Engineering Commons^™

Ahp–Python Framework For Multicriteria Modeling Of Rice Production In Asean, Mayang Anglingsari Putri, Risqy Siwi Pradini, Anuraga Jayanegara, Alexander Dimas Yonanta Putra

Knowledge Engineering and Data Science

Rice production is a key indicator of food security and agricultural stability in Southeast Asia, especially among Association of Southeast Asian Nations (ASEAN) countries. Despite shared regional goals, disparities in rice production remain, and previous studies mainly rely on descriptive statistics, lacking structured multicriteria decision-making frameworks and computational tools for cross-country comparisons. This study addresses these gaps by proposing an integrated Analytic Hierarchy Process (AHP)–Python framework to evaluate and rank ASEAN rice production from 2013 to 2022. Three criteria are used: Total Production Volume (K1), Production Growth Trend (K2), and Recent Year Performance (K3), capturing both long-term consistency and short-term …

Stable Numerical Solution Of An Elliptic Pde Inverse Problem Subject To Incomplete Boundary Conditions, Qasim Abd Ali Tayyeh

Knowledge Engineering and Data Science

This study addresses the challenging problem of solving inverse elliptic Partial Differential Equations (PDE) with incomplete boundary data, data available only on a part of the domain boundary. The aim is to develop a robust, effective numerical framework that consistently recovers parameters and/or sources from incomplete, ill-posed data. In the case of a variational problem discretized by the Finite Element Method (FEM) and solved by an adjoint-based optimization strategy, the framework uses Tikhonov regularization. Morozov's Discrepancy Principle is used to determine regularization parameters that achieve the best balance between accuracy and stability. Even with 5% noise in the measurement data, …

Assessing Deep Learning Models And Hyperparameter Optimization For Stable Time-Series Electricity Load Forecasting, Sukma Patrya, Aji Prasetya Wibawa, Aripriharta Aripriharta

Knowledge Engineering and Data Science

Long-term electricity load forecasting plays an important role in ensuring system reliability, optimizing energy management, and making operational plans in face of continuously rising electricity demands. This study suggests a complete deep learning method for univariate forecasting of future electricity loads based on climatology and electricity consumption data for the period between 2019 and 2023. The initial dataset was cleaned, normalized, and partitioned chronologically into train/test datasets. Four train/test split cases (20/80, 40/60, 60/40, 80/20) were considered to explore the impact of different levels of historical data availability on the performance of the suggested framework from data-poor to data-rich situations. …

Video Comprehension Score (Vcs): A Metric For Long-Form Video Description Evaluation, Harsh Dubey

Electronic Theses and Dissertations

Existing video description evaluation metrics fail to capture the long-range chronology and semantic alignment essential for long-form descriptions. An effective evaluation metric for long-form descriptions must (i) assess global thematic alignment, (ii) measure local semantic alignment, and (iii) evaluate chronological alignment while detecting corrupted content. We introduce Video Comprehension Score (VCS), a reference-based metric, which directly addresses these evaluation requirements through three components: Global Alignment Score for thematic alignment, Local Alignment Score for local semantic alignment, and Narrative Alignment Score for chronological alignment with adjustable tolerance. We evaluate VCS on two large-scale synthetic datasets designed to test corruption detection and …

Classification Of Indonesian Sign Language (Sibi) Using Data Mining Algorithms K-Nearest Neighbor And Random Forest, Muhammad Zaki Wirawan, Achmad Afif, Anik Nur Handayani, Imanuel Hitipeuw, Osamu Fukuda

Knowledge Engineering and Data Science

This study aims to address the communication hallenges faced by the Indonesian deaf community by developing an automatic classification model for Sistem Bahasa Isyarat Indonesia (SIBI) using data mining techniques. The main objective is to identify a practical algorithm for recognizing SIBI hand gestures to enhance accessibility and inclusiveness in digital communication. A comprehensive dataset consisting of 32,850 gesture samples representing SIBI alphabet signs was collected and processed through feature extraction, data cleaning, and normalization using Z-Transform and Min-Max methods. Two classification algorithms, K-Nearest Neighbor (KNN) and Random Forest, were implemented and evaluated using metrics such as accuracy, precision, recall, …

Largely Enhanced Out-Of-Plane Electromechanical Coupling Effects In Two-Dimensional Molybdenum-Disulfide/Boron-Nitride Heterostructures, Qiong Liu, Vijay Kumar Choyal, James E. Morris, Timon Rabczuk, Xiaoning Jiang, Xiaoying Zhuang

Electrical and Computer Engineering Faculty Publications and Presentations

The electromechanical coupling effects in two-dimensional (2D) transition metal dichalcogenides (TMDs) have attracted great interest. However, for 2D TMDs, piezoelectricity is confined to the basal plane, and the flexoelectricity-derived out-of-plane electromechanical response is usually faint, limiting the applications of this material family using the out-of-plane electromechanical effects. Here, this work reports a facile strategy to greatly enhance the out-of-plane electromechanical response of hexagonal molybdenum disulfide (2H-MoS₂) nanoflakes by stacking monolayer hexagonal boron nitride (h-BN) on 2H-MoS₂ nanoflakes to form MoS₂/BN heterostructures. The d^eff/₃₃ coefficient of MoS₂/BN can reach a value comparable …

Old, Flat, And Slow: Interior Greenland Snow And Ice Dynamics Revealed With Gnss, Derek James Pickell

Dartmouth College Ph.D Dissertations

The interior dry snow zone region of the Greenland Ice Sheet can no longer be confidently considered a reliably melt-free region, yet our ability to investigate and quantify the dynamic nature of this landscape is hindered by its remoteness. To overcome this challenge, this work describes a novel, low-cost, low-power GNSS (positioning) instrument that enables simultaneous measurements of (1) ice accumulation/ablation changes and (2) 3D ice flow. Deployed in a dense array, these GNSS instruments achieve similar performance to scientific-grade, commercial options (cm- to mm- precision), yet operate at < 60% of the power and < 34% of the hardware cost. Over a three year campaign, we analyze spatial and temporal patterns of accumulation in this region, using a technique called GNSS interferometric reflectometry (GNSS-IR). Observations with this technique show low bias and high precision relative to a validation study (-2.1 ± 2.9 cm), while we also demonstrate for the first time how GNSS-IR can reveal cm- to m- scale surface roughness, a critical yet often neglected measurement due to longstanding observational challenges. Patterns of accumulation show a spatial dependence linked to surface slope (~+0.7 mm w.e. km^-1 westward away from the divide) while surface roughness has a temporal dependence likely driven by wintertime high winds. Next, we combine GNSS-IR surface heights with the geodetic position time series of the antenna to derive surface elevation changes, which are compared to coincident ICESat-2 laser altimetry elevations. Observations of the surface show a millimeter-level relative bias and cm-level precision (-0.9 ± 3.8 cm) compared with the satellite altimeter, demonstrating for the first time that this technique is a viable ground-truthing method, while ICESat-2 performance has continued to exceed mission performance requirements. Finally, we examine station positioning through time to show a sensitivity to dynamic ice thinning and firn densification, two parameters than cannot be finely observed with space-based methods in this region. Together, these results provide the first ground‑based, high‑resolution picture of interior ice‑sheet change—capturing accumulation, roughness, surface elevation, and strain in one unified dataset. Our approach dramatically lowers logistical barriers, opening the interior of Greenland (and other remote regions) to sustained, quantitative monitoring at unprecedented spatiotemporal resolution.

Smart Qos-Aware Resource Management For Edge Intelligence Systems, Minoo Hosseinzadeh

Theses and Dissertations--Computer Science

There are several definitions for Smart Cities. One common key point of these definitions is that smart cities are technologically advanced cities which connect everything in a complex urban environment including infrastructure, information, and even people to cope with the crucial problems linked with the urban life such as traffic, pollution, city crowding, health, and poverty. Central to this vision are the Internet of Things (IoT) and Big Data, where interconnected devices with sensors collect vast amounts of data for informed decision-making. However, the rapid expansion of IoT devices challenges efficient data processing while meeting diverse Quality-of-Service (QoS) requirements; for …

Twitter-Based Osint For Cyber Event Analytics, Dakota S. Dale, Kylie Mcclanahan, W. Sky Elder, Qinghua Li

Electrical Engineering and Computer Science Faculty Publications and Presentations

Open-Source Intelligence (OSINT) is often regarded as a critical component for cybersecurity intelligence gathering to secure cyber infrastructures. As Artificial Intelligence (AI) and social media progress and become more commonplace, we have the unique opportunity to collect and analyze information from social media in real time. In this paper, we propose an AI-based framework for automatically filtering the Twitter stream for cybersecurity information, analyzing the natural language to aggregate the tweets around specific cybersecurity events, and certifying the information. The system's applications range from discovering events security operators may not have been aware of to helping operators investigate on-going events …

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 …

Neurovascular Coupling Impairments In Acute Traumatic Brain Injury: An Eeg-Nirs Analysis, Zachary Armstrong

Bioengineering Theses - Archive

Traumatic brain injury (TBI) is a major cause of neurological impairment, often leading to variable recovery and uncertain prognosis in the neurocritical care setting. There is a pressing clinical need for robust, physiologically grounded biomarkers to inform prognosis and therapeutic decision-making in acute TBI. This thesis investigates neurovascular coupling (NVC), the physiological coordination between neuronal activity and cerebral blood flow, as a candidate biomarker for brain function and recovery after injury.

A prospective cohort study was performed using simultaneous electroencephalography (EEG) and near-infrared spectroscopy (NIRS) recordings in patients with moderate-to-severe TBI and healthy controls. Wavelet transform coherence (WTC) analysis was …

Nonlinear Control Of Buck-Type Converters For Micro-Wind Generators, Noah Wilding, Shuzan Kumar Sarkar, Shruti Pandey, Michael L. Mcintyre

Electrical and Computer Engineering Faculty Research & Creative Works

Small-scale wind turbines offer a promising solution for distributed renewable energy generation. However, this approach often leads to wasted energy when battery capacity is reached, as excess energy is typically dissipated into resistors. The reliance on batteries further increases the cost and complexity of such systems. This paper presents a nonlinear control algorithm for regulating buck-type converters, providing a more efficient energy management solution. By employing a grid-connected inverter, excess energy is utilized rather than dissipated, potentially eliminating the need for batteries and reducing micro-wind turbine installation costs. The proposed control strategy manages the DC-link voltage for the inverter by …

Filter Based Motor Control For Robotic Applications, Shuzan Kumar Sarkar, Noah Wilding, Shruti Pandey, Nicholas Hawkins, Michael L. Mcintyre

Electrical and Computer Engineering Faculty Research & Creative Works

Controlling coreless DC motor in the field of humanoid robotic application involves considering various surrounding electromagnetic environment interference and sudden change of load with parameters variation of motor dynamics which makes the system complex. This paper presents a filter-based control scheme for a coreless DC motor drive system using an H-bridge inverter as the input circuit of the motor which is easy to implement and cost effective. From the electromagnetic characteristics, the dynamic model of the motor along with the control scheme is implemented in the commercial software PLECS. Then the effectiveness of this approach is validated through simulations demonstrating …

C 3 An: Custom, Compact And Composite Ai Systems - A Neurosymbolic Approach: 4Th-Generation Evolution Of Intelligent Systems, Amit P. Sheth, Kaushik Roy, Revathy Venkataramanan, Venkatesan Nadimuthu

Publications

Artificial Intelligence (AI) systems continue to evolve rapidly. From the architecture perspective, it is evolving from large, monolithic models trained on massive internet data to complex, multi-component “compound” systems and “agentic” frameworks capable of semi-autonomous decision-making. These systems show immense promise yet face numerous challenges in reliability, consistency, transparency, and alignment with user goals. In this article, we propose Custom, Compact and Composite AI with Neurosymbolic (C3AN) approach, a framework that paves way to 4th-generation of AI that integrates data, knowledge, and human expertise to build robust, intelligent and trustworthy AI systems defined by 14 foundation elements.

Custom emphasizes …

Safe Data-Enabled Control Of Human-In-The-Loop Robotic Manipulator Systems, Ritirupa Dey, Avimanyu Sahoo, Vignesh Narayanan

Publications

Safe control of human-in-the-loop (HIL) robotic manipulators is critical for applications such as assistive robotics, teleoperation in hazardous environments, and collaborative manufacturing. However, this remains challenging due to the lack of a unified framework that simultaneously addresses safety constraints, external disturbances, unmodeled dynamics, and dynamic role switching in the HIL setting. In this paper, we propose a novel NN-driven HIL control framework in which human–robot dyadic interaction occurs through the haptic channel. Using Lyapunov stability analysis, we theoretically show that the proposed NN-based controller ensures accurate joint trajectory tracking, compensates for system uncertainties, and adapts to human inputs modeled as …

Characterization, Optimization, And Performance Evaluation Of Pcm With Al2o3 And Zno Hybrid Nanoparticles For Photovoltaic Thermal Energy Storage, Md Golam Kibria, Utpol K. Paul, Md Shahriar Mohtasim, Barun K. Das, N. N. Mustafi

Research outputs 2022 to 2026

The electrical efficiency of the photovoltaic (PV) panel is affected significantly with increased cell temperature. Among various approaches, the use of Phase Change Materials (PCMs) with nanoparticles is currently one of the most effective for reducing and managing the temperature of PV panels. In this study, paraffin wax as PCM with different loading levels (0.5 %, 1 %, and 2 %) of hybrid nanoparticles Al2O3 and ZnO were successfully synthesized and their effects on the performance of the Photovoltaic-Thermal (PVT) system were investigated experimentally. Additionally, a prediction model was developed to analyze the interaction between the operating factors (independent variable) …

An Investigation Into Energy Consumption And V2g Potential Of An Electric School Bus Fleet, Rupesh Dahal

Graduate Theses, Dissertations, and Problem Reports (ETD)

The transition towards sustainable public transportation demands detailed evaluation of alternative fuel technologies. This thesis presents a comprehensive energy assessment and comparative analysis of diesel and electric school buses (ESBs) operating within the Monongalia County school district, its Vehicle-to-Grid (V2G) potential, and efficiency analysis of charging system. Utilizing real-world operational data collected from buses running identical routes, the study quantifies and compares their mileage specific energy consumption. The analysis reveals an average fuel economy of approximately 6.17 miles per gallon (MPG) for the conventional diesel school bus, contrasted with an average energy consumption rate of 0.46 miles per kilowatt-hour (miles/kWh) …

Deep Target Recognition: Semi-Supervised Annotation, Sensor Fusion And Super-Resolution, Shoaib Meraj Sami

Graduate Theses, Dissertations, and Problem Reports (ETD)

Despite the recent expansion of machine learning algorithms to cover a wide range of disciplines, several areas of automatic target recognition (ATR) remain underexplored. This dissertation presents tools developed to improve performance in three significant aspects of ATR: semi-supervised annotation, sensor fusion, and image super-resolution. The aim of the semi-supervised methods is to automatically annotate targets in scenarios where labeled data are scarce in the target domain but available in the source domain. Secondly, to address the limitations of individual image sensors and enhance robustness under different environmental conditions and man-made constraints, a sensor fusion algorithm was developed to improve …

Green Bank Chime/Frb Outriggers Commissioning And Analog System Development, Kholoud Sharif Tag Alkhatem Khairy

Graduate Theses, Dissertations, and Problem Reports (ETD)

The main objective of this thesis is to document the development and commissioning of the Canadian Hydrogen Intensity Mapping Experiment. (CHIME) outrigger at the Green Bank Observatory. (GBO) in Green Bank, WV. This novel cylindrical wide-field radio transient telescope is currently operating in conjunction with CHIME. The CHIME outrigger at GBO aims to contribute significantly to the field of radio astronomy, with implications for both Fast Radio Burst. (FRB) science and broader astronomical research. The construction and commissioning of the CHIME outrigger at the GBO mark a pivotal step forward in pursuing high-precision wide-field detection and localization of radio transients, …

Assessing Techno-Economic Performance Of Synchronous Condensers And Static Synchronous Compensators In Renewable Energy-Integrated Weak-Grids Using Hedge Feedforward Feedback-Based Online Gated Recurrent Unit, Md Ohirul Qays, Iftekhar Ahmad, Daryoush Habibi, Mohammad A.S. Masoum

Research outputs 2022 to 2026

To achieve net-zero targets in many counties, renewable energy generators (REGs) are integrated with grids where less fault current is produced compared to synchronous generators. Accordingly, fault level availability, known as ‘system strength’, is reduced at point of coupling (POC) buses. A minimum system strength level is crucial for REGs to accurately detect and ride-through faults. To maintain adequate system strength in renewable energy-based weak grids, researchers and engineers have recommended supplementary devices, such as synchronous condensers (SynCons) or static synchronous compensators (STATCOMs), to provide the required fault-current. Because SynCons and STATCOMs are costly devices, their optimal sizes and placement …

Design Of An Improved Robust Fractional-Order Pid Controller For Buck–Boost Converter Using Snake Optimization Algorithm, Seyyed Morteza Ghamari, Hasan Molaee, Mehrdad Ghahramani, Daryoush Habibi, Asma Aziz

Research outputs 2022 to 2026

With the increasing complexity of modern power systems, effective control of DC–DC converters has become crucial to ensure stability and efficiency. This paper focuses on optimizing the parameters of a known fractional-order proportional–integral–derivative (FOPID) controller for the control of a DC–DC buck–boost converter. The control of a DC–DC buck–boost converter is achieved using aFOPID approach. The gains of this technique have been enhanced utilizing the snake optimization (SO) algorithm. This converter exhibits unfavourable behaviour due to its non-minimum structure, necessitating a well-regulated controller to guarantee stability. The fractional concept is suggested here to enhance the dynamics of the classical PID …

Joint Channel Allocation And Power Control For Mission-Critical Applications In Sustainable Underwater Acoustic Networks, Sadaf Vahabli, Iftekhar Ahmad, Quoc Viet Phung, Daryoush Habibi

Research outputs 2022 to 2026

Underwater Acoustic Networks (UANs) are gaining popularity for underwater communication due to their long-range capabilities. However, they experience significant challenges, including limited bandwidth and spectrum overlap with marine mammal vocalizations, which raises concerns about the sustainable and ethical use of underwater acoustic resources. This study proposes an eco-friendly approach that integrates joint channel allocation and power control to minimize harmful interference with marine life while ensuring reliable communication for mission-critical applications, such as diver safety. To balance marine protection and communication reliability, this work introduces a strategy for intelligent spectrum management, where nodes first sense the acoustic spectrum to detect …

Multiphysics Modeling Of Solid Oxide Fuel Cells For Gradient Minimization And Inductive Loop Analysis In Impedance Spectroscopy Using Machine Learning-Based Microstructural Property Estimation, Muhammad Usman Khan

College of Graduate Studies: Theses & Dissertations

Solid oxide fuel cells have significant advantages in renewable energy utilization due to their high efficiency, fuel flexibility, and low emissions. However, despite the numerous efforts of technology, thermal and current density gradients and impedance behavior fluctuations are still causing performance degradation. A combined computational framework that integrates machine learning and three-dimensional Multiphysics modeling is needed to investigate and optimize the performance of solid oxide fuel cells. A machine learning model, trained on synthetic microstructure data by percolation analysis, is used to predict important microstructural parameters like triple phase boundary density and geometric tortuosity. These are then employed in a …

Application Of Edge-Enhanced Phase Analysis To Active Microwave Thermographic Measurements, Douglas Blaine Fleetwood

Masters Theses

Nondestructive testing and evaluation (NDT&E) encompasses a range of inspection techniques that assess materials, components, and structures while maintaining their integrity and performance. Among these techniques, thermography is particularly attractive due to its non-contact imaging approach with easy-to-interpret results. Active Microwave Thermography (AMT) has emerged as a promising NDT&E inspection technique that utilizes electromagnetic energy to heat materials through dielectric and magnetic absorption, with subsequent infrared imaging used to detect subsurface defects. While AMT offers significant advantages including material-specific heating optimization and lower power requirements compared to conventional thermographic methods, the technique faces a persistent challenge related to the thermal …

Deep Learning-Driven Biometric Security: Advancing Liveness Detection And Anti-Spoofing Techniques, Banafsheh Adami

Graduate Theses, Dissertations, and Problem Reports (ETD)

Biometric authentication has become a key part of our everyday lives—from unlocking smartphones with a fingerprint or face to verifying identities in banks and airports. These systems rely on our unique physical or behavioral traits, making them both convenient and secure. Unlike passwords, biometrics cannot be forgotten or stolen in the traditional sense. However, they are not without risk. One of the biggest concerns is spoofing: attempts by attackers to fool systems using fake biometric traits, such as silicone fingerprints or AI-generated videos.

As generative AI tools become more powerful and accessible, the ability to create convincing fake biometric data …

Global Spectral Clustering Of Temporally Stable Pixels For Epics Identification, Validation, And Hyperspectral Assignment For Satellite Calibration, Juliana Maria Fajardo Rueda

Electronic Theses and Dissertations

No abstract provided.

Refinement Of Trend-To-Trend Cross Calibration Total Uncertainties Utilizing Extended Pseudo Invariant Calibration Sites (Epics) Global Temporally Stable Target, Minura Samaranayake

Electronic Theses and Dissertations

Cross-calibration is an essential technique for calibrating Earth Observation satellite sensors, which involves taking nearly simultaneous images of a ground target to compare uncalibrated sensor to a well-calibrated reference sensor. This study introduces the hyperspectral Trend-to-Trend (T2T) cross-calibration technique utilizing EPICS Cluster 13 Global Temporally Stable (Cluster 13-GTS) as the calibration target, offering better temporal stability than previous targets used in T2T cross-calibration by an absolute difference of 0.4%, between coefficients of variation across all bands excluding CA band. A multispectral sensor-specific normalized hyperspectral profile was developed using the EO-1 Hyperion hyperspectral profile over Cluster 13-GTS to improve Spectral Band …

Transformer-Based Symbolic Music Generation, Ben Buentello

Master’s Theses

This thesis investigates the capacity of transformer-based architectures to learn generalized musical patterns through symbolic generation. To support this exploration, a complete music generation pipeline was developed, beginning with the construction and classification of a large-scale dataset of over 170,000 MIDI files. The dataset was processed using rule-based heuristics and custom neural classifiers to separate tracks by musical function and contour. A novel tokenization scheme, MINTii, was introduced to encode musical information compactly through interval-based representations, reducing redundancy and promoting generalization. Using this infrastructure, a transformer model was trained to generate single-track melodic sequences. Its performance was evaluated through both …

Design And Control Of A Stroke Therapy Device, Hugh Elliott

Open Access Master's Theses

Stroke is a leading cause of physical disability around the world, and the likelihood of stroke increases as people live longer. The current number of physiotherapists is insufficient to meet the increasing demand for their services. As a result, there has been a focus on developing robotic devices that function similarly to traditional therapy, enabling multiple patients to be seen simultaneously. While many devices have been created and tested, most are expensive, complex, and require trained personnel for supervision, thereby limiting their outreach. This thesis presents the design and control of a low-cost stroke therapy device designed to promote upper …

Dynamic Beamforming And Array Shape Estimation, Nicholas Ryan Costick

Open Access Master's Theses

The accurate estimation of towed sonar array shapes during complex maneuvers is a critical challenge affecting beamforming and target localization performance. When underwater arrays experience sharp turns or rapid movements, sensor positions become difficult to track precisely, negatively impacting the reliability of beamforming methods. This thesis addresses the issue of dynamic array shape uncertainty, motivated by operational challenges faced by the Navy.

A maximum-likelihood estimation (MLE) method is developed to simultaneously estimate the array shape and field directionality (spatial spectrum) during maneuvers. The proposed solution expands upon previous research, specifically the dynamic spatial spectrum estimation techniques described by Rogers and …