Computer Engineering Commons^™

Trustworthy Ai: Prohibited Practices, Ethical Principles, And The Identification Of Problems, Anna Karmańska

Journal of Global Awareness

The following considerations arise from the study of texts of documents of a legal nature and from the author’s judgments. They do not present the results of the author’s own empirical research; however, they constitute a factual study that is important for their undertaking in the next step. Having given concern but also hopes for AI, the author focuses her attention on issues that, not only in her opinion, have a strong bearing on the preservation of humanity in a digital environment and at the same time with technocratic features. These issues (prohibited practices, high-risk systems, and ethics) related to …

Object Tracking Based On Quantum Particle Swarm Optimization, Rajesh Misra, Kumar Ray

Journal of Global Awareness

In Computer Vision domain, moving Object Tracking is considered as one of the toughest problems. As there are so many factors associated like illumination of light, noise, occlusion, sudden start and stop of moving object, shading which makes tracking even harder problem not only for dynamic background but also for static background. In this paper we present a new object tracking algorithm based on Dominant points on tracked object using Quantum particle swarm optimization (QPSO) which is a new different version of PSO based on Quantum theory. The novelty in our approach is that it can be successfully applicable in …

Dynamic Admittance Parameterization For Non-Prehensile Multi-Robot Transport With Optimal Coordinated Planning, Calvin J. Stahoviak

Computer Science ETDs

The Dynamic Admittance Parameterization of Non-Prehensile Multi-Robot Trans- port with Optimal Coordinated Planning (DYNAMO) architecture offers a practical framework for cooperative payload transportation using two robots equipped with nonholonomic mobile bases and four-degree-of-freedom manipulators. Coordinated mobile manipulation is a difficult problem in robotics, and the non-prehensile case is even more challenging than its prehensile counterpart because the robot bases and the payload are dynamically coupled. DYNAMO adapts arm motion in response to interaction forces and generates coordinated base trajectories that account for this coupling. Robust payload transport is achieved through the combination of opti- mal planning and adaptive compliant control, …

Exploring Interactive Robotic Music Therapy Systems For Rehabilitation: A Survey Paper, Hector A. Salinas Gordillo

Discovery Undergraduate Interdisciplinary Research Internship

Interactive robotic music therapy introduces an innovative opportunity, where human guided musical interaction with robotic systems can create adaptive and engaging therapeutic experiences. This survey explores the current state of research at the intersection of robotics, music, and rehabilitation, focusing on emerging technologies such as human robot interaction methods and system designs that help enable real time, interactive music therapy.

Potential patient groups include individuals undergoing motor or cognitive rehabilitation, such as those recovering from stroke, living with Parkinson’s disease, cerebral palsy, or other motor impairments, as well as individuals with developmental disorders or limited mobility.

Traditional rehabilitation exercises may …

Spike Timing Depended Plasticity Produces Unsupervised Learning Of Synergistic Muscle Feedback In A Synthetical Neural Network, Mark Allen Pupkiewicz

Dissertations and Theses

This study investigates how type Ia feedback from muscle spindles can be organized into groups representing agonistic muscle pairs through Spike Timing Dependent Plasticity (STDP). A single degree of freedom joint is actuated with four biologically modeled muscles forming two agonistic pairs. In order to emulate the sensory dynamics of biological muscle spindles, sensors in the model record the active length and velocity states of each muscle, the two primary factors eliciting type Ia afferent responses. In biological networks, synapses from Ia sensory neurons frequently activate interneurons representing agonistic muscle sources. This research investigates whether this organization can emerge in …

3d Printed Portable Automatic Pill Dispenser​, Amber M. Ocasio

Publications and Research

Medication adherence is a major public health concern, particularly among patients with chronic illnesses. Reports from the National Institutes of Health indicate that adherence rates are significantly lower for chronic conditions, with patients taking only ~50% of medications prescribed. Unintentional non-adherence—such as forgetting doses—is more prevalent (62.9%, 47.1%, 46.9%) than intentional non-adherence, and the consequences include medication waste, disease progression, reduced functional abilities, lower quality of life, and increased reliance on medical resources. Because existing automatic pill dispensers cost over $100 on average, they remain inaccessible for many lower-income patients who could benefit from such technology. This project addresses this …

Rapid Prototyping Of Low-Cost Sensor Systems Towards A Platform For Upper Limb Posture Estimation, Russell Rathbun

Electrical Engineering and Computer Science Undergraduate Honors Theses

Physical therapy requires patients to perform repeated actions to achieve meaningful results in rehabilitation. This thesis explores production methods and various sensor systems by utilizing rapid prototyping, inertial measurement units (IMUs), and capacitive sensor arrays (CSAs). CSAs can be made from a wide ar- ray of materials and techniques including 3d printing and laser ablation–to rapidly create CSAs that can be custom fit to enable proximity, force, and touch detection. IMU and CSA systems individually are able to track upper limb movements, ges- tures, and positions. This combination of sensors enables accurate upper limb pos- ture estimation of patients. This …

Hallucination Techniques For Self-Supervised Synthetic Datasets For Mobile Robots, Wyatt D. Colburn

Master's Theses

Classical techniques in autonomous navigation struggle in tightly constrained spaces. Machine learning has been shown to perform better in these difficult environments but most techniques require large amounts of navigation experience for training. Using a new machine learning paradigm learning from hallucination (LfH), training data can be collected in a safe environment and not require supervision. Data is collected in real time while an agent performs a random walk in free space, supervision is not required as there are no obstacles for the robot to run into. After a random walk a post processing pipeline will hallucinate a safety corridor …

Efficient Adaptive Spline-Based Path Planning For In-Space Servicing, Assembly, And Manufacturing Applications, Christian Lozoya

Open Access Theses & Dissertations

Autonomous robotic systems operating in cluttered and partially observed environments require trajectory generation methods that produce smooth and dynamically feasible motion while reacting to locally sensed obstacles. This requirement is especially pronounced for free-flyer and in-space servicing, assembly, and manufacturing (ISAM) platforms, where onboard sensing is sparse, global environmental information is unavailable, and communication or computational resources are constrained. In such settings, motion plans must be updated online using incomplete and rapidly changing local observations, while avoiding excessive replanning that can lead to oscillatory or unstable behavior. Many existing approaches either rely on dense optimization over extended horizons, which is …

Securing Connected And Autonomous Vehicles, Owana Marzia Moushi

Dissertations and Doctoral Documents, University of Nebraska-Lincoln, 2023–

A vehicular network is susceptible to various security flaws and attacks. Cryptographic techniques are used in vehicular networks but these alone cannot provide proper security to the network. Identifying various types of attacks is necessary to secure vehicular communication networks. In this dissertation, we focused on detecting various insider attacks in vehicular networks to enhance the security of the network.

Our first contribution in this dissertation is the detection of both binary and multi-class data replay and data replay Sybil attacks in vehicular networks. A publicly available dataset, VeReMi-Extension is used to detect these attacks. This dataset has been reformulated …

Real-Time, Co-Regulated Design For Cyber-Physical, Multi-Rotor Uas Swarms, Grant Simon Phillips

Dissertations and Doctoral Documents, University of Nebraska-Lincoln, 2023–

Uncrewed Aerial Systems (UAS) have been integrated into a wide range of research and industrial applications, with growing interest in extending mission duration and spatial coverage through coordinated multi-UAS systems, or swarms. While swarming offers the potential for extended mission endurance and robustness through advanced path-planning, control, and estimation algorithms, significant challenges arise when implementing these methods on decentralized platforms composed of size, weight, and power-constrained (SWaP) vehicles. Limitations in onboard computational capacity and congested communication channels can break critical design-time assumptions, which at best, will degrade application quality of service, and at worst, destabilize the fleet through excessive delays …

Qualitative Stereo Vision Using Distributed Extended Waltz Filtering, Ben Mathew

Theses and Dissertations

Stereo vision is a fundamental problem in computer vision, aimed at reconstructing three-dimensional scene structure from two or more two-dimensional images. Traditional stereo algorithms rely on quantitative disparity estimation, often constrained by calibration precision, lighting variations, and surface texture. In contrast, our proposed Qualitative Stereo Vision seeks to understand depth relationships and spatial configurations from multiple planar views through symbolic reasoning and constraint satisfaction, offering a more flexible and cognitively plausible approach to scene interpretation.

This dissertation presents a novel framework called Distributed Extended Waltz Filtering, designed to provide qualitative stereo vision, particularly in the presence of occlusions—a persistent challenge …

Seeing What Matters: Safety-Critical Semantic Segmentation Via Transfer Learning On Construction Sites, Obiora J. Odugu

LSU Master's Theses

Autonomous robots are increasingly deployed on construction sites for tasks such as progress monitoring, inspection, and safety assessment. For these robots to operate effectively, they must perceive and interpret complex, dynamic environments populated by workers, machinery, and unstructured terrain. Achieving reliable perception depends on high performing semantic segmentation models trained on large volumes of annotated data—an expensive and logistically challenging requirement in construction due to privacy restrictions, variable site access, and slow digitalization. This research addresses the challenge of limited labeled data by investigating transfer learning as a label-efficient approach for construction-site segmentation. Specifically, it explores whether road construction imagery—abundant …

Cal Poly (Cp) Legged Robot, Sebastian Barboza, Jonathan Mchale, Isabella Sorensen, Isaac Golan

Mechanical Engineering

The Navy spends $60 billion annually on dangerous ship maintenance performed by sailors. To save lives and resources, the Naval Surface Warfare Center (NSWC) is looking for robots to replace sailors and navigate ships to perform various tasks. Robots with tracks and wheels have been most recently explored by NSWC, however they have encountered significant problems navigating the ships, especially through naval ship doorways with a significant ledge. By using a legged robot, our team hopes to solve these problems and have a robot that can navigate the ship with relative ease and stability.

Environment Mapping And Gps-Based Trailer Parking Using Low-Cost Peripheral Sensors And Post-Processing Algorithms, Connor Best

Journal of Undergraduate Research at Minnesota State University, Mankato

This paper explores the merit of software data optimization through two practical examples: environment mapping & GPS navigation.

Sistemas De Información Geográfica En La Era De La Digitalización, Jairo Eduardo Márquez Díaz, Luis Gonzalo Benavides Ramírez, Arles Prieto Moreno, Martha Andrea Manrique Castro

Ingeniería

En la era digital, la información geográfica es esencial para la toma de decisiones en áreas como la planificación urbana, la gestión de recursos naturales y la seguridad. Los Sistemas de Información Geográfica (SIG) se han establecido como herramientas indispensables para gestionar, analizar y visualizar datos georreferenciados que permite la creación de mapas digitales y la toma de decisiones basada en evidencia. Este libro aborda los fundamentos, las tecnologías y las aplicaciones de los SIG, explorando su evolución y su potencial en un entorno digital en constante transformación. A lo largo de sus cinco capítulos, el libro aborda temas esenciales …

Learn To Fly: Enabling Deep Learning Based Perception And Control In Aerial Robotics, Krishna Muvva

School of Computing: Dissertations, Theses, and Student Research

Uncrewed Aerial Vehicles (UAVs) are increasingly deployed in dynamic, GPS degraded, and cluttered environments, yet their autonomy remains fundamentally constrained by limitations in onboard perception and real-time control. This dissertation addresses these challenges by proposing a unified framework that co-designs deep learning-based perception and model-based control, organized around three core thrusts: Learn to Track, Learn to Localize, and Learn to Evade.

Learn to Track develops dynamic and adaptive perception control mechanisms that optimize CNN inference for target tracking. A control-aware CNN framework dynamically adjusts inference frequency based on UAV motion, reducing latency while maintaining visual lock. An adaptive CNN with …

Csc36000 - Modern Distributed Computing Assignment, Saptarashmi Bandyopadhyay

Open Educational Resources

This assignment covers standard performance metrics for Distributed Systems and the basics of Multiprocessing for CSC36000 - Modern Distributed Computing at the City College of New York CUNY. It is an interactive coding assignment intended to be executed in a Python notebook.

Mixed Reality In Human–Robot Interaction For Collaborative Applications, Evan Reid, Caitlin Osorio

College of Engineering Summer Undergraduate Research Program

This project explores the use of Extended Reality (XR) technologies to enhance human- robot interaction in industrial contexts. Building upon prior research in affective and cognitive state recognition during human-cobot collaboration, this study investigates how natural hand and head gestures, captured through Meta Quest passthrough mode, can be used to communicate human intent to a Universal Robotics e-Series collaborative robot. The XR system provides users with an immersive, real-world visual interface while tracking motion and position in real time. The captured gestures are interpreted through a custom software pipeline that integrates machine learning models and rule-based logic to trigger adaptive …

A Study In Object Detection And Classification Performance By Sensing Modality For Autonomous Surface Vessels, Daniel Lane

Doctoral Dissertations and Master's Theses

This research presents a quantitative performance comparison between light detection and ranging (LiDAR) and vision-based sensing for real-time maritime object detection on autonomous surface vessels. Using Embry-Riddle Aeronautical University’s (ERAU) Minion platform and 2024 Maritime RobotX Challenge data, this study evaluates the detection of six maritime object categories using two representative models. YOLOv8 provides a neural network vision-based method, and GB-CACHE provides a deterministic LiDAR-based method. Both models have been previously demonstrated to run in real time on uncrewed surface vessels (USVs). The evaluation methodology encompasses multi-sensor calibration, real-time performance analysis, and the introduction of a late-fusion strategy in the …

Impacto De La Inteligencia Artificial En La Educación Superior. Guía Reflexiva, Jairo Eduardo Márquez Díaz

Ingeniería

La inteligencia artificial (IA) está revolucionando la educación superior en diversas formas como, por ejemplo, la personalización del aprendizaje, la creación de tutorías inteligentes y el análisis de aprendizaje. Este libro se presenta como una herramienta valiosa para todos aquellos interesados en comprender y aprovechar las oportunidades que la ia ofrece en el campo de la educación superior. Con un enfoque equilibrado y exhaustivo, esta publicación pretende servir como una guía integral para profesores y estudiantes que buscan entender cómo la ia está transformando la enseñanza y el aprendizaje en la actualidad. A lo largo de sus páginas, aborda diversos …

Use Matters: How Different Ways Of Using Chatgpt Drive Ai Acceptance And Solutionism, Florian Golo Flaßhoff, Fabian Anicker, Frank Marcinkowski

Human-Machine Communication

Artificial intelligence is central to solutionism—the vision of a world where all major problems are solved through technology. This study theorizes about how human–AI communication shapes attitudes toward AI and influences the formation of public opinion, sparking solutionist imaginaries. We empirically examine the attitude formation resulting from the non-simulated use of an unmanipulated conversational model in a controlled laboratory experiment. Using a between-subjects design, participants engaged in three semi-structured 20-minute sessions with ChatGPT, providing a novel perspective on the effects of its use. The findings reveal that mere use of ChatGPT causally increases AI acceptance; however, its impact significantly depends …

Generative Ai: Another Chapter Of Human-Machine Communication, Seungahn Nah, Patric R. Spence

Human-Machine Communication

This editorial introduces a special issue of Human-Machine Communication that explores how generative AI reshapes the communicative relationship between humans and machines. It highlights emerging research on technology use, education, interpersonal dynamics, and trust in AI-generated content, emphasizing that generative AI’s significance lies not in novelty but in the social negotiations it provokes around meaning, authority, and credibility.

Uncalibrated Visual Servoing For Spatial Under-Constrained Cable-Driven Parallel Robots, Jarrett-Scott K. Jenny, Matt Marshall

Faculty Articles

Cable-driven parallel robots (CDPRs) offer large workspaces with minimal infrastructure, but their control becomes difficult when the platform is under-constrained and sensing is limited. This paper investigates uncalibrated visual servoing (UVS) with a single monocular camera, asking whether simple global static Jacobians (GSJ) can be sufficient and how an adaptive Jacobian estimator behaves. Two platforms are evaluated: a three-cable (3C) platform and a redundant six-cable du-al-plane platform (RC). Motion-capture (MoCap) validation shows that redundancy improves stability and tracking by reducing platform tilt and making image errors correspond more directly to Cartesian motions. Across static and low-speed tracking tasks, GSJ proved …

Application Of Neural Networks For Intelligent Processing Of Sensor Signals In The Control Of Technological Process Parameters, N.R. Yusupbekov, Yu.Sh. Avazov, G.Kh. Rashidov

Chemical Technology, Control and Management

This scientific article investigates the problem of analyzing technological process parameters in the fields of chemistry, energy, and metallurgy based on sensor data and applying intelligent signal processing methods. The main objective is to evaluate the effectiveness of artificial intelligence and deep learning models for intelligent analysis, forecasting, and anomaly detection of data obtained from sensors. Time-series data collected from industrial sensors were analyzed using LSTM (Long Short-Term Memory) and Autoencoder neural networks, as well as the Kalman filter. At the first stage of the study, sensor signals were denoised and their true state was estimated using the Kalman filter. …

Optimizing Hip And Knee Assistance For Walking And Sit-To-Stand Transitions: An Intrinsic Muscle Mechanics Based Predictive Approach, Neethan Ratnakumar

Dissertations

As the global population ages, the demand for wearable assistive technologies continues to rise, driven by their potential to enhance mobility and independence in older adults. Effectively designed controllers for lower-limb exoskeletons to assist sit-to-stand (STS) and walking are crucial for delivering efficient, safe, and comfortable assistance during daily activities. Traditionally, controller optimization involves biomechanical modeling and user-specific customization. Musculoskeletal simulations play a central role in this process by providing insights into human-exoskeleton interaction dynamics, thereby informing and refining control strategies.

This work presents a simulation-driven approach for developing exoskeleton controllers for walking and STS using two distinct methods: optimal …

Multimodal Learning In Real-World Application: Enhancing Feature Representation And Training Strategies, Nana Lin

Graduate Doctoral Dissertations

Multimodal learning has emerged as a critical paradigm for developing intelligent systems that can understand and reason across diverse inputs such as images, text, and audio data. Despite significant advances, effective deployment of multimodal models in practice remains a challenging task. This dissertation explores how multimodal learning can be effectively applied to high-stakes, real-world scenarios, with a focus on enhancing feature representation and training efficiency. Specifically, this research investigates multimodal learning strategies in two key domains: healthcare and surveillance.

In the healthcare domain, we explored the data fusion and alignment approaches for cognitive decline diagnoses. First, we propose the LOVEMA …

Human-Machine Communication: Complete Volume. Volume 10

Human-Machine Communication

This is the complete volume of HMC Volume 10.

Intelligent Motion Tracking: A Low-Cost Surveillance Framework Using Soc Devices And Sensor Fusion, Aung Myat Khaung, Nicholas Michael Stiffler

Research from the Berry Summer Thesis Institute, 2025

This thesis presents the design and implementation of a lightweight surveillance system capable of realtime motion detection, object tracking, and behavioral history reconstruction in controlled environments. The system uses System-on-Chip devices such as Raspberry Pi boards equipped with NOIR cameras, monocular cameras, and break-beam sensors that work together to detect and track single or multiple moving objects like colored balls. The prototype is validated in structured settings with the goal of eventual deployment in more dynamic environments, addressing the challenge of reliably tracking visually similar objects with minimal distinguishing features. The architecture integrates computer vision with sensor fusion by combining …

Re-Engaging Driver Attention Using Chatgpt: A Multimodal Study On Stress Impact And Driving Performance, Alaa Zaki Elfiqi

Theses and Dissertations

Driving is considered a complex task that requires continuous focus and attention from the driver. Driving performance can also be impacted by changes in the driver’s stress level. However, limited research explores methods to address this challenge. With the current advances in Large Language Models (LLMs) and their ability to engage in human-like interaction, this study investigates the potential of using ChatGPT, designed to speak Egyptian Arabic, in re-engaging driver attention under different driving scenarios for low-stress and high-stress conditions within a virtual reality (VR) environment driving simulator. Drivers were asked to follow a leading car into two scenarios with …