Engineering Commons^™

Ambient Co2 Capture And Valorization Enabled By Tandem Electrolysis Using Solid-State Electrolyte Reactor, Yan-Bo Hua, Bao-Xin Ni, Kun Jiang

Journal of Electrochemistry

Electrocatalytic carbon dioxide reduction is a promising technology for addressing global energy and environmental crises. However, its practical application faces two critical challenges: the complex and energy-intensive process of separating mixed reduction products and the economic viability of the carbon sources (reactants) used. To tackle these challenges simultaneously, solid-state electrolyte (SSE) reactors are emerging as a promising solution. In this review, we focus on the feasibility of applying SSE for tandem electrochemical CO₂ capture and conversion. The configurations and fundamental principles of SSE reactors are first discussed, followed by an introduction to its applications in these two specific areas, …

A Yolo-Based Deep Learning Approach For Vibration-Based Rotating Shaft Imbalance Detection, Abdelhameed Zayed Dr., Ali Siam Dr.

Journal of Engineering Research

One of the prevailing causes of vibrations in machines is rotor imbalance. Rotor balancing can be used to fix the majority of rotating machinery issues. When it comes to high-speed running equipment, even a slight imbalance can lead to serious issues and decrease the operational efficiency of rotating machinery. This work proposed a deep learning approach for the detection of binary and multiclass imbalance in rotating shafts. A YOLOv11 model-based approach is developed to detect imbalance and identify unbalanced rotor positions. To precisely identify unbalanced positions, this method trains the YOLOv11 model using numerous sets of measured response data and …

Impact Of Gamma-Ray Dose On Indoor Visible Light Communication Link Performance: A Geant4 Study, Fayza Mohamed Elamrawy, Adel Mohamed Ismail, Hossm Kasem Prof, Salah Khamis Prof, Ahmed Abdelaziz Youssef Prof

Journal of Engineering Research

This research presents an extended performance analysis of an indoor Visible Light Communication (VLC) system by integrating experimental results with advanced GEANT4 simulations. Building on a prior empirical study that evaluated different LED configurations and photodetectors, this work explores critical environmental and spatial parameters that influence VLC efficiency and reliability. Parameters such as wall reflectivity, emission angles, photon energy, and photon travel time are systematically analyzed using a validated GEANT4 simulation framework. The results confirm the strong impact of narrow emission angles and high wall reflectivity on detection performance, while also highlighting the importance of time delay modeling in high-fidelity …

Multi-Modal Covid-19 Detection Using Cough Sounds And Medical Information With Attention-Enhanced Deep Learning, Mohamed Talaat Saidahmed, Reda Elbasiony, Marwa Reda Bastwesy, Asmaa Aly Hagar

Journal of Engineering Research

The COVID-19 pandemic has highlighted the need for fast, non-invasive, and cost-effective diagnostic tools. Cough sounds, as a prominent symptom of respiratory diseases, present a promising modality for automated COVID-19 detection. In this study, we propose a novel multi-modal deep learning framework for COVID-19 detection that leverages cough sounds and patient-specific medical information. Our approach extracts two types of acoustic features—Mel-Frequency Cepstral Coefficients (MFCCs) and Mel spectrograms—and integrates them with clinical metadata to improve diagnostic ac-curacy. The MFCC branch employs 1D convolutional layers followed by Efficient Channel Attention mechanism. The Mel spectrogram branch utilizes ResNet-50 combined with ECA to capture …

Mutual Coupling Impedance Effect On Ris-Assisted Terahertz Communication, Radwa A. Roshdy Dr., Hossam M. Kasem Prof., Mohammed A. Salem Dr.

Journal of Engineering Research

Terahertz (THz) communication based on reconfigurable intelligent surfaces (RIS) is a promising technology in future wireless networks, with the capabilities of ultra-high data rates and energy-efficient beam communicating. Nevertheless, the mutual coupling impedance of the closely spaced RIS reveals the inefficacy in existing channel estimation schemes, while it is usually ignored in the literature. In this paper, we fill this gap by developing an EM-compliant channel model that integrates mutual coupling explicitly for RIS-assisted THz systems. We evaluate the performance of mutual impedance-induced influence to the accuracy of the root mean square error (RMSE), which is considered as a pivotal …

Performance Enhancement Of Ev Drive System Under Open-Phase Fault Using Reinforcement Learning With Mpc, Ahmed M. Hassan, Mohammed E. Metwally Dr.

Journal of Engineering Research

Improving the operation of electric vehicles (EVs) under fault is a very important subject because it increases their ability to operate in case of an emergency. This paper proposes a fault-tolerant control methodology for an EV drive system under open phase fault (OPF). The 5-ph interior permanent magnet synchronous motor (IPMSM) is employed in the drive system because it has several merits, such as high efficiency and reliability. The proposed technique is based on utilizing a reinforcement learning (RL) control algorithm, based twin-delayed deep deterministic policy gradient (TD3) algorithm, to operate the motor at maximum torque per ampere under OPF. …

Dynamic Resource Allocation For Wireless Networks And Radar Systems Via Deep Reinforcement Learning, Ziyang Lu

Dissertations - ALL

The rapid advancement of wireless communication technologies and the proliferation of smart devices have led to increasingly complex and dynamic network environments. These developments have posed significant challenges to traditional radio resource management (RRM) techniques, which often struggle with scalability, adaptability, and real-time decision-making. In response to these limitations, this dissertation explores the application of advanced machine learning, particularly deep reinforcement learning (DRL), to develop intelligent, adaptive, and data-efficient solutions for resource management in wireless networks and radar systems. We begin by addressing joint channel access and power control in wireless interference networks using centralized, distributed, and federated multi-agent DRL …

Grades Are Bugs, Jordan Freitas

Computer Science Faculty Works

This paper argues that grades are bugs in our educational system, undermining desired behaviors and outcomes. Grades were introduced into higher education for purposes directly at odds with the goals of inclusive pedagogy today, as well as the neuroscience of human motivation and learning. Students enter computer science programs from increasingly varied backgrounds and experiences, and face a rapidly evolving landscape of prospective career paths while higher education costs in the United States are ever increasing. Computer science educators have a responsibility to adapt and carefully re-examine typical approaches to all aspects of the learning environments we build and curricula …

Grid Fragility, Blackouts, And Control Co-Design Solutions, Mario Garcia-Sanz

Faculty Scholarship

The grid is undergoing a large-scale transformation, including a significant reduction of synchronous generators, a high penetration of inverter-based resources and renewables, substantial demand growth, new extra-large loads, aging infrastructure and a concerning vulnerability to contingencies. Some of the recent massive blackouts in Spain/Portugal, Chile and Texas are exposing the fragility of the grid as we know it today. This paper introduces new solutions to stabilize the grid under undesired dynamic interactions and extreme contingencies, with the goal of avoiding cascading failures and blackouts. Using control co-design methodologies, the paper proposes three interdependent categories to improve the reliability and controllability …

Ai-Driven Emi Mitigation For Smart Healthcare: Generative Adversarial Networks And Edge Computing For Reliable Medical Systems, Mona Esmaeili

Electrical and Computer Engineering ETDs

Electromagnetic interference (EMI) from radio frequency (RF) sources poses a major challenge to digital systems, especially in high-electromagnetic environments. Tradi- tional electromagnetic compatibility (EMC) analysis often focuses on continuous wave (CW) interference and overlooks the effects of waveform modulation on EMI coupling. This thesis explores how modulated waveforms influence EMI coupling and introduces a Generative Adversarial Network (GAN)-based classification framework to distinguish between harmful and non-harmful EMI signals. The study extends conventional EMC analysis using machine learning (ML), showing that modulated EMI waveforms can in- crease coupling by up to 27% compared to CW signals. This highlights the need for …

From Assembly Lines To The Open Road: Predicting Rare Events In Autonomous Systems, Ruwan Wickramarachchi

Publications

In the age of embodied AI and smart automation, autonomous agents are increasingly deployed in high-stakes, real-world environments. Ensuring the robustness and resilience of these systems in the face of rare but critical failure events is essential for their safe and reliable operation. Accurate forecasting of such rare events is particularly crucial, as a single overlooked anomaly can lead to catastrophic consequences. In manufacturing, for instance, unplanned downtime due to rare failures costs industries over \$50 billion annually, with sectors like automotive losing more than \$2 million per hour—even with preventive maintenance systems in place.

However, the extreme rarity and …

The Evolution Of Global Gold And Copper Trade Networks, Oleksandr Hulianskyi

Northeast Journal of Complex Systems (NEJCS)

Gold and copper have emerged as two of the most vital commodities in global trade. Despite serving distinct purposes, their international trade networks reveal interconnected patterns, critical to understanding the dynamics of global economics. This paper studies these attributes and their evolution during the last 36 years for both metals and finds correlations between them. The first part of the research is focused on the sustainability of networks through efficiency and robustness indexes; the second part is dedicated to interconnectedness – the Louvain and Bayesian SBM algorithms, partition, and modularity instruments are used. Community detection algorithms provide valuable insights into …

Exploring The Potential Of Large Language Models (Llms) To Simulate Social Group Dynamics: A Case Study Using The Board Game "Secret Hitler", Kaj Hansteen Izora, Christof Teuscher

Northeast Journal of Complex Systems (NEJCS)

This study explores the capacity of large language model-powered agents to simulate human-like behavior in multi-agent social systems. Using Secret Hitler — a hidden-role board game centered on trust, deception, and strategic communication — we evaluate how LLM agents navigate dynamic group interactions. Our findings show that agents exhibit human-like behaviors, including strategic temporal adaptation, contextual reasoning, and complex social cognition such as theory of mind and implicit coordination. Notably, 85% of agent decisions factored in at least two other players’ mental states, highlighting their capacity for multi-agent mental state inference. However, they struggled with key aspects of human gameplay, …

High Field Performance Of Si-Doped N-Aln Layers Grown Using Pulsed Mocvd, Abdullah Al Mamun Mazumder, Tariq Jamil, Mafruda Rahman, Muhammad Ali, Grigory Simin, Asif Khan

Faculty Publications

We report on the study of high-field performance of Si-doped n-AlN layers that were grown using a pulsed metalorganic chemical vapor deposition (PMOCVD) process. In the past we showed this pulsed doping approach to lead to doping efficiency superior to that in the conventional MOCVD process. Here using them as the drift layer for a quasi-vertical conduction Schottky barrier, we show their ability to withstand high reverse bias voltages and sustain an electrical field as high as 9.9 MV cm−1. Our study thus demonstrates the viability of the PMOCVD growth and doping approach to yield n-AlN layers suitable …

Replicating Associative Learning Of Rodents With A Neuromorphic Robot In An Open-Field Arena, Tianze Liu, Kang Jun Bai, Hongyu An

Michigan Tech Publications

This study emulates associative learning in rodents by using a neuromorphic robot navigating an open-field arena. The goal is to investigate how biologically inspired neural models can reproduce animal-like learning behaviors in real-world robotic systems. We constructed a neuromorphic robot by deploying computational models of spatial and sensory neurons onto a mobile platform. Different coding schemes—rate coding for vibration signals and population coding for visual signals—were implemented. The associative learning model employs 19 spiking neurons and follows Hebbian plasticity principles to associate visual cues with favorable or unfavorable locations. Our robot successfully replicated classical rodent associative learning behavior by memorizing …

Benchmarking Model Predictive Control And Reinforcement Learning-Based Control For Legged Robot Locomotion In Mujoco Simulation, Shivayogi Akki, Tan Chen

Michigan Tech Publications

Model Predictive Control (MPC) and Reinforcement Learning (RL) are two prominent strategies for controlling legged robots. RL learns control policies through system interaction, adapting to various scenarios, whereas MPC relies on a predefined mathematical model to solve optimization problems in real-time. Despite their widespread use, there is a lack of direct comparative analysis under standardized conditions. This work addresses this gap by benchmarking MPC and RL controllers on a Unitree Go1 quadruped robot within the MuJoCo simulation environment, focusing on a standardized task, straight walking at a constant velocity. Performance is evaluated based on disturbance rejection, energy efficiency, and terrain …

Development Of A Near Terahertz Backward Wave Oscillator Using Standard Waveguide, Alexander Glick

Electrical and Computer Engineering ETDs

There is a demand for terahertz (THz) frequency radiation sources. Applications include, but are not limited to, imaging for medical and security purposes, biochemical and organic spectroscopy, and velocimetry. Historically, there was a limited supply of THz devices due to technological limitations. In recent years much progress has been made to reduce this “gap” in supply and demand for THz sources. This work proposes a vacuum electronic device that produces high power, extremely high frequency radiation in the G-band, by utilizing a backward wave oscillator (BWO) based on WR3 standard waveguide. This device is compact, fundamentally simple, and has great …

Networking For Power Grid And Smart Grid Communications: Structures, Security Issues, And Features, Biswash Basnet, Varsha Sen

Graduate Student Scholarship

The evolution from traditional to smart grid systems has radically changed communication architectures. It enables secure, efficient, and resilient energy infrastructures. Unlike the centralized, unidirectional communication practices typical of traditional grids with minimal automation, modern smart grids use tiered, bidirectional networks allowing real-time control, integration of distributed energy resources (DER), and active consumer participation. The drive for this development arises from the growing use of renewable energy resources, electric vehicles, and sophisticated digital metering technologies. End-to-end communication architectures now underpin grid reliability, interoperability, and cybersecurity. This research explores the end-to-end architecture of traditional and smart grids, including access technologies, protocol …

How’S It Growing? Tools For Observing Snow And Sea Ice In A Changing Arctic Ocean, Ian Alexander Raphael

Dartmouth College Ph.D Dissertations

September Arctic sea ice extent has diminished by roughly 50% in the 45 years since satellite observations began. The Arctic Ocean may experience ice-free summers within the next decade, with implications for habitat, resource extraction, geopolitics, and local and global climate change. To predict how Arctic sea ice will change in the future, we need to understand its behavior in the present. In situ sea ice mass balance measurements (snow accumulation, ice growth, snow and ice surface melt, and bottom melt) are essential for studying the processes driving rapid changes in the ice pack, and for validating remote sensing measurements …

Devices Related To Electrical Fast-Transient Protection And Detection, Cameron D. Harjes

Electrical and Computer Engineering ETDs

Electrical energy from natural occurring phenomena, such as an indirect lightning strike, can electromagnetically couple with nearby electronics thereby generating unwanted electrical fast-transients (EFT) signals in the circuit. Highly energetic EFT can interfere with a circuit’s normal operation or even destroy some circuit components entirely. Mitigating EFT has been a subject of research for decades and led to the development of devices such as lightning surge arresters (LSAs). These devices are critical for the protection of the electrical power grid from lightning induced EFT, however electromagnetic pulses (EMP) and other high-power radio frequency (RF) sources can generate much higher frequency …

Development Of An Embedded Iot Board For Real-Time Floor Estimation Of Autonomous Robots, Carter J. Sorensen

Electronic Theses and Dissertations

As service robots become more prevalent in multi-story environments such as hospitals, hotels, and laboratories, accurate floor-level detection is critical to ensuring operational reliability. Consider a robot tasked with delivering medical samples in a multi-story laboratory. Without accurate feedback, a robot exiting on the wrong floor could introduce delays, disrupt workflows, or compromise sample integrity. Internet of Things (IoT) technologies offer a way to address these risks by providing real-time error detection and corrective capability. However, current IoT-based floor estimation systems often require invasive modifications to building infrastructure—particularly elevator control panels. These approaches introduce challenges related to cost, liability, backward …

An Advanced Hexacopter For Autonomous Exploration Of Mars: Attitude Control And Navigation Strategies, Laura Sopegno

Electronic Theses and Dissertations

Mars exploration has recently witnessed major interest within the scientific community. Unmanned robotic platforms offer reliable solutions to acquire and collect data and information from the Red Planet. Particularly, rovers, landers, and orbiters have significantly shaped planetary exploration on the Moon and Mars, contributing significantly to past missions while also highlighting limitations in their capacity to cover diverse terrains over wide ranges. Given current advances in Unmanned Aircraft Systems (UASs), Unmanned Aerial Vehicles (UAVs) offer promising alternatives for future scientific missions.

It is argued that hexacopters, with their relatively compact design and redundancy, present a promising …

Piloted Autonomous Crisis Reconnaissance Robot 2.0 (Pacrr 2.0), Awawu Alimi, Urmika Ghosh, Marissa Kuo, Jonathan Santosa, Ethan Wyrick

Electrical and Computer Engineering Senior Theses

PACRR 2.0 (Piloted Autonomous Crisis Reconnaissance Robot, version 2) builds upon the original low-cost, autonomous-capable quadruped platform by enhancing both mobility and environmental perception for first-responder applications such as search and rescue, gas leak detection, and mapping of confined or hazardous areas. In PACRR 2.0, we integrate an RGB-D camera with analytic inverse kinematics and frame-based motion planning to achieve precise foot placement and stable quasi-static gaits even on sloped or uneven terrain. An NVIDIA Jetson processor runs high-level control and mapping alongside a Raspberry Pi 4 to manage motor control. Through simulation, we demonstrate robust 3D map generation and …

Remote Sensing Of Seismic Signals Via Enhanced Moiré-Based Apparatus Integrated With Active Convolved Illumination, Adrian A. Moazzam, Anindya Ghoshroy, Durdu Güney, Roohollah Askari

Michigan Tech Publications

The remote sensing of seismic waves in challenging and hazardous environments, such as active volcanic regions, remains a critical yet unresolved challenge. Conventional methods, including laser Doppler interferometry, InSAR, and stereo vision, are often hindered by atmospheric turbulence or necessitate access to observation sites, significantly limiting their applicability. To overcome these constraints, this study introduces a Moiré-based apparatus augmented with active convolved illumination (ACI). The system leverages the displacement-magnifying properties of Moiré patterns to achieve high precision in detecting subtle ground movements. Additionally, ACI effectively mitigates atmospheric fluctuations, reducing the distortion and alteration of measurement signals caused by these fluctuations. …

Online Hyperparameter Tuning For Llm Optimization, Ethan Lin, Nathan Yu, Jeromy Chang

Computer Science and Engineering Senior Theses

Large Language Models (LLMs) are becoming increasingly popular in modern society. However, despite their popularity, the deployment of LLMs in real-world scenarios is extremely challenging due to substantial computational costs and memory constraints. Edge devices, like smartphones and IoT devices, lack resources needed to run these models locally, instead offloading computations for cloud computing. Cloud computing requires users to send their data over the internet leading to numerous privacy and security concerns. In some domains, such as health and finances, sending such sensitive information is not an option. Existing solutions to compress or increase inference speed include Small Language Models …

Measurement Of Moisture Levels In Oils And Lubricants Using A Novel Moisture Sensor, Aaron Swartz, Ronak Ali

Electrical and Computer Engineering Graduate Research

It is very challenging to measure moisture levels in oils. There is not a good method to measure it. Using the novel moisture sensor invented by Dr. Zhi David Chen, we tried various methods to measure the moisture levels in oils. The initial trials are to immerse the sensor chip into the oils to see any sensor reading changes with the change of moisture levels in oils. It was observed that the sensor reading was unstable even after immersion into the oil for two weeks. The idea for immersion of the sensor chip into the oils failed due to the …

Resilience Oriented Dynamic Bayesian Network For Time Dependent Power Distribution System During Hurricanes, Kehkashan Fatima

Thesis/ Dissertation Defenses

In this dissertation, the overhead line failure due to extreme winds was investigated. The objective of the overhead line failure analysis was to evaluate the failure probability of line due to hurricane. The standard IEEE 33 bus test system was utilized to analyze the impact of hurricane wind speed intensity at distinct time and location on the grid. The test system was divided into four regions based on the number of buses and hurricane category (according to Saffire Simpson Hurricane Wind Scale). Regions 1, 2, and 3 cover 8 buses while region 4 covers 9 buses. Regions 1, 2, 3, …

Llm Music Creation And Recommendation Applications, Curtis Robinson, Allan Yang, Thomas Liu

Electrical and Computer Engineering Senior Theses

This thesis explores the application of large language models (LLMs) in music analysis, creation, and interaction, focusing on their potential to reshape traditional workflows in the music domain. The study begins by contextualizing the role of artificial intelligence in music technology, particularly emphasizing the emergence of LLMs like GPT and their unique capabilities in multimodal and musical contexts. A comprehensive survey of current research and toolsets highlights both creative and analytical implementations, ranging from text-based music generation to music information retrieval. The core contribution is an experimental framework that integrates LLMs with music processing libraries, enabling novel interactions such as …

The Grd Companion, Julius Gamboa, Muti Shuman, Tro Hovasapian

Electrical and Computer Engineering Senior Theses

This project introduces the design and development of the GRD Companion. This gesture-controlled reminder device aims to support individuals with special needs in their daily routines, in the hope of them being more independent. The system we designed integrates a Raspberry Pi Zero 2 W, PAJ7620U2 gesture sensor, and audio playback components, all housed in a custom 3D-printed case. Some of our key design priorities included accessibility by relying only on gesture recognition to control the device, eliminating screens and buttons, as well as portability, enabling all users to carry the device anywhere. The device is paired with our WaveLink …

Emg Vocal Translations, Lucas Amlicke, Raphael Kusuma, Cole Heider, Kayleigh Vu, Monica Sommer

Electrical and Computer Engineering Senior Theses

In this paper, we propose a novel augmentative and alternative communication (AAC) framework for silent speech. Many individuals with speech impairments are unable to vocalize effectively due to various conditions that affect the vocal cords. To engage in social activities, many rely on AAC devices that often lack flexibility and expressiveness. Users may still find self-expression and spontaneity difficult with such devices. This project presents a novel approach to developing a silent speech interface (SSI), providing a more adaptable and user-centered solution to give the vocally impaired a voice. Using surface electromyography (sEMG) alongside machine learning techniques, we aim to …