Abstract

The rapid proliferation of energy-constrained electronic systems, including IoT nodes, biomedical
implants, and portable devices, has intensified the demand for ultra-low-power and area-efficient analog
circuit design. This thesis presents the design and analysis of advanced ultra-low-power analog building
blocks with a focus on current and voltage references, as well as high-speed comparators, optimized for
energy and area efficiency under wide process, voltage, and temperature variations.
A series of resistorless current and voltage reference architectures are explored, leveraging subthreshold operation and the complementary temperature characteristics of CTAT and PTAT voltages to
achieve robust temperature compensation. A sub-1V current reference generating 593 pA is demonstrated, achieving a temperature coefficient of 378 ppm/◦C over a range of −40◦C to 100◦C, with a line
sensitivity of 0.198%/V across a supply range of 0.8 V to 2.5 V. The design consumes only 3.3 nW at
27◦C and occupies 0.074 mm2
, while maintaining ±0.75% (3σ/µ) process variation, enabling trim-free
operation. Further, an integrated ultra-low-voltage reference operating at 0.5 V generates 90.7 pA and
288 mV current and voltage references, respectively, achieving temperature coefficients of 15.2 ppm/◦C
and 36.8 ppm/◦C. The circuit operates across a wide supply range of 0.5 V to 2.6 V with line sensitivities
of 0.028%/V and 0.154%/V, consuming only 275.26 pW and occupying 0.087 mm2
through the use of
gate leakage-based techniques.
To address line sensitivity and enable operation over extreme temperature ranges, a compact voltage
reference architecture employing only four transistors and a dual-loop regulation scheme is proposed.
The design achieves a reference voltage of 451.6 mV while consuming 37.6 pW, with an ultra-low
line sensitivity of 0.009%/V. It operates over a wide temperature range of −30◦C to 160◦C with a
temperature coefficient of 70.2 ppm/◦C, enabled by threshold voltage modulation and leakage control
techniques.
In addition to reference generation, a 0.3 V bulk-driven rail-to-rail comparator with dynamic transient
enhancement is introduced to overcome the speed limitations of subthreshold operation. The proposed
design achieves a 10× improvement in rise time, with a transient response of 7.56 ns and a bandwidth of
1.07 MHz while consuming only 28 nW of power, demonstrating portability across 65 nm and 180 nm
CMOS technologies.
While the previously presented resistorless architectures demonstrate excellent simulated performance in terms of ultra-low power and compactness, their reliance on device-level characteristics makes
them inherently more susceptible to process variations, potentially impacting yield and long-term reliability. To address these limitations, area-efficient resistance-based current references are investigated
and validated through silicon tape-out measurements. A fabricated design in 0.18 µm CMOS delivers
a 10 nA reference current while consuming 40 nW, achieving a temperature coefficient of 136 ppm/◦C
over −40◦C to 100◦C and a line sensitivity of 1.1%/V across a 1.4 V to 1.9 V supply range, with a compact area of 0.03 mm2
. Furthermore, a low-cost single-point auto-calibration technique is proposed,
enabling compensation of temperature and process variations. The calibrated 1 µA current reference
achieves ±2% accuracy with a temperature coefficient below 150 ppm/◦C over −40◦C to 100◦C, offering performance comparable to conventional trimming methods while significantly reducing calibration
complexity, time, and cost.
Additionally, this thesis presents a fully integrated ultra-low-power and area-efficient RC oscillator based on a resistance amplification technique for compact on-chip timing generation. Implemented
in a 0.18 µm CMOS process, the oscillator synthesizes a large effective resistance using amplified
polysilicon resistor characteristics, significantly reducing silicon area while maintaining frequency stability. Continuous-time offset mitigation techniques are employed to suppress low-frequency noise and
amplifier-induced offsets. The proposed oscillator generates a stable 5 kHz clock from a 1.4 V supply
with ultra-low power consumption, achieving a temperature coefficient of 100 ppm/◦C over −40◦C to
100◦C and a line sensitivity of 1%/V across a 1.4 V to 2 V supply range using single-point calibration.
Overall, the proposed architectures demonstrate significant improvements in power consumption,
area efficiency, temperature stability, and robustness, making them well-suited for next-generation ultralow-power analog and mixed-signal systems.

Abstract

Modern Optical character recognition (OCR) systems increasingly rely on deep visual sequence
models, yet they are often evaluated mainly through final recognition accuracy. This leaves an important
question underexplored: how do such models learn, stabilize, and exhibit persistent errors over the
course of training? This question is especially relevant for Indic scripts, where visual text recognition is
shaped by complex orthographic structures such as aksharas, modifiers, conjunct-like forms, and dense
character compositions.
This thesis studies the learning behavior of two representative OCR models, CRNN and PARSeq,
using a five-language corpus covering Hindi, Bengali, Tamil, Telugu, and English. The main chapters
present representative analyses, and the same qualitative trends were observed in the remaining languages. The experiments are conducted on cropped printed word images, allowing the analysis to focus
on recognition behavior at the word level. Rather than comparing models only by final CER and WER,
the thesis examines their behavior across four complementary dimensions: training data scale, sequence
length, subword structure, and optimization geometry.
The results show that learning behavior varies meaningfully across model family, data regime, sequence length, and script structure. Under the tested settings, CRNN is more sensitive to reductions in
training data, while PARSeq remains comparatively more stable. Short words are learned faster than
long words, showing that sequence length remains an important source of recognition difficulty. Frequent and structurally simpler subword patterns stabilize earlier than rarer and more compositionally
involved patterns. Loss landscape visualizations further suggest that training moves from sharper and
more irregular regions toward smoother and more stable basins, with fuller data settings reaching such
regions more readily.
Together, these findings show that final recognition scores alone do not fully characterize OCR model
behavior. The analysis can support more informed model selection, especially when data availability,
script complexity, and word length vary across recognition settings. The findings may also inform
dataset design by indicating where additional examples are likely to be useful. By focusing on learning
trajectories, this thesis offers a structured way to evaluate OCR models beyond final accuracy, with
attention to how their behavior changes with data availability, word length, script structure, and training
stability.

Abstract

Random graph theory studies how global structure emerges from simple local rules. The classical
Erd˝os-R´enyi model is the most prominent example: every pair of vertices is connected independently
with the same probability. This model has a remarkably complete theory and displays sharp transitions
for the appearance of components, cycles, a giant component, and connectivity. Nevertheless, many
mathematical and physical networks are not well represented by a completely homogeneous model. In
systems with an underlying geometry, edges are often constrained by spatial locality. Particles interact
mainly with nearby particles, lattice Hamiltonians are often local or quasi-local, communication links
may be restricted by distance, and random walks on such networks inherit the geometry of the ambient
space.
This thesis introduces a new model in which randomness and geometry coexist: the periodic
band random graph. The vertices are placed on the discrete cycle Zn, two vertices are eligible to be
connected only when their cyclic distance is at most a bandwidth parameter m, and each eligible edge
is then retained independently with probability p
The first main result of the thesis is a sharp connectivity threshold for this model. Since each
vertex has exactly 2m admissible neighbours in the deterministic host graph, the probability that a
fixed vertex is isolated is (1 − p)
2m. This suggests that isolated vertices disappear when
pconn(n) ∼
log n
2m
.
We prove that this is the correct sharp threshold: below this scale, isolated vertices persist with high
probability, while above this scale the graph is connected with high probability.
The proof above the threshold is not a purely mean-field argument. In the Erd˝os-R´enyi graph, the
number of possible edges between a set and its complement depends only on the size of the set. In
the band random graph, it also depends on the shape and location of the set along the cycle. Long
intervals have much smaller boundaries than scattered sets of the same size. To overcome this, we
use a geometric block-decomposition argument: large connected pieces are built inside consecutive
intervals, neighbouring pieces are joined across their interfaces, and all small leftover sets are shown to
attach to the resulting connected chain.
We also analyze the graph just below the connectivity threshold. When
p =
log n − c
2m
, c > 0,
the graph is disconnected, but the disconnectedness is caused only by isolated vertices. More precisely,
the number Niso of isolated vertices converges in distribution to a Poisson random variable with mean
e
c
, and with high probability there is a unique giant component C1 satisfying
|C1| = n − Niso.
Thus every non-isolated vertex lies in the giant component. Moreover, unlike in the homogeneous Erd˝os-R´enyi model, this giant component has an explicit geometric structure: it contains an ordered
chain of large connected pieces inside consecutive m-blocks of the cycle, joined across neighbouring
interfaces.
The thesis also studies the random walk on the band random graph. The underlying cycle geometry
creates bottlenecks which persist even after the graph becomes connected. These bottlenecks are
reflected in the spectral gap of the lazy random walk. We show that, in the regime considered here,
the lazy spectral gap has order
γlazy(G) = Θ
m2
n2

and consequently obtain the corresponding mixing-time estimate. Finally, using electrical-network
methods, effective resistance, and explicit flow constructions, we prove that the maximal hitting time
of the simple random walk has order
Hmax(PG) = Θ
max
n
2
m2
, n .
The first term reflects diffusive motion around the cycle with step range m, while the second term
is the natural order of the time needed to hit a specified vertex when the stationary distribution is
close to uniform.
Overall, the thesis shows that the periodic band random graph is a useful model for studying
how independent randomness interacts with deterministic geometry. On the graph-theoretic side,
the model retains part of the Erd˝os-R´enyi connectivity picture: isolated vertices still determine the
sharp connectivity threshold. On the random-walk side, however, the behaviour is controlled by the
one-dimensional band structure, which produces the spectral-gap and hitting-time scales described
above.
The model also suggests a broader spectral direction. If AH denotes the adjacency matrix of the
deterministic host graph C
m
n
, then the adjacency matrix of the sampled graph can be written as
AG = pAH +
p
2mp(1 − p) W,
where W is a centered random band matrix supported on the same band. Thus AG is the sum of a
full-rank deterministic band operator and a random band fluctuation. While this thesis focuses mainly
on connectivity and random walks, the bulk and edge spectra of AG remain largely open and are a
natural direction for future work, connec

Abstract

Classical computers face fundamental barriers when simulating quantum phenomena due to the exponential
growth of quantum state spaces. This limitation motivates quantum simulation, particularly Hamiltonian simulation, as a central application for quantum computers. Simulating quantum dynamics is essential for studying
materials and molecules, and Hamiltonian simulation is an integral subroutine of important quantum algorithms
including ground state preparation, quantum phase estimation, quantum linear systems, and quantum singular value transformation. While various Hamiltonian simulation methods have been developed, those offering
optimal complexity such as qubitization and LCU-based methods require oracular assumptions, complicated
multi-qubit control, and extensive ancilla qubits, making them unsuitable for near and intermediate-term quantum computers. In contrast, hardware-friendly methods such as Trotterization, qDRIFT, and Single-Ancilla
LCU appear more practical, but their performance for concrete physical systems and applications of interest
remains unclear. This thesis provides comprehensive numerical benchmarks comparing Hamiltonian simulation
algorithms for early fault-tolerant quantum computers. We analyze when and why different methods achieve optimal performance based on system structure, precision requirements, and hardware constraints. We benchmark
Single-Ancilla LCU (SA-LCU), first and second-order Trotterization, and qDRIFT across two distinct classes of
quantum systems. For sparse Ising chains with nearest-neighbor interactions, first-order Trotterization achieves
better performance at moderate precision due to tight commutator bounds, while SA-LCU dominates at high
precision owing to its polylogarithmic scaling. For dense quantum chemistry Hamiltonians, SA-LCU consistently achieves the lowest gate counts across nearly all regimes. These results show that no single algorithm
dominates universally, optimal method selection depends on Hamiltonian structure and target precision. We
extend this comparative framework to two applications that use Hamiltonian simulation as a subroutine. First,
we consider the problem of estimating the magnetization of the ground state of the transverse-field Ising Hamiltonian. We vary the transverse-field coupling strength across a quantum phase transition and benchmark the
performance of various Hamiltonian simulation procedures. Our numerical simulations show that SA-LCU outperforms other techniques, requiring shorter circuit depths while using minimal ancilla overhead. Secondly, we
consider the problem of simulating open systems dynamics, an avenue where simulating Hamiltonian dynamics
finds widespread applications. However, most quantum algorithms for this problem require a lot of resources,
rendering them infeasible for early fault-tolerant quantum computers. We develop a collision model framework,
where a single environment qubit repeatedly interacts with the underlying system. This sequence of simple
interactions allows for the simulation of both Markovian and non-Markovian open systems dynamics, while
naturally incorporating near-term Hamiltonian simulation techniques. Our numerical validation shows that
second-order Trotter methods achieve the best asymptotic scaling for long-time evolution, while SA-LCU remains competitive for high-precision, short-time regimes. Our findings offer concrete guidance for the choice of
Hamiltonian simulation method driven by the structure of the system, target precision, and available hardware
resources. In the early fault-tolerant era, no single algorithm dominates universally, and understanding these
trade-offs is essential.

Abstract

Small centrifugal pumps under 10 HP form the operational backbone of community-scale water supply across rural and peri-urban regions of the developing world, yet they are monitored exclusively
through periodic manual inspection that cannot detect developing faults before they cause service disruption. Digital Twin technology offers a well-established solution, but all existing implementations
target large industrial assets with infrastructure requirements that are economically and technically incompatible with community-scale installations. This thesis bridges that gap by designing, deploying,
and quantitatively validating the first open-source, physics-grounded operational Digital Twin for a
3.7 kW three-phase induction motor driving a live campus water pump, built entirely from commodity
sensors, open-source middleware, and standard cloud infrastructure.
The system integrates a Simulink-based virtual motor model covering coupled electrical, mechanical,
and thermal dynamics with Eclipse Ditto as a Digital Twin (DT) middleware, MongoDB for long-term
telemetry archival, and ThingSpeak for data storage and later integration with MATLAB, all interconnected via application-layer protocols operating over TCP/IP protocols over campus Wi-Fi. Temperature, current, and three-axis vibration data streamed from an ESP32 sensor gateway at five-second
intervals are continuously compared against model predictions; when the weighted composite RMSE
exceeds a statistically derived threshold for three consecutive cycles, the system issues a fault alert and,
under confirmed critical conditions, autonomously shuts down the motor through a solid-state relay,
completing a partially closed-loop operational Digital Twin without any proprietary software or dedicated industrial network.
A four-month deployment from August to November 2024 accumulated 160,000 sensor datapoints
and validated the system against four quantitative design objectives, all of which were met. The
Simulink model achieved normalised prediction errors of 3.2% for motor body temperature and 4.8% for
phase current, with end-to-end latency of approximately 8 seconds satisfying the SCADA supervisory
monitoring standard of below 10 seconds. A Random Forest classifier trained on heuristically labelled
fault data, requiring no documented fault history, achieved precision 0.84, recall 0.98, F1 score 0.90,
and AUC 0.79 across four fault classes; a comparative regression analysis of eight ML-based algorithms
further confirmed that the Random Forest Regressor best characterises the motor's thermal-electrical relationships (R2 = 0.77, MAE = 0.70 ◦C). An 11% reduction in normalised energy consumption was
observed over a 30-day pre/post-deployment comparison (p = 0.032), and a structured usability trial
with five non-technical facilities staff yielded a mean satisfaction score of 4.3 out of 5, with all operational tasks completed within a 15-minute training limit.

Abstract

Large language models now produce fluent text across domains, creating a need for systems
that can distinguish human-authored text from machine-generated text and, when needed, identify the generator. We study whether RoBERTa's multi-layer representations can support both
goals under distribution shift. In our initial SemEval study, we proposed weighted layer averaging (WLA), which learns scalar weights over RoBERTa layer representations, and AdaLoRA,
which adapts the encoder through parameter-efficient low-rank updates. The WLA component
follows the view that different encoder depths can expose different contextual features.
We extend that inquiry from binary detection to generator attribution. The main finding is
task-conditional. For binary detection, aggregation can support a broad human-versus-machine
boundary. For generator attribution, the same kind of compression appears less suitable. Further experiments indicate two practical risks: WLA layer averaging may blur layer-specific
generator cues, and the tested low-rank adaptation setup may be too restrictive for six-way
source identification under domain shift.
To address this compression problem, we evaluate layer-slice concatenation, which preserves
selected RoBERTa layers as independent channels before classification. The strongest reported
region consists of slices ending at layer 9: layers 5-9 and 6-9 reach 0.9323 test accuracy on
OUTFOX, with layers 2-9 marginally lower at 0.9319 and effectively tied after rounding. These
results exceed the plain RoBERTa attribution anchor of 0.8730 accuracy and suggest that
generator attribution benefits from preserving layer channels rather than using early scalar
compression. Overall, robust detection and generator attribution require different architectures:
aggregation for broad invariance, preservation for fine-grained source identification.

Abstract

Virtual Reality (VR) development is largely driven by implementation, where requirements are typically expressed in informal, natural language and manually translated into scene configurations and
interaction logic within development environments such as Unity. This practice introduces a gap between requirements and design, resulting in limited traceability, difficulty in reasoning about system
behavior, and challenges in managing changes as the system evolves across iterations.
This thesis presents VReqDV, a model-driven framework that aims to introduce structure and formalization into the design phase of VR development. The proposed approach represents requirements
using structured, machine-readable specifications, which serve as the basis for generating the corresponding VR scene design templates. These templates capture both the structural aspects of the scene,
such as objects and their spatial configuration, as well as the behavioral aspects, including interactions
defined through an action-response model. By maintaining an explicit relationship between the requirement specifications and the generated design artifacts, the framework supports traceability and enables
systematic reasoning about how changes in requirements affect the resulting VR scene.
An important aspect of the approach is the integration of version management within the design
process. As specifications and designs evolve, the framework maintains synchronized versions of both
representations, allowing users to compare different states of the system and understand the impact
of modifications over time. In addition, a behavioral synthesis mechanism is introduced to generate
executable Unity C# scripts from structured specifications, enabling partial automation of interaction
implementation while preserving alignment with the original requirements.
An empirical evaluation was conducted to assess the effectiveness of the proposed approach in comparison to conventional Unity-based workflows. The results indicate that VReqDV provides strong support for requirement traceability, version comparison, and understanding the impact of design changes.
However, no statistically significant improvement was observed in development efficiency for controlled
modification tasks. These findings suggest that the primary benefit of the approach lies in improving
the organization, clarity, and maintainability of the design process, rather than directly reducing development time.
Overall, this work demonstrates that introducing structured, model-driven representations into the
early stages of VR development can help bridge the gap between requirements and implementation. At
the same time, it highlights the need for further work to improve scalability, extend behavioral expressiveness, and better support practical development workflows.

Abstract

Verifying scientific claims is a critical yet challenging task in the era of automated misinformation detection, characterized by high-stakes technical jargon and the requirement for complex domain-specific
reasoning. Despite its importance, the field suffers from a significant "data bottleneck": existing datasets
are often limited in scale, primarily English-centric, and frequently overlook the nuances of numerical
reasoning and multilingual accessibility. This thesis addresses these gaps by proposing a unified framework that moves from large-scale English baselines to multilingual frontiers in Hindi.
The first contribution of this work is the introduction of SciClaimHunt and SciClaimHunt Num,
two large-scale datasets derived from scientific research papers. These datasets address the scale deficiency in English scientific NLP and provide a specialized subset for numeral-aware verification. We
evaluate these through several baseline models, including Retrieval-Augmented Generation (RAG) and
specialized scientific encoders, demonstrating that SciClaimHunt serves as a robust resource for training
models that generalize across diverse scientific domains.
The second contribution extends this frontier to morphologically rich, low-resource languages with
the introduction of HindiSciVerify. Leveraging a semi-automatic pipeline grounded in NCERT textbooks
and Parameter-Efficient Fine-Tuning (PEFT), we develop a ternary classification dataset (Support, Refute, Not Enough Information) for Hindi. By utilizing LoRA adapter merging and high-capacity LLMs
like Llama-3, we demonstrate a computationally tractable pathway for specializing models for Hindi
scientific prose.
Our findings indicate that the proposed datasets and methodologies significantly improve the reliability of automated scientific fact-checking. By bridging the gap between English-scale heuristics and
the technical nuances of Hindi, this thesis provides a foundational benchmark for future research in
multilingual scientific NLP and offers a scalable blueprint for addressing the digital divide in automated
fact verification.

Abstract

This thesis investigates the dynamics of answer formation in Large Language Models (LLMs) along
two dimensions: vertically across network layers, and horizontally across sequential reasoning tokens.
To evaluate vertical evolution, we apply layer-wise linear probes to open-weight models on a controlled character-counting task. The probing results show that early and middle layers reliably encode
sub-token character counts. Late-stage network components, specifically the penultimate and final multilayer perceptrons, actively suppress this information and default to degenerate prediction strategies. The
models thus fail at utilization, despite having the required representational capacity.
For horizontal evolution, we introduce Forced Answer Completion (FAC) to extract a model's intermediate answer commitment at each step of its chain-of-thought trace. Across multiple-choice, numeric,
and search tasks, FAC shows that models decide their final answer long before the reasoning trace concludes. After filtering out Transient Answer Flips (TAFs) with a causal smoothing operator, we find
that nearly half of all reasoning tokens are generated after the model's decision has stabilized. These
late-stage tokens function primarily as post-hoc justification.
These findings enable direct interventions in LLM inference. Vertical tracking isolates suppressed
knowledge, providing targets for structural adjustments. Horizontal tracking supports lightweight earlystopping gates by training linear probes on intermediate hidden states, we predict when an answer has
stabilized and halt generation. Testing on the Qwen3 architecture demonstrates that this early-stopping
method saves hundreds of tokens per query with a marginal drop in accuracy

Abstract

Large Language Models (LLMs) have demonstrated remarkable capabilities in natural language processing, yet their application to Machine Translation (MT), particularly for lowresource languages, remains an active area of research. This thesis investigates the adaptation
of LLMs for machine translation involving English and Indian languages, addressing challenges
in assessment, fine-tuning, alignment, evaluation, and inference efficiency.
We begin by assessing the translation capabilities of popular LLMs (LLaMA, Mistral, Bloom)
for 22 Indian languages under zero-shot and in-context learning settings. Our findings reveal
that while raw LLMs exhibit limited translation quality, parameter-efficient fine-tuning using
LoRA significantly improves performance, with two-stage fine-tuning achieving results competitive with specialized MT systems.
Building on these insights, we investigate fine-tuning strategies for extremely low-resource
Indic languages through participation in the WMT 2024 shared task. Our experiments across
Assamese, Mizo, Khasi, and Manipuri demonstrate that fine-tuned LLMs substantially outperform models trained from scratch, and that multilingual approaches consistently surpass
monolingual ones.
We then explore data-efficient alignment strategies using preference optimization. Our Progressive Perturbation approach with Kahneman-Tversky Optimization (KTO) demonstrates
that targeted perturbations of training data can enhance translation quality without requiring
extensive parallel corpora.
To address the challenge of evaluating MT for low-resource languages, we conduct a comprehensive analysis of statistical and neural evaluation metrics, examining their stability, consistency, and correlation with human judgments.
Finally, we tackle inference efficiency through DistillBeam, a multi-trajectory knowledge
distillation framework for speculative decoding. By aggregating supervision from multiple
high-probability teacher trajectories, DistillBeam achieves wall-clock speedups of 35-65% over
autoregressive decoding across 20 languages.
This thesis contributes practical methodologies and empirical insights for deploying LLMbased translation systems for Indian languages, balancing translation quality with computational efficiency.