Reactivation kinetics of boron acceptors in hydrogenated silicon during zero bias anneal

The reactivation kinetics of passivated boron accepters in hydrogenated silicon during zero bias annealing in the temperature range of 65-130 degrees C are reported, For large annealing times and high annealing temperatures, the reactivation process follows second-order kinetics and is rate limited by a thermally activated <(H)over tilde (2)> complex formation process, For short annealing times and low annealing temperatures, the reactivation rate is found to be larger than that due to <(H)over tilde (2)> complex formation alone. We conclude that the faster reactivation is caused by the diffusion of the liberated hydrogen atoms into the bulk as well as <(H)over tilde (2)> complex formation. The effective diffusion coefficient of hydrogen is measured and found to obey the Arrhenius relation with an activation energy (1.41 +/- 0.1) eV. (C) 1997 American Institute of Physics.

Correction for Bias in Downscaling GCM Simulations for Hydrologic Impact Assessment

In a statistical downscaling model, it is important to remove the bias of General Circulations Model (GCM) outputs resulting from various assumptions about the geophysical processes. One conventional method for correcting such bias is standardisation, which is used prior to statistical downscaling to reduce systematic bias in the mean and variances of GCM predictors relative to the observations or National Centre for Environmental Prediction/ National Centre for Atmospheric Research (NCEP/NCAR) reanalysis data. A major drawback of standardisation is that it may reduce the bias in the mean and variance of the predictor variable but it is much harder to accommodate the bias in large-scale patterns of atmospheric circulation in GCMs (e.g. shifts in the dominant storm track relative to observed data) or unrealistic inter-variable relationships. While predicting hydrologic scenarios, such uncorrected bias should be taken care of; otherwise it will propagate in the computations for subsequent years. A statistical method based on equi-probability transformation is applied in this study after downscaling, to remove the bias from the predicted hydrologic variable relative to the observed hydrologic variable for a baseline period. The model is applied in prediction of monsoon stream flow of Mahanadi River in India, from GCM generated large scale climatological data.

Effect of substrate bias voltage on the structure, electric and dielectric properties of TiO(2) thin films by DC magnetron sputtering

Titanium dioxide (TiO(2)) films have been deposited on glass and p-silicon (1 0 0) substrates by DC magnetron sputtering technique to investigate their structural, electrical and optical properties. The surface composition of the TiO(2) films has been analyzed by X-ray photoelectron spectroscopy. The TiO(2) films formed on unbiased substrates were amorphous. Application of negative bias voltage to the substrate transformed the amorphous TiO(2) into polycrystalline as confirmed by Raman spectroscopic studies. Thin film capacitors with configuration of Al/TiO(2)/p-Si have been fabricated. The leakage current density of unbiased films was 1 x10(-6) A/cm(2) at a gate bias voltage of 1.5 V and it was decreased to 1.41 x 10(-7) A/cm(2) with the increase of substrate bias voltage to -150 V owing to the increase in thickness of interfacial layer of SiO(2). Dielectric properties and AC electrical conductivity of the films were studied at various frequencies for unbiased and biased at -150 V. The capacitance at 1 MHz for unbiased films was 2.42 x 10(-10) F and it increased to 5.8 x 10(-10) F in the films formed at substrate bias voltage of -150 V. Dielectric constant of TiO(2) films were calculated from capacitance-voltage measurements at 1 MHz frequency. The dielectric constant of unbiased films was 6.2 while those formed at -150 V it increased to 19. The optical band gap of the films decreased from 3.50 to 3.42 eV with the increase of substrate bias voltage from 0 to -150 V. (C) 2011 Elsevier B. V. All rights reserved.

Oscillatory exchange bias and training effects in nanocrystalline Pr0.5Ca0.5MnO3

We report on exchange bias effects in 10 nm particles of Pr0.5Ca0.5MnO3 which appear as a result of competing interactions between the ferromagnetic (FM)/anti-ferromagnetic (AFM) phases. The fascinating new observation is the demonstration of the temperature dependence of oscillatory exchange bias (OEB) and is tunable as a function of cooling field strength below the SG phase, may be attributable to the presence of charge/spin density wave (CDW/SDW) in the AFM core of PCMO10. The pronounced training effect is noticed at 5 K from the variation of the EB field as a function of number of field cycles (n) upon the field cooling (FC) process. For n > 1, power-law behavior describes the experimental data well; however, the breakdown of spin configuration model is noticed at n >= 1. Copyright 2012 Author(s). This article is distributed under a Creative Commons Attribution 3.0 Unported License. http://dx.doi.org/10.1063/1.3696033]

Charge order suppression, emergence of ferromagnetism and absence of exchange bias effect in Bi0.25Ca0.75MnO3 nanoparticles: Electron paramagnetic resonance and magnetization studies

We report the results of magnetization and electron paramagnetic resonance (EPR) studies on nanoparticles (average diameter similar to 30 nm) of Bi0.25Ca0.75MnO3 (BCMO) and compare them with the results on bulk BCMO. The nanoparticles were prepared using the nonaqueous sol-gel technique and characterized by XRD and TEM analysis. Magnetization measurements were carried out with a commercial physical property measurement system (PPMS). While the bulk BCMO exhibits a charge ordering transition at similar to 230 K and an antiferromagnetic (AFM) transition at similar to 130 K, in the nanoparticles, the CO phase is seen to have disappeared and a transition to a ferromagnetic (FM) state is observed at T-c similar to 120 K. However, interestingly, the exchange bias effect observed in other nanomanganite ferromagnets is absent in BCMO nanoparticles. EPR measurements were carried out in the X-band between 8 and 300 K. Lineshape fitting to a Lorentzian with two terms (accounting for both the clockwise and anticlockwise rotations of the microwave field) was employed to obtain the relevant EPR parameters as functions of temperature. The results confirm the occurrence of ferromagnetism in the nanoparticles of BCMO. (C) 2012 American Institute of Physics. http://dx.doi.org/10.1063/1.4730612]

A trust model for routing in MANETs: a cognitive agents based approach

Mobile ad hoc networks (MANETs) is one of the successful wireless network paradigms which offers unrestricted mobility without depending on any underlying infrastructure. MANETs have become an exciting and im- portant technology in recent years because of the rapid proliferation of variety of wireless devices, and increased use of ad hoc networks in various applications. Like any other networks, MANETs are also prone to variety of attacks majorly in routing side, most of the proposed secured routing solutions based on cryptography and authentication methods have greater overhead, which results in latency problems and resource crunch problems, especially in energy side. The successful working of these mechanisms also depends on secured key management involving a trusted third authority, which is generally difficult to implement in MANET environ-ment due to volatile topology. Designing a secured routing algorithm for MANETs which incorporates the notion of trust without maintaining any trusted third entity is an interesting research problem in recent years. This paper propose a new trust model based on cognitive reasoning,which associates the notion of trust with all the member nodes of MANETs using a novel Behaviors-Observations- Beliefs(BOB) model. These trust values are used for detec- tion and prevention of malicious and dishonest nodes while routing the data. The proposed trust model works with the DTM-DSR protocol, which involves computation of direct trust between any two nodes using cognitive knowledge. We have taken care of trust fading over time, rewards, and penalties while computing the trustworthiness of a node and also route. A simulator is developed for testing the proposed algorithm, the results of experiments shows incorporation of cognitive reasoning for computation of trust in routing effectively detects intrusions in MANET environment, and generates more reliable routes for secured routing of data.

Precoder optimization in cognitive radio with interference constraints

We consider precoding strategies at the secondary base station (SBS) in a cognitive radio network with interference constraints at the primary users (PUs). Precoding strategies at the SBS which satisfy interference constraints at the PUs in cognitive radio networks have not been adequately addressed in the literature so far. In this paper, we consider two scenarios: i) when the primary base station (PBS) data is not available at SBS, and ii) when the PBS data is made available at the SBS. We derive the optimum MMSE and Tomlinson-Harashima precoding (THP) matrix Alters at the SBS which satisfy the interference constraints at the PUs for the former case. For the latter case, we propose a precoding scheme at the SBS which performs pre-cancellation of the PBS data, followed by THP on the pre-cancelled data. The optimum precoding matrix filters are computed through an iterative search. To illustrate the robustness of the proposed approach against imperfect CSI at the SBS, we then derive robust precoding filters under imperfect CSI for the latter case. Simulation results show that the proposed optimum precoders achieve good bit error performance at the secondary users while meeting the interference constraints at the PUs.

A novel algorithm for cooperative distributed sequential spectrum sensing in Cognitive Radio

This paper considers cooperative spectrum sensing in Cognitive Radios. In our previous work we have developed DualSPRT, a distributed algorithm for cooperative spectrum sensing using Sequential Probability Ratio Test (SPRT) at the Cognitive Radios as well as at the fusion center. This algorithm works well, but is not optimal. In this paper we propose an improved algorithm- SPRT-CSPRT, which is motivated from Cumulative Sum Procedures (CUSUM). We analyse it theoretically. We also modify this algorithm to handle uncertainties in SNR's and fading.

Performance analysis of data and voice connections in a cognitive radio network

We study the performance of cognitive (secondary) users in a cognitive radio network which uses a channel whenever the primary users are not using the channel. The usage of the channel by the primary users is modelled by an ON-OFF renewal process. The cognitive users may be transmitting data using TCP connections and voice traffic. The voice traffic is given priority over the data traffic. We theoretically compute the mean delay of TCP and voice packets and also the mean throughput of the different TCP connections. We compare the theoretical results with simulations.

Antenna Selection in Interference-Constrained Underlay Cognitive Radios: SEP-Optimal Rule and Performance Benchmarking

In the underlay mode of cognitive radio, secondary users are allowed to transmit when the primary is transmitting, but under tight interference constraints that protect the primary. However, these constraints limit the secondary system performance. Antenna selection (AS)-based multiple antenna techniques, which exploit spatial diversity with less hardware, help improve secondary system performance. We develop a novel and optimal transmit AS rule that minimizes the symbol error probability (SEP) of an average interference-constrained multiple-input-single-output secondary system that operates in the underlay mode. We show that the optimal rule is a non-linear function of the power gain of the channel from the secondary transmit antenna to the primary receiver and from the secondary transmit antenna to the secondary receive antenna. We also propose a simpler, tractable variant of the optimal rule that performs as well as the optimal rule. We then analyze its SEP with L transmit antennas, and extensively benchmark it with several heuristic selection rules proposed in the literature. We also enhance these rules in order to provide a fair comparison, and derive new expressions for their SEPs. The results bring out new inter-relationships between the various rules, and show that the optimal rule can significantly reduce the SEP.

Cognitive radio implementation for a frequency hopping primary signal

Frequency hopping communications, used in the military present significant opportunities for spectrum reuse via the cognitive radio technology. We propose a MAC which incorporates hop instant identification, and supports network discovery and formation, QOS Scheduling and secondary communications. The spectrum sensing algorithm is optimized to deal with the problem of spectral leakage. The algorithms are implemented in a SDR platform based test bed and measurement results are presented.

Assessing severe Drought and wet events over India in a future climate using a nested bias-correction approach

General circulation models (GCMs) are routinely used to simulate future climatic conditions. However, rainfall outputs from GCMs are highly uncertain in preserving temporal correlations, frequencies, and intensity distributions, which limits their direct application for downscaling and hydrological modeling studies. To address these limitations, raw outputs of GCMs or regional climate models are often bias corrected using past observations. In this paper, a methodology is presented for using a nested bias-correction approach to predict the frequencies and occurrences of severe droughts and wet conditions across India for a 48-year period (2050-2099) centered at 2075. Specifically, monthly time series of rainfall from 17 GCMs are used to draw conclusions for extreme events. An increasing trend in the frequencies of droughts and wet events is observed. The northern part of India and coastal regions show maximum increase in the frequency of wet events. Drought events are expected to increase in the west central, peninsular, and central northeast regions of India. (C) 2013 American Society of Civil Engineers.

Subject independent human action recognition using spatio-depth information and meta-cognitive RBF network

In this paper, we present a machine learning approach for subject independent human action recognition using depth camera, emphasizing the importance of depth in recognition of actions. The proposed approach uses the flow information of all 3 dimensions to classify an action. In our approach, we have obtained the 2-D optical flow and used it along with the depth image to obtain the depth flow (Z motion vectors). The obtained flow captures the dynamics of the actions in space time. Feature vectors are obtained by averaging the 3-D motion over a grid laid over the silhouette in a hierarchical fashion. These hierarchical fine to coarse windows capture the motion dynamics of the object at various scales. The extracted features are used to train a Meta-cognitive Radial Basis Function Network (McRBFN) that uses a Projection Based Learning (PBL) algorithm, referred to as PBL-McRBFN, henceforth. PBL-McRBFN begins with zero hidden neurons and builds the network based on the best human learning strategy, namely, self-regulated learning in a meta-cognitive environment. When a sample is used for learning, PBLMcRBFN uses the sample overlapping conditions, and a projection based learning algorithm to estimate the parameters of the network. The performance of PBL-McRBFN is compared to that of a Support Vector Machine (SVM) and Extreme Learning Machine (ELM) classifiers with representation of every person and action in the training and testing datasets. Performance study shows that PBL-McRBFN outperforms these classifiers in recognizing actions in 3-D. Further, a subject-independent study is conducted by leave-one-subject-out strategy and its generalization performance is tested. It is observed from the subject-independent study that McRBFN is capable of generalizing actions accurately. The performance of the proposed approach is benchmarked with Video Analytics Lab (VAL) dataset and Berkeley Multimodal Human Action Database (MHAD). (C) 2013 Elsevier Ltd. All rights reserved.