Abstract

Air pollution is a growing concern in India, specifically in the National Capital Region (NCR) which is one of the world’s most polluted regions. Despite the recognized problem, there is a lack of research focusing on how people actually perceive air pollution, the specific actions they take to protect their well-being from its impact, and how effective these personal strategies really are. This thesis is designed to address these knowledge gaps by examining public awareness of air pollution in India and the effectiveness of solutions used to improve Indoor Air Quality (IAQ) and further explores how urban residents respond to degraded air quality through a combination of personal behaviours, technological interventions, and adaptive strategies such as change in window opening and closing behaviour in response to pollution, change in day-to-day lifestyle choices and willingness to invest in cleaner air. As part of the study, ethnographic interviews were conducted in person to gather detailed socioeconomic perspectives. A mixed-methods approach was employed, involving interviews from 101 households in Delhi and it’s neighbouring cities like Ghaziabad and Noida. Both Qualitative and Quantitative methods were used to corroborate the statistical findings with descriptive accounts. The findings reveal widespread awareness of the Air Quality Index (88% are aware), but limited understanding of PM2.5 and its health implications (only 33% know about it). Air purifiers emerged as a common IAQ intervention, yet 40% of non-owners cited doubts about effectiveness or believe that IAQ in their house is sufficiently good. Cost and habitual behaviour to air purifiers were additional primary barriers. Among purifier owners, 75% reported perceived benefits, but many saw these as temporary or insufficient given the scale of outdoor pollution. Furthermore aspects like frequency of purifier use and regular maintenance were assessed as they impact the efficacy of solution. Over 35% of surveyed households already own purifiers indicating revealed Willingness to pay (WTP), while an additional 23% showed interest if costs were subsidized or if they were offered a different plan scheme once given enough information about Indoor Air Quality. Estimated WTP for clean air interventions ranges from 10,000–15,000 INR per household annually, depending on income and awareness levels. Similarly when asked about trust in government mitigation programs to address air pollution people, more than 60% people revealed that they don’t trust government to address these issues but around 58% of people still want to contribute a monthly amount to government to improve public transportation and other programs that addresses vehicular pollution. The study also assesses how people respond to pollution, and thus tries to bridge the gap between occupant behaviour and optimal natural ventilation practice. The thesis proposes a pollution-aware natural ventilation (NV) strategy, guided by PM2.5 and PM10 thresholds. To evaluate its effectiveness and compare it with common practices, this strategy was modelled using EnergyPlus simulations. The simulation results indicate that aligning natural ventilation (NV) schedules with real-time pollution data can reduce energy consumption by up to 10.5%, compared to typical occupant behaviour during pollution episodes, while maintaining acceptable indoor air quality. This approach promotes energy-efficient and occupant-centric ventilation practices even in high-pollution environments while making sure that IAQ is not compromised. The thesis concludes with recommending a multi-pronged approach: integrating sensor-based NV systems into residential buildings, scaling public awareness campaigns focused on PM2.5 literacy and IAQ awareness, and designing incentive programs to expand access to IAQ technologies including better usage practices of Air Purifiers. By combining interview-based behavioral insights, simulationbased optimization, and economic insights, this research offers actionable pathways for managing and improving Indoor Air Quality in polluted urban regions of India.

Abstract

Flexible pressure sensors based on conductive polymers and porous dielectric materials are gaining significant attention for applications in wearable devices, human-machine interaction, and soft robotics. In this work, we present the fabrication and characterization of flexible pressure sensors using polypyrrolecoated cotton textile (PCC) and bubble-induced PDMS foam. A highly compressible multilayer capacitive pressure sensor was developed using PDMS foam as the dielectric layer and PCC as the electrodes. The PDMS foam was prepared by mixing specific amounts of ethanol into PDMS, and the electrodes were attached using silicone adhesive. The fabricated sensor demonstrated low hysteresis, high repeatability, and good step response. Surface profiling and scanning electron microscopy were used to characterize the bubble structure. Applications such as mouse click detection and grasping objects with varying weights were demonstrated, highlighting the sensor’s versatility for free-form electronics. Additionally, an ultra-soft capacitive pressure sensor was developed using polyurethane foam with polypyrrole-coated surfaces as electrodes. Various concentrations of pyrrole and FeCl3 were used to optimize the sensor performance, achieving a high sensitivity of 1.01 kPa−1 at 25 kPa pressure. Finite element simulations were performed to study mechanical deformation and space charge variations under pressure. This sensor also offers customizable shapes and sizes for diverse applications. Further, a piezoresistive pressure sensor was fabricated using PCC synthesized via in-situ chemical oxidative polymerization. This sensor exhibited high sensitivity in the low-pressure range of 160 Pa to 16 kPa and a response time of 463 ms. It showed excellent repeatability over 1000 loading-unloading cycles, with a gauge factor of 0.32 for compressive strains up to 99.6%, enabled by the fibrous structure of cotton. Finally, a large-area flexible conductive cotton fabric (20 cm × 20 cm) was fabricated using in-situ polymerization of polypyrrole. The fabric maintained stable conductivity after multiple washing cycles, with resistance changes stabilizing after the fourth wash. This demonstrates its potential in interactive textiles and smart fabric application

Abstract

Over the past decade, football analytics has transformed how the game is played, understood, and managed, primarily in elite European contexts. Football analytics refers to the use of data-driven methods to evaluate tactics, player performance, recruitment, and decision-making across all levels of the sport. Yet little is known about how data practices take shape in low-resource environments, especially in the Global South. This thesis investigates the emergence of Indian football analytics as a decentralised, digitally scaffolded ecosystem, driven by informal learning, frugal innovation, and community-led mentorship. Drawing on digital ethnography, semi-structured interviews, and social media shadowing, this research traces how aspiring analysts build careers, collaborate, and gain legitimacy in the absence of formal training pathways. Participants describe a rich peer-to-peer learning culture enabled by opensource tools, public data, and social media platforms. Knowledge circulates through blogs, repositories, newsletters, and YouTube videos, creating a porous but potent apprenticeship network. The thesis identifies four key dynamics that shape this ecosystem: (1) informal learning and community mentorship as alternatives to institutional training, (2) institutional frictions that limit the uptake of data in football clubs, despite analyst expertise, (3) frugal innovation and grassroots tooling to bypass infrastructure gaps, and (4) social media as a hiring and visibility pipeline. Together, these themes reveal an ecosystem that enables participation while denying long-term stability, where analysts must continuously navigate precarious labour conditions, shifting legitimacy structures, and platform pressures. By centring the everyday practices and lived experiences of Indian football analysts, this study makes three core contributions. First, it offers an empirically grounded account of how decentralised analytics infrastructures operate in the Global South. Second, it highlights how frugality and informality can be generative forces in technological learning and labour. Third, it calls attention to the politics of visibility, credibility, and care in informal data ecosystems. This work contributes to ongoing conversations in HCI and ICTD around alternative infrastructures, peer learning, and the ethics of digital labour. It concludes by offering design-oriented suggestions to support more sustainable, equitable pathways for young data workers in sport.

Abstract

The dynamics exhibited by reaction networks is often a manifestation of their steady states. This dissertation show that there exists endotactic and strongly endotactic dynamical systems that are not weakly reversible and possess a family of infinitely many positive steady states. In addition, for some of these systems we prove that there exist no weakly reversible mass-action systems that are dynamically equivalent to mass-action systems generated by these networks. This extends a result by Boros, Craciun and Yu [1], who proved the existence of weakly reversible dynamical systems with infinitely many steady states. We also investigate the possibility that for any given reaction rate vector k associated with a network G, there exists another network G′ with a corresponding reaction rate vector that reproduces the massaction dynamics generated by (G,k). Our focus is on a particular class of networks for G, where the corresponding network G′ is weakly reversible. In particular, we show that strongly endotactic twodimensional networks with a two-dimensional stoichiometric subspace, as well as certain endotactic networks under additional conditions, exhibit this property. Furthermore, we show that being endotactic is necessary for the dynamics of a network to be included in the dynamics of a weakly reversible network. Additionally, we establish a strong connection between this family of networks and the locus in the space of rate constants in which the corresponding dynamics admits globally stable steady states.

Abstract

Error-correcting codes are mathematical objects that are useful for mitigating errors due to noise, but they are also fundamental to other problems, such as designing and ensuring the privacy and efficiency of storage systems. Reed-Muller codes is one such family of codes that is widely used across these diverse applications, but these codes have limited flexibility in parameters like rate and length, which are required in many cases. In this thesis, we propose a new family of codes similar to Reed-Muller codes and study their properties and decoding. We study a family of subcodes of the m-dimensional product code C ⊗m (‘subproduct codes’) that have a recursive Plotkin-like structure, and which include Reed-Muller (RM) codes and Dual Berman codes as special cases. We denote the codes in this family as C ⊗[r,m] , where 0 ≤ r ≤ m is the ‘order’ of the code. These codes allow a ‘projection’ operation that can be exploited in iterative decoding, viz., the sum of two carefully chosen subvectors of any codeword in C ⊗[r,m] belongs to C ⊗[r−1,m−1] . Recursive subproduct codes provide a wide range of rates and block lengths compared to RM codes while possessing several of their structural properties, such as the Plotkin-like design, the projection property, and fast ML decoding of first-order codes. Our simulation results for first-order and secondorder codes, that are based on a belief propagation decoder and a local graph search algorithm, show instances of subproduct codes that perform either better than or within 0.5 dB of comparable RM codes and CRC-aided Polar codes

Abstract

Quadrotors are extensively utilized in applications such as surveillance, mapping, and delivery, owing to their high maneuverability and vertical takeoff and landing (VTOL) capabilities. The deployment of quadrotors in these critical tasks necessitates a high degree of safety and reliability. However, their inherent lack of rotor redundancy poses significant challenges in addressing motor faults, making fault tolerance a crucial concern in their design and operation. This thesis presents a simple method for stabilizing the quadrotor dynamics under complete loss of one actuator using two-stage optimal control. Detailed equilibrium analysis and subsequent selection of the operating point under actuator loss are provided, incorporating constraints on the maximum available thrust. Two distinct cases were considered for equilibrium selection: one in which two actuators have equal inputs, and another in which all actuator inputs are allowed to vary independently. It is shown that when all actuator inputs are allowed to vary independently, the system can track an additional state, thereby enabling attitude tracking. A detailed simulation study using a high-fidelity nonlinear model of the quadrotor is presented, showing the stability and performance of the closed-loop system under complete actuator loss, in the presence of external disturbances. To ensure better maneuverability, the quadrotor under fault may have to shift to multiple equilibrium states along the course of a single flight. This poses a challenge as designing a controller for every equilibrium state across the state space is a tedious task. To address this challenge, a controller capable of stabilizing the quadrotor under fault across multiple equilibrium states was designed by applying Linear Matrix Inequality (LMI) conditions. The controller was tested in simulation using a high-fidelity nonlinear model of the quadrotor. The proposed method not only ensures continued stable flight in fault scenarios but also lays the groundwork for resilient flight control design in safety-critical aerial robotics applications.

Abstract

In this work, we introduce and rigorously characterize a broad class of quantum operations termed quantum ergodic channels. These are completely positive, and trace-preserving (CPTP) maps that possess a unique fixed point toward which any initial quantum state asymptotically evolves. The existence of such a fixed point enables a systematic exploration of ergodicity in quantum dynamics, extending classical notions into the quantum regime. We construct and analyze Lindblad-type master equations governing the evolution under these ergodic channels in arbitrary finite-dimensional Hilbert spaces. These equations describe both Markovian and non-Markovian dynamics, depending on the time dependence and structure of the generator. In particular, the class of ergodic channels considered here encompasses both memoryless and memory-affected evolution, allowing us to explore their differences through established quantitative measures of non-Markovianity. A special focus is placed on the case where the fixed point of the channel is a passive state, i.e., a state from which no work can be extracted via unitary operations. In such settings, evolution under ergodic channels gives rise to non-trivial ergotropy dynamics. Ergotropy is a fundamental thermodynamic quantity that quantifies the maximum extractable work from a quantum state via unitary transformations, assuming no access to additional resources. We show that in the Markovian regime, where the dynamics lack memory, the ergotropy of the system decreases monotonically over time, consistent with the second law of thermodynamics in open systems. However, under non-Markovian dynamics, the situation changes significantly. We demonstrate that ergotropy can temporarily increase during evolution, indicating a backflow of useful energy from the environment into the system. This phenomenon—referred to as ergotropy backflow—is a direct manifestation of memory effects and points to the possibility of temporary reversals in the degradation of thermodynamic resources. Our analysis shows that this backflow is not only physically meaningful but also operationally relevant: it can serve as a dynamical witness or indicator of non-Markovianity. These findings offer a novel perspective on the interplay between non-Markovianity and thermodynamic behavior in open quantum systems. By grounding the discussion in welldefined resource-theoretic and thermodynamic terms, this study deepens our understanding of how memory effects influence the evolution of quantum resources such as ergotropy. In particular, it highlights the role of ergodic channels as a versatile theoretical framework for studying energy dynamics, dissipation, and resource recovery in realistic quantum settings—including potential applications in the design and analysis of quantum batteries. Overall, the results presented here enrich the theoretical framework of open quantum systems and pave the way for future investigations into the thermodynamic implications of quantum memory effects. They suggest practical strategies for identifying, characterizing, and potentially exploiting non-Markovian dynamics to enhance the performance of emerging quantum technologies.

Abstract

Training and evaluating large language models (LLMs) on deductive reasoning tasks has attracted much attention in recent times. There have been various attempts at training and evaluating LLMs to perform deductive reasoning tasks. Some among these have shown interesting results which suggest that LLMs behave like humans by displaying content effect, that is, they reason better on reasoning tasks containing rules that align with our everyday beliefs and reason poorly when the rules are beliefviolating. Others, using chain-of-thought prompting, a technique to make LLMs generate intermediate steps before making them arrive at the final conclusion, suggest that LLMs may emulate human-like reasoning thought processes by generating intermediate reasoning steps. On the other hand, there are studies which conclude that language models do not yet demonstrate reliable deductive reasoning, since their performance is shown to decrease upon introduction of perturbations, such as synonym substitution, and attribute their reasoning to artefacts from the training data. In this study, we look at these three distinct attempts at evaluating LLMs on deductive competence tasks, each employing different criteria such as content-based reasoning (Dasgupta et al., 2023 [1]), chain-of-thought prompting (Wei et. al., 2023 [2]), and introduction of perturbations (Yuan et al., 2023 [3]). In order to make sense of these claims concerning genuine reasoning, we introduce a framework developed by Diane Proudfoot using externalist criteria for machine cognition (Proudfoot 2004 [4]), which is based on Wittgenstein’s argument that deductive reasoning involves rule-following which is normative in nature. Based on the notion that deductive competence involves the following of normative rules, the framework proposes the use of the Wittgensteinian distinction between rule-following and quasi rule-following as a method to distinguish genuine deductive competence from quasi-competence. The criteria distinguishing between rule-following and quasi rule-following shall be adapted to analyse whether these LLMs can be said to reason genuinely or are merely imitating reasoning-like behaviour. We propose this use of Proudfoot’s criteria for rule-following as a framework to distinguish genuine deductive competence from quasi deductive competence. In doing so, we also draw attention to the limitations and implications of Proudfoot’s claims regarding machine cognition through the introduction of a thought experiment. This thought experiment enables us to think through Proudfoot’s argument, according to which it is due to pragmatic considerations–and not in principle–that LLMs are unlikely to possess genuine reasoning.

Abstract

With the advent of Industry 4.0, the demands on industrial robots have expanded beyond simple pick-and-place tasks. Future smart factories require robots capable of a wide range of manipulation skills, including the ability to manipulate objects with variety of actions like pushing. This thesis investigates the capabilities of a manipulator and the system to perform fine or controlled non-prehensile manipulation (without grasping the objects), potentially exceeding the robot arm’s reachable workspace. Key contributions of this research include: • Hybrid Planner combining Striking, Pushing and Pick and Place motions. • Development of sophisticated optimal control strategies for generate manipulation of objects to specific target locations with high precision without necessarily grasping. These algorithms calculate the optimal contact point,force and velocity required for each action. • Conducting experiments primarily in a simulation environment to validate the effectiveness of the end-effector design and its control algorithms, complemented by preliminary tests on a real robot. These evaluations demonstrate the practical viability and robustness of the proposed system under controlled conditions. The thesis further explores the vast practical implications of Non-Prehensile manipulation. In warehouse logistics, this technology can significantly optimize sorting, material transfer, and distribution processes by enabling faster and more precise handling of items. By combining optimization and planning strategies, this research provides a comprehensive framework for designing a framework for hybrid manipulation. This interdisciplinary methodology enhances the versatility, adaptability, and performance of robots, ultimately improving efficiency, safety, and productivity in various industrial and operational settings. Keywords: Mobile Manipulation, Non-Prehensile Manipulation, Redundant robot, Trajectory optimization, Robot Arm Control, Action-based Planning, Hybrid Action Planner, Whole body control, Robot Simulation, Rigid body dynamics, Sim-to-real, residual learning, Planning framework.

Abstract

Enabling effective grasping capabilities in unmanned aerial manipulators (UAMs) presents formidable challenges stemming from the intricate coupling forces that emerge between the flying platform and manipulator arm, compounded by parametric uncertainties and environmental disturbances. Current methodologies predominantly fall into two categories: those demanding precise dynamic models of the entire system, and those addressing the aerial and manipulator subsystems as separate entities , both approaches facing substantial limitations when deployed in practical settings. Despite substantial advancement in research on the topic of aerial manipulation, there is a lack of methods that properly address the effect of uncertain interaction forces between the manipulator interacting with the environment and the floating aerial base. These forces tend to be notoriously hard, if not impossible, to model due to uncertainties in payload characteristics and environmental interaction. This thesis proposes a novel adaptive control architecture for an integrated solution for the UAM system combined with a bistable passive gripper to facilitate dependable aerial grasping operations without necessitating prior knowledge of system dynamics or disturbance characteristics. The bistable gripper employs a pre-stressed spring steel band that transitions between two stable states, autonomously initiating object capture upon contact and thereby minimizing alignment precision requirements. Its innovative cable-driven actuation mechanism, powered by a single compact DC motor, enables efficient gripper release without requiring bulky pneumatic components. The developed adaptive control strategy effectively addresses the challenge of unmodeled coupling dynamics and state-dependent uncertainties inherent in aerial manipulation systems. The controller incorporates adaptation mechanisms that dynamically estimate composite uncertainties, encompassing variations in inertial parameters, Coriolis and centrifugal effects, gravitational influences, and external forces, maintaining reliable tracking performance despite the absence of precise system identification. A rigorous Lyapunov stability analysis demonstrates that the closed-loop system achieves uniform ultimate boundedness under practical operating conditions.