Group testing based spectrum hole search using a simple sub-nyquist sampling scheme

In this paper, we consider the problem of finding a spectrum hole of a specified bandwidth in a given wide band of interest. We propose a new, simple and easily implementable sub-Nyquist sampling scheme for signal acquisition and a spectrum hole search algorithm that exploits sparsity in the primary spectral occupancy in the frequency domain by testing a group of adjacent subbands in a single test. The sampling scheme deliberately introduces aliasing during signal acquisition, resulting in a signal that is the sum of signals from adjacent sub-bands. Energy-based hypothesis tests are used to provide an occupancy decision over the group of subbands, and this forms the basis of the proposed algorithm to find contiguous spectrum holes. We extend this framework to a multi-stage sensing algorithm that can be employed in a variety of spectrum sensing scenarios, including non-contiguous spectrum hole search. Further, we provide the analytical means to optimize the hypothesis tests with respect to the detection thresholds, number of samples and group size to minimize the detection delay under a given error rate constraint. Depending on the sparsity and SNR, the proposed algorithms can lead to significantly lower detection delays compared to a conventional bin-by-bin energy detection scheme; the latter is in fact a special case of the group test when the group size is set to 1. We validate our analytical results via Monte Carlo simulations.

Thermoelectric properties of beta-FeSi2

We investigate the thermoelectric properties of beta-FeSi2 using first principles electronic structure and Boltzmann transport calculations. We report a high thermopower for both p- and n-type beta-FeSi2 over a wide range of carrier concentration and in addition find the performance for n-type to be higher than for the p-type. Our results indicate that, depending upon temperature, a doping level of 3 x 10(20) to 2 x 10(21) cm(-3) may optimize the thermoelectric performance. (C) 2013 AIP Publishing LLC.

Power-Controlled Reverse Channel Training in a Multiuser TDD-MIMO Spatial Multiplexing System With CSIR

This paper considers the design of a power-controlled reverse channel training (RCT) scheme for spatial multiplexing (SM)-based data transmission along the dominant modes of the channel in a time-division duplex (TDD) multiple-input and multiple-output (MIMO) system, when channel knowledge is available at the receiver. A channel-dependent power-controlled RCT scheme is proposed, using which the transmitter estimates the beamforming (BF) vectors required for the forward-link SM data transmission. Tight approximate expressions for 1) the mean square error (MSE) in the estimate of the BF vectors, and 2) a capacity lower bound (CLB) for an SM system, are derived and used to optimize the parameters of the training sequence. Moreover, an extension of the channel-dependent training scheme and the data rate analysis to a multiuser scenario with M user terminals is presented. For the single-mode BF system, a closed-form expression for an upper bound on the average sum data rate is derived, which is shown to scale as ((L-c - L-B,L- tau)/L-c) log logM asymptotically in M, where L-c and L-B,L- tau are the channel coherence time and training duration, respectively. The significant performance gain offered by the proposed training sequence over the conventional constant-power orthogonal RCT sequence is demonstrated using Monte Carlo simulations.

Effect of the Addition of B2O3 on the Density, Microstructure, Dielectric, Piezoelectric and Ferroelectric Properties of K0.5Na0.5NbO3 Ceramics

Boron oxide (B2O3) addition to pre-reacted K0.5Na0.5NbO3 (KNN) powders facilitated swift densification at relatively low sintering temperatures which was believed to be a key to minimize potassium and sodium loss. The base KNN powder was synthesized via solid-state reaction route. The different amounts (0.1-1 wt%) of B2O3 were-added, and ceramics were sintered at different temperatures and durations to optimize the amount of B2O3 needed to obtain KNN pellets with highest possible density and grain size. The 0.1 wt% B2O3-added KNN ceramics sintered at 1,100 A degrees C for 1 h exhibited higher density (97 %). Scanning electron microscopy studies confirmed an increase in average grain size with increasing B2O3 content at appropriate temperature of sintering and duration. The B2O3-added KNN ceramics exhibited improved dielectric and piezoelectric properties at room temperature. For instance, 0.1 wt% B2O3-added KNN ceramic exhibited d (33) value of 116 pC/N which is much higher than that of pure KNN ceramics. Interestingly, all the B2O3-added (0.1-1 wt%) KNN ceramics exhibited polarization-electric field (P vs. E) hysteresis loops at room temperature. The remnant polarization (P (r)) and coercive field (E (c)) values are dependent on the B2O3 content and crystallite size.

Spatially Adaptive Kernel Regression Using Risk Estimation

An important question in kernel regression is one of estimating the order and bandwidth parameters from available noisy data. We propose to solve the problem within a risk estimation framework. Considering an independent and identically distributed (i.i.d.) Gaussian observations model, we use Stein's unbiased risk estimator (SURE) to estimate a weighted mean-square error (MSE) risk, and optimize it with respect to the order and bandwidth parameters. The two parameters are thus spatially adapted in such a manner that noise smoothing and fine structure preservation are simultaneously achieved. On the application side, we consider the problem of image restoration from uniform/non-uniform data, and show that the SURE approach to spatially adaptive kernel regression results in better quality estimation compared with its spatially non-adaptive counterparts. The denoising results obtained are comparable to those obtained using other state-of-the-art techniques, and in some scenarios, superior.

Evolution of Surface Roughness During Electropolishing

The characteristics of surface roughness span a range of length scales determined by the nature of the surface generation process. The mechanism by which material is removed at a length scale determines the roughness at that scale. Electropolishing preferentially reduces the peaks of surface protuberances at sub-micron length scales to produce smooth surfaces. The material removal in electropolishing occurs by two different mechanisms of anodic leveling and microsmoothing. Due to insufficient lateral resolution, individual contribution of these two mechanisms could not be measured by conventional roughness measurement techniques and parameters. In this work, we utilize the high lateral resolution offered by Atomic force microscopy along with the power spectral density method of characterization, to study the evolution of roughness during electropolishing. The power spectral density show two corner frequencies indicating the length scales over which the two mechanisms operate. These characteristic frequencies are found to be a function of the electropolishing time and hence can be used to optimize the electropolishing process.

Single network adaptive critic aided dynamic inversion for optimal regulation and command tracking with online adaptation for enhanced robustness

To combine the advantages of both stability and optimality-based designs, a single network adaptive critic (SNAC) aided nonlinear dynamic inversion approach is presented in this paper. Here, the gains of a dynamic inversion controller are selected in such a way that the resulting controller behaves very close to a pre-synthesized SNAC controller in the output regulation sense. Because SNAC is based on optimal control theory, it makes the dynamic inversion controller operate nearly optimal. More important, it retains the two major benefits of dynamic inversion, namely (i) a closed-form expression of the controller and (ii) easy scalability to command tracking applications without knowing the reference commands a priori. An extended architecture is also presented in this paper that adapts online to system modeling and inversion errors, as well as reduced control effectiveness, thereby leading to enhanced robustness. The strengths of this hybrid method of applying SNAC to optimize an nonlinear dynamic inversion controller is demonstrated by considering a benchmark problem in robotics, that is, a two-link robotic manipulator system. Copyright (C) 2013 John Wiley & Sons, Ltd.

On Precoding for Constant K-User MIMO Gaussian Interference Channel With Finite Constellation Inputs

This paper considers linear precoding for the constant channel-coefficient K-user MIMO Gaussian interference channel (MIMO GIC) where each transmitter-i (Tx-i) requires the sending of d(i) independent complex symbols per channel use that take values from fixed finite constellations with uniform distribution to receiver-i (Rx-i) for i = 1, 2, ..., K. We define the maximum rate achieved by Tx-i using any linear precoder as the signal-to-noise ratio (SNR) tends to infinity when the interference channel coefficients are zero to be the constellation constrained saturation capacity (CCSC) for Tx-i. We derive a high-SNR approximation for the rate achieved by Tx-i when interference is treated as noise and this rate is given by the mutual information between Tx-i and Rx-i, denoted as I(X) under bar (i); (Y) under bar (i)]. A set of necessary and sufficient conditions on the precoders under which I(X) under bar (i); (Y) under bar (i)] tends to CCSC for Tx-i is derived. Interestingly, the precoders designed for interference alignment (IA) satisfy these necessary and sufficient conditions. Furthermore, we propose gradient-ascentbased algorithms to optimize the sum rate achieved by precoding with finite constellation inputs and treating interference as noise. A simulation study using the proposed algorithms for a three-user MIMO GIC with two antennas at each node with d(i) = 1 for all i and with BPSK and QPSK inputs shows more than 0.1-b/s/Hz gain in the ergodic sum rate over that yielded by precoders obtained from some known IA algorithms at moderate SNRs.

A detailed numerical study of NOx kinetics in low calorific value H-2/CO syngas flames

The present study provides an extensive and detailed numerical analysis of NO chemical kinetics in low calorific value H-2/CO syngas flames utilizing predictions by five chemical kinetic mechanisms available out of which four deal with H-2/CO while the fifth mechanism (GRI 3.0) additionally accounts for hydrocarbon chemistry. Comparison of predicted axial NO profiles in premixed flat flames with measurements at 1 bar, 3.05 bar and 9.15 bar shows considerably large quantitative differences among the various mechanisms. However, at each pressure, the quantitative reaction path diagrams show similar NO formation pathways for most of the mechanisms. Interestingly, in counterflow diffusion flames, the quantitative reaction path diagrams and sensitivity analyses using the various mechanisms reveal major differences in the NO formation pathways and reaction rates of important reactions. The NNH and N2O intermediate pathways are found to be the major contributors for NO formation in all the reaction mechanisms except GRI 3.0 in syngas diffusion flames. The GRI 3.0 mechanism is observed to predict prompt NO pathway as the major contributing pathway to NO formation. This is attributed to prediction of a large concentration of CH radical by the GRI 3.0 as opposed to a relatively negligible value predicted by all other mechanisms. Also, the back-conversion of NNH into N2O at lower pressures (2-4 bar) was uniquely observed for one of the five mechanisms. The net reaction rates and peak flame temperatures are used to correlate and explain the differences observed in the peak NO] at different pressures. This study identifies key reactions needing assessment and also highlights the need for experimental data in syngas diffusion flames in order to assess and optimize H-2/CO and nitrogen chemistry. Copyright (C) 2014, Hydrogen Energy Publications, LLC. Published by Elsevier Ltd. All rights reserved.

Effect of strain on electronic and thermoelectric properties of few layers to bulk MoS2

The sensitive dependence of the electronic and thermoelectric properties of MoS2 on applied strain opens up a variety of applications in the emerging area of straintronics. Using first-principles-based density functional theory calculations, we show that the band gap of a few layers of MoS2 can be tuned by applying normal compressive (NC) strain, biaxial compressive (BC) strain, and biaxial tensile (BT) strain. A reversible semiconductor-to-metal transition (S-M transition) is observed under all three types of strain. In the case of NC strain, the threshold strain at which the S-M transition occurs increases when the number of layers increase and becomes maximum for the bulk. On the other hand, the threshold strain for the S-M transition in both BC and BT strains decreases when the number of layers increase. The difference in the mechanisms for the S-M transition is explained for different types of applied strain. Furthermore, the effect of both strain type and the number of layers on the transport properties are also studied using Botzmann transport theory. We optimize the transport properties as a function of the number of layers and the applied strain. 3L- and 2L-MoS2 emerge as the most efficient thermoelectric materials under NC and BT strain, respectively. The calculated thermopower is large and comparable to some of the best thermoelectric materials. A comparison among the feasibility of these three types of strain is also discussed.

Group Testing-Based Spectrum Hole Search for Cognitive Radios

This paper investigates the use of adaptive group testing to find a spectrum hole of a specified bandwidth in a given wideband of interest. We propose a group testing-based spectrum hole search algorithm that exploits sparsity in the primary spectral occupancy by testing a group of adjacent subbands in a single test. This is enabled by a simple and easily implementable sub-Nyquist sampling scheme for signal acquisition by the cognitive radios (CRs). The sampling scheme deliberately introduces aliasing during signal acquisition, resulting in a signal that is the sum of signals from adjacent subbands. Energy-based hypothesis tests are used to provide an occupancy decision over the group of subbands, and this forms the basis of the proposed algorithm to find contiguous spectrum holes of a specified bandwidth. We extend this framework to a multistage sensing algorithm that can be employed in a variety of spectrum sensing scenarios, including noncontiguous spectrum hole search. Furthermore, we provide the analytical means to optimize the group tests with respect to the detection thresholds, number of samples, group size, and number of stages to minimize the detection delay under a given error probability constraint. Our analysis allows one to identify the sparsity and SNR regimes where group testing can lead to significantly lower detection delays compared with a conventional bin-by-bin energy detection scheme; the latter is, in fact, a special case of the group test when the group size is set to 1 bin. We validate our analytical results via Monte Carlo simulations.

First and second law thermodynamic analysis of air and oxy-steam biomass gasification

Gasification is an energy transformation process in which solid fuel undergoes thermochemical conversion to produce gaseous fuel, and the two most important criteria involved in such process to evaluate the performance, economics and sustainability of the technology are: the total available energy (exergy) and the energy conserved (energy efficiency). Current study focuses on the energy and exergy analysis of the oxy-steam gasification and comparing with air gasification to optimize the H-2 yield, efficiency and syngas energy density. Casuarina wood is used as a fuel, and mixture of oxygen and steam in different proportion and amount is used as a gasifying media. The results are analysed with respect to varying equivalence ratio and steam to biomass ratio (SBR). Elemental mass balance technique is employed to ensure the validity of results. First and second law thermodynamic analysis is used towards time evaluation of energy and exergy analysis. Different component of energy input and output has been studied carefully to understand the influence of varying SBR on the availability of energy and irreversibility in the system to minimize the losses with change in input parameters for optimum performance. The energy and exergy losses (irreversibility) for oxy-steam gasification system are compared with the results of air gasification, and losses are found to be lower in oxy-steam thermal conversion; which has been argued and reasoned due to the presence of N-2 in the air-gasification. The maximum exergy efficiency of 85% with energy efficiency of 82% is achieved at SBR of 0.75 on the molar basis. It has been observed that increase in SBR results in lower exergy and energy efficiency, and it is argued to be due to the high energy input in steam generation and subsequent losses in the form of physical exergy of steam in the product gas, which alone accounts for over 18% in exergy input and 8.5% in exergy of product gas at SBR of 2.7. Carbon boundary point (CBP), is identified at the SBR of 1.5, and water gas shift (WGS) reaction plays a crucial role in H-2 enrichment after carbon boundary point (CBP) is reached. Effects of SBR and CBP on the H-2/CO ratio is analysed and discussed from the perspective of energy as well as the reaction chemistry. Energy density of syngas and energy efficiency is favoured at lower SBR but higher SBR favours H-2 rich gas at the expense of efficiency. Copyright (C) 2014, Hydrogen Energy Publications, LLC. Published by Elsevier Ltd. All rights reserved.

A bottom-up approach for optimization of friction stir processing parameters; a study on aluminium 2024-T3 alloy

Friction stir processing (FSP) is emerging as one of the most competent severe plastic deformation (SPD) method for producing bulk ultra-fine grained materials with improved properties. Optimizing the process parameters for a defect free process is one of the challenging aspects of FSP to mark its commercial use. For the commercial aluminium alloy 2024-T3 plate of 6 mm thickness, a bottom-up approach has been attempted to optimize major independent parameters of the process such as plunge depth, tool rotation speed and traverse speed. Tensile properties of the optimum friction stir processed sample were correlated with the microstructural characterization done using Scanning Electron Microscope (SEM) and Electron Back-Scattered Diffraction (EBSD). Optimum parameters from the bottom-up approach have led to a defect free FSP having a maximum strength of 93% the base material strength. Micro tensile testing of the samples taken from the center of processed zone has shown an increased strength of 1.3 times the base material. Measured maximum longitudinal residual stress on the processed surface was only 30 MPa which was attributed to the solid state nature of FSP. Microstructural observation reveals significant grain refinement with less variation in the grain size across the thickness and a large amount of grain boundary precipitation compared to the base metal. The proposed experimental bottom-up approach can be applied as an effective method for optimizing parameters during FSP of aluminium alloys, which is otherwise difficult through analytical methods due to the complex interactions between work-piece, tool and process parameters. Precipitation mechanisms during FSP were responsible for the fine grained microstructure in the nugget zone that provided better mechanical properties than the base metal. (C) 2014 Elsevier Ltd. All rights reserved.

Simulation study of a two-stage adsorber system

The present study simulates a two-stage silica gel + water adsorption desalination (AD) and chiller system. The adsorber system thermally compresses the low pressure steam generated in the evaporator to the condenser pressure in two stages. Unlike a standalone adsorption chiller unit which operates in a closed cycle the present system is an open cycle wherein the condensed desalinated water is not fed back to the evaporator. The mathematical relations formulated in the current study are based on conservation of mass and energy along with isotherm relation and kinetics for RD-type silica gel + water pair. Various constitutive relations for each component namely the evaporator, adsorber and condenser are integrated in the model. The dynamics of heat exchanger are modeled using LMTD method, and LDF model is used to predict the dynamic characteristic of the adsorber bed. The system performance indicators namely, specific cooling capacity (SCC), specific daily water production (SDWP) and coefficient of performance (COP) are used as objective functions to optimize the system. The novelty of the present work is in introduction of inter-stage pressure as a new parameter for optimizing the two-stage operation of AD chiller system. (C) 2014 Elsevier Ltd. All rights reserved.

Quick, Decentralized, Energy-Efficient One-Shot Max Function Computation Using Timer-Based Selection

In several wireless sensor networks, it is of interest to determine the maximum of the sensor readings and identify the sensor responsible for it. We propose a novel, decentralized, scalable, energy-efficient, timer-based, one-shot max function computation (TMC) algorithm. In it, the sensor nodes do not transmit their readings in a centrally pre-defined sequence. Instead, the nodes are grouped into clusters, and computation occurs over two contention stages. First, the nodes in each cluster contend with each other using the timer scheme to transmit their reading to their cluster-heads. Thereafter, the cluster-heads use the timer scheme to transmit the highest sensor reading in their cluster to the fusion node. One new challenge is that the use of the timer scheme leads to collisions, which can make the algorithm fail. We optimize the algorithm to minimize the average time required to determine the maximum subject to a constraint on the probability that it fails to find the maximum. TMC significantly lowers average function computation time, average number of transmissions, and average energy consumption compared to approaches proposed in the literature.