Theses and Dissertations from UMD
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New submissions to the thesis/dissertation collections are added automatically as they are received from the Graduate School. Currently, the Graduate School deposits all theses and dissertations from a given semester after the official graduation date. This means that there may be up to a 4 month delay in the appearance of a give thesis/dissertation in DRUM
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Item Automated Management of Network Slices with Service Guarantees(2024) Nikolaidis, Panagiotis; Baras, John; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Future mobile networks are expected to support a diverse set of applications including high-throughput video streaming, delay-sensitive augmented reality applications, and critical control traffic for autonomous driving. Unfortunately, existing networks do not have the required management mechanisms to handle this complex mix of traffic efficiently. At the same time, however, there is a significant effort from both industry and academia to make networks more open and programmable, leading to the emergence of software-defined networking, network function virtualization, and packet-forwarding programming languages. Moreover, several organisations such as the Open Networking Foundation were founded to facilitate innovation and lower the entry barriers in the mobile networking industry. In this setting, the concept of network slicing emerged which involves the partitioning of the mobile network into virtual networks that are tailored for specific applications. Each network slice needs to provide premium service to its users as specified in a service level agreement between the mobile network operator and the customer. The deployment of network slices has been largely realized thanks to network function virtualization. However, little progress has been made on mechanisms to efficiently share the network resources among them. In this dissertation, we develop such mechanisms for the licensed spectrum at the base station, a scarce resource that operators obtain through competitive auctions. We propose a system architecture composed of two new network functions; the bandwidth demand estimator and the network slice multiplexer. The bandwidth demand estimator monitors the traffic of the network slice and outputs the amount of bandwidth currently needed to deliver the desired quality of service. The network slice multiplexer decides which bandwidth demands to accept when the available bandwidth does not suffice for all the network slices. A key feature of this architecture is the separation of the demand estimation task from the contention resolution task. This separation makes the architecture scalable for a large number of network slices. It also allows the mobile network operator to charge fairly each customer based on their bandwidth demands. In contrast, the most common approach in the literature is to learn online how to split the available resources among the slices to maximize a total network utility. However, this approach is neither scalable nor suitable for service level agreements. The dissertation contributes several algorithms to realize the proposed architecture and provisioning methods to guarantee the fulfillment of the service level agreements. To satisfypacket delay requirements, we develop a bandwidth demand estimator based on queueing theory and online learning. To share resources efficiently even in the presence of traffic anomalies, we develop a network slice multiplexer based on the Max-Weight algorithm and hypothesis testing. We implement and test the proposed algorithms on network simulators and 5G testbeds to showcase their efficiency in realistic settings. Overall, we present a scalable architecture that is robust to traffic anomalies and reduces the bandwidth needed to serve multiple network slices.Item Development of Photonic Reservoir Computers for Radiofrequency Spectrum Awareness(2024) Klimko, Benjamin; Chembo, Yanne K.; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)In this dissertation, we study the use of several optoelectronic oscillator architectures for physical reservoir computing tasks. While optoelectronic oscillator-based reservoir computers have been reported in the literature for over a decade, all reported experimental results have been processed using wideband systems with baseband data sets. Our work focuses on two majorinnovations for physical reservoir computing: (i) narrowband reservoir computers allowing computing tasks to be performed natively on radiofrequency signals and (ii) illustrating that “simplified” optoelectronic oscillators, without external optical modulators, are capable of meeting or exceeding the results from more complex photonic reservoir computers. By their nature, optoelectronic oscillators operate in the radio passband regime and reservoir computers have been shown to be capable on time-series tasks such as waveform prediction and classification data sets. We demonstrated that the optoelectronic oscillator-based reservoir computer can effectively process signals in the radio passband, which is a novel result that could provide an enabling technology for next-generation communication methods such as cognitive networks. The benefits of this innovation would scale with increasing frequency, such as potential use with millimeter-wave cellular networks. In our second physical reservoir innovation, we have shown that external optical modulators, nearly ubiquitous devices in optoelectronic oscillators, may be excluded from the design of a physical reservoir computer without decreasing its accuracy. This is a major result as a reservoir without active optical components could be built on a single integrated circuit using modern semiconductor manufacturing processes. Such integration and miniaturization would be a large step towards photonic reservoir systems that could be more easily put into an operational environment. Up to this point, there has been minimal work on transitioning the optoelectronic oscillator from a benchtop, experimental system to one useful in the real world. Lastly, we investigated the relationship between computational power of the reservoir computer and task error. This is a crucial finding since reservoir computing is often touted as an alternative computing paradigm that is less resource-intensive than other computing methods. By determining a threshold on computational needs for a photonic reservoir computer, we ensure that such systems are utilized efficiently and do not unnecessarily use resources.Item INGESTIBLE BIOIMPEDANCE SENSING DEVICE FOR GASTROINTESTINAL TRACT MONITORING(2024) Holt, Brian Michael; Ghodssi, Reza; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Gastrointestinal (GI) diseases, such as inflammatory bowel disease (IBD), result in dilated adherens and tight junctions, altering mucosal tissue permeability. Few monitoring techniques have been developed for in situ monitoring of local mucosal barrier integrity, and none are capable of non-invasive measurement beyond the esophagus. In this work, this technology gap is addressed through the development of a noise-resilient, flexible bioimpedance sensor integrated ingestible device containing electronics for low-power, four-wire impedance measurement and Bluetooth-enabled wireless communication. Through electrochemical deposition of a conductive polymeric film, the sensor charge transfer capacity is increased 51.4-fold, enabling low-noise characterization of excised intestinal tissues with integrated potentiostat circuitry for the first time. A rodent animal trial is performed, demonstrating successful differentiation of healthy and permeable mice colonic tissues using the developed device. In accordance with established mucosal barrier evaluation methodologies, mucosal impedance was reduced between 20.3 ± 9.0% and 53.6 ± 10.7% of its baseline value in response to incrementally induced tight junction dilation. Ultimately, this work addresses the fundamental challenges of electrical resistance techniques hindering localized, non-invasive IBD diagnostics. Through the development of a simple and reliable bioimpedance sensing module, the device marks significant progress towards explicit quantification of “leaky gut” patterns in the GI tract.Item Active Power Decoupling (APD) Converter for PV Microinverter Applications(2024) Shen, Yidi; Khaligh, Alireza; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Under global challenges in climate change, the demand for renewable energy is continuously growing. Photovoltaic (PV) power and its integration into the utility grid are gaining increasing traction. To lower the levelized cost of energy (LCOE) of PV systems, enhance the adoption of PV applications, and ensure the delivery of high-quality power to the utility grid, there is a growing need for reliable, cost-effective, efficient, and compact PV inverters. One key challenge in single-phase PV systems is the short lifetime and poor reliability of electrolytic capacitors used for decoupling the double line frequency (DLF) power. To eliminate the less reliable electrolytic capacitor, the active power decoupling (APD) technique is widely adopted. Various topologies can be used for APD, but the selection of proper topology, modulation scheme, and circuit components, along with the control strategy, will enhance the efficiency, power density, reliability, and cost of the overall PV microinverter. This Ph.D. dissertation proposes an APD converter circuit suitable for PV microinverters, designed for optimized efficiency, power density, and cost. The proposed APD converter is controlled to achieve good power decoupling performance and to optimize the system's maximum power point tracking (MPPT) efficiency. The proposed APD converter circuit is analyzed in the low-frequency domain for power flow and in the high-frequency domain for modulation strategy, where different topologies are considered, taking into account the voltage and current ratings of active devices and decoupling capacitors. Two modulation approaches, continuous conduction mode (CCM) and critical conduction mode (CRM), are compared, considering detailed zero voltage switching (ZVS) operation and different loss mechanisms. Parametric design and multi-objective optimization are performed for CCM and CRM to select circuit components and switching frequency for each modulation strategy to minimize power loss, volume, and costs. With the results of multi-objective optimization, Pareto-optimal designs for CCM and CRM are analyzed in terms of the impact of various circuit elements, namely: switching device output capacitance and on-state resistance, inductor winding turns and core geometries, as well as capacitor dimensions and capacitance. With the optimal CCM- and CRM-operated APD realizations, closed-loop control algorithms are designed, and the corresponding system characteristics are compared. A simple pulse width modulation (PWM) based control strategy that does not rely on zero-crossing detection (ZCD) is proposed to implement closed-loop CRM modulation. In addition, advanced control technologies, including double sampling-based average current control, current observer-based reduced sensor control, and sensorless predictive control, are proposed to improve APD converter performance, reduce system complexity, and lower circuit cost. The proposed APD converter operation is extended to different application scenarios, including burst-mode operation and non-sinusoidal power delivery, including systems with non-linear circuit components, non-linear local loads, or non-ideal grids. A feed-forward control solution is proposed to enable power decoupling for non-sinusoidal power with improved control accuracy and reduced closed-loop design burden. The circuit design, associated analyses, and control approaches are validated by the design, development, and testing of 400 VA APD hardware prototypes.Item Enhancement and Robustness of Large Timely Gossip Networks(2024) Kaswan, Priyanka; Ulukus, Sennur; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)In this thesis, we explore the subject of fast dissemination of real-time data from a source to multiple users, critical for time-sensitive applications such as autonomous driving sytems, internet of things (IoT), augmented reality (AR), virtual reality (VR), and real-time content sharing in social networks, that feature dense interconnected networks of devices and humans. In face of network resource limitations and increasingly dynamic data generated by various sources in these networks, it is imperative that all nodes within these networks have timely information, i.e., the latest possible updates about the source nodes at all times for seamless functioning of these networks. Although it might seem straightforward to transmit changing data at high speed to all users, practical challenges such as limited bandwidth, server servicing speed, and intermittent connectivity hinder this approach. Therefore, to achieve the goal of timeliness, measured by metrics such as the age of information, this thesis leverages gossip algorithms, which are decentralized algorithms whose popularity stems from their efficiency and scalablility for information dissemination in such constrained and uncertain networks. This thesis aims to deepen our understanding of the capabilities and limitations of timely gossip networks of various topologies in managing large volumes of dynamic data. As importantly, this thesis explores the unique threats and vulnerabilities these next-generation networks face in the evolving landscape of 5G and 6G technologies. We open our analysis of this subject by first exploring efficient strategies for timely dissemination of time-varying data files from a source to a user via a simple network of parallel relays. We find that consolidating file update rates to the minimum number of relays improves timeliness, contrary to using all relays for each file. By solving an auxiliary single-cache problem and adapting its solution to our multi-cache network, we provide a sub-optimal solution, such that the upper bound on its gap to the optimal policy is independent of the number of files. Next, we explore complex network topologies and examine the resilience of gossip networks as a function of their connectivity to jamming attacks. We analyze the average version age in the presence of $\tilde{n}$ jammers for both connectivity-constrained ring and connectivity-rich fully connected topologies of $n$ nodes. Our findings reveal that a ring network is robust against up to $\sqrt{n}$ jammers, while a fully connected network withstands $n\log{n}$ jammers, showing that higher connectivity enhances resilience to jamming. To maximize age deterioration in the network, the jammers should attack in a manner that consolidates all remaining unjammed links into a dense cluster of the fewest possible nodes, leaving a higher number of nodes isolated. Next, we uncover a new type of attack in age-based networks, called timestomping, where an adversary manipulates timestamps of information packets, causing nodes to discard fresh packets for stale ones. We show that in fully connected networks, a single infected node can increase the expected age from $O(\log n)$ to $O(n)$, highlighting how full connectivity can expedite adversarial impacts. Conversely, in unidirectional ring networks with sparse inter-node connectivity, we find that the adversarial impact on node age scaling is confined by its distance from the adversary, maintaining an age scaling of $O(\sqrt{n})$ for a significant fraction of the network. Then, we demonstrate how the age-specific nature of file exchange protocols also makes gossip networks susceptible to the propagation of misinformation. We consider networks where packets can potentially get mutated during inter-node gossiping, creating misinformation. Nodes prefer latest versions of information, however, when a receiving node encounters both accurate information and misinformation for the same version, we consider two models: one where truth prevails over misinformation and another where misinformation prevails over truth. Using stochastic hybrid systems (SHS) modeling, we examine the expected fraction of nodes with correct information and the version age. We show that higher or lower gossiping rates effectively reduce misinformation when truth prevails, whereas moderate rates increase its spread. Conversely, misinformation prevalence rises with increased gossiping under the misinformation-prevailing scenario. Then, we consider the balance between freshness and reliability of information in an age-based gossip network, where two sources (reliable and unreliable) disseminate updates to $n$ network nodes. Nodes wish to have fresh information, however, they have preference for packets that originate at the reliable source and are willing to sacrifice their version age of information by up to $G$ versions to switch from an unreliable packet to a reliable packet. We show that increasing $G$ reduces unreliable packets but raises the network version age, revealing a freshness-reliability trade-off. Next, we develop a theory of timeliness for non-Poisson updating and study cache-aided networks where the inter-update times on the links are not necessarily exponentially distributed. We characterize the expressions for instantaneous age and version age in arbitrary networks, then derive their closed form expressions in case of tree networks, where they exhibit an additive structure. Finally, we analyze age of information in networks where update processes on the links become sparse as network size increases, noting that in symmetric fully connected networks, expected age scales as $O(\log{n})$. Then, we study a system where a group of users, interested in closely tracking a time-varying event and maintaining their expected version ages below a threshold, choose between either preferably relying on gossip from their neighbors or directly subscribing to a server publishing about the event, to meet the timeliness requirements. The server wishes to maximize its profit by boosting subscriptions from users and minimizing event sampling frequency to reduce costs, setting up a Stackelberg game between the server and the users. We analyze equilibrium strategies in both directed and undirected networks, finding that well-connected networks have fewer subscribers since well-connected users dissuade their multiple neighboring nodes from subscribing. Next, we consider a gossip network of $n$ users hosting a library of files, such that each file is initially present at exactly one node, designated as the file source. The source gets updated with newer versions of the file according to an arbitrary distribution in real-time, and the other users in the network wish to acquire the latest possible version of the file. We present a class of gossip protocols that achieve $O(1)$ age at a typical node in a single-file system and $O(n)$ age at a typical node for a given file in an $n$-file system. We show that file slicing and network coding based protocols fall under the presented class of protocols. Finally, we further explore timestomping attacks in a simplified communication model, where a source attempts to minimize the age of a user, but due to a power constraint, the source can only transmit updates directly to the user for a fraction of timeslots over a fixed time horizon. A cache node, which can afford more frequent transmissions, lies in between the source and the user, however the communication link between the cache and the user is under attack by a timestomping adversary. We formulate this adversarial cache updating problem as an online learning problem and study the achievable competitive ratios for this problem.Item POLYMORPHIC CIRCUITS: THE IDENTIFICATION OF POSSIBLE SOURCES AND APPLICATIONS(2024) Dunlap, Timothy; Qu, Gang; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Polymorphic gates are gates whose function depends on some external or environmental conditions. While there has been research into both the creation and applications of polymorphic gates, much remains unknown. This dissertation, motivated by the recent security applications of polymorphic gates, seeks a systematic approach to generating polymorphic gates.Its contributions include a polymorphic interoperability framework, the first study on the source of polymorphism, time-based polymorphic gates, and polymorphism in the sub-threshold design. Polymorphic circuits are commonly created with evolutionary algorithms [3]. Because the evolutionary algorithm operates in ways that are not always obvious, precise mechanisms of polymorphism are not immediately clear in the resulting gates and has not been reported before. This dissertation, for the first time, identifies multiple structures that impact the polymorphic nature of the gates, which sheds light on how to create polymorphic gates. This discovery is based on a categorization methodology that evaluates the quality of polymorphic gates and finds the robust ones for further investigation of polymorphism. By combining the discovered structures with the evolutionary algorithm, high quality polymorphic gates can be generated faster as demonstrated in the subthreshold design domain. Time-based polymorphism was discovered during the time analysis of evolved polymorphic circuits while searching for the sources of polymorphism. This occurs when the function of the circuit depends on the sample rate of the circuit and is based on some input combinations not quickly reaching the output they move towards. Therefore, when the circuit is running at different clock frequency, it may exhibit different functionality. This is time-based polymorphism. As one application of polymorphic gates, this dissertation presents a framework that can enhance the fault coverage of any fault testing method by utilizing polymorphic gates. The proposed framework starts with any traditional fault testing approach, and when it becomes less effective in covering uncovered faults, it employs a gate replacement strategy to selectively replace certain standard logic gates by polymorphic gates of specific polymorphism. This concept is demonstrated in the dissertation with examples of a D flip-flop and the ISCAS85 C17 benchmark. This work has high practical value in subthreshold design where circuit manufacture defects increase significantly. In summary, this dissertation presents multiple contributions to the study of polymorphic circuits. It discovers multiple sources of polymorphism and how the results of an evolutionary algorithm can be filtered into higher quality solutions. It also examines time-based polymorphism as a new form of polymorphism with security applications. Finally, an enhancement to stuck-at fault testing using polymorphic gates is presented. This allows for easier testing of corner-cases that are hard to detect using traditional methodologies and holds promise for improving the reliability of testing, particularly in the subthreshold domain.Item Learning in Large Multi-Agent Systems(2024) Kara, Semih; Martins, Nuno C; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)In this dissertation, we study a framework of large-scale multi-agent strategic interactions. The agents are nondescript and use a learning rule to repeatedly revise their strategies based on their payoffs. Within this setting, our results are structured around three main themes: (i) Guaranteed learning of Nash equilibria, (ii) The inverse problem, i.e. estimating the payoff mechanism from the agents' strategy choices, and (iii) Applications to the placement of electric vehicle charging stations. In the traditional setup, the agents' inter-revision times follow identical and independent exponential distributions. We expand on this by allowing these intervals to depend on the agents' strategies or have Erlang distributions. These extensions enhance the framework's modeling capabilities, enabling it to address problems such as task allocation with varying service times or multiple stages. We also explore a third generalization, concerning the accessibility among strategies. Majority of the existing literature assume that the agents can transition between any two strategies, whereas we allow only certain alternatives to be accessible from certain others. This adjustment further improves the framework's modeling capabilities, such as by incorporating constraints on strategy switching related to spatial and informational factors. For all of these extensions, we use Lyapunov's method and passivity-based techniques to find conditions on the revision rates, learning rule, and payoff mechanism that ensure the agents learn to play a Nash equilibrium of the payoff mechanism. For our second class of problems, we adopt a multi-agent inverse reinforcement learning perspective. Here, we assume that the learning rule is known but, unlike in existing work, the payoff mechanism is unknown. We propose a method to estimate the unknown payoff mechanism from sample path observations of the populations' strategy profile. Our approach is two-fold: We estimate the agents' strategy transitioning probabilities, which we then use - along with the known learning rule - to obtain a payoff mechanism estimate. Our findings regarding the estimation of transitioning probabilities are general, while for the second step, we focus on linear payoff mechanisms and three well-known learning rules (Smith, replicator, and Brown-von Neumann-Nash). Additionally, under certain assumptions, we show that we can use the payoff mechanism estimate to predict the Nash equilibria of the unknown mechanism and forecast the strategy profile induced by other rules. Lastly, we contribute to a traffic simulation tool by integrating electric vehicles, their charging behaviors, and charging stations. This simulation tool is based on spatial-queueing principles and, although less detailed than some microscopic simulators, it runs much faster and accurately represents traffic rules. Using this tool, we identify optimal charging station locations (on real roadway networks) that minimize the overall traffic.Item Studies in Differential Privacy and Federated Learning(2024) Zawacki, Christopher Cameron; Abed, Eyad H; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)In the late 20th century, Machine Learning underwent a paradigm shift from model-driven to data-driven design. Rather than field specific models, advances in sensors, data storage, and computing power enabled the collection of increasing amounts of data. The abundance of new data allowed researchers to fit flexible models directly to observed data. The influx of information made possible numerous advances, including the development of novel medicines, increases in efficiency of markets, and the proliferation of vast sensor networks. However, not all data should be freely accessible. Sensitive medical records, personal finances, and private IDs are all currently stored on digital devices across the world with the expectation that they remain private. However, at the same time, such data is frequently instrumental in the development of predictive models. Since the beginning of the 21st century, researchers have recognized that traditional methods of anonymizing data are inadequate for protecting client identities. This dissertation's primary focus is the advancement of two fields of data privacy: Differential Privacy and Federated Learning. Differential Privacy is one of the most successful modern privacy methods. By injecting carefully structured noise into a dataset, Differential Privacy obscures individual contributions while allowing researchers to extract meaningful information from the aggregate. Within this methodology, the Gaussian mechanism is one of the most common privacy mechanisms due to its favorable properties such as the ability of each client to apply noise locally before transmission to a server. However, the use of this mechanism yields only an approximate form of Differential Privacy. This dissertation introduces the first in-depth analysis of the Symmetric alpha-Stable (SaS) privacy mechanism, demonstrating its ability to achieve pure-Differential Privacy while retaining local applicability. Based on these findings, the dissertation advocates for using the SaS privacy mechanism in protecting the privacy of client data. Federated Learning is a sub-field of Machine Learning, which trains Machine Learning models across a collection (federation) of client devices. This approach aims to protect client privacy by limiting the type of information that clients transmit to the server. However, this distributed environment poses challenges such as non-uniform data distributions and inconsistent client update rates, which reduces the accuracy of trained models. To overcome these challenges, we introduce Federated Inference, a novel algorithm that we show is consistent in federated environments. That is, even when the data is unevenly distributed and the clients' responses to the server are staggered in time (asynchronous), the algorithm is able to converge to the global optimum. We also present a novel result in system identification in which we extend a method known as Dynamic Mode Decomposition to accommodate input delayed systems. This advancement enhances the accuracy of identifying and controlling systems relevant to privacy-sensitive applications such as smart grids and autonomous vehicles. Privacy is increasingly pertinent, especially as investments in computer infrastructure constantly grow in order to cater to larger client bases. Privacy failures impact an ever-growing number of individuals. This dissertation reports on our efforts to advance the toolkit of data privacy tools through novel methods and analysis while navigating the challenges of the field.Item Autonomous Robot Navigation in Challenging Real-World Indoor and Outdoor Environments(2024) Sathyamoorthy, Adarsh Jagan; Manocha, Dr. Dinesh; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The use of autonomous ground robots for various indoor and outdoor applications has burgeoned over the years. In indoor settings, their applications range from waiters in hotels, helpers in hospitals, cleaners in airports and malls, transporters of goods in warehouses, surveillance robots, etc. In unstructured outdoor settings, they have been used for exploration in off-road environments, search and rescue, package delivery, etc. To successfully accomplish these tasks, robots must overcome several challenges and navigate to their goal. In this dissertation, we present several novel algorithms for learning-based perception combined with model-based autonomous navigation in real-world indoor and outdoor environments. The presented algorithms address the problems of avoiding collisions in dense crowds (< 1 to 2 persons/sq.meter), reducing the occurrence of the freezing robot problem, navigating in a socially compliant manner without being obtrusive to humans, and avoiding transparent obstacles in indoor settings. In outdoor environments, they address challenges in estimating the traversabilityof off-road terrains and vegetation, and understanding explicit social rules (e.g. crossing streets using crosswalks). The presented algorithms are designed to operate in real-time using the limited computational capabilities on-board real wheeled and legged robots such as the Turtlebot 2, Clearpath Husky, and Boston Dynamics Spot. Furthermore, the algorithms have been evaluated in real-world environments with dense crowds, transparent obstacles, off-road terrains, and vegetation such as tall grass, bushes, trees, etc. They have demonstrated significant improvements in terms of several metrics such as increasing success rates by at least 50% (robot avoids collisions and reaches its goal), lowering freezing rates by at least 80% (robot does not halt/oscillate indefinitely), increasing pedestrian friendliness up to 100% higher, reducing vibrations experienced in off-road terrains by up to 22%, etc over the state-of-the-art algorithms in various test scenarios. The first part of this dissertation deals with socially-compliant navigation approaches for crowded indoor environments. The initial methods focus on collision avoidance, handling the freezing robot problem in crowds of varying densities by tracking individual pedestrians, and modeling regions the robot must avoid based on their future positions. Subsequent works expand on these models by considering pedestrian group behaviors. The next part of this dissertation focuses on outdoor navigation methods that estimate the traversability of various terrains, and complex vegetation (e.g. pliable obstacles such as tall grass) using perception inputs to navigate on safe, and stable terrains. The final part of the dissertation elaborates on methods designed for detecting and navigating complex obstacles in indoor and outdoor environments. It also explores a technique leveraging recent advancements in large vision language models for navigation in both settings. All proposed methods have been implemented and evaluated on real wheeled and legged robots.Item 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.