Sintering studies of nano-crystalline zinc oxide

Sintering and grain growth of nano-crystalline undoped ZnO has been studied in detail over a wide range of temperature and holding time. Below 800 °C, sintering of over 70% theoretical density is not observed, irrespective of particle size. At 900 °C for 6 h, the nano-crystalline sample sinters to 99% of theoretical density whereas the density for as received sample is 93% of theoretical density. However, at 1300 °C or higher, the densification is found to be much faster and after a few hours becomes independent of holding time. Grain growth studies reveal a similar feature of attaining saturation over holding time. The average saturated grain size is found to be ∼1.5 and ∼2.2 μm at 800 and 900 °C, respectively, while at 1300 °C or higher, it is in between 12 and 13 μm.     

III–V super-lattice SPST/SPMT pin switches for THz communication: theoretical reliability and experimental feasibility studies

Microsystem Technologies - A physics-based Quantum-Modified CLassical Drift–Diffusion (QMCLDD) non-linear mathematical model has been developed for design and characterisation of GaN/AlGaN...     

Solvent Crystallization of the Fat and Fatty Acids of Schleichera trijuga Seed

The crystallizations of the kusum oil and the mixed fatty acids thereof were studied from several solvents at various temperatures (+10° C to ?60° C). The results indicate in general that in the range of temperatures studied, petroleum ether as a single solvent is comparable in efficiency to methanol and superior to both acetone and ethanol in respect of separation of the saturated and unsaturated components of the fatty acid mixture. The saturated and unsaturated fractions of the oil also are better separated by petroleum ether than acetone. Further, oleic acid essentially free from linoleic acid is obtainable by a preliminary crystallization of the fatty acid mixture from petroleum ether at ca. ?12° C, followed by two additional crystallizations from acetone at ca. ?55° C.     

BLAST-SSAHA Hybridization for Credit Card Fraud Detection

A phenomenal growth in the number of credit card transactions, especially for online purchases, has recently led to a substantial rise in fraudulent activities. Implementation of efficient fraud detection systems has thus become imperative for all credit card issuing banks to minimize their losses. In real life, fraudulent transactions are interspersed with genuine transactions and simple pattern matching is not often sufficient to detect them accurately. Thus, there is a need for combining both anomaly detection as well as misuse detection techniques. In this paper, we propose to use two-stage sequence alignment in which a profile analyzer (PA) first determines the similarity of an incoming sequence of transactions on a given credit card with the genuine cardholder's past spending sequences. The unusual transactions traced by the profile analyzer are next passed on to a deviation analyzer (DA) for possible alignment with past fraudulent behavior. The final decision about the nature of a transaction is taken on the basis of the observations by these two analyzers. In order to achieve online response time for both PA and DA, we suggest a new approach for combining two sequence alignment algorithms BLAST and SSAHA.     

Extraction efficiency of saturated-gain high-power dye laser amplifiers: effect of nonlinear signal absorption

The effect of nonlinear signal absorption (NLSA) due to ground-state absorption and excited-state absorption in a transversely pumped high-power dye laser amplifier is theoretically examined with a one-dimensional steady-state model for a dye amplifier pumped by a copper vapor laser. A well-approximated analytical expression for the extraction efficiency is derived, from which the effect of NLSA in reducing the amplifier efficiency can be appreciated immediately and can also be interpreted in terms of certain characteristic lengths. The reduction in efficiency due to NLSA is found to be largely independent of the pump power, provided that the signal power is increased linearly with the pump power to continue to saturate the amplifier gain and suppress amplified spontaneous emission.     

Outage Performance of Uplink (UL) NOMA Network

Wireless Personal Communications - This paper proposes and analyses the power allocation coefficient normalization for successive interference cancellation in power-domain non-orthogonal multiple...     

Bayesian inference and prediction of the inverse Weibull distribution for Type-II censored data

This paper describes the Bayesian inference and prediction of the inverse Weibull distribution for Type-II censored data. First we consider the Bayesian inference of the unknown parameter under a squared error loss function. Although we have discussed mainly the squared error loss function, any other loss function can easily be considered. A Gibbs sampling procedure is used to draw Markov Chain Monte Carlo (MCMC) samples, and they have in turn, been used to compute the Bayes estimates and also to construct the corresponding credible intervals with the help of an importance sampling technique. We have performed a simulation study in order to compare the proposed Bayes estimators with the maximum likelihood estimators. We further consider one-sample and two-sample Bayes prediction problems based on the observed sample and provide appropriate predictive intervals with a given coverage probability. A real life data set is used to illustrate the results derived. Some open problems are indicated for further research.     

Genetic algorithms based robust frequency estimation of sinusoidal signals with stationary errors

In this paper, we consider the fundamental problem of frequency estimation of multiple sinusoidal signals with stationary errors. We propose genetic algorithm and outlier-insensitive criterion function based technique for the frequency estimation problem. In the simulation studies and real life data analysis, it is observed that the proposed genetic algorithm based robust frequency estimators are able to resolve frequencies of the sinusoidal model with high degree of accuracy. Among the proposed methods, the genetic algorithm based least squares estimator, in the no-outlier scenario, provides efficient estimates, in the sense that their mean square errors attain the corresponding Cramér-Rao lower bounds. In the presence of outliers, the proposed robust methods perform quite well and seem to have a fairly high breakdown point with respect to level of outlier contamination. The proposed methods significantly do not depend on the initial guess values required for other iterative frequency estimation methods.     

Multi-objective optimization with artificial weed colonies

Invasive Weed Optimization (IWO) was recently proposed as a simple but powerful metaheuristic algorithm for real parameter optimization. IWO draws inspiration from the ecological process of weeds colonization and distribution and is capable of solving general multi-dimensional, linear and nonlinear optimization problems with appreciable efficiency. This article extends the basic IWO for tackling multi-objective optimization problems that aim at achieving two or more objectives (very often conflicting) simultaneously. The concept of fuzzy dominance has been used to sort the promising candidate solutions at each iteration. The new algorithm has been shown to be statistically significantly better than some state of the art existing evolutionary multi-objective algorithms, namely NSGAIILS, DECMOSA-SQP, MOEP, Clustering MOEA, GDE3, and MOEADGM on a 12-function test-suite (including both unconstrained and constrained problems) from the IEEE CEC (Congress on Evolutionary Computation) 2009 competition and special session on multi-objective optimization algorithms. The following performance metrics were considered: IGD, Spacing, and Minimum Spacing. Our experimental results suggest that IWO holds immense promise to appear as an efficient metaheuristic for multi-objective optimization.     

An inertia-adaptive particle swarm system with particle mobility factor for improved global optimization

Particle Swarm Optimization (PSO) has recently emerged as a nature-inspired algorithm for real parameter optimization. This article describes a method for improving the final accuracy and the convergence speed of PSO by firstly adding a new coefficient (called mobility factor) to the position updating equation and secondly modulating the inertia weight according to the distance between a particle and the globally best position found so far. The two-fold modification tries to balance between the explorative and exploitative tendencies of the swarm with an objective of achieving better search performance. We also mathematically analyze the effect of the modifications on the dynamics of the PSO algorithm. The new algorithm has been shown to be statistically significantly better than the basic PSO and four of its state-of-the-art variants on a twelve-function test-suite in terms of speed, accuracy, and robustness.