Abstract

The continued scaling of CMOS and FinFET technologies has introduced significant challenges in digital circuit design, including increased susceptibility to aging effects and heightened leakage power variability. This thesis presents a comprehensive exploration of machine learning (ML)-integrated methodologies for standard cell characterization and complex circuit analysis in the context of advanced CMOS and FinFET technologies. Through a sequence of these studies, the work addresses key challenges associated with reliability, power efficiency, and aging-aware modeling in nanoscale digital circuits. RelOps demonstrates an ML-assisted, multi-objective optimization framework for 16nm HP FinFET standard cells. By integrating SPICE-driven time-series datasets with accurate regression models, the framework enables efficient tuning of critical FinFET dimensions (e.g., channel length, fin width, and fin height), thereby mitigating aging effects such as NBTI and HCI across PVT corners. This approach achieves substantial improvements in power-delay product (PDP) and reduces simulation overhead, establishing a scalable path for performance-centric design optimization. PIDArc proposes a novel physics-informed machine learning (PIML) architecture based on a deaggregated methodology to model the aging-induced leakage behavior in 7nm and 10nm FinFETs through pattern knowledge. This approach addresses the limitations of conventional ML models in handling high-variance leakage data and captures the complex non-linear relationship between aging, PVT variations, and leakage power. The proposed method significantly outperforms existing deep learning and boosting models in terms of prediction accuracy, offering a robust solution for aging-aware leakage estimation. Optimo proposes an ML-based optimization at the complex circuit level via the OptiMo methodology, which automates the reduction of power and delay across diverse designs in CMOS technology. The experimental evaluations on multi-stage circuits demonstrate up to 64.6% PDP reduction, reinforcing the scalability and adaptability of the ML optimization framework from standard cells to hierarchical digital systems. Collectively, the proposed techniques establish a unified ML-driven design flow that bridges devicelevel physics, circuit-level modeling, and system-level optimization. This work not only advances the state-of-the-art in FinFET-based digital circuit design but also offers practical and computationally efficient tools for reliability-aware characterization and performance enhancement in emerging technology nodes.

Abstract

With the rise of quantum computing, the traditional cryptographic techniques may face potential vulnerabilities, particularly in the security-critical applications, such as Internet of Things (IoT)-enabled blockchain systems. These systems rely heavily on the digital signatures for authentication, data integrity, and secure transactions. However, the quantum algorithms like Shor’s algorithm threaten conventional public-key based cryptographic schemes, such as RSA and elliptic curve cryptography (ECC) which make post-quantum cryptography (PQC) essential for future-proof security. IoT devices often have constrained computational power and storage, and thus, it makes challenging to integrate complex security protocols. Post-quantum signature schemes need to be designed which are resilient against quantum attacks, such as lattice reduction attack, hybrid classical-quantum attacks, side-channel and fault injection attacks, and brute-force attacks (e.g., searching for secret keys) using the quantum Grover’s Search algorithm in the context of lattice-based signature schemes. Thus, these signature schemes should ensure the long-term security of IoT networks without significantly increasing computational overhead. Additionally, blockchain-based applications rely on immutable records, which become vulnerable if past transactions can be decrypted in the future. Moreover, post-quantum digital signatures mitigate this risk by providing quantum-safe authentication and verification, maintaining trust in blockchain networks. In addition, regulatory and compliance frameworks are evolving to address quantum security threats, necessitating the adoption of PQC in IoT-blockchain systems. Organizations deploying IoT solutions in critical sectors, such as healthcare, finance, and smart cities, must adopt quantum-resistant cryptographic methods to ensure data privacy and system integrity. This thesis aims to design and develop blockchain-integrated lattice-based cryptographic schemes, including aggregate signatures, multi-signatures, and Ciphertext-Policy AttributeBased Encryption (CP-ABE) in IoT environments, to improve security, efficiency, and scalability. These schemes are intended to address the specific challenges of IoT networks, where devices are often limited in resources and require lightweight but strong security measures. In the first contribution, we designed a new lattice-based aggregate signature scheme that relies on the complexity of the Ring Learning-with-Errors (Ring-LWE) lattice-based hard problem for security. This scheme is then applied within the Internet of Drones (IoD) systems using the blockchain technology to ensure secure and transparent data storage. In this scheme, we described the comprehensive security analysis and comparative study, which indicates that the proposed scheme offers enhanced security, including quantum resistance, is efficient. We implemented the testbed experiments and blockchain simulations further to confirm that theproposed scheme is viable for real-world drone applications. Next, by utilizing the widely-adopted lattice hardness assumptions, namely Ring-based short integer solutions (Ring-SIS) and Ring-LWE, we designed an efficient multi-signature scheme for blockchain-based IoT applications. Additionally, we incorporated the single round online phase in the signing algorithm to achieve a low round complexity. The blockchain has been used as an add-on service for secure data storage purposes. In this case, each signer engages in pre-processing the offline phase before the single round online phase. In the offline phase, all the signers engage in interactions and exchange a set of commit messages among themselves. Subsequently, each party employs a random linear combination approach to aggregate all the commit messages and utilize them to generate the corresponding signature of the signer. Without the participation of all signers, a multi-signature cannot be generated, thereby making it impossible for an adversary to produce a fraudulent multi-signature without knowledge of all signers’ secret keys. The security analysis confirms that the scheme is capable of withstanding various attacks, including quantum attacks. A blockchain simulation was conducted to measure the computational time required for mining blocks in the blockchain. Moreover, a comparative study was presented, featuring several existing lattice-based multisignature schemes, to showcase the effectiveness of the proposed scheme in IoT applications. Finally, we designed a novel multi-authority CP-ABE scheme based on the lattice structure in a distributed environment for IoT-based smart healthcare applications. The work supports the implementation of the trapdoor generation to substantially reduce the computational overhead associated with the keygen phase and Gaussian Pre-image sampling techniques. The proposed scheme that has been developed regards the numerous auth

Abstract

In decision-making settings such as medical diagnosis, underwriting, or sentencing in a court of law, decisions are often influenced by multiple factors like the individual’s background, experience, and personal biases. Variability in decisions across individuals is thus inevitable, and so are disparities in different systems. Every system has its own way of addressing prevailing disparities—be it through noise audits, standardized guidelines, or other approaches. In the medical field, for instance, prior studies have documented significant inter-observer variations in the interpretation of clinical images such as MRIs, X-rays, etc., leading to inconsistencies in diagnoses and treatments. To address the issue, the medical domain is witnessing active development of AI tools intended to support clinical data analysis and reduce disparities in healthcare outcomes. In this thesis, we explore a data cube-based methodology to explore the issue of disparities in the legal domain. In the legal domain, decisions related to parole or bail grants, child custody, or sentence imposition are often left to the judges’ discretion. Specifically for sentence imposition, guidelines in many countries around the world allow for subjectivity. For example, in India, while sentencing guidelines prescribe minimum and maximum punishments for different offences, the weights assigned to various aggravating and mitigating circumstances are left to the judge’s discretion. This flexibility, though intended to accommodate case-specific nuances, increases variance, leading to inconsistencies in trial outcomes, sentence lengths, and penalties across courts and judges. The literature widely acknowledges that anomalies and disparities exist in sentencing and other legal decisions, often stemming from personal beliefs, biases, and contextual factors. Over the years, several efforts have also been made to analyse and assess sentencing anomalies/disparities in India and other parts of the world, particularly with respect to individual factors such as gender, race, socioeconomic background, etc., through surveys, case studies, and machine learning techniques. Notably, the Online analytical processing (OLAP) methodology has been widely employed in literature to analyse multidimensional data in different domains. The concept of a data cube was proposed to summarise and extract all subcubes from a table, enabling multi-dimensional analysis and derivation of insights from diverse perspectives. It is commonly adopted to extract interesting trends and anomalies from multidimensional data in domains like sales, marketing, etc. However, so far, no effort has been made to extend the data cube-based framework to explore anomalies and disparities in the legal domain. In this thesis, we leverage the OLAP framework and propose a data cube-based approach to explore potential trends and anomalies in judicial decisions, particularly sentences. A major bottleneck in this domain has been the lack of structured datasets. To address this, we employed a large language model (LLM) to curate a structured dataset from unstructured data extracted manually from Indian criminal case judgments. We designed a conceptual schema by identifying relevant attributes, hierarchies, and defining appropriate aggregate measures. We used this schema to build a data cube on the curated dataset and facilitate anomaly detection. This approach enabled us to uncover potential anomalies in court sentences, particularly in terms of quantum and monetary penalties for similar offenses across Indian states. For instance, we observed that Kerala imposed relatively higher monetary penalties in cases of rape and murder compared to other states. Several additional trends were also identified. Our experiments demonstrate that the proposed framework has the potential to identify the anomalies that could help in further understanding the causal factors, such as bias, that contribute to such anomalies and disparities. We also provide a structured dataset annotated by domain experts, which treasures sentencing-related information along with the verdict rationale from judgements of around 10,000 criminal cases adjudicated in the Trial court, High Court, and Supreme Court of India during 2000-2010. We make this dataset public to encourage further research

Abstract

Power, Performance, and Area have been some of the most crucial specification metrics to adjudge the quality of systems. Circuits based on nanoscale transistors operate at lower values of threshold and supply voltages, thereby providing better power-delay specifications. CMOS based nanoscale circuits (≤45nm) show higher static leakage losses than dynamic losses and become highly sensitive to PVT (Process parameters, Supply Voltage and Temperature) variations. Reliability analysis of the circuit in terms of propagation delays are greatly affected factors such as Bias Temperature Instability (BTI) and Hot Carrier Injection. Hence optimization is one of the most important aspects of the design flow. This thesis suggests an algorithm-based transistor sizing approach toward low-power and high-performance optimization of digital circuits. It proposes the use of 2 swarm intelligence based algorithms- Spider Monkey Optimization and Elephant Herding Optimization for transistor resizing in gates/ blocks either for low power or for high performance scenarios. In the first phase discussions, basic digital cells such as logic gates and blocks like Full Adders are optimized by resizing their transistors to meet the required specifications. The second phase involves the optimization of complex circuits comprising several of such basic cells/ blocks. This includes factors such as the critical signal path performance specifications, pairwise driver(s) and load(s) analysis, etc. This thesis proposes a python based top level module that scans through the circuit for potential critical/ near critical signals paths, identifies the types of basic cells present and the loads they drive, optimizes and finally places the appropriate resized cells to improve the overall circuit performances. We could obtain about 65% power and 44% in high performance optimization. Approximate computation deals in fields where the accuracy has a more relaxed tolerance level such asimage processing,speech processing etc. Thisis due to the fact that human senses are cannot perceive minute errors. We have utilized this performance-accuracy trade-off phenomenon to design low power systems with the help of algorithms. We have generated low power Full Adder designs through transistor pruning (selective removal of transistors) for speech processing systems with 67% power optimization. Accuracy has been tested and our design show improved performances as compared to previous works.

Abstract

Stuttering is a neurodevelopmental uency disorder characterized by disruptions in the normal ow of speech, such as repetitions of sounds, syllables, or words, prolongations of sounds, and audible or silent blocks. These speech disuencies can lead to signicant communication challenges and adversely affect an individual’s social, emotional, and occupational well-being. Accurate and timely detection and classication of stuttering events are crucial steps for appropriate clinical intervention, personalized therapy planning, and monitoring treatment efcacy. Traditional assessment methods often rely on perceptual judgments by clinicians, which, while valuable, can be subjective, time-consuming, and may exhibit inter-rater variability, particularly in complex cases or large-scale studies. Consequently, objective assessment methods leveraging computational analysis are gaining prominence due to their potential for enhanced consistency, efciency, and scalability in both clinical practice and research settings. There is a growing impetus for robust automated systems that can reliably identify stuttering events and categorize them, thereby assisting clinicians in the diagnostic process and in objectively tracking therapeutic progress over time. The development of objective assessment tools utilizing acoustic and signal processing techniques for the analysis of speech can effectively uncover the specic manifestations and nuances of stuttered events. Therefore, the extraction of salient acoustic, temporal, and prosodic features from the speech signal is considered a paramount step in building robust automated systems for stuttering analysis. While previous research has made strides in the automatic detection of stuttering, further advancements are necessary to improve the accuracy of detecting the diverse range of disuency types and to reliably differentiate stuttered speech from instances of typical disuency that occur in normally uent speakers. To characterize the distinct acoustic and temporal patterns associated with various stuttered events, a detailed analysis of speech segment durations, spectral characteristics, energy contours, and fundamental frequency variations is essential. Furthermore, the application of advanced signal processing methodologies and machine learning algorithms holds signicant promise for enhancing the performance and discriminative power of stuttering assessment systems. The primary objectives of this thesis are the automatic detection of stuttering from continuous speech signals and the subsequent classication of these identied stuttering events into their respective types. In this thesis, SEP-28k was the primary dataset used in most of the studies and also used Fluency Bank, VCTK stutter corpus. The F1 score and Accuracy were the primary metrics used for the evaluation of the models.

Abstract

Urban air quality monitoring faces a fundamental scalability crisis. With only 800 monitoring stations serving over 4,000 Indian cities, traditional sensor networks cannot capture pollution variations that occur every 300-500 meters in complex urban environments. The sparse coverage, combined with deployment costs of INR 40 lakhs to 1.6 crores per station, renders comprehensive monitoring economically unfeasible. Image-based air quality estimation emerges as a transformative alternative, yet existing approaches are constrained by limited datasets and poor generalization across cities. This thesis introduces two contributions that establish a new paradigm for scalable air quality monitoring. TRAQID (Traffic-Related Air Quality Image Dataset) provides the first comprehensive multiview dataset with 26,678 synchronized front-rear traffic image pairs, co-located environmental sensors, and systematic coverage across three seasons in Indian urban environments. Benchmark evaluation reveals existing state-of-the-art methods achieve only 75% accuracy on TRAQID, exposing critical limitations in current approaches. AQIFormer, our transformer-based architecture, presents image-based air quality estimation through weather-aware attention mechanisms, adaptive dual-view fusion, and multi-task temporal learning. The architecture achieves 89.96% accuracy on TRAQID—a remarkable 14.96% improvement over existing methods. Most significantly, AQIFormer demonstrates unprecedented cross-city generalization, maintaining 81.67% accuracy on independent Nagpur data with minimal adaptation (5% local samples), exhibiting only 8.29% performance degradation compared to typical 30%+ drops in existing approaches. Comprehensive analysis validates each innovation: dual-view integration contributes 11.46% improvement, multi-task learning enhances discriminability by 11.57%, and attention visualization reveals consistent focus on physically meaningful pollution sources across diverse urban environments. The architecture processes standard traffic imagery in real-time, enabling integration with existing camera infrastructure without specialized hardware. This research establishes the practical foundation for city-scale air quality monitoring at unprecedented spatial resolution and dramatically reduced costs. The robust cross-city performance enables immediate deployment across multiple urban environments, transforming air quality monitoring from expensive, sparse sensor networks to comprehensive, camera-based systems that can protect public health through enhanced environmental awareness.

Abstract

In today’s competitive academic landscape, choosing the right college is a crucial decision for students seeking higher education. Traditional ranking frameworks, such as the National Institutional Ranking Framework (NIRF), provide a comprehensive evaluation of colleges across India based on various parameters, including teaching, learning resources, research output, graduation outcomes, outreach, and institutional perception. While these rankings offer a valuable overview of institutional performance, they may not directly cater to the individual preferences and priorities of students, potentially leading to choices that do not align with personal goals or specific needs. This thesis introduces a novel approach to enhance the utility of NIRF data by developing a userspecific ranking system that tailors college rankings to individual preferences. The proposed system allows users to define their criteria, such as median salary, number of students placed, facilities for Physically Challenged Students (PCS), faculty strength, and research funding, to generate personalized rankings. By integrating these user-defined criteria with NIRF data, the system generates dynamic, customized rankings that adapt to evolving student priorities, providing a more nuanced and relevant assessment of educational institutions. The methodology involves collecting NIRF data and supplementing it with JoSAA cutoff data to provide a comprehensive dataset for analysis. The data is processed using novel techniques, including multi-skyline queries, which expand traditional skyline queries to provide a broader set of ranking options that better reflect user preferences. The system’s architecture, built on the MERN stack (MongoDB, Express.js, React.js, Node.js), supports flexible data handling and user-friendly interaction, allowing for efficient customization and visualization of rankings. By empowering students with personalized insights, this approach facilitates informed decisionmaking and enriches the overall college selection process. It allows students to prioritize aspects of higher education that matter most to them, such as research opportunities, employability, or inclusivity, thus enabling a more tailored approach to selecting a college. This user-specific ranking system is not only a valuable tool for students but also for parents and educational counselors, who can better assist in navigating the complex landscape of higher education based on personalized data. This approach not only supports students in making well-informed decisions but also encourages institutions to strive for excellence in areas that matter most to their prospective students. By enhancing the utility of NIRF data, the proposed system offers a comprehensive and personalized college selection experience, positioning itself as a valuable tool in the evolving landscape of higher education.

Abstract

Water quality monitoring of inland waterbodies is a global challenge due to limited monitoring infrastructure and availability of high-resolution hydrological data. Timely assessment and mitigation of water quality threats is constrained by limited data availability. To address this issue, remote sensing techniques have emerged as a robust tool for continuous real-time monitoring of inland water quality. In tropical regions, there has been a greater emphasis on remote sensing techniques for the continuous monitoring of water quality, mainly in eutrophic or hypereutrophic inland water bodies such as estuaries, rivers, and lakes, while reservoirs have received comparatively less attention. However, worldwide, tropical reservoirs are vulnerable to contamination risks due to their multifunctional roles, i.e., drinking, irrigation, and hydropower generation and long periods of storage times without adequate outflows which are critical for maintaining downstream water quality. Furthermore, the surrounding catchment influence contamination dynamics, as rapid changes in land use/land cover (LULC) can introduce sediments, nutrients, and heavy metals, contributing to elevated levels of physical, biological, and chemical characteristics of water quality parameters. Identifying the critical importance of monitoring tropical reservoirs, the present study focuses on employing remote sensing techniques to estimate water quality parameters, focusing on physio-biological characteristics, mainly in oligotrophic and mesotrophic reservoirs, and underscores the significance of catchment-related factors in influencing contamination levels. This approach is essential for maintaining optimal water quality levels, benefiting both local communities and the surrounding environment. The study selected two tropical reservoirs, Bhadra and Tungabhadra, situated within the same river system and experiencing similar climatic conditions but distinct landscape characteristics. The primarily aim is to develop a modeling framework to map and quantify the spatiotemporal variability of Chlorophyll-a (Chl-a), turbidity and surface water temperature (SWT), along with associated water spread for 2016-2021 using Sentinel 2 and Landsat 8 satellite datasets. The second aim is to examine interconnections among selected water quality parameters, to improve the accuracy of reservoir water quality assessments and identify potential correlations and interdependencies. The third aim is to comprehend how the landscape (land use/land cover (LULC)) and meteorological variables (rainfall, air temperature) of the contributing catchment influence contamination dynamics. The remote sensing-based results, validated with field-based observations measured from the Aquaread AP7000 instrument and secondary data include EOMAP (Earth Observation and Mapping) water quality derived datasets were employed to enhance accuracy and reliability. Chl-a, used as a proxy for nutrient contamination primarily from agricultural runoff, estimated using the Maximum Chlorophyll Index (MCI) derived from Sentinel 2 satellite data in the Google Earth Engine (GEE) platform. The results indicate a consistent and extensive distribution of Chl-a across the entire water surface of the Tungabhadra reservoir, while the Bhadra reservoir exhibited a more limited distribution. During the post-monsoon, increase in Chl-a spread in the Tungabhadra reservoir, primarily attributed to nutrient-rich water inflows from agricultural and urban areas. This notable rise linked to the harvesting of Kharif crops. Additionally, the discharge of untreated sewage further contributes to the degradation of water quality in the Tungabhadra reservoir. In contrast, the Bhadra reservoir, surrounded predominantly by forested areas, maintained water cleanliness and served as a riparian boundary. These findings demonstrate how LULC changes drive nutrient contamination in tropical reservoirs. Turbidity was considered as a proxy for sediments, detected and mapped using Sentinel-2 data. The Normalised Difference Turbidity Index (NDTI) values strongly correlated with EOMAP and field-based observations having values > 10 NTU (nephelometric turbidity unit) with R 2 = 0.81 and standard error estimate (SEE) of 5.3, respectively. In addition, the correlation between red band reflectance values with < 10 NTU, demonstrated a strong relation with an R 2 = 0.74, SEE = 1.7. Combining NDTI and red band reflectance allowed classification into low, moderate, and high turbidity zones. Further analyses indicated that diverse land use patterns, particularly high proportions of urban and agriculture area along with rainfall, significantly influenced the annual and seasonal turbidity variations. Surface water temperature (SWT) is a significant physical parameter affecting aquatic metabolism, nutrient cycling, and contaminant interaction was estimated using Landsat 8 Thermal Infrared (TIR) d

Abstract

Understanding how narrative flow reflects cognitive processes such as memory, schema use, and event organization has long been a goal of cognitive science. Recent advancements in natural language processing, particularly the rise of large language models (LLMs), have introduced new tools for modeling and quantifying linguistic phenomena. Among these, a measure known as sequentiality—proposed by Sap et al. (2022)—aims to capture the narrative coherence of a story using LLM-derived probabilities. However, like many LLM-based approaches, its validity as a cognitively meaningful measure remains underexamined, especially in relation to human-annotated data. This thesis begins with a conceptual replication of the original sequentiality study, using a newly constructed dataset of paired autobiographical and biographical narratives written by the same individuals. Despite theoretical expectations of greater narrative structure in biographies, we fail to replicate the significant differences in sequentiality originally reported. To investigate further, we replicate the original analysis on Sap et al.’s dataset and identify a methodological bias stemming from topic-based components in the formulation. Removing this source of bias reduces the observed difference between narrative types, calling into question the suitability of the original metric for capturing narrative coherence. In the second part of this thesis, we propose a rectified version of the sequentiality measure, excluding the topic term and relying solely on sentence-to-sentence contextual prediction. To empirically validate this variant, we turn to the domain of Automated Essay Scoring (AES), where essays are annotated by human raters across multiple dimensions, including coherence. Using two well-established AES datasets, we demonstrate that the context-only sequentiality score aligns more closely with human judgments of narrative flow than the original formulation. Furthermore, we show that this measure improves the performance of AES systems when incorporated as a feature, highlighting its practical utility as an interpretable and cognitively motivated coherence metric. Together, these findings contribute to the growing literature on using LLMs as models of human linguistic behavior, while emphasizing the necessity for rigorous validation and methodological transparency when deploying such models in cognitive research. This work offers a clearer path toward integrating LLM-derived measures into applied NLP systems and opens avenues for future exploration in interpretability, bias mitigation, and discourse-level evaluation.

Abstract

This thesis presents novel contributions in two primary areas: advancing the efficiency of generative models, particularly normalizing flows, and applying generative models to solve real-world computer vision challenges. In the first part, we introduce significant improvements to normalizing flow architectures through six key innovations: (1) Development of invertible 3 × 3 Convolution layers with mathematically proven necessary and sufficient conditions for invertibility, (2) introduction of a more efficient Quad-coupling layer, (3) Design of a fast and efficient parallel inversion algorithm for k×k convolutional layers, (4) Fast & efficient backpropagation algorithm for inverse of convolution, (5) Using inverse of convolution, in Inverse-Flow, for the forward pass and training it using proposed backpropagation algorithm, and (6) AffineStableSR, a compact and efficient super-resolution model that leverages pre-trained weights and Normalizing Flow layers to reduce parameter count while maintaining performance. These advances maintain model expressiveness while substantially improving computational efficiency compared to existing approaches. The second part demonstrates the practical applications of generative modeling advances across diverse computer vision tasks. This thesis develops: (1) An automated quality assessment system for agricultural produce using Conditional GANs to address class imbalance, data scarcity and annotation challenges, achieving good accuracy in seed purity testing; (2) An unsupervised geological mapping framework utilizing stacked autoencoders for dimensionality reduction, showing improved feature extraction compared to conventional methods; (3) We proposed a privacy preserving method for autonomous driving datasets using on face detection and image inpainting; (4) Utilizing Stable Diffusion based image inpainting for replacing the detected face and license plate to advancing privacy-preserving techniques and ethical considerations in the field.; and (5) An adapted diffusion model for art restoration that effectively handles multiple types of degradation through unified fine-tuning. This thesis advances the theoretical understanding of generative models and their practical applications, demonstrating significant improvements in efficiency, scalability, and real-world utility across multiple domains.