Abstract

Unmanned aerial vehicles (UAVs) are considered to be among the most advanced robotics technologies available today. They are capable of supporting and transporting sensors, communication equipment, and a variety of other payloads. UAVs are cost-effective, and with better sensing technology, they are being utilized by a wide variety of enterprises, both military and commercial. They have been acknowledged in recent years for their ability to perform a variety of tasks, particularly in the military domain like surveillance, convoy protection, border patrol. One of the key aspects involved in these military applications is target tracking. The existing literature on target tracking is primarily focused on tracking a single target. In this thesis, a Model Predictive Control (MPC) based algorithm is proposed for tracking multiple ground target swarms of unmanned aerial vehicles (UAVs). Swarm of UAVs have distinct advantages when compared to a swarm of unmanned aerial vehicles (UGVs). When used for applications like multi-target tracking with a sensor like a camera, tracking through aerial view avoids issues like loss of observability which is typically associated with monocular vision-based target tracking from the ground. Another advantage of UAVs over UGVs is that the tracking in case of UAVs is simple while tracking through UGVs due to obstacles and the restricted environment becomes complex. There are two primary types of UAVs, namely fixed-wing aircraft(FWA) and multi-rotor UAVs. FWA because of their ability to cover longer distances at higher speeds, are preferred in military applications, but they come with a disadvantage that they come with lower bound on speed and non-zero-turn radius. Multi-target tracking through UAVs has numerous difficulties, owing to the maneuverability of target, atmospheric disturbances. MPC has been shown to be an efficient method for process control, tracking, path planning, and the ability to handle disturbances well, among other applications. Future commands with a short time horizon are determined with high precision using future prediction and optimization techniques. The primary argument for choosing MPC over other techniques is its ability to adequately account for constraints, allowing all processes to run at their maximum possible performance. All UAVs here belong to the fixed-wing aircraft category having flight velocity, climb rate, and turn rate limits. Each UAV is equipped with a downward-facing camera for the purpose of detecting and tracking the target. Two scenarios are studied in which the total number of UAVs equals the total number of targets in the first scenario. In the second situation, the number of UAVs is less than the number of targets, resulting in a conservative solution in which the objective is to maximize the average time duration that targets are within the FOV of any of the UAV’s cameras. To relate the hyperparameters utilized in MPC to mission efficiency, a data-driven Gaussian process (GP) model is created. The GP model provides black-box identification of non-linear dynamic systems using a probabilistic non-parametric modeling technique. Gaussian processes can identify regions of the input space where prediction accuracy is low due to a lack of data or its complexity by offering a greater degree of variation around the anticipated mean. Bayesian optimization is used to determine the MPC hyperparameters that maximize mission efficiency by treating the algorithm’s generalization performance as a sample from a Gaussian process. The tractable posterior distribution generated by the GP enables the most efficient use of data from previous trials, allowing for the best decisions on which parameters to test next. Both situations are numerically simulated using a distributed MPC formulation technique. Apart from numerical simulations, the following performance comparisons are made by employing constraints in different ways: first, between decentralized and centralized approaches via error vs. time plots for individual UAVs, and then between computing efficiency and root mean square error for different prediction horizons. Second, between the proposed method and greedy approach for both centralized and decentralized MPC implementation

Abstract

Engineering proteins to have desired properties by mutating amino acids at specific sites is commonplace. Such engineered proteins must be stable to function. Experimental methods used to determine stability at throughputs required to scan the protein sequence space thoroughly are laborious. To this end, many machine learning based methods have been developed to predict thermodynamic stability changes upon mutation. They were trained using small and biased datasets. The methods have been previously evaluated for symmetric consistency by testing with hypothetical reverse mutations, and many fail the test. In this work, we propose transitive data augmentation, evaluating transitive consistency with our new S transitive dataset, and a new machine learning based method, first of its kind, that incorporates both symmetric and transitive properties into the architecture. Our method, called SCONES, is an interpretable neural network that predicts small relative protein stability changes for missense mutations that do not significantly alter the structure. It estimates a residue’s contributions towards protein stability (∆G) in its local structural environment, and the difference between independently predicted contributions of the reference and mutant residues is reported as ∆∆G. We show that this self-consistent machine learning architecture is immune to many common biases in datasets, relies less on data than existing methods, is robust to overfitting, and can explain a substantial portion of the variance in experimental data.

Abstract

Automated segmentation of cortical and non-cortical human brain structures has been hitherto approached using nonrigid registration followed by label fusion. We propose an alternative approach for this using a convolutional neural network (CNN) which classifies a voxel into one of many structures. Four different kinds of two-dimensional and three-dimensional intensity patches are extracted for each voxel, providing local and global (context) information to the CNN. The proposed approach is evaluated on five different publicly available datasets which differ in the number of labels per volume. The obtained mean Dice coefficient varied according to the number of labels, for example, it is 0.844 0.031 and 0.743 0.019 for datasets with the least (32) and the most (134) number of labels, respectively. These figures are marginally better or on par with those obtained with the current state-of-the-art methods on nearly all datasets, at a reduced computational time. The consistently good performance of the proposed method across datasets and no requirement for registration make it attractive for many applications where reduced computational time is necessary. Feature-based registration has been popular with a variety of features ranging from voxel intensity to Self-Similarity Context (SSC). In the second part of the thesis, we examine the question of how features learnt using various Deep Learning (DL) frameworks for segmentation can be used for deformable registration and whether this feature learning is necessary or not. We investigate the use of features learned by different DL methods in the current state-of-the-art discrete registration framework and analyze its performance on 2 publicly available datasets. We draw insights about the type of DL framework useful for feature learning. We consider the impact, if any, of the complexity of different DL models and brain parcellation methods on the performance of discrete registration. Our results indicate that the registration performance with DL features and SSC are comparable and stable across datasets whereas this does not hold for low level features.

Abstract

Internet of Vehicles (IoV) is an extension of Vehicle-to-Vehicle (V2V) communication network. IoV is a connected adhoc network (Vehicular Ad Hoc Network (VANET)), where each vehicle in the network is a node, connected to other vehicles and also to the public Internet. IoV helps in enhancing driving aids with the help of vehicle’s Artificial Intelligence (AI), awareness of other vehicles and their actions. The autonomous vehicles can instantaneously communicate with other vehicles surrounding them. Since the communication among various entities involved in the IoV environment is via open channel (e.g. vehicles, pedestrians, fleet management systems, and road-side infrastructure), it gives an opportunity to a passive/active adversary to intercept, modify, delete or even insert fake information during communication. It is then a serious concern for the vehicles users to determine whether the received information is genuine. Many security protocols have addressed this issue by adding authentication mechanism. Authentication of sender is a process of verifying his/her identity claimed by or for a system entity. Authentication of the messages ensures that the messages which is flowing in the network is unintercepted and unforged. Mutual authentication is a type of authentication which allows the involved entities simultaneously authenticate each other in order to establish a secure communication between them via computed session key. In this thesis, we study the IoV paradigm and its comparison and evolution over VANETs. We present a taxonomy of security protocols designed for IoV security. We address the threats and attacks in IoV environment. In particular, we focus on various authentication protocols in IoV. We propose authentication schemes, to accomplish mutual authentication, conditional privacy preserving, batch authentication and blockchain based authentication among the involved entities in the IoV environment for secure communication. For every proposed scheme, a detailed comparative analysis among various state-of-art authentication protocols proposed in the related IoV environment is provided to show their effectiveness as well as security and functionality features. The first study presents a new mutual authentication and key agreement protocol in Internet of Vehicles-enabled Intelligent Transportation System (ITS), using the elliptic curve cryptography (ECC) technique. The proposed protocol deals with the challenges during open communication among entities in IoV environment. An open communication can be targeted by the adversary to eavesdrop, modify, insert fabricated (or malicious) messages, or delete any data-in-transit; thus, resulting in replay, impersonation, man-in-the-middle, privileged-insider, and other related attacks. In addition to providing security, the proposed scheme also achieves anonymity and untraceability. It provides a mutual authentication between vehicle and vehicle and also between a vehicle and a road side unit (RSU). Using both formal and informal security analysis, as well as formal security verification using an Automated Validation of Internet Security Protocols and Applications (AVISPA) tool, the proposed scheme is proved to be secured against several known attacks in an IoV-enabled ITS environment. Furthermore, a detailed comparative analysis shows that the proposed scheme has low communication and computational overheads, and offers better security and functionality attributes in comparison to other competing schemes. We also evaluate the performance of the proposed scheme using NS2. In our second study, we design a new conditional privacy preserving batch verificationbased authentication mechanism in the IoV environment using ECC technique, where a vehicle can authenticate its neighbor vehicle and also an RSU can authenticate its nearby vehicles in a batch. The proposed scheme incurs low computation cost as it is implements batch authentication. The proposed scheme is shown to be highly secure against a passive/active adversary through various security analysis, such as random oracle based formal security, formal security verification via automated simulation tool (AVISPA), and also informal security analysis. An exhaustive comparative analysis reveals that the proposed scheme offers better security and functionality attributes, when compared with the relevant schemes. Finally, in the third study, we focus on designing a novel blockchain-enabled batch authentication scheme in Artificial Intelligence (AI)-envisioned IoV-based smart city deployment. We collaborate mutual authentication, batch authentication and blockchaining to produce an efficient scheme. The vehicles in IoV can be used to opportunistically gather and distribute the data in a smart city environment. However, at the same time, various security threats arise due to insecure communication happening among various entities due to lack of trust. Moreover with the increase in number of vehicl

Abstract

In the first part of this thesis, we analyze a multi-cloud marketplace model consisting of brokers with multi-unit demands and cloud providers with multi-unit supplies. Using tools from cooperative game theory we show the existence of a core allocation which also maximizes the market surplus. We do this by finding an optimal coalition structure and formulate an integer linear programming problem to maximize the overall market surplus. The optimal coalition lies in the core which gives it stability and no incentive to deviate to any other sub-coalition. We then study our model in the real-world scenario in which the brokers don’t reveal their valuations for the cloud providers. For this, we use the auction algorithm due to Bertsekas [2] which leads to a core allocation without requiring the brokers to reveal their utilities. In the second part of this thesis, we study how an oligopolist influences the coalition structure in federated cloud markets. We consider two price offering strategies for an oligopolist: non-adaptive and adaptive. In the non-adaptive strategy, an oligopolist makes a price offer to all the cloud providers simultaneously. It can be noted that the oligopolist can buy-out all the cloud providers by making a price offer which is equal to a core allocation and the total price offer made by the oligopolist is equal to the value of the grand coalition. In the adaptive strategy, the oligopolist approaches the cloud providers one after another in a sequential manner. We show that by using the adaptive strategy, the oligopolist can buy-out all the cloud providers at a total price offer which is less than that of the non-adaptive strategy

Abstract

Plastic optical fibres (POF) can be made sensitive to various parameters. Therefore, a implementation of tomographic imaging based on POF sensors is a good application in pressure sensing domain. The system uses photons to transmit information along the sensor and deliver a signal at a detector of the sensor, this Photonic Guided Path Tomography (PGPT) is used to design carpet. It is found that the POF based pressure sensing platform has several limitations such as photodiode output voltage variations, difference in the sensor response for horizontal and vertical fiber and acquiring data at unexpected intervals. Optical pressure sensors also suffer from the drawback of instability due to LED, photodiode, POF, or all. These limitations can lead to incorrect estimation of output voltage. Stabilization of output voltage is an essential factor to be taken into consideration. To remove such limitations and improve pressure imaging of floor sensor carpet for clear contrast is a big challenge in the optoelectronics systems. To surpass the above limitations, we have developed time based calibration using time windowing technique to reduce the standard deviation of photodiode output voltage variations by 98%. We have also described space and weight calibration techniques based on the convex and concave shape of fiber bend. These techniques help in improving the Landweber image reconstruction algorithm to obtain significant clarity and improvement in object pressure images. The major work demonstrates time, space and weight based calibration techniques for pressure monitoring in the low-cost plastic optical fiber (POF) sensor carpet. The proposed work also reports an artificial intelligence (AI) based algorithm to determine the accuracy of positioning of the load. Experimental results demonstrate that this algorithm gives a mean square error of 0.875 cm in position detection on the carpet. The proposed work also done using IoT application, to get real time data in remote sensing. Here, Message Queuing Telemetry Transport (MQTT) and ThingSpeak server used for communication. The images constructed using wired communication and remote sensing are almost similar in terms of quality and information. The work discusses the potential for a compact and cost-effective pressure sensor carpet, which integrates with the living environment and the outside world.

Abstract

Determination of seismic safety of existing buildings is a time consuming and challenging process. Instead, rapid visual survey methods were developed which also identify deficient structures from a large building stock in a city or town. These rapid visual screening (RVS) methods are quick and require minimal time. The important part of any RVS method is identifying critical architectural and structural features, i.e., irregularities (also known as vulnerable parameters). The score value assigned to each vulnerable parameter plays a vital role in deciding the risk of the building. Therefore, it also matters significantly that how these score values are derived. In the last two decades, several RVS methods were developed by many researchers and civil engineering professionals in India. The study in this thesis begins with a comparison and critical review of existing RVS methods used for seismic assessment of existing reinforced concrete buildings. The study focuses on rapid visual screening methods developed for the safety assessment of reinforced concrete buildings in the Indian subcontinent and a widely used method in the United States. The comparison is carried out in various ways. Initially, a direct comparison is made based on vulnerable parameters and damage grades proposed by each method. Later, a scoring system is developed to highlight the differences and rank the selected methods. It was observed that there are many uncommon vulnerable parameters amongst each selected method. Later, to understand the workability of each selected RVS method, a rapid visual survey was conducted on 100 reinforced concrete buildings in three different cities (viz., Pithoragarh, Gangtok, and Agartala) in India. The results of all five methods were found to have different outputs with significant variation for the same sample of surveyed buildings in each city. From investigation on such varying results, it was observed that relative weights assigned to each vulnerable parameter are different in each RVS method. Therefore, it was felt necessary to fix the relative weights of each vulnerable parameter to have unanimity in the results of different RVS methods. The present study attempts to derive the relative weights of the few selected vulnerability parameters on overall vulnerability of reinforced concrete moment resisting framed buildings using a numerical approach. To determine the relative weight of any vulnerable parameter, it is necessary to understand and effectively quantify its effect on the global response of the building. The quantification of the impact can be determined with the use of an appropriate damage index. The study presents a brief discussion of currently available and widely used damage indices along with their pros and cons. From the literature study on damage indices, the most suitable index with some modifications is adopted in this study. As a modification, the study proposes the hinge state coefficients and the procedure to derive the same. Thus, the vulnerability index can be determined using these hinge state coefficients and the total number of hinges formed in a structure. To derive the relative weights, the inelastic performance of irregular frames is compared with the performance of regular frames. The irregularities (i.e., vulnerable parameters) considered in this study are old building (i.e., building designed with previous code), open ground storey, floating column, and the taller floor. Nonlinear static analysis is performed on all regular as well as irregular frames to study the inelastic behaviour. The regular frames and irregular frames are initially compared based on the results of nonlinear static analysis and later based on calculated vulnerability index values. The vulnerability index values are determined at the global level and storey level as well. The comparison based on capacity curves and hinge patterns show a drastic change in irregular frames. Finally, a statistical analysis of the thypothesis test on data sets of regular and irregular frames was used to estimate the relative weights of selected vulnerability parameters. The results show that the floating column vulnerable parameter has the worst effect on global damage of structure for infill frames, followed by the open ground storey, building designed with previous code, and taller floor.

Abstract

RNA is a versatile biopolymer. Different RNAs build up the protein synthesis machinery in living organisms. Non-protein coding RNAs often participate in gene regulation, catalysis of biochemical reactions, and many other cellular processes. The efficiency of functional RNAs depends on their accurate folding into compact three-dimensional structures. RNAs are composed of only four types of nucleotides having similar chemical properties. So, the sources of structural complexity observed in these folded RNAs remain ambiguous. Instead, hydrogen-bonded planar RNA base pairs have the potential to be the fundamental building block of RNA. They display remarkable diversities in their geometries, stabilities, and physicochemical properties. Properties of RNA base pairs are strongly influenced by nucleobase modification, such as post-transcriptional changes, tautomerization, ionization, etc. One such important modification is nucleobase protonation. Protonation at different polar sites of nucleobases are associated with positive free energy change. However, protonationinduced modifications to nucleobases result in subsequent stabilizing interactions, which compensate for the thermodynamic cost of protonation. For example, protonation converts a hydrogen bond acceptor to a hydrogen bond donor and therefore opens up new base-pairing opportunities through the protonated edge of the nucleobase (Class I). In other cases, the protonation-induced changes may influence the stability of the base pairs formed through the unprotonated edges of the base (Class II). Instances of Class II protonation in RNA bases is nontrivial to detect for both experiments and in-silico techniques, as the loaded proton does not form any inter-base hydrogen bonds. Hence, the contribution of Class II protonation in determining RNA’s structure and function remains majorly unexplored. Recently a strategy has been proposed to study the consequences of Class II protonation on the geometry and stability of RNA base pairs (1). It involves comparing the electronic structure properties of the different base pairs optimized with and without the Class II protonation. In the past, this method revealed that Class II protonation at the N7 and N3 positions of guanine stabilizes different RNA base pairs, which are intrinsically unstable. In this work, the same strategy is applied to explore the consequences of Class II protonation at the N3 position of guanine and N3 & N7 positions of adenine on the geometry and stability of different RNA base pairs. Those are the sites where the thermodynamic cost of protonation is higher than other sites. Analysis of RNA crystal structure datasets and electronic structure calculations carried out at DFT, and DFT-D levels reveal a number of interesting insights. In general, it is observed that charge redistribution caused by Class IIprotonation alters the hydrogen bonding potential at the polar sites located on the unprotonated edges of nucleobases. Such changes collectively determine the geometry and stability of the base pairs formed through the unprotonated edges. A set of RNA base pairs are reported which are sensitive to Class II type nucleobase protonation and hence have putative biotechnological application in pH sensing.

Abstract

Summarization involves reducing the size of the document without the loss of important content. The reduced content is called the summary of a document. Summarization can be divided into two broad categories: Extractive and Abstractive. In Extractive Summarization, the summary of a document is constructed by extracting phrases and sentences from the document itself. In this work, we are mainly concerned with single document extractive text summarization and employing deep-neural-networkbased solutions for the same. While on the surface, extractive summarization seems like a trivial problem since summarizing here involves copying content from the source document, in reality, it is far from trivial. One main reason for the complexity of the task is the absence of gold labels for what sentences/phrases to extract. Typically, extractive summarization systems are evaluated using human-written summaries. However, this summary cannot be directly used to train the neural networks. Indirect methods of labeling or training are commonly used in many of these cases. Extractive Summarization can be divided into two steps: Document Understanding and Document pruning. A document is not simply a bunch of sentences put together. There is an underlying structure between the way sentences are arranged, which makes a document coherent and meaningful. Moreover, summarisation can be said to utilize this exact structure (among other things) to determine which sentences are more important and relevant. Most works assume that the structure of the document is linear and use recursive networks to encode them. But it was shown by Rhetorical Structure Theory (RST) that documents can be represented by tree-like structures. One of the first things we explore is to see if we can use non-linear tree-based networks to encode the document. Moreover, since learning to summarize involves learning to understand documents implicitly, we see if we can try to capture such structures implicitly when learning to summarize. We also explore if such structures can aid in summarisation. Another reason for the complexity of the task is that extractive summarization is different from sentence ranking. Sentence ranking can be defined as ranking sentences based on how good a sentence is for representing the document. However, in summarisation, the selection of each sentence also depends on the other sentences selected. This makes the task very complex due to the exponential number of possibilities in selecting a subset of sentences to represent the document. Each of these possible subsets of sentences can be called a candidate summary of the document. A document of length 25 has over two thousand such candidate summaries, only when considering those of length 2 and 3 sentences. This creates a combinatorial explosion problem that we should overcome by usnig indirect means. Many works deal with this combinatorial problem in their own way. Here we also explore different novel and unique methods for doing the same. We introduce a new pipelined architecture for extractive summarization, which helps in dealing with precisely this problem. We also see how we can adopt different complex reinforcement learning (RL) formulations to extractive summarisation. Many works use one of the two formulations in training their models: either policy-gradient-based algorithms or deep-Q-networks. In this work, we see how we can improve both of them. We also experiment with neural-network-based loss functions for summarisation. At last we see the problems that can arise in training extractive summarisation models when we try to circumvent the combinatorial explosion problem. Many works try to simplify their training objective by approximating summarisation as sentence classification, or use very limited set of samples to train their models. This creates a lot of issues in what the model actually learns. We show this by experiments and analysis, and also provide a glimpse at a solution for solving the same.

Abstract

This thesis revolves around two questions. How have the ideological positions of political parties in India evolved in the 21st century and what changes are these bringing to the participation of people in democratic processes? Literature on ideology and participation are discussed in the initial part of this thesis. It uses election manifestos published by political parties for the elections from 2004 to 2019 to identify the ideology of political parties. A manifesto coding scheme adapted to Indian conditions was used to categorize manifesto points to different heads based on ideological leaning. The evaluation suggested a shift towards capital and private entrepreneurship, increasing focus on urban areas, changing attitude towards decentralisation depending on the power relations. Political parties showed a left leaning view on social affairs while positions varied from central right to left on economic and political affairs. The political parties are becoming more inclusive of different sections of the country though the trend is not consistent. To identify participation, the focus was given to Muslim participation. Detailed election results published by the Election Commission of India from 1977 to 2019 was used to identify participation in elections. Muslim names were identified using a code. geographical distribution was mapped using QGIS. Muslim participation was increasing consistently from 1991 to 2014 before dropping in 2019. The participation is largely through recognized political parties than as minor groups or independents. Muslim identity based political parties and Islamic parties are trying to spread the influence geography wise and vote wise. But they remain minor players in relation to Muslim participation where recognized political parties which are not based on Muslim identity fields more number of candidates.