PhD Theses (Electronics and Electrical Engineering)

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Development of Kalman Filter based Algorithms for Fringe Pattern Analysis
(2024) Sharma, Shikha
The purpose of fringe pattern analysis is to retrieve the phase from the fringe pattern. The phase retrieval is essential from the fringe pattern in order to derive the object information. Therefore, demand for the phase information has promoted the development of fringe analysis techniques. Spatial fringe analysis techniques typically involve different operations such as fringe denoising, fringe normalization, and fringe pattern demodulation for the phase estimation. In some cases, phase aberration compensation is also required to be performed. The thesis presents a number of spatial fringe processing algorithms based on the application of Kalman filter.
Analysis of Speech and Music Content for Movie Genre Classification
(2023) Bhattacherjee, Mrinmoy
Movies are a popular mode of entertainment around the world. The consistent rise in the production and consumption of movies demands more efficient automatic movie content analysis applications. Movie Genre Classification (MGC) is vital for underage censorship, search, retrieval, and targeted publicity. Current trends in MGC literature indicate a focus on short trailers instead of full movies and a multimodal approach. The audio modality is generally used only as an auxiliary channel. However, due to its rich genre-specific information, the audio signal deserves a dedicated study in the current context. Hence, this thesis aims to perform only audio-specific MGC. The thesis has four principal contributions. First, spectral peak tracking-based magnitude spectrum features are proposed for isolated speech and music classification. Second, the underexplored phase component of the audio signals is utilized for discriminating speech and music. The third contribution involves using harmonic-percussive sourceseparated features and classifiers in the multi-task learning framework for identifying speech overlapped with music. Finally, the above proposals are employed for the MGC task. The spectral peak trackingbased method performs better than the other proposals and the baselines. Specific combinations of all the proposed and baseline features provide the overall best performance, even in the cross-dataset scenario. The thesis work can be extended in the future by analyzing the individual constituents of speech and music for a more nuanced representation of movie genres.
Automatic Dialect Identification in Ao, a Low Resource Language
(2023) Tzudir, Moakala
Dialect Identification (DID) is a significant research problem widely explored in major languages like Arabic, Chinese, and Spanish. DID can serve as a frontend for many applications like Automatic Speech Recognition (ASR) that may require special dialect-specific enhancements for improved performance. This thesis proposes an automatic DID system for Ao, an under-resourced language of India. Ao is a Tibeto-Burman language spoken in Nagaland. It is a tonal language with three lexical tones: high, mid, and low. Chungli, Mongsen, and Changki are the three dialects of Ao that differ in their respective tone assignment on lexical words. Four principal contributions are made in this thesis. The first contribution of this thesis is creating a manually collected and annotated novel speech dataset to foster research on the Ao language. The second contribution of the thesis is a detailed acoustic study of the unexplored tone dynamics of the dialects of Ao. Based on the analysis, a tonal feature ($F_0$) to capture the dialect-specific tone information is proposed. The DID performance improves when the proposed tonal feature is combined with other spectral features. As the third contribution, this thesis explores three excitation source features in the DID task. The source features studied are Residual Mel Frequency Cepstral Coefficient (RMFCC), Integrated Linear Prediction Residual Log Mel Spectrogram (ILPR-LMS), and Linear Prediction (LP)-gammatonegram. A notable performance improvement is observed when the source information is combined with the vocal tract information. The fourth contribution of this thesis is the exploration of prosody-related characteristics of speech signals. The prosodic features are observed to provide significant performance improvements in classifying the dialects of Ao. The thesis work is concluded by combining all the proposed approaches to build an efficient DID system for Ao. Among many hurdles in studying under-resourced languages like Ao, the need for more data is the most prominent. Nevertheless, the contributions of this thesis may bridge some of those gaps and spur future research in this direction.
Story Segmentation and Retrieval of News Videos in a Multi-modal Framework
(2024) Haloi, Pranabjyoti
Shot segmentation, categorization, indexing, and news story formation are the most important and primary steps in building an efficient and well-sorted video storage and retrieval system. News channels have evolved as one of the primary sources of information. However, in recent times, with the increase in the number of news channels, a plethora of news content is available on air, and it has become difficult to store and retrieve the news videos effectively. Commercials are also included in a news video, containing considerably less information. These commercials are to be filtered out, and the remaining news video will be segmented meaningfully. Segmentation of news videos is a crucial process for efficient storage and categorizing of the videos. The segmented stories also facilitate the easy retrieval and finding of the desired news. In this work, we developed different algorithms for shot segmentation, categorization, indexing, and retrieval of news videos. Our methods are independent of different temporal and spatial structures of various news channels and require a minimal manual input.
Graph based Classi cation Techniques for Pig Breed Identi cation from Hand-crafted Visual Muzzle Descriptors
(2023) Chakraborty, Shoubhik
Breed classification of pigs based on muzzle images has been attempted in this thesis. Limited, noisy, heterogeneous visual data stemming from MUZZLE images taken from Pigs belonging to different breeds pose many challenges, not just from the point of view of identifying and isolating those features and statistics which are discriminatory in nature, but also from the point of view of constructing a suitable breed-centric model (aided by an inferencing mechanism), which is robust and stable. The work in this light has three primary contributions:  Designing and selecting a set of Handcrafted Colour and Texture based visual descriptors which are breed-discriminatory.  Devising a feature-specific siphoning policy and model for segregating breeds serially.  Using Spanning Trees in DUAL MODE (MIN-tree and MAX-tree forms) for binding breed-specific features and devising a NOVEL test-point INDUCTION procedure for producing an OUTLIER score, whether the point is in the INTERIOR or EXTERIOR of the breed-cluster. Given the diversity of data on hand and the limited training set available to build the model, CROSS-testing results were very promising: DUROC-breed (93.85%), GHUNGROO (97.48%), HAMPSHIRE (94.27%) and YORKSHIRE (100%).
Acoustic Charge Transport in Organic Semiconductors using Surface Acoustic Wave Devices
(2024) Mishra, Himakshi
A surface acoustic wave (SAW) is a periodic deformation of the surface of an elastic material propagating at the surface primarily as a linear wave front. Despite the fact that their existence had already been established by Lord Rayleigh in 1885, it wasn't until 1965, through the development of the interdigital transducer (IDT), that they were first utilised for various applications. It is now feasible to stimulate and detect SAWs on a piezoelectric surface in an effective manner. It is established that SAW devices have a very broad range of applications in several fields. Professional radar and communications systems extensively use SAW delay lines, band pass filters, resonators, oscillators, and matched filters. SAW can also be employed as a pressure, humidity, and temperature sensor for chemical sensing and analysis purposes. SAW has very low velocity and narrow wavelengths, reducing size and weight and hence, can be mass manufactured. When a semiconductor comes into interaction with SAW, the acoustic deformations induced by SAW have a significant impact on the semiconductor's energy bands and, consequently, its electrical characteristics. SAW-induced band edge modulation leads to the spatial separation of charge carriers of a semiconductor. Furthermore, the energy and momentum carried by SAW are transmitted to charge carriers resulting in a dragging force on them. This phenomenon is known as the acoustoelectric effect, and the transport caused by this effect is termed acoustic charge transport (ACT). The process of ACT has been demonstrated by several researchers in inorganic semiconductors either by injecting carriers through an input bias or optically generating carriers. Organic semiconductors are increasingly being used as the active layer in a wide variety of innovative technologies due to their solution-processability, lightweight, and flexibility. In contrast to inorganic semiconductors, organic materials form a polycrystalline layer, and their charge transport is mostly limited by grain boundaries. Numerous studies have been done, throughout the past few years, to investigate the factors affecting and contributing to the charge transport of organic semiconductors. However, the interaction of an acoustic wave with these materials has not been reported yet. The primary objective of the thesis is to observe the charge transport of ambipolar electrons and holes in organic semiconductor films by means of acoustic waves and to investigate potential acousto-optic applications that may result from this interaction.
Evaluation of Out-of-Breath Speech Using Machine Learning Approaches
(2024) Sahoo, Sibasis
Stress alters the speech production mechanism. Factors like emotion, cognitive load, pathology, noisy condition (Lombard effect), physical load, sleep deprivation, etc., affect speech production. Among these, speech under emotional, noisy, and pathological conditions are investigated extensively. Little light has been shed on speech under physical load conditions, called out-of-breath speech. Such evaluation of out-of-breath conditions can be used in context-aware speech interfaces to estimate the workload level, exercise intensity of an athlete, and physical fitness of a person.
Design of RRAM-Based Integrate and Fire Neuron And Programmable Synapse for Neuromorphic Computing
(2024) Dongre, Ashvinikumar Pruthviraj
A human brain can perform compute-intensive tasks, such as multi-object recognition, reasoning, and decision-making, consuming only 20 W power. Whereas, to recognize 1000 different objects, a CPU consumes around 250 W power. Around 1011 neurons in the human brain are interconnected through approximately 1015 synapses responsible for the brain’s exceptional computing capacity. The advancements in processing technology have reduced the technology nodes drastically, which further reduced the power consumption of the processors; still, they cannot match the low power consumption of the human brain. Even with the latest technological advancements, optimizing the processors with Von Neumann architectures for speed and power becomes challenging because of the memory Bottleneck
Segmentation-based Approaches to Pre-operative and Intra-operative Brain Ultrasound Image Registration
(2024) Chel, Haradhan
The human brain is made of soft tissues that floats on cerebrospinal fluid (CSF), and it frequently shifts during a surgical process. The brain-shift prevents a neuro-navigation (NN) system from locating the diseased region. A brain ultrasound (BUS) imaging system is utilized to monitor the surgical procedure. Brain-shift can be corrected by registering the pre-operative brain ultrasound (pBUS) and the corresponding intra-operative brain ultrasound (iBUS) images. The similarity between the pBUS and the corresponding iBUS image is affected for a variety of reasons, which makes the registration difficult. This thesis developed three methods to extract similar regions in pBUS and iBUS images and register using these regions. The first method finds the common edge-rich regions from the registering image pair and is followed by the registration of those edge-rich regions through the minimization of the mean-squared registration error. The second method proposes a fast and fully automatic method for extracting the hyper-echoic(HE) regions from the registering image pairs. The patch-based approach makes the segmentation faster and robust to noise. The segmented HE regions are registered by minimizing the registration error. The third approach adopts a patch-based level-set strategy for segmenting three prominent HE regions namely, the longitudinal fissure, choroid plexus, tumor, and two anechoic regions namely, the ventricles and the resection cavity. A registration method is followed on the segmented image sections. Various gradient-based and heuristic optimizations are used for minimizing the mean-squared registration error during registration. Experiments were conducted on selected image pairs from the RESECT and the BITE datasets. For performance evaluation, the segmented ground truth images are prepared by annotating the boundaries of different regions in coordination with an expert radiologist. For comparing registration performance, common tagpoints are selected from the registering image pairs, and the improvement of mean target registration error (mTRE) after registration is analyzed. Experimental results demonstrate the superiority of the proposed segmentation-based approaches to the state-of-the-art methods.
Design and Analysis of Memelements for Low Power and Area Efficient High Frequency Applications
(2023) Ananda, Y R
Memristor, memcapacitor, and meminductor are the three types of memory elements (memelements). Memristor is the fourth fundamental circuit element based on the missing relationship between two electrical quantities, the charge (q) and the flux (φ). The memristor is considered one of the most promising nano-devices among those currently being studied for possible use in future electronic systems. The best performance features include fast switching speed, high endurance and data retention, low power consumption, high integration density, and CMOS compatibility. Memristors are being explored as a potential technology to replace CMOS for logic-in-memory systems exploiting memristive nonvolatility. It is one of the prominent characteristic features of the memristor, which effectively solves the so-called memory wall problem in conventional von-Neumann architecture. A memristive device is highly nonlinear and non-volatile, which makes this device is better storage element with greater data density than the existing memory devices. In addition, the memristor exhibits switching capability, which is more relevant for implementing logic gates, a realization of Boolean functions, and system designing, such as arithmetic units like adders, subtractors, multipliers and dividers.
Shouted, Overlapped and Competitive Speech Detection in Indian Television News Debates
(2022) Baghel, Shikha
Television (TV) news debates present expert opinions, analysis and discussions on contemporary events. These debates play a critical role in navigating public belief and understanding of socio-politically relevant topics. This encourages several agencies to analyze the TV news debate content for monitoring their influence. The availability of huge (and ever increasing) amount of news debate data calls for the necessity of automatic content analysis. TV news debates are generally argumentative in nature. Such arguments are often associated with the presence of shouted, overlapped, and competitive speech. In this context, the present thesis aims to detect these three speech categories in Indian TV news debates. The first contribution of this thesis is the development of an Indian Broadcast News Debate (IBND) corpus containing audio signals from 15 news debates (approximately 13 hours). A multi-level annotation procedure was followed to obtain the final annotations for the three targeted tasks of the thesis. The second contribution lies in the proposal of excitation source based Shouted Speech Detection (SSD). Both handcrafted and learned features from excitation source-based representations are explored for SSD. An autoencoder with Bi-GRU based architecture is used as classifier. The third aim of the thesis is to identify the overlapped speech in TV news debates. Phase-based representations of the speech signals are established as efficient features for Overlapped Speech Detection (OSD) using a CNN-LSTM based classifier. Finally, the shouted and overlapped speech classification network embeddings and their prediction scores are used as features to identify the competitive speech. It has been shown that the detection of competitive speech can be performed efficiently using high-level information of both shouted and overlapped speech.
Operation and Control of Smart Transformer Based Meshed Hybrid Microgrid
(2023) Das, Dwijasish
The excessive use of fossil fuels for power generation in the previous decades has led to various environmental concerns. Moreover, such fuels are also with limited availability. These factors have encouraged engineers and scientists to look for alternate renewal energy sources (RES) for power generation. Various RES like solar photo-voltaic (PV), wind, geothermal, etc., have been used for power generation and injection into the electric grid. However, such changing trends come with their own limitations. RES are generally intermittent in nature with widely varying levels of availability throughout the day and round the year. In addition to that, such sources also need power electronic interface for power injection into the electric grid. These factors give rise to various challenges like voltage variations, faults, harmonics in voltages and currents, islanded operation, complexity of control, etc. Various power electronic equipment such as distribution static compensator (DSTATCOM), dynamic voltage restorer (DVR), unified power quality conditioner (UPQC), static transfer switch, static current limiter, etc., are used in the electric grid to address such challenges.
Avergage Modeling and DC-Link Capacitor Voltage Regulation of SRF-dq Controlled Single-Phase ANPCI for Solar and Wind Power Applications
(2022) Missula, Jagath Vallabhai
A Voltage Source Inverter (VSI) converts DC voltage to ac voltage with adjustable magnitude and frequency. VSIs have numerous industrial applications, such as, uninterrupted power supplies, adjustable speed drives, High Voltage DC (HVDC) transmission, Flexible AC Transmission Systems (FACTS), renewable power generation, etc. Based on the number of output voltage levels, the VSIs can be classified as two-level inverters and Multi-Level Inverters (MLIs). Due to the high voltage and large power handling capability and reduced Total Harmonic Distortion (THD) in the output voltage, MLIs are preferred to two-level inverters, mostly in the medium and high-power applications. Neutral Point Clamped (NPC) MLI, Flying Capacitor (FC) MLI and cascaded H-bridge MLI are the most popular topologies among the various MLIs available in the literature.
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.
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.
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.
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
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.
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.
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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