Electrical & Computer Engineering Theses and Dissertations

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    Compression and Multi-Spectral Sensing for Video Based Physiological Monitoring
    (2022) Steinhauser, Carl Frederick; Wu, Min; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Remote physiological monitoring is an active area of research that extends monitoring capabilities traditionally found in a clinical setting towards the home, telehealth, and beyond. In particular, there is interest in leveraging consumer electronic devices for sensing physiological characteristics such as heart rate, heart rate variability, and blood oxygen saturation. This thesis focuses on enhancing the understanding and usage of the sensing component for these applications to improve the performance and quality of cardio-physiological monitoring. First, a close relationship between the color spaces used for video compression and the color projection planes commonly used for heart rate estimation is identified. % that results in higher compression of the physiological signal. The study demonstrates the impact of this observation on real and synthetic data to provide a foundation to guide future video coding to optimize its configurations to better preserve the heart rate signal for health related applications. Second, an investigation with a commercial-off-the-shelf (COTS) multi-spectral sensor is presented with key observations related to the sampling rate, exposure settings, and multi-channel processing. These observations will enable better usage of the sensor for future studies and data collections that leverage the more precise spectral measurements from the multi-spectral sensor compared to standard RGB cameras.
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    Wave Chaos Studies and The Realization of Photonic Topological Insulators
    (2022) Xiao, Bo; Anlage, Steven; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Wave propagation in various complex media is an interesting and practical field that has a huge impact in our daily life. Two common types of wave propagation are examined in this thesis: electromagnetic wave propagation in complex wave chaotic enclosures, where I studied its statistical properties and explored time-domain pulse focusing, and unidirectional edge modes propagating in a reciprocal photonic topological insulator waveguide. Several theories, e.g. the Random Matrix Theory and the Random Coupling Model, have been developed and validated in experiments to understand the statistical properties of the electromagnetic waves inside wave chaotic enclosures. This thesis extends the subject from a single cavity to a network of coupled cavities by creating an innovative experimental setup that scales down complex structures, which would otherwise be too large and cumbersome to study, to a miniature version that retains its original electromagnetic properties. The process involves shrinking down the metal cavity in size by a factor of 20, increasing the electromagnetic wave frequency by the same factor and cooling down the cavity by a dilution refrigerator to reduce its ohmic loss. This experimental setup is validated by comparison with results from a full-scale setup with a single cavity and it is then extended for multiple coupled cavities. In the time domain, I utilized the time-reversal mirror technique to focus electromagnetic waves at an arbitrary location inside a wave chaotic enclosure by injecting a numerically calculated wave excitation signal. I used a semi-classical ray algorithm to calculate the signal that would be received at a transceiver port resulting from the injection of a short pulse at the desired target location. The time-reversed version of this signal is then injected into the transceiver port and an approximate reconstruction of the short pulse is observed at the target port. Photonic topological insulators are an interesting class of materials whose photonic band structure can have a bandgap in the bulk while supporting topologically protected unidirectional edge modes. This thesis presents a rotating magnetic dipole antenna, composed of two perpendicularly oriented coils fed with variable phase difference, that can efficiently excite the unidirectional topologically protected surface waves in the bianisotropic metawaveguide (BMW) structure recently realized by Ma, et al., despite the fact that the BMW medium does not break time-reversal invariance. In addition to achieving high directivity, the antenna can be tuned continuously to excite reflectionless edge modes to the two opposite directions with various amplitude ratios. Overall, this thesis establishes the foundation for further studies of the universal statistical properties of wave chaotic enclosures, and tested the limits of its deterministic properties defined by the cavity geometry. It also demonstrated in experiment the excitation of a unidirectional edge mode in a Bianisotropic Meta-waveguide, allowing for novel applications in the field of communications, for example phased array antennas.
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    (2022) Chen, Lei; Anlage, Steven M; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Wave scattering properties in complex scattering systems have been of great interest to both the physics and engineering communities because of their useful characterizations of such systems and significant value for applications. The most common tool for studying such properties – the scattering (S)-matrix, can be fully represented by its zeros and poles in the complex energy/frequency plane. There has been substantial effort to understand the scattering properties and wave phenomena inside complex systems in the past, both theoretical and experimental, which in turn has led to significant advancement in many applications: wavefront shaping (WFS), coherent perfect absorption (CPA), wireless power transfer, electromagnetic interference (EMI), etc. In this dissertation, I will summarize the recent progress and interest regarding an intriguing wave phenomenon – coherent perfect absorption (CPA) in complex scattering systems. We have successfully implemented CPA protocols in generic complex scattering systems without any geometric or hidden symmetries, which greatly extends CPA beyond its initial concept as the time-reversal of a laser cavity. Under such efforts, we have also established a convenient approach for control of the local losses inside the network system, which helped us to uncover the mystery of matching the imaginary part of the S-matrix zero to the uniform loss of the system. We thus developed the theoretical representation of the S-matrix by its zeros and poles, and generalized the traditional Wigner time delay to a complex quantity in sub-unitary scattering systems. We have revealed the inherent connection between the complex Wigner time delay and coherent perfect absorption, and can utilize the new complex Wigner time delay idea for extracting S-matrix zeros and poles in a practical system. We have also studied the statistical properties of the complex generalization of Wigner time delay for subunitary wave-chaotic scattering systems, and demonstrated excellent agreement between theory and experiments. Finally, we have extended this scheme to a comprehensive time delay analysis framework, including Wigner, transmission, and reflection time delays. This approach offers us the capability to systematically analyze the poles and zeros of the scattering matrix of any complex scattering system. We then apply the new transmission time delay method on a two-channel microwave graph realization of the Aharonov--Bohm ring from mesoscopic physics, and demonstrate the dependence of non-reciprocal transport behavior on the de-phasing rate. The ultimate goal is to completely control the scattering properties of complex systems by manipulating the zeros and poles of the S-matrix, for example by adding losses in the system or changing the coupling of the scattering channels, etc. Such a capability will be extremely useful for understanding the wave properties of complex scattering systems, and for controlling the wave behavior in optics, electromagnetics, acoustics, quantum transport in condensed matter systems, etc.
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    Control Theory-Inspired Acceleration of the Gradient-Descent Method: Centralized and Distributed
    (2022) Chakrabarti, Kushal; Chopra, Nikhil; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Mathematical optimization problems are prevalent across various disciplines in science and engineering. Particularly in electrical engineering, convex and non-convex optimization problems are well-known in signal processing, estimation, control, and machine learning research. In many of these contemporary applications, the data points are dispersed over several sources. Restrictions such as industrial competition, administrative regulations, and user privacy have motivated significant research on distributed optimization algorithms for solving such data-driven modeling problems. The traditional gradient-descent method can solve optimization problems with differentiable cost functions. However, the speed of convergence of the gradient-descent method and its accelerated variants is highly influenced by the conditioning of the optimization problem being solved. Specifically, when the cost is ill-conditioned, these methods (i) require many iterations to converge and (ii) are highly unstable against process noise. In this dissertation, we propose novel optimization algorithms, inspired by control-theoretic tools, that can significantly attenuate the influence of the problem's conditioning. First, we consider solving the linear regression problem in a distributed server-agent network. We propose the Iteratively Pre-conditioned Gradient-Descent (IPG) algorithm to mitigate the deleterious impact of the data points' conditioning on the convergence rate. We show that the IPG algorithm has an improved rate of convergence in comparison to both the classical and the accelerated gradient-descent methods. We further study the robustness of IPG against system noise and extend the idea of iterative pre-conditioning to stochastic settings, where the server updates the estimate based on a randomly selected data point at every iteration. In the same distributed environment, we present theoretical results on the local convergence of IPG for solving convex optimization problems. Next, we consider solving a system of linear equations in peer-to-peer multi-agent networks and propose a decentralized pre-conditioning technique. The proposed algorithm converges linearly, with an improved convergence rate than the decentralized gradient-descent. Considering the practical scenario where the computations performed by the agents are corrupted, or a communication delay exists between them, we study the robustness guarantee of the proposed algorithm and a variant of it. We apply the proposed algorithm for solving decentralized state estimation problems. Further, we develop a generic framework for adaptive gradient methods that solve non-convex optimization problems. Here, we model the adaptive gradient methods in a state-space framework, which allows us to exploit control-theoretic methodology in analyzing Adam and its prominent variants. We then utilize the classical transfer function paradigm to propose new variants of a few existing adaptive gradient methods. Applications on benchmark machine learning tasks demonstrate our proposed algorithms' efficiency. Our findings suggest further exploration of the existing tools from control theory in complex machine learning problems. The dissertation is concluded by showing that the potential in the previously mentioned idea of IPG goes beyond solving generic optimization problems through the development of a novel distributed beamforming algorithm and a novel observer for nonlinear dynamical systems, where IPG's robustness serves as a foundation in our designs. The proposed IPG for distributed beamforming (IPG-DB) facilitates a rapid establishment of communication links with far-field targets while jamming potential adversaries without assuming any feedback from the receivers, subject to unknown multipath fading in realistic environments. The proposed IPG observer utilizes a non-symmetric pre-conditioner, like IPG, as an approximation of the observability mapping's inverse Jacobian such that it asymptotically replicates the Newton observer with an additional advantage of enhanced robustness against measurement noise. Empirical results are presented, demonstrating both of these methods' efficiency compared to the existing methodologies.
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    Speech Segregation and Representation in the Ferret Auditory and Frontal Cortices
    (2022) Joshi, Neha Hemant; Shamma, Shihab; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    The problem of separating overlapping streams of sound, and selectively attending to a sound of interest is ubiquitous in humans and animals alike as a means for survival and communication. This problem, known as the cocktail party problem, is the focus of this thesis, where we explore the neural correlates of two-speaker segregation in the auditory and frontal cortex, using the ferret as an animal model. While speech segregation has been studied extensively in humans using various non-invasive imaging as well as some restricted invasive techniques, these do not provide a way to obtain neural data at the single-unit level. In animal models, streaming studies have been limited to simple stimuli like tone streams, or sound in noise. In this thesis, we extend this work to understand how complex auditory stimuli such as human speech is encoded at the single-unit and population level in both the auditory cortex, as well as the frontal cortex of the ferret. In the first part of the thesis, we explore current literature in auditory streaming and design a behavioral task using the ferret as an animal model to perform stream segregation. We train ferrets to selectively listen to one speaker over another, and perform a task to indicate detection of the attended speaker. We show the validity of this behavioral task, and the reliability with which the animal performs this task of two speaker stream segregation. In the second part, we collect neurophysiological data which is post-processed to obtain data from single units in both the auditory cortex (the primary auditory cortex, and the secondary region which includes the dorsal posterior ectosylvian gyrus) as well as the dorsolateral aspect of the frontal cortex of the ferret. We analyse the data and present findings of how the auditory and frontal cortices encode the information required to reliably segregate the speaker of relevance from the mixture of two speakers, and the insights provided into stream segregation mechanisms and the cocktail party solved by animals using neural decoding approaches. We finally demonstrate that stream segregation has already begun at the level of the primary auditory cortex. In agreement with previous attention-modulated neural studies in the auditory cortex, we show that this stream segregation is more pronounced in the secondary cortex, where we see clear enhancement of the attended speaker, and suppression of the unattended speaker. We explore the contribution of various areas within the primary and secondary regions, and how it relates to speaker selectivity of individual neuronal units. We also study the neural encoding of top-down attention modulation in the ferret frontal cortex. Finally, we discuss the conclusions from these results in the broader context of their relevance to the field, and what future directions it may hold for the field.