Digital Commons Network^™

A Feature-Free Deep Learning Approach For Midair Hand Gesture Recognition From Surface Electromyogram (Semg) Data, Yasir Altaf, Abdul Wahid, Mudasir Manzoor Kirmani

Turkish Journal of Electrical Engineering and Computer Sciences

Midair hand gesture recognition plays a crucial role in applications such as sign language recognition and human-computer interaction, particularly for supporting individuals with partial or complete hearing loss. However, recognizing gestures in midair remains challenging due to the rapid and complex nature of hand movements. To address this, noninvasive techniques like surface electromyography (sEMG)—which captures muscle activity through sensors placed on the skin—have gained attention. sEMG provides rich time-series data that reflect both spatial and temporal muscle dynamics. In this study, we propose a deep learning architecture that combines convolutional neural networks (CNNs) and recurrent neural networks (RNNs) to classify …

Integrated Log Spectrogram Convolutional Neural Network (Ils-Cnn) For Robust Spoken Digit Recognition, Awais Ahmed

Turkish Journal of Electrical Engineering and Computer Sciences

Spoken digit recognition (SDR), a type of supervised automatic speech recognition, is essential for various human-machine interaction applications, including banking operations, dialing systems, price extraction, and airline reservation systems. However, designing an effective SDR system presents several challenges, such as developing labeled audio data, selecting appropriate feature extraction methods, and creating high-performance models. To overcome these challenges, a novel approach for robust spoken digit recognition using an integrated log spectrogram convolutional neural network (ILS-CNN) has been proposed. The proposed work presents an efficient SDR method by taking advantage of a log spectrogram layer directly within the neural network to enhance …

Railway Track Condition Monitoring Based On Sensor Data And Artificial Neural Networks, Ivan Kots, Alina Isaeva, Mark Denisenko, Alexander Sinyukin, Andrey Kovalev

Turkish Journal of Electrical Engineering and Computer Sciences

Monitoring the condition of engineering objects is one of the urgent tasks of industry, construction, and transport infrastructure. This article describes a system for condition monitoring and diagnostics of rail tracks in real time. Compared with other similar studies, the proposed system has the advantages of compactness, usability, scalability and versatility of application. The proposed monitoring system is based on an Nvidia Jetson Nano embedded computing board and also includes inertial sensor modules, a microphone, a geolocation module, communication modules, an SSD storage device, and a battery. The prototype of the diagnostic module is a portable device that can be …

Grey Wolf Optimization Of Pi Controller For Power Management In Wind Farms: A Novel Approach, Anis Feddaoui, Lotfi Farah, Abdelouahab Benretem, Mohammed Abdeldjalil Djehaf

Turkish Journal of Electrical Engineering and Computer Sciences

This study proposes a novel power management strategy for wind farms using a grey wolf optimization (GWO)-based PI controller. The method aims to enhance active and reactive power control in systems employing dou bly fed induction generators. Three control strategies are evaluated—namely, a classical frequency-domain PI controller, an Artificial Neural Network (ANN)-based controller, and the proposed GWO-based PI controller—the last of which represents the main contribution. The classical PI and ANN controllers are included strictly for comparative bench marking. MATLAB simulations demonstrate that the GWO-beased PI controller offers superior dynamic performance, particularly in settling time and overshoot reduction. A power …

Performance Analysis Of Ris-Empowered Ofdm-Im Communications Under Weibull Fading And Joint Tx/Rx I/Q Imbalance, Büşra Ceni̇kli̇oğlu, İbrahi̇m Develi̇, Ayşe Eli̇f Canbi̇len

Turkish Journal of Electrical Engineering and Computer Sciences

A modernist technique, reconfigurable intelligent surface (RIS) provides outstanding signal reflection and amplification, making it highly valuable for upcoming communication systems. Besides, a major contributor is index modulation (IM), attaining superior spectral and energy efficiency, and achieving hardware sufficiency. The primary and novel contribution of this work is the derivation of a highly accurate, closed-form approximate expression for the average bit error rate (ABER) of an orthogonal frequency division multiplexing (OFDM)-IM system operating in the complex and challenging environment characterized by joint transmitter/receiver (Tx/Rx) in-phase and quadrature phase imbalance (IQI) and Weibull fading. This essential analytical achievement is facilitated by …

Fpga-Based Takagi-Sugeno Fuzzy Controller For Quadrotor Uav Stabilization And Trajectory Tracking, Hocine Khati, Mohamed Amine Nehmar, Arezki Fekik, Mohand Achour Touat, Hand Talem, Rabah Mellah

Turkish Journal of Electrical Engineering and Computer Sciences

This study presents the implementation of a fuzzy logic–based control system on a field-programmable gate array (FPGA) for a quadrotor autonomous aerial vehicle (UAV). The objective is to design and integrate six Takagi–Sugeno fuzzy controllers to regulate roll, pitch, and yaw angles, along with longitudinal, latitudinal, and altitude movements, thereby stabilizing the UAV and enabling it to follow a desired trajectory. Due to the computational complexity of the six controllers, achieving the desired performance requires considerable processing time, which can adversely affect the quadrotor’s mission. Owing to their high processing power and operating frequency, FPGAs enable the control algorithm to …

Re: Conditional Approval Letter For The Final Butte Mine Waste Repository Geotechnical Investigation Work Plan (Dated November 14, 2025), Emma Rott

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Grp-0176 A Comparative Analysis Of Traditional Virtual Machines And Micro Virtual Machines, Tasnim Akter Onisha

C-Day Computing Showcase

Virtualization technologies form the backbone of modern cloud and serverless platforms, but the balance between performance efficiency and strong isolation remains a key research challenge. Using a comparative literature based methodology, benchmark data from empirical studies are synthesized and normalized across four metrics: startup latency, memory footprint, isolation strength, and scalability. The findings show that traditional VMs such as KVM and Xen provide robust, formally verified isolation but incur higher boot times and memory usage, whereas Micro VMs like Firecracker and Kata Containers achieve lower latency (often < 125 ms) and smaller memory footprints (5–30 MB) while preserving VM-level isolation. Overall, Micro VMs deliver near container responsiveness with VM-grade security, making them suited for serverless and edge environments. As a future direction, this study highlights the need for scalable, formally verified Micro VM architectures for next-generation cloud systems.

Grp-0160 Quantum Anonymous Notification Protocol, Nitin Jha, Prateek Paudel

C-Day Computing Showcase

The scalability of current quantum networks is limited due to noisy quantum components and high implementation costs, thereby limiting the security advantages that quantum networks provide over their classical counterparts. Quantum Augmented Networks (QuANets) address this by integrating quantum components in classical network infrastructure to improve robustness and end-to-end security. To enable such integration, Quantum Anonymous Notification (QAN) is a method to anonymously inform a receiver of an incoming quantum communication. Therefore, several quantum primitives will serve as core tools, namely, quantum voting, quantum anonymous protocols, quantum secret sharing, etc. However, all current quantum protocols can be compromised in the …

Ur-1202 Generative Ai & Cybersecurity​, Emmanuel Okafor, David Nguyen, Alex Lu

C-Day Computing Showcase

Generative AI both strengthens and threatens cybersecurity. This project develops six reproducible, Google-Colab modules that demonstrate real attack paths — direct and indirect prompt injection, deepfake phishing, phishing-URL classification, insecure code/hallucination risks, and a malware reconstruction (Honor) challenge — and evaluates practical defenses including prompt wrapping, input sanitization, output redaction, and LLM-based guard chains. Using open-weights models and transparent data handling, we provide runnable notebooks, a public Google Site, and an IEEE-style paper to support education and defense-in-depth design.

Ur-1167 Owlexchange, Laura Chaplin, Caitlin Johnson, Alexin D'Haiti, Jamaul Gordon, Leonardo Barranco

C-Day Computing Showcase

OwlExchange is a secure, role-based campus marketplace designed to help Kennesaw State University students buy, sell, exchange, and donate items in a trusted digital environment. The platform replaces unorganized and unsafe exchanges occurring across group chats and social media by providing a centralized system with authenticated user accounts, item listings, and transparent communication. Built with a Flask (Python) MVC architecture, Auth0 for secure identity management, and MySQL for persistent storage, OwlExchange supports modular dashboards for buyers, sellers, and administrators, enabling item management, interest requests, and platform oversight. By promoting reuse and donation of textbooks, furniture, electronics, and other student items, …

Grp-0275 Graph Attention Network Based Downlink Channel Prediction Using In Frequency Division Duplexed Nextgen Networks, Jui Mhatre

C-Day Computing Showcase

In Frequency Division Duplex (FDD) 5G networks, downlink channel state information (CSI) must be estimated at the user equipment (UE) and fed back to the base station, a process that requires frequent CSI-RS transmission and uplink feedback, resulting in high overhead and energy consumption. This research proposes a novel base-station–centric framework that predicts the downlink channel matrix directly at the gNB, eliminating the need for continuous CSI-RS–based estimation at the UE. By leveraging uplink channel observations, geometric environment features, and learned mappings between uplink and downlink channel relationships, our model reconstructs the downlink MIMO channel with high fidelity. The system …

Gc-0270 Oncoboost - Hydration Monitoring Application, Blossom Madubike, Aafra Alam, Damola Ojo

C-Day Computing Showcase

Dehydration is a common and preventable complication for oncology patients, especially those undergoing chemotherapy and radiation. Side effects such as nausea, fatigue, and loss of appetite make it difficult for patients to maintain adequate fluid intake, contributing to avoidable discomfort and potential treatment disruptions. This capstone project presents Onco-Boost, a mobile hydration monitoring application designed to help adult oncology patients track daily fluid intake, recognize their intake patterns, and stay engaged in daily self-care between clinic visits. Built with React Native and Expo, and backed by Firebase for authentication and cloud data storage. Onco-Boost translates clinical hydration guidance and research …

Gc-1146 Student Engagement Portal: Enhancing Student Success Through Milestone Tracking, Antonio Brewer, Taylor Bolinger, Tyler Dawkins, Ricartho Franck, Moises Valles

C-Day Computing Showcase

The Student Engagement Portal, also known as the Milestone Map, is a platform developed to help students within KSU’s College of Computing and Software Engineering monitor their academic and professional growth. The system enables students to log milestones, check in at events, and view progress toward personal and departmental goals. Built with a full-stack architecture using NestJS, React, and MongoDB, the portal also includes an administrative dashboard for event management and analytics. The project demonstrates how progress tracking and clear visualization of achievements can improve communication, organization, and engagement between students and the college.

Uc-0205 Enhancing Gimp’S User Experience: Addressing Community Ui/Ux Issues, Alexander Stanley, Ryan Harrison, Dante Galvan, Rami Elmostafa

C-Day Computing Showcase

Water quality monitoring is crucial for environmental protection, public health, and ecosystem sustainability. With increasing pressures from urbanization, agricultural runoff, and climate change, robust data-driven approaches are essential for early detection of water quality degradation and informed decision-making in environmental conservation efforts. Current water quality monitoring relies on reactive threshold exceedances, failing to detect gradual degradation and multi-parameter deterioration patterns. This creates delayed response to pollution events and missed opportunities for preventive intervention in one of Queensland's most vital water systems. The importance objective is to implement and evaluate a Real-Time Multi-Stream Monitoring system for early detection of water quality …

Uc-1140 Riverguard, Grant Versluis, Collin Tucker, Wyatt Bramblett, Pedro Pinto, Geshlee Ruiz

C-Day Computing Showcase

RiverGuard’s mission is to protect and preserve waterways by using technology to identify and reduce pollution. The system uses an object detection model to automatically locate and classify trash within images or video of rivers and lakes, removing the need for slow, manual observation. By providing real-time insight into waste accumulation, RiverGuard helps communities, researchers, and organizations take faster, more effective action to keep waterways clean. Its goal is to create a sustainable monitoring system that empowers people to understand pollution patterns and support long-term environmental responsibility. RiverGuard represents a step toward cleaner water, healthier ecosystems, and a more informed …

Uc-1226 Iknowit: Multilingual Smartphone Tutorial Platform, Jacqueline Juarez, David Bazan, Julissa Rivera

C-Day Computing Showcase

Digital literacy challenges affect millions of adults who struggle with basic smartphone use due to rapidly changing technology and limited support. iKnowIT is a dynamic, web-based learning platform designed to provide clear, visual, and multilingual tutorials that guide users through essential device functions. The goal of iKnowIT is to bridge the digital divide and empower users to engage confidently with modern technology

Uc-1244 Agentic Ai For Intelligent Customer Communication, Lucas Papadopoulos, Jeremy Hopkins, Munir Gargour, Weston Dease

C-Day Computing Showcase

E-commerce web shoppers need fast, reliable responses to a variety of requests: account modifications, order tracking, or policy inquiries. Businesses must address user queries in a fast and efficient manner, or else lose customers. Multi-agent AI models boast the ability to answer customer questions and act upon consumer queries without outside intervention. However, research is sparse as to how agentic models can transfer benefit to large commercial software stacks under realistic commercial load. We sought to ask whether a multi-agent AI architecture can effectively handle commercial-scale e-commerce customer service tasks. Moreover, we investigated how a multi-agent AI architecture compares to …

Uc-1149 Ai Systems For A Stealth Videogame​, Lukas Ehler, Evan Weir, Javier Corona

C-Day Computing Showcase

Shmovement Games 3/5 has been making progress towards creating a videogame that leverages multiple types of AI systems working in unison with one-another. This will be a game in which the player sneaks around enemies while trying to grab objects marked as the player's primary objective. This is a project for our CGDD 4242 AI class in which we are implementing a single agent, multi-agent, and advanced multi-agent system into one game.

Uc-1232 Stronghold, Caitlin Tigani, Adam Tucker, Camden Lloyd, Dale Balsor

C-Day Computing Showcase

Our goal for this game is to create a game using both a single agent and multi agent AIs. In our game you can either play against the current AI or train the AI up as it fights against another AI. The training will use genetic AI. So whichever AI wins that row will be the one that move on. The loser will have their weights adjusted. This means the more that you train the Stronghold AI the harder the AI will be to fight against. The final game mode available is being able to play against another.

Uc-1262 Georgia Watch - Georgia Hospital Accountability Score, Robert Straiton, Patrick Cox, Constant Nortey, Sankalp Amaravadi, Kahmin Keller

C-Day Computing Showcase

Georgia Watch and the members of this team have partnered to change how Georgia residents understand their healthcare by providing a source of objective metrics which affect their care. This project represents the quintessential React Project produced with the purpose of a reactive interface for the end user, built for maintainability for any subsequent developers. The hospital data is maintained in JSON format for its ease of parsing and adjustment pending any changes. The interactive map was developed through the MAPBOX library, and the team is maintaining a deployment via an independent repository with necessary control over the website.

Uc-1269 Website Makeover - Loving Arms Cancer Outreach, Yuliana Pacheco, Alicia Cook, Taylor Tompkins, Schuyler Bridges, Dalton Roberts

C-Day Computing Showcase

Loving Arms Cancer Outreach (LACO) provides financial, emotional, and community support to individuals affected by cancer, making an accessible and reliable website essential to its mission. Our team conducted a quality assurance audit using tools such as Google Lighthouse and axe DevTools, identifying issues with accessibility, navigation, readability, and mobile responsiveness. Using these findings, we redesigned key sections of the site, improved layouts and forms, and recommended updated plugins to enhance usability and long-term performance. We also developed a Website Architecture and Maintenance Guide to support sustainability. This project establishes the foundation for a modern, user-friendly website that strengthens LACO’s …

Mobile Computer Vision Application For Agricultural Disease Detection Of Pepper Diseases Using Two-Stage Deep Learning System, Carlos Jose Estevez, Mai Dang, Ryan Bass

SMU Data Science Review

Plant diseases pose a significant threat to food security, particularly in developing countries where farmers often lack the resources and infrastructure for early detection. In nations like Mexico and the Dominican Republic, the spread of harmful plant diseases impacts key agricultural commodities, such as habanero peppers, leading to substantial yield losses. This study presents a computer vision system based on Convolutional Neural Networks (CNNs) and an object detection model (YOLO) to help farmers detect pepper diseases efficiently. The system uses a two-stage approach: YOLOv11n first detects pepper leaves in images, then a lightweight MobileNetV3Small model classifies whether the detected leaves …

Predicting Simulation Times For Multiphase Thermal-Hydraulic Models, Andrew Yule, Andrew Taylor

SMU Data Science Review

Addressing the challenge of computationally intensive OLGA

simulations in the oil and gas industry, a machine learning framework is

developed for accurate runtime prediction. A specialized feature extraction

pipeline identifies key parameters—such as simulation time, time step,

number of branches, and section count—from OLGA input files that serve as

high-impact predictors. Multiple predictive models, including regression,

tree-based ensembles, and neural networks, are implemented to validate

accuracy and robustness. Results reveal that prioritizing simulations based on

predicted runtimes optimizes licensing resources and reduces operational

costs, making real-time scheduling more efficient. This research demonstrates

the effectiveness of data-driven runtime prediction in enhancing …

Ai-Powered Compliance: Accelerating Efficiency And Decision-Making For Compliance Related Inquiries., Amberly R. Rodriguez

SMU Data Science Review

This research examines the potential of an AI-powered chatbot to streamline compliance workflows by reducing the time and effort required to locate and interpret complex compliance documents. The prototype integrates a centralized MySQL-based document repository, a contextual document querying engine, and a Streamlit web interface, enabling employees to retrieve accurate, document-backed answers within seconds. The system supports both stored and user-uploaded documents, with features such as automated summarization and source citations to enhance transparency and trust. Manual evaluation demonstrated notable gains in efficiency and accuracy compared to traditional search methods, with strong potential to improve adherence to compliance policies. Future …

Ai-Driven Optimization Of Wind Energy Distribution In Texas Using Multi-Agent Reinforcement Learning, Waleed Amer, Owolabi Oluwadamilola, Bassey Ogbonnaya

SMU Data Science Review

Abstract. The integration of large-scale wind power into modern electrical grids presents persistent challenges due to variability, curtailment, and compliance with operational constraints. This study proposes a multi-agent reinforcement learning (MARL) framework for optimizing wind energy distribution within the Texas power grid. The system employs three specialized agents—managing wind curtailment, storage utilization, and load adjustments—to collaboratively balance supply and demand under dynamic grid conditions. Using historical operational data from the Electric Reliability Council of Texas (ERCOT), the framework was trained and evaluated on a range of scenarios encompassing both typical and extreme operating conditions. Results demonstrate substantial performance improvements compared …

Relationship Satisfaction Among College Students, Gracie L. Sager, Makenna A. Stratton, Kyrah Brodie, Autumn R. Thomas

Science University Research Symposium (SURS)

Understanding relationship satisfaction is essential to individual well-being, particularly in college populations, as it relates to happiness, mental and physical health, and everyday functioning. Existing measures lack sensitivity to developmental dynamics shaped by evolving expectations and modern relationship stressors. This study aimed to develop a measure that examines the relationship between overall relationship satisfaction and key factors in romantic relationships, including partner support, relationship conflict, relationship length, and communication quality among college students. We hypothesize that the newly developed measure of relationship satisfaction will demonstrate good internal reliability (Cronbach’s α ≥ .700.) We also expect that perceived partner support would …

Kairiomone Moderated Anti-Predator Responses In Pleurocera Laqueta, Daisy V. Mcleod, John Webb, Amanda Waggoner, John Niedzwiecki

Science University Research Symposium (SURS)

Predator-prey coevolution in freshwater ecosystems often involves chemical communication, where the prey detect predator released compounds known as kairomones. These chemical cues allow prey to alter behavior to reduce risk of predation. In Middle Tennessee streams, the native snail Pleurocera laqueata commonly coexists with predator crayfish, and previous studies have shown that exposure to crayfish kairomones can induce an anti-predator “crawl out” response. This response is commonly demonstrated through snails climbing above the waterline to avoid crayfish predators below. We hypothesized that Pleurocera laqueata would display a similar anti-predator response when exposed to both native and non-native crayfish cues. After …

Final Butte Priority Soils Operable Unit (Bpsou) Insufficiently Reclaimed Sites Remedial Action Work Plan (Rawp): Bres No. 104 (Colorado Dump) North Slope, Pioneer Technical Services, Inc.

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Innovation Curse: The Wastefulness Of Technologies Believed To Mitigate Climate Change, Minh Hoang Nguyen, Valoree Gagnon, Thanh Tu Tran, Thi Mai Anh Tran

Michigan Tech Publications

Technological innovations are increasingly promoted as solutions to climate change. However, many innovations, including Carbon Capture and Storage, bioplastics, and glacier geo-engineering, suffer significant limitations from high costs, speculative efficacy, and adverse ecological consequences. Using Granular Interaction Thinking Theory (GITT), a transdisciplinary framework grounded in information theory, quantum mechanics, and mindsponge theory, in this study, we explain how such technological innovations become systemically favored despite their flaws. We introduce the concept of the “innovation curse,” which arises when institutional and cognitive filtering systems, operating under high informational entropy, default to familiar but ineffective techno-solutions while marginalizing Indigenous and Local Knowledges …