Abstract

The semiconductor industry is driven by the need to design smaller, faster and low power con- suming circuits. A comparator is an integral part of any electronic system and by default the comparators exhibit hysteresis phenomenon. A Schmitt trigger is the most commonly used comparator in circuit design. Rigorous efforts have been made to speed up the circuit perfor- mance by various techniques at the system, circuit and transistor level and they have demon- strated incremental improvement. However, all these efforts are focused on reducing the switch- ing time between the logic levels and shortening the width of hysteretic loop while maintaining noise immunity. This thesis presents a new paradigm shift from the conventional techniques of circuit speed up wherein the hysteretic device itself is replaced with a proteretic one and efforts to improve the circuit speed have been demonstrated successfully. Hysteresis has an inherent switching delay, whereas, proteresis or inverse hysteresis demonstrates an early response to the switching action of the circuit. Proteresis is a known phenomenon across various domains like pharmacokinetics (PK) and pharmacodynamics (PD) drugs, ferro-electric materials and optical bi-stable devices, but their impact and study in area of Integrated circuits has been very limited. This is because proteresis is induced by feed forwarding the input to couple of hysteretic devices (Schmitt trigger) and this leads to output of circuit being stable for a small input ranges and prevalent instability over the remaining input range. However, this thesis proposes a novel mechanism of stabilizing the proteretic output over a wide input voltage range and hence leverages the benefit of early switching characteristics for improving the circuit speed by a factor of 25%. A detailed mathematical model for proteretic circuit design over various input ranges is presented and the trade-off in terms of area and power is also discussed with the ramp generator as a design example. Hysteretic and proteretic devices are bi-stable and their interstate switching depends on triggering voltages. Conventionally, devices can be classified to work as either hysteretic or proteretic. This thesis proposes the first attempt to describe a circuit that switches between two opposite characteristics of hysteresis and proteresis to present a new circuit called Prohys switch. The output of prohys switch demonstrates a limited amount of randomness in its first cycle of operation and this factor is a strong reason to employ a prohys switch for designing a Physical unclonable Function (PUF). In recent times PUF’s has gained immense popularity for securing the IC’s by providing unique identification code to each chip. The key design parameters of a PUF are uniqueness and reliability and designing a highly efficient PUF with optimal values of uniqueness and reliability is quite a challenge. Reliability depends on the chip’s ability to resist changes to supply voltage and temperature variations, whereas, uniqueness depends on process variations during chip fabrication. Multiple PUF designs that employ reliability enhancement circuits and security algorithms achieve these design characteristics. Nonetheless, these techniques are design overheads. Finally, this thesis presents a novel PUF based on the proHys switch called the prohys PUF. The proposed prohys PUF befittingly satisfies both uniqueness and reliability criteria, without any additional circuitry or security algorithms.

Abstract

The safety improvements in the medical systems or devices, specifically non-invasive patient monitoring systems (PMS) and point-of-care (POC) devices of human health monitoring (HHMS) systems, are in great need to perform precise measurements of vital parameters with uninterrupted continuous monitoring for diagnosis of significant human health ailments. During critical or non-critical nursing times, frequent monitoring of short and long-duration measures of vital parameters (like ECG, EEG, Respiratory, SpO2, Blood-pressure, Temperature, etc.) and non-vital parameters (like Glucose levels, Hemoglobin, Urea in blood, etc.) will allow for better nursing and early detection of diseases like cardiovascular diseases, respiratory diseases, liver dysfunction, diabetes, renal diseases, and other psychological disorders. A non-invasive medical instrumentation system for accomplishing these tasks must meet several criteria like accuracy and precision of the measurement, reduction of false alarms, effective detection of faults, and fault tolerance to systemic or random failures with an uninterrupted performance during nursing times. Many research groups have made many efforts worldwide to make strategies and guidelines and standardize the procedures for medical systems based on safety design approaches to raise alerts for any deviance during non-invasive monitoring of health parameters. In addition, health monitoring instruments should be portable, low-cost, and reliable over time. Many modes of diagnosis and related instrument maintenance procedures satisfy these criteria. However, most of them are still in the improvement stage for developing resilient instruments for integrating with critical auto-robotic surgery instruments with minimal human intervention. Several approaches have been proposed, out of which safety-related design approaches like using 2oo2, 2oo3, 1oo2, etc., along with AI-based data analytics, have the advantage of meeting these rigorous demands in fundamental safety improvements of Medical Systems. Based on the safety-related design 2oo2 concept, a configurable system prototype of the cardiac health monitoring system (CHMS) is developed and evaluated to meet the set objectives, such as fault detection effectiveness and fault tolerance with improved safety configurability. This configurable system uses various sensors to collect the bio-medical data in parallel. Primarily, three diverse sensors are used non-invasively in sensing the bio-signals in different forms like electric potential, light, and sound signals for computations. These diverse sensors are used to detect biomedical signals to obtain data from electrocardiogram (ECG), Photo-plethysmogram (PPG), and Phonocardiogram (PCG). Therefore, the accuracy of the vital estimates and the fundamental safety improvements were evaluated using this multimodal system with AI-based fault detection and predictive maintenance techniques. Traditional statistical and AI-based techniques have acquired authentic measurements of human health parameters from diverse signals received simultaneously from various sensing circuits like PPG/ECG/EEG. Secondly, these bio-signals are calibrated independently with known algorithms in diverse. Finally, these obtained parameters, along with the built-in-test (BIT) system for health signals, are processed with implemented safety functions, and the algorithms to generate the correct human health parameters and prognosticate abnormalities of human health 200 patients were available for instrument evaluation trials for HR parameter monitoring and tested after taking informed consent. A MATLAB-based CHMS tool is developed for configurable and then implemented on a field-programmable gate array (FPGA) to minimize the designed circuitry with improved resiliency of the instrument system. The CHMS has been configured to 2oo2 with the selected HR parameter to estimate the system's availability and health. The collected HR output was subjected to data analytics against individually collected data. We found a significant reduction in the generation of unimportant alarms and increased uninterruptable System availability by 45% to 55%, along with normal and abnormal artifact data. The measured normal artifacts are more than 99% accurate and are used for prognosis. The abnormal data is used for edge-AI-based analytics to infer the system's health for prescriptive maintenance. An experimental study has been carried out to effectively segregate normal and abnormal signals in 2oo2 and 2oo3 configurations. A detailed analysis is carried out in various sensor configurations as proposed. Similarly, in the 2oo3 configuration, we significantly improved the system's availability from 55% to 95% by eliminating spurious alarms with reduced downtime and improved accurate data vital parameters. Further, a reliability assessment is performed on CHMS on identified parameters, such as measuring Avai

Abstract

Blockchain and Reinforcement Learning (RL) are two game-changing research areas that have received a lot of attention recently. In recent years, significant advances in RL have resulted in tremendous success in solving various sequential decision-making problems in machine learning. The two most successful RL applications are discussed in this work, unmanned aerial vehicles, and blockchain. Blockchain is a distributed ledger technology with its first application in Bitcoin. The main challenge in blockchain-based cryptocurrencies is to provide a distributed trust environment with high security like in a centralized financial system. The current throughput of Bitcoin is around 4 to 7 transactions per second and confirmation latency is about one hour. If Bitcoin has to go mainstream, the throughput has to be in the order of thousands of transactions per second with very low latency in the order of a few seconds. Recent advances in blockchain research proposed consensus algorithms that scale bitcoin, such as sharding and Prism-based blockchain. However, the security of Bitcoin is very high that it can tolerate up to 50% adversarial nodes and avoids double spending attacks. The current blockchain size is over 260 GB and is growing at an astonishing rate imposing a huge storage requirement on the nodes. Recent developments improving the Bitcoin consensus have shown that there is a tradeoff between decentralization, scaling, and security. In order to scale blockchain, we leverage coding theory and RL in this thesis. Due to the increasing storage requirement for blockchains, the computation can be afforded by only a few miners. Sharding has been proposed to scale blockchains so that the storage and transaction efficiency of the blockchain improves at the cost of a security guarantee. Incorporating coding theory into existing consensus algorithms has demonstrated improvements in terms of storage efficiency and low latency. A Secure-Repair-Blockchain (SRB) is proposed which aims to decrease the storage cost at the miners. In addition, SRB also decreases the bootstrapping cost, which allows for new miners to easily join a sharded blockchain. In order to reduce storage, coding-theoretic techniques are used in SRB. In order to decrease the amount of data that is transferred to the new node joining a shard, the concept of exact repair secure regenerating codes is used. The proposed blockchain protocol achieves lower storage than those that do not use coding and achieves lower bootstrapping costs as compared to the different baselines. Prism is a recent blockchain algorithm that achieves the physical limit on throughput and latency without compromising security. However, like the traditional blockchain systems, Prism also has a trade-off between security, latency, and cost. In recent days, reinforcement learning approaches are investigated in traditional blockchains, to improve performance. In this work, we apply Deep Reinforcement Learning (DRL) to one of the promising blockchain protocols, Prism, to optimize its performance. We propose a Deep Reinforcement Learning-based Prism Blockchain (DRLPB) scheme which dynamically optimizes the parameters of Prism blockchain and helps in achieving a better performance. In DRLPB, we apply two widely used DRL algorithms, Dueling Deep Q Networks (DDQN) and Proximal Policy Optimization (PPO). This work presents a novel approach to applying DDQN and PPO to a blockchain protocol and comparing the performance. The analysis of Prism in terms of latency, and security level considering other blockchain parameters is provided. Using the analysis, the DRLPB scheme adapts the Prism blockchain parameters to enhance the security upto 84% more than Prism, while still preserving the performance guarantees of Prism. The recent advancements in the field of Internet of Things (IoT) motivate the development of a secure infrastructure for storing and sharing vast amounts of data. Blockchain, a distributed and immutable ledger, is best known as a potential solution to data security and privacy for IoT. The scalability of blockchain, which should optimize the throughput and handle the dynamics of the IoT environment, becomes a challenge due to the enormous amount of IoT data. The critical challenge in scaling blockchain is to guarantee decentralization, latency, and security of the system while optimizing the transaction throughput. this paper presents a deep reinforcement learning (DRL)-based performance optimization for blockchain-enabled IoT. We consider one of the recent promising blockchains, Prism as the underlying blockchain system because of its good performance guarantees. We integrate the IoT data to Prism Blockchain and optimize the performance of the system by leveraging Proximal Policy Optimization (PPO) method. The DRL method helps to optimize the blockchain parameters like mining rate and mined blocks to adapt to the environment dynamics of

Abstract

Hardware accelerators are being designed to offload compute-intensive tasks such as deep neural networks from the CPU to improve the overall performance of an application, specifically on the performance-per-watt metric. With the evolution of speech recognition based applications, many deep learning models for Automatic Speech Recognition have been proposed. Encoder-decoder-based sequenceto-sequence models such as the Transformer model have demonstrated state-of-the-art results in end-toend automatic speech recognition systems (ASRs). However, the Transformer model being intensive on memory and computation poses a challenge for an FPGA implementation. This thesis proposes an end-to-end architecture to accelerate a Transformer for an ASR system. The host CPU orchestrates the computations from different encoder and decoder stages of the Transformer architecture on the designed hardware accelerator with no necessity for intervening FPGA reconfiguration. The intermediate stages, like data pre-processing and feature extraction, are performed on the host while the complex recognizer, i.e., the Transformer model, is offloaded onto an FPGA. The communication latency is hidden by prefetching the weights of the next encoder/decoder block while the current block is being processed. The larger computations in the model are split across both the Super Logic Regions (SLRs) of the FPGA, mitigating the inter-SLR communication. The proposed design presents an optimal latency, exploiting the available resources. The accelerator design is realized using Vitis high-level synthesis tool, using OpenCL, a language for heterogeneous computing, and evaluated on an Alveo U-50 FPGA card. The end-to-end ASR system has a latency of ∼120ms, which is suitable for real-time applications. The design demonstrates an average speed-up of 32× compared to an Intel Xeon E5-2640 CPU and 8.8× compared to NVIDIA GeForce RTX 3080 Ti Graphics card for a 32-bit floating point single precision model.

Abstract

Monitoring and analyzing air quality is a challenging task without understanding influencing factors. Conventional air pollution monitoring is limited to few locations and accessing data is difficult. Internet of Things (IoT) provides a solution by enabling cost-efficient networks of particulate matter (PM) monitoring devices that are easily connected to the internet. PM monitoring portable sensors combined with IoT offer a more efficient and widespread PM monitoring solution, overcoming issues with bulky and expensive traditional systems. This thesis focuses on development and deployment of PM monitoring device and the establishment of a dense PM monitoring network. This thesis focuses mainly on four aspects. In the first aspect, the development of an end-to-end low-cost IoT system for a densely deployed PM monitoring network carried out an extensive field study in a region of the Indian metropolitan city of Hyderabad. A total of 49 devices were deployed in an area of 4 km2 , with 43 of them developed specifically for this study and the remaining six obtained from external sources, a density never realized in any metropolitan city worldwide and mostly in developing countries like India in the past. A total of 15 devices were connected to Wi-Fi, primarily within the campus area and wherever Wi-Fi connectivity was available, whereas 34 devices were equipped with 2G eSIM. The low-cost sensors were carefully calibrated to account for seasonal variations by utilizing a highly precise reference sensor. Also, a robust device was made that can cache data to avoid loss due to communication outages. The second aspect of this thesis focuses on gathering a significant quantity of data, nearly 20.7 million. This unique dataset offers great opportunities for future research and is examined to evaluate whether a concentrated deployment of PM monitoring devices was necessary. Third, various analytical methods, such as mean, variance, spatial interpolation, and correlation, were utilized to produce informative findings about the fluctuations of PM over time and across seasons. In order to understand the impact of firecracker detonations during Diwali festival evenings, a spatio-temporal analysis of PM values was conducted. As part of our analysis, we also tried to answer an important question concerning decision-makers about the optimum density required for effectively monitoring street-level pollution. For the considered scenario, we demonstrated that PM monitoring devices should be deployed at most 350 m apart to accurately capture the spatial variability of PM. Finally, this thesis examines various challenges encountered during the pre-deployment and postdeployment of PM monitoring devices. It addresses issues such as the design of low-cost devices, pre-deployment calibration, and seasonal calibration. Also explores challenges related to power supply, theft, environmental factors affecting sensor performance, and hardware failures. Additionally, the study investigates hardware reset and corrupt or redundant data. This thesis examines the challenges that arise during the deployment of PM monitoring devices and to propose feasible solutions to mitigate these issues, thereby offering valuable insights into the effective implementation of PM monitoring technology. Overall, the thesis highlights the importance of dense deployment and addresses the challenges to ensure reliable and accurate data collection.

Abstract

GPCRs are the most prominent family of membrane proteins that serve as major targets for one-third of the drugs produced. A detailed understanding of the molecular mechanism of drug-induced activation and inhibition of GPCRs is crucial for the rational design of novel therapeutics. The binding of the neurotransmitter adrenaline to the β2-adrenergic receptor (β2AR) is known to induce a flight or fight cellular response, but much remains to be understood about binding-induced dynamical changes in β2AR and adrenaline. In Chapter 3, we examine the potential of mean force (PMF) for the unbinding of adrenaline from the orthosteric binding site of β2AR and the associated dynamics using umbrella sampling and molecular dynamics (MD) simulations. The calculated PMF reveals a global energy minimum, which corresponds to the crystal structure of β2AR-adrenaline complex, and a meta-stable state in which the adrenaline is moved slightly deeper into the binding pocket with a different orientation compared to that in the crystal structure. The orientational and conformational changes in adrenaline during the transition between these two states and the underlying driving forces of this transition are also explored. Based on clustering of MD configurations and machine learning-based statistical analyses of time series of relevant collective variables, the structures and stabilizing interactions of these two states of the β2AR-adrenaline complex are also investigated. In Chapter 4, the PMF of G-Protein dissociation, and GDP dissociation are studied. In order to study the entire GPCR activation pathway, models have been constructed for 3 different states (inactive, intermediate, and active states) of the G-Protein-β2AR complex. The results suggest that the binding of GDP to G-Protein favours G-Protein dissociation from β2AR. In summary, the findings enhance our understanding of GPCR activation and the advancement of new therapies aimed at GPCRs by offering comprehensive insights into conformational changes, energetics, and critical residues involved. The methodologies and discoveries presented in this study establish the groundwork for an automated process to explore ligand dynamics and interactions between drugs and GPCRs, thereby facilitating future research endeavors in this domain.

Abstract

The satellite imagery has different types of objects like buildings, vegetation, lakes, roads, grounds etc. Each type of object has unique characteristicslike texture, colour,spatial arrangement etc that differentiates them from other types of objects. Using a single method to extract all these types of objects might not be logical. Detecting buildings from satellite imagery is of major importance for supporting government related activities and a great support in crisis situations for disaster management. This work presents a new way of automating the extraction of buildings from high resolution satellite images using Object Based Image Analysis (OBIA). The proposed algorithm utilizes an active contour model called Chan-Vese segmentation that is capable of accurately locating various objects in the image. Additionally, to ensure accuracy, the algorithm employs Normalized Difference Vegetation Index (NDVI) mask to remove vegetation areas. Moreover, these detected objects pass through a careful filtering process based on regional characteristics like minimum area and object width thereby improving the precision of the identified structures. The correctness of results is confirmed by conducting rigorous quantitative assessments for validation purposes. This means that not only does this comprehensive approach guarantee error-free building identification but also suggests a strong framework for automatic building extraction from high resolution satellite imagery.

Abstract

Videos form an integral part of human lives and act as one of the most natural forms of perception spanning both the spatial and the temporal dimensions: the spatial dimension emphasizes the content, whereas the temporal dimension emphasizes the change. Naturally, studying this modality is an important area of computer vision. Notably, one must efficiently capture this high-dimensional modality to perform different downstream tasks robustly. In this thesis, we study representation learning for videos to perform two key aspects of video-based tasks: classification and generation. In a classification task, a video is compressed to a latent space that captures the key discriminative properties of a video relevant to the task. On the other hand, generation involves starting with a latent space (often a known space, such as standard normal) and learning a valid mapping between the latent and the video manifold. This thesis explores complementary representation techniques to develop robust representation spaces useful for diverse downstream tasks. In this vein, this thesis starts by tackling video classification, where we concentrate on a specific task of “lipreading” (transliterating videos to text) or in technical terms - classifying videos of mouth movements. Through this work, we propose a compressed generative space that self-augments the dataset improving the discriminative capabilities of the classifier. Motivated by the findings of this work, we move on to finding an improved generative space in which we touch upon several key elements of video generation, including unconditional video generation, video inversion, and video superresolution. In the classification task, we aim to study lipreading (or visually recognizing speech from the mouth movements of a speaker), a challenging and mentally taxing task for humans to perform. Unfortunately, multiple medical conditions force people to depend on this skill in their day-to-day lives for essential communication. Patients suffering from ‘Amyotrophic Lateral Sclerosis’ (ALS) often lose muscle control, consequently, their ability to generate speech and communicate via lip movements. Existing large datasets do not focus on medical patients or curate personalized vocabulary relevant to an individual. Collecting large-scale datasets of a patient needed to train modern data-hungry deep learning models is, however, extremely challenging. We propose a personalized network designed to lipread for an ALS patient using only one-shot examples. We depend on synthetically generated lip movements to augment the one-shot scenario. A Variational Encoder-based domain adaptation technique is used to bridge the real-synthetic domain gap. Our approach significantly improves and achieves high top-5 accuracy with 83.2% accuracy compared to 62.6% achieved by comparable methods for the patient. Apart from evaluating our approach on the ALS patient, we also extend it to people with hearing impairment, relying extensively on lip movements to communicate. In the next part of the thesis, we focus on representation spaces for video-based generative tasks. Generating videos is a complex task that is accomplished by generating a set of temporally coherent images frame-by-frame. This approach confines the expressivity of videos to image-based operations on individual frames, necessitating network designs that can achieve temporally coherent trajectories in the underlying image space. We propose INR-V, a video representation network that learns a continuous space for video-based generative tasks. INR-V parameterizes videos using implicit neural representations (INRs), a multi-layered perceptron that predicts an RGB value for each input pixel location of the video. The INR is predicted using a meta-network which is a hypernetwork trained on neural representations of multiple video instances. Later, the meta-network can be sampled to generate diverse novel videos enabling many downstream video-based generative tasks. Interestingly, we find that conditional regularization and progressive weight initialization play a crucial role in obtaining INR-V. The representation space learned by INR-V is more expressive than an image space showcasing many interesting properties not possible with the existing works. For instance, INR-V can smoothly interpolate intermediate videos between known video instances (such as intermediate identities, expressions, and poses in face videos). It can also in-paint missing portions in videos to recover temporally coherent full videos. We evaluate the space learned by INR-V on diverse generative tasks such as video interpolation, novel video generation, video inversion, and video inpainting against the existing baselines. INR-V significantly outperforms the baselines on several of these demonstrated tasks, clearly showcasing the potential of the proposed representation space. In summary, this thesis makes a significant cont

Abstract

Fingerprints are a widely recognized and commonly used method of identification. Contact-based fingerprints, which involve pressing the finger against a surface to obtain images, are a popular method of capturing fingerprints. However, this process has several drawbacks, including skin deformation, unhygienic conditions, and high sensitivity to the moisture content of the finger. These factors can negatively impact the accuracy of the fingerprint. Moreover, fingerprints are three-dimensional anatomical structures, and two-dimensional fingerprints do not capture the depth information of the finger ridges. While 3D fingerprint capture is less sensitive to skin moisture levels and avoids skin deformation, it is limited in adoption due to the high cost and system complexity associated with it. The complexity and cost are mainly attributed to the use of multiple cameras, projectors, and sometimes synchronously moving mechanical parts. Photometric stereo offers a promising solution to build low-cost, simple sensors for high-quality 3D capture using only a single camera and a few LEDs. However, the method assumes that the surface being imaged is lambertian, which is not the case for human fingers. Existing 3D fingerprint scanners based on photometric stereo also assume that the finger is lambertian, resulting in poor reconstruction results. In this context, we introduce the Split and Knit algorithm (SnK), a 3D reconstruction pipeline based on Photometric Stereo for finger surfaces. The algorithm splits the reconstruction of the ridge-valley pattern and finger shape and combines them to obtain the 3D fingerprint reconstruction for the full finger with a single camera for the first time. To reconstruct the ridge-valley pattern, SnK introduces an efficient way of estimating the direct illumination component by using a trained U-Net without extra hardware, which reduces the non-Lambertian nature of the finger image and enables a higher-quality reconstruction of the entire finger surface. To obtain the finger shape using a single camera, the algorithm introduced two novel approaches, a) using IR illumination and b) using a mirror and parametric modeling for the finger shape. Finally, we combine the overall finger shape and the ridge-valley point cloud to obtain a 3D finger phalange. The high-quality 3D reconstruction results in better matching accuracy of the captured fingerprints. Splitting the ridge-valley pattern from the finger provides an implicit way to convert 3D fingerprint into 2D fingerprint, making the SnK algorithm compatible with the 2D fingerprint recognition systems. To apply the SnK algorithm to fingerprints, we designed a 3D printed photometric stereo-based setup that captures contactless finger images and obtains their 3D reconstructions

Abstract

Breast cancer continues to be a major worldwide health burden, and therapy and prognosis are greatly influenced by hormone receptor status. Immunohistochemistry (IHC) is currently the gold standard for evaluating the status of the estrogen receptor (ER), the progesterone receptor (PR), and the human epidermal growth factor receptor 2 (HER2). However, this approach has drawbacks, such as the potential for labelling errors and inconsistency with intrinsic subtypes. To increase the precision of predicting hormone receptor status, this thesis introduces a unique predictive modelling approach employing DNA methylation and gene expression data. We created machine learning models that use data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) to include DNA methylation profiles and gene expression data to forecast ER, PR, and HER2 status. Using MSigDB and STRINGDB, we discovered genes that had varying levels of methylation in relation to each receptor status and looked into the functional significance of these findings. Additionally, we used machine learning models based on noisy label training to address the issue of noisy labels that may be present as a result of potential mislabeling by IHC-based approaches. The results of the study revealed that gene expression information and DNA methylation profiles are reliable indicators of hormone receptor status. Our models performed well as compared to traditional IHC techniques, indicating potential clinical value. Additionally, our method might predict brand-new biomarkers and offer deeper perceptions into the epigenetic pathways underlying breast cancer. However, there are still certain drawbacks, such as class imbalance problems and the high-dimensionality of DNA methylation data, which may be resolved with the development of machine learning techniques and larger, more representative datasets. This thesis emphasises the potential of DNA methylation-based prediction models to increase the precision of determining hormone receptor status, provide useful information for individualised therapy approaches, and enhance patient prognosis