Abstract

Super structures are mostly founded on layers of soil unless bedrock is very close to the ground surface. It is clear that seismic waves pass through kilometers of bedrock and usually less than 100 meters of soil; soil layers play a signifi cant role in assigning the characteristics of the ground surface movement. When the ground is stiff enough, the dynamic response of the structure will not be influenced significantly by the soil properties during the earthquake and the structure can be ana lyzed under the fixed base condition. When the structure is resting on a flexible medium, the dynamic response of the structure will be different from the fixed base condition owing to the interaction between the soil and the structure. This difference in behavior is because of the phenomenon, commonly referred to as soil - structure interaction (SSI), which if not taken into account in the analysis and design properly; the accuracy in assessing the structural safety, facing earthquakes, could not be reliable . During some past earthquakes several damages have been observed due to not accounting the effect of interaction in the analysis. The 1985 Mexico earthquake high rise building collapsed due to the partial bearing capacity failure of foundation soil and i ncrease in a fundamental time period of soil from about 1.0 s to nearly 2.0 s induced due to the interaction phenomenon. In the 1995 Kobe earthquake (M=6.9) the interaction effect played a vital role in the sudden increase of natural period where the colla pse and overturning of Hanshin expressway is observed. During the 2001 Bhuj earthquake (MW= 7.6) caused extensive damage to life and property due to the attenuation effect of the wave travelling through the soil layers with a high impedance contrast of the supporting soil layers. Coming to the Indian s cenario, Geographical statistics of India show that almost 54% of the land is vulnerable to earthquakes. The seismic risk in the country has been increasing rapidly in the recent years as there were large m agnitude earthquakes in recent times even in the s continental region. Performance - based engineering (PBE) is a technique for seismic evaluation and in design using performance level prediction for safety and risk assessment. Soil structure interaction , pa rticularly for unbraced structures resting on relatively soft soils may significantly amplify the lateral displacements and inter - storey drifts. This amplification of lateral deformations may change the performance level of the building frames. Thus a comp rehensive dynamic analysis to evaluate the realistic performance level of a structure should consider the effects of SSI in the model. In the present study the research has been primarily motivated to reduce the complexity in the modeling a soil structure foundation interaction model. In interaction problem the flexible interface is needed to implement in the model between soil to raft and soil to pile in order to achieve the continuity in the analysis and to obtain the model nearest to the reality. The so il half space can be represented in two ways like near field and far field. The near field behavior can be considered as the nonlinear and the far filed is modeled as the linear with the application of the non reflecting viscous boundary to avoid the multi ple wave reflection during the dynamic analysis. The soil structure interaction problem is computationally very expensive due to big size which includes interfaces, nonlinear soil behavior, and structure. This cost has been exponentially increasing with th e volume of soil, interfaces area and structure geometry is increasing. In case of pile foundation the soil domain is needed to consider deeper and wider thus it is essential to reduce the model with some technique s . In the present study the focus has been given to reduce the soil domain by replacing the pile group with the equivalent pier method. This approach for soil - structure interaction analysis has been fully investigated. With this the volume of the interfaces can be reduced up to a certain extent an d the solution can be obtained within the shortest time. The finite element program for dynamic analysis has been developed using a versatile low level object oriented language C++. The time domain displacement and stress histories have been estimated for the given set of dynamic loading of the original pile configuration and the equivalent pier. The applicability of the equivalent pier method has been checked for different set of l

Abstract

Parsing Indian languages has always been a challenging task. In recent years there have been various approaches explored for improving parsing accuracy for Hindi and other Indian languages. In this work, we present our experiments to improve dependency parsing accuracy for Hindi language as part of COLING-MTPIL 2012 shared task. We explored three data driven parsers on grounds of large feature pool consisting of morphological, chunk and syntactic features. We tried with different parser config- urations by considering different parsing strategies, classifiers and feature templates. We explored the usage and adoption of the Turbo Parser for parsing Indian languages. In addition to Turbo parser we have also explored other data-driven parsers: Malt and MST. We have also experimented on getting the best out of these parsers by using two approaches. We selected the best configuration for each set of data and were able to produce the best average accuracy in the shared task. We achieved a best result of 96.50% unlabeled attachment score (UAS), 92.90% labeled accuracy (LA), 91.49% labeled attachment score (LAS) using voting method on data with gold POS tags. In case of data with automatic POS tags, we achieved a best result of 93.99% (UAS), 90.04% (LA) and 87.84% (LAS). The second part of this thesis focuses on using statistical dependency parsing technique to detect NULLs or Empty Categories in the sentences. In these experiments we have worked with Hindi de- pendency treebank. There were some rule based approaches tried out before to detect Empty heads for Hindi language but statistical learning for automatic prediction was not demonstrated. In this approach we used a technique of introducing complex labels into the data to predict Empty Categories in sentences.We have mapped the problem of Empty category prediction to data-driven parsing and explored various parsers to find the best one for this approach. The motivation comes from using data-driven parsing to solve other tasks in Natural Language Processing. The system was able to predict Empty categories with a decent F-score of 76.26. We have discussed about shortcomings and difficulties of this approach.

Abstract

Robots playing games human are adept in playing is a challenge. It is an area where the researcher has to deal with real world properties [47]. Catch-Catch and Chain Catch are common playground “Tag” games where a single player as a Catcher chases other players to catch them while others try to escape. To extend the game further in form of Chain Catch game, Catcher and the just caught players form a chain and chase other players in that formation. We implement the idea of robotic agents playing Catch-Catch and Chain Catch as a Multi-Agent System (MAS), where each agent is an autonomous entity with capability to communicate with other agents and to use different strategies based on their roles (Catcher, Escapee, Chain). Our solution is handled both in physical (with actual robots) and simulated environment (simulating software agents). These are fast games that require team of robotic agents to cooperate among themselves to compete with other group of agents through quick decision making. Developing such robotic agents and designing their strategies is a challenging research problem. Idea of “Catch” in our game can be seen as a variant of pursuit evasion problem, but game rules and constraints on field space make our problem more complex and hence solutions to pursuit domain are not completely suitable for Catch-Catch. Also, the extension of game into Chain Catch makes it a combination of two challenges- pursuit domain and robotic chain formation. Thus our Chain Catch game is a novel and challenging problem. In this thesis, we develop a front end Catch-Catch and Chain Catch simulator that allows us to incor- porate game rules, design strategies and simulate the game play. We use the concept of cost heuristics to design strategies for each of Escapee, Catcher and Chain. Formation plays a key role among Es-capees to evade a Catch. We propose Maximize distance, K circle, K circle with rotation and Sliding slope strategies for the Escapees. These strategies are implemented, tested and evaluated against pursuit domain strategy and random movement. Two techniques- Tagging method and Variance heuristics are developed for chain formation and its movement in Chain Catch game scenario. We also built and used our own production quality robots to implement the game play in physical environment and study the behavior of our game strategies in real robot scenario. A number of empirical experiments are per-formed to evaluate, compare and form the best strategy for each of chain, Escapee and Catcher. Our results show that Sliding slope strategy is the best strategy for Escapees whereas Tagging method is the best method for chain’s movement in Chain Catch. Our robot implementation in different scenarios shows that game strategies work as expected and a complete chain formation takes place successfully in each game.

Abstract

Earthquakes and crustal deformations are well observed and recorded all over the world. Geological, geodesy and geodetics studies have been on pivotal to understand crustal deformations and to pridict earthquakes. Many plate tectonic models have been proposed describing interaction types. Experimental, analytical numerical models are being developed to understand the whole tectonic process. Indian tectonic plate is experiencing one of the most dramatic crustal deformations by forming Himalayas due to collision between Indian plate and Eurasian plate. The convergence rate is making the whole region including peninsular India seismically very active leading to minor to great earthquakes. Indian tectonic plate boundary is undergoing continental-continental plate collision in the northern part, oceanic-continental plates subduction in the eastern part, oceanic-oceanic plates divergence in the southern part and oceanic-continental plates transform in the western part. Over all, all kinds of plates interactions and their aects are seen in and around Indian tectonic plate. With increasing computing power over the last few decades several computer models have been developed, at the same time many numerical methods have also been under enhancements. Out of many numerical methods, nite element method is used widely for all kinds of elds and applications because of its simplicity in understanding and coding. ABAQUS is nite element based commercially available software which has the power of modeling and simulating all kinds of complex physical problems with material (elastic, visco-elastic), geometry and boundary non-linearity (friction, contact analysis). The main objectives of this research is to simulate and understand the crustal deformations in general and apply the same to Indian tectonic plate and its boundaries. Starting with interaction between Indian and Eurasian plates, undersatnd subduction process for generic scenerio for arc-subduction geometry and to claculate the energy released to during Assam great earthquake (1897) using various nite element based models.

Abstract

Interesting physiochemical properties of gold nanoclusters have attracted the scientific community towards their extensive applications in catalysis, biosensing, protein engineering, gene technology, etc. The role of gold clusters as catalysts has been of intense interest in recent years, being regarded as new generation of catalysts due to their unusually high catalytic performance. The nontoxic and biocompatible nature of gold nanoclusters has put them on the center stage for designing therapeutic drug delivery vehicles and efficient biosensors. In the present work, computational studies based on density functional theory (DFT) was performed to study the electronic structure modulation on increasing the size of the gold cluster and how the biomolecular tagging effects the electronic structure and properties of these gold clusters. These modulations exhibit great potential in influencing the catalytic properties and biosensing mechanism of the gold nanoclusters. In order to choose a reliable level of theory, an extensive benchmarking of DFT functionals was done, based on which PBE0 functional was employed for carrying out all the calculations related to geometry and electronic modulation of small gold clusters on increasing the system size and also tagging them with natural/synthetic biomolecules. The work demonstrates how the quantum size effects play an instrumental role in tuning the catalytic potential of these clusters, in case of CO oxidation. An even-odd oscillation is found in the fragmentation energy of clusters indicating a preferentially higher stability for even numbered clusters. A correlation between the electronic properties of the clusters and the activation energy barrier was established, suggesting that modulating the electronic structure is the key to fine-tune the catalytic properties of the cluster. The catalytic properties of the gold clusters can also be modulated by tagging them with biomolecules. In this respect, complexes of nucleobases (natural/size-expanded) tagged to gold cluster (Au3) are studied. The CO oxidation reaction on the nucleobase-tagged gold cluster indicates an interesting implication of tagging the cluster to a nucleobase resulting in a mechanistic crossover which is responsible for making nucleobase tagged gold clusters a better catalyst in comparison to pristine Au3 cluster. The results open up prospect of using these as building blocks of new nanoscale catalysts with tailored properties and having wider technological applications. Interaction between metal nanoparticles and biomolecules is important from the view point of developing and designing biosensors. In this respect, studies on proline tagged with gold nanoclusters of varying size are reported. DNA and RNA molecules tagged to gold nanoparticles, have been shown to exhibit potential applications in designing and fabrication of novel electronic nano-devices. Complexes of RNA with gold nanoparticles are known to exhibit potential application in gene silencing and as siRNA delivery vehicles which find application in developing novel therapeutic and diagnostic modalities. However, the binding mechanism between gold nanoparticles and these molecules and its implications are not completely understood. Hence, a comprehensive study to examine the effect of structural perturbations offered to DNA and RNA base pairs in terms of adsorption on a gold cluster (Au3) has been done. Geometric and electronic features of these complexes provide evidences for distortion of certain base pairs depending on the binding site of the cluster. This study demonstrates the possibility of combining structural modifications to DNA and RNA base pairs and subsequent binding to gold nanoparticles to modulate and achieve molecular systems with desired optoelectronic properties.

Abstract

Abstract Morphological analysis is a fundamental task in most Natural Language Processing (NLP) applications, especially for morphologically rich languages such as Hindi. Morphological analysis for Hindi involves predicting lemma, POS, gender, number, person, case-marker, TAM and vibhakti. In general, morphological analyzers predicts all possible analyses for a given word. For most of the NLP tasks, instead of having multiple multiple analyses for a word, we need to disambiguate those multiple analyses and arrive at one analysis which best fits in the given sentential context. The prime motivation for carrying out the research in this thesis comes from the initial set of Hindi parsing experiments carried out by us. The existing morphological analyzers for Hindi do not predict context based morphological analysis. Because of lack of automatic context based morphological information, parsing accuracy could not be improved. In this thesis, we try to predict a single analysis for a word in a given context. This thesis deals with predicting context based morph information for the attributes viz. lemma, gender, number, person, case-marker, TAM and vibhakti. The existing analyzers also perform poorly for Out-Of-Vocabulary (OOV) words. We also aim to address this issue and make an exhaustive evaluation of our predictions for OOV words. For lemma prediction, we adopt a machine translation approach. For gender, number, person and case-marker prediction we perceive it as a classification problem. TAM and vibhakti are better predicted by rule based approach. For lemma, gender, number, person and case prediction, we achieved an overall accuracy and OOV accuracy of 84.25% and 63.06% respectively. To present our case that the predicted morphological information helps in NLP applications, we carry out parsing experiments without and with the predicted morph information and report Labeled Attachment Score of 87.75% and 89.41% for both the experiments respectively. Building machine translation models are time complex. Hence, in the later part of the thesis we conceive lemma prediction also as a classification problem. We also experiment with other features for gender, number, person and case prediction. We report overall and OOV accuracies of 85.87% and 65.96% respectively. We have seen an 1-2% improvement from our earlier set of experiment results. We extend our approach to other Indian languages viz. Urdu and Telugu for predicting context based morphological information.

Abstract

Strong ground motion plays a vital role in engineering for design of important structures such as dams and nuclear power plants. The site of construction seldom contains past strong motion records, which is a major constraint in earthquake resistant design. Simulated strong motion records at such sites serve the purpose of safe design criteria. Researchers have been producing ground motions using analytical models, for the earthquakes that have occurred in the past. In the area of ground motion analysis, numerical studies on actual eld conditions are quite few. Proposed research is to generate ground motions in near fault regions for both strike-slip and dip-slip faults. For this purpose, these faults are modeled to simualte the ground motion records using numerical approach. Semi-empirical approach is used to validate the characteristics of ground motions of both the earthquakes. It is found that the PGA values follow attenuation with respect to epicentral distance. It is observed that the simulated ground motions are in good agreement with the observed ground motions along FN and FP directions. A ground motion record of PGA 0.62 g is estimated at maximum horizontal slip (8.8 m) of Denali fault during 2002 Denali earthquake. Further the analysis has been carried out to derive and propose geophysical parameters such as ground motion prediction equation (GMPE), stress drop, kappa parameter and frequency dependent quality parameter for Alaskan region. These parameters are important and play vital role in generating synthetic ground motions. The variation in magnitude of these parameters is significantly changed the peak ground acceleration and spectral acceleration. For this purpose, a study has been carried out on effect of geophysical parameters on ground motion. It is observed that there is no effect of quality factor on spectral acceleration in high period range, but slight effect is seen in low period range. Significant increase in spectral acceleration and PGA values is observed with increase in stress drop. As shear wave velocity increases, the rupture velocity increases as well and consequently the fault will be fractured more rapidly. Generally, the rupture velocity is assumed as 80-90% of shear wave velocity, which is also important parameter in accessing the ground motion. A simplied approach has been proposed to estimate Vr=Vs through numerical approach. It is observed that the ratios of rupture and shear wave velocities at 20 s, 25 s, 30 s, 35 s and 50 s are 0.75, 0.63, 0.84, 0.76 and 0.53, respectively. The maximum rupture velocity is equal to 0.76 times the shear wave velocity, whereas, v the assumed value was 0.8 times the shear wave velocity. Further, a uniform hazard spectrum (UHS) has been developed for Alaska region. The seismicity parameters a and b are generated for Alaska region and those are 5.76 and 0.776, respectively. A 10 % probability in 50 years corresponding to a 475 year return period for uniform hazard spectrum has been assumed. A maximum of 0.51 g spectral acceleration has been observed at the bed rock level.

Abstract

In future, it is possible to have small sized autonomous vehicles traveling along with larger vehicles on the same roads. Autonomous vehicles are no longer in the realm of science fiction, thanks to the artificial intelligence and integration of numerous auto motive technologies that have matured over many years. Autonomous vehicles could save lives, chase criminals, reduce accidents, and help the environment by reducing fuel demands and pollution. One such example can be police motorcycles. Such autonomous vehicles has to both keep track of the other vehicles and also drive his/her vehicle safely and fast. In this paper, we present AUTOSTEPS, a multi-agent simulation environment where autonomous small vehicles perceive and learn about the environment for fast navigation to reach their goal. The goal for a agent is defined as the destination he wants to reach. The key idea is the notion of spatial temporal spaces into which the small vehicles continue to move till they reach their goal. These spaces are present on the road between vehicles and may change with time and position of vehicles. We built a simulator and ran experiments to show that our strategies for AUTOSTEPS ensures fast and safer navigation for small autonomous vehicles.

Abstract

Abstract Machine Translation (MT) is a task in Natural Language Processing (NLP), where the automatic systems are used to translate the text from one language to another while preserving the meaning of source language. In this work, we provide our efforts in developing a rule-based translation system on the Analyze-Transfer-Generate paradigm which employs morphological and syntactic analysis of source language. We utilized shallow parser for Hindi language along with dependency parse labels for syntactic analysis of Hindi language, developed modules for transfer of Hindi to English and generation of English language. Due to wide difference in word order of the two languages (Hindi following SOV and English SVO word order), a lot of re-ordering rules need to be crafted to capture the irregularity of the language pair. As a result of drawbacks of the aforementioned approach, we shifted to statistical methods for developing a system. A wide variety of machine translation approaches have been developed in past years. As each model has its pros and cons, we propose an approach where we try to capture the advantages of each system, thereby developing a better MT system. We then incorporate semantic information in phrase-based machine translation using monolingual corpus where the system learns semantically meaningful representations. Recent studies in machine translation support the fact that multi-model systems perform better than the individual models. In this thesis, we describe a Hindi to English statistical machine translation system and improve over the baselines using multiple translation models. We work on MOSES which is a free statistical machine translation framework, which allows automatically training translation model using parallel corpus. MOSES provides support for multiple algorithms for training a model. We propose an approach for computing the quality score for each translation by using automatic evaluation metric as our quality score. The computed quality score is used for guided selection among the translations from multiple models, thereby providing a better system. We have used support vector regression to train a model using syntactic,textual and linguistic features extracted from the source and target translation with evaluation metric as our regression output. Quality Estimation of Machine Translation is a task where the system tries to predict the quality of output on the basis of features extracted from source and target languages. The system dynamically(run time) computes a quality score corresponding to each translation, which is the measurement for the correctness of the output. Different from MT evaluation, quality estimation systems do not rely on reference translations and are generally addressed using machine learning techniques. The approach offers a great advantage to the readers of target language as well as for professional translators.

Abstract

Histopathology, the study of tissues, has a major contribution in determining prognosis of diseases at the tissue level. Quantitative analysis of histopathological images is used to determine the future course of treatment. However, the analysis is a very laborious process given the complexity of structures in tissues. Pathologists require a lot of expertise and skill to perform the analysis. Hence, development of Computer Aided Diagnostic (CAD) algorithms which rely on automated image analysis, is of great interest. CAD development in histopathology is a relatively new field of research. Some of the major hurdles in this field are non - availability of public datasets, difficulty in acquiring data and ground - truth marking. This thesis aims at developing CAD tools for assisting pathologists. In our work, we have identified two problems in this field and provided solution for the same. The first is identification of stromal regions in tissue and the second is detecting mitotic candidates. Both these are ne cessary for assessing and staging cancer. For stroma identification we propose a novel block - based approach of suppressing foreground texture to learn discriminative textural features in a block. The results of the bloc k - based stage are refined to obtain t he stroma segments. We have teste d our method on 3 datasets (two public and one locally acquired) varying in size and type (breast and prostate tissue) and have obtained around 80% accuracy and 90% sensitivity. Both the qualitative as well as the quantitat ive results show an improvement over the existing methods. The existing algorithms for mitosis detection employ a traditional approach of classification post feature - extraction. A major road - block in this problem is the presence of visual clutter ( false positives , similar in appearance to mitotic candidates ). We use biological cues to design rejection stages to get rid of the false positives for mitosis detection followed by classification. The pipeline design comprising of rejection stages adapt to variability in the magnification levels of datasets. This was confirmed by determining percentage of rejection after each stage on a locally acquired dataset at different magnification settings. We further improve the performance by incorporating an eff icient classifier regenerative random forests . The algorithm was validated on MITOS dataset against the ground truth and a recall of ,0.81,precision of 0.84 and f - measure of 0.83 was obtained.