Electrical and Computer Engineering Commons^™

Multi-Agent Reinforcement Learning Driven Package Pdn Design Automation, Haran Manoharan, Chulsoon Hwang

Electrical and Computer Engineering Faculty Research & Creative Works

The design of package-level power delivery networks (PDNs) has become increasingly challenging as modern high-performance systems demand higher currents. Existing PDN design flows treat ball map assignment, stackup selection, and power plane routing as separate, largely manual steps, leading to long iteration times and limited scalability. This work proposes a unified and automated package PDN design framework based on multi-agent reinforcement learning (MARL). Each power domain is modeled as an agent, with specialized agents responsible for ball map assignment, routing layer selection, and power plane synthesis. A central controller coordinates agent decisions using a global reward that captures electrical and …

Deep Learning Based High-Resolution Electromagnetic Inversion Imaging Using Deep Convolutional Double-Module Structure, He Ming Yao, Shiji Song, Lijun Jiang, Michael Ng

Electrical and Computer Engineering Faculty Research & Creative Works

In this paper, a novel deep learning (DL) approach has been proposed to realize high-resolution electromagnetic (EM) inversion imaging. The newly proposed approach is based on the deep convolutional double-module structure (DCDMS), consisting of the pixel-interpolating module and the corresponding quality-improving module. While the pixel-interpolating module roughly increases the 'resolution' of the initial input, the following quality-improving module realizes quantitative EM imaging in high resolution. The input of the proposed DCDMS adopts the mixed input scheme, consisting of the received EM scattered field and the initial reconstruction in much low resolution computed from Gauss-Newton method. The output of the proposed …

Enhancing Explainable Ai For Medical Imaging: Improved Lime Interpretation With Influence Mapping, Abiha Tahsin Chowdhury, Dhanush Bavisetti, Daniel B. Hier, Rahul Dubey, Tayo Obafemi-Ajayi

Electrical and Computer Engineering Faculty Research & Creative Works

The integration of artificial intelligence (AI) into medical imaging is progressing rapidly. It is essential for these AI tools to be transparent, interpretable, and explainable to gain the trust of clinicians and regulators. Current state-of-the-art explainable AI (XAI) techniques in imaging includes Local Interpretable Model-Agnostic Explanations (LIME), Shapley Additive Explanations (SHAP), and Gradient-weighted Class Activation Mapping (Grad-CAM). Recent studies have shown that LIME often suffers from inconsistency and unreliability which limits their utility in sensitive fields like medical imaging. This paper proposes Influence Map based Explanation (IME), an enhanced variant of the original LIME framework, that aggregates multiple runs to …

Event-Based Predefined-Time Synchronization For Complex Networks With Deception Attacks: An Asynchronously Intermittent Strategy, Lei Xue, Jiong Yu, Haoyu Zhou, Yongbao Wu, Jian Liu, Donald C. Wunsch

Electrical and Computer Engineering Faculty Research & Creative Works

This article studies the practical predefined-time synchronization (PPTS) for complex networks (CNs) under deception attacks based on the asynchronously intermittent event-triggered control (AIE-TC). Notably, AIE-TC effectively integrates the advantages of asynchronously intermittent control (AIC) and event-triggered control, where AIC provides each subsystem node with independent control and rest intervals. Besides, all synchronization errors of the CNs converge to an adjustable neighborhood within the predefined time by designing a bounded time-varying function into the controller. Moreover, this article considers that the transmission network is subjected to stochastic deception attacks modeled by a Markov process, which captures the state-driven dynamic transition characteristics …

Effect Of Sample Properties On Short-Circuited Waveguide Measurements For Materials Characterization, Alexander Hook, Jared Sinkey, Kristen M. Donnell

Electrical and Computer Engineering Faculty Research & Creative Works

Microwave materials characterization measurements can be performed using a number of well-established approaches. One such approach, the filled transmission line approach featuring a short-circuited rectangular waveguide (SC-RWG) sample holder, is known to have sample placement restrictions related to measurement viability. This work focuses on this approach and addresses the measurement restrictions within the context of sample length and dielectric properties. The complex reflection properties, S11, of a sample placed in a SC-RWG sample holder are studied to quantitively define when a sample must be offset from the SC-end. The impact of the sample holder is also studied from a measurement …

Femtosecond-Laser-Inscribed Cladding Fiber Bragg Gratings: Architectures, Design Trade-Offs And Future Sensing Opportunities, Koustav Dey, Rex E. Gerald, Chen Zhu, Jie Huang

Electrical and Computer Engineering Faculty Research & Creative Works

Femtosecond (fs) laser inscription enables fiber Bragg gratings (FBGs) to be positioned beyond the fiber core, including within the cladding, thereby expanding control over modal interactions and directional sensing. These cladding fiber Bragg gratings (CLFBGs) extend FBG functionality through interactions with core, cladding, and evanescent fields. Their operation does not involve fundamentally different grating physics; rather, core-mode, cladding-mode, and coupled-mode responses depend on power distribution, modal overlap with the laser-inscribed refractive-index modulation (RIM), grating position, fiber geometry, modal excitation, and interrogation configuration. This Perspective reviews advances in CLFBG fabrication, optical characteristics, sensing mechanisms, and applications. Cladding-waveguide-assisted, evanescent-field-coupled, and direct cladding-mode-excitation …

Deep Learning Frameworks For Functional Genomics: Lncrna–Mirna Interactions, Dose-Specific Graph Modeling, And Complete Graph-Based Lncrna Functional Annotation Using Cross-Layer Attention, Sachit Satyal

Computer Science and Engineering Dissertations

Functional genomics seeks to understand how genes and regulatory elements orchestrate biological processes and adapt under diverse conditions. Two critical challenges in this field are deciphering the regulatory roles of long noncoding RNAs (lncRNAs) and modeling cellular transcriptional dynamics under therapeutic perturbations. While lncRNAs are increasingly recognized as key regulators in health and disease, their functions remain incompletely characterized. Concurrently, advances in single-cell transcriptomics reveal how gene and pathway activities rewire across drug dosage gradients, highlighting the need for computational frameworks that are predictive, interpretable, and capable of integrating molecular interactions with dynamic cellular states. This dissertation develops a unified …

End-To-End Development And Experimental Validation Of A 1/10-Scale Autonomous Vehicle, Rikkin Pankaj Panchal

Electrical Engineering Theses

Autonomous vehicle development demands vast resources, making scaled down platforms a critical alternative for solving core algorithmic challenges. The primary contribution of this thesis is the end to end development and validation of a complete real time autonomous driving pipeline deployed on a one tenth scale vehicle. To streamline platform development, an AI assisted annotation framework automates dataset generation, significantly reducing manual labor while improving training data quality. The system perception stack features a reinforcement learning guided online multi camera calibration framework that enables adaptive surround view stitching without the need for offline recalibration. This is paired with robust lane …

Performance Evaluation Of Thick Carbon Fiber-Reinforced Laminates Manufactured Using Six-Magnetron Microwave System, Nayan Pundhir, Sourav Bolar, Kumbla Chandrashekhara, Kristen Donnell, Jim Lua, Kalyan Shrestha, Rui Li

Mechanical and Aerospace Engineering Faculty Research & Creative Works

Microwave curing is a fast, energy-efficient, and a viable alternative to conventional thermal curing processes. It has been widely adopted for processing carbon fiber-reinforced polymer composites because the high electrical conductivity of carbon fibers enables strong microwave coupling. In this study, IM7/Cycom 5320-1 unidirectional prepreg has been used to fabricate 64-layer laminated composites. Symmetric cross-ply ([0°/90°]16S) and a quasi-isotropic ([45°/90°/−45°/0°]8S) layup have been investigated. A custom-built six-magnetron microwave applicator and an autoclave were employed to manufacture the composite panels. Degree of cure of the manufactured laminates was evaluated via differential scanning calorimetry. Interfacial bonding and porosity of the microwave-cured laminates …

A Full Polymer Piezoelectric Flextensional Energy Harvester, Nadia Ahbab, Sidra Naz, Bingqi Zhao, Tian-Bing Xu

Mechanical & Aerospace Engineering Faculty Publications

This study presents a full polymer piezoelectric flextensional energy harvester (FPPFEH) comprising a single-layer poly(vinylidene fluoride) (PVDF) film bonded to a 3D-printed polylactic acid (PLA) flextensional frame. For an arm inclination angle of θ=10°, the free-body model gives a theoretical geometric force-amplification factor of M_F=cot θ ≈ 5.67; this value represents an ideal upper bound and was not independently validated by local force or strain measurements. During assembly, the film was tensioned only to remove visible slack and maintain a flat configuration. No intentional pretension was applied, and any residual tension was not measured. Off-resonance force-controlled tests showed …

Skin Type Diversity In Image Datasets, Neda Alipour

Doctoral

Image-based AI systems that analyse human skin are increasingly used in healthcare and computer vision applications. However, many human skin-based image datasets do not provide reliable information about skin type, making it difficult to assess whether these systems perform consistently across the full spectrum of skin colour. The objective of this thesis is to examine how skin type diversity is represented and measured in image datasets, and to evaluate the reliability of image-based skin type measurement methods under different imaging conditions. Using publicly available skin lesion image datasets as a well-defined and widely used sub-class of skin image datasets, this …

Design, Modeling, And Experimental Development Of Nanoscale Confinement Structures On Planar Silicon-Based Microelectrode Arrays For Single-Entity Electrochemical Sensing, Parinaz Eskandari

Dissertations, Master's Theses and Master's Reports

Electrochemical sensing is widely used for chemical and biological detection due to its high sensitivity, label-free operation, and compatibility with miniaturized electronic systems. However, conventional microelectrode platforms operate in an ensemble-averaged regime in which the measured current represents the collective response of many molecules interacting with the electrode surface. This ensemble averaging masks localized nanoscale electrochemical events and limits the ability to detect rare interactions, such as single molecules or nanoparticles. Achieving single-entity electrochemical detection therefore requires strategies that confine electrochemical reactions to nanoscale regions while maintaining compatibility with scalable planar microfabrication.

This dissertation investigates nanoscale electrochemical confinement on planar …

Open Source Tools For Ecological Research, Alex P. Riebe

Dissertations, Master's Theses and Master's Reports

This thesis presents the development of an open source wireless sensor network for hibernacula manipulation with an emphasis on accessibility and reproducibility. It addresses the design of the electrical hardware, guidance on antennas and RF implementation, and design of an application-specific communication protocol, all with the explicit goal of enabling ecologists and other conservationists to be able to manufacture, deploy, operate, and maintain the system for their research. The resulting sensor network designed in this thesis is to control the temperature inside bat hibernacula during the winter to study the relationship between temperature and bat mortality rate due to White …

Design And Validation Of A Low-Cost Wearable Electromyography (Emg) System For Monitoring Exercise-Induced Changes In Muscle Activity, Ingrid E. Halverson

Dissertations, Master's Theses and Master's Reports

Wearable technologies have expanded opportunities for monitoring athletic performance, but many existing systems remain costly and confined to laboratory or medical settings. This thesis presents the design, development, and evaluation of a low-cost, wearable EMG platform for monitoring neuromuscular activity during exercise. A wireless device incorporating a surface EMG sensor, an ESP32 microcontroller, and Wi-Fi transmission was developed to acquire muscle activation data. Signal processing techniques, including filtering, root-mean-square (RMS), mean frequency (MNF), and median frequency (MDF) analyses, were used to evaluate changes in muscle activation. Experimental testing demonstrated reliable wireless data acquisition and successful capture of physiological changes before …

Control System Emendation And Ai-Driven Optimization For Enhancing A Smart Residential Microgrid, Anthony Nyoyoko

Master’s Theses

This thesis began with a simple idea: to restore the control system of a smart residential microgrid to respond intelligently to electricity prices while remaining safe, reliable, and practical on low-cost hardware. At the start, the goal was to design an economically aware microgrid that could look at electricity prices from the PJM market and make better operational decisions than traditional rule-based control. The motivation was straightforward. As residential renewable energy adoption increases, microgrids are expected to do more than just supply power. They are expected to respond to price volatility, integrate renewable generation, and operate reliably using embedded controllers …

Multi-Objective Optimization Of Energy Costs And Ev Battery Health In V2g Enabled Homes, Dzifa M. Hodey

Theses and Dissertations--Computer Science

Electric vehicles (EVs) and rooftop solar photovoltaic (PV) systems are increasingly being integrated into residential settings, creating new opportunities for vehicle-to-grid (V2G) and vehicle-to-home (V2H) operations. In these systems, the EV battery functions as a controllable energy storage unit that can charge from the grid or PV and discharge energy to supply household load or export to the grid for a profit. By intelligently scheduling this bidirectional power exchange, households can reduce electricity costs and enhance PV utilization. Realizing these benefits requires optimization strategies that balance cost reduction with EV battery health preservation. However, existing V2G/V2H studies largely emphasize cost …

Advancing Task-Oriented Dialog Systems: Scalability, Generalization, And Evaluation, Adib Mosharrof

Theses and Dissertations--Computer Science

Task-oriented dialog (TOD) systems enable conversational interfaces for complex tasks like flight booking and restaurant reservations. However, deploying TOD systems at scale faces three critical barriers: scalability, generalization, and evaluation. Scalability is primarily restricted by the human-annotation bottleneck, as current systems depend on vast quantities of manually labeled data for every new domain, making deployment prohibitively expensive. Generalization remains a persistent challenge, as systems optimized for known domains often suffer significant performance degradation when encountering new, unseen ones. Existing evaluation metrics measure response quality and fluency, but fail to measure functional task success. As TOD systems are deployed across diverse …

Vision‑Based Online Quality Tracking In Wire Arc Additive Manufacturing Via Hybrid Unsupervised Deep Learning–Statistical Process Monitoring, Giulio Mattera, Yue Cao, Yuming Zhang, Luigi Nele

Electrical and Computer Engineering Faculty Publications

Vision-based monitoring of Wire Arc Additive Manufacturing (WAAM) using supervised deep learning represents the state of the art in anomaly detection, but such approaches require large labeled datasets that are costly to obtain and typically limited to laboratory conditions. To address these limitations, this work proposes a hybrid deep learning–statistical process monitoring (SPM) framework tailored to the stochastic nature of conventional arc welding processes such as GMAW-based additive manufacturing, where existing methods often overfit. The framework integrates a residual convolutional autoencoder (Res-CAE) with skip connections, which jointly analyzes video frames to generate refined latent-space features that are subsequently monitored using …

Machine Learning-Based Lifetime Prediction Of Lithium Batteries: A Comparative Assessment For Electric Vehicle Applications, Abdelilah Hammou, Raffaele Petrone, Demba Diallo, Boubekeur Tala-Ighil, Philippe Makany Boussiengue, Hicham Chaoui, Hamid Gualous

Electrical & Computer Engineering Faculty Publications

This paper evaluates and compares four data-driven methods (Gaussian Process Regression (GPR), echo state network (ESN), gated recurrent unit (GRU), and long short-term memory (LSTM)) for lithium-ion capacity prognostics adapted to electric vehicle conditions. This comparison aims to find the most efficient prognosis method considering two constraints: the limitation of computational power and the unavailability of on-board capacity measurement that requires full charge and discharge conditions. The machine learning models are trained using capacity values estimated under vehicle conditions. The ageing data is collected from cycling tests of two battery chemistries, Lithium Fer Phosphate (LFP) and Nickel Manganese Cobalt (NMC), …

Unified Bayesian And Machine Learning-Based State Of Charge Estimation In A Pv Battery System, Showrov Rahman

Open Access Dissertations

The state of charge (SOC) of a battery indicates the remaining charge in the battery relative to its nominal maximum charge capacity. The accurate estimation of SOC is of utmost importance for efficient energy management as it indicates the level of energy stored in the battery.

Bayesian and machine learning (ML) approaches represent two distinct estimation strategies for battery state estimation. Bayesian filters, such as the Kalman filter (KF) and particle filter (PF), operate sequentially by incorporating physical models of battery dynamics. In contrast, ML-based methods are data-driven, and their performance largely depends on the quantity and quality of training …