Engineering Commons^™

An Iterative Shifting Disaggregation Algorithm For Multi-Source, Irregularly Sampled, And Overlapped Time Series, Colin O. Quinn, Ronald H. Brown, George F. Corliss, Richard J. Povinelli

Electrical and Computer Engineering Faculty Research and Publications

Accurate time series forecasting often requires higher temporal resolution than that provided by available data, such as when daily forecasts are needed from monthly data. Existing temporal disaggregation techniques, which typically handle only single, uniformly sampled time series, have limited applicability in real-world, multi-source scenarios. This paper introduces the Iterative Shifting Disaggregation (ISD) algorithm, designed to process and disaggregate time series derived from sensor-sourced low-frequency measurements, transforming multiple, nonuniformly sampled sensor data streams into a single, coherent high-frequency signal. ISD operates in an iterative, two-phase process: a prediction phase that uses multiple linear regression to generate high-frequency series from low-frequency …

Tidal Flooding Contributes To Eutrophication: Constraining Nonpoint Source Inputs To An Urban Estuary Using A Data Driven Statistical Model, Alfonso Macías-Tapia, Margaret R. Mulholland, Corday R. Selden, Sophie Clayton, Peter W. Bernhardt, Thomas R. Allen

OES Faculty Publications

In coastal urban areas, tidal flooding brings water carrying nutrients and particles back from land to estuarine and coastal waters. A statistical model to predict nutrient loads during tidal flooding events can help estimate nutrient loading from previous and future flooding events and adapt nutrient reduction strategies. We measured concentrations of dissolved inorganic nitrogen and phosphorus in floodwater at seven sentinel sites during 15 tidal flooding events from January 2019 to September 2020. The study area was the Lafayette River watershed in Norfolk, VA, USA, which is prone to tidal flooding and is predicted to experience more frequent and intense …

Ensemble Machine Learning Approaches For Bathymetry Estimation In Multi-Spectral Images, Kazi A. Islam, Omar Abdul-Hassan, Hongfang Zhang, Victoria Hill, Blake Schaeffer, Richard Zimmerman, Jiang Li

OES Faculty Publications

Traditional bathymetry measures require a large number of human hours, and many bathymetry records are obsolete or missing. Automated measures of bathymetry would reduce costs and increase accessibility for research and applications. In this paper, we optimized a recent machine learning model, named CatBoostOpt, to estimate bathymetry based on high-resolution WorldView-2 (WV-2) multi-spectral optical satellite images. CatBoostOpt was demonstrated across the Florida Big Bend coastline, where the model learned correlations between in situ sound Navigation and Ranging (Sonar) bathymetry measurements and the corresponding multi-spectral reflectance values in WV-2 images to map bathymetry. We evaluated three different feature transformations as inputs …

A Century Of Sediment Metal Contamination Of Mar Menor, Europe's Largest Saltwater Lagoon, Irene Alorda-Montiel, Valentí Rodellas, Ariane Arias-Ortiz, Albert Palanques, Andrea G. Bravo, Júlia Rodriguez-Puig, Aaron Alorda-Kleinglass, Carlos Green-Ruiz, Marc Diego-Feliu, Pere Masqué, Javier Gilabert, Jordi Garcia-Orellana

OES Faculty Publications

Coastal enclosed ecosystems, such as lagoons, are vulnerable to anthropogenic impacts because they favor the accumulation of contaminants from the surrounding watersheds, particularly in their sediments. Europe's largest saltwater lagoon, the Mar Menor (SE, Iberian Peninsula), is a highly impacted ecosystem and the first in the continent to be granted personhood rights. Based on a high-resolution spatial and temporal dataset, we present the historical reconstruction of metal contamination in this ecosystem during the last century. Our results highlight that sediment metal contamination has been mainly driven by the development of the mining industry in the nearby Sierra Minera de Cartagena-La …

Development Of A Compliant Soft Gripper With Embedded Force Sensors, Ayman Abbas, Abdelrahman Said, Tamer Kahlil, Mahmoud Magdy

Mechanical Engineering

Robotic manipulators have the capability to perform repetitive tasks with a level of efficiency and precision that exceeds the capabilities of human operators. Currently, they are commonly found and extensively utilized across various disciplines. However, robotic manipulators have gradually begun to make inroads into fields beyond manufacturing, such as medicine and agriculture. Due to the diverse range of industries utilizing robotic manipulators, the nature of tasks assigned to these devices can often be more intricate and multifaceted than conventional assignments. Hence, conventional mechanical grippers may not always be suitable, prompting a growing need for grippers capable of adapting to effectively …

Design And Implementation Of Smart Automation For Sustainable Plastic Recycling With Iot Integration, Mostafa Abdelaziz, Mahmoud Khedr, Amr Bakkar, Yossef Zidan, Abdelrahman Youssef, Mahmoud Magdy

Mechanical Engineering

Plastic pollution poses a critical environmental challenge, infiltrating ecosystems and impacting human health. One innovative solution involves repurposing recycled plastics into interlocking blocks as a sustainable alternative to concrete in construction. However, manual production methods hinder scalability, efficiency, and product consistency while generating material waste. This research addresses these limitations by automating the production process using PLC and IoT technologies. The findings demonstrate that automation significantly enhances production rates, ensures consistent quality, and minimizes waste, thereby improving operational efficiency and environmental sustainability. IoT is integrated to enhance the system's capabilities by providing real-time connectivity and data exchange. The IoT dashboard, …

Design Of Modular Soft Gripper For Harvesting, Mostafa Abdelaziz, Ahmed Reda, Abdulrhman Atef, Mahmoud Magdy

Mechanical Engineering

Soft robotics presents a transformative approach to agricultural automation, addressing the limitations of traditional mechanical systems in handling delicate crops. This paper introduces a modular soft robotic gripper designed for gentle harvesting applications, with a focus on adaptability, precision, and crop safety. The gripper's performance was evaluated through Finite Element Analysis (FEA) and experimental testing, targeting its ability to conform to objects of varying shapes, sizes, and fragility. The FEA confirmed the gripper's capability to apply a load based on the gripped crop of the range 0.6 – 0.75 N, sufficient for damage-free harvesting of sensitive produce like strawberries and …

Non-Invasive Glucose Monitoring Using Ppg, Ai, And Iot-Driven Mobile Integration For Real-Time Diabetes Management, Mostafa Abdelaziz, Amr Refaie

Mechanical Engineering

Diabetes mellitus patients must regularly monitor their blood glucose levels to manage glycaemia, typically requiring capillary tests at least three times daily and laboratory tests one to two times per month. These conventional methods involve finger pricking, causing significant discomfort and stress. This study introduces an innovative non-invasive glucose monitoring approach by integrating photoplethysmography (PPG) technology with an artificial intelligence (AI) algorithm, complemented by a mobile application using Flutter, IoT systems, and Firebase cloud for real-time data access. Among the AI models tested, polynomial regression demonstrated superior accuracy in glucose prediction, achieving a Mean Squared Error (MSE) of 14.76 and …

Pre-Programmable Directional Stiffness In Continuum Robotic Arms: Effects Of Truss Configurations, Mostafa Abdelaziz, Zahy Elgendy, Mahmoud Magdy

Mechanical Engineering

This paper investigates the implementation of trussed designs in modular tendon-driven continuum arms (CAs) to address stiffness and stability limitations while maintaining adaptability. Various truss configurations— Single-Level, Combined-Level, and Alternated trusses—were analyzed using Finite Element Analysis (FEA) to evaluate their impact on flexural and axial stiffness under realistic loading conditions. Results demonstrate that truss placement significantly influences performance, with bottom-level trusses improving flexural stiffness by up to 33%, middle-level trusses enhancing axial stiffness by 43%. Further, Combined-Level configurations provide superior overall stiffness, with MiddleBottom trusses achieving a 66.5% improvement in flexural stiffness and a 70.9% increase in axial stiffness, while …

Ai-Driven Robotic System For Predictive Maintenance: Urban Road Defect Detection In Smart Cities, Mostafa Abdelaziz, Mahmoud Khedr

Mechanical Engineering

This paper presents the development and evaluation of an integrated AI & robotic solution for predictive maintenance in smart cities, focusing on urban road defect detection. The proposed system integrates a mobile robot equipped with a high-resolution camera and a GPS module to capture video footage and geolocation data of road surfaces. The collected data is processed using cloud-based AI models, with YOLOv9 and Roboflow 3.0 Object Detection (Fast) identified as the most effective for analyzing footage to detect cracks and potholes. Experimental results validate the system's ability to accurately identify defects and generate timely reports for maintenance teams. While …

Multi-Objective Monitoring Of Cvd Diamond Micro-Grinding Tools Using Acoustic Emission And Force Signals With Neural Network Optimization, Ahmed Elkaseer, Jianfei Jia, Bianbian Meng, Bing Guo, Jun Qin, Guicheng Wu, Huan Zhao, Zhenfei Guo, Qingyu Meng, Qingliang Zhao, Honghui Yao, Amr Monier

Mechanical Engineering

Micro-grinding has been widely used in aerospace and other industry, and its application was mainly the asymmetric microstructure. Chemical Vapor Deposition (CVD) diamond has drawn attention for its good wear resistance. However, the small diameter and high spindle speed may cause difficulties on the monitoring of the micro-grinding processes. In order to solve the mentioned problem, a novel multi-objective monitoring method of structured CVD diamond micro-grinding tool based on acoustic emission (AE) and force signals is presented in this study to achieve the high efficiency of the tool condition and grinding quality. The relationship between the grinding quality, tool condition, …

Integrating Machine Learning And Symbolic Regression For Predicting Damage Initiation In Hybrid Frp Bolted Connections, Sherif Sorour

Mechanical Engineering

No abstract provided.

Bi-Objective Dynamic Tugboat Scheduling With Speed Optimization Under Stochastic And Time-Varying Service Demands, Xiaoyang Wei, Hoong Chuin Lau, Zhe Xiao, Xiuju Fu, Xiaocai Zhang, Zheng Qin

Research Collection School Of Computing and Information Systems

With the growing emphasis on green shipping to reduce the environmental impact of maritime transportation, optimizing fuel consumption with maintaining high service quality has become critical in port operations. Ports are essential nodes in global supply chains, where tugboats play a pivotal role in the safe and efficient maneuvering of ships within constrained environments. However, existing literature lacks approaches that address tugboat scheduling under realistic operational conditions. To fill the research gap, this is the first work to propose the bi-objective dynamic tugboat scheduling problem that optimizes speed under stochastic and time-varying demands, aiming to minimize fuel consumption and manage …

Sogar: Self-Supervised Spatiotemporal Attention-Based Social Group Activity Recognition, Naga Venkata Sai Raviteja Chappa, Pha Nguyen, Alexander H. Nelson, Han-Seok Seo, Xin Li, Page Daniels Dobbs, Khoa Luu

Electrical Engineering and Computer Science Faculty Publications and Presentations

Social group activity recognition is crucial for various applications including surveillance, human-robot interaction, and behavioral analysis. Current approaches often require extensive manual annotations and rely heavily on pre-trained detectors, limiting their practical applications. Additionally, existing methods struggle to effectively model long-term spatiotemporal relationships in group activities. This paper introduces a novel approach to Social Group Activity Recognition (SoGAR) using Self-supervised Transformers network that can effectively utilize unlabeled video data. To extract spatio-temporal information, we create local and global views with varying frame rates. Our self-supervised objective ensures that features extracted from contrasting views of the same video are consistent across …

Investigation Of New Superconducting Materials For The Next Generation High-Performance Rf Superconducting Cavities For Particle Accelerators, Alex Gurevich, Jean Delayen, Chang-Beom Eom, Gianluigi Ciovati

Physics Faculty Publications

In this DOE-funded project DE-SC0010081-020 Old Dominion University (ODU) in collaboration with University of Wisconsin (UW) and Jefferson Laboratory have investigated both experimentally and theoretically electromagnetic response and losses in multilayered superconducting structures made of new SRF materials which can push the field and Q performance limits of accelerating cavities.

Semi-Quantitative Elemental Imaging Of Corrosion Products From Bioabsorbable Mg Vascular Implants In Vivo, Weilue He, Keith W. Macrenaris, Adam Griebel, Maria P. Kwesiga, Erico Freitas, Amani Gillette, Jeremy Schaffer, Thomas V. O'Halloran, Roger J. Guillory Ii

Michigan Tech Publications

While metal materials historically have served as permanent implants and were designed to avoid degradation, next generation bioabsorbable metals for medical devices such as vascular stents are under development, which would elute metal ions and corrosion byproducts into tissues. The fate of these eluted products and their local distribution in vascular tissue largely under studied. In this study, we employ a high spatial resolution spectrometric imaging modality, laser ablation inductively coupled plasma time-of-flight mass spectrometry (LA-ICP-TOF-MS) to map the metal distribution, (herein refered to as laser ablation mapping, or LAM) from Mg alloys within the mouse vascular system and approximate …

Normalizing Images In Various Weather And Lighting Conditions Using Colorpix2pix Generative Adversarial Network, Sanjida Tasnim, Ashif Mahmud Mostafa, Azmain Morshed, Namreen Shaiyaz, Shakib Mahmud Dipto, Saad Aloteibi, Mohammad Ali Moni, Md. Golam Rabiul Alam, Md. Ashraful Alam

Computer Science Faculty Publications

Autonomous vehicles (AVs) are widely regarded as the future of transportation due to their tremendous benefits and user comfort. However, the AVs have been struggling with very crucial challenges, such as achieving reliable accuracy in object detection as well as faster computation required for quick decision-making. In recent years, perception systems in driverless cars have been significantly enhanced, mainly due to advances in deep-learning-based object detection systems. However, these perception systems are still heavily affected by environmental variables, such as changes in illumination, refractive interference, and adverse weather conditions, which may compromise their reliability and safety. This research proposes an …

Insights In Adaptation: Examining Self-Reflection Strategies Of Job Seekers With Visual Impairments In India, Akshay Kolgar Nayak, Yash Prakash, Sampath Jayarathna, Hae-Na Lee, Vikas Ashok

Computer Science Faculty Publications

Significant changes in the digital employment landscape, driven by rapid technological advancements and the COVID-19 pandemic, have introduced new opportunities for blind and visually impaired (BVI) individuals in developing countries like India. However, a significant portion of the BVI population in India remains unemployed despite extensive accessibility advancements and job search interventions. Therefore, we conducted semi-structured interviews with 20 BVI persons who were either pursuing or recently sought employment in the digital industry. Our findings reveal that despite gaining digital literacy and extensive training, BVI individuals struggle to meet industry requirements for fulfilling job openings. While they engage in self-reflection …

Energy-Based Deep Incomplete Multi-View Clustering, Ziyu Wang, Yiming Du, Rui Ning, Lusi Li

Computer Science Faculty Publications

Incomplete multi-view clustering (IMVC) deals with real-world scenarios where certain views are partially missing, posing significant challenges to effective clustering. Most existing IMVC approaches face a trade-off: imputation-free methods suffer from information bias and imbalance, while full-imputation methods risk introducing and propagating noise. To overcome these limitations, we propose Energy-Based Deep Incomplete Multi-View Clustering (Energy-DIMC), a novel selective-imputation framework that leverages energy-based models (EBMs) to guide reliable imputations and robust clustering. EBMs assess data compatibility by assigning lower energy to more coherent structures, effectively modeling complex inter-view and inter-sample dependencies. Inspired by EBMs, Energy-DIMC integrates four key components: 1) a …

Delivery Of Medical Supplies To Remote Locations Via Unmanned Aerial Vehicles: Approaches, Challenges, And Solutions, Meshari Aljohani, Ravi Mukkamala, Stephanie Olariu

Computer Science Faculty Publications

Unmanned Aerial Vehicles (UAVs) are becoming more important in improving healthcare logistics, in particular due to their cost effectiveness, minimized risk, and versatile operational capabilities. This study explores the deployment of autonomous UAVs to deliver medical supplies to remote areas. Advances in ledger technology, smart contracts, and machine learning have transformed tasks previously managed by human teams or manually controlled UAVs into fully autonomous missions. We present a comprehensive analysis of the challenges and initial solutions vital for the effective use of autonomous UAVs in the delivery of medical supplies. In addition, we propose a machine-learning model to optimize UAV …

Contextual Memory Recall: A Novel Metric For Class Incremental Learning, Balasubramanian S, Sai Subramaniam M., Sai Sriram Talasu, Yedu Krishna P., Pranav Phanindra Sai M., Darshan Gera, Ravi Mukkamala

Computer Science Faculty Publications

We propose a novel metric for class incremental learning (CIL) called Contextual Memory Recall (CMR), which evaluates how well a CIL model recalls previously learned classes when given relevant past cues. Inspired by human memory, CMR offers newer insights into continual aspects of a CIL model that were not addressed by previously proposed metrics for CIL. Specifically, the standard metric, average incremental accuracy (AIA), overlooks the quality of evolving feature representations, whereas our proposed CMR accounts for it. As a result, methods using feature distillation perform well under AIA but poorly under CMR, while those without feature distillation excel under …

Coldstartcpi: Induced-Fit Theory-Guided Dti Predictive Model With Improved Generalization Performance, Qichang Zhao, Haochen Zhao, Linyuan Gao, Kai Zheng, Yajie Li, Qiao Ling, Jing Tang, Yaohang Li, Jianxin Wang

Computer Science Faculty Publications

Predicting compound-protein interactions (CPIs) plays a crucial role in drug discovery. Traditional methods, based on the key-lock theory and rigid docking, often fail with novel compounds and proteins due to their inability to account for molecular flexibility and the high sparsity of CPI data. Here, we introduce ColdstartCPI, a framework inspired by induced-fit theory, which leverages unsupervised pre-training features and a Transformer module to learn both compound and protein characteristics. ColdstartCPI treats proteins and compounds as flexible molecules during inference, aligning with biological insights. It outperforms state-of-the-art sequence-based models, particularly for unseen compounds and proteins, and shows strong generalization capability …

Increased Visual Response Delay Impact On Sensorimotor Control In Persons With Multiple Sclerosis, Julie C. Wagner, Frankie M. Ingram, Vincenzo Daniele Boccia, Matilde Inglese, Maura Casadio, Camilla Pierella, Andrea Canessa, Robert A. Scheidt, Scott A. Beardsley

Biomedical Engineering Faculty Research and Publications

Multiple Sclerosis negatively affects hand function in 60% of cases. Upper extremity dysfunction in persons with Multiple Sclerosis (PwMS) has previously been linked to slower, more variable movement, increased visual response delays (T_v), and neuroimaging evidence of altered brain activity; yet no one knows how these aspects relate to each other. This work combines clinical, kinematic, sensorimotor control, and neural imaging techniques to gain a more complete understanding of how upper extremity dysfunction arises in PwMS. Twenty PwMS and 20 Controls completed a reach and hold task with simultaneous electroencephalography recorded to determine if increased T_v in …

Lung Uptake Of Two Spect Markers Identifies Sensitivity To Hyperoxia-Induced Acute Respiratory Distress Syndrome In Rats, Anne V. Clough, Taheri Pardis, Guru P. Sharma, Ming Zhao, Elizabeth R. Jacobs, Said H. Audi

Biomedical Engineering Faculty Research and Publications

Introduction: Exposure of adult rats to hyperoxia is a well-established model of human Acute Respiratory Distress Syndrome (ARDS). Although rats exposed to 100% O₂ display clinical evidence of lung injury after ∼40 h and death by 72 h, rats exposed to 60% O₂ for up to 7 days show little sign of injury. However, when subsequently exposed to hyperoxia, these pre-exposed rats become more susceptible to ARDS. The objective of this study is to evaluate the ability of imaging biomarkers to track this hyperoxia susceptibility and to elucidate underlying mechanisms.

Methods: Sprague-Dawley rats were exposed to either room …

Scoping Review Of Machine Learning Techniques In Marker-Based Clinical Gait Analysis, Kevin N. Dibbern, Maddalena G. Krzak, Alejandro Olivas, Mark V. Albert, Joseph J. Krzak, Karen M. Kruger

Biomedical Engineering Faculty Research and Publications

The recent proliferation of novel machine learning techniques in quantitative marker-based 3D gait analysis (3DGA) has shown promise for improving interpretations of clinical gait analysis. The objective of this study was to characterize the state of the literature on using machine learning in the analysis of marker-based 3D gait analysis to provide clinical insights that may be used to improve clinical analysis and care. Methods: A scoping review of the literature was conducted using the PubMed and Web of Science databases. Search terms from eight relevant articles were identified by the authors and added to by experts in clinical gait …

Agency, Index & Process: Investigating The Role Of The Artist’S Body In Digital Sculpture Production, Alan Magee

Doctoral

Contemporary Digital Sculpture has emerged out of recent developments in digital art, 3d modelling and virtual modes of production. These advances range from the creation of more powerful software and hardware systems to the to nascent XR sectors, and the potential for materialisation through technologies such as 3d printing, laser-cutting, or CNC1 machining. However, critical discourse remains predominantly focused on the end product, often overlooking the embodied labour processes inherent in its creation. As revealed through indexical traces of the artist’s body, these processes encapsulate the gestures, actions and subjective agency of artistic activity. Consequently, with the limitations of digital …

Forecasting Data-Driven System Strength Level For Inverter-Based Resources-Integrated Weak Grid Systems Using Multi-Objective Machine Learning Algorithms, Md Ohirul Qays, Iftekhar Ahmad, Daryoush Habibi, Mohammad A.S. Masoum

Research outputs 2022 to 2026

Shortage of grid-fault level, known as system strength inadequacy, impacts on grid instability and can lead to blackouts. System strength is generally measured by short circuit ratio index at point of coupling (POC) of inverter-based resources (IBRs) and the grid system. Nowadays, accurate knowledge of system strength forecasting for ‘next day’ to ‘next week’ duration is essential to power system operators, owing to the higher-growth of IBRs. However, releavant publications about this subject remain limited when compared with load demand, active and reactive power prediction. Therefore, a data-driven system strength forecasting scheme is presented in this paper to surmount these …

Antennas For Emerging Satellite Services, Jakub Przepiorowski

Doctoral

The NewSpace era has revolutionized access to space, driving advancements in satellite communication (SATCOM) systems and creating demand for low-cost, compact, and high-performance user terminals. This thesis addresses these needs by developing innovative antenna solutions tailored for user terminals designed for emerging satellite constellations and satellite Internet-of-Things (S-IoT) applications.

Actuator Disk Model For Aeropropulsive Coupling Effects In Vortex Particle Method, Eduardo Alvarez, Vineet Ahuja, Vinod Lakshminarayan, Aaron Perry, Ryan Anderson, Andrew Ning

Faculty Publications

Blown lift and distributed electric propulsion aircraft pose strong aeropropulsive coupling effects that cannot be ignored during the early stages of design. In this study, we develop an advanced actuator disk model (ADM) for aeropropulsive coupling effects of ducted fans with the vortex particle method (VPM). The advanced ADM consists of (1) an actuator disk at the rotor plane, (2) a surface vortex sheet modeling the mixing and convection of blade tip vorticity along walls, and (3) a powered wake at the exhaust. With these three components, a propulsion jet with an arbitrary velocity profile is formed and interactions with …

Power Saving In Open Ran By Using Advanced Cpu Scheduling Algorithm, Saish Urumkar, Sachin Sharma

Conference papers

Open RAN is an emerging wireless technology that is gaining significant attention for its potential to enable flexi- ble, cost-efficient, and interoperable networks. Reducing power utilization in Open RAN, particularly for 5G base stations (gNodeBs) deployed in remote areas, remains a critical challenge due to limited power availability. In our previous work, we developed a CPU scheduling algorithm that optimized core allocation based on load conditions, reducing power utilization for gNodeB in a virtualized Open RAN environment. Extending our previous work, this paper introduces an advanced CPU scheduling for Open RAN designed to reduce power utilization in real hardware Open …