Computer Engineering Commons^™

Multimodal Emotion Recognition For Human-Robot Interaction Across Neuro-Diverse Populations., Ruchik Mishra

Electronic Theses and Dissertations

This dissertation explores the integration of multimodal data streams and artificial intelligence pipelines to understand human affect in neurotypical and children with Autism Spectrum Disorder (ASD). This dissertation captures human affect in the context of human-robot interaction. For this, multiple studies have been presented with both children with ASD and neurotypical adults. This dissertation makes four contributions: 1) The first study introduces autonomy during perspective-taking teaching sessions by making verbal content generation through large language models (LLMs). This system is the first of its kind for teaching perspective-taking in a semi-autonomous manner under the supervision of domain experts. Furthermore, this …

Integrating Blockchain Technology Into Telemedicine: A Framework For Enhancing Data Privacy And Security, Harsha Sammangi, Aditya Jagatha, Jun Liu

Research & Publications

This paper presents a blockchain-based framework designed to enhance data privacy, integrity, and security in telemedicine systems. The proposed architecture employs distributed ledger technology to ensure transparency, traceability, and immutability of patient data exchanges among healthcare providers. Smart contracts automate access permissions and auditing processes, reducing the risks of unauthorized data sharing. The study evaluates the framework’s performance in secure data handling, highlighting blockchain’s role in fostering patient trust and resilience against cyberattacks in remote healthcare environments.

Decentralized Multi-Hop Federated Reinforcement Learning For Energy-Efficient And Secure Routing In Lorawan-Based Smart City Infrastructure, Harsha Sammangi, Aditya Jagatha, Jun Liu

Research & Publications

This paper proposes a decentralized hybrid framework that integrates Federated Learning (FL) and Reinforcement Learning (RL) to enable energy-efficient and secure multi-hop routing in LoRaWAN-based smart city networks. The method allows local model training at gateways and relay nodes, combining real-time routing decisions with privacy-preserving federated aggregation. Key metrics such as residual energy, link quality, and node trust levels are embedded into the reward function, and lightweight encryption plus differential privacy safeguard routing metadata. Simulation results demonstrate substantial gains in packet-delivery ratio, latency, network lifetime and resilience to adversarial attacks when compared to traditional routing protocols.

Sensor Data Fusion For Air Quality Monitoring, Mirna Hesham

Theses and Dissertations

Since traditional air quality monitoring methods often rely on geographically sparse and costly air quality monitoring stations, image-based air quality method- ologies are recently offering a compelling alternative that utilizes images from sources like satellites, traffic cameras, and even smartphones to monitor pollution levels by using estimation models, image-processing techniques, and deep-learning models. In this thesis, we first conduct a systematic review, in which we categorize and discuss the existing literature work. Moreover, we introduce a novel, multi- modal dataset designed to address the limitations of existing datasets, which are restricted in size, geographical coverage, and fixed-scene imagery, impeding the …

Vision-Based Multimodal Frameworks For Human Behavioral Analysis: Applications In Group Activity Understanding And Public Health, Naga Venkata Sai Raviteja Chappa

Graduate Theses and Dissertations

Group Activity Recognition (GAR) has emerged as a crucial problem in computer vision, with wide-ranging applications in sports analysis, video surveillance, and social scene understanding. Unlike traditional action recognition focused on individuals, GAR requires understanding complex spatiotemporal relationships between multiple actors, their interactions, and the broader context in which these activities occur. This complexity introduces unique challenges, including the need for accurate actor localization, modeling of inter-actor dependencies, and understanding of temporal evolution in group behaviors. While recent advances have shown promise, existing approaches often rely heavily on extensive annotations such as ground-truth bounding boxes and action labels, creating significant …

Conversion Of Thermal Energy Stored In Conductive Concrete To Electricity With Thermoelectric Generators, Moustafa M. Al Adawi

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

This paper presents a proof-of-concept experimental study on the use of conductive concrete as thermal energy storage and its conversion into electricity. The conductive concrete is heated to 100°C by supplying electricity, and the stored thermal energy is converted back into electricity using thermoelectric generators (TEGs). Measurement results demonstrate that the conductive concrete effectively stores thermal energy up to 100°C, and this energy can be successfully converted into electricity. The findings highlight the potential of conductive concrete as a reliable medium for thermal energy storage.

Advisor: Lim Nguyen

Propasafe: A Bert-Based Offline Tool For Propaganda Detection, Vivek Sharma, Mohammad Mahdi Shokri, Shweta Jain, Sarah Ita Levitan, Elena Filatova

Publications and Research

In an era marked by the rapid consumption of digital news, the proliferation of propaganda poses significant threats to democratic values and informed decision-making. We propose a novel framework that prioritizes user privacy while assisting in the automatic detection of propaganda in news articles, allowing users to sift through the rhetoric to read the relevant objective news. As a demonstration of this framework, we present Propasafe, a BERT-based browser extension designed to alert users to propagandist tones in news articles. By highlighting instances of propaganda-like language in real-time, Propasafe empowers users to critically engage with news content, promoting objective understanding …

Inference Per Joule: A Performance Metric For Artificial Intelligence In Space Applications, Eduardo Macias Zugasti

Open Access Theses & Dissertations

The use of artificial intelligence (AI) has grown exponentially in recent years. This growth is driven in part by the significant advancements in computing capabilities, which have also increased exponentially. Computers have not only become more powerful but also smaller in size, thanks to the evolution of transistor technology. These developments have enabled AI to become a widely accessible tool, even in recreational activities such as image creation and entertainment videos.

More recently, the use of AI has extended to space applications, where it can enhance and optimize various tasks. However, space conditions pose significant challenges for conventional computers due …

The Impact Of System Transparency On Perceived System Reliability, Perceived System Usability, And Information Clarity In Self-Driving Car Systems, Uditkumar Nair

Theses and Dissertations

In human-computer interaction (HCI), the development of autonomous vehicle (AV) technology has created new difficulties, especially in building user confidence as well as understanding of system functioning. The effect of system transparency on user- centered outcomes, such as perceived usability, perceived system reliability, and information clarity, is examined in this thesis. In order to evaluate their experiences in both ordinary and high-stakes driving situations, participants engaged with both system- transparent user interfaces (TUIs) and non-transparent user interfaces (NTUIs) across a number of experimental scenarios. In order to assess how well each interface conveyed system logic and actions, the study included …

Advanced Day-Ahead Scheduling Of Hvac Demand Response Control Using Novel Strategy Of Q-Learning, Model Predictive Control, And Input Convex Neural Networks, Rahman Heidarykiany, Cristinel Ababei

Electrical and Computer Engineering Faculty Research and Publications

In this paper, we present a Q-Learning optimization algorithm for smart home HVAC systems. The proposed algorithm combines new convex deep neural network models with model predictive control (MPC) techniques. More specifically, new input convex long short-term memory (ICLSTM) models are employed to predict dynamic states in an MPC optimal control technique integrated within a Q-Learning reinforcement learning (RL) algorithm to further improve the learned temporal behaviors of nonlinear HVAC systems. As a novel RL approach, the proposed algorithm generates day-ahead HVAC demand response (DR) signals in smart homes that optimally reduce and/or shift peak energy usage, reduce electricity costs, …

Cellular Telemetry For Real-Time River Flow Velocity Data From Fluvial Acoustic Tomography Loggers, Joshua Lee Seymour

Honors Theses

Streamflow is a critical element in understanding watershed processes and the effects of land use on those processes because it is the primary medium through which water, sediment, nutrients, organic material, thermal energy, and aquatic species move. Fluvial Acoustic Tomography (FAT) systems offer accurate direct measurements of river section-averaged flow velocity by measuring reciprocal acoustic travel times between at least two acoustic nodes positioned on opposite riverbanks. However, similar to many in-situ sensors used in estuarine and riverine environments, FAT loggers require manual data retrieval, which is time-consuming, labor-intensive, and limits real-time access and increases maintenance costs. This project presents …

Real Time Object Detection Using Yolo, Rohit Malik, Manisha Kumari, Sanghoon Lee

Symposium of Student Scholars

This project explores the implementation of real-time object detection using the You Only Look Once (YOLO) architecture. Leveraging its speed and accuracy, we developed a system capable of identifying and localizing multiple objects within live video streams. Our implementation focused on optimizing YOLO's performance for real-time applications, specifically addressing the trade-off between speed and accuracy.

We employed a pre-trained YOLO model and fine-tuned it on a custom dataset tailored to specific object classes. This fine-tuning process aimed to enhance the model's ability to recognize objects in our target environment. The system was implemented using Python and the OpenCV library, enabling …

Campus Navigator: A Mobile App For Seamless University Navigation, Hafsa Mohammed, Ali Rahimzadehfard, Rachab Wilson, Mathias Rossi, Turaj Ashuri, Amir Ali Amiri Moghadam

Symposium of Student Scholars

Navigation services play a big role in everyone’s daily lives, from directing them on new roads to guiding them through buildings. Outdoor navigation services have evolved from physical maps to digital ones like Google Maps for ease of use and accessibility. Upon conducting literature research, the team found that many navigation apps lack clear and accurate instructions for how to navigate university campuses such as Marietta campus. Due to the size of KSU’s Marietta campus and its buildings, effortless navigation has been a common challenge for students, faculty, and visitors alike. Additionally, all buildings are referred to by letters, numbers, …

Sandrapp: A Digital Intervention To Enhance Social Connectivity And Emotional Support Among Older Adults, Pavan Chowdary Chilukuri, Purna Chandu Anukula

Symposium of Student Scholars

Social isolation and loneliness are significant challenges affecting the well-being of older adults, often leading to adverse mental and physical health outcomes. Prolonged isolation has been linked to increased risks of depression, anxiety, cognitive decline, and chronic illnesses such as hypertension and cardiovascular diseases. As digital technology continues to evolve, innovative solutions have emerged to address these issues and foster meaningful social interactions for older adults.

SANDRApp is a user-friendly digital platform designed to reduce loneliness and enhance social connectivity among older adults. The platform caters to three primary user groups: (1) families with elderly loved ones living far away, …

Low Cost Additive Manufacturing Of Segmented Stator Composite Polymer Permanent Magnet Dc Motors, Ben Goldberg, Jordan Bailey, Connor Hawkins, Colin Haskins, Razvan Voicu

Symposium of Student Scholars

This study presents a novel approach to the design, manufacture, and optimization of segmented stators for composite construction axial flux permanent magnet DC motors. Traditional axial flux stator manufacturing is both challenging and expensive, creating a bottleneck in rapid prototyping and innovation. To overcome these limitations, the stator is divided into individually fabricated segments using advanced composite polymer materials and low-cost additive manufacturing techniques. This segmentation not only drastically reduces production complexity and cost but also allows for customized coil geometries that maximize the surface area for improved heat dissipation.

A key innovation of our design is the integration of …

Preparing Students For The Quantum Era: Qml Training And Applications, Triveni Kandimalla, Valentina Nino

Symposium of Student Scholars

Quantum Machine Learning (QML) emerges as a transformative approach to addressing the growing complexities of modern data processing and computational challenges. Classical machine learning (CML) techniques, while powerful, face limitations in handling vast amounts of high-dimensional data and solving complex optimization problems efficiently. Despite its potential, QML remains underrepresented in academia, highlighting the need for accessible, hands-on learning experiences and knowledgeable faculty. This project seeks to advance QML education by incorporating it into diverse curricula, creating practical learning materials, and fostering workforce readiness. Using Google Colab, an open source labware has been designed to provide interactive learning modules (M0 to …

Human-Ai Teaming For Academic Performance Analysis, Kendarius Ward, Elise Hernandez, Tom Antony, Md Abdullah Al Hafiz Khan, Kazi Aminul Islam, Abm Adnan Azmee

Symposium of Student Scholars

Educators today often work with students who are struggling academically, but limited time and resources make it difficult to uncover the root causes and provide timely assistance. Artificial intelligence (AI) is a growing, viable tool for analyzing large datasets and solving problems in different domains; however, human expertise is required to enhance the AI model’s performance. This study will utilize human-AI teaming to assess student performance based on factors such as their academic involvement, hours spent studying, and grade-point-average, among others. These findings will help instructors better grasp each student's academic needs. By incorporating humans into the AI pipeline, we …

Sustainable Smart Farming Device With Leafit Adaptive Growth Technology, Saville Atkins, Anthony Iwejuo, Julian Pitts, Luis Mercado, Rachnicha Rojjhanarittikorn, Sandip Das, Hai Ho

Symposium of Student Scholars

For farmers, gardeners, and horticulture enthusiasts worldwide, one immutable reality is that maintaining a consistent physical presence to care for plants is not always feasible. In addition, different plants have unique needs for watering, nutrients, and environmental conditions to thrive. Failing to meet these specific needs can result in poor plant health, reduced yields, and inefficient resource usage. In this research project, we have designed and developed ‘LeaFit’ – a cutting-edge Internet of Things (IoT) device that offers a sophisticated and sustainable smart farming and gardening solution. Equipped with intelligent soil moisture, ambient temperature, humidity, and light sensors, LeaFit autonomously …

A Comprehensive Analysis Of Recognition Of Hand Gestures Using Machine Learning, Shivani Shivani, Satinder Bal Gupta

Makara Journal of Technology

Hand gestures are a natural means of conveying information and thus, there is an increasing interest in utilizing gestures for communication with computers. This study focuses on systematically reviewing different machine learning algorithms while assessing their working mechanisms and accuracy. Articles were analyzed for comparing the performance of K-Nearest Neighbors (KNN), Random Forest (RF), Support Vector Machines (SVM), Naive Bayes (NB), Convolutional Neural Networks (CNN), and Recurrent Neural Networks (RNN). In accordance with input data, intricacy of gestures, processing resources, and real-time demands, the study shows that each technique has distinct advantages and disadvantages. RNN showed the best accuracy of …

Design Of A Teleoperabletendon-Driven Robotic Hand, Zachary Giese, Joseph Gober, Samuel Grisham, Avery Mahan, Aspen White, Brayden May, Aspen White

ATU Scholars Symposium

This work presents the design, implementation, and validation of a 3D-printed tendon-driven robotic hand with a bi-directional wireless control system. The robotic hand emulates human anatomical principles, focusing on replicating flexion and extension mechanisms of the metacarpophalangeal (MCP), proximal interphalangeal (PIP), and distal interphalangeal (DIP) joints via a tendon-pulley system. Iterative prototyping resolved initial challenges in joint stiffness, tendon routing, and servo interference, culminating in a design where a single servo drives both flexion and extension for each joint using a bidirectional tendon control mechanism. A reliable bidirectional wireless communication framework, using Arduino Nano Every microcontrollers and NRF24L01 modules, enables …

Scalable Distributed Ai: Low-Cost, High-Performance Computing With Jetson Nano And Dask, Kesava Manikanta Chirumamilla

ATU Scholars Symposium

No abstract provided.

Deep Learning-Based Multi-Class Classification Of Breast Cancer Ultrasound Images Using Convolutional Neural Networks, Andres E. Dewendt Urdaneta

ATU Scholars Symposium

The National Cancer Institute forecasts 2,001,140 cancer diagnoses in 2024, with approximately 600,000 expected deaths. Breast cancer is projected to be the most prevalent, with about 310,000 cases. Early diagnosis is critical to improving outcomes, and various diagnostic technologies, including imaging, biopsies, and blood tests, play a vital role. Image testing methods include X-rays, ultrasounds, magnetic resonance imaging (MRI), and PET scans. Artificial intelligence (AI) has recently significantly improved cancer detection, improving speed, accuracy, and effectiveness. This research project uses a convolution neural network (CNN) to analyze ultrasound breast images, classifying them as benign, malignant, or normal. Our CNN model …

Empowering Mental Support Health Through Ai Chatbot, Anish Ilapaka

ATU Scholars Symposium

The growing prevalence of mental health concerns worldwide underscores the urgent need for accessible, scalable, and supportive solutions. Artificial Intelligence (AI) has emerged as a promising tool in this domain, capable of delivering immediate and empathetic interactions to complement traditional methods of mental health care. This project introduces a conversational AI system to assist individuals experiencing mental health challenges. The proposed system is built on a LLaMA model fine-tuned with a dataset of 10,000 mental health-related dialogues; the system leverages advanced natural language processing and machine learning techniques for meaningful engagement. The core functionality of this tool lies in its …

Empowering Students Through Project-Based Learning In Computer Science: Connecting Real-World Problems To Classroom Innovation, Roza Shaimurat, Faith Bogrek, Bethany Ratermann, Shruti Bhandari, Tolga Ensari

ATU Scholars Symposium

Project-Based Learning (PBL) is a powerful approach in computer science education that moves beyond technical instruction to cultivate problem-solving, adaptability, and resilience. Unlike traditional methods, PBL immerses students in real-world challenges, where failure becomes a steppingstone to success. This paper explores how PBL fosters deep learning through hands-on projects, such as robotics competitions and smart technology solutions, bridging the gap between classroom concepts and real-world innovation. Despite its unpredictability and instructional challenges, PBL ignites student engagement, instills a sense of ownership, and prepares learners for the evolving demands of the tech industry. By embracing PBL, educators not only equip students …

The Hidden Cost Of Intelligence: Environmental Impacts And The Rise Of Green Ai Solutions, Hayin Tamut

ATU Scholars Symposium

Artificial Intelligence (AI) has rapidly transformed industries and daily life, but beneath its advancements lies a growing environmental concern. This paper explores the often-overlooked ecological footprint of AI technologies, focusing on real-world data and tangible impacts. Notably, training OpenAI’s GPT-3 model alone consumed approximately 1,287 megawatt-hours (MWh) of electricity—equivalent to the annual consumption of over 120 U.S. homes—and emitted 500 metric tons of CO₂, comparable to driving 112 gasoline-powered cars for a year.

Furthermore, the environmental cost extends beyond electricity consumption. Data centers powering AI require vast amounts of water for cooling, contributing to local water shortages—highlighted …

An Empirical Evaluation Of Communication Technologies And Quality Of Delivery Measurement In Networked Microgrids, Ruairí De Fréin, Yasin Emir Kutlu

Articles

Networked microgrids (NMG) are gaining popularity as an example of smartgrids (SG), where power networks are integrated with communication technologies. Communication technologies enable NMGs to be monitored and controlled via communication networks. However, ensuring that communication networks in NMGs satisfy quality of delivery (QoD) metrics such as the round trip time (RTT) of NMG control data is necessary. This paper addresses the communication network types and communication technologies used in NMGs. We present various NMG deployments to demonstrate real-life applicability in different contexts. We develop a real-time NMG testbed using real hardware such as Cisco 4331 Integrated Services Routers (ISR). …

Humanités Numériques, Langue Et Culture Synthèse, Fatima Chnane-Davin

Dirassat

The Digital Humanities: Digitalization and Artistic and Cultural Expression conference, organized from May 2 to 4, 2024 at the FLSH in Agadir, invites you to explore the vast field of digital humanities, from the challenges of digitalization to the opportunities it offers for cultural creativity, while highlighting the importance of preserving a rich heritage in the face of digital transformations.

Netflix : D’Une Digitalisation Cinématographique Vers Une Déconstruction Du 7èMe Art, Karim El Mahjoubi

Dirassat

According to a study conducted by GroupM1, 76% of French people have gone to the cinema less since the 2018 health crisis. The first reason given is "the fear of being contaminated by Covid-19". This is how the streaming platform "Netflix" saw, here, a godsend to seize this market and innovate in cinematographic digitalization. It therefore decided to rent via a monthly subscription, through a Smartphone or Smart TV application, films and series to watch "without moderation" (unlike the old video clubs). The cinema then becomes "Smart or pocket" and accessible, at a low price, for any film buff because …

Le Numérique En Administration Pédagogique Des Écoles : État Des Lieux, Impacts Et Défis. Cas De La Direction Provinciale D’Ouarzazate, Mohamed Boukhch, Abdessamad Boulkhalil

Dirassat

This article attempts to explore the subject of the digitalization of school administration. The digitization of the sector was initiated by the Minister of Education already ten years ago through the implementation of digital systems available to school administrators. We are wondering here about the probable existence of a link between the digitalization of the school administration system and student’s learning. the question we asked is: To what extent does the digitalisation put in place relate to student’s learning ?

to answer this question, we have opted for a practical exploratory approach in order to provide elements of clarification on …

Les Droits D’Auteur À L’Ère De La Révolution Numérique : Enjeux Et Perspectives, Ismail Madani Alaoui, Said Ouhamou, Mohamed Boukhch

Dirassat

This article explores the challenges and issues associated with the development of digital technologies in the context of copyright law. The authors examine significant changes in the creation, distribution, and consumption of digital content, as well as the consequences for copyright holders. Our article focuses on different approaches to copyright protection in the digital environment, such as DRM licenses and Creative Commons. It also evaluates their effectiveness in protecting creators' rights while promoting access to works. The issue of piracy and illegal sharing of digital content is also addressed, as well as the measures taken by the government and industry …