PhD Theses
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Item Open Access Computational and Data Driven Approaches for Investigation of Microwave-Plasma Interaction(Dhirubhai Ambani Institute of Information and Communication Technology, 2023) Ghosh, Pratik; Chaudhury, BhaskarMicrowave-plasma interaction and High power microwave (HPM)breakdown involving plasma formation have been studied theoreticallyas well as experimentally since the 1950s for a wide variety of applications.Microwave plasma interaction can be classified into two broad categories,firstly involving low power non-ionizing waves and secondly high powerionizing waves leading toHPMbreakdown. Early studies onHPMbreakdownprimarily focused on the determination of the breakdown field as a functionof pressure, frequency and pulse duration. However, only recently, detailedexperimental investigations of the plasma dynamics during breakdownhave been possible with the use of sophisticated high-speed ICCD cameras.Particularly, in the past few years, several experiments and numericalsimulations using millimeter and sub-millimeter wave irradiation ( 100 GHz)at high pressures (ten to hundreds of Torr) have been carried out. The renewedinterest in this area is primarily because of two reasons. Firstly, the potentialapplications of such discharges to aerodynamic flow control, combustionignition, flame stabilization and to propulsion have been investigated veryrecently. Secondly, the dynamics of high frequency wave breakdown at highpressures leading to formation of complex plasma structures (spatio-temporalpropagation of plasma) such as self-organized plasma arrays is a subject ofgreat interest from scientific point of view.To completely understand the physics and properties of different types ofdischarges associated with microwave breakdown, it is crucial to furtherimprove our current understanding of the microwave-plasma interactionand plasma formation at high pressures. To fully utilize the potential of thispromising area of research, it is crucial to understand microwave-plasmainteractions, both in the context of low-power non-ionizing microwaves andwhen the power is sufficient to ionize the gaseous species and form plasma.Modeling and simulation of the strong coupling between the high frequencyEM waves and the plasma is still a challenging research problem due tothe different time and space scales involved in the process. Particularlyaccurate 2D/3D simulations are computationally very expensive and werequire new efficient computational approaches to investigate this problemfor real life applications. Most of the computational studies reported in theliterature till now (particularly recent 2D simulations) have focused only onthe wave scattering by the plasma and ionization-diffusion mechanism forplasma evolution (time scale of 100s of nanoseconds) due to computationalconstraints. Researchers have primarily studied this problem using asimple model wherein Maxwell�s equations have been coupled with plasmacontinuity equations and these models have been used to investigate theplasma dynamics in nanosecond timescales.As a first step, we have developed a comprehensive computational modelfor investigating microwave-plasma interaction and different kinds ofmillimeter wave breakdown at high pressures. An in-house 2D simulatorhas been implemented in C language and the validity of the code has beenestablished by directly comparing the simulation results with the experimentalobservations available in the literature. The computational tool consist ofthree computational solvers (EM wave solver, Plasma solver and Fluid solver)coupled with each other. The inputs to this computational tool are the fieldstrength of the EM wave, frequency of the wave, pressure and gas details. Theimportant output required for investigating the physics of plasma dynamicsare: plasma density, electric field distribution, electron temperature, gasdensity distribution etc.As a second step, to address the computational challenges associated withsuch simulations, a self-aware mesh refinement algorithm has been presentedthat uses a coarse mesh and a fine mesh that dynamically expands based on theplasma profile topology to resolve the sharp gradients in E-fields and plasmadensity in the breakdown region. The dynamic mesh refinement (DMR)technique is explained in detail, and its performance has been evaluatedusing two metrics, the accuracy and efficiency, on a standard benchmarkmicrowave breakdown problem. Different 2-D simulations are performed tocapture the front velocity and the filamentary pattern formation, and, resultsare compared for DMR (different refinement factors (r = 2, 4)) with the resultsobtained from uniform fine mesh. From the efficiency analysis, we observea speedup of 8 (of the order of O(r3), when the refinement factor (r) is 2)compared to a traditional single uniform fine mesh-based simulation. Thetechnique is scalable and performs better when the problem size increases.Two applications related to HPM breakdown have been explored usingour in-house 2D simulator, one associated with the protection of electroniccomponents and the second on HPM swtching. Breakdown thresholds, thefield strength and the initial plasma density that determines breakdowntime for such applications are reported. The dependence of cutoff time oninitial plasma as well as strength of microwave E-field are investigated. Thetransmission and rejection capability of plasma for certain frequencies areinvestigated. Additionally, effect of gas heating on the HPM breakdowninduced plasma and the cutoff time is studied for switching and limiter action.We propose a completely new machine learning based data driven approachfor investigation of microwave-plasma interaction. Complete deep learning(DL) based pipeline to train, validate and evaluate the model has beendiscussed in this thesis. A convolutional neural network (CNN)-based deeplearning model, inspired from UNet with series of encoder and decoder unitswith skip connections, for the simulation of microwave-plasma interactionhas been discussed. The microwave propagation characteristics in complexplasma medium pertaining to transmission, absorption and reflectionprimarily depends on the ratio of electromagnetic (EM) wave frequency andelectron plasma frequency, and the plasma density profile. The scattering ofa plane EM wave with fixed frequency (1 GHz) and amplitude incident ona plasma medium with different Gaussian density profiles (in the range of1 � 1017 ? 1 � 1022m?3) have been considered. The training data associatedwith microwave-plasma interaction has been generated using 2D-FDTD(Finite Difference Time Domain) based simulations. The trained deep learningmodel is then used to reproduce the scattered electric field values for the1GHz incident microwave on different plasma profiles with error margin ofless than 2%. We compare the results of the network, using various metricslike SSIM index, average percent error and mean square error, with thephysical data obtained from well-established FDTD based EM solvers. Theproposed deep learning technique is significantly fast as compared to theexisting computational techniques, and can be used as a new, prospectiveand alternative computational approach for investigating microwave-plasmainteraction in a real time scenario.Most of the plasma applications and research in the area of low-temperatureplasmas (LTPs) is based on accurate estimation of plasma density and plasmatemperature. The conventional methods for electron density measurementshave major disadvantages of operational range (not very wide), cumbersomeinstrumentation, and complicated data analysis procedures. To address suchpractical concerns, the thesis further proposes a novel machine learning(ML) assisted microwave-plasma interaction based strategy which is capableenough to determine the electron density profile within the plasma. Theelectric field pattern due to microwave scattering is measured to estimate thedensity profile. The proof of concept is tested for a simulated training data setcomprising a low-temperature, unmagnetized, collisional plasma. Differenttypes of Gaussian-shaped density profiles, in the range 1016 ? 1019m?3,addressing a range of experimental configurations have been considered inour study. The results obtained show promising performance in estimatingthe 2D radial profile of the density for the given linear plasma device.The performance of the proposed deep learning based approach has beenevaluated using three metrics- SSIM, RMSLE and MAPE. The favourableperformance affirms the potential of the proposed ML based approach inplasma diagnostics and in future to replace existing plasma diagnostics.In conclusion, the thesis presents new approaches for investigation ofmicrowave-plasma interaction and HPM breakdown, which are significantlyefficient compared to existing simulation techniques. To the best of ourknowledge, this is the first effort towards exploring a data-driven DL basedapproach for the simulation of complex microwave plasma interaction. Thesimulations presented in the thesis provide a better understanding of bothionizing and non-ionizing applications of microwave-plasma interaction.They contribute to the study of complex plasma dynamics associated withhigh-frequency HPM breakdown-induced plasma, with potential applicationssuch as switching/limiters, and plasma diagnostics.Item Open Access Desertification characterization using predictive soil modelling and pattern recognition(Dhirubhai Ambani Institute of Information and Communication Technology, 2023) Dave, Viral A.; Ghosh, RanenduThis thesis presents a hierarchical methodology for land degradation mapping,land use land cover classification, degradation process identification and map-ping using multispectral LISS-3 images. The study aims to demonstrate the im-portance of remote-sensing images for various applications, both social and en-vironmental. The study compares the results of different algorithms for differentterrains, demonstrating that Simple Linear Iterative Clustering (SLIC) segmenta-tion with the random forest(RF) method outperforms CNN and pixel-based Sup-port Vector Machine (SVM) with an accuracy of 85% for level 1 land cover clas-sification. Vegetation degradation in forest areas is assessed in central parts ofGujarat, India, and land degradation in agricultural areas due to soil salinity isstudied, particularly in southeastern parts of Gujarat, India. ML algorithms likesupport vector machine(SVM) and RF was applied to different features to identifythe degradation process. Temporal data were used to find the severity of deserti-fication using the change in degraded areas.Further, it discusses soil degradation causing desertification and severely re-ducing potential soil productivity. The study uses machine learning algorithmsand an ANN-based model to predict soil properties like EC, pH, and OC, whichare important indicators of soil degradation. Environmental parameters are takenas covariates in prediction models, including vegetation indices, terrain indices,soil parameters, spatial attributes, and meteorological parameters of the study re-gion. Field soil sampling data of the study region obtained from Soil Health Card(SHC) for the year 2014 is incorporated in training the model. The SHC data isdivided into different ratios for training and testing the model. The SCORPANmodel is considered the base approach for the development of the ANN-basedprediction model. Moreover, the thesis also discusses the mapping of vulnera-ble areas to desertification. The study combines remote sensing and geographicinformation system (GIS) to map sensitive areas. Two different approaches wereused for vulnerability assessment: Mediterranean Desertification and Land Use(MEDALUS) approach and the fuzzy logic (FL) method. Soil, climate, land uti-lization, geography, and vegetation contribute to the land degradation of anyarea. However, man�s intervention leads to significant changes in the environ-ment, making socio-economic factors a considerable input to assess desertificationvulnerability. Indices related to these factors are generated, and both methods areused to find the severity level of the desertification vulnerability in the Panchma-hal district.Lastly, the role of climate in the process of desertification is discussed. Thestudy uses the aridity index (AI), which incorporates most of the weather datalike temperature, rainfall, humidity, wind, and solar radiation, to identify the de-sertification hot-spot using AI over the Gujarat state. The study uses weatherdata from more than 18 locations all over Gujarat for the past 20 years to calcu-late AI, and the FAO Penman-Monteith method was used to calculate PET. Thestudy generates an annual AI map for the whole of Gujarat using these valuesand compares it with a globally published AI map. It also compares the changein climate with the change in vegetation over the years using the vegetation in-dex for Gujarat. In summary, this thesis provides a comprehensive approach toland degradation mapping using degradation process identification, soil predic-tion, and climate variable using geospatial technology and machine learning. Thestudy demonstrates the importance of remote sensing images in various applica-tions, including social and environmental. The study employs different machinelearning algorithms and approaches to achieve high accuracy and identify vul-nerable areas to desertification. The study also highlights the importance of soilproperties and climate in the process of desertification.Item Open Access Water Footprint in the Context of Urban Water Management: Challenges and Opportunities(Dhirubhai Ambani Institute of Information and Communication Technology, 2023) Banerjee, Alik; Parikh, Alka; Tiwari, MukeshWater, a crucial resource in preserving the ecology in good shape, has becomescarce. Water footprint (WF) measure has been proposed in the literature to understandthis prevailing water crisis. The WF, which consists of green, blue, andgrey, can be defined as the green water footprint (WFgreen) that shows how muchwater is used by forests and non-irrigated agriculture; the blue water footprint(WFblue), shows the amount of water used by irrigated agriculture, industry, andresidences, and grey water footprint (WFgrey) shows how much water would berequired to neutralize the pollution in the water and bring it back to the acceptabledischarge water quality. This study conducted a comprehensive WF calculationin Purulia, Dhanbad, and Ranchi municipalities of West Bengal and Jharkhand,India. The primary reasons for choosing these municipalities were that they arewater-scarce and have an inadequate municipal water supply system.The researcher used published data to estimate WF. The results show WFgreenvalues depict that Purulia reports the highest mean values (182.6 to 296.3 (M3*103)per square kilometer (sq km)), followed by Dhanbad (170.3 to 241.2 (M3*103) persq km), and then Ranchi (131 to 219.2 (M3*103) per sq km) for four consecutiveyears (2016-19). These figures imply that Purulia overuses its water resourcesin agriculture, and hence its high WF green needs to be corrected by increasingwater productivity. Dhanbad�s high WF is because of the water consumption byits forests. The high WF is not of concern given that the forests help hold up thesoil and water. Ranchi�s WF is low because it has less land under forests andagriculture.Moreover, WFblue values of 2019 illustrate that Ranchi reports the highest (108M3 per capita), followed by Purulia (81.5M3 per capita), and Dhanbad reports theleast (68.8M3 per capita). The primary factor for getting such results is high runofffollowed by evaporation, and then the municipality supplies water. Therefore,Steps should be taken to retain the rainwater in some form in the soil and manmadechannels.In addition, this study examines the per capita per-day water availability among272 sample households of different income classes to understand the ground-levelsituation. The result reports that slum dwellers are the worst sufferers since theydo not get even the bare minimum amount of water � 70 lpcd, while affluent peopleliving in apartments or bunglows suffer no shortage. The study finds that thisinequality prevails because the primary water source is groundwater, accessibilityto which depends on wealth ownership. As the residences change from poorto non-poor, people depend less on centralized water supply and more on tubewells/bore wells. This is because the water supplied through the municipality isnot enough. Also, the correlation between sources of water and seasonal dearthshows significantly less value, which signifies that seasonal dearth does not relateto which water sources households are fetching the water from. Water quality isterrible for all income classes, but the rich can purify it through R-O.Furthermore, the study also found that in the case of municipality water balance,all three municipalities are going through a deficit water balance. For Purulia,it is 14 (M3*103) per day; for Dhanbad, it is 490.4 (M3*103) per day; andfor Ranchi, it is 439.6 (M3*103) per day, respectively. This means that water withdrawalis far more than the recharge rate. Water availability is expected to be evenmore compromised as we move forward.In such a situation, check dams, ponds, wells, reservoirs, etc., seem to be helpingin water conservation. In addition, water recycling, as tried out by Surat MunicipalCorporation, can also reduce WF. Based on these practical solutions, in theend, some policy recommendations are proposed for water conservation.Item Open Access Feature for Live and Spoofed Speech Detection(Dhirubhai Ambani Institute of Information and Communication Technology, 2023) Gupta, Priyanka; Patil, Hemant A.The authorization to access specific information is given by a biometric system.Biometric systems are used for security purposes in a way that they prevent unauthorized access to important information or data (information privacy). The accessgranted by the biometric is done by capturing traits of humans, which make allhuman beings unique w.r.t. that particular trait. This thesis focuses on voicebased biometric systems, also known as Automatic Speaker Verification (ASV)systems, given that speech is the most natural and powerful form of communication used by humans to communicate with the outside world. It is the most intuitive, simple, and easy-to-produce characteristic. Since ASV systems have beenused for applications, such as in banking transactions and access to buildings associated with classified information, only authorized legitimate or genuine usersare granted access.ASV systems suffer from vulnerabilities to attacks and can be compromisedat various stages. The attacks may be categorized as direct and indirect attacks,depending on the extent of the attacker�s accessibility to the ASV framework. Besides, due to the recent commercial success of several Intelligent Personal Assistants (IPAs), also known as voice assistants, such as Speech Interpretation andRecognition Interface (SIRI), Amazon Alexa, Google Home, and so on, manyvoice-enabled devices in Internet of Things (IoT) have been commonly prone tospoofing attacks. To that effect, there is active research in the direction of designing countermeasure systems for ASV systems, particularly for spoofing attacks,namely, Speech Synthesis (SS), Voice Conversion (VC), and replay.This thesis is a humble attempt to alleviate some of the research gaps in designing features for countermeasure systems. In particular, this thesis proposesQuadrature Energy Separation Algorithm (QESA) in the light of incorporating thequadrature-phase component with the in-phase component of the signal. To thateffect, an existing feature set for replay Spoofed Speech Detection (SSD), namely,CFCCIF-ESA is extended to the CFCCIF-QESA feature set for enhanced performance of the countermeasure system. The performance of the proposed CFCCIFQESA feature set is evaluated on various datasets for various spoofing attacksgiven in the literature. Furthermore, the existing Linear Frequency Residual Cepstral Coefficients (LFRCC) feature set is optimized w.r.t. to its Linear Prediction(LP) order for the replay SSD task. In particular, it is found that the LP orderneeded for a good prediction of speech is not the same as that needed for thereplay SSD task. The resulting optimized LFRCC feature set is evaluated on theASVSpoof 2019 PA dataset. In addition to this, another feature, known as the uncertainty vector (u-vector), is developed from the Heisenberg�s uncertainty principle in the signal processing framework. The proposed u-vector is evaluated usingthe ASVSpoof 2017 dataset for replay attacks.Furthermore, in the direction to make countermeasure systems independent ofthe type of spoofing attack, features have been proposed for the Voice LivenessDetection (VLD) task. VLD is performed by the detection of pop noise which is thediscriminating acoustic cue present in live speech, produced due to the breathingeffect captured by the microphone when the speaker�s mouth is close to the microphone. The work on VLD in this thesis is based on two key hypotheses, namely,Parseval�s energy equivalence for STFT, CWT, and analytic CWT, whereas the second hypothesis is that the energy of pop noise decreases with the distance of a microphone from the speaker that is used to capture genuine speech. The proposedfeatures for VLD in this thesis are wavelet-based, wherein three wavelets are used,namely, Bump, Morlet, and Morse wavelet, where Morse wavelet is presented as asuperfamily of analytic wavelets, called as Generalized Morse Wavelets (GMWs).Detailed experimental analysis such as speaker-microphone proximity, the effectof phoneme type, and the effect of frequency range is studied.Apart from this, the security of speech data is also taken into account and thisthesis proposes an improved Voice Privacy (VP) system, which is based on Linear Prediction (LP) of speech. Furthermore, the VP system is studied along withthe attacker�s perspective using the target selection approach, and particularly,target selection w.r.t. twins is studied, wherein the most vulnerable twin-pair(i.e., target) is selected. Lastly, some of the proposed feature sets in this thesis arealso evaluated for tasks related to other Assistive Speech Technologies (AST) applications, such as the classification of healthy vs. pathological infant cries, anddysarthric severity-level classification.Item Open Access Design of Quasi-periodic and Aperiodic Array Lattices to Improve Array Antenna Performance(Dhirubhai Ambani Institute of Information and Communication Technology, 2023) Mevada, Pratik; Gupta, SanjeevThe thesis addresses one of the possible solutions to the grating lobe occurrence in the beam steerable periodic arrays for large angular beam scans. The controlled aperiodicity has been introduced to the periodic array to achieve the same and the object is set to design the beam steerable array antenna offering improvement in the peak SLL performance with beam scan and reducing the number of array elements. The designs of such aperiodic and quasi-periodic array antennas have been carried out using the innovative strip projection (SP) based method. The strip projection method uses the area of rotated higher dimensional lattice and projects it to lower dimensions to generate an aperiodic array. The designs of aperiodic linear and planar arrays have been carried out to achieve �30� conical beam scan range with peak SLL <-10dB over -90� to 90o angular range. The novelty of the proposed SP method is that the number of optimization variables is fixed and independent of the size of the aperiodic array. The reported techniques to generate the aperiodic arrays lack in this aspect. The proposed method facilitates a significant reduction of the design efforts, especially in the case of the larger beam-steerable arrays. The proposed method is relatively straightforward to implement compared to the reported algorithms.The performance of the aperiodic linear array antenna has been compared with the aperiodic arrays designed using evolutionary optimization algorithms, namely, genetic algorithm (GA), particle swarm optimization (PSO) and Jaya algorithms and it is found that the proposed design method is comparatively more efficient and faster. The aperiodic array lattice is also populated with X-band electromagnetically coupled patch antenna integrated with a phase shifter and simulated. The aperiodic patch array antenna has been fabricated and characterized in the anechoic chamber. The comparison of the measured and simulated results is presented. In measurement, a significant improvement of 5.72dB in peak SLL is achieved at �30� beam scan angle.The design of an aperiodic planar array antenna has been carried out for a 15 x 15? aperture size. The optimized array has 21.9% less elements than the conventional periodic rectangular lattice. The Pinwheel based aperiodic array lattice has also been designed for the same beam scan requirement and presented for comparison. It is observed that the peak SLL performance is maintained at <-11.63dB and <-12.70dB over the 0�-30� beam scan range by the proposed aperiodic array and Pinwheel based array, respectively. Moreover, both types of lattice have been populated with S-band cubic-shaped dielectric resonator (DR) antenna element and their simulations have been carried out using a 3D electromagnetic solver. For the quantification of the aperiodicity in the structure, position standard deviation (O) is also defined and computed.The projection concept is generalized and implemented to design a quasi-periodic beam steering array antenna by projecting the vertices of co-centric polyhedrons on the 2D aperture plane. The modelling and design of quasi-periodic array lattice are carried out by projecting the vertices of co-centric polyhedrons, namely dodecahedrons and icosahedrons, on the aperture plane. The angular orientation of the polyhedrons is optimized to achieve a 4.2dB peak SLL improvement for a �30� beam scan. The optimized array lattice is populated with cubic shaped DR based elements and integrated with a Voronoi based metallic fence and decoupling network (DCN) for mutual coupling improvement between the elements. The polyhedron projection based concept has been extended to design interferometric arrays for radio astronomy. The stereographic projection has been used for the projection of vertices of the rotated polyhedron and forms the aperiodic array, whose performance is subsequently evaluated for radio interferometric imaging. The necessary test framework for imaging of 1" sample image using the designed array lattice has been developed in Matlab and the array lattice has been optimized to achieve the maximum fidelity index (FI). The various cases of the aperiodic array with various combinations of polyhedrons have been evaluated and compared with the Giant Metrewave Radio Telescope (GMRT) array. The aperiodic array generated by the three co-centric polyhedrons, i.c., dodecahedron, octahedron and tetrahedron, is proved to have a better fidelity index (FI) over the various declinations (8). In addition, the projection based aperiodic array antenna has also been evaluated for minimum variance distortionless response (MVDR) type of beamformer, which is a widely used technique in various fields like communication, radar, acoustics, and sonar. Matlab codes are developed to implement DoA estimation using the MVDR technique and applied to the conventional periodic and proposed aperiodic linear arrays. It is shown that aperiodicity in the element position has eliminated the unwanted lobes in the detection range.Item Open Access Handcrafted Features for Anti-Spoofing(Dhirubhai Ambani Institute of Information and Communication Technology, 2023) Patil, Ankur T.; Patil, Hemant A.Amongst various biometrics, voice is the most natural and convenient way of the communication for human-machine interaction. To that effect, the use of AutomaticSpeaker Verification (ASV) for authentication is increasing in various sensitiveapplications, which create a chance for fraudulent attack as attackers canbreach the authentication by using various spoofing attacks. To alleviate this issue,we can either develop an ASV system, which is inherently protected fromthe spoofing attacks or develop a separate countermeasure (CM) system that canassist the ASV system in tandem against the spoofing attacks. The earlier approacheshave trade-off between performance of the ASV system and robustnessagainst spoofing attacks. Hence, it would be advantageous to implementthe separate Spoof Speech Detection (SSD) system, and hence majority researchattempts are focusing upon the later approach. To that effect, various internationalchallenge campaigns were organized during INTERSPEECH conferences,such as ASVSpoof 2015, ASVSpoof 2017, and ASVSpoof 2019, which providesstandard datasets, protocol, and evaluation metrics. This thesis focuses on developingthe handcrafted feature sets for CM systems against the spoofing attacks,namely, Speech Synthesis (SS), Voice Conversion (VC), and replay. These featuresets are either developed by applying the subband filtering on the speech signalsor derived from the spectrogram representations.In this thesis work, various subband filtering-based feature sets are developed,namely, Enhanced Teager Energy-Based Cepstral Coefficients (ETECC), Cross-Teager Energy Cepstral Coefficients (CTECC), and Energy Separation AlgorithmbasedInstantaneous Frequency estimation for Cochlear Cepstral Features (CFCCIFESA).These feature sets are either modification in Teager Energy Operator (TEO)-based representations or utilization of Energy Separation Algorithm (ESA) for InstantaneousFrequency (IF) estimation. The ETECC feature set is developed byaccurately estimating the energies in high frequency regions using compensationof the signal mass. In Teager Energy-Based Cepstral Coefficients (TECC), TEO isutilized to estimate the energy, which considers the approximation sin(?) ? ?,which is applicable for low frequencies. However, the discriminative information or the replay detection is prominently present in the mid and high frequency regions.Hence, ETECC feature set is proposed to obtain the efficient representationfor SSD task by accurately estimating the energies at high frequency regions. Furthermore,signal processing-based approach is presented for replay SSD in VoiceAssistants (VAs). It utilizes the Cross-Teager Energy Operator (CTEO) for extractingthe acoustic cues from replay speech. CTEO gives the interactions amongthe multi-channel signal by estimating the cross-Teager energies between signals.To that effect, it is necessary to efficiently represent the acoustic cues for replayspoofs and hence, maximum cross-Teager energies among the subband filteredmulti-channel signal is utilized for feature representation. Thus, the rationale behindoptimal channel selection is to find the most noisy (distorted) transmissionchannel. The cepstral features extracted using CTEO are referred as Cross-TeagerEnergy Cepstral Coefficients (CTECCmax). The experiments are performed usingRealistic Replay Attack Microphone Array Speech Corpus (ReMASC), which is speciallydesigned for the replay SSD in VAs. The proposed CTECCmax feature setperforms better than other state-of-the-art feature sets. The proposed CFCCIFESAfeature set combines the magnitude and phase (in the form of IFs) informationto develop the efficient feature representation for SS, VC, and replay spoofingattacks. The proposed CFCCIF-ESA utilizes ESA to accurately estimate themodulation patterns due to their relatively low computational complexity, hightime resolution, and instantaneously adapting nature. In previously proposedCochlear Filter Cepstral Coefficient Instantaneous Frequency (CFCCIF) featureset, IFs were estimated using Hilbert transform-based approach, whose time resolutionis relatively low (as it requires a segment of speech) as compared to theESA-based approach.Furthermore, Constant-Q Transform (CQT)-based feature representation andSpectral Root Cepstral Coefficients (SRCC) are developed using spectrogram representationsand effectively utilized for anti-spoofing. According to Heisenberg�suncertainty principle in signal processing framework, the CQT has variable spectrotemporalresolution, in particular, better frequency resolution for low frequencyregion and better temporal resolution for high frequency region. This property ofthe CQT representation is effectively utilized to identify the low frequency characteristicsof pop noise. Here, pop noise is attributed to the live speaker and hence, itis exploited for Voice Liveness Detection (VLD) task. SRCC feature set is derivedfrom the theory of homomorphic filtering, which obeys the generalized superpositiontheory. In spectral root homomorphic deconvolution system, convolutionallycombined vectors are mapped to another convolutionally combined vector space, where signal components are more easily separable by liftering operation.Logarithm operation in Mel Frequency Cepstral Coefficients (MFCC) extractionis replaced by power-law nonlinearity (i.e., (�)?) to derive SRCC feature set. Theproper choice of the ? depends upon the pole-zero arrangements in the transferfunction obtained from the speech signal and it helps to capture the system informationof the speech signal, with a minimum number of cepstral coefficients. Inthis thesis, optimum ?-value is chosen by estimating the energy concentration incepstral coefficients and by visualizing the spectrogram w.r.t. ?-value.To validate performance of our proposed feature sets, the experiments are performedusing various datasets, state-of-the-art feature sets, classifiers, and evaluationmetrics. The development and performance analysis of each proposedfeature set is provided in the corresponding chapters. Furthermore, other contributionsin the thesis, namely, feature normalization for anti-spoofing, analysis onDelay and Sum (DAS) vs. Minimum Variance Distortionless Response (MVDR)beamforming techniques for anti-spoofing in VAs, severity-level classification ofdysarthric speech, and classification for normal vs. pathological cries, are alsodiscussed. Thesis concludes with potential future research directions and openresearch problems.Item Open Access Heavy Metal Detection in Crops and Soil Clay Mineral Abundance Mapping using Hyperspectral Data(Dhirubhai Ambani Institute of Information and Communication Technology, 2023) Priya, Swati; Ghosh, Ranendu; Mandal, SrimantaPresence of heavy metal in crops is an indicator of environmental pollution. Theheavy metals found in the plant indicate that the specific metal exists in the terrestrialenvironment. These metals affect leaves� spectral characteristics and interferewith plants� biochemical features, such as chlorophyll concentration and photosynthesis.Accurate detection of heavy metals in plants is necessary for agriculturalmanagement and to preserve ecological balance. Field spectroscopy techniquesare used to measure the spectral changes triggered due to contaminationwith heavy metals. The advantage of these remote sensing approaches to assessheavy metal contamination is that they can frequently collect data across a widegeographic area.The study mainly focuses on detecting different levels of heavy metal pollutionfrom airborne hyperspectral data using reference data from in situ controlledpot experiments. We constructed a training set spectrum from a controlled experimenton cotton and tobacco for two important heavy metals, Lead (Pb) andCadmium (Cd). Cotton and tobacco crops were grown in pots after artificiallycontaminating the soil with four Pb and Cd heavy metal treatments. The hyperspectraland biochemical data generated spectra of heavy metal concentrations atdifferent crop growth stages. Standard reflectance spectra at different contaminationlevels do not show significant changes at different wavelengths due to thepresence of heavy metal. These spectra were further decomposed using wavelettransform at different levels to capture the subtle changes in spectra using thedetailed component of wavelets. The reconstructed detailed wavelet reflectanceat the third level of decomposition was found to be significant with heavy metalstress. The correlation analysis established that the wavelength range of 651-742nano meter (nm) in cotton was sensitive to Pb stress, and 631-802 nm was sensitiveto Cd stress in tobacco. The reconstructed detail reflectance at a particularwavelength was then further used as reference spectra with different heavy metallevels to map heavy metal pollution.The AVIRIS-NG data obtained for the study area was first classified to identifythe tobacco crop in the Anand region and the cotton crop in the Surendranagarregion using a combination of Autoencoder (AE) for feature extraction followedby an artificial neural network for classification. The training data obtained fromthe pot experiment were utilized to map Pb and Cd pollution from classified airbornehyperspectral data from Airborne Visible InfraRed Imaging Spectrometer- Next Generation (AVIRIS-NG) using a spectral matching algorithm known asDynamic Spectral Warping (DSW). The results confirm the efficiency of the developedalgorithm in estimating Cd content in tobacco and Pb content in cottoncrops. The model was validated by collecting the exact field points and heavymetal concentration, which shows a promising result for this algorithm.Diverse soil minerals may be easily identified through modern hyperspectraltechnology for remote sensing. The aerial hyperspectral sensor�s enhanced spatialand spectral resolution can identify the abundance of several clay minerals, suchas Kaolinite, Montmorillonite, and Illite. This study maps the clay mineral distributionin the Udaipur area of Rajasthan and the Ambaji region of Gujarat usinghyperspectral data acquired by the AVIRIS-NG sensor on an airborne platform.The representative soil sampling sites were selected from hyperspectral datausing the Spectral Feature Fitting (SFF) algorithm. X-ray Diffraction (XRD) analysiswas carried out to find different clay minerals in the samples. Then the regressionanalysis was carried out to find the relation between Absorption PeakDepth (APD) extracted from hyperspectral data corresponding to the actual locationof sampling sites and the corresponding clay percentage obtained from XRDanalysis. Regression analysis between absorption peak depth values estimatedfrom hyperspectral data at 2205 nm � 2214 nm spectral region of soil samplingsites and corresponding clay content value showed a significant relationship. Theregression line obtained for the known pixel is used to prepare the mineral abundance map over the study area. The study over the Udaipur region shows thedominance of montmorillonite clay minerals, and the Ambaji region showed anabundance of kaolinite.Item Open Access Resource Utilization for Raw Data Query Processing : Optimizing Required Resources & Maximizing Utilization of Existing Resources(Dhirubhai Ambani Institute of Information and Communication Technology, 2023) Patel, Mayankkumar Lalabhai; Bhise, MinalScientific experiments and modern applications generate large amount of dataevery day. Many such applications store the data in raw format initially, as theschema is not known. The traditional database management system (DBMS) requiresthe entire dataset to be loaded before querying it. Data loading requires asignificant amount of time and resources, which increases application latency andrunning costs. In-situ engines eliminate the data loading requirement, therebyreducing upfront resource utilization. However, they suffer from high query executiontime (QET) and reparsing. It has been observed that state-of-the-art in-situand DBMS do not utilize available resources efficiently.This thesis proposes ResourceAvailability andWorkload aware Hybrid Framework(RAW-HF) to tackle underutilization of resources. It optimizes requiredresources (ORR) and maximizes utilization of existing resources (MUER) for resourceefficient processing of raw datasets. It is a hybrid system consisting of anin-situ engine and DBMS. The in-situ engine reduces data to query time whileDBMS moderates the raw data reparsing. Hybrid framework for raw data queryprocessing and resource monitoring is developed during the initial phase. Analysisof resource monitoring indicated substantial underutilization of resources.The optimization of required resources is done using Query Complexity Aware(QCA) and Workload and Storage Aware Cost-based (WSAC) algorithms. QCAand WSAC also improved workload execution time (WET). Further resource utilizationis improved by Maximizing Utilization of Available Resources (MUAR)algorithm.RAW-HF is demonstrated using scientific experiment datasets like Sloan DigitalSky Survey (SDSS) and Linked Observation Data (LOD). RAW-HF query andresource performances are compared with state-of-the-art techniques. The stateof-the-art techniques which allocate resources accurately based on historical resourceconsumption data do not address ad-hoc queries and multi-format joins.On the other hand, RAW-HF addresses ad-hoc queries and also supports multiformatjoins. The ORR phase of RAW-HF reduced the WET by 26% compared tothe state-of-the-art Partial Loading technique. MUAR component of RAW-HF iscapable of estimating work memory value with 15-20% error required to achievethe best query performance with only single query run data. A comparison ofMUAR with machine learning based techniques like PCC and AutoToken is alsopresented. The overall CPU, RAM, and IO resource utilization has been improvedby 61-91% over traditional database management systems. Although the Partialloading technique requires 33% lesser RAM than RAW-HF, it needs 24% more IO.The improvement in dataset size processing capacity is also estimated for SDSSdataset. The estimation proposes that RAW-HF framework can be used to processlarge application datasets efficiently using existing resources.Item Open Access Study of frames in Hilbert C?-modules and their representation as regular k-distance sets(Dhirubhai Ambani Institute of Information and Communication Technology, 2022) Rajput, Ekta; Sahu, Nabin KumarThe definition of basis in the study of vector space is very antagonistic. As a resultof that, one might look for a prominent substitute. Frames are such a notion, asthe linear independence between the frame elements is not required. Further, theadditional degree of freedom coming from the structure of C?-algebra A enrichesthe theory of frames in Hilbert C?-modules. This thesis aims to introduce variousnotions of frame theory in Hilbert C?-modules as they are the subjects of the recentstudy. We also introduced the notion of a regular k-distance frame in Hilbert space.The thesis is planned to be organized into six chapters, along with the introductoryand literature survey chapter and a chapter for conclusions and the scope offuture research. Chapter 1 of the thesis is the introductory chapter, where a briefintroduction of frame theory in Hilbert space as well as in Hilbert C?-module hasbeen discussed. The interest in taking the particular research problem has beenoutlined. A concise but sufficient literature survey has been presented. In Chapter2, we introduced the concept of a regular k-distance frame in Hilbert space as wellas focused on k-distance tight frames for the underlying space. We have introducedthe definition of dual frames for a regular k-distance set. Finally, the perturbationresult for regular k-distance frames is established. The objective of Chapter 3 is tointroduce woven g-frames in Hilbert C?-modules and to develop its fundamentalproperties. This study establishes sufficient conditions under which two g-framespossess weaving properties. We also investigated the sufficient conditions underwhich a family of g-frames includes weaving properties. Chapter 4 is concernedwith weaving K-frames in Hilbert C?-module. We introduced the concept ofweaving K-frames and defined an atomic system for weaving K-frames in HilbertC?-module. We studied weaving K-frames in this chapter from the operator theoretic approach. Moreover, we gave an equivalent definition for weaving Kframes.In Chapter 5, we introduced the notion of a controlled K-frame in HilbertC?-modules. We established the equivalent condition for controlled K-frame inHilbert C?-modules. We investigated some operator theoretic characterizations ofcontrolled K-frames and controlled Bessel sequences. Moreover, we established therelationship between the K-frames and controlled K-frames. We also investigatedthe invariance of a C-controlled K-frame under a suitable map T. At last, weproved a perturbation result for controlled K-frame in Hilbert C?-modules.An equivalent definition is much easier to apply and permits us to study the varioustypes of frames from the operator theory point of view. The multiple notions offrame theory developed in this thesis will draw the attention of researchers to workin this area. At last, in Chapter 6, we summarize all the work that has been doneso far and feature the potential avenues for the future scope of research.Motivation and Objective of the ThesisIn a vector space, a set of vectors is referred to as a basis if every element in theunderlying space can be expressed in terms of a finite linear combination of thebasis vectors uniquely. The definition of basis in the study of vector space is veryantagonistic. As a result of that, one might look for a prominent substitute. Framesare such a notion as the linear independence between the frame elements is notrequired. In addition to that, the additional degree of freedom coming from thestructure of C?-algebra A enriches the theory of frames in Hilbert C?-modules.We intend to see whether the results of frame theory in Hilbert spaces hold forframe theory in Hilbert C?-modules and, if not, then to study what modificationswe need. In Chapter 2, we investigated the concept of a regular k-distance framein Hilbert space which is the extension of a regular two-distance frame in Hilbertspace. A regular two-distance frame is a particular type category of frame whichhas some nice properties. Motivated by this, we studied regular k-distance frames,in particular, regular tight k-distance frames in Hilbert space. Tight frames are thosein which the lower and upper frame bounds are equal. Tight frames play a key rolein wide applications as tight frames look like a more natural way to reconstruct vectors. Tight frames are closest to orthonormal bases as they are a redundant setof vectors and have properties like basis. In Chapter 3 and Chapter 4, we studiedthe concept of woven frames in Hilbert C?-modules. Recently many people gotsignificant results in frame theory by generalizing the results which are present inHilbert space to Hilbert C?-modules. The concept of weaving frames is applicablein wireless sensor networks that require distributed processing under differentframes, as well as pre-processing of signals using Gabor frames. Generalizedframes (or g-frames) include standard frames, bounded invertible linear operators,and many recent generalizations of frames. g-frames in Hilbert C?-modules interestmany useful properties with their comparable tools in Hilbert space. As we know,K-frames and standard frames diverge in many aspects; we introduce the conceptof weaving K-frames and define an atomic system for weaving K-frames in HilbertC?-modules. As it is easier to work, we gave an equivalent definition for weavingK-frames and characterized weaving K-frames from the operator theory point ofview. In Chapter 5, we introduced the notion of controlled K-frames in Hilbert C?-modules. Controlled frames have been an area of interest because of their expertisein improving the numerical efficiency of iterative algorithms for inverting theframe operator.Item Open Access Investigation of plasma transport across magnetic filter in low temperature plasmas using 2D-3V PIC-MCC simulations: application to negative ion sources(Dhirubhai Ambani Institute of Information and Communication Technology, 2022) Shah, Miral; Chaudhury, BhaskarThe LTP (hydrogen) based negative ion source plays an important role inthe neutral beam injection system - one of the primary means of plasmaheating in magnetic fusion. In this thesis, we have performed PIC-MCCbased simulations of such plasmas wherein the ROBIN negative ion source(consisting of an LTP source with a magnetic filter) installed at IPR,Gandhinagar is taken as a testbed problem for the validation of the model.ROBIN has a driver, an expansion chamber, a magnetic filter, and extractionsystem consisting of 3 different grids. Plasma is generated in the RF driverregion, and that expands in the expansion chamber before encountering themagnetic filter field. A magnetic filter is a localized magnetic field (few tensof gauss) perpendicular to the plasma flow (diffusion flux or transport) andcontrols the plasma flux flowing from the expansion chamber to the extractionsystem. As a first step, we have performed 1D-3V PIC-MCC simulations, andwe observe a good qualitative match between the simulation and experimentalresults in terms of plasma density and electron temperature. The quantitativemismatch between the ROBIN experiment and 1D-simulation results is due tothe fact that the effect of drifts and instabilities (present in real experiments)are not captured properly in the 1D model. However, even with severallimitations, we find that 1D-3V PIC-MCC simulations can predict plasmabehavior in such LTP experiments with acceptable accuracy.As a second step in this direction, we have developed an in-house serial 2D-3VPIC-MCC code and also validated it with results available in the literature.However, stringent numerical constraints associated with a 2D PIC codemake it computationally prohibitive on CPUs in the case of real experimentalgeometry (total number of particles, number of grid points and simulationtime-scale). Therefore, we parallelized our 2D-3V PIC-MCC codes for sharedas well as distributed memory systems consisting of multi-core and many-corearchitectures (GPUs). We have also proposed a hybrid parallel scheme(OpenMP+MPI) which can be used to perform such expensive simulations onan HPC cluster with several nodes. One of the novel contribution towardsthe PIC-MCC code development has been made in terms of using differentparticle sorting strategies which significantly improved the memory accesstime leading to a remarkable enhancement in speedup compared to traditionalstrategies used for PIC-MCC implementation.The parallel 2D-3V PIC-MCC code have been used to simulate ROBINexperiment with real physical dimensions to understand the plasma transportacross magnetic filter. Most of the previous works in this area used ascaled geometry as well as relaxed the stringent numerical criteria for suchsimulations due to computational requirements, however we performedsimulations by satisfying all the strict numerical constraints such as time step,grid spacing and PPC required for kinetic modelling of such LTP experiments.Plasma density and electron temperature profiles from our 2D-3V PICsimulations follow similar trends (qualitative as well as quantitative) asseen in experimental results. This immensely helped us to understand therole of instabilities as well as different diffusion and collisional processes,and subsequently quantifying the plasma transport accurately. Even withcertain limitations present in our model, simulation results show a reasonablygood match with the phase-1 ROBIN experimental results. Particularly thesimulations are showing similar important patterns in plasma characteristicsas seen in the experiments. Comparison of the simulation and experimentalresults from ROBIN gives us sufficient confidence to do further case studiesfor future ROBIN experiments. Several case studies have been performedto understand the role of the magnetic filter profile on plasma transport,which will help in planning future experiments by using the magnetic filteras a switching mechanism to achieve the required density and electrontemperature profiles for efficient operation of negative ion source.Various collision dependent physical phenomena, having different time scalesand length scales are studied using 2D-3V PIC-MCC simulations. We havereported instabilities, observed near the filter field region. It is also observedthat the frequencies of those instabilities are close to some of the electronicand ionic collision frequencies which may create resonant phenomena in themagnetic filter region and influence the cross-field transport, and heating.From our investigations, we find that the application of a bias voltage (appliedto the extraction boundary) changes the potential profile and thereby plays animportant role in controlling the ion temperature near the extraction boundary.The nature of the instabilities also depend on the bias voltage. We areanticipating an ion heating due to instabilities originating in the filter fieldregion. 2D snapshots clearly shows discrete band structure which correspondsto drifts and instabilities, and the frequencies of the instabilities are identifiedusing Fast Fourier Transform (FFT) analysis. The instability correspondingto 105 Hz is identified as E B drift instability whereas, 106 Hz still requiresfurther investigation. In this study, we have shown these instabilities are oneof the causes for ion heating.Drifts and instabilities observed in our simulations may lead to double layer(DL) formation which has not been studied yet in the context of negativeion sources. This motivated us to perform detailed analysis with differentmagnetic field values and different bias voltages. Plasma profiles (such aspotential, electron and ion temperature, and ion velocities) are studied tounderstand the formation of DL and its effect on plasma transport. Ionacceleration is found near both source and extraction boundaries either due tosheath, instabilities, or DL.We observe DL formation under specific conditions(magnetic field and bias voltage). Two velocities components (one due to thefree ions and the other due to the trapped ions) are visible in our simulations.We found that DL depends on both the magnetic field and the differencebetween bias voltage and plasma potential. DL does not occur when the biasvoltage is more or equal to the plasma potential. When the bias voltage isgreater than plasma potential, electron sheath forms and reflects ions from theextraction boundary.A detailed investigation of Energy Distribution Functions (EDFs) helps ininterpreting the complex physics involved in such LTP problems. We havestudied the temporal and spatial evolution of EDFs using our PIC-MCCcode. We have observed that EEDF is Maxwellian in nature, but IEDF isnon-Maxwellian in nature. Our detailed Spatio-temporal analysis of EDFsrevealed that IEDF is more sensitive to changes in the filter field and biasvoltage compared to EEDF.All the past studies have focused on understandingelectron transport, however, our simulations suggest that to completelyunderstand the physics of plasma transport in such low-temperature sources,ion transport is equally important and needs to be investigated in more detail.Efficient negative ion generation in the negative ion source is a critical stepin the neutral beam injection (NBI) system of the future fusion reactor ITER.Achieving few tens of Amperes of H?? current in the negative ion sourceis technically challenging and needs more understanding of the physicsof the plasma transport in such sources. The important contributions ofthis thesis such as identification of instabilities, double layer formationand understanding of EDFs in the context of negative ion sources usingcomprehensive kinetic simulations will further improve our understanding ofphysics of plasma transport and help in enhancing the efficiency of negativeion generation process in such sources. The results are also relevant for similarkinds of different LTP based applications involving magnetic field such as Hallthrusters, ECR source, end-Hall source and magnetron discharge.