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.

Abstract

Users navigating the web often deviate from the intended path and find themselves in a state of being lost. Early research shows, different cognitive factors such as domain knowledge, spatial ability, working memory, and prior experience contributes to the successful navigation. Existing cognitive models such as CoLiDeS, CoLiDeS+, CoLiDeS+Pic, MESA, and SNIF-ACT provide deeper analysis to understand the user behavior on the web. At the end, the goal of these models is to help the practitioners of the web in evaluating “the effectiveness of their designs”, in terms of user navigation. These models consider the selection process of a link as the key step in navigation and do so by measuring the semantic similarity of the link with respect to the goal. However, they do not completely account for all types of cognitive factors, and also the position of the links and content on a web page in the selection process. In this work, we present the results from two eye tracking studies conducted to understand the user behaviour during the link selection process. We observed that the users select a link with maximum similarity to the goal (information scent) and the information scent is always increased in a successful navigation path. We have also observed that the position and the information scent has not affected the way users attend the links. Although existing models do not consider the content while evaluating the links, we noticed that users spent significant amount of time in attending the content during the selection process. By comparing CoLiDes+ with Information Search process, we also found that the users feel more uncertain during the link selection stage of CoLiDes+. Incorporating all these observations, we modelled a tool to assist in navigation to reduce the user uncertainty and disorientation. While the above studies consider the user behavior as goal-oriented, often users browse without any predefined goal to start with. In this context, we carried out a behavioral study to understand the role of cognitive factors spatial ability and prior knowledge, on information seeking patterns. We observed the high spatial ability resulted in surface navigation whereas the low spatial ability resulted in deep navigation pattern. Users with high domain knowledge spent more time per page and visited less number of pages. These findings lead us to design an assistive feature by presenting the information from other sections on a given page. We believe this will result in better information discovery for the low spatial ability and the domain knowledge.

Abstract

Handwriting recognition (HWR) in Indic scripts is a challenging problem due to the inherent subtleties in the scripts, cursive nature of the handwriting and similar shape of the characters. Though a lot of research has been done in the field of text recognition, the focus of the vision community has been primarily on English. Furthermore, a lack of publicly available handwriting datasets in Indic scripts has also affected the development of handwritten word recognizers and made direct comparisons across different methods an impossible task in the field. Also, due to this lack annotated data, it becomes challenging to train deep neural networks which contain millions of parameters. These facts are quite surprising considering the fact that there are over 400 million Devanagari speakers in India alone. We first tackle the problem of lack of annotated data using various approaches. We describe a framework for annotating large scale of handwritten word images without the need for manual segmentation and label re-alignment. Two new word level handwritten datasets for Telugu and Devanagari are released which were created using the above mentioned framework. We synthesize synthetic datasets containing millions of realistic images with a large vocabulary for the purpose of pre-training using publicly available Unicode fonts. Later on, pre-training using data from Latin script is also shown to be useful to overcome the shortage of data.Capitalizing on the success of the CNN-RNN Hybrid architecture, we propose various improvements in the architecture and it’s training pipeline to make it even more robust for the purposes of handwriting recognition. We now change the network to use a Resnet-18 like structure for the convolutional part along with adding a spatial transformer network layer. We also use an extensive data augmentation scheme involving multi-scale, elastic, affine and test time distortion. We outperform the previous state-of-the-art methods on existing benchmark datasets for both Latin and Indic scripts by quite some margin. We perform an ablation study to empirically show how the various changes we made to the original CNN-RNN Hybrid network have improved its performance with respect to handwriting recognition. We dive deeper into the working of our networks convolutional layers and verify the robustness of convolutional-features through layer visualizations. We hope the release of the two datasets mentioned in this work along with the architecture and training techniques that we have used instill interest among fellow researchers of the field.

Abstract

Videos engulf the media archive in every capacity, generating a rich and vast source of information from which machines can learn how to understand such data and predict useful attributes that can help technologies make human lives better. One of the most significant and instrumental part of a computer vision system is the comprehension of human actions from visual sequences – a paragon of machine intelligence that can be achieved through computer vision. The problem of human action recognition has high importance as it facilitates several applications built around recognition of human actions. Understanding actions from monocular sequences has been studied immensely, while comprehending human actions from a skeleton video has developed in recent times. We propose action recognition frameworks that use the skeletal data (viz. 3D locations of some joints) of human body to learn spatio-temporal representations necessary for recognizing actions. Information about human actions is composed of two integral dimensions, space and time, along which the variations occur. Spatial representation of a human action aims at encapsulating the config- uration of human body essential to the action, while the temporal representation aims at capturing the evolution of such configurations across time. To this end, we propose to use geometric relations betweenhuman skeleton joints to discern the body pose relative to the action and physically inspired kinematic quantities in order to understand the temporal evolution of body pose. Spatio-temporal understanding of human actions is thus conceived as a comprehension of geometric and kinematic information with the help of machine learning frameworks. Using a representation inculcating an amalgamation of geo- metric and kinematic features, we recognize human actions from skeleton videos (S-videos) using such frameworks. We first present a non-parametric approach for temporal sub-segmentation of trimmed action videos using angular momentum trajectory of the skeletal pose sequence. Meaningful summarization of the pose sequence is a product of the sub-segmentation achieved through systematic sampling of the seg- ments. Descriptors capturing geometric and kinematic statistics encoded as histograms and spread across a periodic range of orientations are computed to represent the summarized pose sequences, which are fed to a kernelized classifier for recognizing actions. This framework for understanding human ac- tions instils the effects of using geometric and kinematic properties of human pose sequences, important to spatio-temporal modeling of the actions. However, a downside of this framework is the inability to scale with availability of large amount of visual data.To mitigate the impending drawback, we next present geometric deep learning frameworks and specifically, graph convolutional networks, for the same task. Representation of human skeleton as a sparse spatial graph is intuitive and a structured form which lies on graph manifolds. A human action video hence results in the formation of a spatio-temporal graph and graph convolutions facilitate the learning of a spatio-temporal descriptor for the action. Inspired by the success of Deformable Part-based Models (DPMs) for the task of object understanding from images, we propose a part-based graph con- volutional network (PB-GCN) that operates on a human skeletal graph divided into parts. Incorporating the culmination of understandings from the success of geometric and kinematic features, we propose to use relative coordinates and temporal displacements of the 3D joints coordinates as features at the vertices in the skeletal graph. Owing to these signals, the prowess of graph convolutional networks is further boosted to attain state-of-the-art performance among several action recognition systems using skeleton videos. In this thesis, we meticulously examine the growth of the idea about using geometry and kinematics, transition to geometric deep learning frameworks and design a PB-GCN with geometric + kinematic signals at the vertices, for the task of human action recognition using skeleton videos.

Abstract

360° field of view (FOV) is becoming a vital part of various human-robot/computer applications, such as urban search and rescue task. Despite growing importance of 360° FOV, very few studies have assessed the effect of 360° visual display on users' remote spatial ability, specifically in the case of 2D 360° User Interface (UI), which is the focus of the current study. We aim to evaluate the efficacy and efficiency of desktop 360° display designs on teleoperators' egocentric spatial ability, specifically navigation and direction judgment in an unknown environment. A previous study[1] showed an advantage of the segmented interface over seamless 360° UI on angle estimation error. However, we are still not clear as to the determining factors that lead to a difference in interface interaction ability. We conducted two studies that studied the scanning effort while using a 360° interface and the ability to estimate the mental egocentric bearing of a target in an environment. We measured the effects of gaming experience, and time constriction, with metrics consisting of time to task completion, target bearing estimation, and latency of target estimation. Further data was collected by an eye-tracking device to help support and complement the data from the studies. We found that gaming experience is a significant positive factor in accuracy and time to task completion. Further, a seamless panoramic view was found to be least effective compared to a segmented interface view of the 360° video feed for target bearing estimation across the entire field of vision presented through the interfaces. Eye tracking data supports the behavioral data collected. It was found that the estimation frequencies in the first study were high for forward heading direction, which was seen in the increased eye movement activity in the same region. In the second study, it was found that angle estimation error was the lowest along mental egocentric axes. This was again observed in the eye tracker data that reported increased activity along the mental egocentric axes.

Abstract

Forest fire is a major ecological disaster, which has economic, social and environmental impacts on humans and also causes the loss of biodiversity. Forest officials issue warnings to the public on the basis of fire danger index classes. Generally, fire danger indices are developed based on the meteorological stations in countries like Canada, United States of America, Australia, etc. Geospatial techniques such as satellite remote sensing based approaches can be useful to develop the fire danger indices in those countries that lack sufficient meteorological stations. In general, fire danger indices have been developed based on the parameters which are associated for the cause of ignition and spreading of forest fires. These properties include forest fuel type, topographic conditions and moisture conditions. Vegetation and topographic conditions are static, i.e. they do not change frequently, whereas moisture conditions are dynamic. Dynamic properties such as air temperature, relative humidity, moisture conditions changes regularly in a day. In this study, Static Fire danger Index has been developed using MODIS (Moderate Resolution Imaging Spectro Radiometer) Land cover type yearly L3 global 500 m SIN grid (MCD12Q1) and ASTER Global Digital Elevation Model datasets. International Geosphere-Biosphere Programme (IGBP) land cover type has been generated from MCD12Q1, which has been used to compute the forest fuel type index based on historical fire data. Fuel type danger index, Terrain ruggedness danger index, Slope danger index, Aspect danger index and Elevation danger index were computed from the ASTER GDEM datasets. Uttarakhand state has very few meteorological stations so geospatial techniques can be useful to derive the fire danger indices. Several authors developed the fire danger indices based on satellite derived parameters such as land surface temperature, vegetation and moisture indices such as “Normalized Difference Vegetation Index, Normalized Difference Water Index, Normalized Multiband Drought Index, Visible Atmospheric Resistant Index” etc. In this study, Dynamic Fire Danger Index (DFDI) has been developed from three parameters i.e. potential surface temperature, Perpendicular Moisture Index (PMI) and Modified Normalized Multiband Fire Index (MNDFI) using the MODIS Terra satellite datasets. DFDI has been calculated from the Near Real Time (NRT) Level 2 MODIS Terra Land Surface Temperature datasets (MOD11_L2) and MODIS Terra NRT surface reflectance dataset (MOD09). Finally, Forest Fire Danger Index (FFDI) has been developed by integrating both the Static and Dynamic fire danger indices and also used the near real time data sets that can be available for download through NASA FTP server Developing Forest Fire Danger index using geo-spatial techniques after one hour of the satellite overpass. The overall fire danger prediction accuracy was around 81.27% for the year 2016. Thus, the FFDI has been useful to assess the fire danger accurately over the study area and can be useful anywhere, where the meteorological stations are un-available. The entire procedure of calculating FFDI from NRT datasets was semi-automated so that the fire danger maps will be disseminated to the fire officials for taking timely action in controlling the forest fires.

Abstract

Sentiment analysis is a discipline of Natural Language Processing which deals with identifying and analyzing the subjectivity of data. It is an important task with both commercial and academic function- ality. It has acquired wide commercial uses including social media monitoring tasks, survey responses, review systems, etc. In review systems and surveys, sentiment analysis allows us to understand the opinions of multiple recipients. It expresses opinions such as like/dislike, for/against of general population. The merits of sentiment analysis do not stop there. The applications of sentiment analysis are broad and powerful. The ability to extract meaningful insights from social data is being widely adopted by organizations across the world.The human language is complex, teaching a machine to analyze the various grammatical nuances, cultural variations, slang and misspellings that occur in online mentions is a difficult process. To perform various sentiment analysis tasks we created a platform to employ different techniques and statistical methods to evaluate the data. By doing text analysis using computational linguistic techniques, we are able to capture and quantify affective states and subjective information. Sentiment analysis/opinion mining is done on human generated data. Since sentiment knowledge in humans grow with age and daily interactions, cognitive knowledge is required to understand emotion. In order for machines to understand underlying emotion, we need to transfer this cognitive knowledge in some form. Majority of the computational approaches to opinion mining comes using pre-existing lexicons. It has been understood that such lexicons are vital for any sentiment analysis system. These lexicons are the closest representation of our cognitive knowledge. These computational lexicon wrap sentiment information to assist in the task of opinion mining. Most research efforts in sentiment lexicon creation are bound to the English language. SentiWordNet for English is one such important lexical resource that contains subjective polarity for each lexical item. It was devised to aid in the task of opinion mining and sentiment classification. With growing data in native vernacular, there is a need for language-specific sentiment lexicon. For resource poor languages, creating such a sentiment lexicon is a difficult task to achieve. Described in this thesis are two different methods on how to build such a resource for an Indian language Tamil, using existing resources in other languages. Such a resource would serve as a baseline for further improvements in the task of sentiment analysis specific to Tamil data. Furthermore, the architecture followed in the two methods can be easily adapted to other languages.

Abstract

Forests play a key role in carbon cycle by sequestering the atmospheric carbon dioxide from various sources. Approximately 33% of the world’s land area is covered by forest ecosystems. It is thus essential to monitor forests at a regular interval for better management. Remote sensing datasets (both satellite and aerial) forms an key sources for providing a synoptic view of forest cover at landscape level to describe land use changes for monitoring purposes. However, the complexities of the forest ecosytems are not well understood with the traditional 2D remote sensing datasets (optical and radar remote sensing images) and need for the 3D structural assessment of forest ecosystems are essential. Terrestrial Laser Scanning (TLS) or Terrestrial LiDAR (Light Detection And Ranging), is an active remote sensing based ground measurement technique capable of capturing 3D information of the scene of interest with milli-meter accuracy. It transmits the laser pulse and analyses the return pulse to position the reflected object in 3D space. The main goal of this thesis is to evaluate the potential of TLS for forest biomass estimation and to develop the relevant methods to extract relevant information from TLS data. Accurate estimations of forest aboveground biomass at regional and national scales heavily depend on the field data and are upscaled using remote sensing datasets (both optical and radar). However,Field data collection of forest inventory parameters (mainly species name, tree count, tree diameter, height etc.,) using conventional methods are both labour-intensive and time consuming. So, firstly, we target the feasibility of TLS to extract forest inventory parameters accurately using the automated algorithm. We provide an automated solution to extract forest inventory parameters at individual tree level from 3D TLS data. The algorithm is tested over deciduous forests of Betul, Madhya Pradesh. Data filtering followed by a RANSAC based circle fitting algorithm was used to extract tree diameter and tree heights. An overall detection rate of 97% along with high correlation of field based easurements and TLS based measurements (Diameter R2 = 0.99 & Height R2 = 0.98) was obtained. The estimated RMSE was found to be 2.2 cm in diameter and 1.65 m in height. Further, to envisage the potential of TLS in diverse forestry applications, it was desirable to identify individual trees in the 3D TLS data to better describe tree architecture. So the focus was made to segment individual trees from the 3D point cloud by employing supervoxels and graph-cut based segmentation methods. High dense point cloud was first converted into supervoxels to reduce the data complexity and a graph-cut was performed to extract individual tree objects. The individual trees paved way for the advanced inventory parameter extraction such as crown dimensions (diameter and height) and branching patterns which would have been near impossible using conventional field measurements. Also, they form as the basic ground information for high resolution image based studies focusing on crown detection and delineations. Next, trees with relatively high completeness are selected and are described by highly accurate piecewise cylinders based on branching structure. This method helped to estimate the tree volume as the sum of the volumes of individual cylinders. Thus, an attempt has been made to accurately describe the actual tree volume in a non-destructive way. The volumes derived for selected trees were used to construct local volume equations of the study site along with the uncertainty in the total volume estimate. Tree volumes are a direct proxy of tree biomass (biomass is generally calculated by multiplying tree volumes with wood density), which help in carbon stock assessment of the forest area. Based on our estimates, tree volumes derived from conventional volume equations have underestimated the actual TLS volume by 28%. However, this estimate needs to be substantiated with destructive sampling. Also, the volume equations constructed using TLS along with uncertainty estimates are used in upscaling the field measurements to the regional biomass estimates using satellite remote sensing datasets. The standard way of producing a regional biomass map is by using a regression model using the field biomass estimates with the remote sensing variable (optical or radar). However, the uncertainty of this measurement at the spatial scale is often not calculated accurately. Both field level and the remote sensing model level errors attribute to the uncertainty of the spatial biomass estimate. Accurate assessment of biomass is essential to several social and economic incentive schemes of United Nations Framework Convention on Climate Change (UNFCCC). In this context, a possible framework to integrate uncertainties from field level measurements to regional-level estimates was established and spatial biomass maps along with uncertainty maps are gene