Don't-cares in the concurrency matrix for bit steering

The concurrency matrix aids the detection of bit steerability of microcommand sets in a microprogram. In the present work, the concept of don't-cares is introduced into the concurrency matrix to identify the bit steerable microcommand sets.

Evolution of three-dimensional character across the Lan+1NinO3n+1 homologous series with increase in n1

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.

Improved optical character recognition by matched filtering

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).

Improved optical character recognition by matched filtering

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).

The role of polar and facial amphipathic character in determining lipopolysaccharide-binding properties in synthetic cationic peptides

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.

Artificial database for character recognition research

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.

Character recognition — A review

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.

Two Dimensional Principal Component Analysis for Online Tamil Character Recognition

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.

Online character recognition using regression techniques

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.

Texture and grain boundary character distribution during Equal Channel Angular Extrusion of some two-phase copper alloys

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.

Comparison of HMM and SDTW for Tamil handwritten character recognition

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.

Time Series Classification for Online Tamil Handwritten Character Recognition – A Kernel Based Approach

In this paper, we consider the problem of time series classification. Using piecewise linear interpolation various novel kernels are obtained which can be used with Support vector machines for designing classifiers capable of deciding the class of a given time series. The approach is general and is applicable in many scenarios. We apply the method to the task of Online Tamil handwritten character recognition with promising results.

Interplay of localized and itinerant character of Ru ions: Tl2Ru2O7 versus Hg2Ru2O7

We carry out a comparative study of the electronic structure of two pyrochlore ruthenate compounds, Tl2Ru2O7 and Hg2Ru2O7, in terms of first principles calculations. Our study reveals the Ru d electrons in Hg2Ru2O7 to be much more delocalized compared to that in Tl2Ru2O7. The subtle change in the Ru-d bandwidths in the two compounds, triggered by the differences in Hg 5d-Ru 4d hybridization compared to that of Tl 5d-Ru 4d, bring in the observed differences in behavior. Our study further shows that the development of long range noncollinear antiferromagnetic structure at low temperature is sufficient to produce the insulating solution in Hg2Ru2O7, in line with the prediction from recent nuclear magnetic resonance study.

When good neighbors don't need fences: temporal landscape partitioning among baboon social groups

Intraspecific competition is a key factor shaping space-use strategies and movement decisions in many species, yet how and when neighbors utilize shared areas while exhibiting active avoidance of one another is largely unknown. Here, we investigated temporal landscape partitioning in a population of wild baboons (Papio cynocephalus). We used global positioning system (GPS) collars to synchronously record the hourly locations of five baboon social groups for similar to 900 days, and we used behavioral, demographic, and life history data to measure factors affecting use of overlap areas. Annual home ranges of neighboring groups overlapped substantially, as predicted (baboons are considered non-territorial), but home ranges overlapped less when space use was assessed over shorter time scales. Moreover, neighboring groups were in close spatial proximity to one another on fewer days than predicted by a null model, suggesting an avoidance-based spacing pattern. At all time scales examined (monthly, biweekly, and weekly), time spent in overlap areas was greater during time periods when groups fed on evenly dispersed, low-quality foods. The percent of fertile females in social groups was negatively correlated with time spent in overlap areas only during weekly time intervals. This suggests that broad temporal changes in ecological resources are a major predictor of how intensively overlap areas are used, and groups modify these ecologically driven spacing patterns at short time scales based on female reproductive status. Together, these findings offer insight into the economics of territoriality by highlighting the dynamics of spacing patterns at differing time scales.