Open Access. Powered by Scholars. Published by Universities.®

Engineering Commons™

Open Access. Powered by Scholars. Published by Universities.®

Discipline
Institution
Keyword
Publication Year
Publication
Publication Type
File Type

Articles 14341 - 14370 of 198066

Full-Text Articles in Engineering

Whipped Into Shape: Infrastructure As Punishment For The Unruly Citizen, Eliza Mortimer Jan 2025

Whipped Into Shape: Infrastructure As Punishment For The Unruly Citizen, Eliza Mortimer

Anthós

This article interrogates how inflexible designs and policies of airline seating, as well as critical socio-political rhetoric, discipline and marginalize bodies that do not conform to normative standards. Situating airline seating within fat and disability studies models, Mortimer argues that these punitive measures are not neutral, but rather reflective of cultural narratives of productivity, morality, and bodily discipline. Mortimer draws from ethnographic analyses of first-person accounts and policy reviews to demonstrate how shrinking seats, rigid policies, and moralized discourse construct a “space of calculability” that pressures fat individuals to monitor and minimize their bodies to justify their presence in public …

Go to article

Introducing Sustainable Development Goals In College Curricula: A Way Forward, Christy Ashley, Jason D. Oliver, Hillary Leonard Jan 2025

Introducing Sustainable Development Goals In College Curricula: A Way Forward, Christy Ashley, Jason D. Oliver, Hillary Leonard

Markets, Globalization & Development Review

The commentary proposes a blueprint to help guide curriculum innovations, partnerships, and teaching interventions that incorporate the United Nations sustainable development goals (SDGs) into the curriculum. It suggests the utilization of AACSB’s Societal Impact Canvas with Ancona et al.’s (2007) Leadership Capabilities (Sensemaking, Relating, Visioning, Inventing) to provide a blueprint for mission-aligned SDG integration at a local level. It provides an illustrative example from the University of Rhode Island (USA), where the focus is on the Blue Economy. It aims to provide practical guidance for how a college or university can efficiently organize to gain stakeholder input that helps enhance …

Go to article

Incorporating Insulin Into Alginate-Chitosan 3d-Printed Scaffolds: A Comprehensive Study On Structure, Mechanics, And Biocompatibility For Cartilage Tissue Engineering, Afsaneh Jahani, Mohammad Sagdegh Nourbakhsh, Ali Moradi, Marzieh Mohammadi, Lobat Tayebi Jan 2025

Incorporating Insulin Into Alginate-Chitosan 3d-Printed Scaffolds: A Comprehensive Study On Structure, Mechanics, And Biocompatibility For Cartilage Tissue Engineering, Afsaneh Jahani, Mohammad Sagdegh Nourbakhsh, Ali Moradi, Marzieh Mohammadi, Lobat Tayebi

Electrical & Computer Engineering Faculty Publications

Osteoarthritis is a leading cause of disability worldwide, challenging current treatments to limited cartilage self-healing capacity. Cartilage tissue engineering (CTE) integrates cells, scaffolds, and signaling molecules, with Insulin being utilized as a differentiation biomolecule due to cost-effectiveness, dose-dependent influence on chondrogenesis, suitable biological activity, and ability to activate relevant receptors. Yet, administering differentiation biomolecules through conventional scaffolds poses a persistent challenge. Alginate (Alg) is commonly employed in CTE for its biocompatibility, though it lacks sufficient mechanical properties. Chitosan (Cs), while enhancing scaffold mechanical properties, but does not independently provide optimal support for chondrogenesis. While Alg-Cs scaffolds have garnered attention, challenges …

Go to article

Personalized Prediction Of Tumor Recurrence With Image-Guided Physics-Informed Computational Model In High-Grade Gliomas, Walia Farzana, Khan M. Iftekharuddin Jan 2025

Personalized Prediction Of Tumor Recurrence With Image-Guided Physics-Informed Computational Model In High-Grade Gliomas, Walia Farzana, Khan M. Iftekharuddin

Electrical & Computer Engineering Faculty Publications

High grade gliomas are infiltrating tumors characterized by their diffusive invasion and proliferative growth. Across and within patients heterogeneity of tumors makes it challenging to determine tumor spatial extent after surgical resection. Traditionally, tumor growth predictions after surgical resections rely on generalized models and population-based observations, which do not account for individual patient differences. To address this gap, we propose a personalized approach with image-guided computational model (digital twin) that incorporates physics-based modeling to predict tumor recurrence. Our digital twin involves an inverse modeling step, followed by a recurrence model that accounts for varying surgical effects. The physics-guided inverse model …

Go to article

Data-Driven Gradient Optimization For Field Emission Management In A Superconducting Radio-Frequency Linac, S. Goldenberg, K. Ahammed, A. Carpenter, J. Li, R. Suleiman, C. Tennant Jan 2025

Data-Driven Gradient Optimization For Field Emission Management In A Superconducting Radio-Frequency Linac, S. Goldenberg, K. Ahammed, A. Carpenter, J. Li, R. Suleiman, C. Tennant

Electrical & Computer Engineering Faculty Publications

Field emission can cause significant problems in superconducting radio-frequency linear accelerators (linacs). When cavity gradients are pushed higher, radiation levels within the linacs may rise exponentially, causing degradation of many nearby systems. This research aims to utilize machine learning with uncertainty quantification to predict radiation levels at multiple locations throughout the linacs and ultimately optimize cavity gradients to reduce field emission-induced radiation while maintaining the total linac energy gain necessary for the experimental physics program. The optimized solutions show over 40% reductions for both neutron and gamma radiation from the standard operational settings.

Go to article

Biomedical Mxene-Polymer Nanocomposites: Advancing Photothermal Therapy, Antibacterial Action, And Smart Drug Delivery: A Review, Ali Mohammad Amani, Lobat Tayebi, Ehsan Vafa, Mohammad Javad Azizli, Milad Abbasi, Ahmad Vaez, Hesam Kamyab, Daniel Simancas-Racines, Shreeshivadasan Chelliapan Jan 2025

Biomedical Mxene-Polymer Nanocomposites: Advancing Photothermal Therapy, Antibacterial Action, And Smart Drug Delivery: A Review, Ali Mohammad Amani, Lobat Tayebi, Ehsan Vafa, Mohammad Javad Azizli, Milad Abbasi, Ahmad Vaez, Hesam Kamyab, Daniel Simancas-Racines, Shreeshivadasan Chelliapan

Electrical & Computer Engineering Faculty Publications

MXenes are hydrophilic, conductive, tunable, and biocompatible two-dimensional ceramic materials prepared by etching the 'A' layer from their precursor MAX phases. Although MXenes show exceptional promise in photothermal therapy, biosensing, and regenerative medicine, they face challenges such as oxidative instability in physiological environments, limited drug-loading capacity, and unpredictable immune responses. To address these limitations, MXene/polymer nanocomposites incorporating both synthetic polymers (e.g., polyethylene glycol (PEG), polyvinyl alcohol (PVA), polyvinylpyrrolidone (PVP), and polylactic-co-glycolic acid (PLGA)) and natural biopolymers (e.g., cellulose nanofiber, gelatin, chitosan, hyaluronic acid, and soybean phospholipids) have been developed. These composites enhance functionality for biomedical applications such as photothermal cancer …

Go to article

Adaptive Fusion Neural Networks For Sparse-Angle X-Ray 3d Reconstruction, Shaoyong Hong, Bo Yang, Yan Chen, Hao Quan, Shan Liu, Minyi Tang, Jiawei Tian Jan 2025

Adaptive Fusion Neural Networks For Sparse-Angle X-Ray 3d Reconstruction, Shaoyong Hong, Bo Yang, Yan Chen, Hao Quan, Shan Liu, Minyi Tang, Jiawei Tian

Electrical & Computer Engineering Faculty Publications

3D medical image reconstruction has significantly enhanced diagnostic accuracy, yet the reliance on densely sampled projection data remains a major limitation in clinical practice. Sparse-angle X-ray imaging, though safer and faster, poses challenges for accurate volumetric reconstruction due to limited spatial information. This study proposes a 3D reconstruction neural network based on adaptive weight fusion (AdapFusionNet) to achieve high-quality 3D medical image reconstruction from sparse-angle X-ray images. To address the issue of spatial inconsistency in multi-angle image reconstruction, an innovative adaptive fusion module was designed to score initial reconstruction results during the inference stage and perform weighted fusion, thereby improving …

Go to article

The Impact Of Chromium Ion Implantation On Ald Lead Chalcogenide Thin Films, Haifeng Cong, Charlotte Poterie, Jean Francois Barbot, Helmut Baumgart Jan 2025

The Impact Of Chromium Ion Implantation On Ald Lead Chalcogenide Thin Films, Haifeng Cong, Charlotte Poterie, Jean Francois Barbot, Helmut Baumgart

Electrical & Computer Engineering Faculty Publications

Inherently the synthesis of semiconducting materials by Atomic Layer Deposition ALD produces only intrinsic undoped films which require the introduction of small amounts of impurities for doping to change them into extrinsic semiconductors. Apart from various in-situ diffusion doping techniques like delta doping during the ALD process, post deposition doping by ion implantation affords the best control of dose and doping profile. The present study investigates the impact of 180 keV Cr+ ion implantation on the properties of semiconducting ALD lead chalcogenide thin films to improve their thermoelectric figure of merit. The implantation was accomplished with 180 keV Chromium …

Go to article

Energy-Aware Swarm Robotics In Smart Microgrids Using Quantum-Inspired Reinforcement Learning, Mohamed Shili, Salah Hammedi, Hicham Chaoui, Khaled Nouri Jan 2025

Energy-Aware Swarm Robotics In Smart Microgrids Using Quantum-Inspired Reinforcement Learning, Mohamed Shili, Salah Hammedi, Hicham Chaoui, Khaled Nouri

Electrical & Computer Engineering Faculty Publications

The integration of autonomous robots with intelligent electrical systems introduces complex energy management challenges, particularly as microgrids increasingly incorporate renewable energy sources and storage devices in widely distributed environments. This study proposes a quantum-inspired multi-agent reinforcement learning (QI-MARL) framework for energy-aware swarm coordination in smart microgrids. Each robot functions as an intelligent agent capable of performing multiple tasks within dynamic domestic and industrial environments while optimizing energy utilization. The quantum-inspired mechanism enhances adaptability by enabling probabilistic decision-making, allowing both robots and microgrid nodes to self-organize based on task demands, battery states, and real-time energy availability. Comparative experiments across 1500 grid-based …

Go to article

Energy-Aware Sensor Fusion Architecture For Autonomous Channel Robot Navigation In Constrained Environments, Mohamed Shili, Hicham Chaoui, Khaled Nouri Jan 2025

Energy-Aware Sensor Fusion Architecture For Autonomous Channel Robot Navigation In Constrained Environments, Mohamed Shili, Hicham Chaoui, Khaled Nouri

Electrical & Computer Engineering Faculty Publications

Navigating autonomous robots in confined channels is inherently challenging due to limited space, dynamic obstacles, and energy constraints. Existing sensor fusion strategies often consume excessive power because all sensors remain active regardless of environmental conditions. This paper presents an energy-aware adaptive sensor fusion framework for channel robots that deploys RGB cameras, laser range finders, and IMU sensors according to environmental complexity. Sensor data are fused using an adaptive Extended Kalman Filter (EKF), which selectively integrates multi-sensor information to maintain high navigation accuracy while minimizing energy consumption. An energy management module dynamically adjusts sensor activation and computational load, enabling significant reductions …

Go to article

Chondrogenic And Chondroprotective Response Of Composite Collagen I/Ii-Hyaluronic Acid Scaffolds Within An Inflammatory Osteoarthritic Environment, Carly M. Battistoni, Javier Munoz Briones, Douglas K. Brubaker, Alyssa Panitch, Julie C. Liu Jan 2025

Chondrogenic And Chondroprotective Response Of Composite Collagen I/Ii-Hyaluronic Acid Scaffolds Within An Inflammatory Osteoarthritic Environment, Carly M. Battistoni, Javier Munoz Briones, Douglas K. Brubaker, Alyssa Panitch, Julie C. Liu

School of Chemical Engineering Faculty Publications

Inflammation plays a key role in cartilage damage that occurs in osteoarthritis (OA). However, in vitro assessments of tissue-engineered constructs for cartilage regeneration generally do not consider their performance in the presence of inflammation. In this work, the chondrogenic differentiation potential of mesenchymal stromal cells (MSCs) was evaluated in the presence of both chondrogenic factors and inflammatory cytokines, and cartilage formation, degradative response, and inflammatory response were characterized. The addition of cytokines reduced cartilage production, increased cell proliferation, and resulted in an increase in inflammatory markers. Incorporation of hyaluronic acid (HA) had little impact on both collagen fibril microstructure and …

Go to article

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 Jan 2025

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 (Tv), 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 Tv in …

Go to article

Virtual Reality-Based Training To Augment Recovery Of Hand Dexterity After Surgery For Degenerative Cervical Myelopathy, Viprav B. Raju, Roxanne Hauer, Mohammad Ghassemi, Anjishnu Banerjee, Derek Kamper, Brian D. Schmit, Aditya Vedantam Jan 2025

Virtual Reality-Based Training To Augment Recovery Of Hand Dexterity After Surgery For Degenerative Cervical Myelopathy, Viprav B. Raju, Roxanne Hauer, Mohammad Ghassemi, Anjishnu Banerjee, Derek Kamper, Brian D. Schmit, Aditya Vedantam

Biomedical Engineering Faculty Research and Publications

Degenerative cervical myelopathy (DCM), the leading cause of non-traumatic spinal cord injury, frequently results in impaired hand dexterity. While surgical decompression is the primary treatment, over 40% of patients report residual hand disability after surgery. There are no therapies to restore hand function after surgery for DCM. In this single-arm clinical trial, post-surgical DCM participants (within 12 months after surgery) underwent a 4-week VR training protocol using the Virtual Keyboard system, which promotes practice of finger individuation. Assessments of hand dexterity were performed at baseline (at week 1), post-training (at week 6) and follow-up (at week 10). The primary outcome …

Go to article

Vaim-Cff: A Variational Autoencoder Inverse Mapper Solution To Compton Form Factor Extraction From Deeply Virtual Compton Scattering, Manal Almaeen, Tareq Alghamdi, Brandon Kriesten, Douglas Adams, Yaohang Li, Huey-Wen Lin, Simonetta Liuti Jan 2025

Vaim-Cff: A Variational Autoencoder Inverse Mapper Solution To Compton Form Factor Extraction From Deeply Virtual Compton Scattering, Manal Almaeen, Tareq Alghamdi, Brandon Kriesten, Douglas Adams, Yaohang Li, Huey-Wen Lin, Simonetta Liuti

Computer Science Faculty Publications

We develop a new methodology for extracting Compton form factors (CFFs) from deeply virtual exclusive reactions such as the unpolarized DVCS cross section using a specialized inverse problem solver, a variational autoencoder inverse mapper (VAIM). The VAIM-CFF framework not only allows us access to a fitted solution set possibly containing multiple solutions in the extraction of all 8 CFFs from a single cross section measurement, but also accesses the lost information contained in the forward mapping from CFFs to cross section. We investigate various assumptions and their effects on the predicted CFFs such as cross section organization, number of extracted …

Go to article

Human Perception Of Ai Capabilities At Classifying Perturbed Roadway Signs, Katherine R. Garcia, Jing Chen, Yanru Xiao, Scott Mishler, Cong Wang, Bin Hu Jan 2025

Human Perception Of Ai Capabilities At Classifying Perturbed Roadway Signs, Katherine R. Garcia, Jing Chen, Yanru Xiao, Scott Mishler, Cong Wang, Bin Hu

Computer Science Faculty Publications

Artificial Intelligence (AI) is crucial to numerous functions required for driving automation systems, including the computer vision techniques used to detect the roadway environment and make real-time decisions. However, the images used as inputs to the AI system may be maliciously perturbed, or manipulated, causing the AI system to make an incorrect classification. In this study, we examined humans’ perception of the AI’s computer vision capability of classifying various road sign images, including the original images, images with two different types of malicious attacks, and images that are scrambled randomly at the pixel level. Our results showed that participants rated …

Go to article

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 Jan 2025

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 …

Go to article

An Optimized Generalized Multi-Color Point Implicit Solver For Intel Gpus Using Oneapi Esimd, Joseph Wassell, Mohammad Zubair, Aaron Walden, Gabriel Nastac, Eric Nielsen, Timothée Ewart Jan 2025

An Optimized Generalized Multi-Color Point Implicit Solver For Intel Gpus Using Oneapi Esimd, Joseph Wassell, Mohammad Zubair, Aaron Walden, Gabriel Nastac, Eric Nielsen, Timothée Ewart

Computer Science Faculty Publications

This paper presents an efficient implementation of a linear-solver kernel relevant to FUN3D, a suite of computational fluid dynamics software developed at NASA’s Langley Research Center. The linear solver is optimized for a range of block sizes commonly used in FUN3D. The implementation targets Aurora, the Argonne Leadership Computing Facility’s (ALCF) exascale machine featuring Intel Data Center Max 1550 GPUs. The linear solver’s performance is memory bandwidth-bound due to its low arithmetic intensity. The primary performance challenges stem from variable matrix row lengths and indirect memory access patterns inherent in unstructured-grid applications. Variable block sizes introduce additional complexity through differing …

Go to article

Energy-Based Deep Incomplete Multi-View Clustering, Ziyu Wang, Yiming Du, Rui Ning, Lusi Li Jan 2025

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 …

Go to article

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 Jan 2025

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 …

Go to article

Security Enhancement In Uav Swarms: A Case Study Using Federated Learning And Shap Analysis, Sushmitha Halli Sudhakara, Lida Haghnegahdar Jan 2025

Security Enhancement In Uav Swarms: A Case Study Using Federated Learning And Shap Analysis, Sushmitha Halli Sudhakara, Lida Haghnegahdar

School of Cybersecurity Faculty Publications

As cyber-physical systems (CPSs) increasingly integrate physical and digital realms, securing critical infrastructure, such as the Port of Virginia, becomes paramount. Among CPSs, Unmanned Aerial Vehicles (UAVs) are vital for monitoring, communication, and supporting the command and control through remote reconnaissance and surveillance missions. These UAV applications often require coordination, planning, and runtime reconfiguration, traditionally managed by human decision-makers. However, this approach has limitations, as extensively documented in the literature. Artificial Intelligence (AI) has emerged as a pivotal tool to address these limitations, enhancing risk mitigation and informed decision-making. This research proposes a machine learning (ML) based security mechanism, leveraging …

Go to article

Security Enhancement In Aav Swarms: A Case Study Using Federated Learning And Shap Analysis, Sushmitha Halli Sudhakara, Lida Haghnegahdar Jan 2025

Security Enhancement In Aav Swarms: A Case Study Using Federated Learning And Shap Analysis, Sushmitha Halli Sudhakara, Lida Haghnegahdar

School of Cybersecurity Faculty Publications

As cyber-physical systems (CPSs) increasingly integrate physical and digital realms, securing critical infrastructure, such as the Port of Virginia, becomes paramount. Among CPSs, Autonomous Aerial Vehicles (AAVs) are vital for monitoring, communication, and supporting the command and control through remote reconnaissance and surveillance missions. These AAV applications often require coordination, planning, and runtime reconfiguration, traditionally managed by human decision-makers. However, this approach has limitations, as extensively documented in the literature. Artificial Intelligence (AI) has emerged as a pivotal tool to address these limitations, enhancing risk mitigation and informed decision-making. This research proposes a machine learning (ML) based security mechanism, leveraging …

Go to article

Long-Term Traffic Prediction Using Deep Learning Long Short-Term Memory, Ange-Lionel Toba, Sameer Kulkarni, Wael Khallouli, Timothy Pennington Jan 2025

Long-Term Traffic Prediction Using Deep Learning Long Short-Term Memory, Ange-Lionel Toba, Sameer Kulkarni, Wael Khallouli, Timothy Pennington

School of Cybersecurity Faculty Publications

Traffic conditions are a key factor in our society, contributing to quality of life and the economy, as well as access to professional, educational, and health resources. This emphasizes the need for a reliable road network to facilitate traffic fluidity across the nation and improve mobility. Reaching these characteristics demands good traffic volume prediction methods, not only in the short term but also in the long term, which helps design transportation strategies and road planning. However, most of the research has focused on short-term prediction, applied mostly to short-trip distances, while effective long-term forecasting, which has become a challenging issue …

Go to article

Tl-Convlstm: A Transfer-Learning-Based Convolutional Lstm To Identify And Forecast Traffic In The Nextg Environments, Bikash Chandra Singh, Peter Foytik, Rafael Diaz, Sachin Shetty Jan 2025

Tl-Convlstm: A Transfer-Learning-Based Convolutional Lstm To Identify And Forecast Traffic In The Nextg Environments, Bikash Chandra Singh, Peter Foytik, Rafael Diaz, Sachin Shetty

School of Cybersecurity Faculty Publications

Forecasting and categorizing cellular traffic flows and their types are essential functions in intelligent network systems to ensure efficient network optimization. The ever-evolving nature of 5G networks results in fluctuations in traffic patterns over time, leading to a phenomenon known as model drift. Consequently, accurately predicting and identifying cellular traffic patterns becomes a complex task. To tackle this challenge, this article introduces an innovative approach called TL-ConvLSTM, which combines transfer learning with convolutional long short-term memory (ConvLSTM) to effectively combat model drift and provide precise forecasting and recognition of cellular traffic within the network. To accomplish this, we initiate the …

Go to article

From Spark To Wildfire: Predicting Wildfire Origins And The Efficacy Of “Fire Safe” Cigarettes In California Grasslands, Trevor Blackwell Haltermann Jan 2025

From Spark To Wildfire: Predicting Wildfire Origins And The Efficacy Of “Fire Safe” Cigarettes In California Grasslands, Trevor Blackwell Haltermann

Cal Poly Humboldt theses and projects

As wildfires in North America increase in size and frequency, the societal impacts become greater as well. With greater risk and high fire suppression costs it is pertinent to improve fire investigation methods and limit the potential for human caused ignitions. Two distinct studies were carried out to assess the accuracy of fire pattern indicators and the efficacy of “fire safe” cigarette technology in lowland grass fuel types in California. The first study utilized 24 experimental small-scale fires within a wind tunnel to examine the accuracy of microscale fire pattern indicators and the factors that influence the accuracy of predictions …

Go to article

Ieee 802.11n And Ieee 802.11ax Networks Under Various Propagation Models, Jun Peng Jan 2025

Ieee 802.11n And Ieee 802.11ax Networks Under Various Propagation Models, Jun Peng

Electrical and Computer Engineering Faculty Publications

IEEE 802.11n networks are the first generation of Wi-Fi networks to reach data rates of hundreds of megabits per second. They are also the first to use MIMO technologies in Wi-Fi networks. They can operate in both 2.4 GHz and 5 GHz frequency bands and use 20 or 40 MHz channel width. Similarly, IEEE 802.11ax networks can operate in both 2.4 GHz and 5.0 GHz frequency bands and use 20, 40, 80, or 160 MHz channel width. For channel access, IEEE 802.11ax networks use CSMA/CA, multi-user MIMO (MU-MIMO), and OFDMA. They can reach data rates of multiple gigabits per second. …

Go to article

Integrating Machine Learning And Symbolic Regression For Predicting Damage Initiation In Hybrid Frp Bolted Connections, Sherif Sorour Jan 2025

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

Mechanical Engineering

No abstract provided.

Go to article

Artificial Intelligence For Digital Deception: A Study On Detection, Generation, And Evaluation, Tasnim Akter Onisha Jan 2025

Artificial Intelligence For Digital Deception: A Study On Detection, Generation, And Evaluation, Tasnim Akter Onisha

College of Graduate Studies: Theses & Dissertations

The rapid advancement of artificial intelligence has significantly influenced digital media, enabling both the detection and generation of synthetic content. This thesis, titled Artificial Intelligence for digital deception: A Study on Detection, Generation, and Evaluation, explores AI’s role in digital deception through three distinct studies focused on facial expression analysis for deepfake detection, machine learning-based spam classification on cloud platforms, and the evaluation of generative AI state-of-the-art text to video models. The first study investigates the effectiveness of facial expression analysis in distinguishing between deepfake and genuine videos. Using Noldus FaceReader 7, participant’s emotional responses were analyzed while viewing deep-fake …

Go to article

Ease Of Product Disassembly Through A Systematic Structured Time-Based Design For Disassembly Methodology, Emeka S. Igwe Jan 2025

Ease Of Product Disassembly Through A Systematic Structured Time-Based Design For Disassembly Methodology, Emeka S. Igwe

College of Graduate Studies: Theses & Dissertations

This research introduces a systematic time-based design for disassembly (DfD) framework aimed at optimizing product disassembly by addressing important features like liaisons between components in product, component accessibility and the overall modularity of the product. This study specifically covers electromechanical and mechatronic systems in both household and industrial setup, identifying their disassembly challenges and high value pointers for improvement. The methodology involves using a design for disassembly framework called LeanDfD in carrying out a holistic disassembly process and evaluating quantitative metrics like disassembly time and complexity and suggesting further redesign strategies to minimize disassembly time and cost. Adopting this systematic …

Go to article

Ranking Of Non-Destructive Testing Methods For Ductile Iron Castings For Industry 4.0, Michael W. Jones Jan 2025

Ranking Of Non-Destructive Testing Methods For Ductile Iron Castings For Industry 4.0, Michael W. Jones

College of Graduate Studies: Theses & Dissertations

Non-destructive testing techniques provide a unique solution to provide quality assurance of parts rapidly and efficiently without altering the functional properties of the workpiece. The metal casting industry requires an elevated level of quality assurance, as castings are critical components to several industries. The wrong selection of non-destructive testing methods could increase production costs, quality costs, and potentially permit non-conforming castings to enter the market. The aim of this research is to develop a ranking model for non-destructive testing techniques for ductile iron castings within the context of the fourth industrial revolution. The ranking model analyzes the effectiveness of the …

Go to article

An Infrared Thermography Assessment For Monitoring Aging Mse Walls, Charles Lawal Jan 2025

An Infrared Thermography Assessment For Monitoring Aging Mse Walls, Charles Lawal

College of Graduate Studies: Theses & Dissertations

Mechanically Stabilized Earth (MSE) walls play a vital role in ensuring the stability of transportation infrastructure, yet traditional inspection techniques often struggle to detect subsurface defects and demand significant labor. This research explores the application of Infrared Thermography (IRT) as a non-destructive, cost-effective approach for inspecting MSE walls across Southeast Georgia. By capturing thermal images under diverse environmental and operational conditions, the study aims to identify potential issues, including cracks, voids, delamination, and honeycombing, which can undermine integrity over time. Utilizing advanced image processing tools, the research implements specialized software to enhance the accuracy of defect detection, facilitating the generation …

Go to article

First Prev Next Last