Electrical and Computer Engineering Commons^™

St-Hybrid: Dynamic Graph Learning With Multi-Scale Spatio-Temporal Attention For Traffic Forecasting, Dhe Yeong Ewaza Tchalla

Electronic Theses and Dissertations

Accurate short-term traffic forecasting is central to modern Intelligent Transportation Systems, supporting route guidance, adaptive signal control, and incident response. Yet producing reliable predictions remains difficult because traffic is highly non-stationary. The relationships among roadway sensors shift during congestion, incidents, weather changes, or fluctuations in demand, and the temporal structure of traffic spans several scales from abrupt minute-level variations to broader daily and weekly rhythms. Models that rely on fixed spatial graphs or a single temporal scale tend to miss these evolving and layered dependencies. This thesis addresses these challenges by developing a graph-learning framework that adapts to changing traffic …

Powersynth 2: Automated Power Electronics Physical Design Synthesis With Custom And Heterogeneous Components, Mehran Sanjabiasasi, Alan H. Mantooth, Yarui Peng

Electrical Engineering and Computer Science Faculty Publications and Presentations

Electronics Design Automation (EDA) has shown significant importance in the power electronics industry. As power electronic circuits become more complex, the traditional trial-and-error approach in physical design becomes less effective and time-consuming. Novel packaging technologies and intelligent physical design automation solutions are crucial to overcome these challenges and produce reliable solutions. PowerSynth 2 is an EDA tool for generating and optimizing power module layouts. To extend the layout synthesis capability beyond power modules, the layout engine needs to consider various custom components such as capacitors, inductors, and gate drivers. This research presents a novel framework for the layout synthesis process …

Multi-Objective Deep Reinforcement Learning For Dynamic Algorithm Selection In Open Ran, Saish Urumkar, Byrav Ramamurthy, Sachin Sharma

Conference papers

Open Radio Access Networks (Open RAN) provide flexible, modular multi-vendor interoperability. Growing mobile data demand requires balancing network performance with power efficiency. Mobile operators need intelligent resource management to achieve Key Performance Indicator (KPI) targets while maintaining operational efficiency. This paper proposes a solution using a multi-objective deep reinforcement learning (MODRL) model deployed on the Open RAN Intelligent Controller (RIC). Three customizable operator profiles (Power Saving, Balanced, and Performance) are used which define specific priority ratios between performance and power saving objectives.

To evaluate, individual algorithms (CPU scheduling and UE connection state switching) are implemented in Open RAN, achieving 5–20%CPU …

Pac-Oran: Power Saving Cpu Scheduling Approach For Scalable Mobile Ues In Open Ran, Saish Urumkar, Byrav Ramamurthy, Somayeh Mohammady, Sachin Sharma

Conference papers

Open RAN is a next-generation wireless network technology that promotes flexibility, cost efficiency, and interoperability through disaggregation and open interfaces. Energy efficiency remains a key challenge, especially in scalable deployments with many connected User Equipments (UEs), where dynamic power management at the gNodeB is critical. In this paper PAC-ORAN (Power-saving Adaptive CPU Scheduling for Open RAN) is proposed which is an advanced CPU scheduling algorithm evaluated with a large number of connected UEs to gNodeb. PAC-ORAN includes an advanced method using moving average adaptive threshold selection and dynamically adjusting CPU core states and frequency tuning based on real-time CPU metrics. …

Performance Evaluation Of Managed Switch Configurations For Secure And Efficient Plc-Based Industrial Automation Networks, Ahmed Salama

All Graduate Theses, Dissertations, and Other Capstone Projects

This thesis explores how managed switches can improve network performance and security in PLC-based industrial systems. Using simulations in GNS3 and Cisco Packet Tracer, and packet analysis via Wireshark, the study compares unmanaged and managed switch configurations. Redundancy protocols, particularly STP and RSTP, are evaluated under failure scenarios. Results show that RSTP offers faster recovery times, making it more suitable for time-sensitive environments. Additionally, managed switch features like VLANs, port security, and MAC filtering significantly reduce vulnerabilities and improve network segmentation. The findings highlight the importance of incorporating both cybersecurity and redundancy in industrial network design as systems move toward …

Nanomaterial-Enabled Enhancements In Thylakoid-Based Biofuel Cells, Amit Sarode, Gymama Slaughter

Center for Bioelectronics Publications

Thylakoid-based photosynthetic biofuel cells (TBFCs) harness the inherent light-driven electron transfer pathways of photosynthesis to enable sustainable solar-to-electrical energy conversion. While TBFCs offer a unique route toward biohybrid energy systems, their practical deployment is hindered by sluggish electron transfer kinetics, unstable redox mediators, and inefficient interfacing between biological and electrode components. This review critically examines recent advances in TBFCs, with a focus on three key surface engineering strategies: (i) incorporation of nanostructured materials to enhance electrode conductivity and surface area; (ii) application of redox mediators to facilitate charge transfer between photosynthetic proteins and electrodes; and (iii) functional exploitation of individual …

Battery Energy Storage, Control, Dc Microgrid, Integrated Power Systems, Power Electronics, Supercapacitor, Jack Elliott Hannum

Theses and Dissertations

DC distribution has enabled a variety of new features and opportunities for naval power systems. At the same time, pulsed loads are proliferating, and pose unique challenges to power systems, especially smaller systems like those onboard ships. Energy storage resources, including Battery Energy Storage Systems (BESS) and supercapacitors, can be used to compensate pulsed loads of varying frequencies with proper control. However, standard methods of frequency-based load allocation have only limited ability to integrate sources with diverse power and energy capabilities. To overcome this limitation, a new droop control scheme, Frequency Targeting Droop (FTDC), has been devised that increases the …

Multirotor Aerial Vehicle Morphology Design, Modeling, And Control, Samuel Allen Dauchert

Theses and Dissertations

Multirotor aerial vehicles (MAVs) have seen explosive popularity since the early 2000s, with microprocessors enabling small-scale Autonomous Aerial Vehicles (AAVs) or First Person Aerial Vehicles (FPVs). Additionally, the use of these MAVs in commercial applications has continued to grow since then with expansion into delivery, search and rescue, and smart farming applications. With this rise in popularity, specialized MAVs have undergone development for these applications due to the environments in which these vehicles operate.

From this development, a new area of Morphological Multirotor Aerial Vehicles (MMAVs) has emerged. Unlike traditional MAVs, these platforms adjust their shape based on environmental factors. …

Deep Target Recognition: Semi-Supervised Annotation, Sensor Fusion And Super-Resolution, Shoaib Meraj Sami

Graduate Theses, Dissertations, and Problem Reports (ETD)

Despite the recent expansion of machine learning algorithms to cover a wide range of disciplines, several areas of automatic target recognition (ATR) remain underexplored. This dissertation presents tools developed to improve performance in three significant aspects of ATR: semi-supervised annotation, sensor fusion, and image super-resolution. The aim of the semi-supervised methods is to automatically annotate targets in scenarios where labeled data are scarce in the target domain but available in the source domain. Secondly, to address the limitations of individual image sensors and enhance robustness under different environmental conditions and man-made constraints, a sensor fusion algorithm was developed to improve …

From Tpu To Qpu: Bridging Fidelity Gaps Across Next-Generation Computing Systems, Mohammad Walid M. Charrwi

Dissertations and Theses

Modern computing systems continue to advance rapidly, enabling unprecedented computational capabilities across diverse domains. Among these powerful architectures are AI accelerators such as Tensor Processing Units (TPUs) and quantum computing systems utilizing Quantum Processing Units (QPUs) stand out as trans-formative technologies. Despite their potential, these systems share significant challenges related to hardware infidelity, manifested as error-induced degradation in computational accuracy and reliability. This thesis explicitly addresses cost-effective methods to screen-out defective AI accelerators across next-generation computing architectures and boost quantum circuit output fidelity through hierarchical circuit optimization using advanced machine learning methodologies.

This thesis first proposes a cost-effective testing methodology …

Renewable Energy Integration In Nyc Subway: Solar Panels (Under Railroads And At The Subway's Edge) & Vertical Axis Wind Turbines (Vawts) Near Tracks, Pengdwinde Olivier Saba

Dissertations and Theses

As urban populations continue to expand, the demands on public transit systems in densely populated regions like New York City are escalating. Multiple cities face the dual challenge of increasing ridership and the imperative to minimize their environmental footprints.According to Law 97 in NYC, the goal is to reduce emissions by 40% and to reach net zero by 2050. An innovative solution will need to be integrated into the NYC environment, such as the subway system, where there are a potential unuse space, building rooftop, since we know that spaces in New York City are very critical, such as these …

Metasurfaces And Their Applications In Photonic Devices, Shuwei Guo

Dissertations and Theses

Metasurfaces are flat artificial optical elements composed of dielectric and/or metallic nanostructures. They can manipulate light in unprecedented ways by altering amplitude, phase, and polarization at subwavelength scales. The ability of these two-dimensional elements to perform multiple optical functions within compact optical systems—while offering high functionality, small form factors, and easy integration into optoelectronic devices—has sparked significant interest across various fields and industries. These applications span imaging, sensing, nonlinear and quantum optics, optical computing, automotive technology, augmented and virtual reality, and more.

In this dissertation, we explore and design new multifunctional metasurfaces with a focus on manipulating spectral responses. First, …

C 3 An: Custom, Compact And Composite Ai Systems - A Neurosymbolic Approach: 4Th-Generation Evolution Of Intelligent Systems, Amit P. Sheth, Kaushik Roy, Revathy Venkataramanan, Venkatesan Nadimuthu

Publications

Artificial Intelligence (AI) systems continue to evolve rapidly. From the architecture perspective, it is evolving from large, monolithic models trained on massive internet data to complex, multi-component “compound” systems and “agentic” frameworks capable of semi-autonomous decision-making. These systems show immense promise yet face numerous challenges in reliability, consistency, transparency, and alignment with user goals. In this article, we propose Custom, Compact and Composite AI with Neurosymbolic (C3AN) approach, a framework that paves way to 4th-generation of AI that integrates data, knowledge, and human expertise to build robust, intelligent and trustworthy AI systems defined by 14 foundation elements.

Custom emphasizes …

การพัฒนาเกมกระตุ้นการรู้คิดด้วยเทคโนโลยีเสมือนจริงสำหรับผู้สูงอายุที่มีภาวะการรู้คิดบกพร่องเล็กน้อย, ธนกร ตันมาลาภรณ์

Chulalongkorn University Theses and Dissertations (Chula ETD)

ภาวะการรู้คิดบกพร่องเล็กน้อยเป็นระยะเปลี่ยนผ่านระหว่างภาวะสมองปกติกับภาวะสมองเสื่อม โดยมีอาการที่เกี่ยวข้องกับความจำ ภาษา การรู้คิด และการตัดสินใจ แต่ยังไม่ส่งผลกระทบต่อการใช้ชีวิตประจำวัน งานวิจัยนี้จึงมุ่งเน้นการนำเสนอแนวคิดการออกแบบเพื่อพัฒนาเกมที่สามารถช่วยกระตุ้นการรู้คิดของคนที่มีภาวะการรู้คิดบกพร่องเล็กน้อย ซึ่งได้ประยุกต์ใช้เทคโนโลยีเสมือนจริงด้วยแว่น Meta Quest 2 ยูนิตีและเบลนเดอร์ถูกใช้ในการพัฒนาเกม เนื่องจากเป็นซอฟต์แวร์โอเพนซอร์สและไม่มีค่าใช้จ่าย เกมถูกออกแบบมาเน้นการกระตุ้นการรู้คิดผ่านการฝึกความจำและการรับรู้ตำแหน่ง โดยผู้เล่นจะต้องจดจำตำแหน่งและสีของเฟอร์นิเจอร์ภายในห้อง และมีการพัฒนาเกมในระบบผู้เล่นหลายคนสำหรับ 2 บทบาท คือ ผู้เคลื่อนย้าย และ ผู้ช่วย เพื่อส่งเสริมการสื่อสารและการทำงานร่วมกัน นอกจากนี้ ยังมีการพัฒนาเกมในรูปแบบเว็บไซต์ควบคู่กันไป เพื่อให้เป็นทางเลือกที่ผู้สูงอายุสามารถเข้าถึงง่าย โดยไม่จำเป็นต้องใช้อุปกรณ์วีอาร์เฉพาะทาง จากอาสาสมัครทั้งบนเวอร์ชันวีอาร์และเวอร์ชันเว็บไซต์ พบว่าเวอร์ชันวีอาร์ให้ความสมจริงและช่วยให้ผู้เข้าทดลองมีสมาธิกับงานด้านความจำและการรับรู้ตำแหน่งมากขึ้น ขณะที่เวอร์ชันเว็บไซต์เข้าถึงง่ายและใช้งานสะดวกสำหรับผู้สูงอายุ โดยรวมแล้วทั้งสองแพลตฟอร์มน่าจะกระตุ้นการรู้คิดได้และได้รับผลตอบรับที่ดีจากอาสาสมัคร

Solid Loading Effects On The Assembly Of Alumina Particles In Aqueous Suspensions Due To The Dielectrophoretic Forces, Sivakumar Chithamallu, Rohan Kiran Parai, Dipankar Ghosh

Mechanical & Aerospace Engineering Faculty Publications

Current work in situ investigated the mechanisms of the interparticle interactions that evolve in dilute aqueous alumina suspensions subjected to alternating current (AC) electric field and the effects of solid loading of suspensions. The interactions were investigated for alumina suspension compositions in the 0.005‒0.04 vol.% solid loading range. Field‐induced interactions evolved via particle motion and dynamic assembly, chain formation parallel to the direction of the applied field and chain growth, chain cross‐linking, and chain thickening. The evolution time of each of those events was rapidly accelerated with solid loading. While chain cross‐linking was negligible in low solid loading suspensions, a …

Auto-Tuning And Applications Of Transient Voltage Suppressor Models In Full-Wave Simulations, Daniel Peter Szanto

Masters Theses

System efficient electrostatic design (SEED) combines full-wave geometry information with SPICE models of non-linear protection devices, typically transient voltage suppression (TVS) diodes, to allow optimization and validation of electrostatic design (ESD) protection early in the design process. TVS models have previously been developed which may be used in SPICE simulation tools like Keysight ADS, but these models could not be used directly in full-wave simulation tools like CST Studio. A process was developed for converting existing ADS models of TVS devices to a form that can be used within a CST full wave/SPICE hybrid simulation. Three TVS models were converted …

Power Flow Control Of The Triple Active Bridge Converter, Lauryn Reece Morris

Masters Theses

With the rise of renewable energies, electric vehicles, and microgrids, the development of power electronic topologies that can seamlessly integrate these systems into the grid is crucial. The triple active bridge is an extension of the dual active bridge topology and is capable of energy storage or renewable energy integration into a power electronic converter and thus the power grid. Consisting of three H-Bridge converters, the triple active bridge topology is an expanding area of research to meet these growing demands. With the addition of the third bridge, the system has additional nonlinear characteristics which introduce complexity in solving for …

Frequency-Tracker And Power Supply For Piezoelectric Lunar Dust Removal Actuator, Praneeth Uddarraju

Masters Theses

"This work presents a reconfigurable phase-locked loop (PLL)-based control system for the resonant excitation of piezoelectric actuators aimed at automated removal of particulate contaminants from photovoltaic (PV) surfaces—particularly in extraterrestrial environments such as the lunar surface. Regolith or lunar dust buildup on solar panels is a serious hazard to the effectiveness of energy harvesting on extended missions. By using high-frequency structural excitation and inertial forces the suggested system removes surface impurities. Tunable Sallen-Key low-pass filters for reliable feedback conditioning are used in conjunction with a digitally implemented PLL on an FPGA (XLR8 platform) to precisely lock the drive frequency to …

Enabling Drone-Integrated Active Microwave Thermography Via A Slot Antenna Design, Alec P. Fitzmaurice

Masters Theses

Civil infrastructure inspection quality and inspector safety may be enhanced from the advancement in the capabilities of nondestructive testing and evaluation of remote or otherwise hard-to-reach areas such as nuclear power plants, wind turbines, bridges, or other civil infrastructure using drone-based Active Microwave Thermography (AMT). AMT is a nondestructive testing technique that utilizes high frequency energy (often radiated from an antenna) to induce heating in a specimen. Following this thermal excitation, an infrared camera is used to measure the resulting surface thermal profile. From this, defect indications may be detected. To enable drone-based deployment of AMT, where the antenna size …

Application Of Edge-Enhanced Phase Analysis To Active Microwave Thermographic Measurements, Douglas Blaine Fleetwood

Masters Theses

Nondestructive testing and evaluation (NDT&E) encompasses a range of inspection techniques that assess materials, components, and structures while maintaining their integrity and performance. Among these techniques, thermography is particularly attractive due to its non-contact imaging approach with easy-to-interpret results. Active Microwave Thermography (AMT) has emerged as a promising NDT&E inspection technique that utilizes electromagnetic energy to heat materials through dielectric and magnetic absorption, with subsequent infrared imaging used to detect subsurface defects. While AMT offers significant advantages including material-specific heating optimization and lower power requirements compared to conventional thermographic methods, the technique faces a persistent challenge related to the thermal …

Power Supply Induced Jitter Analysis In High-Speed Drivers, Yifan Ding

Doctoral Dissertations

The Input/Output Buffer Information Specification (IBIS) model faces challenges in accurately simulating power-supply-induced jitter (PSIJ), particularly under nonlinear and time-varying power noise conditions with pre-driver stages. This work introduces advancements in IBIS model modification algorithms to enhance PSIJ simulation accuracy for high-speed drivers.

First, a correction coefficient-based approach was developed to adjust switching coefficients K_pu and K_pd using pre-driver DC jitter sensitivity, significantly improving model robustness under DC and AC power noise. However, its effectiveness diminished under large noise amplitudes due to coefficient shape dominance.

To address this, a simplified algorithm bypassed correction coefficient, directly correlating switching transitions with jitter …

Metal-Organic Thin Film Coated On Optical Fiber, Nahideh Salehifar

Doctoral Dissertations

This dissertation explores the development of metal-organic framework (MOF)-based optical fiber sensors for detecting volatile organic compounds (VOCs) at low concentrations (parts-per-billion to parts-per-million). In the first part of the study, theoretical calculations were performed using effective medium approximation (EMA) models, including Lorentz–Lorentz, Maxwell–Garnett, and Bruggeman equations, to predict the refractive index changes of MOFs upon gas adsorption. These models were applied to MOFs such as ZIF-7, ZIF-8, ZIF-90, MIL-101(Cr), and HKUST-1 to evaluate their potential for gas sensing.

In the second part of the dissertation, experimental work was conducted to validate the theoretical predictions. MOF-coated optical fibers were fabricated …

Measurement Method And Applications Of Transfer Function In Rf Desensitization Problem, Xiangrui Su

Doctoral Dissertations

Radio frequency (RF) desensitization issues comprise two components: noise radiation sources and the transfer function from noise sources to the victim antenna. RFI is a critical challenge in modern electronic systems, particularly in densely packed environments. This work presents a comprehensive study of RFI, addressing key aspects through three novel contributions. First, a transfer function measurement method is developed for compact metallic enclosures. This method provides a precise characterization of the electromagnetic (EM) environment within confined spaces, enabling accurate identification of interference pathways. Second, an EM emission management analysis framework is proposed, leveraging transfer functions to quantify and mitigate interference …

New Topologies For High Voltage Gain Dc-Dc Power Electronic Converters – Theoretical Analysis And Experimental Validation, Saeed Habibi

Doctoral Dissertations

"This dissertation is focused on developing and analyzing new high voltage gain DC-DC power electronic converters. The proposed converters in this dissertation are suitable candidates for connecting low voltage sources such as photovoltaic (PV) panels or fuel cell (FC) stacks to a DC microgrid or DC link of an inverter. The proposed converters provide desired characteristics such as high voltage gain at a low or medium duty cycle values, low voltage stress on power semiconductors, and continuous input current. Initially, the research introduces three novel converter topologies: First, two quadratic converters based on the combination of a three-winding coupled inductor …

Lifelong Machine Learning With Adaptive Resonance Theory, Sasha Petrenko

Doctoral Dissertations

"This publication option dissertation is composed of three papers concerning the study of the problem lifelong machine learning with Adaptive Resonance Theory (ART) algorithms. Lifelong learning (L2) is a challenging machine learning paradigm that both encompasses and formalizes the fields of continual learning and incremental learning. The field is concerned with the mitigation of the phenomenon of catastrophic forgetting whereby learning agents that are faced with incrementally novel information deleteriously overwrite previous knowledge if that learning process is not regularized to counteract this consequence. ART algorithms solve this stability-plasticity dilemma by optimally assigning learning to categories or instantiating new knowledge …

Selected Topics In Blockchain-Aided Iot Inference, Yiming Jiang

Doctoral Dissertations

"Blockchain has recently been considered in the Internet of Things (IoT) to secure data exchanges and storage across various engineering applications. This newly emerging blockchain-aided IoT (BIoT) network has been applied to a great deal of security-related applications. However, the BIoT still has the vulnerability that can be exploited by attackers, potentially impairing the performance of BIoT applications. Despite this, research addressing the vulnerability has been limited. This work presents four studies aimed at addressing critical gaps in current BIoT research.

The first study introduces a Time-Restricted Double-Spending Attack (TR-DSA) model for Proof-of-Work (PoW)-based blockchains, where the adversary only conducts …

Optimal Control Of Queueing Systems With Error-Prone Servers, Junqi Hu, Sigrún Andradóttir, Hayriye Ayhan

Information Technology & Decision Sciences Faculty Publications

Consider a Markovian tandem line with finite intermediate buffers and an equal number of stations and servers. Servers are flexible but noncollaborative, so that a job can be processed by at most one server at any time. When a job is being processed, it can be damaged and wasted depending on the proficiency of the server. We identify the dynamic server assignment policy that maximizes the long-run average throughput of the system with two stations and two servers. We find that the optimal policy is either a single or a double threshold policy on the number of jobs in the …

Modeling And Analysis Of Amorphous Steel Transformer For Potential Loss Reduction In Power Systems., Daniel A. Muchow

Theses and Dissertations--Electrical and Computer Engineering

The energy consumed in power systems can be reduced directly by implementing new technologies and materials. The effort to reduce the carbon emissions expelled from the production of electrical energy has become a major focus. According to the U.S. energy Information Administration approximately 5% of all the electric generated and transmitted in the U.S. electrical grid is lost. The energy profile calculated from the usage of electrical power from all 50 states in 2021 is estimated to be 3.8 billion megawatts per hour, with a state average of just over 11 cents per MWh, producing just over 41.8 billion in …

Analysis And Design Optimization Of Synchronous Machines With Excitation Through Stator Dc Coils Or Magnets And Reluctance Rotors, Oluwaseun A. Badewa

Theses and Dissertations--Electrical and Computer Engineering

The design and optimization of electric machines face increasing demands for higher efficiency, improved torque density, manufacturability, and effective utilization of materials, particularly in applications such as electric traction and propulsion, where performance, reliability, and cost are critical. This work explores innovative synchronous machine configurations with reluctance rotors and stator-combined excitation, including permanent magnet and DC-excited topologies, to address these challenges through advanced design methodologies, computational modeling, and experimental validation. This research is relevant to address the growing demand for high-performance electric machines that combine high power density with costeffective manufacturing. The conventional limitations in power and torque density, thermal …

Optimal Control And Structurally-Informed Gradient Optimization Of A Custom 4-Dof Rigid-Body, Brock Marcinczyk, Logan E. Beaver

Mechanical & Aerospace Engineering Faculty Publications

This work develops a control-centric framework for a custom 4-DOF rigid-body manipulator by coupling a reduced-order Pontryagin’s Maximum Principle (PMP) controller with a physics-informed Gradient Descent stage. The reduced PMP model provides a closed-form optimal control law for the joint accelerations, while the Gradient Descent module determines the corresponding time horizons by minimizing a cost functional built directly from the full Rigid-Body Dynamics. Structural-mechanics reaction analysis is used only to initialize feasible joint velocities—most critically the azimuthal component—ensuring that the optimizer begins in a physically admissible region. The resulting kinematic trajectories and dynamically consistent time horizons are then supplied to …