PhD Theses (Electronics and Electrical Engineering)

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    Design and Implementation of Hardware-Efficient Architectures for FFT Algorithms
    (2024) Hazarika, Jinti
    The Fast Fourier Transform (FFT) holds significance across diverse applications in wireless communications, audio, and signal processing. This doctoral thesis addresses the imperative need to enhance hardware efficiency while concurrently minimizing area and power consumption in FFT processors. Extensive efforts by researchers have centered on optimizing FFT algorithms, determining the requisite number of multipliers, adders, and registers, all of which intricately influence power consumption and overall area. These considerations become pivotal constraints in FFT applications, necessitating a judicious trade-off between complexity and performance.
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    Investigation on Multi-dynamic Radar System: A concept for Airborne Surveillance Application
    (2024) Qumar, Javed
    During the last few decades, Stealth technology has proven to be one of the most effective approaches to hiding the target from radar systems. The basic concept of low observable is mainly the reduction of Radar Cross Section (RCS) in direction of the receiver. So, for detecting such targets, concepts of bistatic and multi-static radar attracted substantial attention. However more challenges lie when radar platforms are mobile or airborne. The geometrical structures are studied with different spacing of radars, it is one of the parameters for Bi-static Radar (Baseline distance between transmitter and receiver) to extend the detection coverage over the mono-static radar. The simulation is also made to extend further for multi-dynamic scenarios. Transmitted waveform identification is very important to know the info about the waveform to processing the returned signal accordingly. The simulation is made for transmitter identity based on augmented BPSK/BASK based waveform ID tailored with standard LFM. However, another way of Transmitter ID info is simulated using IFF Mode-S waveform so that IFF waveform can be utilized for waveform ID of the radar.
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    Application of Reduced Switch Single DC Source based Cascaded H-bridge Multilevel Inverter for Power Quality Improvement
    (2022) Chakrabarty, Ramyani
    Distribution static compensator (DSTATCOM) is a shunt connected custom power device which is widely used for load compensation and improvement of power quality. The conventional MLIs used for DSTATCOM implementation pose some drawbacks like large component count, multiple DC-link capacitors and capacitor voltage balancing issues which complicate its design and control. To overcome these issues, a 7-level reduced switch single DC source based cascaded H-bridge multilevel inverter (RSDCHBMLI) topology is presented in this thesis. RSDCHBMLI utilizes lesser number of switches, single DC source and has no requirement of additional diodes or capacitors. As the number of switches in this topology is reduced, a single carrier level shifted pulse width modulation (SC-LS-PWM) technique is developed which is implemented in low-cost controller. The RSDCHBMLI operation is implemented and analyzed in both open-loop and closed loop using state-feedback control (SFB). SFB controller combined with SC-LS-PWM results in constant switching frequency operation of the inverter. The 7-level RSDCHBMLI is implemented as a DSTATCOM connected to weak distribution system. Both SFB current control and finite-control-set model- predictive-control (FCS-MPC) is implemented for load compensation, and it is observed that FCS-MPC gives better dynamic response. It also develops a current based DC-bus voltage controller whose gains can be easily computed and gives better performance than conventional controllers. Detailed case studies for DSTATCOM operation under various conditions of loads and source voltages are also presented.
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    Ripple Minimization of Input and Inductor Currents in Coupled Inductor Single Input Dual Output DC-DC Converters
    (2023) Nupur
    The single input multiple outputs (SIMO) DC-DC converters with two outputs are called single input dual output (SIDO) DC-DC converters. The SIDO converters can generate two different DC voltage levels from only one available DC voltage level with the reduced component count, reduced losses, reduced physical size and increased efficiency. The introduction of an inversely coupled inductor further reduces the physical size as the flux due to the two windings cancels each other, resulting in a reduced flux in the core. Therefore, the coupled inductor single input dual output (CI-SIDO) buck, boost, and buck-boost converters are analyzed in this thesis. The analysis of each CI-SIDO converter for all possible values of the coupled inductor parameters, duty ratios, and the gate pulse shift is very tedious and repetitive. An approach to unify inductor current waveforms and inductor current ripples in CI-SIDO converters is presented by forming sectors of duty ratios. The CI-SIDO converters have two MOSFETs with two gate pulses. It is found that the shift of one gate pulse with respect to the starting point of another gate pulse reduces the ripples in two inductor currents and input currents. The ripples in inductor currents reduce up to $92.46\%$ with respect to the zero shifting. The condition of the ripple-free input current is also proposed. This work also proposes a unified coupled inductor design with reduced inductor current ripples. An approach to design a CI-SIDO boost converter is also proposed such that the ripple in input current is either zero or less than the maximum specified ripple limit. It is further found that there is no undesirable effect of gate pulse shifting on any of the variables of the CI-SIDO converter, such as the average value of inductor current, input current, and output voltage ripples. Also, the range of CCM operation increases as the shifting is introduced by decreasing the CCM/DCM boundary. The discontinuous conduction mode (DCM) of the CI-SIDO boost converter is also analysed. The input-output voltage relations of DCM are found, and the effect of change in load currents, input voltages and duty ratios are analysed for all possibilities of the CI-SIDO boost converter
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    Theoretical and Experimental Investigation of Graphene based Devices for Bio-Sensing Applications
    (2022) Singh, Rajan
    Biosensors are widely used to expedite the estimation of vital health signatures and provide techniques that aid in the continuous health monitoring of patients. Over the last few decades, the advancement in the silicon industry caused significant improvement in the development of sensing devices, marketing sensitive and robust sensors. However, multidisciplinary expertise is needed for the furtherance of developed biosensors. Biotechnology provides simple yet efficient assays to detect biomarkers and hence plays an imperative role in sensor development. Recent developments in two-dimensional nanomaterials such as graphene have also proved to be of great importance to achieve better sensor performances. However, the estimation and modelling of the interaction between the biomarkers and newly discovered nanomaterials are not yet matured, which is indispensable for developing a new generation of sensors. The traditional methods to evaluate these interactions are based on experimentally determined parameters that are difficult to estimate. Therefore, methods for electronic structure calculation, such as density functional theory (DFT), can be a good candidature for the study of interactions between biomarkers and nanomaterials. The primary research objective of this thesis is to employ DFT for estimating the electronic structure of pristine graphene and doped graphene nanostructures and evaluating their interactions with numerous gaseous molecules and biomarkers by calculating binding energies, electronic structures, and charge transfer. The doped nanostructures exhibit superior sensing capabilities over pristine nanostructures. Further, the synthesis of graphene and its functionalization routes to employ them as Glutathione and Lactate sensors are explored. Later, for experimental proof, the chem-resistive-based sensing devices were fabricated, and sensor parameters were estimated. The high sensitivity and better limit of detection of the fabricated devices make them suitable for devices for point of care applications for detecting various health disorders.
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    Analysis and simulation of piezoelectric vibration energy harvesters having uniform stress profile
    (2023) Kundu, Sushanta
    The objective of the presented work is to improve the electrical power output by modifying the geometry of the piezoelectric vibration energy harvester (PVEH). The key contributions of the thesis are, modification of the thickness profile of conventional cantilever-based bimorph PVEH to achieve uniform stress along the beam length. In this regard, two thickness-tapered geometries are proposed: PVEH-1, which consists of a thickness-tapered substrate sandwiched between two uniform thickness piezoelectric layers, and PVEH-2, which consists of a bimorph cantilever with a substrate of uniform thickness sandwiched between two thickness-tapered piezoelectric layers. In diaphragm-based PVEH, radial cuts are introduced to reshape it in identical slices (sectors) of 32.7o each resulting in improved harvested power. Further, a broadband energy harvester is designed using several slices of different central angles.
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    Visual Object Tracking in Dynamic Scenes
    (2023) Francis, Mathew
    Visual object tracking is one of the popular and legacy problems in computer vision. Tracking has applications in a wide spectrum of domains with applications in agriculture, automobile, surveillance, defence, and entertainment. It remains a challenging research problem on account of various factors like occlusions, shape deformations, illumination changes and background clutter. The object tracking performance depends on the joint efficiencies of a target object modeling scheme and a localization strategy. Accordingly, this thesis proposes three major contributions. First, a colour feature based representation that models the foreground while considering the background context. The target is further localized by using a meta-heuristic search strategy based on breeding fireflies. The breeding fireflies technique is realized through a combination of Real Coded Genetic Algorithm (RGA) and the Firefly Algorithm (FA). The second contribution involves target representation in the sparse representation framework. Here, the object is modeled using a (weighted) distribution of sparse codes. The weights are derived from a foreground-background classifier. Here, the target is localized by using the Firefly-RGA approach. The third contribution uses target representation using deep network embeddings. Here, a Siamese network is used to derive the object location predictions. Additionally, a multi-part object model is explored for handling occlusions. All three proposals use the appearance features to predict the object positions. Thus, complementary information is derived by inter-frame dense motion estimation. The motion based predictions are used to enhance the accuracy of the three proposed appearance based trackers. Thus, all the proposed trackers are realized in an ensemble framework of appearance and motion based predictions. The colour feature based tracker has an object model based on handcrafted features derived from colour distribution. The sparse representation-based tracker learns the model by using features from only the first frame of the sequence. In the Siamese tracker, a pre-trained deep network is used for extracting image features. The performances of the classical methods are limited by the feature used. For example, colour distribution-based features get disturbed by illumination changes and structure-based features face challenges from geometric transformations. In contrast, the deep network used in the Siamese tracker is trained on a very large set of general images (for image classification task) and is thus capable of generating rich feature representations. It is also observed that the Siamese tracker has significantly outperformed the other two trackers. The proposed trackers are benchmarked on the VOT2018, OTB2015 and UAV123 datasets and are compared against a number of baseline methods formulated in traditional and deep learning framework.
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    Design of Hybrid (De)multiplexers for Optical Interconnects on SOI Platform
    (2022) Minz, Manoranjan
    The ever increasing data traffic due to technological advancements as well as increase in the number of users has ignited tremendous interest in the research and development of high performing computing systems. However, the performance increase is limited by the usage of electrical interconnects, as it suffers from high power dissipation and propagation delay. To overcome these issues optical interconnect has emerged as a viable solution owing to its low power consumption and potential for link capacity improvement by employing various multiplexing techniques such as wavelength division multiplexing (WDM), polarization division multiplexing (PDM), and mode-division multiplexing (MDM). Silicon photonics platform has attracted a lot of attention as it has enabled monolithic integration of photonics and microelectronics due to its compatibility with the standard complementary metal oxide semiconductor technology. Considering the demands of high-link capacity in the future, research is in progress to combine multiple multiplexing schemes to realize a multi-dimensional hybrid multiplexing technology. This thesis explores the design of such hybrid (de)multiplexers, which are based on the contra-directional coupling principle using periodic grating structures. To the best of our knowledge, for the first time, a grating assisted collaterally contra-directional coupled TE and TM MDMs are introduced using silicon slab waveguides. Then by cascading the TE and TM MDMs, a hybrid MDM-PDM device has been reported on silicon-on-insulator platform. The theory of supermodes has been utilized to determine a suitable gap between the waveguides such that contra-directional coupling occurs with low reflection at the input. Mathematical models of the proposed devices are presented with coupled-mode equations and their solutions. A hybrid MDM-WDM device is demonstrated using the buried strip waveguides and silicon grating structures by integrating the wavelength and mode division multiplexing schemes. To further increase the link capacity, PDM is combined with the MDM-WDM structure to realize a hybrid MDM-PDM-WDM device. The devices are designed and analyzed using the numerical finite-difference time-domain method.
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    Condition Assessment of Natural Ester Oil and its Nanofluid filled Transformer
    (2022) Baruah, Niharika
    This thesis presents the condition assessment of transformers filled with alternative dielectric fluids such as natural ester oil (NEO) and its nanofluids (NFs) by evaluating the oil properties. It also presents the development of a NEO modified with nanoparticles (NPs) with enhanced physicochemical, thermal and electrical performance for transformer application. The dissolved gas analysis (DGA) is performed to monitor the emission pattern of gases when the oil is subjected to thermal stress in a sealed beaker setup at 150oC. The classic Duval triangle, Dornenburg ratio, Rogers ratio and IEC method are applied to ascertain the type of incipient faults. The total dissolved combustible gases (TDCG) of the oils are done to examine the composition of combustible gases. To study of the ageing characteristics of the NEO, the frequency domain spectroscopy (FDS) is performed to obtain the dielectric parameters over a wide range of frequencies. The oil is subjected to thermal stress in an open beaker ageing test setup at 115oC for up to 500 hours, and samples are taken out at intervals of 100 hours for testing. A statistical model is developed to relate frequency, ageing duration and dielectric parameters based on the FDS results. The temperature dependence on the FDS characteristics of the dielectric liquids is also studied by considering semi-conductive titanium oxide (TiO2) NP dispersed in the base fluids, in the frequency range of 10-3 to 104 Hz and the temperature range from 30 to 90oC. The Cole-Cole double relaxation model is used to ascertain the number of relaxations in the samples. It is observed that the conductivity increases with increase in temperature for all the oil samples. A predictive analysis model is proposed using machine learning (ML) algorithms to predict the dielectric properties based on FDS. To understand the breakdown probability of the fresh and aged NEO and NEO-NFs, statistical analysis is carried out on the AC breakdown voltage (ACBDV) data. The ACBDV tests are performed for all the samples and a comparative examination is done among them to estimate the behaviour of the new and aged oils. To understand the statistical behaviour of the ACBDV of the oil samples, normal, 2-parameter Weibull and 3-parameter Weibull distributions are considered and hypothesis testing with Shapiro-Wilk test and Anderson-Darling test is carried out. The goodness of fit using the correlation coefficient shows that all the 4 oil datasets follow a 3-parameter Weibull distribution quite well. As NEOs are being advocated for usage in transformer applications, a new NEO is developed in this work known as the Pongamia pinnata oil (PPO). The crude PPO is modified by the transesterification process and converted to pongamia oil methyl ester (POME) as the crude oil is not suitable for direct use because of its high viscosity, high pour point and acid number. To enhance the properties further, an insulating hexagonal boron nitride (h-BN) NP is used. The bulk h-BN NP of size 1 μm is exfoliated into 2-D nanosheets of size 50-100 nm subsequently to enhance the surface area of exfoliated h-BN (Eh-BN), which is then dispersed in the base fluid. The various thermophysical and electrical properties of NFs are also studied. The results of the POME are also compared with two more NEOs, one is the FR3 and the other is JAT and it is observed that the POME shows comparable results and thus may be used as a potential substitute to MO for transformer application.
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    Analysis and Evaluation of Electromagnetic Losses in Induction Machines and their Impact on Motor Design and Performance
    (2022) Kumar, Rajendra
    In this thesis, various electromagnetic losses, viz. core losses and stray losses of an induction machine have been studied and characterized for their fast and accurate representation in the iterative motor design process. To accomplish this, the first part of the work uses the measurement results of various medium power three-phase induction motors ranging from 1kW to 375kW output power. The factors affecting the stray losses and the magnetic circuit parameters of a motor are then investigated for all these motors to develop various empirical correlations of stray loss.
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    Development of Robust State of Charge Estimation Algorithms for Lithium-ion Batteries in Electric Vehicles
    (2022) Sethia, Gautam
    With increasing innovation and environmental awareness, electric vehicles (EVs) are becoming more popular than the conventional fuel based vehicles for emission-free future transportation. Lithium-ion battery (LIB) is the most suitable choice of energy storage system that works as the core of an EV. Along with the battery, a micro-controller known as battery management system (BMS) is required for reliable and secure operation of the battery. In BMS, real-time access to the information of one of the most critical battery states, known as state of charge (SOC), is vital as it indicates the remaining capacity of the battery, helps to prevent overcharging and undercharging, increases capacity utilization and lifespan, improves reliability, reduces cost, and ensures safety of the battery and its surroundings. Being an internal state, SOC is not available for direct measurement by any sensor and estimating it accurately for an LIB is non-trivial due to the highly nonlinear nature of the battery and various uncertain operating conditions. The literature reports several different approaches to estimate SOC of an LIB with each having its own advantages and drawbacks. It is important to note that each method of SOC estimation in literature possess some drawbacks either in accuracy or in real-time implementation. Hence, there is a scope for further improvement of these methods to enhance the performance of SOC estimation.
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    (An) Intelligent Electric Vehicle Charging Infrastructure
    (2022) Sah, Bikash
    Electric vehicle charging infrastructure is the foundation for ensuring wider acceptance of electric vehicles (EVs). Governments and organisations worldwide are working on policies for deeper penetration of EVs in the transportation sector. The infrastructure comprises various stakeholder who communicates with each other. The communication ensures an optimal operation of each entity in the infrastructure, meeting the requirements of all the stakeholders. Although the present electricity infrastructure is able to support the charging of EVs, there are challenges of integrating bidirectional power flow between EVs and electric grid, developing appropriate communication infrastructure and support systems, motivating users to perform coordinated charging, and options to utilise EVs for ancillary services. Apart from the challenges in the electric grid, the requirement of fast charge with constrained battery degradation is another major challenge to persuade user acceptance. Henceforth, the work presented in the thesis proposes controllers, algorithms, estimation procedures, and techniques to develop an intelligent infrastructure that can perform coordinated charging with the least disturbances in the electric grid and meet the requirement of fast charge with constrained degradation at the user end.
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    Movement Epenthesis Detection in Compressed and Uncompressed Sign Language Videos
    (2022) Talukdar, Anjan Kumar
    Continuous sign language recognition (SLR) system suffers from a problem called coarticulation. Movement epenthesis (me) is a special type of coarticulation which is a transition from one sign to other. So, sign spotting is a process of segmenting out signs and fingerspellings from continuous utterance sentences by removing movement epenthesis components, which is a challenging task.
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    Digital Hologram Processing Methods and its Applications in Particle Imaging
    (2023) Ghosh, Anok
    Digital holography is a three-dimensional imaging technique, which involves digital recording of a hologram as the intensity of interference pattern of an object scattered light and a reference light. The complex light field at the object plane is retrieved by numerical reconstruction of the hologram. As a result, digital holography provides both amplitude and quantitative phase imaging which is one of its unique features. Especially, digital in-line holographic microscopy (DIHM) has found its way in microscopic imaging applications and microfluidic studies due to its unique features and cost effectiveness. DIHM has found numerous applications in particle imaging due to its three-dimensional particle localization capability, compact and cost-effective experimental setup. The particle objects placed at any given distance between source to camera can be detected in reconstructed images numerically. In general, the reconstructed images in digital in-line holography suffers from twin image effect and background noise. This affect the particle detection and its size estimation accuracy. The improvement of reconstructed image quality can result in improved particle detection accuracy. Conventional reconstruction method takes high computation time for the accurate particle localization as the hologram reconstruction is required to be performed at different planes within the measurement. This problem can be overcome with a computationally efficient reconstruction algorithm. Accurate detection of object focal plane is of crucial importance in digital holography as it offers numerical autofocusing capability. In that context, establishing an appropriate autofocusing criteria is essential. Thus, taking these observations into consideration, a number of digital hologram processing algorithms are reported in the thesis. Novel hologram reconstruction algorithms are developed which provide computational efficiency without compromising the reconstruction quality. In the case of quantitative phase imaging, an important problem of phase unwrapping is addressed by developing an quality-guided, computationally efficient phase unwrapping algorithm. The thesis proposes different autofocusing algorithm for accurate detection of focal plane of amplitude, phase and mixed type objects. The performance of all these algorithms were evaluated and compared with state-of-art method with different simulation examples. Furthermore, practical validation of the algorithms were performed with experimental data. A comprehensive study on the particle size distribution of fly ash samples is also reported. We believe that the proposed hologram processing methods can be important additions in the field of digital holography.
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    Design of CMOS Integrated Circuits for Full-Duplex Radios
    (2021) Sharma, Prateek Kumar
    In-band Full-duplex (FD) radio is an emerging technology, which transmits and receives the signal simultaneously at the same frequency. FD has the potential of doubling the throughput with low latency. The main challenge associated with an FD system is the self-interference (SI). The SI in an FD radio is caused by the leakage and the echo signals from the transmitter to its own receiver. Due to the direct or the parasitic path, the transmitted signal gets leaked into the receiver in an FD radio. Whereas an echo signal occurs due to the reflection of the transmitted signal in free space. The large in-band SI signal makes the demodulation of the weak desired signal a hard task. To mitigate the problem of SI in an FD radio, an SI canceling circuit which generates an SI canceling signal is often used. The SI canceling circuit should be capable of providing variable attenuation, variable time-delay, and variable phase delay. Apart from the SI signal, blockers from different bands can degrade the performance of the FD receiver, especially in the sub-6 GHz range. The main objective of this research is to investigate the fully integrated circuit techniques for SI cancellation in FD receivers
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    Design, Optimization, and Modelling of Hybrid Organic-Inorganic Perovskite Memristors
    (2023) Gogoi, Himangshu Jyoti
    Memristor (memory + resistor) is the fourth basic circuit element beyond resistor, capacitor and inductor. It establishes a relationship between flux and charge and its present resistance depends on its past state. With fast switching speeds (~picoseconds), low power consumption (~femto-joule/bit), long write/erase endurance, and high scalability (nano-meter scale), memristors have strongly captured the attention of research community in the fields of neuromorphic computation, artificial intelligence, ultra-dense data storage, and logic circuits.
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    Automated Diagnosis of Cardiac Disorders from Electrocardiogram Signals using Deep Learning
    (2023) Prabhakararao, Eedara
    Cardiac disorders are the leading cause of human mortality worldwide. Most disorders progressively worsen over time; as a result, if left untreated can lead to severe complications such as heart attack and stroke. Therefore, early diagnosis and a better understanding of the disease progression are crucial to timely initiating appropriate treatments that can help prevent further disease progression and severe cardiac events. Widely used by clinicians as a routine modality in hospitals, electrocardiogram (ECG) signals non-invasively capture the heart's electrical activity from the body surface. Therefore, many cardiac electrophysiological abnormalities have a signature on the ECG, and their identification can aid in the early diagnosis of cardiac disorders. In practice, an experienced cardiologist manually examines the morphological changes in ECG to diagnose disorders. Manual examination of enormous amounts of ECG data can be tedious, time-consuming, and prone to human errors. Hence, research on automated ECG interpretation methods is gaining popularity as they can aid in rapid and improved objective clinical decision-making, allowing clinicians to provide timely treatment. This thesis documents our investigations on developing efficient deep learning-based automated methods that can effectively handle the pathological variabilities of single- and 12-lead ECG signals for the reliable diagnosis of various cardiac disorders. First, a new multi-lead diagnostic attention-based recurrent neural network (MLDA-RNN) architecture is proposed for classifying myocardial infarction (MI) severity stages such as early MI, acute MI, and chronic MI using 12-lead ECG signals. The method systematically processes input 12-lead ECG using lead-specific RNNs and intra- and inter-lead attention layers to effectively encode the clinically relevant 12-lead ECG information for improved MI severity staging. The second work presents an attention-based deep residual RNN (A-DRRNet) to diagnose congestive heart failure (CHF) from single-lead ECG beats. The method incorporates multi-layered RNNs with residual connections followed by an attention module to comprehend the complex pathological ECG changes associated with CHF syndrome. In the third work, a multi-task deep convolutional neural network (MT-DCNN) is investigated as a new robust framework for accurately estimating the atrial fibrillation (AF) burden (the percentage of the time patient is in AF rhythm) from the long-term ambulatory ECG recordings. This method incorporates ECG reconstruction as an auxiliary task for the primary task of AF detection, which aided in robust AF burden estimation. The estimated daily AF burden demonstrates improved diagnosis and stroke risk assessment in AF patients. Lastly, a novel multi-scale deep temporal CNN ensemble (MS-DTCE) framework is investigated for the simultaneous classification of multiple cardiac disorders such as MI, bundle branch blocks, and hypertrophic cardiomyopathy using 12-lead ECGs. This method effectively handles the disease variabilities by combining diagnosis decisions from several scale-dependent expert classifiers designed using dilated and causal convolutional filters with different receptive fields. Evaluation of the proposed methods on the diverse clinical ECG datasets shows significant improvements over the existing approaches.
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    Universal Identity Independent Face Counter-spoofing
    (2023) Rao, Balaji K
    "Owing the ubiquitous deployment of face-recognition units at various points, ""face-masquerading"" is certainly possible via synthetic facial-imitations of some target individual. These ""imitations"" can be executed either in a crude way with a planar print/digital image or in a much more sophisticated manner via ""prosthetics"". Since the spoofing modality/type is hard to predict, what is well understood is the INTRINSIC NATURAL FACE-SPACE.Furthermore, counterspoofing should operate at a layer where subject-content must be ignored. What should be learnt is the high-level presentation form of the face or face-like object presented to the still-camera. On this front, a CONTIGUOUS RANDOM SCAN based architecture is proposed and deployed to build a content-agnostic/subjectindependent model for the NATURAL FACE CLASS ALONE. Works effectively against planar-spoofing and even prosthetics despite depth variability (due to the one-mask-fits-many, over-smoothing constraint). The ""contiguous random scan"" is a technology transfer from an application invented by Matias and Shamir (1987) which involved application of SPACE FILLING CURVES for compressing encrypted videos. As second contribution, to attack PRINT-SPOOF-presentations, a CONTRAST REDUCTIONISTIC IMAGE LIFE-TRAIL based on ITERATIVE FUNCTIONAL MAPS is proposed, to segregate natural contrast rich faces with SELF-SHADOWS from PRINT-SPOOF-presentations. NATURAL face-images tend to have longer LIFE-trails as compared to PRINT-SPOOF-images."
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    Model Predictive based Coordinated Voltage Control of Active Distribution Networks
    (2022) Dutta, Arunima
    Electricity generation and transportation sectors are significant contributors to the increasing carbon foot print of the society. The utilization of greener generation technologies in both these sectors is the solution to the increasing carbon emissions. However, increased penetration of DERs in distribution networks brings new challenges in the operation and management of power system, among which voltage fluctuation is the most severe. In this thesis, a rule-based model predictive control-based centralized control approach has been developed that optimally coordinates the different entities, such as, actions of on-load tap changer, distribution static synchronous compensator, and active and reactive power set-points of PV and EV inverters to manage the node voltage variations and fulfill other objectives, such as minimizing energy losses and line congestions. To investigate different volt/var control devices based on different temporal characteristics, the proposed control strategy has been further converted to a two-stage control structure. The two functionalities of active distribution management system, i.e., demand response and conservation voltage reduction strategies are included in this two-stage voltage control methodology to enhance energy efficiency of the distribution networks. Due to the availability of on-board chargers, opportunities emerge for EV to provide services to the distribution network operators through vehicle-to-grid technology. Although EV infrastructure benefits the distribution system through V2G services, the increasing number of EVs creates congestion in the feeders, resulting in network overloading. Thus, a dual-stage centralized control strategy has been developed to mitigate voltage variations and line congestion. Further, a three-stage MPC-based centralized coordinated approach has been further developed to schedule charging of EV and volt/var devices. The approach aims at maintaining bus voltage magnitudes and SoC of EV battery within desired limits with minimal usage of control resources and cost of electricity consumption. The economic aspects of EV aggregators in charge scheduling and reserve scheduling have been further evaluated to add value to the third stage. Moreover, Furthermore, the reactive power set-points achieved from the centralized control scheme follow the integrated local Q(V) characteristics according to DER integration standards so as to establish the control problem as multi-level control structure. The 33-bus and 38-bus radial distribution networks are used to validate all the proposed control approaches.
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    Investigation on Certain Issues Related to Development of Next-Generation Broadband Wireless Networks
    (2022) Bhattacharjee, Arijit
    The day-by-day increasing popularity of mobile phones, wearable devices, and other such personal wireless devices has resulted in a huge surge in user-generated data. This trend has been augmented further by the inclusion of new technologies, services, and applications centered on wireless systems. As a result, the expectations from the mobile networks to cater to the new services and traffic conditions are also rising at an equally concerning rate. To tackle this crisis, researchers from academia and industry are looking for ways to improve the existing network capacities by enhancing the current technologies. The challenge is not just limited to upgrading the existing network capabilities but also to ramp up their link-level capacity and augmenting them in providing reliable last-mile connectivity. For this reason, the need for high-capacity wireless links has become particularly significant owing to the prominence of high data-rate enhanced multimedia broadband (eMBB) applications like HD videos, gaming, and Internet-of-Things (IoT) in the overall share of network usage. However, supporting these applications using the existing commercial communication spectrum seems unlikely, as these bands are already congested, and there is hardly any scope for capacity improvement. Therefore, the prospect of communication using the millimeter-wave (mmWave) bands ranging from 30 GHz to 300 GHz frequencies has been explored thoroughly. Due to their large bandwidths and cheap spectrum costs, these frequencies are considered ideal for supporting the eMBB services and other high data-rate applications.