An Estimator Algorithm For Learning Automata With Changing Number Of Actions

In many problems of decision making under uncertainty the system has to acquire knowledge of its environment and learn the optimal decision through its experience. Such problems may also involve the system having to arrive at the globally optimal decision, when at each instant only a subset of the entire set of possible alternatives is available. These problems can be successfully modelled and analysed by learning automata. In this paper an estimator learning algorithm, which maintains estimates of the reward characteristics of the random environment, is presented for an automaton with changing number of actions. A learning automaton using the new scheme is shown to be e-optimal. The simulation results demonstrate the fast convergence properties of the new algorithm. The results of this study can be extended to the design of other types of estimator algorithms with good convergence properties.

New Look at Kirchhoff's Theory of Plates

KIRCHHOFF’S theory [1] and the first-order shear deformation theory (FSDT) [2] of plates in bending are simple theories and continuously used to obtain design information. Within the classical small deformation theory of elasticity, the problem consists of determining three displacements, u, v, and w, that satisfy three equilibrium equations in the interior of the plate and three specified surface conditions. FSDT is a sixth-order theory with a provision to satisfy three edge conditions and maintains, unlike in Kirchhoff’s theory, independent linear thicknesswise distribution of tangential displacement even if the lateral deflection, w, is zero along a supported edge. However, each of the in-plane distributions of the transverse shear stresses that are of a lower order is expressed as a sum of higher-order displacement terms. Kirchhoff’s assumption of zero transverse shear strains is, however, not a limitation of the theory as a first approximation to the exact 3-D solution.

A Hierarchical System of Learning Automata

A learning automaton operating in a random environment updates its action probabilities on the basis of the reactions of the environment, so that asymptotically it chooses the optimal action. When the number of actions is large the automaton becomes slow because there are too many updatings to be made at each instant. A hierarchical system of such automata with assured c-optimality is suggested to overcome that problem.The learning algorithm for the hierarchical system turns out to be a simple modification of the absolutely expedient algorithm known in the literature. The parameters of the algorithm at each level in the hierarchy depend only on the parameters and the action probabilities of the previous level. It follows that to minimize the number of updatings per cycle each automaton in the hierarchy need have only two or three actions.

Partial Integrated Guidance and Control of Surface-to-Air Interceptors for High Speed Targets

An important limitation of the existing IGC algorithms, is that they do not explicitly exploit the inherent time scale separation that exist in aerospace vehicles between rotational and translational motions and hence can be ineffective. To address this issue, a two-loop partial integrated guidance and control (PIGC) scheme has been proposed in this paper. In this design, the outer loop uses a recently developed, computationally efficient, optimal control formulation named as model predictive static programming. It gives the commanded pitch and yaw rates whereas necessary roll-rate command is generated from a roll-stabilization loop. The inner loop tracks the outer loop commands using the Dynamic inversion philosophy. Uncommonly, Six-Degree of freedom (Six-DOF) model is used directly in both the loops. This intelligent manipulation preserves the inherent time scale separation property between the translational and rotational dynamics, and hence overcomes the deficiency of current IGC designs, while preserving its benefits. Comparative studies of PIGC with one loop IGC and conventional three loop design were carried out for engaging incoming high speed target. Simulation studies demonstrate the usefulness of this method.

A method of fraud & intrusion detection for e-payment systems in mobile

The need for paying with mobile devices has urged the development of payment systems for mobile electronic commerce. In this paper we have considered two important abuses in electronic payments systems for detection. The fraud, which is an intentional deception accomplished to secure an unfair gain, and an intrusion which are any set of actions that attempt to compromise the integrity, confidentiality or availability of a resource. Most of the available fraud and intrusion detection systems for e-payments are specific to the systems where they have been incorporated. This paper proposes a generic model called as Activity-Event-Symptoms(AES) model for detecting fraud and intrusion attacks which appears during payment process in the mobile commerce environment. The AES model is designed to identify the symptoms of fraud and intrusions by observing various events/transactions occurs during mobile commerce activity. The symptoms identification is followed by computing the suspicion factors for event attributes, and the certainty factor for a fraud and intrusion is generated using these suspicion factors. We have tested the proposed system by conducting various case studies, on the in-house established mobile commerce environment over wired and wire-less networks test bed.

Nonlinear ensemble prediction of chaotic daily rainfall

The significance of treating rainfall as a chaotic system instead of a stochastic system for a better understanding of the underlying dynamics has been taken up by various studies recently. However, an important limitation of all these approaches is the dependence on a single method for identifying the chaotic nature and the parameters involved. Many of these approaches aim at only analyzing the chaotic nature and not its prediction. In the present study, an attempt is made to identify chaos using various techniques and prediction is also done by generating ensembles in order to quantify the uncertainty involved. Daily rainfall data of three regions with contrasting characteristics (mainly in the spatial area covered), Malaprabha, Mahanadi and All-India for the period 1955-2000 are used for the study. Auto-correlation and mutual information methods are used to determine the delay time for the phase space reconstruction. Optimum embedding dimension is determined using correlation dimension, false nearest neighbour algorithm and also nonlinear prediction methods. The low embedding dimensions obtained from these methods indicate the existence of low dimensional chaos in the three rainfall series. Correlation dimension method is done on th phase randomized and first derivative of the data series to check whether the saturation of the dimension is due to the inherent linear correlation structure or due to low dimensional dynamics. Positive Lyapunov exponents obtained prove the exponential divergence of the trajectories and hence the unpredictability. Surrogate data test is also done to further confirm the nonlinear structure of the rainfall series. A range of plausible parameters is used for generating an ensemble of predictions of rainfall for each year separately for the period 1996-2000 using the data till the preceding year. For analyzing the sensitiveness to initial conditions, predictions are done from two different months in a year viz., from the beginning of January and June. The reasonably good predictions obtained indicate the efficiency of the nonlinear prediction method for predicting the rainfall series. Also, the rank probability skill score and the rank histograms show that the ensembles generated are reliable with a good spread and skill. A comparison of results of the three regions indicates that although they are chaotic in nature, the spatial averaging over a large area can increase the dimension and improve the predictability, thus destroying the chaotic nature. (C) 2010 Elsevier Ltd. All rights reserved.

The nucleus of Saccharomyces bayanus

The extra-vacuolar nucleus is visible in a small percentage of living cells from 72–96 hour wort cultures. The vacuoles show a luminous boundary under dark ground illumination. The details observed in living nuclei could be stained with haematoxylin after fixation in iodine-formaldehyde-acetic acid mixture. The Feulgen-negative nature of the vacuole and the limitation of the Feulgen-positive material to the area bounded by the nuclear membrane would imply that the ‘centrosome’ described by Lindegren and Rafalko (1950) is the real nucleus. The nucleus ofS. bayanus conforms in its structure to those of higher organisms.

Transcriptional switching in Escherichia coli during stress and starvation by modulation of Sigma 70 activity

During active growth of Escherichia coli, majority of the transcriptional activity is carried out by the housekeeping sigma factor (Sigma 70), whose association with core RNAP is generally favoured because of its higher intracellular level and higher affinity to core RNAP. In order to facilitate transcription by alternative sigma factors during nutrient starvation, the bacterial cell uses multiple strategies by which the transcriptional ability of Sigma 70 is diminished in a reversible manner. The facilitators of shifting the balance in favour of alternative sigma factors happen to be as diverse as a small molecule (p)ppGpp (represents ppGpp or pppGpp), proteins (DksA, Rsd) and a species of RNA (6S RNA). Although 6S RNA and (p)ppGpp were known in literature for a long time, their role in transcriptional switching has been understood only in recent years. With themelucidation of function of DksA, a new dimension has been added to the phenomenon of stringent response. As the final outcome of actions of (p)ppGpp, DksA, 6S RNA and Rsd is similar, there is a need to analyse hese mechanisms in a collective manner. We review the recent trends in understanding the regulation of Sigma 70 by (p)ppGpp, DksA, Rsd and 6S RNA and present a case for evolving a unified model of RNAP redistribution during starvation by modulation of Sigma 70 activity in E. coli.

Droplet Epitaxy of InN Quantum Dots on Si(111) by RF Plasma-Assisted Molecular Beam Epitaxy

InN quantum dots (QDs) were fabricated on Si(111) substrate by droplet epitaxy using an RF plasma-assisted MBE system. Variation of the growth parameters, such as growth temperature and deposition time, allowed us to control the characteristic size and density of the QDs. As the growth temperature was increased from 100 C to 300 degrees C, an enlargement of QD size and a drop in dot density were observed, which was led by the limitation of surface diffusion of adatoms with the limited thermal energy. Atomic force microscopy (AFM) and scanning electron microscopy (SEM) were used to assess the QDs size and density. The chemical bonding configurations of InN QDs were examined by X-ray photo-electron spectroscopy (XPS). Fourier transform infrared (FTIR) spectrum of the deposited InN QDs shows the presence of In-N bond. Temperature-dependent photoluminescence (PL) measurements showed that the emission peak energies of the InN QDs are sensitive to temperature and show a strong peak emission at 0.79 eV.

A LSQR-type method provides a computationally efficient automated optimal choice of regularization parameter in diffuse optical tomography

Purpose: Developing a computationally efficient automated method for the optimal choice of regularization parameter in diffuse optical tomography. Methods: The least-squares QR (LSQR)-type method that uses Lanczos bidiagonalization is known to be computationally efficient in performing the reconstruction procedure in diffuse optical tomography. The same is effectively deployed via an optimization procedure that uses the simplex method to find the optimal regularization parameter. The proposed LSQR-type method is compared with the traditional methods such as L-curve, generalized cross-validation (GCV), and recently proposed minimal residual method (MRM)-based choice of regularization parameter using numerical and experimental phantom data. Results: The results indicate that the proposed LSQR-type and MRM-based methods performance in terms of reconstructed image quality is similar and superior compared to L-curve and GCV-based methods. The proposed method computational complexity is at least five times lower compared to MRM-based method, making it an optimal technique. Conclusions: The LSQR-type method was able to overcome the inherent limitation of computationally expensive nature of MRM-based automated way finding the optimal regularization parameter in diffuse optical tomographic imaging, making this method more suitable to be deployed in real-time. (C) 2013 American Association of Physicists in Medicine. http://dx.doi.org/10.1118/1.4792459]

Study of dynamic behaviour of recession curves

Since Brutsaert and Neiber (1977), recession curves are widely used to analyse subsurface systems of river basins by expressing -dQ/dt as a function of Q, which typically take a power law form: -dQ/dt=kQ, where Q is the discharge at a basin outlet at time t. Traditionally recession flows are modelled by single reservoir models that assume a unique relationship between -dQ/dt and Q for a basin. However, recent observations indicate that -dQ/dt-Q relationship of a basin varies greatly across recession events, indicating the limitation of such models. In this study, the dynamic relationship between -dQ/dt and Q of a basin is investigated through the geomorphological recession flow model which models recession flows by considering the temporal evolution of its active drainage network (the part of the stream network of the basin draining water at time t). Two primary factors responsible for the dynamic relationship are identified: (i) degree of aquifer recharge (ii) spatial variation of rainfall. Degree of aquifer recharge, which is likely to be controlled by (effective) rainfall patterns, influences the power law coefficient, k. It is found that k has correlation with past average streamflow, which confirms the notion that dynamic -dQ/dt-Q relationship is caused by the degree of aquifer recharge. Spatial variation of rainfall is found to have control on both the exponent, , and the power law coefficient, k. It is noticed that that even with same and k, recession curves can be different, possibly due to their different (recession) peak values. This may also happen due to spatial variation of rainfall. Copyright (c) 2012 John Wiley & Sons, Ltd.

Raman Spectroscopic Study of Serum Components

Early diagnosis of disease is important, because therapeutic intervention is most successful before it spread to the subject. The best health screenings method could be the blood test because the blood contains thousands of bio-molecules coming as by-products from the diseased part of the organism and would be non-invasive approach. The major limitation of this approach is the very low concentrations of the analytes need to be detected. Raman spectroscopy has been proven as one of the cutting edge technique applied in the field of histology, cytology and clinical chemistry. The primary obstacle of Raman spectroscopy is the low signal intensities. One of the promising approaches to overcome that is surface enhanced Raman spectroscopy (SERS) which has opened novel opportunities for chemical and biomedical analytics. Albumin is one of the most abundant proteins in blood, produced by liver. The state of albumin in serum determines the health of the liver and kidney. Serum albumin helps to transport many small molecules such as fatty acids, bilirubin, calcium, drugs through the blood. In this study, SERS is being used for the quantification and to understand of binding mechanism serum albumin.

A non-resonant mass sensor to eliminate the ``missing mass'' effect during mass measurement of biological materials

Resonant sensors and crystal oscillators for mass detection need to be excited at very high natural frequencies (MHz). Use of such systems to measure mass of biological materials affects the accuracy of mass measurement due to their viscous and/or viscoelastic properties. The measurement limitation of such sensor system is the difficulty in accounting for the ``missing mass'' of the biological specimen in question. A sensor system has been developed in this work, to be operated in the stiffness controlled region at very low frequencies as compared to its fundamental natural frequency. The resulting reduction in the sensitivity due to non-resonant mode of operation of this sensor is compensated by the high resolution of the sensor. The mass of different aged drosophila melanogaster (fruit fly) is measured. The difference in its mass measurement during resonant mode of operation is also presented. That, viscosity effects do not affect the working of this non-resonant mass sensor is clearly established by direct comparison. (C) 2014 AIP Publishing LLC.

Toward a High-Resolution Monitoring of Continental Surface Water Extent and Dynamics, at Global Scale: from GIEMS (Global Inundation Extent from Multi-Satellites) to SWOT (Surface Water Ocean Topography)

Up to now, high-resolution mapping of surface water extent from satellites has only been available for a few regions, over limited time periods. The extension of the temporal and spatial coverage was difficult, due to the limitation of the remote sensing technique e.g., the interaction of the radiation with vegetation or cloud for visible observations or the temporal sampling with the synthetic aperture radar (SAR)]. The advantages and the limitations of the various satellite techniques are reviewed. The need to have a global and consistent estimate of the water surfaces over long time periods triggered the development of a multi-satellite methodology to obtain consistent surface water all over the globe, regardless of the environments. The Global Inundation Extent from Multi-satellites (GIEMS) combines the complementary strengths of satellite observations from the visible to the microwave, to produce a low-resolution monthly dataset () of surface water extent and dynamics. Downscaling algorithms are now developed and applied to GIEMS, using high-spatial-resolution information from visible, near-infrared, and synthetic aperture radar (SAR) satellite images, or from digital elevation models. Preliminary products are available down to 500-m spatial resolution. This work bridges the gaps and prepares for the future NASA/CNES Surface Water Ocean Topography (SWOT) mission to be launched in 2020. SWOT will delineate surface water extent estimates and their water storage with an unprecedented spatial resolution and accuracy, thanks to a SAR in an interferometry mode. When available, the SWOT data will be adopted to downscale GIEMS, to produce a long time series of water surfaces at global scale, consistent with the SWOT observations.