Abstract

Deep Reinforcement Learning (DRL) has emerged as a prominent method for enhancing the training of autonomous Unmanned Aerial Vehicles (UAVs). At the heart of this advancement lies the critical and complex challenge of obstacle avoidance, which is essential for ensuring that UAVs can navigate safely and efficiently. The incorporation of DRL into UAV training protocols enhances their ability to autonomously navigate through and adapt to diverse environments. This, in turn, is pivotal for expanding the operational capabilities of UAVs, enabling them to tackle a broader array of applications across various challenging and complex scenarios. In our research, we undertake a multifaceted examination of the impact of measurement uncertainty on the performance of DRL-based waypoint navigation and obstacle avoidance for UAVs covering both static and dynamic obstacles environment scenario. The challenge increases when the obstacles changes from static to dynamic as now these obstacles can change position in time and affect the navigation. The measurement uncertainty primarily stems from sensor noise, which impacts localization accuracy and obstacle detection capabilities. We model this uncertainty as adhering to a Gaussian probability distribution, characterized by an unknown mean and variance. Our research unfolds by assessing the performance of a DRL agent, meticulously trained using the Proximal Policy Optimization (PPO) algorithm, operating within an environment characterized by continuous state and action spaces. This investigation takes place in unseen randomized environments, each exposed to varying degrees of state-space noise, effectively emulating the effects of noisy sensor readings. Our primary objective is to pinpoint the threshold at which the policy becomes susceptible to noise, ultimately paving the way for us to explore diverse filtering techniques to mitigate these detrimental effects. Our findings reveal that the DRL agent exhibits a remarkable degree of inherent robustness against specific types of noise. We leverage this inherent robustness to bolster its performance in scenarios where it would otherwise succumb to the deleterious influence of state-space noise. To empirically validate our research, we undertake extensive training and testing of the DRL agent across a spectrum of UAV navigation scenarios within the PyBullet physics simulator. In a significant stride towards practical applicability, we port the policy distilled through simulation directly onto a real UAV without any additional modifications, and subsequently, we meticulously verify its performance in a real-world operational setting. This transformative approach holds the potential to inform the selection of sensors with varying biases and variances, and intriguingly, it suggests that artifcially injecting noise into measurements can yield performance enhancements in certain scenarios. The substantiation of this proposition is achieved through rigorous testing on a real UAV, thereby solidifying the practicality and real-world utility of our approach. In summation, our research contributes to the burgeoning field of UAV autonomy by shedding light on the intricate interplay between measurement uncertainty and Deep Reinforcement Learning-based navigation and obstacle avoidance. The insights garnered from our study not only advance our understanding of UAV operation but also provide actionable guidance for sensor selection and noise injection strategies to bolster real-world performance.

Abstract

In this thesis, we work towards improving the representation of low-resource languages in the digital world by easing the access and participation of these communities to reliable information hubs like Wikipedia. Although the internet has brought in an information age, it is disproportionately distributed amongst language communities since content and tools for low-resource languages are less readily available. Recognizing the importance of Wikipedia as the primary source of reliable, unbiased information, we seek to improve the information available by automatically generating Wikipedia articles in low-resource languages to improve the quality and quantity of articles available. Our work begins with XWikiGen, a cross-lingual multi-document summarization task that aims to generate Wikipedia articles using reference texts and article outlines. We propose the XWikiRef dataset to facilitate this, which spans eight languages and five distinct domains, laying the groundwork for our experimentation. We observe that existing Wikipedia text generation tools rely on Wikipedia outlines to provide a structure for the article. Hence, we also propose Multilingual Outlinegen, a task focused on generating Wikipedia article outlines with minimal input in low-resource languages. To support this task, we introduce another novel dataset, WikiOutlines, which encompasses ten languages over eight domains, further enriching available multilingual tools for further research work. An important question with text generation is the reliability of the generated information. For this, we propose the task of Cross-lingual Fact Verification (FactVer). In this task, we aim to verify the facts in the source articles against their references, addressing the growing concern over hallucinations in Language Models. We manually annotate the FactVer dataset for this task to benchmark our results against it. By exploring these three tasks, we highlight the disparity in content and tools available in low-resource languages, underscore the importance of multilingual and cross-lingual tools in global participation and propose innovative solutions to enhance Wikipedia�s accessibility and reliability for low-resource languages. Overall, we contribute multiple novel datasets and methodologies to automatic text generation and highlight the importance of inclusivity in the Internet age. By tackling the challenges of article generation, outline generation and fact verification, we pave the way for future advancements that promise to improve the quality and quantity of information available to low-resource language communities of the world.

Abstract

In analog and radio frequency (RF) circuit design, innovation is a cornerstone driving progress amid escalating demands for energy efficiency, reliability, and performance optimization. This thesis, titled �Innovative Design Approaches with Self-Adaptive Methods for Analog and RF Circuits,� presents a comprehensive exploration of cutting-edge methodologies to revolutionize circuit design paradigms. Divided into two distinct yet interconnected sections, the thesis embarks on a journey through the intricate landscape of analog and RF circuitry. The first section delves into the intricate art of low-power analog and RF design, driven by the relentless pursuit of energy efficiency. Here, novel architectures, circuit topologies, and optimization techniques have been explored to minimize power consumption while preserving signal integrity and performance metrics. The journey begins with a meticulous investigation into two low-supply 0.5V current/voltage reference designs tailored for ultra-low power IoT and biomedical applications. The first one, designed in CMOS 90nm technology, proposes a current and voltage reference that achieves a typical accuracy of 34.6ppm/?C (29.68ppm/ ?C) over a wide temperature range of -55?C to 75?C with typical value 63.32pA(0.35V). We observe excellent line sensitivities of 0.0318%/V and 0.0576%/V for voltage and current references in a supply range of 0.5V - 2.3V. The second reference focuses on reducing the power to half compared to the previous one. The design generates reference values of 90.7pA and 288mV, giving an excellent temperature coefficient of 15.2ppm/�C and 36.8ppm/�C for compensated current and voltage values, respectively, at a nominal supply of 0.5V for a wide temperature range of -55�C to 100�C. The circuit works for a wide voltage range of 0.5V - 2.6V with a supply sensitivity of 0.028%/V and 0.154%/V for current and voltage reference, respectively. The thesis then introduces a new nW range gate-leakage-based Sub-Bandgap Voltage Reference (SUB-BGR) for low-power, high-temperature IoT applications. Designed in TSMC 65nm technology, the proposed architecture achieves an accuracy of 94ppm/?C. It achieves an excellent line sensitivity of 0.0066%/V for a supply range of 0.7V to 4V and PSRR of 89dB at DC 1V supply. As the spectrum broadens, the focus shifts to the high-frequency domain, where the work explores the intricacies of on-chip Vector Network Analyzers (VNAs). It introduces a fully integrated low-power, low-area on-chip single-port CMOS VNA, designed explicitly for bio-molecule detection, operating in a tunable frequency range of 0.5GHz to 2.5GHz. This design incorporates innovative features such as an IDC sensor and a high-linearity Low Noise Amplifier (LNA), demonstrating superior performance metrics within a compact footprint. The process, voltage, and temperature (PVT) variations immensely affect the circuit performance of the Analog and RF circuits. The second section of the thesis delves into self-adaptive methodologies tailored to mitigate variations across PVT, addressing the challenge of ensuring consistent circuit performance. Leveraging adaptive control and feedback mechanisms, self-adaptive loops are developed and implemented in simulation, dynamically adjusting circuit parameters in real-time. For efficient self-adaptation, the information of P, V, and T is necessary to provide accurate real-time feedback, enabling dynamic parameter adjustments in analog and RF circuits. This thesis significantly contributes to the advancement of analog and RF circuit design methodologies through a synthesis of theoretical analyses, simulation studies, and practical implementations. By embracing innovation and harnessing the power of self-adaptive methods, this research paves the way for a new era of agile, efficient, and resilient analog and RF circuits.

Abstract

Multi-agent systems (MAS) are distributed systems composed of multiple autonomous agents interacting to achieve a common or conflicting goal. MAS tackles complex and dynamic problems that a single agent cannot solve, resulting in better problem-solving skills, enhanced reliability, and improved scalability. This thesis explores the challenges facing MAS, particularly related to their game-theoretic, fairness, security, and privacy guarantees. A game-theoretically sound MAS is one where the agent interaction can be modeled as a game and analyzed using game-theoretic concepts. This leads to a more stable and efficient system, as agents are incentivized to make decisions that align with the system goals. This thesis focuses on civic crowdfunding, a method for raising funds through voluntary contributions for public projects (e.g., public parks). Our work enriches the existing literature by designing more inclusive mechanisms and providing fairer rewards and efficiency over the blockchain. Fairness is also an essential aspect of MAS as it ensures that the actions and outcomes of agents are equitable and just, resulting in MAS�s long-term stability and sustainability. This thesis looks at fair incentives in Transaction Fee Mechanisms (TFM). Blockchains employ TFMs to include transactions from the set of outstanding transactions in a block. We argue that existing TFMs� incentives are misaligned for a cryptocurrency�s greater market adoption. We propose TFMs that provide fairer rewards to the transaction creators and minimize the surplus collected to the creators Last, security and privacy are crucial aspects of MAS, as the autonomy and decentralization of agents in MAS can lead to the exposure of sensitive information. In this thesis, we specifically focus on privacy guarantees for MAS like (i) auctions, (ii) voting, and (iii) distributed constraint optimization (DCOPs). We propose privacy-preserving applications that preserve agents� sensitive information while proving the computation�s verifiability

Abstract

Embodied AI, where artificial agents interact with their environment through sensors and actuators, holds immense potential for real-world applications such as robotic assistance. Efficient object navigation and locating strategies are crucial for robotic assistance in real-world environments. However, existing methodologies often encounter challenges in adapting to dynamic environments and incorporating human-like reasoning for optimal decision-making. This thesis aims to bridge these gaps by addressing two fundamental challenges in embodied AI: Multi-Object Navigation (MultiON) and optimal object location within household environments. First, we tackle MultiON, where a robot is tasked with localizing multiple instances of diverse object classes in dynamic environments. This is a fundamental task for an assistive robot in a home or a factory. Existing methods for this task have viewed this as a direct extension of Object Navigation (ON), the task of localising a single instance of an object class, and are pre-sequenced, i.e., the sequence in which the object classes are to be explored is provided in advance. This is a strong limitation in practical applications characterized by dynamic changes. We present Sequence-Agnostic MultiON (SAM), which is the task of locating an instance each of multiple objects in a household environment in no pre-defined order. We present a deep reinforcement learning framework for an actor-critic architecture and a reward specification. It exploits past experiences and seeks to reward progress towards individual as well as multiple target object classes. We use photo-realistic scenes from Gibson benchmark dataset in the AI Habitat 3D simulation environment to experimentally show that our method performs better than a pre-sequenced approach and a state-of-the-art ON method extended to MultiON. Next, we present CLIPGraphs, a novel method for determining the best room to place or find objects within home environments. Existing approaches predominantly rely on large language models (LLMs) or reinforcement learning (RL) policies, neglecting commonsense domain knowledge. CLIPGraphs effectively integrates domain knowledge, data-driven methods, and multimodal learning to ascertain object-room affinities. Specifically, it (a) encodes a knowledge graph of prior human preferences about the room location of different objects in home environments, (b) incorporates vision-language features to support multimodal queries based on images or text, and (c) uses a graph network to learn object-room affinities based on embeddings of the prior knowledge and the vision-language features. We demonstrate that our approach provides better estimates of the most appropriate location of objects from a benchmark set of object categories in comparison with state-of-the-art baselines.

Abstract

Metabolism is an integral part of cellular physiology, with the liver as the central organ for a wide range of metabolic functions and homeostasis. The liver, unlike other organs, has a remarkable capacity to regenerate after partial loss of its mass, thus maintaining a constant liver-to-body weight ratio to preserve homeostasis. In an injury-free liver, the turnover of regeneration is very slow, but in the presence of an injury or perturbation, the regenerative response is triggered as a reparative strategy. Perturbations interfering with liver functions and disturbing the homeostatic state have serious repercussions leading to metabolic disorders like hepatic steatosis, non-alcoholic fatty liver disease (NAFLD), non-alcoholic steatohepatitis (NASH) and hepatocellular carcinoma (HCC). Ageing is one risk factor that increases the susceptibility to these diseases. The regenerative ability of the liver has been used to treat these diseases in the form of liver transplantation and partial liver resection, but recurrence of the disease is often observed after a few years. Understanding the molecular network that controls liver function and its regenerative ability in health and disease is crucial for developing clinical applications. The emergence of high throughput omics technologies provides a scope to develop a systems-level understanding of the disease. In this direction, we attempt to understand the pathophysiology of the liver by adopting systems biology approach for omics data interpretation. Tissue homeostasis and regeneration depend on the reversible transitions between quiescence (G0) and cell proliferation. During regeneration, the liver needs to maintain the essential metabolic tasks along with the fulfilment of metabolic requirements for hepatocyte growth and division. To understand the regulatory mechanisms involved in balancing the liver function and proliferation demand after injury or resection, we analyzed RNA sequencing temporal data of liver regeneration after two-thirds partial hepatectomy (PH) using network inference and mathematical modelling approaches. The reconstruction of the dynamic regulatory network revealed the overall temporal coordination of metabolism, RNA splicing and cell cycle during liver regeneration. A temporal shift in the gene expression pattern corresponding to increased hepatocyte proliferation and decreased hepatocyte function is observed with HNF4A as a key transcriptional activator. Based on these key observations, we developed a mathematical model of the HNF4A regulatory circuit, which showed the emergence of different states corresponding to compensatory metabolism, proliferation, and epithelial-to-mesenchymal transition. We showed that a mutually exclusive behaviour emerges due to the bistable inactivation of HNF4A, which controls the initiation and termination of liver regeneration and different population-level behaviour. Through our approach of modelling a regulatory circuit from high-throughput gene expression data, we proposed a mechanistic explanation of different states observed in single-cell RNA sequencing data of liver regeneration. The functional impairment of the liver with ageing reduces its regenerative capability and predisposes it to NAFLD and HCC. Mapping the molecular network of the liver encompassing these physiological (ageing) and pathological conditions may help to understand the crosstalk of ageing with different liver diseases. We performed networklevel analyses by integrating mouse transcriptomic data with protein-protein interaction (PPI) network. A sample-wise analysis using network entropy measure was performed, which showed an increasing trend with ageing and helped to identify ageing genes based on local entropy changes. To gain further insights, we also integrated the differentially expressed genes (DEGs) between young and different age groups with the PPI network and identified core modules and nodes associated with ageing. Finally, we computed the network proximity of the ageing network with different networks of liver diseases and regeneration to quantify the effect of ageing. Our analysis revealed the complex interplay of immune, cancer signalling, and metabolic genes in the ageing liver. We found significant network proximities between ageing and NAFLD, HCC, liver damage conditions, and the early phase of liver regeneration with common nodes, including NLRP12, TRP53, GSK3B, CTNNB1, MAT1 and FASN. A common theme involving pathways in cancers connects ageing, regeneration and liver diseases. Overall, our study maps the network-level changes of ageing and their interconnections with the physiology and pathology of the liver. While the regulated process of liver regeneration is crucial for damage-induced repair, its dysregulation may lead to HCC, the most common type of liver cancer. Understanding the molecular pathogenesis of HCC sequentially from precancerous state to cancer

Abstract

Cognitive and psychological studies on human reading indicate that the effort required to read and understand text increases with sentence complexity (Klein and Kurkowski, 1974). Modern natural language processing (NLP) applications face similar challenges. Processing complex sentences with high accuracy remains difficult in computational linguistics, necessitating automatic sentence simplification techniques (Chandrasekar et al., 1996). Sentence complexity can be classified into �lexical complexity� and �syntactic complexity�. Lexical complexity can be managed by utilizing resources like lexicons, dictionaries, and thesauruses to replace infrequent words with common ones. To address syntactic complexity, analyzing sentence structure and applying simplification operations is essential. There are many applications of sentence simplification in NLP. Machine translation systems struggle with long, complex sentences, especially when dealing with divergent language pairs. For parsing, (McDonald and Nivre, 2007) showed that syntactic parsing of long sentences and identifying long-distance dependencies remain challenging. Simplifying sentences can aid parsing and machine translation by breaking down sentences into smaller parts. In automatic summarization, simplification can improve accuracy by extracting smaller units of information. In this work, we studied and analyzed existing sentence simplification methods for Hindi. We developed new approaches, both rule-based and statistical, to design a better system that overcomes the limitations of existing systems while improving quality and readability. As an application, we examined the effects of sentence simplification on Hindi to English machine translation systems.

Abstract

Recommender systems play a crucial role in guiding users towards items they are likely to appreciate (13). Traditional collaborative filtering (CF) methods have been widely used (1), but they often face challenges such as data sparsity and cold start problems (10). This research explores the integration of category-specific algorithms with traditional CF to enhance the performance of recommender systems. By considering item categories, we aim to create hybrid models that leverage the strengths of both approaches, resulting in more accurate and personalized recommendations. Our study is grounded in the extensive evaluation of different models, including user-based CF (2), category-based CF (CCF), and a hybrid model combining both approaches (3). We utilized the MovieLens 1M dataset, which contains over a million ratings from thousands of users, to validate our models. The performance of these models was assessed using precision, recall, and F1-score metrics, which are standard measures in recommender system research. The results indicate that incorporating category-specific information significantly improves the performance of recommender systems. The CCF model outperformed the traditional CF model, demonstrating the value of considering item categories. Furthermore, the hybrid model, which combines CF and CCF, achieved the highest performance, illustrating the effectiveness of leveraging the strengths of both methods. This research contributes to the field of recommender systems by providing a novel approach that enhances recommendation accuracy and personalization. The findings suggest that integrating categoryspecific algorithms with traditional CF methods can lead to significant improvements in recommendation performance, offering valuable insights for future research and practical applications in various domains such as e-commerce, streaming services, and social media platforms.

Abstract

Accidental fires in public and large buildings pose significant risks, demanding effective evacuation strategies to ensure the safety of occupants and minimize property loss. This study aims to address the challenges associated with building evacuations by integrating spatial, temporal, and path planning approaches specifically for 2D building plans. By incorporating a geospatial framework, the study enables the evaluation of dynamic changes in the building environment and their impact on evacuation outcomes. The current studies do not show how the cascading effect of the person in the queue and space constraints, static or dynamic, affect the evacuation times and can exacerbate the disaster. Also, the presence of multiple exits, if any, can change the outcomes. This research studies how spatial arrangement and floor plans play an important part in evacuation strategies and advocates for their integration into building planning and design processes. The study delves into the impact of obstacles on evacuation processes, by introducing a subspace model that significantly improves computation time in various geometric spaces. The findings emphasize the hindrance of obstacles, either static or dynamic, on evacuation efficiency and propose efficient path generation to mitigate increased evacuation times. The primary objective of this study is to evaluate dynamic changes in the building environment during evacuations and assess their impact on evacuation outcomes. It also addresses the challenges associated with building evacuations during accidental fires in public and large buildings. The focus is on ensuring occupant safety and minimizing property loss by developing effective evacuation strategies. The technical approach adopted in this study involves the integration of spatial, temporal, and path planning methodologies for 2D building plans within a geospatial framework. The study employs a computational approach that combines occupancy-based path planning. This allows for a complete evaluation of dynamic changes in the building environment, highlighting the interactions between individuals and their chosen paths toward their respective exits. Notably, the study focuses on time-dependent path planning to recognize the evolving nature of emergencies, incorporating dynamic elements to enhance the accuracy of evaluating the building environment in emergency scenarios. The study explores the effectiveness of integrating floor plans into path generation and people flow analysis, recognizing the influence of spatial arrangement on evacuation strategies. The proposed method employs a graph-based path generation approach using the subspace model for an effective methodology for evacuating occupants by adapting to changing paths based on a 2D structural plan. The path planning accounts for not just current position to the main exit, but incorporates other conditions like crowding and lag on some path segments, time delays in utilizing alternate exits like windows, and finally reaching to the Evacuation point. The study shows that in comparison to the base scenario, the adverse situation resulted in a more than 12x increase in evacuation time for the 89 building occupants. This underscores the impact of changed dynamics and emphasizes the necessity for well-planned alternative exits with sufficient capacity. The study gives the influence of main exit capacity and alternate exit access time on overall evacuation time, calling for comprehensive evaluation and appropriate definition of capacities for doors and windows. The subspaces offer a clear line of sight to the exit and are integral to the evacuation path. In scenarios with obstacles, the approach, where the main door served as the primary exit, resulted in only a marginal increase of 215 seconds for 70 occupants compared to clear spaces. However, when obstacles led to disconnection of the network, alternate exits like windows were used, causing an almost 4x rise in evacuation time to 996 seconds in Case 2. In cases with critical node blockage, the last person took around 1295 seconds to exit, a 6x increase compared to the base scenario with obstacles. This gives the need for strategic planning of alternate exits to reduce delays and optimize people flow rates. The study called for further work to seamlessly integrate the proposed method with static and dynamic components of indoor spaces, emphasizing the development of an occupant-friendly information dissemination model. The use of an agent-based model facilitated dynamic decision-making and adaptation to changing graph networks. The study's technical approach thus encompasses a holistic evaluation of evacuation strategies, considering spatial path planning aspects, with practical implications for building design and emergency planning. In conclusion, this study significantly advances the field of evacuation planning and modeling by addressing the complexities as

Abstract

Unmanned Aerial Vehicles (UAVs), commonly known as drones, have revolutionized various sectors including surveillance, agriculture, and disaster management due to their versatility and maneuverability. Hexacopter UAVs, equipped with six rotors, offer enhanced stability and payload capacity compared to their quadcopter counterparts. However, their operational effectiveness is contingent upon reliable fault detection and isolation mechanisms. In the dynamic operational contexts of hexacopter UAVs, potential faults such as motor failures, sensor malfunctions, or communication disruptions can lead to catastrophic consequences including loss of control, collisions, or data loss. Detecting and isolating these faults accurately and swiftly is imperative to ensure safe and efficient UAV operations. Our objective is to improve the reliability and accuracy of fault detection and isolation for a single motor failure in hexacopter UAVs. Commencing with a foundational exposition on hexacopter UAV dynamics, prevalent fault conditions, and classical machine learning classifiers, the research subsequently introduces LSTM networks and conducts a review of pertinent literature in fault detection and isolation, laying the groundwork for the proposed methodology. The principal contribution of this thesis revolves around the formulation of a fault detection and isolation paradigm that combines LSTM networks for latent temporal feature extraction with ensemble classifiers, notably Random Forests, aimed at enhancing fault detection efficacy. By harnessing the temporal intricacies inherent in UAV data using LSTM networks, the proposed model exhibits robust performance under measurement noise in fault detection and isolation tasks. The evaluation of the proposed approach encompasses comprehensive analyses conducted on synthetic and real-world datasets, encompassing examinations of noise resilience, fault detection and isolation timing, and the deployment on resource-constrained platforms such as the Raspberry Pi. Comparative assessments are conducted with benchmark models from both classical and deep learning domains, wherein our proposed approach demonstrates superior performance with an accuracy of 96.8% for stimulated datasets and 83.2% for real-world datasets. Furthermore, noise analysis highlights the resilience of our proposed method across varying noise intensities, underscoring its adaptability and robustness. The analysis conducted on synthetic data serves as a crucial validation step, instilling confidence in the model�s efficacy for real-world applications where data collection is inherently challenging. Subsequent experiments investigate fault detection and inference times, yielding insights into the temporal efficiency of the proposed approach. On an onboard microcontroller like the Raspberry Pi, the average inference time for our model is measured at 6.512 milliseconds, with additional statistical analyses providing further insights into performance metrics such as minimum and maximum algorithm run time and delay in prediction, alongside standard deviation. In conclusion, the thesis summarizes key findings and contributions, emphasizing the efficacy of the proposed approach in enhancing fault detection and isolation in hexacopter UAVs. Furthermore, it outlines potential avenues for future research, thereby underscoring the practical applicability of advanced machine learning techniques within real-world UAV systems. In essence, this thesis presents a novel fault detection and isolation framework that integrates LSTM networks with ensemble classifiers, thereby advancing fault detection and isolation capabilities in hexacopter UAVs operating in real-world scenarios