Electrical and Computer Engineering Commons^™

Competitive Programming Primer, Jon Ayuco, Pedro Marcet, Ivan Marriott

Electrical Engineering and Computer Science Student Publications

Helps first-year CS/SWE Students that are confused on how advanced algorithms work.

Professors can give a visual aid in-class.

Encourages unsure students interested in Competitive Programming a low-risk place to practice

Remotely Controlled Car Via Lte/Wi-Fi, Nicholas Shenk, Christian Prieto, Joseph Digafe, Donoven Nicolas

Electrical Engineering and Computer Science Student Publications

PROBLEM 

• Professionals must enter dangerous environments just to gather information 
• Existing robotic systems are expensive and complex
• Communication links are often unreliable or high-latency
• Limited real-time feedback reduces decision-making ability

Cali: Cobot Autonomous Living Interface, Nicholas Santamaria, Heber Lopez, Berke Celal Dogan

Electrical Engineering and Computer Science Student Publications

The CALI (Cobot Autonomous Living Interface) system is an assistive robotic platform designed to help individuals with limited mobility eat independently. CALI integrates computer vision, neural networks, and robotic motion control to detect food items on a plate and deliver them safely to a user. By combining AI perception with precise robotic control, the system demonstrates a practical application of assistive robotics.

Advances In The Design Of Bio-Organic Resistive Switching Memory, Muhammad Awais, Yi Sheng Wong, Feng Zhao, Kuan Yew Cheong

Electrical and Computer Engineering Faculty Research & Creative Works

Bio-organic materials have garnered significant attention as sustainable candidates for non-volatile resistive switching memory (RSM) because of their specialized chemical, structural, and environmental advantages. This review presents a design-centered perspective on bio-organic RSM by outlining the key device components required for effective device engineering, including electrode materials, memristive thin films, intermediate layers, substrates, and electrical measurement strategies. Each component is discussed in detail with respect to the material properties and operational parameters that influence overall device performance, such as functional groups, interfacial interactions, and processing conditions. The review further analyses the critical roles of electrode pairing, interfacial chemistry, additive incorporation, …

A Trojan Attack On Tdma Synchronization In Energy-Harvesting Wireless Networks, Ethan Berei

ONU Student Research Colloquium

This paper investigates a Trojan attack targeting the time-division multiple access (TDMA) synchronization mechanism in single-hop energy-harvesting wireless networks. The attack compromises a single node, which subtly skews its transmission timing to operate outside its assigned time slot, causing localized transmission overlaps and triggering repeated network-wide resynchronization events. This behavior shortens the synchronization interval, significantly increases control-plane traffic, and leads to higher energy consumption and delay in energy-constrained networks. The attack is modeled within a finite state machine (FSM) framework and experimentally evaluated under varying energy-harvesting conditions. Experimental results show that the number of synchronization events can increase by up …

Integration Of Bayesian Networks And Neural Networks For High-Dimensional Data Analysis, Cooper Schmer

Department of Electrical and Computer Engineering: Dissertations, Theses, and Student Research

High-dimensional biomedical datasets, such as omics data, present significant challenges for predictive modeling due to noise, redundancy, and computational complexity. This thesis proposes a hybrid framework that integrates Bayesian Networks (BNs) and Artificial Neural Networks (NNs) to improve classification performance of such data sets while reducing input dimensionality. Central to this work is a novel feature selection method based on d-separation, a structural property of Bayesian networks that encodes conditional independence relationships.

The proposed approach introduces a count-based d-separation metric to quantify the relevance of variables to a target outcome, along with a thresholding scheme to balance feature selection robustness …

​ Planning, Control, And State Estimation For Chain-Style Modular Robots​, Sebastian Theiler, Yucheng Nie, Joshua P. Kevil

Electrical and Systems Engineering Capstone Design Projects

Traditional robots have fixed shapes, limiting their ability to adapt to complex or dynamic environments. Modular robotic systems are composed of identical robot modules that can connect and disconnect to form different structures. This allows the robots to form bridges to cross voids and split apart to fit into tight corridors. However, while previous modular robotics research heavily emphasizes reconfiguration planning, comprehensive motion planning remains underexplored. We introduce a novel, end-to-end framework for the planning, state estimation, and control of chain-style modular robot swarms. Our system includes a custom network flow planner that maps polygonal environments into a Reeb graph …

Piano Aid, Christopher G. Sayers, Tyler Baugus, Gabby Taunton

Create@State

Pianos provide recreational and educational value and is a cornerstone of the culture experienced worldwide today. Despite the many benefits and enrichment music brings these benefits are often inaccessible or difficult to learn by most people but especially to those of the Deaf and Hard of Hearing (DHH) community due to its innate auditory nature. While adaptive instruments have been proposed to address this gap, many remain conceptual or fail to reach production because of high development costs and limited commercial markets. This project presents an economically feasible alternative in the design of an adaptive digital piano that enables both …

Physiobridge: Physiology-Constrained Self-Supervised Foundation Model For Cross-Device Ecg–Ppg Learning With Conformal Risk Control, Abbas Alzubaidi, Ali Al-Shuwaili, Ali Al-Bayaty

Electrical and Computer Engineering Faculty Publications and Presentations

Wearable and bedside sensors continuously generate electrocardiograms (ECG), photoplethysmograms (PPG), and related physiological waveforms that could enable earlier detection of deterioration and more personalized care. However, current deep learning pipelines in biomedical signal processing often remain taskand device-specific, degrade under domain shift (new hospitals, sensors, skin tones, motion), and provide limited uncertainty information for safety-critical decisions. We propose PhysioBridge, a foundation-model approach that learns a shared representation space for ECG and PPG via self-supervised pretraining and explicit physiology constraints, then supports downstream adaptation with distribution-free risk control. PhysioBridge introduces (i) multi-rate patch tokenization that preserves clinically meaningful morphology across heterogeneous …

Improving The Circuit Realization Of Grover’S Quantum Search Algorithm By Replacing Hadamard With √ × Gates, Ali Al-Bayaty, Ali Al-Shuwaili, Abbas Alzubaidi, Marek Perkowski

Electrical and Computer Engineering Faculty Publications and Presentations

Jozsa, Bernstein-Vazirani, and Grover, utilize Hadamard gates to create uniform superposition states for the input qubits of an oracle. However, Hadamard gates are non-native (non-supported) gates in all real quantum computers. For this reason, Hadamard gates are considered cost-expensive gates when realizing (transpiling) such algorithms into a real quantum computer. This paper introduces a new methodology for cost-effective transpilation of Grover’s algorithm into real quantum computers, by replacing all Hadamard gates with √ X gates. In quantum computing, the Hadamard and √ X gates create uniform superposition states of a qubit on the Xaxis and Y-axis of the Bloch sphere, …

Techno-Economic Analysis Of Hybrid Systems As A Solution For Electricity Supply During The Dry Season At The Bakaru Run-Of-River Hydropower Plant, Zamharir Aditya Febri, Mohammad Akita Indianto, Sheila Tobing

Journal of Materials Exploration and Findings

Within the South Sulawesi power system (Sulbagsel), the Bakaru Hydro Power Plant serves as a key facility expected to provide consistent and reliable electricity supply. However, since the Bakaru plant operates under a Run of River scheme, its energy output is highly dependent on river discharge rates. In 2024, a significant decrease in water flow was recorded between August and October, which led to a drastic reduction in power generation. To address this challenge, a hybrid energy system is proposed to ensure continuous load coverage, particularly during the dry season. The optimal configuration of this hybrid system was modeled and …

Temperature Determination And Scene Change Artifact Mitigation When Using Fourier-Transform Spectroscopy On Targets With Time-Varying Temperature, Kode A. Wilson, Michael L. Dexter, Benjamin F. Akers, Anthony L. Franz

Faculty Publications

Fourier-transform spectroscopy is a widely used technique for determining the spectral and thermal properties of a target. However, target temperature variations during measurement can compromise the spectral accuracy. Temperature fluctuations induce oscillations superimposed on the target spectrum. These oscillations, referred to as scene-change artifacts, degrade the spectral accuracy. The literature is divided, with theoretical predictions suggesting negligible artifacts and growing experimental evidence reporting significant artifacts. This paper presents a theory and experimental validation of scene-change artifacts originating from target temperature variations. Traditionally, the interferogram offset is assumed to be constant, an invalid assumption for a changing scene. The error is …

Leveraging Convolutional Neural Networks For Through-The-Wall Radar Imaging: Challenges, Impacts, And Future Directions, Tumaini Edgar, Abdulla F. Ally, Abdi T. Abdalla

Tanzania Journal of Engineering and Technology (TJET)

Through-the-wall radar imaging (TWRI) is an essential technology for military and rescue applications; however, its performance in detecting and visualizing high-quality images of targets behind walls is significantly degraded by multipath reflections and signal attenuation. This paper reviews the current state of TWRI and its challenges, and explores the transformative potential of deep learning, particularly convolutional neural networks (CNNs), in addressing these challenges. Peer-reviewed articles published from 2018 to 2024 were analysed to examine CNN applications in addressing TWRI challenges. The analysis reveals that using CNNs, TWRI systems can be more effective by filtering wall distortions, reducing noise, lowering computational …

Emergent Dynamics In Multiplex Social Networks: Agent-Based Modeling Of Information Diffusion For Misinformation Control, Harshvardhan Prabhakar Ghongade, Anjali Ashokrao Bhadre, Shivani Agarwal, Harjitkumar Uttamrao Pawar, Harshal Subhash Rane

Northeast Journal of Complex Systems (NEJCS)

Information misrepresentation is widespread in multi-layered social networks which provide multiple avenues to communicate information. As such, it presents significant opportunities for both information integrity and public discourse to be undermined by disinformation. This paper outlines a new agent-based model, developed to capture emergent dynamics of multi-layered social networks and to help identify technical means to mitigate information misrepresentation in complex systems. A key component of this research includes a novel Multi-Layer Information Diffusion Model (MLIDM), integrating both cross-layer communication among agents, as well as heterogeneous agent behaviors and adaptive intervention strategies. Our methods employ a three-stage process to model …

Efficient Intrusion Detection For Iomt: Integrating Machine Learning, Feature Selection, And Fuzzy Logic, Ghaida Mansour Balhareth

Electronic Theses and Dissertations

The internet of medical things (IoMT) has transformed healthcare by enabling real-time patient monitoring, remote diagnoses, and effective data exchange among connected medical devices and clinical systems. The increasing reliance on interconnected medical equipment has also intensified cybersecurity risks, as resource-constrained devices and wireless communication channels are vulnerable to attacks such as man-in-the-middle, spoofing, data injection, and ransomware. Intrusion Detection Systems (IDSs) play a critical role in mitigating these threats; however, traditional IDS approaches often struggle with high-dimensional IoMT data, class imbalance, and uncertainty in traffic patterns, which can increase false alarms and reduce reliability in safety-critical environments. This dissertation …

Design And Modeling Of A Piezoelectric Bimorph Energy Harvester For Automotive Structural Vibrations, Ali Abu Shawish

Thesis/ Dissertation Defenses

The focus of this thesis is on the design and modelling piezoelectric bimorph energy harvesters for vehicular utilization, particularly on harvesting energy from local structural vibrations of automotive components. The vibrations resulted from road–tire interaction and drivetrain dynamics offer the potential for harnessing electrical energy for low power electronic systems when coupled with low damping resonance harvesting devices. The main purpose of this thesis is to evaluate the potential of a piezoelectric bimorph cantilever tuned to 100–300 Hz local automotive structural vibrations for electrical energy harvesting, and to analyze its actual performance under realistic excitation conditions. The harvester's dynamic response, …

Modeling Flood-Induced Cascading Disruptions In The Indian Electronics Supply Chain Using Influence Network Analysis, Surendra Orupalli, Hiroki Sayama

Northeast Journal of Complex Systems (NEJCS)

This study investigates flood induced disruptions in the Indian electronics supply chain using influence network analysis. Monsoon floods are recurring hazards that significantly impact economic activities, logistics, and industrial productivity. This study integrates district-level rainfall data (2020 to 2025) with supply chain network models to quantify cascading failures. The methodology applies rainfall thresholds (≥ 300 mm/month) to identify flood-prone districts and constructs a stochastic influence matrix representing inter-firm dependencies. Flood propagation dynamics are modeled iteratively with a propagation coefficient (α = 0.6) and convergence threshold (ε = 10⁻⁴). The resulting disruption profiles are mapped onto company-level revenues calibrated to India-specific …

Rioi: A Microwave-Photonic Rf-Interferometric Interrogation Technique For Enhanced Fiber Optic Sensing, Chen Zhu, Ruimin Jie, Chenxi Huang, Jie Huang

Electrical and Computer Engineering Faculty Research & Creative Works

Fiber optic interferometric (FOI) sensors are widely recognized for their high sensitivity, design flexibility, and multiplexing capabilities, making them ideal for applications ranging from structural health monitoring to biomedical diagnostics. However, conventional optical-domain interrogation techniques are often limited by the performance constraints of spectrometers. In this work, we present a radiofrequency (RF)-interferometric optical interrogation (RIOI) method for FOI sensors. This approach leverages microwave photonic (MWP) processing to encode the optical interference phase into an RF signal, which is then combined with a reference RF signal to produce a microwave-domain interferogram. By tracking spectral shifts in the RF domain, RIOI achieves …

Performance Analysis Of Back-To-Back Multilevel Tnpc Converters For Wave Energy Converters, Kappala Raveendrababu

Thesis/ Dissertation Defenses

A Wave Energy Converter (WEC) is a device that transforms the kinetic energy of ocean waves into usable electrical energy. Wave energy has the potential to become a major renewable energy source soon, as its energy density is higher than that of solar and wind power, especially as its technology advances. This thesis addresses the integration of wave energy conversion systems into the electrical grid using back-to-back multilevel power converter topologies. The study focuses on the design and implementation of a back-to-back three-level T-type Neutral Point Clamped (T-NPC) converter for a single wave energy conversion system to enhance efficiency, reliability, …

Predictive Analytics In Oncology And Ophthalmology: Machine Learning Applications For Diabetic Retinopathy And Breast Cancer, Ali Abidalkareem

Electronic Theses and Dissertations

The convergence of artificial intelligence and healthcare represents one of the most transformative developments in modern medicine, with deep learning technologies emerging as powerful tools for addressing complex diagnostic challenges. This dissertation develops and validates machine learning frameworks that address critical challenges in medical diagnosis through innovative approaches to data augmentation, feature learning, and classification, focusing on two fundamental problems: Diabetic Retinopathy (DR) severity classification using multi-model convolutional neural networks (CNNs), and breast cancer stage identification using microRNA (miRNA) gene expression biomarkers. For diabetic retinopathy classification, this work proposes an ensemble deep learning framework that integrates Diffusion-based data augmentation for …