Engineering Commons^™

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

Electrical and Computer Engineering Faculty Research & Creative Works

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

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

Electrical and Computer Engineering Faculty Research & Creative Works

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

Nash Equilibrium Strategies For Multicluster Pursuit–Evasion Game With Disturbances: A Prescribed-Time Convergence Approach, Lei Xue, Xian Yu, Yongbao Wu, Jian Liu, Changyin Sun, D. C. Wunsch

Electrical and Computer Engineering Faculty Research & Creative Works

This article investigates the problem of prescribed-time Nash equilibrium (NE) seeking for a multicluster pursuit–evasion game (PEG) subject to external disturbances. To mitigate the impact of disturbances and reach the NE within a user-defined prescribed time, a prescribed-time disturbance observer (PTDO) is devised to estimate and compensate for them. Based on this observation, a novel control algorithm is developed, which facilitates collaboration among multiple pursuers to capture multiple evaders within the prescribed time. It is theoretically demonstrated that the designed algorithm ensures prescribed-time convergence to the NE of the multicluster PEG with disturbances. Finally, numerical simulations are conducted to verify …

Lidar-Based Framework For Detecting Suspicious Human Activities, Ahd Aljumah, Charalampos Antoniadis, Hakim Ghazzai, Nawfal Guefrachi, Ahmad Alsharoa, Gianluca Setti

Electrical and Computer Engineering Faculty Research & Creative Works

This study explores the development of Human Activity Recognition (HAR) systems capable of identifying suspicious activities to enhance security in public spaces. We propose an innovative solution that integrates LiDAR sensors with deep learning technologies. Our method employs advanced models operating on LiDAR point cloud, PV-RCNN for human detection, and LidarGait++ for classifying activities into categories such as standing or walking (non-suspicious) and sneaking or fighting (suspicious). Due to the scarcity of suitable real-world datasets for training such systems, we utilize a 3D simulation tool, Blender, to create realistic environments and generate labeled point cloud data. This synthetic dataset allows …

Deep Learning Approach For Microwave Imaging Based On Deep Convolutional Asymmetric Encoder-Decoder Structure And Physics-Induced Loss, He Ming Yao, Shiji Song, Michael Kwok Po Ng, Lijun Jiang

Electrical and Computer Engineering Faculty Research & Creative Works

In this paper, we introduce an innovative deep learning (DL) methodology designed for real-time quantitative microwave imaging (MWI). Our approach is centered around the utilization of a deep convolutional asymmetric encoder-decoder structure (DCAEDS), which requires only a single-frequency far-field measurement of the electromagnetic (EM) scattered field as input and subsequently predicts the contrasts (permittivities) of the target materials. During the offline training process, we incorporate an EM forward solver specifically crafted to compute the EM scattered field generated by the predicted target contrasts (permittivities) produced by the DCAEDS. The DCAEDS is seamlessly integrated with this EM forward solver to optimize …

Effect Of Sample Properties On Short-Circuited Waveguide Measurements For Materials Characterization, Alexander Hook, Jared Sinkey, Kristen M. Donnell

Electrical and Computer Engineering Faculty Research & Creative Works

Microwave materials characterization measurements can be performed using a number of well-established approaches. One such approach, the filled transmission line approach featuring a short-circuited rectangular waveguide (SC-RWG) sample holder, is known to have sample placement restrictions related to measurement viability. This work focuses on this approach and addresses the measurement restrictions within the context of sample length and dielectric properties. The complex reflection properties, S11, of a sample placed in a SC-RWG sample holder are studied to quantitively define when a sample must be offset from the SC-end. The impact of the sample holder is also studied from a measurement …

Undergraduate Student Support Survey And Scoring Guide, Kerrie A. Douglas, Adrian Gentry, Julie P. Martin, Tiantian Li

School of Engineering Education Working Papers

No abstract provided.

A Three-Stage Causal Root-Cause Diagnostic Protocol For Nonstationary Industrial Time Series Data, Cansu Yalim, Resit Unal, Holly A. H. Handley

Engineering Management & Systems Engineering Faculty Publications

Predictive maintenance (PdM) systems effectively forecast failures, but they often fail to find root causes, particularly when system dynamics change over time. This limitation arises from applying static causal models or decoupled segmentation to handle nonstationary industrial time series. For regime-aware causal diagnostics and interventional effect estimation, we introduce a three-stage time-varying dynamic Bayesian network (TV-DBN) protocol. Using a minimum description length (MDL) objective that connects segmentation to mechanism changes, Stage I jointly infers change points and regime-specific graph structure. Stage II produces a completed partially directed acyclic graph (DAG) by orienting edges within each regime using a combination of …

Seeing The Invisible Load: Xr+ Multimodal Sensing For Cognitive Ergonomics In Industrial Training, Jessica M. Johnson, Andwele Grant

Virginia Digital Maritime Center (VDMC) Faculty Publications

Extended reality (XR) technologies are increasingly positioned as disruptive Industry 5.0 tools for human-centric industrial training and intelligent human–system integration. Coupled with multimodal sensing (eye tracking, EEG, HRV, GSR, and other physiological signals), XR environments promise to make otherwise invisible cognitive demands observable, especially for novice trainees entering complex industrial settings. Yet the evidence base is fragmented: (1) there is no quantitative synthesis of the cognitive ergonomics benefits of XR plus sensing; (2) little is known about which XR–sensor configurations yield the strongest effects; (3) prior reviews rarely focus on industrial and manufacturing tasks; (4) multimodal signals are used predominantly …

Make It Maritime: A Simulation-Based Engineering Design Challenge Using Multimodal Technologies, Jessica M. Johnson, Jennifer Renne, Jason Dudley

Virginia Digital Maritime Center (VDMC) Faculty Publications

This paper presents The Incredible Bulk, a multimodal, simulation-based engineering challenge that introduces Grades 4–12 to maritime careers and digital transformation. Students assume the roles of different maritime careers to assemble aircraft carrier bulkhead systems using: VR for spatial orientation and task preview; a 3D model as visual work instruction; paper blueprints for part identification and dimensioning; 3D-printed components for hands-on assembly; and an AR app for quality assurance/inspection. Grounded in constructionism and cognitive apprenticeship, the plan–build–inspect loop externalizes thinking and situates coached, authentic practice that advances engineering design, systems thinking, and technological literacy.

Course Improvement Strategies For Effective Learning, Omar Ahmed Raheem Al-Shebeeb

2026 Scholarly Teaching Conference: Poster Session Papers

Manufacturing processes Lab (IENG 302L) is one of the courses that is taught in the Industrial Engineering Department. The students perform several manufacturing processes in this course and for every one of these manufacturing processes, the students need to submit a project report. One of these manufacturing processes is a CNC turning process. The turning project of this course has historically had extensive average time for completion. As such, it was deemed necessary that a way to improve the quality of a turning project be generated. Industrial Quality Control (IENG 316) is also taught as part of the industrial engineering …

Teaching Strategies In The First-Year Engineering Seminar Course, Akua Oppong-Anane, Wen Juan Mo, Marshal Miezah, Gia Huy Pham, Susie Huggins, Lizzie Santiago

2026 Scholarly Teaching Conference: Poster Session Papers

This poster highlights the teaching strategies that are implemented in the First-Year Engineering Seminar Course at West Virginia University (WVU). The teaching team incorporated active learning techniques into the course to enhance student engagement, promote critical thinking, support the development of engineering identity, and encourage major and career exploration.

A Rapid, Accessible Active Learning Framework For Stem At Wvu Tech, Somenath Chakraborty, Zahmeeth Sayed Sakkaff

2026 Scholarly Teaching Conference: Poster Session Papers

Many undergraduates find it difficult to move from listening to doing—especially in STEM courses that require sustained practice, timely feedback, and confidence. This poster describes a rapid, low-cost active-learning routine that can be added to existing lectures at WVU Tech with minimal redesign. Instructors insert brief engagement micro-cycles every 10–15 minutes: a concept question or mini-problem, a one-minute individual attempt, a short pair discussion, and a concise explanation that targets common misconceptions.

Towards Increasing The Retention Of Freshman Engineering Students: Implementing A New Course, Ee 101 Introduction To Electrical And Computer Engineering, Omer Lateef, Mingyu Lu, Waseem Alaqqad, Charan Litchfield, Kenan Hatipoglu

2026 Scholarly Teaching Conference: Poster Session Papers

To assist with improving student retention in the undergraduate electrical and computer engineering program, a course was developed and implemented at the freshman level in the Spring of 2021.  This course was named EE 101: Introduction to Electrical and Computer Engineering and served to give freshman students an opportunity to meet the professors of the ECE department and experience live sessions dedicated to presenting “upcoming” topics and research endeavors of their expertise.  Students typically will not be attending the electrical or computer engineering subjects until their sophomore year and this course, set in second semester for a freshman, attempts to …

Seeing Feedback Differently: Enhancing Learning Through Individualized Video Feedback, Makenzie Khristine Keepers

2026 Scholarly Teaching Conference: Concurrent Session Papers

Students often seek feedback that goes beyond rubric scores, especially for complex assignments like project reports where expectations are nuanced. Transitioning from lengthy written comments to personalized video responses has proven to be an effective alternative. These videos provide students with clear explanations of strengths and areas for growth, while walking them through their work in detail. Feedback from learners suggest that video feedback feels more comprehensive and accessible, helping them better understand mistakes and apply corrections. Importantly, producing video feedback requires comparable effort to traditional written comments, yet offers greater impact for formative assessments that shape performance on summative …

Rubric Enhanced Assessment Of Coursework And Teaching (React): Using Portfolios And Rubrics In An Engineering Classroom To Refocus From Grades To Learning, Nathan Lee Galinsky

2026 Scholarly Teaching Conference: Poster Session Papers

The goal of using rubrics in an engineering classroom is to make grading clearer for students and to enhance the learning efforts. Point based grading of work leaves students looking for the correct numerical solution or choice without thinking about their assumptions, process, or approximations. A rubric dissects grading to include the entire solution procedure emphasizing key steps in a solution procedure. Students also don’t have to wonder where partial credit will be focused or what emphasis a solution will be graded on when using rubrics. By combining the rubric assessment techniques with portfolios, students will be empowered to feel …

Lecture Notes On Cloud Computing (Ver. Winter 2026), Jun Li

Open Educational Resources

This collection of lecture notes provides a comprehensive technical foundation for modern cloud computing, spanning from physical infrastructure to high-level application patterns. The text explores how warehouse-scale computers and virtualization transformed traditional data centers into flexible, on-demand resource pools characterized by elasticity and a pay-as-you-go economic model. Detailed chapters examine core architectural components, including Kubernetes orchestration, serverless computing (FaaS), and distributed key-value stores like Dynamo. The sources also emphasize the critical nature of fault tolerance, utilizing techniques like erasure coding and replication to manage the statistical inevitability of hardware failure. Security and management are addressed through frameworks like the Shared …

Assessment Of Soybean Response To Irrigation Variability In Eastern Nebraska Using The Aquacrop Model, Anmol Singh, Saleh Taghvaeian, Yufeng Ge, Derek M. Heeren, Frank Bai

Department of Agricultural and Biological Systems Engineering: Faculty Publications

Soybean [Glycine max (L.) Merr.] is a major irrigated crop in eastern Nebraska, where recent expansions in irrigated agriculture and projected trends in irrigation demand necessitate thorough investigations of approaches that can optimize its irrigation management. In this study, the AquaCrop model was calibrated and validated using 19 variable irrigation treatments from a five-year field experiment. The model accurately simulated canopy cover, soil water content, and grain yield, with validation normalized root mean square error (nRMSE) of 12%, 7%, and 7%, respectively. The model was subsequently applied to estimate soybean yield and water requirement during a 15-year period (2010–2024) …

Efficient And Fair Resource Unit(Ru) Allocation Methods For The Ofdma Mechanism Under Ieee 802.11ax, Yang Yu

Doctoral

The IEEE 802.11ax standard was approved in February 2021. This standard allows users to access different channel bandwidths to transmit their packets under the Orthogonal Frequency Division Multiple Access (OFDMA) mechanism. As the number of Internet users and the number of Internet applications increase, the allocation of channel resources has been a challenge in WLANs. Different types of applications, such as real-time video conferencing, online gaming and large-scale data transfers, generate different requirements in terms of throughput, latency and reliability. Consequently, the diversity in users’ channel bandwidth demands is increasing. Under the OFDMA mechanism, the packets can be allocated into …

Sharing Patient-Generated Health Data With Electronic Health Record: A Standardised Provenance And Context-Rich Information Model And Clinician Evaluation, Abdullahi Abubakar Kawu

Dissertations

With the advent of Patient-Generated Health Data (PGHD) through wearable, mobile, and home monitoring systems, there is immense potential for ongoing monitoring and patient engagement. But integrating PGHD with Electronic Health Record (EHR) is challenged by sub-optimal support for contextual metadata and its relevant elements, lack of semantic interoperability among disparate systems, poor knowledge regarding the factors that impact clinician acceptance, and absence of globally agreed standards for data exchange. This thesis explores how contextually relevant patient-generated health data can be shared with EHRs through a FAIR standardized information model that ensures semantic and syntactic interoperability.

The study addresses six …

Deep Learning Framework For Sow Posture Classification From Depth Images: Comparison With Data Transformation Techniques, Models, And Cross-Validation, Md Towfiqur Rahman, Tami M. Brown-Brandl, Gary A. Rohrer, Sudhendu R. Sharma, Suzanne M. Leonard, Yeyin Shi

Department of Agricultural and Biological Systems Engineering: Faculty Publications

Piglet preweaning mortality (PWM) in the United States averages ≈14–15%, with sow overlaying causing about one third proportion of these losses. This research aimed to develop and evaluate deep learning models to classify six sow postures using depth images to monitor behaviors linked to overlaying risk. Top-down depth images were captured with Kinect V2^® cameras at 10 frames min^-1 for five consecutive days (2 days before to 2 days after farrowing), yielding 26,506 training images from 18 sows, 17,901 testing images from 12 sows, and 4,697 additional images from three sows in diagonal stalls for external validation. Three …

Beyond The Honeypot: Increasing Adversarial Cognitive Load Through Interactionless Cyber Deception, Marcus Rozier, Connor Dedic, Ted Mcdaniel, Hadley Westover

Space Dynamics Laboratory Publications

Traditional cyber deception mechanisms, such as honeypots and honey-tokens, typically rely on adversary interaction to trigger alerts and provide defensive value. This paper introduces the concept of interactionless deception—a defensive paradigm focused on the strategic manipulation of the environment to disrupt the cyber kill chain without requiring direct engagement. By utilizing a greedy top-k selection method to analyze MITRE ATT&CK® data across 178 Advanced Persistent Threat (APT) groups, this research identifies seven high-frequency sub-techniques that represent common attacker behavior. To counter these, the authors developed a series of proof-of-concept tools, including deceptive web proxies, simulated command-and-control (C2) beacons, and …

A Low Noise, High Qe, Large Format Ccd Camera System For The Nasa Mighti Instrument, Jed Hancock, Joel Cardon, Mike Watson, James Cook, Mitch Whiteley, James Beukers, Chris Englert, Charlie Brown, John Harlander

Space Dynamics Laboratory Publications

Outline

• MIGHTI Instrument Overview and Configuration
• Camera System Block Diagram
• Flight Camera Heads, Electronics Box, and CCDs
• CCD Testing at e2v
• AI&T at the Space Dynamics Lab
• Electro-Optical Characterization and Performance Results
• Conclusions

Climate Change Impacts On Hydrology In The Upper James Watershed, Imiya Mudiyanselage Chathuranika, Dalya Ismael

Engineering Technology Faculty Publications

Hydrological modeling of the Upper James Watershed (UJW), Virginia, is critical for predicting water availability, flood management, agriculture, ecosystem protection, and hydropower production under increasing climate change. The Hydrologic Engineering Center-Hydrologic Modeling System (HEC-HMS) is applied to evaluate climate change impacts on key hydrological components within the watershed. Future climate conditions were assessed for the near (NF: 2026-2050), mid (MF: 2051-2075), and far (FF: 2076-2100) periods using three Global Climate Models (GCMs) under Shared Socioeconomic Pathways SSP 2-4.5 and SSP 5-8.5. Climate data were bias-corrected using the Linear Scaling Method (LSM) and used to drive the HEC-HMS model. Results project …

Identifying Industry-Preferred Automation Software In Engineering: An Indeed-Based Analysis To Inform Engineering Technology Curriculum Design, Triet Minh Phan, Collins Okafor, Winifred Okafor, Devang Mehta, Mohsen Souissi, Jenora Waterman, Connie Mayberry, Misty Thomas, Orlando Ayala, Angie Price, Maurizio Manzo

Engineering Technology Faculty Publications

In the evolving field of automation engineering, staying aligned with industry software demands is critical to preparing graduates for the modern workforce. This study investigates the prevalence of leading industrial automation platforms—Rockwell / Allen-Bradley (RSLogix / Studio 5000), Siemens (TIA Portal / Step 7), and Schneider Electric (EcoStruxure / Unity Pro)—across job postings collected from Indeed using the keyword "automation engineering." The research compiles a structured dataset of job postings with seven fields: ID, Job Title, Organization, Rockwell / Allen-Bradley, Siemens, Schneider Electric, and Posting URL. Each entry is manually coded to indicate whether the listed software platforms are mentioned, …

Analytical And Semi-Analytical Modeling Of Solar Cells Using The Lambert W Function: A Comprehensive Review Of Equivalent Circuits, Adel El-Shahat, Martin Ćalasan, Snežana Vujoševic, Shady H. E. Abdel Aleem

Engineering Technology Faculty Publications

The modeling of photovoltaic (PV) cells through equivalent circuits forms a central element in the analysis, simulation, and optimization of solar energy systems. Traditional approaches often depend on iterative numerical methods to solve the implicit current–voltage (I–V) equations. In contrast, the Lambert W function has emerged as an effective mathematical tool that enables closed-form or semi-analytical expressions for a wide range of PV models. This paper presents a Lambert W-centered review of analytical and semi-analytical formulations for PV equivalent-circuit models, covering classical single-diode and multi-diode structures and modern variants incorporating additional elements, voltage-dependent parameters, and topology rearrangements. The models are …

Improving Medical Diagnostics With Vision-Language Models: Convex Hull-Based Uncertainty Analysis, Ferhat Ozgur Catak, Murat Kuzlu, Taylor Patrick, Michel Audette

Engineering Technology Faculty Publications

In recent years, vision-language models (VLMs) have been applied to various fields, including healthcare, education, finance, and manufacturing, with remarkable performance. However, concerns remain regarding VLMs’ consistency and uncertainty, particularly in critical applications such as healthcare, which demand a high level of trust and reliability. This paper proposes a novel approach to evaluate uncertainty in VLMs’ responses using a convex hull approach on a healthcare application for visual question answering (VQA). For any VLM, temperature refers to a sampling parameter used in probabilistic generation, which controls the randomness of the model’s output. The LLM-CXR model is selected as the medical …

Artificial Intelligence (Ai) In Educating Next Generation Of Engineering Technology Students, Adel El-Shahat, Murat Kuzlu, Vukica M. Jovanovic, Katherine Smith, Abdullah Al Mamun, Otilia Popescu

Engineering Technology Faculty Publications

Artificial Intelligence (AI) is transforming education, particularly for electrical engineering technology (EET) students, by presenting adaptive learning, immediate responses, and unconventional tools. Therefore, this paper proposes investigating modern learning to employ AI in educating future electrical engineering technology students. Firstly, the paper explores how to shape AI knowledge for EET students, supplying them with hands-on skills in AI tasks, clarifying coding, data analysis, and AI ethical usage. Then, as educators, what are the efficient AI tools to utilize in teaching, such as tailored tutoring, automated code assessment, AI-driven design/simulation, lecture dictation, and smart content creation? Key tools, for instance, Google …

Wip: Arduino-Based Pbl To Foster Entrepreneurial Mindset, Nathan Q. Holland, Vukica M. Jovanovic

Engineering Technology Faculty Publications

This work-in-progress paper examines a semester-long project-based learning initiative begun in an introductory engineering course during the Fall 2025 semester. The project aims to enhance students' hands-on experience by integrating information literacy, the engineering design process (EDP), and an entrepreneurial mindset (EM) [1]. The objective is to boost student confidence in teamwork, technical problem-solving, and application of skills, preparing them for future courses in their discipline. Students in this introduction to engineering course received an Arduino Uno R3 Controller board kit and additional sensors. They were tasked to develop a device addressing engineering in the medicine challenge of their choosing. …

Wearable Biosensors For Continuous Monitoring Of Chronic Kidney Disease: Materials, Biofluids, And Digital Health Integration, Anupamaa Sivasubramanian, Shankara Narayanan, Gymama Slaughter

Center for Bioelectronics Publications

Chronic kidney disease (CKD) is a progressive and irreversible disorder affecting over 850 million individuals globally and is associated with significant morbidity, mortality, and healthcare burden. Conventional diagnostic approaches rely on intermittent laboratory measurements, including serum creatinine, estimated glomerular filtration rate (eGFR), and urinary albumin, which provide limited temporal resolution and fail to capture dynamic physiological changes. Recent advances in wearable biosensing technologies offer new opportunities for continuous, non-invasive monitoring of biochemical and physiological markers relevant to renal function. This review provides a comprehensive analysis of wearable biosensors for CKD monitoring, focusing on sensing mechanisms (electrochemical, optical, and field-effect transistor), …