Abstract

In the last few years, implantable medical devices are being used for the treatment of a growing number of pathologies. Implantable medical devices are sophisticated electronic circuits that are implanted into the body to restore or improve various body functions in patients. These devices can perform a range of tasks including sensing, control, and stimulation, which enables them to monitor and regulate the health of the patient. The electronics of a general biomedical device typically consist of several subsystems that work together to perform the desired functions. For example, energy delivery subsys-tem, analog-to-digital conversion subsystem, signal processing subsystem, communication subsystem. The stringent energy constraints dominate the design of biomedical systems. Additionally, systems powered through energy harvesting must be able to work with lower supply voltages. Low power consumption, low supply voltage and accuracy are few of the most important parameters for bio-implants. With all these constraints in play, we have put efforts to design current reference and voltage reference as they are present in almost every analog and mixed signal system. Efforts were put to design a low power current reference with high line sensitivity using peaking current sourceby exploiting the back-gate effect of a MOSFET in subthreshold region. The design works from a 0.55V supply voltage and generates a reference current of 5.6nA. A low line sensitivity of 0.022%/V is achieved and the design works for a temperature range of 0-80◦C. This current reference showsthe best result of line sensitivity among the previous state-of-the-art works. Consequently, we have come up with a switched capacitor network-based bandgap voltage reference (BGR) circuit designed to achieve high accuracy and low power consumption for implantable biomedical applications. A low- power clock generator circuit is proposed, to reduce the leakage current thereby reducing the circuit’s power consumption to 18.5nW at typical conditions. The design works from a supply voltage of 0.5V and has a TC of 74.5ppm/◦C over a temperature range of 0-80◦C. Lastly, we have expanded our research to the field of RF circuits for biomedical applications. An on-chip Vector Network Analyzer (VNA) isdesigned to detect a biomolecule for medical diagnosis. This technique, known as RF sensing, can be used to detect changes in the dielectric properties of a sample, which can indicate the presence of certainbiomolecule. In this part of the research, various blocks of the onchip VNA is demonstrated and the future scope of this work is explained.

Abstract

When volcanoes erupt explosively, the ash gets airborne and reaches till stratosphere vertically, and then spreads laterally to synoptic scales due to wind. Detecting the presence of ash in the atmosphere accurately is a challenge. Although several remote, in-situ, and near-sensing techniques exist, due to the variety and complexity of the physical and chemical properties of ash, that get ejected, even within between spells of the eruption of a given volcanic event, it is hard to distinguish it from other aerosols such as desert sand, ice clouds, etc. The false positives in the detection render these solutions unreliable and inconsistent. As a result, weather parameters are explored as an alternative strategy to predict the presence of ash. In specific, the temperature variable is identified as the proxy variable to study the spatial distribution of ash in the atmosphere. There are several beneficial reasons to study temperature because the values do not vary randomly, low-cost equipment suffice to gather the data, the diurnal variations can be accounted for easily, the ability to convert scales from negative to positive metrics for numerical calculations does not vary significantly with ash type, etc. For this research, the eruption of the Icelandic volcano, Eyjafjallajokull is chosen, due to the severe negative impact it created on the economy across Europe in April and May 2010. World Meteorological Organisation (WMO) and International Civil Aviation Authority (ICAO) together have created Volcanic Ash Advisory Center (VAAC) to model the concentration and simulate the transportation of ash to inform the hazards of ash fall to various stakeholders across the world. The London VAAC used a VAFTDM known as Numerical Atmosphericdispersion Modeling Environment (NAME) to model the ash spread. This theoretical model had several limitations, chief of them being related to the accuracy of the Numerical Weather Prediction (NWP) models that were supported for ash modeling. The UK Met Office dealing with the NWP associated with the NAME model supported and offered multiple models such as Unified Model (UM), ECMWF, and a few others to predict the weather variables. Since each VAAC uses its own NWP model that varies spatially and temporally, a benchmarking exercise was conducted to compare the Volcanic Ash Forecast Transport and Dispersion (VAFTD) models. The benchmarking allowed the use of either NCEP or ECMWF NWP. For this research, NCEP NWP was chosen for analysis since 6 out of 12 VAFTD models used this NWP.

Abstract

Microfluidic systems are effective tools for carrying out a wide range of chemical and biological analyses. Utilizing colorimetry in conjunction with microfluidic devices has proven to be a highly effective method for conducting various analyses that require rapid results. Such systems have significant applications in lab-on-a-chip systems and other medical devices that necessitate a colorimetric measurement to obtain quantitative results about the chemical composition of a given sample. Currently, most state-of-the-art techniques for performing microfluidic color detection use expensive cameras to study the captured image or are paper-based analytical devices that use capillary action to analyze color information. However, paper-based microfluidic devices are limited by the evaporation of test samples during experiments and therefore require high volumes of samples. Additionally, the surface tension of the liquid sample plays a crucial role in this process, and if not properly managed, the device may not provide accurate results. To overcome these limitations, a low-cost automated color detection system that could be used in conjunction with planar microfluidic pumps is presented in this work. The color detection system uses a light-emitting diode (LED) and a light-dependent resistor (LDR) to obtain color information. The LED is used to sequentially shine the primary color component wavelengths - red, green, blue - on the sample, and the color information is obtained from the light intensity incident onto the LDR after reflecting from the sample. The results showed that the system is accurate up to 92% and could be readily readily used to conduct different types of chemical analyses using colorimetry and can be employed in various industries to automate chemical and other biomedical processes completely. The flexible, planar micropumps discussed in this work are based on a nozzle-diffuser design that employs a central chamber connected to two trapezoidal flow diodes. The pump was fabricated using polydimethylsiloxane (PDMS) as a two-layer structure: a bottom layer with molded channels and the chamber, encapsulated by a planar top layer. The transparency of the pump and biocompatibility of PDMS make it an excellent material for biomedical and colorimetric applications. The pump design was characterized for its performance, both when placed on a flat surface and surfaces of different bending radii, by counting the number of compression cycles required for a known volume of fluid, here water, to flow from the inlet to the outlet. The pump produced a flow rate per compression cycle of 5.53 µl on a flat surface and that of 4µl, 3.6 µl when placed on surfaces of bending radii of 71cm and 45 cm, respectively. Finally, the pumping mechanism was completely automated by replacing the diaphragm with a dielectric elastomer actuator (DEA) and using a tesla valve for fluidic channels. The pressure difference imparted by the deformation of the actuator when a sinusoidal voltage is applied is converted to fluid flow (velocity). We also propose a microfluidic system that employs three microfluidic pumps and a central color detection module to perform effective microfluidic color detection. The proposed system provides a cost-effective solution for conducting chemical analyses using colorimetric assays.

Abstract

The estimation of the state of dynamic systems using measurements is a frequently arising challenge. State estimation is used in applications like robotics, industrial manufacturing, weather forecasting, target tracking, etc. The hidden state in the dynamic systems can be estimated using the recursive Bayesian filters. These filters estimate the state by tracking the posterior distribution. These filters include two steps, prediction based on solving the equation modeling the state evolution and update of the state using the measurements. Most real-world systems have non-linear dynamic and measurement models. Additionally, both models can be affected by arbitrary noise sources, which have either Gaussian or nonGaussian characteristics. For state estimation in linear Gaussian models, Kalman filters are employed. Extended Kalman filters (EKF) are utilized for state estimation in non-linear Gaussian models. For state estimation in non-linear non-Gaussian models, particle filters are generally employed. Particle filters, however, are ineffective in high-dimensional non-linear non-Gaussian models because they experience weight degeneracy. In this thesis, we propose incorporating the invertible particle flow methods in the Gaussian particle filter (GPF) framework to generate a proposal distribution. These methods are derived under Gaussian assumptions for the flow equation. The resulting particle flow Gaussian particle filter (PFGPF) method preserves the asymptotic characteristics of Gaussian particle filters and has the potential to perform better at state estimation in high-dimensional spaces. Secondly, we construct a particle flow Gaussian sum particle filter (PFGSPF), which roughly approximates the predictive and posterior as Gaussian mixture models, using a bank of PFGPF filters. In complicated estimating issues where a simple Gaussian approximation is insufficient, this approximation is helpful. We compare the performance of the proposed filters with the existing particle flow filters and particle flow particle filters (PFPF) in high dimensional complex numerical simulations.

Abstract

The market is an indispensable aspect of our lives, enabling us to exchange valuable goods and services. Allocation and pricing are two significant challenges in determining the equilibrium dynamics of a market. With the advent of AI and technology, the way markets function has evolved greatly. With data analytics tools, sellers can engage in personalized pricing to maximize revenue. However, it is shown that personalized pricing is prone to unfairness among consumers. This thesis precisely studies the fairness challenges in personalized pricing. We aim to examine the concept of fairness in the context of monopoly markets that implement feature-based pricing. Our proposal introduces a new form of individual fairness, referred to as 𝛼-fairness, which ensures that individuals with similar characteristics are subjected to comparable pricing. To evaluate the loss in revenue to the seller in pursuit of fairness, we additionally introduce the notion of Cost of Fairness (CoF) – the ratio of the expected revenue in optimal feature-based pricing to the expected revenue in the given fair feature-based pricing. First, we investigate the discrete valuation space and present an analytical solution for the most suitable fair feature-based pricing strategy. Our findings indicate that CoF, can be arbitrarily high for any fair pricing strategy. We observe that such valuation spaces are uncommon, so we focus on continuous valuation spaces that are well-studied in economics. With the standard assumption of the revenue function being continuous and concave with respect to the prices, we demonstrate that CoF is strictly less than two, regardless of the model parameters. Finally, we present a polynomial time algorithm that computes a fair feature-based pricing approach, successfully achieving CoF less than two.

Abstract

In the recent past, math word problem solvers have received wide attention from the NLP community at large. With the advancement of deep learning techniques, solvers have started to show better performance than traditional rule based semantic parsing techniques. Standard accuracy metrics have shown that math word problem solvers have achieved high performance on benchmark datasets. However, these performances are based on datasets that have limited problem statements and equation templates, thus providing very limited diversity and a low probability of generalization on different word problems for practical purposes. Hence, the extent to which existing MWP solvers truly understand natural language problem statements and its relationship with numerical quantities is still unclear. In this work, we first generate adversarial attacks to evaluate the robustness of state-of-the-art MWP solvers. We propose two methods Question Reordering and Sentence Paraphrasing to generate adversarial attacks. We conduct experiments across three neural MWP solvers over two benchmark datasets. On average, our attack method is able to reduce the accuracy of MWP solvers by over 40 percentage points on these datasets. Our results demonstrate that existing MWP solvers are sensitive to linguistic variations in the problem text. We verify the validity and quality of generated adversarial examples through human evaluation. These results showcase that math word solvers do not generalize well and rely on superficial cues to achieve high performance. Next, we conduct experiments to showcase that this behaviour is mainly associated with the limited size and diversity present in existing MWP datasets. We modify the problem statements by altering the text in different settings such that either the problem statement does not make much sense or no question has been asked in the problem statement. The preliminary results from these analysis did not show significant drop in accuracy metric as was expected. Then, we propose several data augmentation techniques broadly categorized into Substitution and Paraphrasing based methods to mitigate the issues found by our analysis. By deploying data augmentation methods we increase the size of existing datasets by five folds. Extensive experiments on two benchmark datasets across three state-of-the-art MWP solvers show that the proposed methods increase the generalization and robustness of existing solvers. On average, the proposed methods significantly increase the state-of-the-art results by over five percentage points on benchmark datasets. Further, the solvers trained on the augmented dataset performs comparatively better on the challenge test set. We also show the effectiveness of proposed techniques through ablation studies and verify the quality of augmented samples through human evaluation

Abstract

In this study, we carried out a comprehensive analysis of SARS-CoV-2 mutations and their spread in India over the past two years of the pandemic (27th Jan 2020 – 8th Mar 2022). The analysis covers four important timelines, viz., the early phase, followed by the first, second, and third waves of the pandemic in the country. Phylogenetic analysis of the isolates indicated multiple independent entries of coronavirus in the country, while principal component analysis identified few state-specific clusters. Genetic analysis of isolates during the first year revealed that though lockdown helped in controlling the spread of the virus, region-specific sets of shared mutations were developed during the early phase due to local community transmissions. We thus report the evolution of state-specific subclades, namely, I/GJ20A (Gujarat), I/MH-2 (Maharashtra), I/Tel-A-20B, I/Tel-B-20B (Telangana), and I/AP-20A (Andhra Pradesh) that explain the demographic variation in the impact of COVID-19 across states. In the second year of the pandemic, India faced an aggressive second wave while the third wave was quite mild in terms of severity. Here we also discuss the prevalence and impact of different lineages and Variants of Concerns/Interests, viz., Delta, Kappa, Omicron, etc. observed during this period. From the genetic analysis of mutation spectra of Indian isolates, the insights gained into its transmission, geographic distribution, containment, and impact are discussed. Next, we evaluated the impact of some important India-specific mutations on protein function using structure and network-based analysis. Finally, we performed epidemiological modeling of the dominant variants in India during the Second (Delta and Kappa) and Third (Omicron) waves of the pandemic using multi-strain SEIR models to estimate the transmission factor (β) for the different variants. This interdisciplinary study provides valuable insights into the demographic, structural, and epidemiological factors influencing the spread of COVID-19 in India. The findings can inform and aid policymakers and public health officials in responding to future waves of COVID-19 or any other pandemic caused by a novel pathogen.

Abstract

Spatial Data has increased tremendously in the last two decades from various data sources including satellite sensor data, Internet of Things data, data collected from remote sensing instruments and crowd sourced data. With the advent of mobile phones, devices and internet applications, a vast amount of spatial data is also available from various mobile and web applications. These datasets have lots of applications in different fields like Urban planning, waste management, Traffic monitoring and planning, Climate science studies, Climate resilience, Agriculture, Forest survey, disaster management, smart city planning etc. These datasets have huge volume, variety, veracity, and velocity, making the problem of spatial data analysis a big data problem. To convert these datasets to usable applications in timely manner will ensure the effective utilization of the application. A unified framework that collects, processes, and helps users analyze the output over internet will be beneficial to the geospatial domain researchers and policy makers for enabling faster decision making. Apart from these, web based geospatial applications also has applications for general citizens like smart homes, transportation etc. Climate change has been significantly affecting our environment and society since ages. Another important application of web based geospatial framework is climate change study. This is one of the urgent needs for which informed action has to be taken immediately. Sustainable Development Goal 13 (SDG-13) calls for urgent climate action to combat climate change [1]. This calls for focused study to create mitigation plans at global, country and regional levels. For this, climate research should be carried out in faster way. Also, along with climate science expertise, traditional climate research involves significant data analysis and knowledge in computing is essential. Considering this, there is a need for common platform that provide web-based climate services for climate researchers, decision makers and policy makers that enable faster research and availability of research output results. It is also required for these services to be discoverable, available, interoperable, and reusable. Hence, using published and widely used standards for this platform is also essential for enabling wider use. Open geospatial Consortium (OGC) has been developing and maintaining the standards needed for Geospatial data management, publishing, sharing, and processing such datasets. OGC Web Services standards WMS, WFS, and WPS are suitable for providing data access to users in the form of maps and data. OGC Web Services standard WPS, allows users to utilize the geospatial data processes that are published. Hence OGC Web Services standards are suitable for implementing this framework. Also, these services are widely used and available as open-source solutions. However, OGC Web Services are implemented in Remote-Procedure-Call (RPC) architectural style using Extensible Markup Language (XML) over HTTP protocol. Currently, resource-oriented architecture like RESTful API is being used by various modern web applications and are competing against traditional service-oriented architectures like RPC. Hence, standards that implement RESTful APIs like OGC API standards are more suitable for this framework. OGC API components maps, tiles, features, coverages, and processes offer similar services to OGC Web services. Additionally, OGC API records implements a mechanism for the available datasets and tools to be discoverable. Considering this, OGC APIs are more suitable for this framework. Extensive work is being carried out by the OGC community to publish all the standards of OGC APIs and many opensource software like pygeoap, GeoServer and MapServer are also implementing these standards. Considering this, we chose both OGC API and OGC Web Services standards for the design of this framework. Also, most of the geospatial software worldwide offers tools and services compatible with OGC Web Services currently. By implementing both standards, our design will follow the W3C best practices for sharing data as well as being in compliant with most available and widely used geospatial software. Phasing out the OGC web services can be carried out when the OGC APIs are published and implemented by major geospatial software and service providers. SensorThings API is another standard that is used in this design for ingesting sensor data into the portal. After ingestion, this data is designed to be served using OGC API Features, Maps and Environmental Data Retrieval components making sensor data useful to different sectors of users. Considering the current requirements and the above-mentioned standards, in our research work, we identify the challenges involved in developing a unified web based spatial data framework and design a web based spatial data framework that will address these challenges. Further, we chose climate science as a use case to

Abstract

While face recognition and verification in controlled settings is already a solved problem for machines, the uniqueness of face as a biometric is that the mode of capture is highly diverse. A face could be captured nearby or at distance, at different poses, with different lighting, and by different devices. Face recognition/verification has several challenges to overcome to effectively perform under these varying conditions. Most current methods, try to find salient features of an individual by ignoring these variations. This can be looked at from the paradigm of signal and noise. The signal here refers to that information that is unique to an individual, but not varying as per the condition. Noise represents those aspects that are not related to the identity itself and are influenced by the capture mechanism, physical setting, etc. This is usually done through metric learning approaches in addition to the use of loss functions such as cross-entropy (e.g., Siamese networks, angular loss, and other margin losses such as ArcFace). There are certain aspects that lie between signal and noise such as facial attributes (such as eyeglasses). These may or may not be unique to the individual subject, but introduces artifacts into the face image. The question then arises, why can’t these variations be detected using learning methods, and the knowledge thus attained about the variations be put to good use during the matching process? It is this curiosity that has resulted in aggregation strategies for matching, which were previously implemented for aspects such as pose, age, etc. However, in the wild, humans demonstrate significant variability in facial attributes such as facial hair, eyeglasses, hairstyles, and make-up. This is common as one of the primary mechanisms of face image acquisition is covert capture in public (with ethics of consent in place), where people usually display significant variability in facial attributes. Hence it is very important to address this variability during the matching process. This work attempts to do the same. The curious question that arises however is if indeed matching performance varies if the attribute prior is known. Even if it does, how does one conceptualize a system that exploits the same? It is here that this thesis proposed two frameworks. One of the configuration-specific operating points and the other involves suppression of attribute information in face embedding prior to matching. The attribute suppression is attempted both directly at the final embedding, and suppression of intermediary layers of a Vision Transformer Deep Neural network. Both of these require the facial attribute of each image to be detected prior to passing the images into the proposed framework for matching. The above naturally adds another task to the face verification pipeline. It is therefore extremely necessary to find efficient and effective ways of performing face attribute detection (and face template generation), since efficiently performing parts, mitigates the pipeline expansion overhead and makes this a viable pipeline to consider for face verification. We observe that face attribute detection usually employs end-to-end networks, which results in a lot of parameters for inference. A feasible alternative is to constantly leverage the SOTA (state-of-the-art) face recognition networks and use the earlier feature layers to perform the face attribute classification task. Since the highly accurate SOTA is currently DNNs (Deep Neural Networks) for face, the same is dealt with in this thesis. More narrowly, we focus on open-set face verification, where DNNs aim to find unique representation even for subjects not used for training the DNN.

Abstract

Physical exercises and gross motor skills support the spine and the body’s lower extremities. Un- derstanding the relationship between physical characteristics and activity to kinematic properties of the spine has implications for sports, occupational tasks, clinical diagnosis and prognosis. This study aimed to categorize spine kinematics and the lumbopelvic-hip segments from flexion, lateral-bend, twist, and squat exercises by examining the correlations to body-mass index (BMI), waist-to-hip ratio (WHR), and physical activity levels in healthy young adults. The data was collected from a marker-based opto- electronic motion capture setup and a marker-less motion capture with the RGB camera of a standard mobile phone. Comparing the accuracies of the two techniques on the same data set is important to de- velop inexpensive (RGB camera) diagnostics. Sixty-two participants (40 Male, 22 female) participated in the study. The angular displacement of the spine and knee/hip for each exercise was extracted by vector analysis using a single reference node/marker. The analysis showed no significant correlations of angular displacements values for the four movements with BMI or WHR. The physical activity level of male participants is significantly correlated with angular displacements for the flexion (p<0.001) and lateral-bend (p-value ranges from 0.001-0.04) and weakly correlated (but not significant) for twist ex- ercises. The physical activity level shows a significant correlation for only flexion (p<0.001) in female participants. BMI is negatively correlated in male participants for the squat movement, while WHR and physical activity show positive associations. In the female cohort, a negative correlation with BMI, WHR, and physical activity level is observed for the squat movement. The findings emphasize the criti- cal role of physical activity on musculoskeletal flexibility in young, healthy adults. The angles estimated using both techniques were comparable and significantly correlated across participants and exercises.