Electrical & Computer Engineering Theses and Dissertations

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Quantum Dots in Photonic Crystals for Hybrid Integrated Silicon Photonics
(2024) Rahaman, Mohammad Habibur; Waks, Edo Prof.; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
Quantum dots are excellent sources of on-demand single photons and can function as stable quantum memories. Additionally, advanced fabrication techniques of III-V materials and various hybrid integration methods make quantum dots an ideal candidate for integration into fiber- and silicon-based photonic circuits. However, efficiently extracting and integrating quantum dot emissions into fiber- and silicon-based photonic circuits, particularly with high efficiency and low power consumption, presents a continued challenge. This dissertation addresses this challenge by utilizing photonic crystals to couple quantum dot emissions into fiber- and silicon-based photonic circuits. In this dissertation, we first demonstrate an efficient fiber-coupled single photon source at the telecom C-band using InAs/InP quantum dots coupled to a nanobeam photonic crystal. The tapered nanobeam structure facilitates directional emission that is mode-matched to a lensed fiber, resulting in a collection efficiency of up to 65% from the nanobeam to a single-mode fiber. Using this approach, we demonstrate a bright single photon source with a 575 ± 5 Kcps count rate. Additionally, we observe a single photon purity of 0.015 ± 0.03 and Hong-Ou Mandel interference from emitted photons with a visibility of 0.84 ± 0.06. A high-quality factor photonic crystal cavity is needed to further improve the brightness of the single-photon source through Purcell enhancement. However, photonic crystal cavities often suffer from low-quality factors due to fabrication imperfections that create surface states and optical absorption. To address this challenge, we employed atomic layer deposition-based surface passivation of the InP photonic crystal nanobeam cavities to improve the quality factor. We demonstrated 140% higher quality factors by applying a coating of Al2O3 via atomic layer deposition to terminate dangling bonds and reduce surface absorption. Additionally, changing the deposition thickness enabled precise tuning of the cavity mode wavelength without compromising the quality factor. This Al2O3 atomic layer deposition approach holds great promise for optimizing nanobeam cavities, which are well-suited for integration with a wide range of photonic applications. Finally, we propose a hybrid Si-GaAs photonic crystal cavity design that operates at telecom wavelengths and can be fabricated without the need for careful alignment. The hybrid cavity consists of a patterned silicon waveguide that is coupled to a wider GaAs slab featuring InAs quantum dots. We show that by changing the width of the silicon cavity waveguide, we can engineer hybrid modes and control the degree of coupling to the active material in the GaAs slab. This provides the ability to tune the cavity quality factor while balancing the device’s optical gain and nonlinearity. With this design, we demonstrate cavity mode confinement in the GaAs slab without directly patterning it, enabling strong interaction with the embedded quantum dots for applications such as low-power-threshold lasing and optical bistability (156 nW and 18.1 µW, respectively). In addition to classical applications, this cavity is promising for alignment-free, large-scale integration of single photon sources in a silicon chip.
Advances in Concrete Cryptanalysis of Lattice Problems and Interactive Signature Schemes
(2024) Kippen, Hunter Michael; Dachman-Soled, Dana; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
Advanced cryptography that goes beyond what is currently deployed to service our basic internet infrastructure is continuing to see widespread adoption. The enhanced functionality achieved by these schemes frequently yields an increase in complexity. Solely considering the asymptotic security of the underlying computational assumptions is often insufficient to realize practical and secure instantiations.In these cases, determining the risk of any particular deployment involves analyzing the concrete security (the exact length of time it would take to break the encryption) as well as quantifying how concrete security can degrade over time due to any exploitable information leakage. In this dissertation, we examine two such cryptographic primitives where assessing concrete security is paramount. First, we consider the cryptanalysis of lattice problems (used as the basis for current standard quantum resistant cryptosystems). We develop a novel side-channel attack on the FrodoKEM key encapsulation mechanism as submitted to the NIST Post Quantum Cryptography (PQC) standardization process. Our attack involves poisoning the FrodoKEM Key Generation (KeyGen) process using a security exploit in DRAM known as “Rowhammer”. Additionally, we revisit the security of the lattice problem known as Learning with Errors (LWE) in the presence of information leakage. We further enhance the robustness of prior methodology by viewing side information from a geometric perspective. Our approach provides the rigorous promise that, as hints are integrated, the correct solution is a (unique) lattice point contained in an ellipsoidal search space. Second, we study the concrete security of interactive signature schemes (used as part of many Privacy Enhancing Technologies). To this end, we complete a new analysis of the performance of Wagner’s k-list algorithm [CRYPTO ‘02], which has found significant utility in computing forgeries on several interactive signature schemes that implicitly rely on the hardness of the ROS problem formulated by Schnorr [ICICS ‘01].
Graph-based Methods for Efficient, Interpretable and Reliable Machine Learning
(2024) Ma, Yujunrong; Bhattacharyya, Shuvra SSB; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
Machine learning algorithms have revolutionized fields such as computer vision, natural language processing, and speech recognition by offering the capability to analyze and extract information from vast datasets, a task far beyond human capacity. The deployment of these algorithms in high-stakes applications, including medical diagnosis, computational finance and criminal justice, underscores their growing importance. However, the decision-making processes of the so-called black-box models used in such areas raise considerable concerns. Therefore, enhancing the interpretability of these models is crucial, as it helps address issues like biases and inconsistencies in predictions, thereby making the models more comprehensible and trustworthy to end-users. Moreover, interpretability facilitates a deeper understanding of model behavior, such as the distribution of contributions across inputs. This deeper understanding can be applied to significantly improve efficiency. This is especially relevant as machine learning models find applications on edge devices, where computational resources are often limited. For such applications, significant improvements in energy efficiency and resource requirements can be obtained by optimizing and adapting model implementations based on an understanding of the models' internal behavior. However, such optimization introduces new challenges that arise due to factors such as complex, dynamically-determined dependency management among computations. This thesis presents five main contributions. The first contribution is the development of a novel type of interpretable machine learning model for applications in criminology and criminal justice (CCJ). The model involves graphical representations in the form of single decision trees, where the trees are constructed in an optimized fashion using a novel evolutionary algorithm. This approach not only enhances intrinsic interpretability but also enables users to understand the decision-making process more transparently, addressing the critical need for clarity in machine learning models' predictions. At the same time, the application of evolutionary algorithm methods enables such interpretability to be provided without significant degradation in model accuracy. In the second contribution, we develop new multi-objective evolutionary algorithm methods to find a balance between fairness and predictive accuracy in CCJ applications. We build upon the single-decision-tree framework developed in the first contribution of the thesis, and systematically integrate considerations of fairness and multi-objective optimization. In the third contribution, we develop new methods for crime forecasting applications. In particular, we develop new interpretable, attention-based methods using convolutional long short-term memory (ConvLSTM) models. These methods combine the power of ConvLSTM models in capturing spatio-temporal patterns with the interpretability of attention mechanisms. This combination of capabilities allows for the identification of key geographic areas in the input data that contribute to predictions from the model. The fourth contribution introduces a dynamic dataflow-graph-based framework to enhance the computational efficiency and run-time adaptability of inference processes, considering the constraints of available resources. Our proposed model maintains a high degree of analyzability while providing greater freedom than static dataflow models in being able to manipulate the computations associated with inference process at run-time. The fifth contribution of the thesis builds on insights developed in the fourth, and introduces a new parameterized design approach for image-based perception that enables efficient and dynamic reconfiguration of convolutions using channel attention. Compared to switching among sets of multiple complete neural network models, the proposed reconfiguration approach is much more streamlined in terms of resource requirements, while providing a high level of adaptability to handle unpredictable and dynamically-varying operational scenarios.
Dynamic EM Ray Tracing for Complex Outdoor and Indoor Environments with Multiple Receivers
(2024) Wang, Ruichen; Manocha, Dinesh; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
Ray tracing models for visual, aural, and EM simulations have advanced, gaining traction in dynamic applications such as 5G, autonomous vehicles, and traffic systems. Dynamic ray tracing, modeling EM wave paths and their interactions with moving objects, leads to many challenges in complex urban areas due to environmental variability, data scarcity, and computational needs. In response to these challenges, we've developed new methods that use a dynamic coherence-based approach for ray tracing simulations across EM bands. Our approach is designed to enhance efficiency by improving the recomputation of bounding volume hierarchy (BVH) and by caching propagation paths. With our formulation, we've observed a reduction in computation time by about 30%, all while maintaining a level of accuracy comparable to that of other simulators. Building on our dynamic approach, we've made further refinements to our algorithm to better model channel coherence, spatial consistency, and the Doppler effect. Our EM ray tracing algorithm can incrementally improve the accuracy of predictions relating to the movement and positioning of dynamic objects in the simulation. We've also integrated the Uniform Geometrical Theory of Diffraction (UTD) with our ray tracing algorithm. Our enhancement is designed to allow for more accurate simulations of diffraction around smooth surfaces, especially in complex indoor settings, where accurate prediction is important. Taking another step forward, we've combined machine learning (ML) techniques with our dynamic ray tracing framework. Leveraging a modified conditional Generative Adversarial Network (cGAN) that incorporates encoded geometry and transmitter location, we demonstrate better efficiency and accuracy of simulations in various indoor environments with 5X speedup. Our method aims to not only improve the prediction of received power in complex layouts and reduce simulation times but also to lay a groundwork for future developments in EM simulation technologies, potentially including real-time applications in 6G networks. We evaluate the performance of our methods in various environments to highlight the advantages. In dynamic urban scenes, we demonstrate our algorithm’s scalability to vast areas and multiple receivers with maintained accuracy and efficiency compared to prior methods; for complex geometries and indoor environments, we compare the accuracy with analytical solutions as well as existing EM ray tracing systems.
Nonlinear and Stochastic Dynamics of Optoelectronic Oscillators
(2024) Ha, Meenwook; Chembo, Yanne K.; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
Optoelectronic oscillators (OEOs) are nonlinear, time-delayed and self-sustained microwave photonic systems capable of generating ultrapure radiofrequency (RF) signals with extensive frequency tunabilities. Their hybrid architectures, comprising both optical and electronic paths, underscore their merits. One of the most notable points of OEOs can be unprecedentedly high quality factors, achieved by storing optical energies for RF signal generations. Thanks to their low phase noise and broad frequency tunabilities, OEOs have found diverse applications including chaos cryptography, reservoir computing, radar communications, parametric oscillator, clock recovery, and frequency comb generation. This thesis pursues two primary objectives. Firstly, we delve into the nonlinear dynamics of various OEO configurations, elucidating their universal behaviors by deriving corresponding envelope equations. Secondly, we present a stochastic equation delineating the dynamics of phases and explore the intricacies of the phase dynamics. The outputs of OEOs are defined in their RF ports, with our primary focus directed towards understanding the dynamics of these RF signals. Regardless of their structural complexities, we employ a consistent framework to explore these dynamics, relying on the same underlying principles that determine the oscillation frequencies of OEOs. To comprehend behaviors of OEOs, we analyze the dynamics of a variety of OEOs. For simpler systems, we can utilize the dynamic equations of bandpass filters, whereas more complex physics are required for expressing microwave photonic filtering. Utilizing an envelope approach, which characterizes the dynamics of OEOs in terms of complex envelopes of their RF signals, has proven to be an effective method for studying them. Consequently, we derive envelope equations of these systems and research nonlinear behaviors through analyses such as investigating bifurcations, stability evaluations, and numerical simulations. Comparing the envelope equations of different models reveals similarities in their dynamic equations, suggesting that their dynamics can be governed by a generalized universal form. Thus, we introduce the universal equation, which we refer to as the universal microwave envelope equation and conduct analytical investigations to further understand its implications. While the deterministic universal equation offers a comprehensive tool for simultaneous exploration of various OEO dynamics, it falls short in describing the stochastic phase dynamics. Our secondary focus lies in investigating phase dynamics through the implementation of a stochastic approach, enabling us to optimize and comprehend phase noise performance effectively. We transform the deterministic universal envelope equation into a stochastic delay differential form, effectively describing the phase dynamics. In our analysis of the oscillators, we categorize noise sources into two types: additive noise contribution, due to random environmental and internal fluctuations, and multiplicative noise contribution, arising from noisy loop gains. The existence of the additive noise is independent of oscillation existence, while the multiplicative noise is intertwined with the noisy loop gains, nonlinearly mixing with signals above the threshold. Therefore, we investigate both sub- and above-threshold regimes separately, where the multiplicative noise can be characterized as white noise and colored noise in respective regimes. For the above-threshold regime, we present the stochastic phase equation and derive an equation for describing phase noise spectra. We conduct thorough investigations into this equation and validate our approaches through experimental verification. In the sub-threshold regime, we introduce frameworks to experimentally quantify the noise contributions discussed in the above-threshold part. Since no signal is present here and the oscillator is solely driven by the stochastic noise, it becomes feasible to reverse-engineer the noise powers using a Fourier transform formalism. Here, we introduce a stochastic expression written in terms of the real-valued RF signals, not the envelopes, and the transformation facilitates the expressions of additive and multiplicative noise contributions as functions of noisy RF output powers. The additive noise can be defined by deactivating the laser source or operating the intensity modulator at the minimum transmission point, given its independence from the loop gains. Conversely, the expression for the multiplicative noise indicates a dependence on the gain, however, experimental observations suggest that its magnitude may remain relatively constant beyond the threshold.
Quantum and Stochastic Dynamics of Kerr Microcombs
(2024) Liu, Fengyu; Chembo, Yanne K.; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
Kerr microcombs are sets of discrete, equidistant spectral lines and are typically generated by pumping a high-quality factor optical resonator with a continuous- or pulse-wave resonant laser. They have emerged as one of the most important research topics in photonics nowadays, with applications related to spectroscopy, sensing, aerospace, and communication engineering. A key characteristic of these microcombs is the threshold pump power. Below the threshold, two pump photons are symmetrically up- and down-converted as twin photons via spontaneous four-wave mixing, and they can be entangled across up to a hundred eigenmodes. These chipscale, high-dimensional, and room-temperature systems are expected to play a major role in quantum engineering. Above the threshold, the four-wave mixing process is stimulated, ultimately leading to the formation of various types of patterns in the spatio-temporal domain, which can be extended (such as roll patterns), or localized (bright or dark solitons). The semiclassical dynamics of Kerr microcombs have been studied extensively in the last ten years and the deterministic characteristics are well understood. However, the quantum dynamics of the twin-photon generation process, and the stochastic dynamics led by the noise-driven fluctuations, are still not so clear. In the first part of our investigation, we introduce the theoretical framework to study the semiclassical dynamics of the Kerr microcombs based on the slowly varying envelope of the intracavity electrical fields. Two equivalent models -- the coupled-mode model and the Lugiato-Lefever model are used to analyze the spectro- and spatio-temporal dynamics, respectively. These models can determine the impact of key parameters on the Kerr microcomb generation process, such as detuning, losses, and pump power, as well as critical values of the system, such as threshold power. Various types of patterns and combs can be observed through simulations that follow experimental parameters. Furthermore, we show an eigenvalue analysis method to determine the stability of the microcomb, and this method is applied to an unstable microcomb solution to understand the generation of subcombs surrounding the primary comb. In the second and third parts, we investigate a stochastic model where noise is added to the coupled-mode equations governing the microcomb dynamics to monitor the influence of random noise on the comb dynamics. We find the model with additive Gaussian white noise allows us to characterize the noise-induced broadening of spectral lines and permits us to determine the phase noise spectra of the microwaves generated via comb photodetection. Our analysis indicates that the low-frequency part of the phase noise spectra is dominated by pattern drift while the high-frequency part is dominated by pattern deformation. The dynamics of the Kerr microcomb with multiplicative noises, including thermal and photothermal fluctuations, are also investigated in the end. We propose that the dynamics of the noise can be included in the simulation of stochastic dynamics equations, introduce the methods to solve the dynamics of the noise, and study a quiet point method for phase noise reduction. In the fourth part, we use canonical quantization to obtain the quantum dynamics for Kerr microcombs generated by spontaneous four-wave mixing below the threshold and develop the study of them using frequency-bin quantum states. We introduce a method to find the quantum expansion of the output state and explore the properties of the eigenkets. A theoretical framework is also developed to obtain explicit solutions for density operators of quantum microcombs, which allows us to obtain their complete characterization, as well as for the analytical determination of various performance metrics such as fidelity, purity, and entropy. Finally, we describe a quantum Kerr microcomb generator with a pulse-wave laser and propose the time-bin entangled states generated by it.
Dynamics and applications of long-distance laser filamentation in air
(2024) Goffin, Andrew; Milchberg, Howard; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
Femtosecond laser pulses with sufficient power will form long, narrow high-intensity light channels in a propagation medium. These structures, called “filaments”, form due to nonlinear self-focusing collapse in a runaway process that is arrested by a mechanism that limits the peak intensity. For near-infrared pulses in air, the arrest mechanism is photoionization of air molecules and the resulting plasma-induced defocusing. The interplay between plasma-induced defocusing and nonlinear self-focusing enables high-intensity filament propagation over long distances in air, much longer than the Rayleigh range (~4 cm) corresponding to the ~200 µm diameter filament core. In this thesis, the physics of atmospheric filaments is studied in detail along with several applications. Among the topics of this thesis: (1) Using experiments and simulations, we studied the pulse duration dependence of filament length and energy deposition in the atmosphere, revealing characteristic axial oscillations intimately connected to the delayed rotational response of air molecules. This measurement used a microphone array to record long segments of the filament propagation path in a single shot. These results have immediate application to the efficient generation of long air waveguides. (2) We investigated the long-advertised ability of filaments to clear fog by measuring the dynamics of single water droplets in controlled locations near a filament. We found that despite claims in the literature that droplets are cleared by filament-induced acoustic waves, they are primarily cleared through optical shattering. (3) We demonstrated optical guiding in the longest-filament induced air waveguides to date (~50 m, a length increase of ~60×) using multi-filamentation of Laguerre-Gaussian LG01 modes with pulse durations informed by experiment (1). (4) We demonstrated the first continuously operating air waveguide, using a high-repetition-rate laser to replenish the waveguide faster than it could thermally dissipate. For each of the air waveguide experiments, extension to much longer ranges and steady state operation is discussed.
NOVEL QUASI-FREESTANDING EPITAXIAL GRAPHENE ELECTRON SOURCE HETEROSTRUCTURES FOR X-RAY GENERATION
(2024) Lewis, Daniel; Daniels, Kevin M; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
Graphene, the 2D allotrope of carbon, boasts numerous exceptional qualities like strength, flexibility, and conductivity unmatched for its scale, and amongst its lesser-known capabilities is electron emission at temperatures and electric fields too low to allow for conventional thermionic or field emission sources to function. Driven by the mechanism of Phonon-Assisted Electron Emission (PAEE), planar microstructures fabricated from quasi-freestanding epitaxial graphene (QEG) on silicon carbide have exhibited emission currents of up to 8.5 μA at temperatures and applied fields as low as 200 C and 1 kV/cm, orders of magnitude below conventional electron source requirements.These emission properties can be influenced through variations in microstructure design morphology, and performance is controllable via device temperature and applied field in the same manner as thermionic or field emission sources. As 2D planar devices, graphene microstructure electron emitters can also be encapsulated with a thermally evaporated oxide, granting electrical isolation and environmental resistance, and can even exhibit emission current enhancement under these conditions. Graphene electron emitters expressed as heterostructure material stacks could see implementation as electron emission sources in environments or devices where conventional thermionic or field emission sources can’t be supported due to thermal, power system, or physical size limitations, the presence of contaminants, or even poor vacuum containment. An explorable application could see an oxide-encapsulated graphene electron source paired with a layered interaction-emission anode to create a micron-scale vertical alignment x-ray source with no need of vacuum containment. We investigate these properties with using hydrogen-intercalated quasi-freestanding bilayer epitaxial graphene, a rare and difficult to manufacture formulation that allows the graphene to behave as if it were a freestanding structure, while still benefiting from the macro-scale mechanical strength and fabrication process compatibility afforded by its silicon carbide substrate. The quasi-freestanding nature of the graphene limits substrate phonon interactions, allowing the graphene phonon-electron interactions to dominate, in turn empowering the PAEE mechanic. Our devices benefit from an ease of interaction that is untenable for processes not employing QEG, with the speed and simplicity of fabrication being a hallmark of our investigations. We begin our exploration of how the PAEE mechanism itself can be influenced in our designs, and how process and fabrication optimizations can be leveraged for device applications. Graphene’s role in the fields of microelectronics, condensed matter physics, and materials science is still novel, and rapidly expanding, and our investigations explore a unique facet of this wonder material’s capabilities.
Image Reconstruction for Hyperpolarized Carbon-13 Metabolic Magnetic Resonance Imaging with Iterative Methods
(2024) Zhu, Minjie; Babadi, Behtash; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
Magnetic resonance imaging (MRI) with hyperpolarized carbon-13 (13C) agents is an emerging in vivo medical imaging technique. 13C MRI gives a series of images that show the evolution of the injected substrate and its metabolic products in the imaging volume, which leads to various medical applications including monitoring tumor progression and post-treatment response in both animal models and clinical trials. This dissertation focuses on the application of novel iterative image reconstruction methods for 13C MRI that aim to improve image quality and temporal resolution.One of the challenges for the existing 13C MRI reconstruction method is the difficulty in quantification of lower intensity metabolites due to noise and overlapping peaks in the aliased spectrum. In the first part of the dissertation, a model-based iterative reconstruction method is proposed to overcome such difficulty. The proposed method utilizes prior knowledge of the properties of the metabolites in the imaging volume, including off-resonance frequency, T2* decay constants, and the image acquisition trajectory in spatial and frequency domain. Metabolic images are reconstructed through solving the linear equation between acquired signal and images with least square error estimation. The reconstruction results on in vivo imaging data sets demonstrate that the proposed method can separate two overlapped peaks in an aliased spectrum while the conventional method fails. Another challenge for 13C MRI is to reconstruct metabolic images from under-sampled acquisitions. Due to the short lifetime of the injected substrate and the physical limitation of the MRI scanner, only a few temporal frames can be acquired for 13C MRI with one injection. Under-sampling in the image acquisition can provide more frames, but certain reconstruction methods are required to remove the artifacts from direct reconstruction on the under-sampled data. In the second part of the dissertation, a customized low-rank plus sparse (L+S) reconstruction method is proposed to produce artifact-free images from under-sampled data. Digital phantom simulations are performed to evaluate the optimal reconstruction parameters. Simulation with digital phantom and in vivo mouse imaging on 2D and 3D dynamic imaging data demonstrate the effectiveness in acceleration without introducing image artifacts using the proposed reconstruction method. In the third part of the dissertation, we present a preclinical application of 13C MRI to study brain metabolism and identify the source of metabolic products based on the metabolic images derived. In vivo metabolic imaging with different flow-suppression levels was performed on rats in the brain region. Results show that metabolic product, lactate, has no significant dependence on the level of suppression while the substrate pyruvate is strongly dependent. This supports our hypothesis that lactate seen in metabolic images is generated in the brain. Additional high-resolution metabolic imaging was performed to show different signal distributions for pyruvate and lactate clearly. Our proposed L+S reconstruction method was applied to the dynamic image data to reduce the background noise. The derived dynamic images show distinct dynamics for pyruvate and lactate, further supporting our hypothesis.
Statistical Models of Neural Computations and Network Interactions in High-Dimensional Neural Data
(2023) Mukherjee, Shoutik; Babadi, Behtash; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
Recent advances in neural recording technologies, like high-density electrodes and two-photon calcium imaging, now enable the simultaneous acquisition of several hundred neurons over large patches of cortex. The availability of high volumes of simultaneously acquired neural activity presents exciting opportunities to study the network-level properties that support the neural code. This dissertation consists of two themes in analyzing network-level neural coding in large populations, particularly in the context of audition. Namely, we address modeling the instantaneous and directed interactions in large neuronal assemblies; and modeling neural computations in the mammalian auditory system.In the first part of this dissertation, an algorithm for adaptively modeling higher-order coordinated spiking as a discretized mark point process is proposed. Analyzing coordinated spiking involves a large number of possible simultaneous spiking events and covariates. We propose the adaptive Orthogonal Matching Pursuit (AdOMP) to tractably model dynamic higher-order coordination of ensemble spiking. Moreover, we generalize an elegant procedure for constructing confidence intervals for sparsity-regularized estimates to greedy algorithms and subsequently derive an inference framework for detecting facilitation or suppression of coordinated spiking. Application to simulated and experimentally recorded multi-electrode data recordings reveals significant gains over several existing benchmarks. The second part pertains to functional network analysis of large neuronal ensembles using OMP to impose sparsity constraints on models of neuronal responses. The efficacy of functional network analysis based on greedy model estimation is first demonstrated in two sets of two-photon calcium imaging data of mouse primary auditory cortex. The first dataset was collected during a tone discrimination task, where we additionally show that properties of the functional network structure encode information relevant to the animal’s task performance. The second dataset was collected from a cohort of young and aging mice during passive presentations of pure-tones in noise to study aging-related network changes in A1. The constituency of neurons engaged in functional networks changed by age; we characterized these changes and their correspondence to differences in functional network structure. We next demonstrated the efficacy of greedy estimation in functional network analysis in application to electrophysiological spiking recordings across multiple areas of songbird auditory cortex, and present initial findings on interareal network structure differences between responses to tutor songs and non-tutor songs that suggest the learning-related effects on functional networks. The third part of this dissertation concerns neural system identification. Neu- rons in ferret primary auditory cortex are known to exhibit stereotypical spectrotem- poral specificity in their responses. However, spectrotemporal receptive fields (STRF) measured in non-primary areas can be intricate, reflecting mixed spectrotemporal selectivity, and hence be challenging to interpret. We propose a point process model of spiking responses of neurons in PEG, a secondary auditory area, where neurons’ spiking rates are modulated by a high-dimensional biologically inspired stimulus rep- resentation. The proposed method is shown to accurately model a neuron’s response to speech and artificial stimuli, and offers the interpretation of complex STRFs as the sparse combination of higher-dimensional features. Moreover, comparative analyses between PEG and A1 neurons suggest the role of such an hierarchical model is to facilitate encoding natural stimuli.The fourth part of this dissertation is a study in computational auditory scene analysis that seeks to model the role of selective attention in binaural segregation within the framework of a temporal coherence model of auditory streaming. Masks can be obtained by clustering cortical features according to their instantaneous coincidences with pitch and interaural cues. We model selective attention by restrict- ing the ranges of pitch or interaural timing differences used to obtain masks, and evaluate the robustness of the selective attention model in comparison to the baseline model that uses all perceptual cues. Selective attention was as robust to noise and reverberation as the baseline, suggesting the proposed attentive temporal coherence model, in the context of prior experimental findings, may describe the computations by which downstream unattended-speaker representations are suppressed in scene analysis. Finally, the fifth part of this dissertation discusses future directions in studying network interactions in large neural datasets, especially in consideration of current trends towards the adoption of optogenetic stimulation to study neural coding. As a first step in these new directions, a simulation study introducing a reinforcement learning-guided approach to optogenetic stimulation target selection is presented.
STUDYING PRODUCT REVIEWS USING SENTIMENT ANALYSIS BASED ON INTERPRETABLE MACHINE LEARNING
(2023) Atrey, Pranjal; Dutta, Sanghamitra; Wu, Min; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
Consumers’ reliance on product reviews and ratings has been making substantial impacts on purchasing behaviors in e-commerce. However, the relationship between reviews and ratings has received limited attention. For instance, a product may have a high rating but average reviews. Such feedback can cause confusion and uncertainty about the products, leading to decreased trust in the product. This thesis carries out a natural-language based machine learning study to analyze the relationship from e-commerce big data of product reviews and ratings. Towards answering this relationship question using natural-language-processing (NLP), we first employ data-driven sentiment analysis to obtain a numeric sentiment score from the reviews, which are then used for studying the correlation with actual ratings. For sentiment analysis, we consider the use of both glass-box (rule-based) and black-box opaque (BERT) models. We find that while the black-box model is more correlated with product ratings, there are interesting counterexamples where the sentiment analysis results by the glass-box model are better aligned with the rating. Next, we explore how well ratings can be predicted from the text reviews, and if sentiment scores can further help improve classification of reviews. We find that neither opaque nor glass- box classification models yield better accuracy, and classification accuracy mostly improves when BERT sentiment scores are augmented with reviews. Furthermore, to understand what different models use to predict ratings from reviews, we employ Local Interpretable Model- Agnostic Explanations (LIME) to explain the impact of words in reviews on the decisions of the classification models. Noting that different models can give similar predictions, which is a phenomenon known as the Rashomon Effect, our work provides insights on which words actually contribute to the decision-making of classification models, even in scenarios where an incorrect classification is made.
COUPLING MECHANISMS USING 3D-INTEGRATION FOR NONLINEAR INTEGRATED PHOTONICS
(2023) Rahman, Tahmid Sami; Srinivasan, Kartik; Waks, Edo; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
Improving coupling between integrated photonics chips and optical fibers is an important topic of study for many applications. For photonic integrated circuits, different coupling methods have been implemented including edge coupling, grating coupling and 3D-integration using direct laser writing. Silicon nitride is a widely proven material for non linear optical phenomena such as frequency comb, optical parametric oscillation etc. Here in this thesis, coupling mechanisms based on direct laser writing are presented for use in nonlinear integrated photonics. Simulation works show that a polymer tapered coupler printed on a single mode fiber could be a good alternative to a cleaved fiber and equivalent to a lensed fiber. It is also shown that an out-of-plane polymer coupler on a silicon nitride access waveguide could be a prospective alternative for coupling to nonlinear integrated photonic circuits while avoiding chip separation and facet polishing. Both mechanisms could be good coupling options for shorter wavelength applications.
Design and Optimization of 5G and Beyond Hybrid Communication Systems
(2023) Torkzaban, Nariman; Baras, John JB; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
5G and beyond communication systems are envisaged to fulfill three key promises that enable novel use cases and applications such as telemedicine, augmented reality/virtual reality (AR/VR), smart manufacturing, autonomous vehicles (AVs), etc. These three key promises are i) Enhanced mobile broadband (eMBB), ii) Ultra-reliable low latency Communications (URLLC), and iii) Massive machine-type communications (mMTC). In other words, 5G is required to achieve key performance indicators (KPIs) in terms of low latency, massive device connectivity, consistent quality of service (QoS), and high security. For instance, user bit-rates up to 10 Gbps and round-trip times (RTTs) as small as 1–10 ms are demanded in specific application scenarios in 5G. Toward achieving the 5G key promises, it is essential to utilize the capacity of all sorts of communications networks (terrestrial, space, aerial) and supporting technologies (SDN, NFV, etc.) simultaneously, leading to the so-called hybrid communication networks as opposed to the traditional stand-alone ones. This signifies the importance of a seamless integration and configuration policy tailored to specific use cases and QoS requirements of 5G and beyond services and will spawn several challenging design and optimization problems from the control and management to the physical layer of next-generation systems. In this thesis, we will address such critical problems in the course of 9 chapters. In the second chapter, we study the benefits of incorporating trust into decision-making for resource provisioning in next-generation communications networks. In this regard, we study the trust-aware service chain embedding problem for enhancing the reliability of virtual network function (VNF) placement on the trusted infrastructure. The problem of placing the VNFs onthe NFV infrastructure (NFVI) and establishing the routing paths between them, according to the service chain template, is termed SFC embedding. The objectives and constraints for the optimization problem formulation of SFC embedding may vary depending on the corresponding network service. We introduce the notion of trustworthiness as a measure of security in SFC embedding and thus network service deployment. We formulate the resulting trust-aware SFC embedding problem as a Mixed Integer Linear Program (MILP). We relax the integer constraints to reduce the time complexity of the MILP formulation and obtain a Linear Program (LP). We investigate the trade-offs among the two formulations, seeking to strike a balance between results accuracy and time complexity. The space-air-ground integrated network (SAGIN) offers potential benefits that are not possible otherwise, including global coverage, low latency, and high reliability. On the other hand, the heterogeneity of the integrated network with non-unified interfaces, and the diversity of 5G use cases with large-scale applications highlight the need for a unified management structure and a dynamic resource allocation policy that are both scalable and flexible enough to handle the increasing complexity. In the third chapter, on one hand, we optimize the integration of the hybrid network by deployment of satellite gateways on the ground segment of the network to ensure proper connection between the layers with minimum latency, and on the other hand, we aim at providing a seamless management and control scheme for the hybrid network utilizing the capacities of the supportive technologies, software-defined networking (SDN) and network function virtualization (NFV); In particular, we study the problem of SDN controller placement with the goal maximizing the reliability of the hybrid network. In the fourth chapter, we propose trust as a metric to measure the trustworthiness of the FL agents and thereby enhance the security of the FL training. We first elaborate on trust as a security metric by presenting a mathematical framework for trust computation and aggregation within a multi-agent system. We then discuss how this framework can be incorporated within an FL setup introducing the trusted FL algorithm for both centralized and decentralized FL. Next, we propose a framework for decentralized FL in UAV-enabled networks which involves the placement of the UAVs while ensuring the connectivity of the network of deployed UAVs. We dedicate the remaining chapters to studying the novel design problems and the key technologies for the physical layer of next-generation wireless systems with an emphasis on millimeter-wave communications, massive MIMO, and hybrid beamforming. We introduce a novel antenna configuration called twin-ULA (TULA) and its composite configurations to generate sharp beams with maximal and uniform gain. We introduce a novel beam alignment technique to maximize the utility of transmission in the presence of multipath, efficiently utilize reconfigurable intelligent surfaces (RIS) to enhance mmWave coverage in urban environments, and synchronize and calibrate in distributed massive MIMO networks for 6G systems, where the synchronization involves the carrier frequency offset estimation and compensation, and the calibration involves mitigating reciprocity mismatches in digital and analog RF chains of the access points (APs) implementing hybrid beamforming, enabling efficient downlink channel estimation.
COORDINATION AND LEARNING ALGORITHMS FOR MULTI-ROBOT INFORMATION GATHERING
(2023) Shi, Guangyao; Tokekar, Pratap; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
With the rapid improvement in perception and planning technology, robots are being increasingly used as smart, adaptive sensors to gather information in applications such as environment monitoring, infrastructure inspection, and security and surveillance. To fully exploit the potential offered by robotic sensing, we need efficient and reliable decision-making techniques to decide when, where, and how to gather information. Such decision-making techniques need to account for the uncertainty and partial knowledge inherent in the working environment. The goal of this dissertation is to design algorithms to enable a multi-robot team to collectively and efficiently gather information on spatiotemporal fields without full knowledge of the environment. Our contributions span the full spectrum of the knowledge of the environmental conditions: from one extreme where the environmental model is fully known to the other extreme where the environmental model is unknown but can be learned from empirical data. We present several efficient (i.e., polynomial time) and effective (i.e., optimal or bounded approximation guarantees) algorithms for multi-robot information gathering. In the first part of the dissertation, we study coordination algorithms when the environmental model is fully or partially known. Specifically, for the case where the environmental model is fully known, we consider the challenge imposed by the connectivity requirement of the team. We present an algorithm for connectivity-constrained submodular maximization for information gathering that requires intermittent communication among the robotic team. For the case where the environment is partially known, and uncertainty exists, we seek to make the multi-robot team robust to the possible failures caused by the uncertainty. When the uncertainty is upper-bounded, we present a constant-factor approximation algorithm for robust multiple-path submodular orienteering. When the uncertainty is stochastic, and the distribution is known, we introduce two risk-sensitive coordination problems for aerial-ground long-term information gathering. In the second part of the dissertation, we study the case where the environmental model is initially unknown and needs to be learned from the data. Classically, such a learning process is independently conducted without considering the downstream task. By contrast, we present a framework that incorporates the downstream decision-making problem into the learning process. Such integration will help reduce the misalignment between the prediction model and the downstream task. The misalignment refers to a predictor that despite achieving high predictive accuracy in the learning phase may not necessarily result in good decisions in the downstream task. The general methodology to achieve such integration is tomake the combinatorial optimization differentiable, which then can be treated as a differentiable module in the learning process. In addition to algorithm design, we present empirical results for applications such as active target tracking, ocean monitoring, and persistent monitoring.
Leveraging Deep Generative Models for Estimation and Recognition
(2023) PNVR, Koutilya; Jacobs, David W.; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
Generative models are a class of statistical models that estimate the joint probability distribution on a given observed variable and a target variable. In computer vision, generative models are typically used to model the joint probability distribution of a set of real image samples assumed to be on a complex high-dimensional image manifold. The recently proposed deep generative architectures such as Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), and diffusion models (DMs) were shown to generate photo-realistic images of human faces and other objects. These generative models also became popular for other generative tasks such as image editing, text-to-image, etc. As appealing as the perceptual quality of the generated images has become, the use of generative models for discriminative tasks such as visual recognition or geometry estimation has not been well studied. Moreover, with different kinds of powerful generative models getting popular lately, it's important to study their significance in other areas of computer vision. In this dissertation, we demonstrate the advantages of using generative models for applications that go beyond just photo-realistic image generation: Unsupervised Domain Adaptation (UDA) between synthetic and real datasets for geometry estimation; Text-based image segmentation for recognition. In the first half of the dissertation, we propose a novel generative-based UDA method for combining synthetic and real images when training networks to determine geometric information from a single image. Specifically, we use a GAN model to map both synthetic and real domains into a shared image space by translating just the domain-specific task-related information from respective domains. This is connected to a primary network for end-to-end training. Ideally, this results in images from two domains that present shared information to the primary network. Compared to previous approaches, we demonstrate an improved domain gap reduction and much better generalization between synthetic and real data for geometry estimation tasks such as monocular depth estimation and face normal estimation. In the second half of the dissertation, we showcase the power of a recent class of generative models for improving an important recognition task: text-based image segmentation. Specifically, large-scale pre-training tasks like image classification, captioning, or self-supervised techniques do not incentivize learning the semantic boundaries of objects. However, recent generative foundation models built using text-based latent diffusion techniques may learn semantic boundaries. This is because they must synthesize intricate details about all objects in an image based on a text description. Therefore, we present a technique for segmenting real and AI-generated images using latent diffusion models (LDMs) trained on internet-scale datasets. First, we show that the latent space of LDMs (z-space) is a better input representation compared to other feature representations like RGB images or CLIP encodings for text-based image segmentation. By training the segmentation models on the latent z-space, which creates a compressed representation across several domains like different forms of art, cartoons, illustrations, and photographs, we are also able to bridge the domain gap between real and AI-generated images. We show that the internal features of LDMs contain rich semantic information and present a technique in the form of LD-ZNet to further boost the performance of text-based segmentation. Overall, we show up to 6% improvement over standard baselines for text-to-image segmentation on natural images. For AI-generated imagery, we show close to 20% improvement compared to state-of-the-art techniques.
Advances in the Application of Superconducting and Photonic Circuits to Microwave Radiometers
(2023) Turner, Charles Josiah; Murphy, Thomas E; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
In the fields of radio astronomy and remote sensing, there are application-driven requirements for wideband radiometers, hyperspectral spectrometers, and Radio Frequency Interference (RFI) mitigation. This work investigates the implementation of superconducting filters for RFI mitigation in ground-based radio astronomy where cryogenic cooling is available. It also explores the feasibility of implementing Photonic Integrated Circuits (PICs) in spaceborne radiometers. Spaceborne instruments have strict size, weight, and power consumption (SWaP) requirements. PICs are intrinsically wideband and offer significant SWaP benefits for enhanced performance in radiometers. This thesis presents three topics in technology development for the advancement of radiometers. The first topic is the development of a thin-film, high-temperature superconductor (HTS) notch filter to reject a local, high-power, RFI signal. The resonator topology was devised to minimize the necessary coupling between the transmission line and resonators. As demonstrated through measurements, this filter has an operating frequency range of 2-12 GHz and provides over 50 dB of rejection around 9.41 GHz. The measured maximum insertion loss is 0.6 dB in the lower pass-band and 2 dB in the upper pass-band, which can be reduced through improved packaging and operating the device at lower temperatures. This device currently demonstrates the largest 50-dB-rejection stop-band reported in literature for thin-film HTS filters at 4.3% fractional bandwidth. The second topic is a stochastic, non-linear, power-response model with supporting laboratory measurements for a photonics-enabled, heterodyne, microwave radiometer. The measurements are taken from a single-channel test device and the results can be applied to improve the design and simulation accuracy of a multi-channel spectrometer. This model is tested by comparing the measured gain of a photonic down-converter (PDC) under an applied continuous wave microwave signal versus an adjustable microwave noise source. The PDC consists of a dual-drive Mach Zehnder Modulator with a microwave local oscillator (LO) used for down-conversion of the microwave carrier signal. Using these results, the dynamic range of the proposed instrument is quantified with improved accuracy. The third topic is the demonstration of thermo-reflectance microscopy (TRM) on a polymer-based photonic device. A spaceborne, photonics-enabled, microwave radiometer needs to survive and operate in a space environment. Measuring the thermal profile of PICs is essential for creating more environmentally-robust designs, but many feature sizes fall below the diffraction limit for traditional infrared thermography. TRM offers a means of measuring thermal profiles by using visible-wavelength light to reduce the diffraction limit and achieving sub-micron spatial resolutions. Photonic Wire Bond (PWB) is an important component for coupling different PICs without requiring active optical alignment between chips. Although TRM has been tested before with semiconductors, it has not been demonstrated before on PWB. These results demonstrate the possibility of using TRM to test complete, multi-material PIC devices.
TOWARDS EXTENDING ACOUSTIC-TO-ARTICULATORY SPEECH INVERSION AND LEARNING ARTICULATORY REPRESENTATIONS
(2023) H P Elapatha Rajapaksha Siriwardena, Yashish Maduwantha; Espy-Wilson, Carol; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
Acoustic-to-articulatory speech inversion involves the challenging task of deducing the kinematic state of various constriction synergies, including the lips, tongue tip, tongue body, velum, and glottis, based on their respective constriction degree and location coordinates. These coordinates are referred to as vocal tract variables (TVs). Developing Speech Inversion (SI) systems have gained attention over the recent years mainly due to its potential in a wide range of speech applications like Automatic Speech Recognition (ASR), speech synthesis, speech therapy, and mental health assessments. Over the past few years, deep neural network (DNN) based models have propelled the development of SI systems to new heights. However, the current SI systems still struggle with the lack of sufficiently larger articulatory datasets, speaker dependence, poor performance with noisy speech, and the lack of generalizability across different articulatory datasets. Moreover, one of the major drawbacks of the existing articulatory datasets is the lack of ground-truth data capturing velar and glottal activity of speech. With this work, we try to address some of the aforementioned challenges pertaining to the development of effective SI systems. Our experiments are based on two publicly available articulatory datasets; the University of Wisconsin X-ray microbeam (XRMB) dataset, and the HPRC dataset. We show that the use of appropriate audio augmentation techniques to synthetically create data can further improve the performance of SI systems both on clean and noisy speech data. We also show that the use of multi-task learning frameworks to carry out an auxiliary, but a related task can also improve the TV prediction. A key improvement came about when the SI systems were forced to learn source features (aperiodicity, periodicity, and pitch) as additional targets. Moreover, the use of self-supervised speech representations (HuBERT) and fine tuning them to the downstream task of speech inversion resulted in improved performance. With the aim of extending the current SI systems to estimate velar and glottal activity, data from an ongoing data collection was used to derive and validate two parameters; nasalance to capture velar constriction degree and electroglottography (EGG) envelope to capture voicing. A separate speaker-independent SI system was subsequently trained to estimate the derived parameters and is one of the first systems to achieve the feat. This SI system along with the conventional SI systems (trained to estimate lip and tongue TVs), provide a framework to estimate a complete articulatory representation of speech in speaker-interdependent fashion. While improving and extending the current SI frameworks, we also explored an unsupervised learning algorithm inspired by sensorimotor interactions in the human brain to perform audio and speech inversion. The proposed “MirrorNet”, a constrained autoencoder architecture is first used to learn, in an unsupervised manner, the controls of an off-the-shelf audio synthesizer (DIVA) to produce melodies only from their auditory spectrograms. The results demonstrate how the MirrorNet discovers the synthesizer parameters to generate the melodies that closely resemble the original and those of unseen melodies, and even determine the best set of parameters to approximate renditions of complex piano melodies generated by a different synthesizer. To extend the same idea of learning to vocal tract controls for speech, we developed a DNN based articulatory synthesizer (articulatory-to-acoustic forward mapping) to be incorporated as the motor plant of the MirrorNet. The MirrorNet with this motor plant, once initialized with a minimal amount of ground-truth data (~ 30 mins of speech), can learn the articulatory representations (6 TVs + source features) with significantly better accuracy. Overall, this highlights the effectiveness and power of the MirrorNet’s learning algorithm in enabling to solve the conventional acoustic-to-articulatory speech inversion problem with minimal use of ground-truth articulatory data. In order to assess the practical utility of articulatory representations in real-world scenarios, we employed articulatory coordination features derived from TVs to detect and analyze articulatory-level alterations in the speech of individuals with schizophrenia. We show that the schizophrenia subjects with strong positive symptoms (e.g. hallucinations and delusions), and who are markedly ill, pose a more complex articulatory coordination pattern in facial and speech gestures compared to healthy controls. This distinction in speech coordination pattern is used to train a multimodal convolutional neural network (CNN) which uses video and audio data to distinguish schizophrenia subjects from healthy controls. Furthermore, we used TVs estimated by the best performing SI system to detect mispronunciation of \ɹ\, a common speech sound disorder in children. The classification model trained with TVs performed better compared to the state-of-the-art hand-crafted age-and-sex normalized formants. In essence, the work in this dissertation presents steps taken towards developing effective acoustic-to-articulatory speech inversion frameworks, and highlights the importance of utilizing articulatory representations in real-world applications.
CORTICAL REPRESENTATIONS OF INTELLIGIBLE AND UNINTELLIGIBLE SPEECH: EFFECTS OF AGING AND LINGUISTIC CONTENT
(2023) Karunathilake , I.M Dushyanthi; Simon, Jonathan Z.; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
Speech communication requires real-time processing of rapidly varying acoustic sounds across various speech landmarks while recruiting complex cognitive processes to derive the intended meaning. Behavioral studies have highlighted that speech comprehension is altered by factors like aging, linguistic content, and intelligibility, yet the systematic neural mechanisms underlying these changes are not well understood. This thesis aims to explore how the neural bases are modulated by each of these factors using three different experiments, by comparing speech representation in the cortical responses, measured by Magnetoencephalography (MEG). We use neural encoding (Temporal Response Functions (TRFs)) and decoding (reconstruction accuracy) models which describe the mapping between stimulus features and the cortical responses, which are instrumental in understanding cortical temporal processing mechanisms in the brain.Firstly, we investigate age-related changes in timing and fidelity of the cortical representation of speech-in-noise. Understanding speech in a noisy environment becomes more challenging with age, even for healthy aging. Our findings demonstrate that some of the age-related difficulties in understanding speech in noise experienced by older adults are accompanied by age-related temporal processing differences in the auditory cortex. This is an important step towards incorporating neural measures to both diagnostic evaluation and treatments aimed at speech comprehension problems in older adults. Next, we investigate how the cortical representation of speech is influenced by the linguistic content by comparing neural responses to four types of continuous speech-like passages: non-speech, non-words, scrambled words, and narrative. We find neural evidence for emergent features of speech processing from acoustics to linguistic processes at the sentential level as incremental steps in the processing of speech input occur. We also show the gradual computation of hierarchical speech features over time, encompassing both bottom-up and top-down mechanisms. Top-down driven mechanisms at linguistic level demonstrates N400-like response, suggesting involvement of predictive coding mechanisms. Finally, we find potential neural markers of speech intelligibility using a priming paradigm, where intelligibility is varied while keeping the acoustic structure constant. Our findings suggest that segmentation of sounds into words emerges with better speech intelligibility and most strongly at ~400 ms in prefrontal cortex (PFC), in line with engagement of top-down mechanisms associated with priming. Taken together, this thesis furthers our understanding on neural mechanisms underlying speech comprehension and potential objective neural markers to evaluate the level of speech comprehension.
WIRELESS SENSING AND ANALYTICS FOR MOTION MONITORING AND MAPPING
(2023) Zhu, Guozhen; Liu, K. J. Ray; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
Environmental perception is pivotal for intelligent systems, enabling them to adeptly capture, interpret, and act upon contextual cues. Grasping the intricacies of the environment—its objects, occupants, floor plan, and dynamics—is fundamental for the effective deployment of technologies, including robotics, the Internet of Things (IoT), and augmented reality. Traditional perception mechanisms, such as video surveillance and sensor-based monitoring, are often hampered by privacy concerns, substantial infrastructural costs, energy inefficiencies, and limited coverage. In contrast, WiFi sensing stands out for its non-intrusive, cost-effective, and pervasive attributes. Capitalizing on ubiquitous WiFi signals that permeate both indoor and outdoor spaces, WiFi sensing delivers unparalleled advantages over its traditional counterparts, sidestepping the need for extra hardware yet offering profound environmental insights. Its capability to penetrate walls and other obstructions further broadens its range, covering areas beyond the reach of conventional sensors. These unique edges of WiFi sensing elevate its value across diverse applications, spanning smart homes, health monitoring, location-based services, and security systems. Amplifying environmental perception via WiFi sensing is more than just an innovation in ubiquitous computing; it's a leap towards forging safer, more efficient, and smarter environments. This dissertation explores monitoring and mapping environments leveraging motion analytics based on commodity WiFi. In the first part of this dissertation, we introduce an efficient and cost-effective system for precise floor plan construction by integrating RF and inertial sensing techniques. The proposed system harnesses detailed insights from RF tracking and broad context from inertial metrics, such as magnetic field strength, to produce an accurate map. The system employs a robot for trajectory collection and requires only a single Access Point to be arbitrarily installed in space, both of which are widely available nowadays. Impressively, the system can produce detailed maps even with minimal data, making it adaptable for diverse structures such as shopping centers, offices, and residences without significant expenses. We validated the efficacy of the proposed system using a Dji RoboMaster S1 robot equipped with standard WiFi across three distinct buildings, demonstrating its capability to produce reliable maps for the intended regions. Given the widespread presence of WiFi setups and the increasing prevalence of domestic robots, the proposed approach paves the way for universal intelligent systems offering indoor mapping services. In the second and third parts, we present two innovative strategies leveraging WiFi to identify the motion of human and various non-human subjects. Initially, we detail a novel passive, non-intrusive methodology tailored for edge devices. By extracting and analyzing motion's physically and statistically plausible features, our system recognizes human and diverse non-human subjects through walls using a singular WiFi link. Experimental results from four distinct buildings with various moving subjects validate its efficiency on edge devices. Advancing to more intricate cases, we put forth a deep learning-based WiFi sensing paradigm. This delves into the efficacy of diverse deep learning models on human and non-human object recognition and probes the feasibility of transferring image-trained models to fulfill the WiFi sensing task. Designed with a robust statistic invariant to the environment and position, this system efficiently adapts to new surroundings. Comprehensive experimental evaluations affirm our framework's precision in pinpointing intricate human and non-human subjects, and readiness for integration into prevalent intelligent systems, thereby boosting their perceptual capacities. In the final part of this dissertation, we propose a pioneering through-wall indoor intrusion detection system that adeptly filters out interference from non-human subjects using ubiquitous WiFi signals. A novel deep learning architecture is proposed for single-link WiFi signal analysis. It employs a ResNet-18-based module to extract features of indoor moving subjects and an LSTM-based module to incorporate temporal information for efficient intrusion detection. Notably, the system is invariant to environmental changes, angles, and positions, enabling swift deployment in new environments without additional training. Evaluation in five indoor environments with various interference yielded high intrusion detection accuracy and a low false alarm rate, even without model tuning for unseen settings. The results underscore the system's exceptional adaptability, positioning it as a top contender for widespread intelligent indoor security applications.
MULTI-ACTIVE-BRIDGE (MAB) DERIVED CONVERTER FOR ENERGY ROUTER APPLICATIONS
(2023) Singhabahu, Chanaka Manoj; Khaligh, Alireza; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
Single-stage power conversion with high frequency transformer isolation has gained interest as a key enabler in improving the efficiency and power density of electrical systems. Traditionally, the power conversion from one voltage level to multiple voltage levels is performed using discrete modules of AC-DC and/or DC-DC converters that meet the isolation requirements. Due to low level of integration in terms of high frequency magnetic link and driving power electronic converters, such solutions suffer from large volume/weight and low efficiencies. Regardless, such multiple input/output converter architectures are extensively used in a wide range of applications, including electric vehicles, DC smart homes, data centers and personal computers. While these architectures are realized using a combination of interconnected discrete power converters, this Ph.D. dissertation presents a multi-port energy router which is capable of integrating multiple systems with different voltage levels, resulting in substantial improvements in power density and efficiency. The proposed energy router employs multi-active-bridge (MAB) converter derived topologies as the fundamental building blocks to create an electrically and magnetically integrated, scalable, single-stage, power electronic converter which can be extended to n-ports. Several key challenges that have impeded the use of MAB converters are investigated in detail. The estimation of the optimal modulation parameters of an MAB converter is vital for achieving desired converter performance. The accurate modeling of the high frequency ac-link plays a major role in determining modulation parameters due to sophisticated magnetic coupling relationships. As the first contribution of this dissertation, a full-order n x n impedance matrix-based model which captures all the coupling information of the magnetic link is used to obtain desired power flow, minimize conduction losses, and analyze zero-voltage-switching (ZVS) conditions of the MAB converter topology. A frequency domain model of the MAB converter is developed which uses the impedance matrix to solve for port currents. Subsequently, the proposed model is used to formulate a constrained numerical optimization routine to find the optimal modulation parameters, which minimizes the conduction and switching losses. The inductance matrix of the high-frequency ac-link is further used in conjunction with the frequency domain model to analyze the port ZVS conditions by investigating the port equivalent inductive energy in the high frequency ac-link. The broad range of operating points (port loading conditions and voltage levels) in an MAB converter presents a complex problem in the design of efficient and power-dense magnetic components. As such, it is not feasible to use traditional optimization approaches developed for two-winding transformers, due to the presence of a high number of design parameters, modulation variables, and the effect of the port loading conditions on the dynamic AC resistance and core losses. As the second contribution, comprehensive planar PCB-based magnetics are developed using a multi-objective design and optimization framework to realize a highly efficient and compact planar magnetic link for the MAB converter. As a key component of this framework, accurate and scalable analytical models for conduction and core loss estimation are developed, which capture loss mechanisms distinctive to multi-winding transformers. Using the proposed loss models, the design framework integrates multi-objective optimization methods for all magnetic components in the high-frequency link, namely, the multi-winding transformer and the series branch inductors. The proposed approach determines the optimal combination of magnetic core geometries, turns ratios, number of turns, branch inductances, and winding interleaving configuration, with the objectives of minimizing the operating point weighted-efficiency drop and the magnetic volume. Finally, a Pareto-optimal magnetic link design is selected. The proposed concepts of obtaining optimal modulation parameters and the design of high frequency planar magnetic link are validated using comprehensive circuit and finite-element-analysis (FEA) simulations. The experimental verification is performed on a Gallium Nitride based 4-port 1-kW DC-DC MAB converter with its ports rated at 420V, 48V, 24V, 12V. With the modeling, design and optimization methodologies obtained from the above two works, a new family of MAB derived converter topologies with AC ports is proposed as the third contribution of this dissertation. Particularly, the single-stage power conversion between DC and three-phase AC is investigated. The operating principles of the proposed topologies are discussed in detail along with systematic modeling and optimal modulation methods by using the concepts developed above for DC-DC MAB converters. The circuit operation is also investigated in terms of ZVS. To validate the topology configurations and the modulation methods, comprehensive Simulink simulation models are developed. Compared to traditional two-stage converter systems comprised of DC-DC and DC-AC stages, the proposed topologies provide multiple benefits in terms of single-stage power conversion, ZVS, high-efficiency and galvanic isolation.

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