Abstract

In a last few years, there has been a surge of interest in the field of autonomous navigation and exploration using drones. While there have been many advances made in the field, there has been various challenges, yet to be tackled, for a safe navigation of Quad-copters amidst uncertain dynamic obstacles. In this work, we attempt to tackle this problem by incorporating positional uncertainties of drone and moving obstacles into a joint trajectory optimization framework. In this thesis, we formulate a novel trajectory optimization scheme that takes into consideration the state uncertainty of the robot and obstacle into its collision avoidance routine. The collision avoidance under uncertainty is modeled here as an overlap between two distributions that represent the state of the robot and obstacle respectively. Our framework is a generic framework and the idea proposed here can be used to for any sets of distributions with characterizable overlap. In the scope of this thesis, we model these distributions as Gaussian distributions. We adopt the minmax procedure to characterize the area of overlap between two Gaussian distributions, and compare it with the method of Bhattacharyya distance. Bhattacharyya distance is an approximate closed form characterization of overlap between two Gaussian distribution. The Bhattacharyya distance is generalizable to other distributions too, if their overlap can be characterized through some analytical closed form solution. In this work, We provide closed form expressions that can characterize the overlap as a function of control. We establish that our closed form approximations to characterize overlap is less erroneous when compared with entropic distances like Bhattacharyya distance. Our proposed algorithm can avoid overlapping uncertainty distributions in two possible ways. Firstly when a prescribed overlapping area that needs to be avoided is posed as a confidence contour lower bound, control commands are accordingly realized through a MPC framework such that these bounds are respected. Secondly in tight spaces control commands are computed such that the overlapping distribution respects a prescribed range of overlap characterized by lower and upper bounds of the confidence contours. We test our proposal with extensive set of simulations carried out under various constrained environmental configurations. We show usefulness of proposal under tight spaces where finding control maneuvers with minimal risk behavior becomes an inevitable task.

Abstract

With an immense amount of data growing on the web in Hindi, a text summariser is helpful in summarising government data, medical reports, news, and research articles or any other digitalized documented data. The volume of available text has increased beyond the reading capacity of a single user — a search term on the web results in thousands of relevant pages. Recent improvements in the search engines yield better ranked documents that aim to provide precise information. However, these documents in itself are significantly huge to be read in a few minutes. Hence, the availability of summarisation tools in this domain is of utmost importance.According to Wikipedia, Hindi is the fourth most-spoken first language in the world. Hindi is written in the Devanagari script. Hindi is growing digitally and serves nearly a billion people residing in India. Summarising Hindi news articles is essential. As of now, not enough work has been done for summarising Hindi news articles automatically. Although the dominant form of news consumption in India is through newspapers, digital news consumption is becoming increasingly popular. With the world moving towards virtual reality and digitalization using smartphones, there is a need to summarise the data to make it more readable and concise. Summaries provide an efficient way to deal with the growth of data. A news summariser can be built to manage the issue of data growth on a large scale. A gradual move towards summarization of the digitally available Hindi news would lead to a higher population reading news efficiently. We have aimed to analyze the following approaches for text summarization: 1. Frequency-based approach 2. Graph-based approach 3. Feature-based approach We developed an end to end extractive text summariser for news articles and tested it on a dataset of about 24 thousand articles. We achieved excellent results so far. The extractive dataset of 200 article-summary pair developed as a part of this thesis is of great use for the research community. Currently, there is no public dataset for extractive summarisation available in Hindi. To solve the issue, we extracted a dataset of 24253 news articles. We evaluated extractive summary results on various parameters with manual gold abstractive summaries of exactly 60 words each. For evaluation purposes, we created a standard article summary pair, of the same type of summaries. In the gold data of extracted news articles, summaries were in abstractive form, and we developed an extractive summariser. So we manually created the gold dataset of extractive summaries of 200 articles to test the summariser on the ideal platform.

Abstract

In recent years, with the proliferation of devices capable of capturing and consuming multimedia content, there is a phenomenal increase in multimedia consumption. And most of this is dominated by video content. This creates a need for efficient tools and techniques to create videos and better ways to render the content. Addressing these problems, in this thesis we focus on (a) Algorithms for efficient video content adaptation (b) Automating the process of video content creation. To address the problem of efficient video content adaptation, we present a novel approach to optimally retarget videos for varied displays with differing aspect ratios by preserving salient scene content discovered via eye tracking. Our algorithm performs editing with cut, pan and zoom operations by optimizing the path of a cropping window within the original video while seeking to (i) preserve salient regions, and (ii) adhere to the principles of cinematography. Our approach is (a) content agnostic as the same methodology is employed to re-edit a wide-angle video recording or a close-up movie sequence captured with a static or moving camera, and (b) independent of video length and can in principle re-edit an entire movie in one shot. The proposed retargeting algorithm consists of two steps. The first step employs gaze transition cues to detect time stamps where new cuts are to be introduced in the original video via dynamic programming. A subsequent step optimizes the cropping window path (to create pan and zoom effects), while accounting for the original and new cuts. The cropping window path is designed to include maximum gaze information and is composed of piecewise constant, linear and parabolic segments. It is obtained via L(1) regularized convex optimization which ensures a smooth viewing experience. We test our approach on a wide variety of videos and demonstrate significant improvement over the state-of-the-art, both in terms of computational complexity and qualitative aspects. A study performed with 16 users confirms that our approach results in a superior viewing experience as compared to the state of the art and letterboxing methods, especially for wide-angle static camera recordings. As the retargeting algorithm takes a video and adapts it to a new aspect ratio, we can only use the existing information in the video which limits the applicability. In the second part of the thesis, we address the problem of automatic video content creation by looking into the possibility of using deep learning techniques for automating cinematography. This type of formulation gives more freedom to the users to create content according to some their preferences. Specifically, we investigate the problem of predicting shot specification from the script by learning this association from real movies. The problem is posed as a sequence classification task using Long Short Term Memory (LSTM) network, which takes as input the sentence embedding and a few other high level structural features (such as sentiment, dialogue acts, genre etc.) corresponding a line of dialogue and predicts the shot specification for the corresponding line of dialogue in terms of Shot-Size, Act-React and Shot-Type categories. We have conducted a systematic study to find out effect of the combination of features and the effect of input sequence length on the classification accuracy. We propose two different formulations of the same problem using LSTM architecture and extensively studied the suitability of each of them to the current task. We also created a new dataset for this task which consists of 16000 shots and 10000 dialogue lines. The experimental results are promising in terms of quantitative measures (such as classification accuracy and F1-score).

Abstract

Relational databases have been around for a long time, and Spatial databases have exploited this feature for close to two decades. The recent past has seen the development of NoSQL non-relational databases, which are now being adopted for spatial object storage and handling too. Moreover, this is gaining ground in the context of a rising shift towards Geospatial Web Services on both the Web and mobile platforms, primarily the user-centric services, where there is a need to improve the query response time. There is a need to assess the spatial functionalities supported by NoSQL databases and gauge the appropriateness of NoSQL databases for a full-scale Geospatial application. This thesis attempts to analyse the performance and adaptability of an existing NoSQL database(MongoDB) with its inbuilt spatial functions compared with that of a SQL database(PostgreSQL) with spatial extension(PostGIS). We compare the performance of both databases on the spatial functions they currently support on simulated as well as real-world data sets. In addition, to evaluate its utility in a GIS application we implemented a routing engine, which is a primary GIS application due to its usage of a large amount of spatial data in the form of network and topology. This implementation helps assess the usability of NoSQL databases in a spatial context against the current SQL implementation (pgRouting). For spatial queries, it was found that MongoDB achieves a gain of 25x in time performance for topological queries and about 10x for aggregate queries. In the routing application for Dijkstra's implementation over the two databases indicate that the performance is better in case of small networks with less number of queries. We expect the routing application to outperform the current server-client model of pgRouting once appropriate optimisations are incorporated into it. Based on the observations from this study, it is anticipated that NoSQL databases may be better suited for simultaneous multiple-user query systems including on Web-GIS and mobile-GIS platforms. While this study has helped to show the potential of NoSQL databases in the context of spatial queries and spatial data handling, further studies may be carried out on other spatial query functions as well as other spatial algorithms to evaluate the use or adoption of NoSQL based spatial databases. Also, performance of MongoDB for simultaneous queries over distributed systems can provide appropriate insights to its utility.

Abstract

Urea assisted denaturation of protein and RNA has been shown to be a valuable tool for studying the stabilities and the folding phenomena of these biomolecules. It has been shown that stacking interactions between nucleobases and urea are one of the driving forces of denaturation. In this study, the ability of urea to form unconventional stacking interactions with RNA bases is investigated by performing high level quantum calculations (RI-MP2/aug-cc-pVDZ level) on few thousands of model systems. Four systems were considered based on the RNA nucleobases (GUA, ADE, CYT, and URA) for the investigation. For each system, a set of models were designed to study the role of hetero-atoms/groups of the nucleobases on stacking interactions with urea moiety with respect to every possible pair. Furthermore, for each model, several plane parallel complexes were generated with urea on top of aromatic systems to exhaustively study all possible factors for urea-nucleobases stacking interactions. Energy decomposition analysis (EDA) and natural bond orbital (NBO) analysis were performed on stacked complexes. AIM analysis was done for the most stable system of each model for all systems to gain better insights on non-covalent stacking interactions. Dispersion component was found to be heavily stabilizing, while the EHF was found to be repulsive for all the four systems indicating lack of hydrogen bonding (HB) type interactions and presence of dispersion type interactions. Interactions of most stable complex for each nucleobase with Urea moeities were also studied to identify the importance of each part of Urea in these stacking interactions. Amide and Carbonyl group of urea molecule was found to play a major role in stacking interactions. We demonstrate that along with functional groups present on the nucleobases, the orientation of urea molecules plays a vital role in stabilizing the urea-nucleobase non-covalent interactions. The proposed study quantifies and provides a comprehensive theoretical description of urea nucleobase unconventional stacking interactions which helps to unravel urea driven RNA unfolding mechanism.

Abstract

Understanding speech in the absence of audio, from the visual perception of lip-motion can aid a variety of computer vision applications. System comprehending ‘silent speech presents a promising potential for low bandwidth video-calling, speech transmission in auditory noisy environment to aid for hearing impaired. While presenting numerous opportunities, it is highly difficult to model lips in silent speech video by observing lip-motion of speaker. Albeit developments in automatic-speech recognition (ASR) has yielded better audio-speech recognition systems in last two decades, in the presence of noise their performance drastically deteriorates. This calls for a computer vision solution to the speech under- standing problem. In this thesis, we present two solutions for modelling lips in silent speech videos. In the first part of the thesis, we propose a word-spotting solution for searching spoken keywords in silent lip-videos. In this work on visual speech recognition our contributions are twofold: we develop a pipeline for recognition-free retrieval, and show its performance against recognition-based retrieval on a large-scale dataset and another set of out-of-vocabulary words. 2) We introduce a query expansion technique using pseudo-relevant feedback and propose a novel re-ranking method based on maximizing the correlation between spatio-temporal landmarks of the query and the top retrieval candidates. The proposed pipeline improves baseline performance by over 35% for word-spotting task on one of the largest lipreading corpus. We demonstrate the robustness of our method through a series of experiments, by investigating domain invariance, out-of-vocabulary prediction and careful analysis of results on dataset. We also present the qualitative results showing success and failure cases. We finally show the application of our method by spotting words in an archaic speech video. In the second part of our work, we propose a lip-synchronization solution for ‘visually redubbing speech videos in a target language. Current methods of adapting a native speech video in foreign lan- guage either through placement of subtitle in the video, which distracts the viewer or through audio redubbing the video in the target language. This causes unsynchronized lip-motion of the speaker with respect to the redubbed audio, resulting in video appearing unnatural. In this work, we propose two lip synchronization methods: 1) cross-accent lip-synchronization for change in accent of the same language audio dubbing, and 2) cross-language lip-synchronization for speech videos dubbed in a differentlanguage. Since viseme remains the same in cross-accent dubbing, we propose a dynamic programing algorithm to align the visual speech from the original video with the accented speech in the target audio. In cross-language dubbing overall linguistics changes, hence we propose a lip-synthesis model conditioned upon on the redubbed audio. Finally, a user-based study is conducted, which validates our claim of better viewing experience in comparison to baseline methods. We present the application of both these methods by ‘visually redubbing Andrew Ngs machine learning tutorial video clips in Indian accented English and Hindi language respectively. In the final part of this thesis, we propose an improved method of 2D lip-landmark localization method. We investigated the current landmark localization techniques in facial domain and human- pose estimation to discover the shortcoming in adapting these methods for the task of lip-landmark localization. Present state-of-the-art methods in the domain considers lip-landmarks as a subset of facial landmarks and hence doesn’t explicitly optimizes for it. In this work we propose a new lip-centric loss formulation on the existing stacked-hourglass architecture which improves the baseline performance. Finally we use 300W and 300VW faces dataset to show the performance of our methods and compare them with the baselines. Overall, in this thesis we examined the current methods of lip modelling, investigated them for their shortcomings and proposed solutions to overcome those challenges. We perform detailed studies, and ablation studies to study our proposed methods and reported both success and failure cases for the same. We compare our solutions with the current baseline on challenging datasets, reporting quantitative results and demonstrating qualitative performances. Our proposed solutions improves the baseline performances in their individual domains.

Abstract

Future graphics and imaging applications from self-driving cars, to pervasive sensing, demand orders of magnitude more computation than we currently have. Real graphics and imaging applications appear embarrassingly parallel, but have complex dependencies, and are limited by locality and synchronization. Increasingly, the cost of communication, both within a chip and over a network dominates computation and power consumption. Driven by these trends, writing high-performance image processing code is challenging. To implement these upcoming sophisticated DIP algorithms and to process the large amount of data captured from sources such as satellites or medical instruments, intelligent high speed real-time systems have become imperative. Image processing algorithms implemented in hardware (instead of software) have recently emerged as the most viable solution for improv- ing the performance of image processing systems. The outstanding features which FPGAs offer such as optimization, high computational density, low cost etc, make them an increas- ingly preferred choice of experts in image processing field today. Despite of huge advancements in High-Level Synthesis (HLS) for FPGAs, designers are still required to have detailed knowledge about coding techniques and the targeted architecture to achieve efficient solutions. To address the challenge of productively building efficient,high- performance programs, this thesis describes an automatic approach to accelerate image processing pipelines using FPGAs. An image processing pipeline can be viewed as a graph of interconnected stages that processes images successively. Each stage typically performs a point-wise, stencil, or other more complex operations on image pixels. Recent efforts have led to the development of domain-specific languages (DSL) and optimization frameworks for image processing pipelines. We develop an approach to map image processing pipelines ex- pressed in the PolyMage DSL to efficient parallel FPGA designs. Our approach exploits reuse and available memory bandwidth (or chip resources) maximally. When compared to Dark-room, a state-of-the-art approach to compile high-level DSL to FPGAs, our approach (a) leads to designs that deliver significantly higher throughput, and (b) supports a greater variety of filters. Furthermore, the designs we generate obtain an mprovement even over pre-optimized FPGA implementations provided by vendor libraries for some of the benchmarks

Abstract

Growing infrastructure is demanding huge amounts of construction and fill materials, though the source available is limited. This unavoidable scenario in civil engineering needs alternative materials which can be substitutes to the conventional fill materials. It would be more advantageous if waste materials are made substitutes. Out of the motivation gained from the past research on waste utilization, waste materials considered for the present study are rice husk ash (RHA), quarry waste (QW) and granulated rubber (GR). Geotechnical properties of combinations of materials were investigated for the under two different studies namely, Study-I and Study-II. Under Study-I, light weight rice husk ash has been strengthened using quarry waste as additive and under Study-II, a light weight high strength material has been proposed using granulated rubber as additive to quarry waste. A series of experimental tests have been performed in Study-I and Study-II to explore their geotechnical behavior. Based on the outcomes of the experimental investigation of Study-I, the RQ50 mix (consisting equal amounts of rice husk ash and quarry waste by weight) was found to be the optimum mix. Numerical analysis under seismic loading has been performed on earth dam with RQ50 mix, QW as shell materials, in order to check their suitability to utilize in earth dam. The seismic performance of earth dam with RQ50 as shell material has been compared to that of dam with dense quarry waste as shell material, using finite element software PLAXIS 2D. The results from numerical study concluded the better seismic performance of earth dam with RQ50 than that with quarry waste, though the mix was lighter compared to quarry waste material. Thus, this combination can form an efficient material to be used as shell material in earth dam. The laboratory outcomes of Study-II have concluded the reduction in maximum dry density of quarry waste by about 0.77gm/cc upon adding 30% of granulated rubber for which the friction angle was found to improve by about 14 ̊ . The experimental results concluded QGR mixes (quarry waste mixes with granulated rubber) to be efficient backfill material with high strength and lightweight. QGR30 mix (70% QW+30% GR by weight) has been identified as best performing mix from the laboratory tests. The lateral earth pressure reduction on retaining wall by utilizing QGR30 as backfill compared to sand, quarry waste would be about 85%, 73% respectively. The advantage of lightweight and high strength fill is very economical as it would result in reduced lateral pressure, thereby the reduction in dimensions of supporting wall. The savings in the cost of backfill material using QGR30 over sand would be around 8.35%. Thus, from this study, the QGR mixes were found to be promising alternatives to the conventional fill materials, having high strength and low weight.

Abstract

DNA is the storehouse of genetic information in biological cells. It plays a vital role in transmission of the stored hereditary information from one generation to the next via the gene expression process. DNA damages caused by various damaging agents including free radicals, UV radiation, and anticancer drugs can alter genetic information and adversely affect gene expression pathways leading to various complex genetic disorders and cancers. To safeguard genetic information, biological cells have sophisticated survival mechanisms to recognize and rectify DNA damages with high fidelity. The task of recognition and repair of damaged DNA is carried out by a group of specific proteins. It is essential to understand the molecular basis of protein-mediated DNA damage repair to comprehend on the initiation and evolution of various complex genetic diseases and disorders. A critical molecular event that occurs during most protein-mediated DNA repair processes is the extrusion of orphaned bases at the damaged site facilitated by specific repairing enzymes. The molecular-level understanding of the mechanism, dynamics, and energetics of base extrusion is necessary to elucidate the molecular basis of protein-mediated DNA damage repair. The present thesis investigates the molecular mechanism and energetics of protein-induced base flipping and of DNA bending in various damaged DNA-protein complexes using molecular dynamics simulation and enhanced sampling free energy methods. In particular, we have explored the molecular mechanism of dinucleotide extrusion in a mismatched DNA (containing a stretch of three contiguous thymidine-thymidine base pairs) facilitated by Radiation sensitive 4 (RAD4), a key DNA repair protein, on an atom-by-atom basis using molecular dynamics (MD) and umbrella-sampling (US) simulations. Using atomistic models of RAD4-free and RAD4-bound mismatched DNA, the free energy profiles associated with extrusion of mismatched partner bases are determined for both systems. The mismatched bases adopted the most stable intra-helical conformation and their extrusion was unfavorable in RAD4-free mismatched DNA due to the presence of prohibitively high barriers (> 12.0 kcal/mol) along the extrusion pathways. Upon binding of RAD4 to the DNA, the global free energy minimum is shifted to the extra-helical state indicating the key role of RAD4-DNA interactions in catalyzing the dinucleotide base extrusion in the DNA-RAD4 complex. The critical residues of RAD4 contributing to the conformational stability of the mismatched bases are identified and the energetics of insertion of a -hairpin of RAD4 into the DNA duplex is examined.The conformational landscape-based mechanistic insight into RAD4-mediated base extrusion provided here may serve as a useful baseline to understand the molecular basis of xeroderma pigmentosum C (XPC)-mediated DNA damage repair in humans. In order to understand the molecular basis of why RAD4 recognizes certain mismatched DNA sequences with higher specificity, we have examined the base flipping dynamics in different RAD4-DNA complexes with different mismatched DNA sequences. The sequence dependent variations in the activation barrier for base extrusion and the most-stable conformations of mismatched partner bases are examined. The simulation results reveal that the energetics of base flipping and stability of the damaged bases are sensitive to the sequence of the damaged DNA. We have also explored the molecular basis of DNA damage recognition by enzyme 8-oxoguanine DNA glycosylase (MutM) and examined the base flipping energetics of guanine (G) and 8-oxoguanine (8OG) in the presence and absence of enzyme MutM. A detailed analysis of free energy profiles have revealed atomistic insights into enzyme-binding induced base flipping energetics, pathways and barriers for all the four systems. Similarly, to understand the sequence dependent damage (8OG)-induced distortions, the free energies of base flipping and DNA bending are calculated for 32 different DNA sequences. Our simulations revealed that base flipping is highly sequence dependent. Depending on the position of nucleotides on either (3' and 5') side of the base, significant alterations have been observed in base flipping and DNA bending energetics.

Abstract

Non-canonical base pairs are found to play a crucial role in folding of RNA molecules. Some of these non-canonical base pairs have been found to occur in nature in intrinsically unstable geometries. An understanding of how various interactions stabilize these base pairs will not only help in predicting higher order structures from secondary structures, but also provide insights into the sequence-structure function landscape of RNA molecules. One such intrinsically unstable non-canonical base pair is the G:G W:H Cis base pair, which converges to a stable G:G W:W Trans geometry when independently optimized using quantum mechanical (QM) methods. This study aims to uncover the external factors that stabilize this base pair using a combination of structural bioinformatics tools and QM methods. Such investigations were earlier used successfully to show that formation of higher order structures with external factors stabilized G:C W:W Trans base pair. Structural bioinformatics tools were used on a non-redundant set of 167 RNA crystal structures. G:G W:H Cis base pair was found to occur 123 times in this dataset. These occurrences of G:G W:H Cis base pair were categorized into 6 different environmental contexts. Environmental contexts are defined based on the external factors (other bases and metal ions) present in the vicinity of G:G W:H Cis base pairs,similar to the higher order structures identified in the study of G:C W:W Trans base pair. Quantum mechanics based geometry optimization calculations were performed on models, incorporating these environmental contexts at B3LYP level and using 6-31++(2d,2p) basis set to investigate the possible roles of these external factors towards stabilizing the geometry of this base pair. Interaction energies were calculated at MP2 level and using aug-cc-pVDZ basis set.The external factors that were found to stabilize the G:G W:H Cis base pair geometry are 1) Triplet formation by the guanine base G H , whose hoogstein edge is involved in G:G W:H Cis base pair, forming additional base pair with either adenine or cytosine through it’s watson crick edge, 2) Quartet formation of G:G W:H Cis base pair, with the G H base forming two additional base pairs, one with adenine though it’s sugar edge and the other with cytosine through it’s watson crick edge and 3) co-ordinate covalent bonds formed by Mg +2 cation with the O6 atoms of both the guanine bases. It is found that, in the absence of external factors, the G:G W:H Cis geometry is unstable even with two good hydrogen bonds and this could be due to the positioning the highly electronegative, and electron rich, O6 atoms closer to each other resulting in electrostatic repulsion. The aforementioned external factors hold the oxygen atoms together by 1) hydrogen bonds with hydrogen atoms of the exocyclic amino groups of the another partner base or 2) co-ordinate covalent bonds with metal ions which act like clamps, to maintain its observed crystal geometry.