Multiobjective PSO based adaption of neural network topology for pixel classification in satellite imagery

The proposed work involves the multiobjective PSO based adaption of optimal neural network topology for the classification of multispectral satellite images. It is per pixel supervised classification using spectral bands (original feature space). This paper also presents a thorough experimental analysis to investigate the behavior of neural network classifier for given problem. Based on 1050 number of experiments, we conclude that following two critical issues needs to be addressed: (1) selection of most discriminative spectral bands and (2) determination of optimal number of nodes in hidden layer. We propose new methodology based on multiobjective particle swarm optimization (MOPSO) technique to determine discriminative spectral bands and the number of hidden layer node simultaneously. The accuracy with neural network structure thus obtained is compared with that of traditional classifiers like MLC and Euclidean classifier. The performance of proposed classifier is evaluated quantitatively using Xie-Beni and β indexes. The result shows the superiority of the proposed method to the conventional one.     

Study of metal assisted anisotropic chemical etching of silicon for high aspect ratio in crystalline silicon solar cells

Textured surface is commonly used to enhance the efficiency of silicon solar cells by reducing the overall reflectance and improving the light scattering. In this study, a comparison between isotropic and anisotropic etching methods was investigated. The deep funnel shaped structures with high aspect ratio are proposed for better light trapping with low reflectance in crystalline silicon solar cells. The anisotropic metal assisted chemical etching (MACE) was used to form the funnel shaped structures with various aspect ratios. The funnel shaped structures showed an average reflectance of 14.75% while it was 15.77% for the pillar shaped structures. The average reflectance was further reduced to 9.49% using deep funnel shaped structures with an aspect ratio of 1:1.18. The deep funnel shaped structures with high aspect ratios can be employed for high performance of crystalline silicon solar cells.     

The investigation of gold/zirconia as a photocatalyst for the direct synthesis of imines from alcohols and aniline

Au/ZrO₂ nanoparticles have been widely used as photocatalysts in various organic syntheses because of their localized surface plasmon resonance (LSPR) effects. In our work, Au/ZrO₂ has been synthesized by a solution method and it was used as a heterogeneous catalyst in the synthesis of imines from alcohols and aniline with irradiation by visible light. The reaction occurred in two steps: step 1 was the aerobic oxidation of the alcohols and step 2 was the nucleophilic addition of aniline. Dimethyl sulfoxide (DMSO) was used as a solvent in the reaction. The selectivity in the synthesis of imines over 3 wt% Au/ZrO₂ (with mean particle size of 5 nm) was high (over 90%) with irradiation by visible light at room temperature, and an obvious difference in the conversion was observed between the reactions with light irradiation and those without light. The intensity and wavelength of the light strongly affected the reaction. The Au/ZrO₂ could be used at least 5 times. A reaction mechanism was proposed based on the experimental results. The results indicate that the reaction of alcohols and aniline using Au/ZrO₂ as the photocatalyst can proceed under mild conditions. Furthermore, this process is environmentally friendly and green.     

Optimal-correlation-based reconstruction for distributed compressed video sensing

Distributed compressed video sensing (DCVS) is a framework that integrates both compressed sensing and distributed video coding characteristics to achieve a low-complexity video coding. However, how to design an efficient joint reconstruction by leveraging more realistic signal models is still an open challenge. In this paper, we present a novel optimal-correlation-based reconstruction method for compressively sampled videos from multiple measurement vectors. In our method, the sparsity is mainly exploited through inter-signal correlations rather than the traditional frequency transform, wherein the optimization is not only over the signal space to satisfy data consistency but also over all possible linear correlation models to achieve minimum-l₁-norm correlation noise. Additionally, a two-phase Bregman iterative based algorithm is outlined for solving the optimization problem. Simulation results show that our proposal can achieve an improved reconstruction performance in comparison to the conventional approaches, and especially, offer a 0.7–9.9 dB gain in the average PSNR for DCVS.     

Multiobjective optimization and decision-making for DG planning considering benefits between distribution company and DGs owner

Environmental concerns and fossil fuels uncertainties have resulted in promotion of multi-source and multi-type distributed generation (DG). However, the development of DG has brought new challenges to distribution system. This paper proposes a multiobjective optimization and decision-making methodology for determining size and site of multi-source and multi-type DG in distribution networks. The proposed method is based on the combination of analytical method and multi-objective optimization method and set pair of analysis (SPA). The comprehensive analysis of the loss sensitivity factor, voltage profile and reliability gave DG candidate locations. The multi-objective optimization method is based on an already-known but suitably modified Non-Dominated Sorting Genetic Algorithm (NSGA) to solve the constructed formulations, which include maximizing benefits of DG owner and Distribution Companies (DisCo) while meeting some constraints. The objective not only includes costs for DG investment, DG operation and maintenance, purchase of power by DisCo but also involving quantization for improvement of losses, voltage, reliability, etc. SPA, which is a multi-attribute decision analysis, is applied to obtain the synthetic priority of pareto solutions and carry out rank stability analysis. Furthermore, the proposed technique is applied to 37-bus distribution network. The results show that the proposed method is fast, reliable and available to determine size and site of DG as well as balance benefits between DG owner and DisCo.     

The effect of Cu addition on the thermoelectric properties of Cu2CdGeSe4

Recently, research in copper based quaternary chalcogenide materials has focused on the study of thermoelectric properties due to the complexity in the crystal structure. In the present work, stoichiometric quaternary chalcogenide compounds Cu_2+xCd_1−xGeSe₄ (x = 0, 0.025, 0.05, 0.075, 0.1, 0.125) were prepared by solid state synthesis. The powder X-ray diffraction patterns of all the samples showed a tetragonal crystal structure with the space group I-42m of the main phase, whereas the samples with x = 0 and x = 0.025 revealed the presence of an orthorhombic phase in addition to the main phase as confirmed by Rietveld analysis. The elemental composition of all the samples characterized by Electron Probe Micro Analyzer showed a slight deviation from the nominal composition. The transport properties were measured in the temperature range of 300 K–723 K. The electrical conductivity of all the samples increased with increasing Cu content due to the enhancement of the hole concentration caused by the substitution of Cd (divalent) by Cu (monovalent). The positive Seebeck coefficient of all the samples in the entire temperature ranges indicates that holes are the majority carriers. The Seebeck coefficient of all the samples decreased with increasing Cu content and showed a reverse trend to the electrical conductivity. The total thermal conductivity of all the samples decreased with increasing temperature which was dominated by the lattice contribution. The maximum figure of merit ZT = 0.42 at 723 K was obtained for the compound Cu_2.1Cd_0.9GeSe₄.     

Wavelet-based hybrid natural image modeling using generalized Gaussian and α-stable distributions

Natural image is characterized by its highly kurtotic and heavy-tailed distribution in wavelet domain. These typical non-Gaussian statistics are commonly described by generalized Gaussian density (GGD) or α-stable distribution. However, each of the two models has its own deficiency to capture the variety and complexity of real world scenes. Considering the statistical properties of GGD and α-stable distributions respectively, in this paper we propose a hybrid statistical model of natural image’s wavelet coefficients which is better in describing the leptokurtosis and heavy tails simultaneously. Based on a clever fusion of GGD and α-stable functions, we establish the optimal parametric hybrid model, and a close-formed Kullback–Leibler divergence of the hybrid model is derived for evaluating model accuracy. Experiment results and comparative studies demonstrate that the proposed hybrid model is closer to the true distribution of natural image’s wavelet coefficients than the single modeling using GGD or α-stable, while is beneficial for applications such as image comparison.     

Resource allocation for multi-class services in multipath networks

In multipath networks, multiple paths are available for each pair of source and destination and can be used to carry data packets parallelly. It has been recognized that using multipath could promote the transmission reliability and fault tolerance, and improve the performance of increasingly bandwidth-hungry multi-media applications. In this paper we propose the resource allocation model for multi-class services in multipath networks with the objective of utility maximization, which is an intrinsically difficult problem of nonconvex optimization. We firstly analyze the model for only elastic services and obtain the optimal rate allocation for them. Then we also discuss the model for inelastic services with nonconcave (sigmoidal or discontinuous) utilities which share common links with elastic ones, and obtain some sufficient conditions under which the global optimum for both elastic and inelastic services can be obtained. For the nonconvex optimization problem, we present a heuristic algorithm using Particle Swarm Optimization (PSO), which can lead to improved solutions over existing approaches. Finally, some numerical examples are given to verify the results obtained.     

Formation of high-density scintillation ceramic from LuAG:Ce + Lu2O3 powders obtained by co-precipitation method

The results of formation of the high density effective scintillation ceramics consisting of two compounds of the cubic symmetry, LuAG:Ce and Lu₂O₃ (LuAG:Ce + Lu₂O₃), are described. Powders of a novel material LuAG:Ce + Lu₂O₃ were synthesized by co-precipitation method. The introduction of Lu₂O₃ into LuAG:Ce was shown to increase the density of the ceramics obtained and modify its scintillation properties.     

Finite-point method for efficient timing characterization of sequential elements

Timing characterization of sequential elements, such as latches and flip-flops, is one of the critical steps for timing closure in the pipelined design. Traditional characterization of setup and hold time constraints is computationally intensive, due to the demand on high accuracy in monitoring the operation failure. To improve the efficiency, this work proposes a finite-point based method for the characterization of setup and hold time constraints. The finite-point method identifies several critical data points in the non-linear curve of timing characteristics, and abstracts the essential setup/hold information from them. Moreover, compact models are derived for each point, further reducing the computation cost. The proposed method is general for all sequential elements in the standard cell library. It is comprehensively validated using benchmark circuits at 45 nm node. Experimental results demonstrate approximately 25× reduction in characterization time, with the prediction error in setup and hold time within 9% of FO₄ nominal delay, as compared to that of SPICE simulation results.