20 resultados para Place Character

em Indian Institute of Science - Bangalore - Índia


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The literature on the subject of the present investigation is somewhat meagre. A rotary converter or synchronous motor no! provided with any special starting devices forms, when started from the alternating current side, a type of induction motor whoso Htator is provided with a polyphase winding, and whoso rotor has a single-phase (or single magnetic axis) winding.

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Electrical and magnetic properties of La3Ni2O7 and La4Ni3O10 have been investigated in comparison with those of La2NiO4, LaNiO3, and LaSrNiO4. The results suggest an increasing 3-dimensional character across the homologous series Lan+1NinO3n+1 with increase in n. Accordingly, the electrical resistivity decreases in the order La3Ni2O7, La4Ni3O10, and LaNiO3 and this trend is suggested to be related to the percolation threshold. Magnetic properties of these oxides also show some interesting trends across the series.

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The effectiveness of linear matched filters for improved character discrimination in presence of random noise and poorly defined characters has been investigated. We have found that although the performance of the filter in presence of random noise is reasonably good (16 dB gain in signal-to-noise-ratio) its performance is poor when the unknown character is distorted (linear shift and rotation).

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The effectiveness of linear matched filters for improved character discrimination in presence of random noise and poorly defined characters has been investigated. We have found that although the performance of the filter in presence of random noise is reasonably good (16 dB gain in signal-to-noise-ratio) its performance is poor when the unknown character is distorted (linear shift and rotation).

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Two series of peptides, designated K and NK were synthesized and tested for lipid A binding and neutralizing properties. K-2, which has an 11-residue amphiphilic core, and a branched N-terminus bearing two branched lysinyl residues does not bind lipid A, while NK2, also with an 11-residue amphiphilic core comprised entirely of non-ionizable residues, and a similarly branched, cationic N-terminus, binds lipid A very weakly. Both peptides do not inhibit lipopolysaccharide (LPS) activity in the Limulus assay, nor do they inhibit LPS-induced TNF-alpha and NO production in 5774 cells. These results are entirely unlike a homologous peptide with an exclusively hydrophobic core whose LPS-binding and neutralizing properties are very similar to that of polymyxin B [David SA, Awasthi SK, Wiese A et al. Characterization of the interactions of a polycationic, amphiphilic, terminally branched oligopeptide with lipid A and lipopolysaccharide from the deep rough mutant of Salmonella minnesota. J Endotoxin Res 1996; 3: 369-379]. These data suggest that a clear segregation of charged and apolar domains is crucial in molecules designed for purposes of LPS sequestration and that head-tail (polar) orientation of the cationic/hydrophobic regions is preferable to molecules with mixed or facial cationic/amphipathic character.

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This paper describes a technique for artificial generation of learning and test sample sets suitable for character recognition research. Sample sets of English (Latin), Malayalam, Kannada and Tamil characters are generated easily through their prototype specifications by the endpoint co-ordinates, nature of segments and connectivity.

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The machine replication of human reading has been the subject of intensive research for more than three decades. A large number of research papers and reports have already been published on this topic. Many commercial establishments have manufactured recognizers of varying capabilities. Handheld, desk-top, medium-size and large systems costing as high as half a million dollars are available, and are in use for various applications. However, the ultimate goal of developing a reading machine having the same reading capabilities of humans still remains unachieved. So, there still is a great gap between human reading and machine reading capabilities, and a great amount of further effort is required to narrow-down this gap, if not bridge it. This review is organized into six major sections covering a general overview (an introduction), applications of character recognition techniques, methodologies in character recognition, research work in character recognition, some practical OCRs and the conclusions.

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Transliteration system for mobile phone is an area that is always in demand given the difficulties and constraints we face in its implementation. In this paper we deal with automatic transliteration system for Kannada which has a non-uniform geometry and inter-character spacing unlike non-oriental language text like English. So it is even more a challenging problem. Working model consists of part of the process taking place on a mobile with remaining on a server. Good results are achieved.

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This paper presents a new application of two dimensional Principal Component Analysis (2DPCA) to the problem of online character recognition in Tamil Script. A novel set of features employing polynomial fits and quartiles in combination with conventional features are derived for each sample point of the Tamil character obtained after smoothing and resampling. These are stacked to form a matrix, using which a covariance matrix is constructed. A subset of the eigenvectors of the covariance matrix is employed to get the features in the reduced sub space. Each character is modeled as a separate subspace and a modified form of the Mahalanobis distance is derived to classify a given test character. Results indicate that the recognition accuracy using the 2DPCA scheme shows an approximate 3% improvement over the conventional PCA technique.

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This paper introduces a scheme for classification of online handwritten characters based on polynomial regression of the sampled points of the sub-strokes in a character. The segmentation is done based on the velocity profile of the written character and this requires a smoothening of the velocity profile. We propose a novel scheme for smoothening the velocity profile curve and identification of the critical points to segment the character. We also porpose another method for segmentation based on the human eye perception. We then extract two sets of features for recognition of handwritten characters. Each sub-stroke is a simple curve, a part of the character, and is represented by the distance measure of each point from the first point. This forms the first set of feature vector for each character. The second feature vector are the coeficients obtained from the B-splines fitted to the control knots obtained from the segmentation algorithm. The feature vector is fed to the SVM classifier and it indicates an efficiency of 68% using the polynomial regression technique and 74% using the spline fitting method.

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In the present work, a thorough investigation of evolution of microstructure and texture has been carried out to elucidate the evolution of texture and grain boundary character distribution (GBCD) during Equal Channel Angular Extrusion (ECAE) of some model two-phase materials, namely Cu-0.3Cr and Cu-40Zn. Texture of Cu-0.3Cr alloy is similar to that reported for pure copper. On the other hand, in Cu-40Zn alloy, texture evolution in α and β (B2) phases are interdependent. In Cu-0.3Cr alloy, there is a considerable decreases in volume fraction of low angle boundaries (LAGBs), only a slight increase in CSL boundaries, but increase in high angle grain boundaries (HAGBs) from 1 pass to 4 passes for both the routes. In the case of Cu-40Zn alloy, there is an appreciable increase in CSL volume fraction.

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In this paper, we compare the experimental results for Tamil online handwritten character recognition using HMM and Statistical Dynamic Time Warping (SDTW) as classifiers. HMM was used for a 156-class problem. Different feature sets and values for the HMM states & mixtures were tried and the best combination was found to be 16 states & 14 mixtures, giving an accuracy of 85%. The features used in this combination were retained and a SDTW model with 20 states and single Gaussian was used as classifier. Also, the symbol set was increased to include numerals, punctuation marks and special symbols like $, & and #, taking the number of classes to 188. It was found that, with a small addition to the feature set, this simple SDTW classifier performed on par with the more complicated HMM model, giving an accuracy of 84%. Mixture density estimation computations was reduced by 11 times. The recognition is writer independent, as the dataset used is quite large, with a variety of handwriting styles.

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In this article, we describe our ongoing efforts in addressing the environment and energy challenges facing the world today. Tapping solar thermal energy seems to be the right choice for a country like India. We look at three solar-thermal technologies in the laboratory — water purification/distillation, Stirling engine, and air-conditioning/refrigeration.