Digital Commons Network^™

Carbon Efficiency Of Natural Organic Honey-Memristor Based Neuromorphic Computing, Harshvardhan Uppaluru, Zoe Templin, Shah Zayed Riam, Feng Zhao, Jinhui Wang

Electrical and Computer Engineering Faculty Research & Creative Works

Natural organic memristors manufactured using honey have exhibited promising synaptic behavior, offering notable advantages such as environmental sustainability, low production and disposal costs, non-volatile storage capabilities, and bio/CMOS compatibility. In this paper, experimental evaluations of honey-memristor based neuromorphic systems are reported with a focus on estimating their carbon footprint. First, manufacturing and testing of honey-memristors is briefly described. To suppress variations and improve accuracy, an optimization technique - parallel memristors is applied. Experimental results indicate that this optimization method significantly improves the device performance of up to. However, it also leads to higher energy consumption and a higher carbon footprint …

Redox-Active Monolayers On Ito Prepared By Post-Amidation And Direct Esterification And Their Spectroelectrochemical Characterization, Takamasa Sagara, Sae Nakai, Ryusuke Yofu, Shota Kojin

Journal of Electrochemistry

A redox-active monolayer on an optically transparent electrode constitutes a typical platform for spectroelectrochemical sensing. The necessity for its sophistication arises from the availability of multi-dimensional sensing signals. Simultaneous monitoring of the redox current and color change synchronized with the oxidation state change significantly enhances sensitivity and selectivity. This study aimed to elucidate the modification of an indium tin oxide (ITO) electrode with a viologen monolayer with an ordered orientation. Novel methods were developed to immobilize a viologen molecule bearing a carboxyl group to form assembled monolayers through a condensation reaction using 1-ethyl-3-(3-dimethylaminopropyl)-carbodiimide with N-hydroxy-succinimide (EDC/NHS). In the two methods …

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 …

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. …

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 …

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, …

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 …

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 …

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 …

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 …

Ergonomic Human Robot Handovers Using Surface Electromyography (Semg) Sensors, Maya Murphy, Michael Mishkanian

Electrical and Computer Engineering Senior Theses

For decades, robots have been kept in cages in industry. With the advances of collaborative robots and Artificial Intelligence (AI), there is a shift towards humans and robots working together. In this research, we propose an ergonomically friendly collaborative robotic cell that enables a human and a collaborative robot to work synergistically to assemble a mobile robot. The collaborative robot provides the parts while explaining the process through a computer, and the human co-worker follows the instructions to complete the assembly. The proposed collaborative robotic cell is evaluated in a user study to ensure that the handovers of the parts …

Orchestrating Complexity: The Art Of Virtual Leadership In System Modelling, Vijay Kumar Sonawane, Bipllab Roy, Purnendu Bikash Acharjee, Indu Pv

Northeast Journal of Complex Systems (NEJCS)

This paper explores the dynamics of virtual leadership within global remote work environments, focusing on the application of complex system modelling to understand and enhance leadership efficacy. The application of computational modelling has been a regular feature in economics, science and technology fields, however its application in virtual leadership with linkage to sport leadership appears to be a novel concept. Adopting a multidisciplinary approach, this paper incorporates Game Theory as a conceptual framework to make the leadership model more relevant and applicable that can offer simpler understanding of complex play of leadership drivers. The model incorporates five key leadership dimensional …

Passive Radar In Metropolitan Environments (Prime), Hannah Bajakian, Carson Crisafulli, Dylan Olson

Electrical and Computer Engineering Senior Theses

Radio Detection and Ranging (radar) is a core application of RF engineering, traditionally implemented through active systems that transmit high-power signals and process their reflections to estimate target range and velocity. However, active radar systems require significant power, cost, and regulatory overhead. This thesis explores the design and implementation of a passive radar system as a low-power, cost-effective alternative for real-time aircraft tracking.

The system capitalizes on existing ultra-high frequency (UHF) television broadcast signals as illuminators of opportunity. These signals, along with their reflected signals from airborne targets, are received using Yagi-Uda directional antennas and captured using a USRP B210 …

Leveraging Usage Of Ai In Education: Knowledge, Attitude And Behavioral Analysis On Students, Bipllab Roy, Purnendu Bikash Acharjee, Rohit Kumar Sharma, Ruptaheen Kramsapi, Shruti P

Northeast Journal of Complex Systems (NEJCS)

The paper explores the possible advantages and drawbacks of artificial intelligence (AI) on sustainability, with an emphasis on using AI to positively achieve SDGs. The study finds a significant vacuum in the literature on the association between knowledge, attitudes, and behaviors towards the use of AI tools and techniques in education and demographic characteristics (sex, age, education level, area of study, and city of origin). The purpose of this research is to close this knowledge gap and advance our understanding of how these demographic factors affect the integration of AI in educational environments. The study specifically aims to comprehend how …

Tellings Of The Pacific Ocean: A Landscape-Based Approach For Multispecies Design And Hci, Maliheh Ghajargar

Engineering Faculty Articles and Research

Environmental disturbances induced by climate change have caused significant changes in our ecosystems and are threatening the health of our environments. As a response to this issue, a growing body of work has emerged in HCI and design, which seeks to foreground more-than-human stories in support of making more sustainable and just futures. This research contributes to this broad agenda by probing graphic novels as a multispecies storytelling method for design and HCI. Combining ideas from Anna Tsing’s adventures of landscape and from HCI and design’s use of sequential art (e.g., storyboards), we use landscape as the main protagonist of …

Secure Your Hardware With Randomization And Redundancy, Dennis Cao, Joseph Khamisy, Sumeet Upadhya

Computer Science and Engineering Senior Theses

Differential Fault Analysis (DFA) is a potent hardware attack that threatens cryptographic security by injecting faults into a cipher implementation to reveal secret keys. This project aims to mitigate DFA attacks on the Advanced Encryption Standard (AES) by implementing targeted countermeasures in an embedded AES-128 encryption core. Two key techniques are explored: Randomization and Triple Modular Redundancy (TMR). The randomization approach introduces unpredictability into the encryption process, which involves inserting dummy rounds, artificial noise, and random delays, to disrupt an attacker’s timing and analysis, while TMR provides redundancy by replicating critical rounds of computation and using majority voting to correct …

Control Strategies In Wind Energy Systems: A Comprehensive Overview, Abeer Awad, Mohamed Al Hasheem

Future Engineering Journal

This paper reviews different control methods used in wind energy systems to improve how they work and connect to the power grid. It covers simple control methods like Proportional-Integral (PI) controllers, as well as more advanced ones like Sliding Mode Control and intelligent controls such as fuzzy logic and neural networks. The strengths and weaknesses of each method are discussed, focusing on how well they handle changes in wind and load, keep power quality high, and stay stable. Challenges like system complexity and unpredictable wind are also explained. The paper ends by suggesting that future work should focus on combining …

Review Of Energy Storage Systems For Optimal Planning And Operation Of Microgrid Resources, Ibrahim M. Ibrahim Dr., Walid A. Omran Prof., Almoataz Y. Abdelaziz Prof.

Future Engineering Journal

Energy storage systems (ESSs) play a central role in microgrid (MG) research and are widely used in related studies. This is due to the many applications of ESSs that enhance the performance of MGs. These include power quality applications and energy management applications. This paper presents a literature review of various studies that address the use of ESSs in microgrid (MG) systems to perform functions aimed at enhancing overall performance. These studies are classified into two main categories: planning and operational planning, with the MG being either grid-connected or islanded in each case. Finally, the optimization techniques used to obtain …

D.A.M.P.E.R.: Delay And Attenuation Modules For Propagation Emulation And Response, Matthew Guntz

Electrical and Computer Engineering Senior Theses

The DAMPER project, which stands for Delay and Attenuation Modules for Propagation Emulation and Response, is a hands-on system designed to help test how radio signals behave in the real world. When radio waves travel over long distances or through different environments, they often get weaker (attenuated) or arrive later than expected (delayed). DAMPER lets students, researchers, and engineers simulate those conditions in a lab using simple, low-voltage electronics.

Vector Extension On A Risc-V Cpu, Muhammad Ibrahim Lawai, Niels Holzmann, Khondakar Mujtaba

Electrical and Computer Engineering Senior Theses

Electricians face significant health and safety hazards when inspecting drop ceilings, including exposure to dust, asbestos, and the risk of falls. To address these challenges, this project proposes a lightweight, autonomous robot capable of inspecting drop ceilings and assisting with wire tracing tasks—thereby distancing electricians from hazardous environments. The robot employs tread-based mobility to navigate fragile ceiling panels, integrated ultrasonic sensors and bumpers for obstacle avoidance, and an antenna system to detect and follow energized wire signals. Visual feedback is provided to the operator through a real-time video feed over a secure NoMachine interface, with manual and semi-autonomous operation modes …

Hybrid Fea And Meta-Modeling For De Optimization Of A Pm Stator-Excited Motor With A Reluctance Rotor, Oluwaseun A. Badewa, Dan M. Ionel

Electrical and Computer Engineering Graduate Research

This paper presents an innovative method for designing high-performance electric motors by integrating machine learning (ML) based meta-modeling with a differential evolution (DE) optimization algorithm. The approach utilizes finite element analysis (FEA) data to train the ML meta-model, allowing for efficient optimization of high-power-density machines, such as the reluctance rotor and permanent magnet (PM) stator combined excitation motor, which is characterized by nonlinearities. The meta-modeling process employs an Artificial Neural Network (ANN) with 3 hidden layers and uses the motor’s geometrical variables as inputs. The accuracy of the meta-model in capturing the nonlinear relationships between design parameters, core losses, and …

Framework For Multi-Agent Coordination And Distributed Localization In Micro-Uavs, Minwoo Park, Nikolas Tambornini

Electrical Engineering

Micro-UAVs (unmanned aerial vehicles) due to their inexpensive nature and compact form factor have shown an increase in prevalence throughout a multitude of applications including, but not limited to: search and rescue, military reconnaissance, and agriculture monitoring. However, for a majority of these high impact applications, a swarm of micro-UAVs are required and furthermore mandate that they are able to cooperatively and autonomously coordinate with each other. For long, controlling and communicating between a user and a singular micro-UAV has been a well known and solved problem, however the same can't be said for swarms of micro-UAVs. This project seeks …