Other Electrical and Computer Engineering Commons^™

Ai-Optimized Resource Management In Next-Gen Wireless Networks, Fatemeh Lotfi

All Dissertations

Next-generation wireless networks must deliver highly adaptive, scalable, and intelligent connectivity to satisfy the heterogeneous demands of emerging services, including enhanced mobile broadband, massive machine-type communications, and ultra reliable low latency applications. The Open Radio Access Network (O-RAN) paradigm has emerged as a key enabler of this vision, introducing openness, virtualization, and artificial intelligence (AI)-driven control into the RAN ecosystem. O-RAN’s disaggregated architecture facilitates multi-vendor interoperability and empowers intelligent management through the RAN Intelligent Controller (RIC). However, achieving real-time, autonomous, and generalized optimization in such a dynamic environment remains a significant challenge due to its distributed nature, non-stationary traffic, and …

Self-Poled P(Vdf-Trfe) Based Composites For Energy Harvesting And Wearable Sensor Applications, Lavanya Muthusamy

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The growing demand for flexible, low-power, and self-powered wearable electronic systems has accelerated research interest in polymer-based sensors and energy harvesting technologies. Among piezoelectric polymer materials, Poly(vinylidene fluoride-trifluoro ethylene) [P(VDF-TrFE)], over the years, has garnered significant attention due to its unique piezoelectric properties, high dielectric constant, mechanical flexibility, thermal stability, chemical resistance, biocompatibility and compatibility with scalable fabrication processes. Despite its advantages, conventional P(VDF-TrFE)-based devices often require external poling and face limitations in integration with low-cost, flexible substrates. To overcome these limitations, this research study explores the nanofiller approach, along with facile fabrication processes, and structural design strategies aimed at …

A Generalizable And Privacy-Preserving Framework For Anomaly Detection In Heterogeneous Iot Environments, Mahshid Rezakhani

All Theses

With the rapid growth of Internet of Things (IoT) devices across various sectors, detecting anomalies in such systems has become increasingly challenging. IoT environments produce diverse and evolving data streams, often lacking labeled examples, which limits the effectiveness of traditional machine learning models. These models typically require frequent retraining and struggle to adapt to new deployment conditions. This thesis proposes a flexible, privacy-aware framework for anomaly detection in multivariate time series data generated by heterogeneous IoT systems. The approach integrates a long short-term memory variational autoencoder (LSTM-VAE) with contrastive learning and adversarial adaptation, enabling the model to generalize across domains, …

Emerging Applications On Reconfigurable Computing Platforms, Zhenyu Xu

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Security and high-performance computing have become two of the most critical demands in modern technology. The increasing complexity of digital systems, the need for real-time processing, and the emergence of sophisticated cyber threats require computing solutions that balance computational power with adaptability and security. Reconfigurable computing platforms, particularly Field-Programmable Gate Arrays (FPGAs), offer a promising solution to these challenges by combining flexibility with hardware acceleration. Many new applications have emerged with the developing of reconfigurable platforms.

FPGAs can be utilized in two primary directions: as control and high-precision measurement units for security-sensitive applications, and as accelerators capable of outperforming GPUs …

Convex Approach To Data-Driven Optimal Control With Safety Constraints Using Linear Transfer Operator, Joseph Raphel Moyalan

All Dissertations

This thesis is concerned with the data-driven solution to the optimal control problem with safety constraints for a class of control-affine nonlinear systems. Designing optimal control satisfying safety constraints is a problem of interest in various applications, including robotics, power systems, transportation networks, and manufacturing. This problem is known to be non-convex. One of this thesis's main contributions is providing a convex formulation to this non-convex problem. The second main contribution is providing a data-driven framework for solving the control problem with safety constraints. The linear operator theoretic framework involving Perron-Frobenius and Koopman operators provides the convex formulation and associated …

Optical Wavelength Measurement Based On Microwave-Photonics And Fiber Dispersion, Yongji Wu

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In modern photonics, combining microwave techniques with optical measurements has introduced a novel research direction. This study introduces a novel Microwave Photonics Wavelength Measurement System (MP-WMS), integrating microwave photonics with fiber chromatic dispersion to achieve direct wavelength measurement. Utilizing cost effective single mode optical fibers as dispersion devices, this system employs chromatic dispersion to convert optical frequency domain measurements into microwave time domain measurements. We established a mathematical model to describe how different wavelengths are detected, enhancing the system's effectiveness. The MP-WMS system offers an affordable solution with high resolution. In the experiment, we used a 35km long SM-28 single-mode …

Digital Twins And Artificial Intelligence For Applications In Electric Power Distribution Systems, Deborah George

All Theses

As modern electric power distribution systems (MEPDS) continue to grow in complexity, largely due to the ever-increasing penetration of Distributed Energy Resources (DERs), particularly solar photovoltaics (PVs) at the distribution level, there is a need to facilitate advanced operational and management tasks in the system driven by this complexity, especially in systems with high renewable penetration dependent on complex weather phenomena.

Digital twins (DTs), or virtual replicas of the system and its assets, enhanced with AI paradigms can add enormous value to tasks performed by regulators, distribution system operators and energy market analysts, thereby providing cognition to the system. DTs …

Generalizable Deep-Learning-Based Wireless Indoor Localization, Ali Owfi

All Theses

The growing interest in indoor localization has been driven by its wide range of applications in areas such as smart homes, industrial automation, and healthcare. With the increasing reliance on wireless devices for location-based services, accurate estimation of device positions within indoor environments has become crucial. Deep learning approaches have shown promise in leveraging wireless parameters like Channel State Information (CSI) and Received Signal Strength Indicator (RSSI) to achieve precise localization. However, despite their success in achieving high accuracy, these deep learning models suffer from limited generalizability, making them unsuitable for deployment in new or dynamic environments without retraining. To …

Procedural City Generation With Combined Architectures For Real-Time Visualization, Griffin Poyck

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The work and research of this paper sought to build upon traditional city generation and simulation in creating a tool that both realistically simulates cities and their prominent features and also creates aesthetic and artistically rich cities using assets that combine several contemporary or near contemporary architectural styles. The major city features simulated are the surrounding terrain, road networks, individual buildings, and building placement. The tools used to both create and integrate these features were created in Houdini with Unreal Engine 5 as the intended final destination. This research was influenced by the city, town, and road networking of Ghost …

Soft Web-Based Continuum Robot Grippers, Anthony Carambia

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We discuss the potential of soft webs to enhance robotic grasping. Specifically, we explore a novel combination of compliant continuum digits interspersed with a flexible material. The resulting webbed structure offers the potential for new modes of robust and adaptive object grasping. We introduce and describe two webbed grippers featuring alternate modes of actuation: pneumatic muscles and remotely actuated tendons. Experiments with the grippers demonstrate their ability to gently capture small, fragile, and non-cooperative objects.

Deep Learning-Guided Prediction Of Material’S Microstructures And Applications To Advanced Manufacturing, Jianan Tang

All Dissertations

Material microstructure prediction based on processing conditions is very useful in advanced manufacturing. Trial-and-error experiments are very time-consuming to exhaust numerous combinations of processing parameters and characterize the resulting microstructures. To accelerate process development and optimization, researchers have explored microstructure prediction methods, including physical-based modeling and feature-based machine learning. Nevertheless, they both have limitations. Physical-based modeling consumes too much computational power. And in feature-based machine learning, low-dimensional microstructural features are manually extracted to represent high-dimensional microstructures, which leads to information loss.

In this dissertation, a deep learning-guided microstructure prediction framework is established. It uses a conditional generative adversarial network (CGAN) …

Analysis Of Deep Learning Methods For Wired Ethernet Physical Layer Security Of Operational Technology, Lucas Torlay

All Theses

The cybersecurity of power systems is jeopardized by the threat of spoofing and man-in-the-middle style attacks due to a lack of physical layer device authentication techniques for operational technology (OT) communication networks. OT networks cannot support the active probing cybersecurity methods that are popular in information technology (IT) networks. Furthermore, both active and passive scanning techniques are susceptible to medium access control (MAC) address spoofing when operating at Layer 2 of the Open Systems Interconnection (OSI) model. This thesis aims to analyze the role of deep learning in passively authenticating Ethernet devices by their communication signals. This method operates at …