Effect of scaling on the non-quasi-static behaviour of the MOSFET for RF ICs

The effect of scaling (1 μm to 0.09 μm) on the non-quasi-static (NQS) behaviour of the MOSFET has been studied using process and device simulation. It is shown that under fixed gate (Vgs) and drain (Vds) bias voltages, the NQS transition frequency (fNQS) scales as 1/Leff rather than 1/L2eff due to the velocity saturation effect. However, under the practical scaling guidelines, considering the scaling of supply voltage as well, fNQS shows a turn around effect at the sub 100 nm regime. The relation between unity gain frequency (ft) and fNQS is also evaluated and it is shown that ft and fNQS have similar trends with scaling.

Quasi-static crack tip fields in rate-sensitive FCC single crystals

In this work, the effects of loading rate, material rate sensitivity and constraint level on quasi-static crack tip fields in a FCC single crystal are studied. Finite element simulations are performed within a mode I, plane strain modified boundary layer framework by prescribing the two term (K-T) elastic crack tip field as remote boundary conditions. The material is assumed to obey a rate-dependent crystal plasticity theory. The orientation of the single crystal is chosen so that the crack surface coincides with the crystallographic (010) plane and the crack front lies along 101] direction. Solutions corresponding to different stress intensity rates K., T-stress values and strain rate exponents m are obtained. The results show that the stress levels ahead of the crack tip increase with K. which is accompanied by gradual shrinking of the plastic zone size. However, the nature of the shear band patterns around the crack tip is not affected by the loading rate. Further, it is found that while positive T-stress enhances the opening and hydrostatic stress levels ahead of crack tip, they are considerably reduced with imposition of negative T-stress. Also, negative T-stress promotes formation of shear bands in the forward sector ahead of the crack tip and suppresses them behind the tip.

Fast Likelihood Computation in Speech Recognition using Matrices

Acoustic modeling using mixtures of multivariate Gaussians is the prevalent approach for many speech processing problems. Computing likelihoods against a large set of Gaussians is required as a part of many speech processing systems and it is the computationally dominant phase for Large Vocabulary Continuous Speech Recognition (LVCSR) systems. We express the likelihood computation as a multiplication of matrices representing augmented feature vectors and Gaussian parameters. The computational gain of this approach over traditional methods is by exploiting the structure of these matrices and efficient implementation of their multiplication. In particular, we explore direct low-rank approximation of the Gaussian parameter matrix and indirect derivation of low-rank factors of the Gaussian parameter matrix by optimum approximation of the likelihood matrix. We show that both the methods lead to similar speedups but the latter leads to far lesser impact on the recognition accuracy. Experiments on 1,138 work vocabulary RM1 task and 6,224 word vocabulary TIMIT task using Sphinx 3.7 system show that, for a typical case the matrix multiplication based approach leads to overall speedup of 46 % on RM1 task and 115 % for TIMIT task. Our low-rank approximation methods provide a way for trading off recognition accuracy for a further increase in computational performance extending overall speedups up to 61 % for RM1 and 119 % for TIMIT for an increase of word error rate (WER) from 3.2 to 3.5 % for RM1 and for no increase in WER for TIMIT. We also express pairwise Euclidean distance computation phase in Dynamic Time Warping (DTW) in terms of matrix multiplication leading to saving of approximately of computational operations. In our experiments using efficient implementation of matrix multiplication, this leads to a speedup of 5.6 in computing the pairwise Euclidean distances and overall speedup up to 3.25 for DTW.

Smoothed functional and Quasi-Newton algorithms for routing in multi-stage queueing network with constraints

We consider the problem of optimal routing in a multi-stage network of queues with constraints on queue lengths. We develop three algorithms for probabilistic routing for this problem using only the total end-to-end delays. These algorithms use the smoothed functional (SF) approach to optimize the routing probabilities. In our model all the queues are assumed to have constraints on the average queue length. We also propose a novel quasi-Newton based SF algorithm. Policies like Join Shortest Queue or Least Work Left work only for unconstrained routing. Besides assuming knowledge of the queue length at all the queues. If the only information available is the expected end-to-end delay as with our case such policies cannot be used. We also give simulation results showing the performance of the SF algorithms for this problem.

Fast acoustic likelihood computation using low-rank matrix approximation

Acoustic modeling using mixtures of multivariate Gaussians is the prevalent approach for many speech processing problems. Computing likelihoods against a large set of Gaussians is required as a part of many speech processing systems and it is the computationally dominant phase for LVCSR systems. We express the likelihood computation as a multiplication of matrices representing augmented feature vectors and Gaussian parameters. The computational gain of this approach over traditional methods is by exploiting the structure of these matrices and efficient implementation of their multiplication.In particular, we explore direct low-rank approximation of the Gaussian parameter matrix and indirect derivation of low-rank factors of the Gaussian parameter matrix by optimum approximation of the likelihood matrix. We show that both the methods lead to similar speedups but the latter leads to far lesser impact on the recognition accuracy. Experiments on a 1138 word vocabulary RM1 task using Sphinx 3.7 system show that, for a typical case the matrix multiplication approach leads to overall speedup of 46%. Both the low-rank approximation methods increase the speedup to around 60%, with the former method increasing the word error rate (WER) from 3.2% to 6.6%, while the latter increases the WER from 3.2% to 3.5%.

Quasi-based hierarchical clustering for land cover mapping using satellite images

This paper presents an improved hierarchical clustering algorithm for land cover mapping problem using quasi-random distribution. Initially, Niche Particle Swarm Optimization (NPSO) with pseudo/quasi-random distribution is used for splitting the data into number of cluster centers by satisfying Bayesian Information Criteria (BIC). Themain objective is to search and locate the best possible number of cluster and its centers. NPSO which highly depends on the initial distribution of particles in search space is not been exploited to its full potential. In this study, we have compared more uniformly distributed quasi-random with pseudo-random distribution with NPSO for splitting data set. Here to generate quasi-random distribution, Faure method has been used. Performance of previously proposed methods namely K-means, Mean Shift Clustering (MSC) and NPSO with pseudo-random is compared with the proposed approach - NPSO with quasi distribution(Faure). These algorithms are used on synthetic data set and multi-spectral satellite image (Landsat 7 thematic mapper). From the result obtained we conclude that use of quasi-random sequence with NPSO for hierarchical clustering algorithm results in a more accurate data classification.

Maximum likelihood algorithms for joint estimation of synchronisation impairments and channel in multiple input multiple output-orthogonal frequency division multiplexing system

Maximum likelihood (ML) algorithms, for the joint estimation of synchronisation impairments and channel in multiple input multiple output-orthogonal frequency division multiplexing (MIMO-OFDM) system, are investigated in this work. A system model that takes into account the effects of carrier frequency offset, sampling frequency offset, symbol timing error and channel impulse response is formulated. Cramer-Rao lower bounds for the estimation of continuous parameters are derived, which show the coupling effect among different impairments and the significance of the joint estimation. The authors propose an ML algorithm for the estimation of synchronisation impairments and channel together, using the grid search method. To reduce the complexity of the joint grid search in the ML algorithm, a modified ML (MML) algorithm with multiple one-dimensional searches is also proposed. Further, a stage-wise ML (SML) algorithm using existing algorithms, which estimate less number of parameters, is also proposed. Performance of the estimation algorithms is studied through numerical simulations and it is found that the proposed ML and MML algorithms exhibit better performance than SML algorithm.

A Phase Transition for the Uniform Distribution in the Pattern Maximum Likelihood Problem

In this paper, we consider the setting of the pattern maximum likelihood (PML) problem studied by Orlitsky et al. We present a well-motivated heuristic algorithm for deciding the question of when the PML distribution of a given pattern is uniform. The algorithm is based on the concept of a ``uniform threshold''. This is a threshold at which the uniform distribution exhibits an interesting phase transition in the PML problem, going from being a local maximum to being a local minimum.

Electronic band structure and Fermi surfaces of the quasi-two-dimensional monophosphate tungsten bronze, P4W12O44

The electronic structure of quasi-two-dimensional monophosphate tungsten bronze, P4W12O44, has been investigated by high-resolution angle-resolved photoemission spectroscopy and density functional theoretical calculations. Experimental electron-like bands around Gamma point and Fermi surfaces have similar shapes as predicted by calculations. Fermi surface mapping at different temperatures shows a depletion of density of states at low temperature in certain flat portions of the Fermi surfaces. These flat portions of the Fermi surfaces satisfy the partial nesting condition with incommensurate nesting vectors q(1) and q(2), which leads to the formation of charge density waves in this phosphate tungsten bronzes. The setting up of charge density wave in these bronzes can well explain the anomaly observed in its transport properties. Copyright (C) EPLA, 2014

Raman Evidence for Coupling of Superconducting Quasi-Particles with a Phonon and Crystal Field Excitation in Superconductor Ce0.6Y0.4FeAsO0.8F0.2

We report inelastic light scattering experiments on superconductor Ce0.6Y0.4FeAsO0.8F0.2 from 4K to 300K covering the superconducting transition temperature T-c similar to 48.6K. A strong evidence of the superconductivity induced phonon renormalization for the A(1g) phonon mode near 150cm(-1) associated with the Ce/Y vibrations is observed as reflected in the anomalous red-shift and decrease in the linewidth below T-c. Invoking the coupling of this mode with the superconducting gap, the superconducting gap (2 Delta) at zero temperature is estimated to be similar to 20meV i.e the ratio 2 Delta(0)/k(B)T(c) is similar to 5, suggesting Ce0.6Y0.4FeAsO0.8F0.2 to belong to the class of strong coupling superconductors. In addition, the mode near 430cm(-1) associated with Ce3+ crystal field excitation also shows anomalous increase in its linewidth below T-c suggesting strong coupling between crystal field excitation and the superconducting quasi-particles.

Synthesis and photocatalytic performance of quasi-fibrous ZnO

The combustion of oxidizer zinc nitrate and fuel oxalic acid results in quasi-fibrous zinc oxide. The processing parameters including oxidizer to fuel ratio, time and temperature were optimized for the resultant crystal structure and morphology. Pure hexagonal phase formation does not depend on the fuel ratio, but a stoichiometric ratio of oxidizer to fuel at 450 degrees C and 30 min results in highly crystalline ZnO with 3 mu m length and 0.5 mu m width. This quasi-fiber originates from partial fusion of near spherical, similar to 60 nm particles during the rapid rate of reaction in the combustion process. Transmission electron microscopic analysis confirms the anisotropic primary particle orientation and pore distribution within the developed quasi-fibrous particles. The degradation of methyl orange was assessed by degrading the dye in the presence of the synthesized ZnO (2.95 eV) under both UV and visible light. Quasi-fibrous zinc oxide exhibits effective photocatalytic efficiency under visible light irradiation.

Maximum likelihood analysis of multi-stress accelerated life test data of series systems with competing log-normal causes of failure

This article presents frequentist inference of accelerated life test data of series systems with independent log-normal component lifetimes. The means of the component log-lifetimes are assumed to depend on the stress variables through a linear stress translation function that can accommodate the standard stress translation functions in the literature. An expectation-maximization algorithm is developed to obtain the maximum likelihood estimates of model parameters. The maximum likelihood estimates are then further refined by bootstrap, which is also used to infer about the component and system reliability metrics at usage stresses. The developed methodology is illustrated by analyzing a real as well as a simulated dataset. A simulation study is also carried out to judge the effectiveness of the bootstrap. It is found that in this model, application of bootstrap results in significant improvement over the simple maximum likelihood estimates.

MRT Letter: A Unified Accelerated Maximum Likelihood Technique for Widefield, Confocal, and Super-Resolution 4Pi Microscopy

We propose an algorithmic technique for accelerating maximum likelihood (ML) algorithm for image reconstruction in fluorescence microscopy. This is made possible by integrating Biggs-Andrews (BA) method with ML approach. The results on widefield, confocal, and super-resolution 4Pi microscopy reveal substantial improvement in the speed of 3D image reconstruction (the number of iterations has reduced by approximately one-half). Moreover, the quality of reconstruction obtained using accelerated ML closely resembles with nonaccelerated ML method. The proposed technique is a step closer to realize real-time reconstruction in 3D fluorescence microscopy. Microsc. Res. Tech. 78:331-335, 2015. (c) 2015 Wiley Periodicals, Inc.

DETERMINATION OF THE MASS OF IGR J17091-3624 FROM ``SPECTRO-TEMPORAL'' VARIATIONS DURING THE ONSET PHASE OF THE 2011 OUTBURST

The 2011 outburst of the black hole candidate IGR J17091-3624 followed the canonical track of state transitions along with the evolution of quasi-periodic oscillation (QPO) frequencies before it began exhibiting various variability classes similar to GRS 1915+105. We use this canonical evolution of spectral and temporal properties to determine the mass of IGR J17091-3624, using three different methods: photon index (Gamma)-QPO frequency (nu) correlation, QPO frequency (nu)-time (day) evolution, and broadband spectral modeling based on two-component advective flow (TCAF). We provide a combined mass estimate for the source using a naive Bayes based joint likelihood approach. This gives a probable mass range of 11.8 M-circle dot-13.7 M-circle dot. Considering each individual estimate and taking the lowermost and uppermost bounds among all three methods, we get a mass range of 8.7 M-circle dot-15.6 M-circle dot with 90% confidence. We discuss the possible implications of our findings in the context of two-component accretion flow.