Abstract

Model Predictive Control(MPC) is a powerful optimization strategy for feedback control which is being used increasingly for Motion planning in autonomous driving. However there are two key restrictions on using MPC. One is the requirement of high performance computational resources as it performs full optimization for every time step. The other is requirement of an accurate predictive model which is less variant to various initializations in multiple scenarios.In this thesis, We propose a new model predictive control formulation for autonomous driving. The novelty of our MPC stems from the following results. Firstly, we adopt an alternating minimization approach wherein linear velocities and angular accelerations are alternately optimized. We show that in contrast to the joint optimization, the alternating minimization exploits the structure of the problem better, which in turn translates to a reduction in computation time. Secondly, our MPC explicitly incorporates the time-dependent non-linear actuator dynamics that captures the transient response of the vehicle for a given commanded velocity. This added complexity improves the predictive component of MPC, resulting in an improved margin of inter-vehicle distance during complex urban scenarios like occlusion during overtaking, lane-change, etc. Although past works have also incorporated actuator dynamics within MPC, there have been very few attempts towards coupling actuator dynamics to collision avoidance constraints through the non-holonomic motion model of the vehicle and analyzing the resulting behavior. We use a high fidelity simulator to benchmark our actuator dynamics augmented MPC with other related approaches in terms of metrics like inter-vehicle distance, trajectory smoothness, and velocity overshoot. Planning frameworks for autonomous vehicles must be robust and computationally efficient for realtime realization. At the same time, they should accommodate the unpredictable behavior of the other participants and produce safe trajectories. In this thesis, we also present a computationally, the efficient hierarchical planning framework for autonomous vehicles. This framework can generate safe trajectories in complex driving scenarios, which are commonly encountered in urban traffic settings. The first level of the proposed framework constructs a Model Predictive Control(MPC) routine using an efficient difference of convex programming approach, that generates smooth and collision-free trajectories. The constraints on curvature and road boundaries are seamlessly integrated into this optimization routine. The second layer is mainly responsible for handling the unpredictable behaviors that are typically exhibited by the other participants of traffic. It is built along the lines of time scaled collision cone(TSCC) which optimize for the velocities along the trajectory to handle such disturbances. We additionally show that our framework maintains an optimal balance between temporal and path deviations while executing safe trajectories. To demonstrate the efficacy of the presented framework, we validated it in extensive simulations in different driving scenarios like overtaking, lane merging and jaywalking amongst many dynamic obstacles.

Abstract

The discovery of several non(protein)coding RNAs, which directly participate in structural, catalytic or regulatory functions, have drawn progressively greater scientific attention to RNA studies. Since molecular structures strongly correlate with functions of biomolecules, a lot of interest has been generated towards detailed molecular level understanding of RNA structures. With the advancement in crystallographic and NMR techniques, high resolution structure of several noncoding RNA molecules have been reported since last decade. These include ribozymes, t-RNAs, group I and group II introns, riboswitches and even the ribosome itself. In this context, with the advent of new biophysical techniques on the one hand and enhancement in computational power on the other, progressively larger amounts of data addressing the structure-function paradigm of RNA molecules are being generated on a regular basis. In silico analysis of these data, has the potential to address many of the important biological problems. For automated and high throughput analysis of available experimental data, it is important to facilitate their retrieval as per the requirements and implementation of efficient algorithms to analyze this data from various perspectives. All this has led to the rapid emergence of the new research domain of “RNA Structural Bioinformatics”, which in the larger context, bridges the gap between the RNA structural biology and the bioinformatics communities. In other words, it can be said that the domain Structural Bioinformatics bring the complementary, and yet largely separated, areas of computational investigations involving structure, dynamics, and functions within a single comprehensive fold. The work described in this thesis addresses different important aspects of RNA structural bioinformatics research, involving detailed structural and contextual analysis of RNA structural elements, which can facilitate the formulation of rules governing the remarkable structural and functional diversities of large RNA molecules. To understand the complex RNA structures and respective functions, a bottom-up approach is followed in my work, where characterization of the smaller and simpler structural elements have been carried out first to understand their putative functional roles in overall structural contexts. With the larger goal of strengthening the basis for future research within the RNA structure-function paradigm, the scope of this thesis may be defined, though not limited, by the following specific objectives: (i) meaningful annotation and classification of higher order RNA structural elements (assemblies of more than one nucleotide residues connected by base-pairing interactions), (ii) implementation of different algorithms for mining recurrent structural elements present in larger RNA molecules, (iii) systematic organization of available data related to occurrences, structures and stability of different RNA structural elements and development of databases which can interactively support complex queries for easy retrieval of customized information, and (iv) computational study to understand the stability of few important RNA structural elements using quantum mechanical (QM) methods. In addition, to demonstrate the importance of the work done for this thesis, a thorough background study has been carried out and reported. This provides a comprehensive review, highlighting the landmarks in the course of developments leading to its contemporary status, in the field of RNA structural bioinformatics.

Abstract

Autonomous micro-aerial vehicles have potential to assist us in several critical tasks like exploration, inspection, cooperative construction and mapping, but before they can be used for those tasks we need to solve several practical challenges dealing with localization, mapping, planning and trajectory gener- ation. With the sharp rise in research along various applications using aerial platforms, the need for an affordable, simple, modular and open-source design which can be used as a template to easily accom- modate and test state-of-the-art innovations and research became of paramount importance. To design a system that is fully autonomous and capable of flying in constrained environments, we have to keep in mind the size constraints while satisfying the required sensing and processing needs. In this work we present a complete onboard system for autonomous navigation of a micro-aerial vehicle using vision- based flight in indoor cluttered environments. We develop a single integrated aerial platform capable of onboard localization, mapping and planning for autonomous exploration using a single monocular cam- era and an IMU(Inertial Measurement Unit). We demonstrate the utility of this platform by deploying it for the task of autonomous exploration and payload transfer. Although a lot of work has been done in the domain of localization, mapping, planning and trajectory generation, there is a large scope for improvement in each of these. One of the problems that is frequently encountered by an autonomous explorative robot is scenarios where multiple future trajectories can be pursued. Often these are cases with multiple paths around an obstacle or trajectory options towards various frontiers. Classical tra- jectory generation methods either requires a prior information about immediate goal/goals or perform an exhaustive search of the known region to sample possible immediate goals which takes significant computation time and resources. Another method of acquiring subsequent goals is based on heuristics which computes the information gain on the available occupancy map and provide optimal immediate goal locations. Humans in such situations can inherently perceive and reason about the surrounding environment to identify several possibilities of either maneuvering around the obstacles or moving to- wards various frontiers. Inspired from the above fact we propose a two-stage Convolutional Neural Network architecture which mimics such an ability to map the perceived surroundings to multiple trajectories that a robot can choose to traverse. The first stage is a Trajectory Proposal Network which suggests diverse regions in the environment that can be occupied in the future. The second stage is a Trajectory Sampling network which provides a fine grained trajectory over the regions proposed by Trajectory Proposal Network. We evaluate our framework in diverse and complicated real life settings.The above proposed technique was tested on standard datasets like KITTI dataset and our own outdoor driving dataset collected in the University campus. For exploration of the environment using the trajectories proposed by our frame- work on actual robots in real world scenarios we used our custom made autonomous drone for indoor scenarios, ClearPath Husky robot for outdoor alleys and a Mahindra E2O electric vehicle for road set- tings. Our experiments suggest that the framework is able to develop a semantic understanding of the obstacles, open regions and identify diverse trajectories that a robot can traverse. Our comparisons por- tray the performance gain of the proposed architecture over a diverse set of methods against which it is compared.

Abstract

RNA molecules show enzymatic activities and several other complex functionalities in living systems. Several of the functional RNAs exist as RNA-protein complexes and in order to achieve specific biological goals, RNAs and proteins often interact with each other at the cellular level. The ribosome is the largest and one of the most well known examples of such complexes. Studies on RNA-protein interactions predominantly have focused on structural properties of RNA binding proteins. Even though the conventional approach to investigate the RNA-protein interface region includes studies on non-covalent interactions, binding affinity and surface properties of the interacting RNA and protein, detailed struc- tural studies of the interacting RNA, at a molecular level, are mostly neglected. However, some recent RNA-centric research reveals the involvement of small RNA structural motifs, role of sugar O2’ atom and the importance of nucleobase specific interactions in the RNA-protein interface region.In our present work, we have re-visited the nucleobase specific interactions, identified within a eukaryotic 80S ribosome (Saccharomyces cerevisiae or yeast), with a resolution of 2.9Å, which includes 4 chains of ribosomal RNAs interacting with 81 protein chains in each biological assembly. Here, specif-ically we have explored the structural features of RNA interacting with protein at the interface, in terms of different types of non-covalent interactions between them, the canonical and non canonical base pairs made by interface RNA residues, and their involvement in secondary structural motifs like internal and hairpin loops. A detailed statistical and contextual analysis of all the observed interface RNA residues have been carried out and reported here.

Abstract

Street dogs are a ubiquitous reality in urban cities in India and are part of the everyday of urban dwellers navigating the city, whether to their liking or not. But this finds a strong exception in a relatively new form of urban transit—the metro system. This thesis examines metro systems in three Indian cities, Mumbai, Delhi and Hyderabad, to argue that the metro corporations assigned to build and maintain their respective metros exert an immense amount of control on its users, which also plays out negatively for nonhuman animals such as street dogs. This control is visible in its design choices and rules and regulations, which are anomalous in comparison to other public spaces. This thesis goes on to argue that the unique space that the metro has built in India is a reflection of a “modern” imagination of space, one that excludes nonhumans such as street dogs.

Abstract

In the present situation of building construction around the world, Flat slab is becoming widely popular in the multi-storied buildings due to its advantage and ease of construction over the slabs. However, during past earthquakes, many building with flats have performed poorly. This is mainly due to inadequate resistance for punching shear under the lateral loading. Many codes that are available for the design are for the analysis of flat slab under gravity loading. Some provisions exist for punching shear resistance, however, they are inadequate for the analysis of flat slab under lateral loading. Therefore, making it essential to study the behavior of flat slab under lateral loading. As a flat slab is widely constructed all over the world, so to understand the design code followed across different country a flat slab example was designed with IS 456, ACI 318, NZS 3101, Euro code design codes and comparisons are made. But as the design code has limitations and past punching shear failures, As part of this research, punching shear behavior of flat slab was studied when subjected to lateral loading. Nonlinear finite element method is used to carry out the numerical modeling of flat slab subjected to appropriate lateral loading and boundary conditions. A MATLAB tool was developed to carry out nonlinear finite element analysis of concrete flat slab subjected to the lateral static loading. flat plate along with 4 column was modeled, lateral loads are applied and Nonlinear analysis is carried out by using the Newton-Raphson method. Stress and Displacement behavior is studied. Further as a part of this study, to understand the punching shear behavior of a reinforced concrete flat slab was subjected to dynamic lateral loading in the form of earthquake loading. The 3D modeling of the reinforced flat slab was done using a finite element method in ABAQUS by providing appropriate mesh size and boundary conditions. The material parameter taken is the Concrete damaged plasticity model in ABAQUS. Considering Carreira and Chu concrete stress and strain behavior. Linear and Nonlinear finite element analysis are performed. This model was used to study the punching shear behavior of reinforced concrete flat slab under varying reinforcement ratio, column dimensions, flat slabs types, etc. are shown.

Abstract

Social dynamics is based on human needs for trust, support, resource sharing, irrespective of whether they operate in real life or in a virtual setting. Massively multiplayer online role-playing games (MMORPGS) serve as enablers of leisurely social activity and are important tools for social interactions. Past research has shown that socially dense gaming environments like MMORPGs can be used to study important social phenomena, which may operate in real life, too. We describe the process of social exploration to entail the following components 1) finding the balance between personal and social time; 2) making a choice between a large number of weak ties or few strong social ties; 3) finding a social group. In general, these are the major determinants of an individual’s social life. This research looks into the phenomenon of social exploration in an activity-based online social environment. We study this process through the lens of the following research questions, 1) What are the different social behavior types? 2) Is there a change in a player’s social behavior over time? 3) Are certain social behaviors more stable than others? 4) Can longitudinal research of player behavior help shed light on the social dynamics and processes in the network? We use an unsupervised machine learning approach to come up with 4 different social behavior types - Lone Wolf, Pack Wolf of Small Pack, Pack Wolf of a Large Pack and Social Butterfly. The types represent the degree of socialization of players in the game. Our research reveals that social behaviors change with time. While lone wolf and pack wolf of small pack are more stable social behaviors, pack wolf of large pack and social butterflies are more transient.We also observe that players progressively move from large groups with weak social ties to settle in small groups with stronger ties. In addition to the above, this research also analyzes the churn behavior of different social types. Churn broadly refers to the loss of users from a system or network. The churn behavior of different social behavior types was analyzed to see if there is a link between social behavior and churn activity. The analysis reveals that churn propensity of different social behavior types is different. Behaviors characterized by a higher degree of socialization have a significantly lower propensity to churn than behavior characterized by a lower degree of socialization. We studied the social trends by looking at the data from a popular MMORPG and aimed to look into the dynamics and social phenomena of the network through the lens of different social behaviors.

Abstract

The ever-growing demand for livestock product because of increase in population, urbanisation and income create a need for development in this sector. Advancements in nutrients and creature well-being will keep on adding to expanding potential generation and further proficiency. Sorghum is important forage crop used in the development of livestock. It is equipped for delivering significant amounts of feed amid the hotter months of the year. However, the livestock production not only relies on the accessibility of grain but the continuous availability of good quality forage. Assessment of quality of a forage depends on the estimation of nutrients present in it. There is a need for improvement in the time-consuming and exorbitant traditional methods for measuring nutrients in sorghum fodder. Near InfraRed Spectroscopy (NIRS) is the new and growing technology in this field. It is the quickest method for estimating nutrient, yet the high expense of the spectroscopic instrument limits its utilization. Therefore, there is a necessity to explore techniques which are low cost, inexpensive and are not labourintensive. NIRS technology scans the spectra for a wide range of wavelengths, which makes it expensive. We have applied Partial Least Square Regression (PLSR) on NIRS data of Sorghum to select significant wavelengths for Crude-Protein (CP), Neutral Detergent Fiber (NDF) and Acid Detergent Fiber (ADF) content estimation. Calibration models proposed using a single wavelength (1449nm) and a pair of wavelength (1485nm and 1486nm) give the best predictive squared correlation coefficient (Q2 ) as 0.7928 and 0.8334 respectively for CP. A pair of wavelengths (1165 nm and 2391 nm) is giving the maximum Q2 value as 0.4045 for NDF. For ADF, the maximum value of Q2 obtained is 0.2208 using 354 nm and 1204 nm wavelengths are used in combination. An LED based chemical detection technique has been used to calculate absorbance for CP estimation at one of its important wavelengths. The advantages of LEDs are that they are low cost, low power and readily available important optical device. The wavelength of 1450 nm giving the Q2 value as it is a commercial LED which is easily available in the market and has been used to design hardware in combination with a Photo Diode (PD) of same peak wavelength to measure the absorbance of collected samples of sorghum. The absorbance values, obtained by using the above mentioned hardware design, are then used in a calibration model equation of 1450 nm wavelength, derived using NIRS dataset by applying PLSR. A laboratory analysis method for CP calculation known as the Kjeldahl method is applied to the same samples for verification purposes. On comparing the CP value obtained using LED based method with that of the Kjeldahl method, we are getting a deviation of 19.48% Through this work, we can implement the fastest, low cost and precise design for the estimation of CP values for sorghum. Further, we can incorporate this design for other nutrient values such as NDF and ADF using their respective set of significant wavelengths.

Abstract

Extensive research is being carried out in the field of Natural Language Processing (NLP) in the context of Indian languages. Spelling correction, word segmentation, and grammar checking are the fundamental problems in NLP. The aim of these problems is to identify noise in the data and correct it. These tools are important for many NLP applications like web search engines, text summarization, sentiment analysis, machine translation etc. Many methods have been developed for these problems for English, which usually exploit linguistic resources like parsers, large amounts of real world data etc. making it difficult to adapt them to other languages. For these problems, deep learning models have also been implemented for English. These models use parallel data of noisy and correct mappings from different sources as training data for automatic correction tasks. Indian languages are resource-scarce and do not have such parallel data due to low volume of queries and non-existence of such prior implementations. Spell correction is very crucial for applications like web search engines. In speech recognition and optical character recognition (OCR), word boundaries are not often comprehensible. Grammar checking is important to clean up the corpus before using it in any application. It is also useful as an aid for second language users and for applications like automatic essay grading. In applications like speech-to-text, handwritten text-to-text, and Machine Translation, applying spell checking, word segmentation, and grammar checking on the output will result in improved accuracy. Most of the existing language processing systems for Indian languages are rule-based and language specific. Majority of the works have a dictionary-based approach and check the errors against the dictionary. Due to lack of availability of training data for machine learning models, we create synthetic datasets using language rules. In this work, we present approaches to build automatic language correction systems for Hindi. We have proposed deep learning models for spelling correction, word segmentation, and grammar checking. The proposed approaches are applicable to any resource-scarce language. A comparative evaluation for each of these three problems shows that the proposed models are competitive with the existing correction techniques for Indian languages.

Abstract

Semantic role labeling, also known as shallow semantic parsing is a task in Natural Language Processing to determine labels of the words or the phrases (group of words) in a sentence. The labels are of the type agent, patient/receiver, goal, temporal, locative or object. It comes under the domain of Artificial Intelligence for the reason that we want to do this task automatically. Formally, the task can be broken into various steps. The first step is to identify the verbs or the predicates in a sentence. The second step consists of detection of the semantic arguments associated with the corresponding predicates in the sentence and the final step would be to label these arguments pertaining to their respective predicates. For instance, in the sentence, “Ram killed Shyam with a gun”, the semantic role labeler should be able to recognize ‘killed’ (represents the phrase “to kill”) as a predicate. Then ‘Ram’ as representing the killer (agent), ‘Shyam’ as the recipient/receiver and “a gun” as representing the theme/object. Such representations are an important step towards figuring the meaning of a sentence. Semantic role labeling (SRL) gives a lower level of abstraction compared to a full syntactic parsing of a sentence. Therefore, it has higher number of classes and groups fewer clauses in each class. For example, “the car belongs to Ram” will need two labels like ‘owned’ and ‘owner’ whereas “the car was sold to Ram” will need two different labels altogether such as ‘theme/goal’ and ‘receiver’ even though these two clauses would be similar if we use subject and object functions. Hence, the labels largely depend on the whole clause rather than just the individual phrase or chunk. In this thesis, we begin with a detailed overview of the literature in the field of semantic role labeling. This includes discussion over the various techniques used to tackle shallow semantic parsing in the past, the development of different datasets built for semantic analysis such as the Propbank and the Framenet and at last we talk about semantic role labeling task for Indian languages. Since most of the Indian Languages are very low-resourced, we have kept our work restricted to only Hindi and Urdu, for which the respective propbanks were good in size. We also give a detailed analysis of the Hindi/Urdu Propbank data sections used by us in our experiments.Next, we present a statistical semantic role labeler for Hindi which is extended to Urdu as well. We propose a set of new features enriching the existing baseline system for these languages. We break the system into two subsequent tasks - Argument Identification and Argument Classification respectively. Our experiments show a reasonable improvement with respect to the previous baseline for Hindi, mainly for the classification step. We also report significant improvements for Argument Identification task in Urdu. We propose a new baseline system for Indian languages using 5-fold cross-validation and we capture results excluding the ‘null’ class and including the ‘null’ class exclusively. Lastly, we give an error analysis of our model compared with the previous baseline and look at both the improvements and limitations of this model. Finally, we introduce a supervised deep learning model for the same Indian languages namely, Hindi and Urdu which uses minimal syntax and yet improves over our statistical model significantly. We progress with three different models inspired from the recent advancements in this field. In the first model we make use of sequence modeling to generate dependency path embeddings and jointly learning the classification process i.e., both Identification and Labeling of arguments. The second model is a syntax agnostic model where we encode the full sentence using a bi-directional LSTM encoder only using the raw words/tokens. The third and the final model adds dependency label to the previous model making it slightly syntax-aware and performs very well compared to the other models. At last, we talk about evaluation metrics and analysis of the three models as well as the statistical model.