Digital Commons Network^™

Rf-Attennet: A Hybrid Attention-Enhanced Network For Mixed Signal Classification In Uav Swarm Detection, Prajoy Podder, Mohammad Atikur Rahman, Maciej Zawodniok, Sanjay Madria

Electrical and Computer Engineering Faculty Research & Creative Works

The continuous increase of UAVs, particularly in swarms, creates significant challenges for security and airspace regulation. Traditional RF fingerprinting methods struggle to detect and classify UAV swarms due to overlapping signals and interference. This study introduces RF-AttenNet, a hybrid deep learning model designed to classify mixed UAV signals by analyzing composite RF spectrograms. RF-AttenNet uses dual attention mechanisms, channel and spatial attention to focus on critical spectral features, enabling the model to effectively separate and identify overlapping UAV signals. We have developed custom composite UAV datasets that simulate real-world swarm interference, incorporating both single and mixed UAV classes. RF-AttenNet achieves …

Fixed-Time Consensus Tracking For Nonlinear Multi-Agent Systems Under Aperiodically Intermittent Control, Lei Xue, Tong Wu, Wenwen Jia, Donald C. Wunsch

Electrical and Computer Engineering Faculty Research & Creative Works

This article explores the problem of fixed-time consensus tracking (FT-CT) for nonlinear multi-agent systems utilizing the a periodically intermittent control (AIC) strategy. In contrast to existing control algorithms, the proposed algorithm utilizes the AIC strategy instead of the conventional continuous-time control strategy, effectively reducing the consumption of communication resources. Moreover, the problem of intermittent FT-CT is well handled by proposing the average control rate of the AIC strategy. Two theorems based on the cases of directed and undirected graphs are proposed, respectively. Finally, the validity of these results is confirmed through numerical simulations on a general nonlinear system and a …

Honey-Cnt Memristive Artificial Synaptic Device For Sustainable Neuromorphic Computing System, Md Mehedi Hasan Tanim, Zoe Templin, Harshvardhan Uppaluru, Jinhui Wang, Kuan Yew Cheong, Feng Zhao

Electrical and Computer Engineering Faculty Research & Creative Works

Brain-inspired neuromorphic computing systems require hardware components analogous to biological neurons and synapses. Honey based natural organic memristor has demonstrated promising nonvolatile memristive behaviors, with the advantages of sustainability, environmentally friendliness, and low-cost manufacturing. In this study, carbon nanotubes (CNTs) are added in honey to fabricate honey-CNT memristive artificial synaptic devices. Honey-CNT film is characterized by micro-Raman spectroscopy and the distribution of CNT bundles embedded in the honey-CNT composite layer by cross-sectional scanning electron microscopy for the first time. Critical synaptic functions of the honey-CNT memristor, including spike-rate-dependent plasticity, spike voltage dependent plasticity, learn-forget-relearn, and supralinear spatial summation are revealed, …

Resolving Stiffness Trade-Offs In Simultaneous Pressure And Vibration Sensing Using A Corrugated-Tube Fiber-Optic Sensor, Yizheng Chen, Yan Tang, Jie Huang, Qi Zhang, Biyao Shi, Zewei Wu

Electrical and Computer Engineering Faculty Research & Creative Works

This article proposes and experimentally demonstrates a corrugated-tube-based fiber-optic sensor capable of measuring pressure, vibration, or both simultaneously. To address the limited sensitivity of conventional diaphragm-based designs, the sensor incorporates an optimized corrugated tube that balances the conflicting stiffness requirements for pressure and vibration measurements. The corrugated tube, acting as a mechanical transducer, is integrated with an extrinsic fiber-optic Fabry–Perot interferometer (EFPI). The EFPI cavity is formed between a reflective surface at the sealed end of the corrugated tube and the cleaved end face of an optical fiber fixed within a mounting assembly. In this configuration, displacement of the corrugated …

Corrections To: Enhancing Measurement Accuracy: The Impact Of Missing Data On Parameter Estimation In Mass-Spring-Damper Systems (Ieee Transactions On Instrumentation And Measurement (2026) 75 (1–12) Doi: 10.1109/Tim.2026.3676091), Michkath Omanda Bouraima, Steven Thompson, Maciej J. Zawodniok

Electrical and Computer Engineering Faculty Research & Creative Works

In the above article [1], a wording ambiguity appears in Proposition 4 regarding the description of the missing at random (MAR) mechanism. The published sentence states that the probability of observing the kth sample depends on the realized measurement value. This wording may be interpreted as dependence on the current unobserved value y[tk], which could suggest a missing not at random (MNAR) mechanism. The intended MAR mechanism is that the observation probability for the kth sample depends only on previously observed measurement information, such as y[tk-1], and not on the current unobserved value y[tk]. Therefore, the corrected wording clarifies that …

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 …

University Education Model; Co-Development, Socialisation, Support, Influence And Implementation 2021-2026, Shaun Ferns, Barry J. Ryan, Fionnuala Darby

Report

This report provides a reflective institutional analysis of TU Dublin’s University Education Model (UEM), appraising its co-development, socialisation, support structures, influence, and the status of its implementation. It has been informed by the institutional conditions that shaped integration and considered with reference to the UEM external reporting requirements associated with the Technological Sector Advancement Funding (TSAF).

Perspectives Of Rural Educators On The Dependencies And Drawbacks Of Chat Gpt In Nigerian Higher Education Practices, Adedayo Olayinka Theodorio Dr, Francisca Jumoke Theodorio, Olumide Gbenga Olugbodi, Peter Adebowale Samson, Olusegun Olawale Olakotan

Journal of Educational Technology Development and Exchange (JETDE)

ChatGPT remains a valuable tool for transforming teaching strategies and supporting students’ comprehension of subject matter. However, its adoption and research remain uneven within the context of higher education in Nigeria. Specifically, there is limited empirical research that exemplifies real-world experiences regarding the dependencies and drawbacks in rural educational settings. This qualitative single-case study explored this phenomenon at a southwestern institution in Nigeria, involving a purposively selected group of educators. Data were gathered through participatory observation and focus group interviews, which were thematically analysed. The results indicated that the effective use of ChatGPT in rural higher education heavily depends on …

An Incidental Standard For Medical Ai, Nicholson W. Price Ii

Articles

Medical AI is poised to make a major difference in the provision of health. It brings major challenges, though: how can developers and implementers ensure that it will work safely and effectively—especially within the context of complex and highly variable health-care systems? Standards provide one key tool, potentially providing guidelines for everything from privacy to accuracy to how AI interacts with human clinicians. This Essay considers the last of these, describing a powerful quasi-standard from a surprising source: an FDA guidance document that tells developers when certain AI systems are not considered medical devices, and are therefore not regulated by …

A Case Study Of Gender And Online Team Communication In Software Engineering Education, Rita Garcia, Christoph Treude

Research Collection School Of Computing and Information Systems

Collaboration is crucial in Software Engineering (SE), yet factors like gender bias can shape team dynamics and behaviours. This descriptive case study examines an eight-week project involving 39 SE students across eight teams contributing to GitHub projects. Focusing on gender, we used a mixed-methods approach to analyse Slack communications, identifying gender differences in how students respond to initiated communications and comparing how students’ communications influenced other aspects of students’ performance, including learning gains. We found higher help-seeking and leadership behaviours in the all-woman team involved in this case study, while men responded more slowly. Although communication did not directly affect …

A General Algorithm For Assortment Optimization Under Random Utility Choice Models, Tien Mai, Andrea Lodi

Research Collection School Of Computing and Information Systems

This work concerns the assortment optimization problem that refers to selecting a subset of items that maximizes the expected revenue in the presence of the substitution behavior of consumers specified by a random utility choice model. The key challenge lies in the computational difficulty of finding the best subset solution, which often requires exhaustive search. The literature on constrained assortment optimization lacks a practically efficient method that is general to deal with different types of customer choice models (e.g., the multinomial logit, mixed logit or general multivariate extreme value models). In this work, we propose a new approach that allows …

Integrating Symbolic And Waveform Music Into Large Language Models, Teng Tu, Xiaohao Liu, Yunshan Ma, Ji Qi, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

Music, as a unique and integral element of human life, is characterized by its complex structures, intricate details, and the fusion of multimodal information. Recent study advance music understanding by leveraging knowledge and reasoning capabilities derived from Large Language Models (LLMs). However, they often lack compatibility and fail to fully utilize the complementary strengths of diverse representations (e.g., ABC, MIDI, Waveform). To address these limitations, we propose a unified music-language model framework, named UniMuLM, transitioning from single-representation approaches to the integration of multiple music representations for LLM. Unifying different music representation formats poses challenges such as patch integrity and boundary …

Food Recognition With Visual Language Models: Search Re-Ranking Or Retrieval-Augmented Generation?, Kian Yu Gan, Phuong Anh Nguyen, Chong-Wah Ngo

Research Collection School Of Computing and Information Systems

Despite the rapid advances in Visual Language Models (VLMs), these models struggle to recognize culture-specific food items. While VLMs are effective in recognizing popular cultural dishes, their performance is suboptimal for dishes that are unique but not widely known internationally. Specifically, VLMs often generate either generic labels or hallucinated names for dishes that are localized to a particular culture. As a result, retrieval-augmented generation (RAG), which retrieves relevant recipes as references for VLMs, emerges as a promising approach. Nevertheless, recipe retrieval, which is itself imperfect, could mislead VLMs into generating inaccurate or culturally inappropriate dish names. This paper presents a …

Security-Enhanced Decentralized Conditional Privacy-Preserving Authentication In Vanets, Suqin Luo, Xinghua Li, Yinbin Miao, Xuelin Cao, Zhan Zhang, Yunwei Wang, Deng R.H.

Research Collection School Of Computing and Information Systems

To ensure the legitimacy of communicators while ad dressing the privacy concerns of vehicles in vehicular ad-hoc networks (VANETs), conditional privacy-preserving authentication (CPPA) schemes have been proposed. Given that existing schemes suffer from single point of failure due to centralized authorities, several distributed CPPA schemes have been proposed. However, these schemes all ignore the tight cementation between system secret keys and the authority, which could be a serious threat to system security, that the compromised authority may leak the system secret key. To address these issues, we propose a security enhanced decentralized conditional privacy-preserving authentication (DCPPA) scheme. DCPPA first introduces …

Decentralized Linear Solvers: Communication Cost And Privacy, Nelson G. Brasil, Vinay A. Vaishampayan

Publications and Research

We consider the problem of multiple parties iteratively solving a system of linear equations in a decentralized manner. Specifically, we solve for $\negr{x} \in \R^n$, the $n \times n$ system of linear equations $M\negr{x} = \negr{b}$ when each party only knows their row of the matrix $M$ and a single component of $\negr{b} \in \R^n$. Our objective is to determine the tradeoff between the accuracy of the solution and the total communication cost measured in bits. A fully connected, reliable mesh network is assumed to connect the different parties. We develop a general formulation that applies to a large class …

Tempo: Training-Time Equilibration Of Modalities For Per-Sample Optimization In Multimodal Sentiment, Yi Zhao, Erik Cambria, Xiaosong E, Xianxun Zhu

Research Collection School Of Computing and Information Systems

Multimodal sentiment models often become over-reliant on the “easiest” modality (typically text), leading to three coupled sub-problems: (i) representation-level dominance, where weaker modalities contribute little to the fused representation; (ii) optimization-level dominance, where the strongest modality drives most gradient updates and suppresses learning in others; and (iii) robustness degradation, where audio or vision fail under noise or missing inputs at test time. We present TEMPO, a plug-and-play training framework that mitigates these issues by rebalancing learning pressure across modalities while leaving inference unchanged. For each mini-batch, TEMPO estimates relative modality strength and applies two synchronized, training-only controls: selective forward attenuation …

Nondeterministic Polynomial-Time Problem Challenge: An Ever-Scaling Reasoning Benchmark For Llms, Chang Yang, Ruiyu Wang, Junzhe Jiang, Qi Jiang, Qinggang Zhang, Yanchen Deng, Shuxin Li, Shuyue Hu, Bo Li, Florian T. Pokorny, Xiao Huang, Xinrun Wang

Research Collection School Of Computing and Information Systems

Reasoning is the fundamental capability of large language models (LLMs). Due to the rapid progress of LLMs, there are two main issues of current benchmarks: i) these benchmarks can be crushed in a short time (less than 1 year), and ii) these benchmarks may be easily hacked. To handle these issues, we propose the ever-scalingness for building the benchmarks which are scaling over complexity against crushing, instance against hacking and exploitation, oversight for easy verification, and coverage for real-world relevance. This paper presents Nondeterministic Polynomial-time Problem Challenge (NPPC), an ever-scaling reasoning benchmark for LLMs. Specifically, the NPPC has three main …

Voltage Stability Enhancement Of Large Load Interconnections Using Syncronous Condensers, Muhammad Ibrahim Abbas

Graduate Studies Theses and Dissertations 2026

The rapid integration of hyperscale data centers as large, concentrated loads presents a growing voltage stability challenge in grids weakened by synchronous generator retirement and increasing inverter-based resource penetration. This work proposes a Jacobian-based sensitivity framework for systematically identifying voltage-critical buses and optimally siting synchronous condensers as voltage support resources. A voltage-weighted sensitivity index, extracted from the full Jacobian inverse, is introduced to combine network-wide reactive coupling strength with observed voltage drops into a single deployable placement criterion. Validation on IEEE 14-bus and 30-bus test systems under multiple loading scenarios demonstrates that the proposed criterion consistently identifies the correct placement …

An Analysis Of The Influence Of Project-Based Assessments In Middle School Mathematics, Andrea Fettkether

Dissertations and Theses @ UNI

In today’s diverse classrooms, teachers face the challenge of educating students with varied academic abilities, including those with disabilities, gifted learners, and students at grade level. The Individuals with Disabilities Education Act (IDEA) ensures inclusive access to core curriculum, but educators must differentiate instruction to meet all learners’ needs. Traditional assessments focus on rote memorization, often failing to reflect deeper understanding or real-world problem-solving.

In middle school math, standard curricula often lack project-based assessments, despite research supporting their effectiveness in improving academic achievement and student engagement. Students benefit from working in collaborative environments where they can discuss mathematical ideas, receive …

Robust Linking Estimation In Item Response Theory Using Quantile Regression: Applications To The Graded Response And Generalized Partial Credit Models, Ibrahim Almansour

Graduate Studies Theses and Dissertations 2026

This dissertation investigates robust estimation of linking coefficients in Item Response Theory (IRT) using quantile regression and its extensions. Linking procedures are essential for placing item and ability parameters from different test forms onto a common scale, thereby ensuring comparability of examinee scores across administrations. Traditional approaches such as moment methods, characteristic curve methods, and mean-based regression procedures like Ordinary Least Squares (OLS) and Generalized Least Squares (GLS) perform adequately under ideal conditions but tend to lose accuracy when ability distributions deviate from normality or include outliers.

To address these limitations, this research proposes a quantile-based regression framework that estimates …

Restoring Digital Trust: Decentralized Frameworks For Identity, Consent, And Media Provenance, Raghu N. Avula

Graduate Studies Theses and Dissertations 2026

Digital platforms are deeply embedded in modern society through centralized network architectures. As online interactions transition to the physical world handling sensitive data, user privacy and cryptographic security require enhancement beyond traditional limits. This dissertation presents a decentralized “Tri-Layer Trust Architecture” that leverages Verifiable Trust Registries, Cryptographic Binding, and Edge Verification to prioritize data sovereignty and local authentication. To demonstrate the architecture’s applicability across distinct operational environments, we implement three solutions: (1) Verifiable Presence for peer-to-peer ride-sharing via D-RideX, which encrypts biometric data on physical hardware (TEEs) and Soulbound Tokens (SBTs) to prevent impersonation without cloud database exposure; (2) Verifiable …

Learning-Based Optimization For Collaborative Truck-Drone Routing And Scheduling, Ahmad A. Bany Abdelnabi

Graduate Studies Theses and Dissertations 2026

Modern delivery networks increasingly rely on innovative technologies such as drones working alongside trucks to meet rising demands for fast and efficient last–mile transportation. Hybrid truck–drone delivery offers strong potential, but its effectiveness depends on careful synchronization between trucks and drones. Although drones provide fast, direct travel, their limited endurance and the need to coordinate launch and recovery with trucks create tightly coupled routing and scheduling interactions. Poor dispatch decisions can cause truck waiting, reduce parallelism, and increase mission completion time. These challenges motivate decision frameworks that explicitly model synchronization and develop scalable methods for coordinated truck–drone operations. This dissertation …

An Integrated Framework For Last-Mile Delivery Optimization Using Reinforcement Learning And Social Media-Based Traffic Prediction In Underdeveloped Megacities, Vasanth Bhat

Graduate Studies Theses and Dissertations 2026

Supply chain management has become increasingly critical as transportation costs rise and urban delivery systems place additional pressure on already congested networks. Last-mile delivery—the movement of goods from distribution centers to end users—represents the most expensive and operationally complex segment of the logistics process, particularly in underdeveloped megacities characterized by unpredictable traffic, limited infrastructure, and sparse real-time data. This dissertation proposes an integrated framework for optimizing last-mile delivery in such environments by combining social media–based traffic intelligence, machine learning, and deep reinforcement learning. The framework leverages unstructured, real-time data from social media platforms to enhance traffic prediction and incorporates these …

Investigation Of Exotic Phenomena In Topological Quantum Materials, Iftakhar Bin Elius

Graduate Studies Theses and Dissertations 2026

he First Quantum Revolution established quantum mechanics as the foundation of modern physics, enabling transformative technologies such as semiconductors, lasers, and transistors through prin-ciples of quantization, wave-particle duality, and uncertainty. The ongoing Second Quantum Rev-olution harnesses entanglement, superposition, and coherence to advance quantum computation, communication, simulation, and sensing. Central to this endeavor is the study of quantum mate-rials hosting novel electronic structures and emergent phases, including topological insulators and Dirac, Weyl, and nodal-line semimetals. In this thesis, using angle-resolved photoemission spec-troscopy (ARPES), transport measurements, and density functional theory (DFT) calculations, we investigate three classes of materials: (i) magnetic topological semimetals, …

Computational Modeling Of Bubble Dispersion In Alginate Hydrogel Foams For Radiologically Equivalent Biomedical Phantoms, Godson N. Brako

Graduate Studies Theses and Dissertations 2026

The foaming of hydrogels presents a promising strategy for tailoring mechanical and radiological properties to replicate biological soft tissues for biomedical phantoms. Achieving uniform and predictable void fraction distributions in alginate hydrogel foams remains a challenge due to the complex interplay between bubble dynamics, matrix rheology, and interfacial forces during the pre-gelation aeration stage. This thesis develops a Computational Fluid Dynamics framework using the transient Eulerian two-fluid approach to predict void fraction distribution in alginate hydrogel precursor solutions aerated by air injection through a bottom nozzle. The objective is to use the framework for design of the foaming system to …

Detection And Discrimination Of Targets In Infrared Imagery, Adam T. Cuellar

Graduate Studies Theses and Dissertations 2026

Automated infrared (IR) imagery analysis is essential for persistent surveillance, defense, and security, yet it remains difficult when sensors move and when unknown objects appear. This dissertation addresses two fundamental problems: (1) detecting small moving targets amid platform-motion induced parallax and (2) distinguishing between known stationary targets and out-of-distribution (OOD) objects.   For detecting moving targets while the platform itself is in motion, auxiliary Global Positioning System and Inertial Navigation System data are combined with image analysis. Direction Cosine Matrices from calibrated inertial measurements enable sub-pixel frame alignment unattainable with purely image-based registration. The stabilized sequence is processed by a Reed–Xiaoli …

Temperature And Speciation Measurements Of Aluminum-Laden Detonation Flows Via A Three-Color Wavelength Modulation Spectroscopic Sensor, Marc B. Etienne

Graduate Studies Theses and Dissertations 2026

Tunable diode lasers are widely used and are known to be great options for continuous wave lasing applications. Because of their stability and spectral selectivity, they have become an important diagnostic tool for modern combustion research. Previous combustion research, particularly those focused on solid and hybrid rocket systems, have shown that the interaction of aluminum with a reacting flow can greatly affect the dynamics and the thermochemical properties of the surrounding gas, therefore understanding the effects of aluminum-laden flows is critical for the advancement of these propulsion systems. However, due to the high degree of optical interference, high temperatures, and …

An Examination Of The Impact Of School Accountability On Data-Driven Decision-Making By Florida Educational Leaders, Emily Galucho

Graduate Studies Theses and Dissertations 2026

This mixed-methods study examined how accountability influences educational leaders' data-driven decision-making (DDDM) practices in Central Florida. Grounded in the principal-agent and consequential accountability frameworks, the study investigated demographic predictors, implementation challenges, and the role of accountability in DDDM. Educational leaders completed online surveys measuring DDDM perceptions across five subscales and responded to open-ended questions. The responses were analyzed through multiple regression, correlation, and thematic coding.

Accountability emerged as DDDM's strongest predictor, significantly exceeding demographic and organizational variables. Five implementation challenges were identified: Data Literacy Gaps, Data Timeliness Issues, Resource Constraints, Implementation Fidelity, and Systemic Barriers. Accountability demonstrated dual effects: many …

Reflection In Still Waters: Generative Ai's Production Of Language, Jonathan D. Hawks

Graduate Studies Theses and Dissertations 2026

Language is a conceptual process of meaning-making as well as a social construction as defined by Saussure, Bakhtin, and Lacan. Large Language Models (LLMs) engage with neither process and instead present language as a finished and consumable product which undermines its fundamental aspects. The difference between representing language through probability and its structural components instead of its more inherent qualities is not a pointless distinction. This paper utilizes Narcissus as a central metaphor to show how people misread the outputs of LLMs as language which imparts to those reflected words meaning on par with our own conceptualizations of reality. People …

Predicting Safety And Mobility Parameters Based On Comprehensive Analytics Of Connected Vehicle Data, Lei Han

Graduate Studies Theses and Dissertations 2026

Traffic safety and mobility remain critical challenges for modern transportation systems. The emergence of connected vehicle (CV) data offers unprecedented opportunities to capture microscopic, non-aggregated driving dynamics to support precise and finer-grained traffic safety and mobility research. This dissertation develops a comprehensive CV data–driven analytical framework to advance traffic safety and mobility research across multiple roadway contexts, including intersections, segments, freeways, and urban arterial networks. For traffic safety, this research extracts both longitudinal and lateral risky driving behaviors from CV trajectories and integrates them with macro-level roadway, traffic, and visual environment features. A spatial machine learning framework is proposed for …