2D shape classification and retrieval

We present a novel correspondence-based technique for efficient shape classification and retrieval. Shape boundaries are described by a set of (ad hoc) equally spaced points – avoiding the need to extract “landmark points”. By formulating the correspondence problem in terms of a simple generative model, we are able to efficiently compute matches that incorporate scale, translation, rotation and reflection invariance. A hierarchical scheme with likelihood cut-off provides additional speed-up. In contrast to many shape descriptors, the concept of a mean (prototype) shape follows naturally in this setting. This enables model based classification, greatly reducing the cost of the testing phase. Equal spacing of points can be defined in terms of either perimeter distance or radial angle. It is shown that combining the two leads to improved classification/retrieval performance

Biodiversity of Cyanobacteria in industrial effluents

Biodiversity of cyanobacteria in industrial effluents. In order to study the biodiversity of cyanobacteria in industrial effluents, four different effluents such as dye, paper mill, pharmaceutical and sugar were selected. The hysicochemical characteristics of all the effluents studied were more or less similar. Totally 59 species of cyanobacteria distributed in four different effluents were recorded. Among the effluents, sugar mill recorded the maximum number of species (55) followed by dye (54), paper mill (45) and pharmaceutical (30). Except pharmaceutical effluent, others recorded heterocystous cyanobacteria. In total 26 species of cyanobacteria were recorded in common to all the effluents analysed. Of them, Oscillatoria with 13 species was the dominant genus which was followed by Phormidium (8), Lyngbya (2), Microcystis (2) and Synechococcus with single species. The abundance of cyanobacteria in these effluents was due to favourable contents of oxidizable organic matter, rich calcium and abundant nutrients such as nitrates and phosphates with less dissolved oxygen. Indicator species from each effluent and their immense value for the future pollution abatement programmes have also been discussed.Biodiversidad de cianobacterias en vertidos industriales. Se ha estudiado la biodiversidad de cianobacterias presente en vertidos industriales de diferente naturaleza (colorantes, fabricación de papel, sector farmacéutico y azúcar). Las características físico-químicas de los vertidos fueron más o menos similares. Se han identificado un total de 59 especies de cianobacterias en los cuatro tipos de vertidos. La mayor riqueza específica se encontró en el vertido de la industria azucarera (55 especies), seguida luego por la industria de colorantes, papel y productos farmacéuticos (54, 45 y 30 especies, respectivamente). Con la excepción del vertido de la industria farmacéutica, en los restantes vertidos se detectaron cianobacterias heterocistadas. El número de especies comunes a los cuatro vertidos ascendió a 26. Entre éstas, Oscillatoria, con 13 especies, fue el género dominante, seguido por Phormidium (8), Lyngbya y Microcystis (ambas con 2) y Synechococcus (1). La abundancia de cianobacterias en estos vertidos se debió al contenido en materia orgánica oxidable, altos niveles de calcio y nutrientes inorgánicos (nitratos y fosfatos) y bajos niveles de oxígeno disuelto. Se discuten el valor del indicador de especies de cada vertido y su importancia para los programas de reducción de la contaminación

Scaling Reinforcement Learning Paradigms for Motor Control

Reinforcement learning offers a general framework to explain reward related learning in artificial and biological motor control. However, current reinforcement learning methods rarely scale to high dimensional movement systems and mainly operate in discrete, low dimensional domains like game-playing, artificial toy problems, etc. This drawback makes them unsuitable for application to human or bio-mimetic motor control. In this poster, we look at promising approaches that can potentially scale and suggest a novel formulation of the actor-critic algorithm which takes steps towards alleviating the current shortcomings. We argue that methods based on greedy policies are not likely to scale into high-dimensional domains as they are problematic when used with function approximation – a must when dealing with continuous domains. We adopt the path of direct policy gradient based policy improvements since they avoid the problems of unstabilizing dynamics encountered in traditional value iteration based updates. While regular policy gradient methods have demonstrated promising results in the domain of humanoid notor control, we demonstrate that these methods can be significantly improved using the natural policy gradient instead of the regular policy gradient. Based on this, it is proved that Kakade’s ‘average natural policy gradient’ is indeed the true natural gradient. A general algorithm for estimating the natural gradient, the Natural Actor-Critic algorithm, is introduced. This algorithm converges with probability one to the nearest local minimum in Riemannian space of the cost function. The algorithm outperforms nonnatural policy gradients by far in a cart-pole balancing evaluation, and offers a promising route for the development of reinforcement learning for truly high-dimensionally continuous state-action systems. Keywords: Reinforcement learning, neurodynamic programming, actorcritic methods, policy gradient methods, natural policy gradien

Calibration approach to electron probe microanalysis: A study with PWA-1480, a nickel base superalloy

The utility of an indirect calibration approach in electron probe microanalysis is explored. The methodology developed is based on establishing a functional relationship between the uncorrected k-ratios and the corresponding concentrations obtained using one of the ZAF correction schemes, for all the desired elements in the concentration range of interest. In cases where a very large number of analyses are desired, such a technique significantly reduces the total time required for the microprobe analysis without any significant loss of precision in the data. A typical application of the method in the concentration mapping of the transverse cross-section of a dendrite in directionally solidified PWA-1480, a nickel-based superalloy, is described

Secondary arm coarsening and microsegregation in superalloy PWA-1480 single crystals: Effect of low gravity

Single crystal specimens of nickel base superalloy PWA-1480 were directionally solidified on ground and during low gravity (20 sec) and high gravity (90 sec) parabolic maneuver of KC-135 aircraft. Thermal profiles were measured during solidification by two in-situ thermocouples positioned along the sample length. The samples were quenched during either high or low gravity cycles so as to freeze the structures of the mushy zone developing under different gravity levels. Microsegregation was measured by examining the solutal profiles on several transverse cross-sections across primary dendrites along their length in the quenched mushy zone. Effect of gravity level on secondary arm coarsening kinetics and microsegregation have been investigated. The results indicate that there is no appreciable difference in the microsegregation and coarsening kinetics behavior in the specimens grown under high or low gravity. This suggests that short duration changes in gravity/levels (0.02 to 1.7 g) do not influence convection in the interdendritic region. Examination of the role of natural convection, in the melt near the primary dendrite tips, on secondary arm spacings requires low gravity periods longer than presently available on KC-135. Secondary arm coarsening kinetics show a reasonable fit with the predictions from a simple analytical model proposed by Kirkwood for a binary alloy

Sequential support vector classifiers and regression

Support Vector Machines (SVMs) map the input training data into a high dimensional feature space and finds a maximal margin hyperplane separating the data in that feature space. Extensions of this approach account for non-separable or noisy training data (soft classifiers) as well as support vector based regression. The optimal hyperplane is usually found by solving a quadratic programming problem which is usually quite complex, time consuming and prone to numerical instabilities. In this work, we introduce a sequential gradient ascent based algorithm for fast and simple implementation of the SVM for classification with soft classifiers. The fundamental idea is similar to applying the Adatron algorithm to SVM as developed independently in the Kernel-Adatron [7], although the details are different in many respects. We modify the formulation of the bias and consider a modified dual optimization problem. This formulation has made it possible to extend the framework for solving the SVM regression in an online setting. This paper looks at theoretical justifications of the algorithm, which is shown to converge robustly to the optimal solution very fast in terms of number of iterations, is orders of magnitude faster than conventional SVM solutions and is extremely simple to implement even for large sized problems. Experimental evaluations on benchmark classification problems of sonar data and USPS and MNIST databases substantiate the speed and robustness of the learning procedure

Electromechanical and Dynamic Characterization of In-House-Fabricated Amplified Piezo Actuator

A diamond-shaped amplified piezo actuator (APA) fabricated using six multilayered piezo stacks with maximum displacement of 173 μm at 175V and the amplification factor of 4.3. The dynamic characterization of the actuator was carried out at different frequencies (100 Hz–1 kHz) and at different AC voltages (20V–40V). The actuator response over this frequency range was found neat, without attenuation of the signal. Numerical modeling of multilayered stack actuator was carried out using empirical equations, and the electromechanical analysis was carried out using ABAQUS software. The block force of the APA was 81 N, calculated by electromechanical analysis. This is similar to that calculated by dynamic characterization method

Deferring the learning for better generalization in radial basis neural networks

Proceeding of: International Conference Artificial Neural Networks — ICANN 2001. Vienna, Austria, August 21–25, 2001The level of generalization of neural networks is heavily dependent on the quality of the training data. That is, some of the training patterns can be redundant or irrelevant. It has been shown that with careful dynamic selection of training patterns, better generalization performance may be obtained. Nevertheless, generalization is carried out independently of the novel patterns to be approximated. In this paper, we present a learning method that automatically selects the most appropriate training patterns to the new sample to be predicted. The proposed method has been applied to Radial Basis Neural Networks, whose generalization capability is usually very poor. The learning strategy slows down the response of the network in the generalisation phase. However, this does not introduces a significance limitation in the application of the method because of the fast training of Radial Basis Neural Networks