Engineering Commons^™

Understanding The Role Of Interactivity And Explanation In Adaptive Experiences, Lijie Guo

All Dissertations

Adaptive experiences have been an active area of research in the past few decades, accompanied by advances in technology such as machine learning and artificial intelligence. Whether the currently ongoing research on adaptive experiences has focused on personalization algorithms, explainability, user engagement, or privacy and security, there is growing interest and resources in developing and improving these research focuses. Even though the research on adaptive experiences has been dynamic and rapidly evolving, achieving a high level of user engagement in adaptive experiences remains a challenge. %????? This dissertation aims to uncover ways to engage users in adaptive experiences by incorporating …

Explainable Physics-Informed Deep Learning For Rainfall-Runoff Modeling And Uncertainty Assessment Across The Continental United States, Sadegh Sadeghi Tabas

All Dissertations

Hydrologic models provide a comprehensive tool to calibrate streamflow response to environmental variables. Various hydrologic modeling approaches, ranging from physically based to conceptual to entirely data-driven models, have been widely used for hydrologic simulation. During the recent years, however, Deep Learning (DL), a new generation of Machine Learning (ML), has transformed hydrologic simulation research to a new direction. DL methods have recently proposed for rainfall-runoff modeling that complement both distributed and conceptual hydrologic models, particularly in a catchment where data to support a process-based model is scared and limited.

This dissertation investigated the applicability of two advanced probabilistic physics-informed DL …

Beyond Just Money Transactions: Redesigning Digital Peer-To-Peer Payments For Social Connections, Lingyuan Li

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Financial activities, such as the exchange of money between individuals, have long been considered a crucial aspect of how people build and maintain their interpersonal relationships (i.e., a strong, deep, or close association/acquaintance between two or more people) with individuals they know because money is a sensitive social construct. In particular, over the past decade, how to conduct, manage, and experience money exchanges and processes between individuals has been dramatically transformed due to the increasing popularity of digital peer-to-peer (P2P) payment services (i.e., performing one to one online money transactions via a digital device). In this sense, digital P2P payments …

Vanet Applications Under Loss Scenarios & Evolving Wireless Technology, Adil Alsuhaim

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In this work we study the impact of wireless network impairment on the performance of VANET applications such as Cooperative Adaptive Cruise Control (CACC), and other VANET applications that periodically broadcast messages. We also study the future of VANET application in light of the evolution of radio access technologies (RAT) that are used to exchange messages. Previous work in the literature proposed fallback strategies that utilizes on-board sensors to recover in case of wireless network impairment, those methods assume a fixed time headway value, and do not achieve string stability. In this work, we study the string stability of a …

Enabling High Throughput And Reliable Low Latency Communication Over Vehicular Mobility In Next-Generation Cellular Networks, Snigdhaswin Kar

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The fifth-generation (5G) networks and beyond need paradigm shifts to realize the exponentially increasing demands of next-generation services for high throughputs, low latencies, and reliable communication under various mobility scenarios. However, these promising features have critical gaps that need to be filled before they can be fully implemented for mobile applications in complex environments like smart cities. Although the sub-6 GHz bands can provide reliable and larger coverage, they cannot provide high data rates with low latencies due to a scarcity of spectrum available in these bands. Millimeter wave (mmWave) communication is a key enabler for a significant increase in …

Deep Reinforcement Learning And Game Theoretic Monte Carlo Decision Process For Safe And Efficient Lane Change Maneuver And Speed Management, Shahab Karimi

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Predicting the states of the surrounding traffic is one of the major problems in automated driving. Maneuvers such as lane change, merge, and exit management could pose challenges in the absence of intervehicular communication and can benefit from driver behavior prediction. Predicting the motion of surrounding vehicles and trajectory planning need to be computationally efficient for real-time implementation. This dissertation presents a decision process model for real-time automated lane change and speed management in highway and urban traffic. In lane change and merge maneuvers, it is important to know how neighboring vehicles will act in the imminent future. Human driver …

Enhanced Mobile Networking Using Multi-Connectivity And Packet Duplication In Next-Generation Cellular Networks, Prabodh Mishra

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Modern cellular communication systems need to handle an enormous number of users and large amounts of data, including both users as well as system-oriented data. 5G is the fifth-generation mobile network and a new global wireless standard that follows 4G/LTE networks. The uptake of 5G is expected to be faster than any previous cellular generation, with high expectations of its future impact on the global economy. The next-generation 5G networks are designed to be flexible enough to adapt to modern use cases and be highly modular such that operators would have the flexibility to provide selective features based on user …

Adversarial Deep Learning And Security With A Hardware Perspective, Joseph Clements

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Adversarial deep learning is the field of study which analyzes deep learning in the presence of adversarial entities. This entails understanding the capabilities, objectives, and attack scenarios available to the adversary to develop defensive mechanisms and avenues of robustness available to the benign parties. Understanding this facet of deep learning helps us improve the safety of the deep learning systems against external threats from adversaries. However, of equal importance, this perspective also helps the industry understand and respond to critical failures in the technology. The expectation of future success has driven significant interest in developing this technology broadly. Adversarial deep …