Abstract

A set X together with a finite collection of self-maps on X is called a multiple discrete flow. We develop the theory of fair convergence for multiple flows and show how the UNITY programs of Chandy and Misra can be reformulated in terms of this theory.

Abstract

Prediction of electric load is very important issue for modern day power system engineers and a very good day ahead prediction of electric load is required for efficient performance of various Energy Management System (EMS) functions such as unit commitment, economic dispatch, fuel scheduling, and unit maintenance. A fuzzy based approach for day ahead prediction of electric load using Mahalanobis distance has been chosen in this work. Mahalanobis distance provides the similar characteristic days from the historical data set based on some independent variables generally of climate and time (such as temperature, day of the week, month etc.) and that are used to predict the dependent variable, i.e., day ahead electric load demand. The independent variables considered for the distance measure include the hourly humidity values, hourly temperatures values, and the day type variable.The similarity between the load on the day of prediction and that on similar characteristic days, which is evaluated using fuzzy system

Abstract

IISc Bangalore has developed a recognition engine for Tamil printed text, which has been tested on 1000 document images of pages scanned from books printed between 1950 and 2000. IIIT Hyderabad has developed a XML based annotated database for storing the 5000 images of scanned pages and the corresponding typed text in Unicode. CDAC, Noida has developed an efficient evaluation tool, which compares the OCR output text to the reference typed text (ground truth) and flashes the substitution, deletion and insertion errors in different colours on the screen, so that the design team can quickly identify the issues with the OCR and make corrective steps for improving the performance. IIT Delhi has proposed and developed a novel scheme for segmenting only the text regions from document images containing pictures. The OCRs uses Karhunen-Louve transform (KLT) as features and a support vector machine (SVM) classifier with RBF kernel in a discriminative directed acyclic graph (DDAG) configuration. They assume an uncompressed input image of the document page, scanned at a minimum of 300 dpi with 256 gray levels (not binary or two-level). Tamil OCR currently gives over 94% recognition accuracy at the Unicode level, evaluated on over 1000 printed pages, some of them also containing old Tamil letters.

Abstract

ndexing image data for content-based image search is an important area in Computer Vision. The state of theart uses the 128-dimensional SIFT as low level descriptors. Indexing even a moderate collection involves severalmillions of such vectors. The search performance depends on the quality of indexing and there is often a need tointeractively tune the process for better accuracy. In this paper, we propose a a visualization-based tool to tunethe indexing process for images and videos. We use a feature selection approach to improve the clustering of SIFTvectors. Users can visualize the quality of clusters and interactively control the importance of individual or groupsof feature dimensions easily. The results of the process can be visualized quickly and the process can be repeated.The user can use a filter or a wrapper model in our tool. We use input sampling, GPU-based processing, and visualtools to analyze correlations to provide interactivity. We present results of tuning the indexing for a few standarddatasets. A few tuning iterations result in an improvement of over 4% in the final classification performance, whichis significant.

Abstract

Graphics processing units provide a large compu-tational power at a very low price which position them as anubiquitous accelerator. General purpose programming on thegraphics processing units (GPGPU) is best suited for regulardata parallel algorithms. They are not directly amenable foralgorithms which have irregular data access patterns suchas list ranking, and finding the connected components of agraph, and the like. In this work, we present a GPU-optimizedimplementation for finding the connected components of agiven graph. Our implementation tries to minimize the impactof irregularity, both at the data level and functional level.Our implementation achieves a speed up of 9 to 12 timesover the best sequential CPU implementation. For instance, ourimplementation finds connected components of a graph of 10million nodes and 60 million edges in about 500 millisecondson a GPU, given a random edge list. We also draw interestingobservations on why PRAM algorithms, such as the Shiloach-Vishkin algorithm may not be a good fit for the GPU and howthey should be modified.

Abstract

Genetic algorithms are effective in solving many optimiza-tion tasks. However, the long execution time associated withit prevents its use in many domains. In this paper, we pro-pose a new approach for parallel implementation of geneticalgorithm on graphics processing units (GPUs) using CUDAprogramming model. We exploit the parallelism within achromosome in addition to the parallelism across multiplechromosomes. The use of one thread per chromosome byprevious efforts does not utilize the GPU resources effec-tively. Our approach uses multiple threads per chromosome,thereby exploiting the massively multithreaded GPU moreeffectively. This results in good utilization of GPU resourceseven at small population sizes while maintaining impressivespeed up for large population sizes. Our approach is mod-eled after the GAlib library and is adaptable to a varietyof problems. We obtain a speedup of over 1500 over theCPU on problems involving a million chromosomes. Prob-lems of such magnitude are not ordinarily attempted due tothe prohibitive computation times.

Abstract

Large-scale 3D reconstruction has received a lot of attentionrecently. Bundle adjustment is a key component of the reconstructionpipeline and often its slowest and most computational resource intensive.It hasn’t been parallelized effectively so far. In this paper, we present ahybrid implementation of sparse bundle adjustment on the GPU usingCUDA, with the CPU working in parallel. The algorithm is decomposedinto smaller steps, each of which is scheduled on the GPU or the CPU. Wedevelop efficient kernels for the steps and make use of existing libraries forseveral steps. Our implementation outperforms the CPU implementationsignificantly, achieving a speedup of 30-40 times over the standard CPUimplementation for datasets with upto 500 images on an Nvidia TeslaC2050 GPU.

Abstract

Conventionally optimal control theory has been used in the theoretical design of laser pulses through the direct variation in the electric field of the laser pulse as a function of time. This often leads to designed laser pulses which contain a broad and seemingly arbitrary frequency structure that varies in time in a manner which may be difficult to realize experimentally. In contrast, the experimental design of laser pulses has used a genetic algorithm (GA) approach, varying only those laser parameters actually available to the experimentalist. We investigate in this paper the possibility of using GA optimization methods in the theoretical design of laser pulses to bring about quantum state transitions in molecules. This allows us to select only a small limited number of parameters to vary and to choose these parameters so that they correspond to those available to the experimentalist. In the paper we apply our methods to the vibrational-rotational excitation of the HF molecule. We choose a small limited number of frequencies and vary only the associated electric field amplitudes and pulse envelopes. We show that laser pulses designed in this way can lead to very high transition probabilities.

Abstract

Structural and electronic properties of diastereomers of tetrahydrofuran amino acids (TAA) derived tripeptide, Boc-TAA-Leu-Val-OMe, are studied using density functional theory. Predicted secondary folding patterns with hydrogen bonded pseudocycles of different sizes in peptides containing (2R,5S)-cis-TAA and (2S,5R)-cis-TAA are confirmed by detailed NMR studies of both, and single crystal X-ray analysis of the former. A novel unusual folding pattern emanating from three-centered hydrogen bond is found in peptide with (2R,5S)-cis relationship. Stereochemical control on the orientation of interacting sites is substantiated by structural analysis of the peptides. Using natural bonding orbital and atoms in molecules analyses, charge transfer interactions are analyzed.

Abstract

Optimal control theory is used to design a laser pulse for the multiphoton dissociation of the Fe– CO bond in the CO-heme compounds. The study uses a hexacoordinated iron–porphyrin– imidazole–CO complex in its ground electronic state as a model for CO liganded to the heme group. The potential energy and dipole moment surfaces for the interaction of the CO ligand with the heme group are calculated using density functional theory. Optimal control theory, combined with a timedependent quantum dynamical treatment of the laser-molecule interaction, is then used to design a laser pulse capable of efficiently dissociating the CO-heme complex model. The genetic algorithm method is used within the mathematical framework of optimal control theory to perform the optimization process. This method provides good control over the parameters of the laser pulse, allowing optimized pulses with simple time and frequency structures to be designed. The dependence of photodissociation yield on the choice of initial vibrational state and of initial laser field parameters is also investigated. The current work uses a reduced dimensionality model in which only the Fe–C and C–O stretching coordinates are explicitly taken into account in the timedependent quantum dynamical calculations. The limitations arising from this are discussed in Sec. IV.