 0.5; 0.05 less than or equal to y less than or equal to 0.1; z = 2x + 3y/2) with the complete disappearance of the blue band. This cannot be explained by the simple point defect model as the EPR studies do not show any evidence for the presence of electron or hole centers. TEM investigations show the presence of exsolved nanophases of SrAl12O19 and/or TiO2 in the grain boundary region as well as grain interiors as lamellae which, in turn, form the solid-state defects, namely, dislocation networks, stacking faults and crystallographic shear planes whereby the framework of corner shared TiO6 octehedra changes over to edge-sharing TiO5-AlO5 strands as indicated from the Al-27 MAS NMR studies. The presence of transitional nanophases and the associated defects modify the excitation-emission processes by way of formation of electronic sub-levels at 3.40 and 4.43 eV, leading to magnetic-dipole related red emission with enhanced intensity. This is evidenced by the fact that SrAl12O19:Pr3+,Ti4+ shows bright red emission whereas SrAl12O19:Pr3+ does not show red photoluminescence.