Self-image (autoidolon) techniques for the realisation of optical computing type operations

Abstract is not available.

The effect of particle size distribution on TG

The effect of a particle size distribution on the fractional reaction has been analysed. The analysis shows that for non-isothermal TG the activation energy and frequency factor evaluated from the fractional reaction by conventional method depend on the particle size distribution, and this may lead to a kinetic compensating effect. Particle size distribution may also lead to an erroneous conclusion about the change in the mechanism of reaction.

A semi-analytical particle filter for identification of nonlinear oscillators

Particle filters find important applications in the problems of state and parameter estimations of dynamical systems of engineering interest. Since a typical filtering algorithm involves Monte Carlo simulations of the process equations, sample variance of the estimator is inversely proportional to the number of particles. The sample variance may be reduced if one uses a Rao-Blackwell marginalization of states and performs analytical computations as much as possible. In this work, we propose a semi-analytical particle filter, requiring no Rao-Blackwell marginalization, for state and parameter estimations of nonlinear dynamical systems with additively Gaussian process/observation noises. Through local linearizations of the nonlinear drift fields in the process/observation equations via explicit Ito-Taylor expansions, the given nonlinear system is transformed into an ensemble of locally linearized systems. Using the most recent observation, conditionally Gaussian posterior density functions of the linearized systems are analytically obtained through the Kalman filter. This information is further exploited within the particle filter algorithm for obtaining samples from the optimal posterior density of the states. The potential of the method in state/parameter estimations is demonstrated through numerical illustrations for a few nonlinear oscillators. The proposed filter is found to yield estimates with reduced sample variance and improved accuracy vis-a-vis results from a form of sequential importance sampling filter.

Mechanics of Deformation under Traction and Friction of a Micrometric Monolithic MoS2 Particle in Comparison with those of an Agglomerate of Nanometric MoS2 Particles

Tribology of small inorganic nanoparticles in suspension in a liquid lubricant is often impaired because these particles agglomerate even when organic dispersants are used. In this paper we use lateral force microscopy to study the deformation mechanism and dissipation under traction of two extreme configurations (1) a large MoS2 particle (similar to 20 mu m width) of about 1 mu m height and (2) an agglomerate (similar to 20 mu m width), constituting 50 nm MoS2 crystallites, of about 1 mu m height. The agglomerate records a friction coefficient which is about 5-7 times that of monolithic particle. The paper examines the mechanisms of material removal for both the particles using continuum modeling and microscopy and infers that while the agglomerate response to traction can be accounted for by the bulk mechanical properties of the material, intralayer and interlayer basal planar slips determine the friction and wear of monolithic particles. The results provide a rationale for selection of layered particles, for suspension in liquid lubricants.

Accurate Parameter Estimation of Contaminant Transport Inverse Problem using Particle Swarm Optimization

Swarm Intelligence techniques such as particle swarm optimization (PSO) are shown to be incompetent for an accurate estimation of global solutions in several engineering applications. This problem is more severe in case of inverse optimization problems where fitness calculations are computationally expensive. In this work, a novel strategy is introduced to alleviate this problem. The proposed inverse model based on modified particle swarm optimization algorithm is applied for a contaminant transport inverse model. The inverse models based on standard-PSO and proposed-PSO are validated to estimate the accuracy of the models. The proposed model is shown to be out performing the standard one in terms of accuracy in parameter estimation. The preliminary results obtained using the proposed model is presented in this work.

A discrete wavelet transform approach to multiresolution complex SAR image generation

We present a signal processing approach using discrete wavelet transform (DWT) for the generation of complex synthetic aperture radar (SAR) images at an arbitrary number of dyadic scales of resolution. The method is computationally efficient and is free from significant system-imposed limitations present in traditional subaperture-based multiresolution image formation. Problems due to aliasing associated with biorthogonal decomposition of the complex signals are addressed. The lifting scheme of DWT is adapted to handle complex signal approximations and employed to further enhance the computational efficiency. Multiresolution SAR images formed by the proposed method are presented.

Oxide Particle Surface Chemistry and Ion Transport in "Soggy Sand" Electrolytes

The crucial role of oxide surface chemical composition on ion transport in "soggy sand" electrolytes is discussed in a systematic manner. A prototype soggy sand electrolytic system comprising aerosil silica functionalized with various hydrophilic and hydrophobic moieties dispersed in lithium perchlorate-ethylene glycol solution was used for the study. Detailed rheology studies show that the attractive particle network in the case of the composite with unmodified aerosil silica (with surface silanol groups) is most favorable for percolation in ionic conductivity, as well as rendering the composite with beneficial elastic mechanical properties: Though weaker in strength compared to the composite with unmodified aerosil particles, attractive particle networks are also observed in composites of aerosil particles with surfaces partially substituted with hydrophobic groups. The percolation in ionic conductivity is, however, dependent on the size of the hydrophobic moiety. No spanning attractive particle network was formed for aerosil particles with surfaces modified with stronger hydrophilic groups (than silanol), and as a result, no percolation in ionic conductivity was observed. The composite with hydrophilic particles was a sol, contrary to gels obtained in the case of unmodified aerosil, and partially substituted with hydrophobic groups.

Performance evaluation of elitist-mutated multi-objective particle swarm optimization for integrated water resources management

Optimal allocation of water resources for various stakeholders often involves considerable complexity with several conflicting goals, which often leads to multi-objective optimization. In aid of effective decision-making to the water managers, apart from developing effective multi-objective mathematical models, there is a greater necessity of providing efficient Pareto optimal solutions to the real world problems. This study proposes a swarm-intelligence-based multi-objective technique, namely the elitist-mutated multi-objective particle swarm optimization technique (EM-MOPSO), for arriving at efficient Pareto optimal solutions to the multi-objective water resource management problems. The EM-MOPSO technique is applied to a case study of the multi-objective reservoir operation problem. The model performance is evaluated by comparing with results of a non-dominated sorting genetic algorithm (NSGA-II) model, and it is found that the EM-MOPSO method results in better performance. The developed method can be used as an effective aid for multi-objective decision-making in integrated water resource management.

Eye-view: A retinal image acquisition system

This paper presents a low cost but high resolution retinal image acquisition system of the human eye. The images acquired by a CMOS image sensor are communicated through the Universal Serial Bus (USB) interface to a personal computer for viewing and further processing. The image acquisition time was estimated to be 2.5 seconds. This system can also be used in telemedicine applications.

Memory-efficient spatial prediction image compression scheme

In prediction phase, the hierarchical tree structure obtained from the test image is used to predict every central pixel of an image by its four neighboring pixels. The prediction scheme generates the predicted error image, to which the wavelet/sub-band coding algorithm can be applied to obtain efficient compression. In quantization phase, we used a modified SPIHT algorithm to achieve efficiency in memory requirements. The memory constraint plays a vital role in wireless and bandwidth-limited applications. A single reusable list is used instead of three continuously growing linked lists as in case of SPIHT. This method is error resilient. The performance is measured in terms of PSNR and memory requirements. The algorithm shows good compression performance and significant savings in memory. (C) 2006 Elsevier B.V. All rights reserved.

Nonlinear structural dynamical system identification using adaptive particle filters

The problem of identifying parameters of nonlinear vibrating systems using spatially incomplete, noisy, time-domain measurements is considered. The problem is formulated within the framework of dynamic state estimation formalisms that employ particle filters. The parameters of the system, which are to be identified, are treated as a set of random variables with finite number of discrete states. The study develops a procedure that combines a bank of self-learning particle filters with a global iteration strategy to estimate the probability distribution of the system parameters to be identified. Individual particle filters are based on the sequential importance sampling filter algorithm that is readily available in the existing literature. The paper develops the requisite recursive formulary for evaluating the evolution of weights associated with system parameter states. The correctness of the formulations developed is demonstrated first by applying the proposed procedure to a few linear vibrating systems for which an alternative solution using adaptive Kalman filter method is possible. Subsequently, illustrative examples on three nonlinear vibrating systems, using synthetic vibration data, are presented to reveal the correct functioning of the method. (c) 2007 Elsevier Ltd. All rights reserved.

Vector evaluated particle swarm optimization (VEPSO) for multi-objective design optimization of composite structures

We present a generic method/model for multi-objective design optimization of laminated composite components, based on vector evaluated particle swarm optimization (VEPSO) algorithm. VEPSO is a novel, co-evolutionary multi-objective variant of the popular particle swarm optimization algorithm (PSO). In the current work a modified version of VEPSO algorithm for discrete variables has been developed and implemented successfully for the, multi-objective design optimization of composites. The problem is formulated with multiple objectives of minimizing weight and the total cost of the composite component to achieve a specified strength. The primary optimization variables are - the number of layers, its stacking sequence (the orientation of the layers) and thickness of each layer. The classical lamination theory is utilized to determine the stresses in the component and the design is evaluated based on three failure criteria; failure mechanism based failure criteria, Maximum stress failure criteria and the Tsai-Wu failure criteria. The optimization method is validated for a number of different loading configurations - uniaxial, biaxial and bending loads. The design optimization has been carried for both variable stacking sequences, as well fixed standard stacking schemes and a comparative study of the different design configurations evolved has been presented. (C) 2007 Elsevier Ltd. All rights reserved.

Automatic Road Extraction using High Resolution Satellite Image Based on Texture Progressive Analysis and Normalized Cut Method

In this paper the approach for automatic road extraction for an urban region using structural, spectral and geometric characteristics of roads has been presented. Roads have been extracted based on two levels: Pre-processing and road extraction methods. Initially, the image is pre-processed to improve the tolerance by reducing the clutter (that mostly represents the buildings, parking lots, vegetation regions and other open spaces). The road segments are then extracted using Texture Progressive Analysis (TPA) and Normalized cut algorithm. The TPA technique uses binary segmentation based on three levels of texture statistical evaluation to extract road segments where as, Normalizedcut method for road extraction is a graph based method that generates optimal partition of road segments. The performance evaluation (quality measures) for road extraction using TPA and normalized cut method is compared. Thus the experimental result show that normalized cut method is efficient in extracting road segments in urban region from high resolution satellite image.

Vector Evaluated Particle Swarm Optimization (VEPSO) of Supersonic Ejector for Hydrogen Fuel Cells

Fuel cells are emerging as alternate green power producers for both large power production and for use in automobiles. Hydrogen is seen as the best option as a fuel; however, hydrogen fuel cells require recirculation of unspent hydrogen. A supersonic ejector is an apt device for recirculation in the operating regimes of a hydrogen fuel cell. Optimal ejectors have to be designed to achieve best performances. The use of the vector evaluated particle swarm optimization technique to optimize supersonic ejectors with a focus on its application for hydrogen recirculation in fuel cells is presented here. Two parameters, compression ratio and efficiency, have been identified as the objective functions to be optimized. Their relation to operating and design parameters of ejector is obtained by control volume based analysis using a constant area mixing approximation. The independent parameters considered are the area ratio and the exit Mach number of the nozzle. The optimization is carried out at a particularentrainment ratio and results in a set of nondominated solutions, the Pareto front. A set of such curves can be used for choosing the optimal design parameters of the ejector.

Strength design of composite beam using gradient and particle swarm optimization

This work addresses the optimum design of a composite box-beam structure subject to strength constraints. Such box-beams are used as the main load carrying members of helicopter rotor blades. A computationally efficient analytical model for box-beam is used. Optimal ply orientation angles are sought which maximize the failure margins with respect to the applied loading. The Tsai-Wu-Hahn failure criterion is used to calculate the reserve factor for each wall and ply and the minimum reserve factor is maximized. Ply angles are used as design variables and various cases of initial starting design and loadings are investigated. Both gradient-based and particle swarm optimization (PSO) methods are used. It is found that the optimization approach leads to the design of a box-beam with greatly improved reserve factors which can be useful for helicopter rotor structures. While the PSO yields globally best designs, the gradient-based method can also be used with appropriate starting designs to obtain useful designs efficiently. (C) 2006 Elsevier Ltd. All rights reserved.