Abstract

Disaster Demand for electricity in urban areas is increasing world over, because electricity is the prime lifeline support. Due to increase in fossil fuel prices coupled with concerns related to global warming, demand for nuclear energy is increasing. Around 30 countries are now using nuclear energy for production of electricity. However, its share in electricity production is varying from country to country. In some countries it is only 3% and in some countries it is around 50%. In light of March 2011 nuclear energy are reviewing the safety of their reactors against natural disasters. In this regard, authors proposed to study the current and projected nuclear energy demand for electricity supply in urban areas of India on one hand and scenario of seismic hazard and risk on the other hand.

Abstract

Biometric identification often involves explicit com-parison of a probe template against each template stored in a database. This approach becomes extremely time-consuming as the size of the database increases. Filtering approaches use alight-weight comparison to reduce the database to smaller set of candidates for explicit comparison. However, most existing filtering schemes use specific features that are hand-crafted for the biometric trait at each stage of the filtering. In this work,we show that a cascade of simple linear projections on random lines can achieve significant levels of filtering. Each stage offiltering consists of projecting the probe onto a specific line and removal of database samples outside a window around the probe.The approach provides a way of automatic generation of filters and avoids the need of developing specific features for different biometric traits. The method also provides us with a variety of parameters such as the projection lines, the number and order of projections, and the window sizes to customize the filtering process to a specific application. Experimental results show that using an ensemble of projections reduce the search space by 60%without increasing the false negative identification rate.

Abstract

In order to incorporate various writing styles orfonts in a character recognizer, it is critical that a large amount of labeled data is available, which is difficult to obtain. In this work, we present a semi-supervised SVM based framework that can incorporate the unlabeled data for improvement of recognition performance. Existing semi-supervised learning methods for SVMs work well only for two-class problems. We propose a method to extend this to large-class problems by incorporating a participation termin to the optimization process. The proposed system uses a Decision Directed Acyclic Graphs (DDAG) of SVM classifiers,which have proven to be very effective for such recognition problems. We present experimental results on three different digits dataset with varying complexity, as well as additional multi-class datasets from the UCI repository for comparison with existing approaches. In addition we show that approximate annotations at the word or sentence level can be used for evaluation as well as active learning to further improve there cognition results.

Abstract

This paper deals with extraction of fingerprint features directly from gray scale images by the method of ridge trac­ing. While doing so, we make substantial use of contextual information gathered during the tracing process. Narrow bandpass based filtering methods for fingerprint image en­hancement are extremely robust as noisy regions do not af­fect the result of cleaner ones. However, these method often generate artifacts whenever the underlying image does not fit the filter model, which may be due to the presence of noise and singularities. The proposed method allows us to use the contextual information to better handle such noisy regions. Moreover, the various parameters used in the al­gorithm have been made adaptive in order to circumvent human supervision. The experimental results from our al­gorithm have been compared with those from Gabor based filtering and feature extraction, as well as with the origi­nal ridge tracing work from Maio and Maltoni [11]. The results clearly indicate that the proposed approach makes ridge tracing more robust to noise and makes the extracted features more reliable.

Abstract

We propose a novel approach to enhance the finger­print image and extract features such as directional fields, minutiae and singular points reliably using a Hierarchical Markov Random Field Model. Unlike traditional finger­print enhancement techniques, we use previously learned prior patterns from a set of clean fingerprints to restore a noisy one. We are able to recover the ridge and valley structure from degraded and noisy fingerprint images by formulating it as a hierarchical interconnected MRF that processes the information at multiple resolutions. The top layer incorporates the compatibility between an observed degraded fingerprint patch and prior training patterns in addition to ridge continuity across neighboring patches. A second layer accounts for spatial smoothness of the orienta­tion field and its discontinuity at the singularities. Further layers could be used for incorporating higher level priors such as the class of the fingerprint. The strength of the pro­posed approach lies in its flexibility to model possible vari­ations in fingerprint images as patches and from its abil­ity to incorporate contextual information at various resolu­tions. Experimental results (both quantitative and qualita­tive) clearly demonstrate the effectiveness of this approach.

Abstract

Building construction in India has been on rise since last two decades. More recently construction of tall buildings ranging from 30 to 50 stories have been proposed in many cities. For these buildings, wind analysis is as important as earthquake analysis, since in taller buildings, wind loads are dominant than earthquake loads at greater heights. In India, wind analysis is done according to IS 875 (Part III)-1987 (here after IS 875). But this code doesn’t provide sufficient information analysis of different structural forms. Quite often, engineers generally rely wind tunnel experiments to understand the behaviour. However, for arriving at the best orientation several tests have to be conducted which is tedious job. In the paper a necessity to adopt a different approach such as wind tunnel experiments and CFD simulation is proposed. Case study on wind analysis of Y-shaped tall building is illustrated using Ansys CFX and SAP 2000.

Abstract

Pipeline generally extends over long distances traversing through wide variety of different soils, geological conditions and regions with different seismicity. Majority of the past works in the area of pipeline subjected to fault motion is restricted in several ways. There were many analytical models developed in the past for pipeline fault crossing, however, they are of limited usage, for example analytical model developed for pipeline fault crossing can be useful for strike slip fault crossing only. Likewise incorporating the large geometric changes in analytical study is a tricky task; however pipeline subjected to the fault motion itself is a phenomenon of large geometric changes. Especially when pipeline subjected to compression, where in addition to material deformation it also undergoes general as well as local buckling with bending, contradictorily past work mostly assumed that pipeline is under tension. With day by day increasing capacity of computation and advancement in numerical modeling, one can find more facts for pipeline subjected fault motions including cases of pipe under compression as well. In this paper, past work is reviewed for pipeline subjected to large fault motion. A three dimensional FE based numerical model is suggested to carry out pipeline performance of buried pipeline subjected to fault motion. A proposed model includes material nonlinearity, as well as effect of the large geometric changes. For this purpose, three dimensional FE program is developed in MATLAB. Displacement controlled Arc-length technique is implemented to solve the nonlinear behavior. To reduce the computation time of analysis here parallelization tool kit of MATLAB is utilized.

Abstract

Sandhi splitting is a primary and an important step for any natural language processing (NLP) application for languages which have agglutinative morphology. This paper presents a statistical approach to build a sandhi splitter for agglutinative languages. The input to the model is a valid string in the language and the output is a split of that string into meaningful word/s. The approach adopted comprises of two stages namely Segmentation and Word generation, both of which use conditional random fields (CRFs). Our approach is robust and language independent. The results for two Dravidian languages viz. Telugu and Malayalam show an accuracy of 89.07% and 90.50% respectively

Abstract

This paper introduces a vision based localization method for large scale urban environments. The method is based upon Bag-of-Words image retrieval techniques and handles problems that arise in urban environments due to repetitive scene structure and the presence of dynamic objects like vehicles. The localization system was experimentally verified it localization experiments along a 5km long path in an urban environment.

Abstract

This paper presents a realtime, incremental multibody visual SLAM system that allows choosing between full 3D reconstruction or simply tracking of the moving objects. Motion reconstruction of dynamic points or objects from a monocular camera is considered very hard due to well known problems of observability. We attempt to solve the problem with a Bearing only Tracking (BOT) and by integrating multiple cues to avoid observability issues. The BOT is accomplished through a particle filter, and by integrating multiple cues from the reconstruction pipeline. With the help of these cues, many real world scenarios which are considered unobservable with a monocular camera is solved to reasonable accuracy. This enables building of a unified dynamic 3D map of scenes involving multiple moving objects. Tracking and reconstruction is preceded by motion segmentation and detection which makes use of efficient geometric constraints to avoid difficult degenerate motions, where objects move in the epipolar plane. Results reported on multiple challenging real world image sequences verify the efficacy of the proposed framework.