Engineering Commons^™

Harnessing Generative Ai And Large Language Models For Revolutionizing Cybersecurity In The Internet Of Things: Ethical And Privacy Implications, Harsha Sammangi, Aditya Jagatha, Jun Liu

Research & Publications

Generative artificial intelligence (AI) and large language models (LLMs) have in- troduced transformative capabilities in cybersecurity, particularly in securing Internet of Things (IoT) environments. These technologies can synthesize vast datasets, support real-time anomaly detection, and generate predictive insights through simple prompts. However, their deployment also presents ethical and privacy-related concerns, including algorithmic bias, data leakage, and misuse for malicious content creation. This paper conducts a systematic literature review to evaluate how LLMs and generative AI contribute to IoT cybersecurity. We propose an ethical AI-IoT security framework, examine key challenges, and offer recommendations for integrating responsible AI governance. We aim to …

Cst110.1 Analysing Everyday Interfaces Example 1, Sae University College

Exemplars

A Case Study on MyTime Interface analysing the usability of it.

Camino—Career Advancement, Mentorship, Inspiration, And Opportunities: A Stem K-12 Outreach Initiative, Hector A. Pulgar, Francisco Zelaya-Arrazabal, Erick Salvador Vasquez, Sebastian N. Martinez Lizana

Chemical and Materials Engineering Faculty Publications

CAMINO is a STEM program designed to promote career advancement, provide mentorship, give inspiration, and offer opportunities for K-12 Hispanic students located in East Tennessee. As part of the educational component of an NSF CAREER grant, the program launched in 2021 and is a collaboration between a research group from the University of Tennessee and one of the high schools with the largest Hispanic populations in the Knoxville Metropolitan area. These students from minority backgrounds may limit their aspirations and may not pursue higher education and/or professional development for numerous reasons, such as limited motivation, insufficient exposure to higher education, …

Entrepreneurially Minded Learning (Eml) Micromoment Activities Generated Using Students' Experiences In A Fluid Flow And Heat Transfer Course, Erick Salvador Vasquez, Megan Morin

Chemical and Materials Engineering Faculty Publications

The Entrepreneurially Minded Learning (EML) Framework involves the 3 C's: curiosity, connections, and creating value. Several design courses, open-ended assignments, and laboratory experiences can successfully lead to EML implementations. However, these implementations require extensive class time and instructor feedback, limiting their use in core engineering courses. Developing EML activities that are active, engaging, and rapid to deploy in a classroom setting can promote the sustained growth of an entrepreneurial mindset (EM). EML micromoment activities are emerging as a practical tool to facilitate the incorporation of the 3 Cs through rapid activity implementations that only last 2 – 30 minutes. These …

Cst110.1 Analysing Everyday Interfaces Example 2, Sae University College

Exemplars

A Case Study on usability analysis of the Apple iOS fitness app.

Modeling And Estimation Of Co2 Capture By Porous Liquids Through Machine Learning, Farid Amirkhani, Amir Dashti, Hossein Abedsoltan, Amir H. Mohammadi, John L. Zhou, Ali Altaee

Chemical and Biochemical Engineering Faculty Research & Creative Works

Porous liquids (PLs) are newly developed porous materials that combine unique fluidity with permanent porosity, which exhibit promising functionalities. They have shown ability to efficiently absorb greenhouse gases such as carbon dioxide (CO₂). Experimental measurement is one approach to determining the solubility of various greenhouse gases in PLs, which has drawbacks such as being expensive and time-consuming. Hence, simulation models are valuable to predict the solubility of CO₂ in various PLs. This work aims to develop machine learning (ML) modeling methods for accurately estimating CO₂ solubility under varying conditions (e.g. PLs, temperature, pressure). Adaptive Neuro-Fuzzy Inference …

Designing Electric Vehicle Infrastructures And Opportunities To Benefit All Residents, Polly Parkinson, Emma Mecham, Fawn Groves, Ivonne Santiago, Amy Wilson-Lopez

Teacher Education and Leadership Student Research

Countries around the globe have set electric vehicle adoption goals to address environmental and health concerns, but engineering planners and community policy experts cannot separate the socioeconomic factors from transportation needs. This mixed-methods case study indicates that because transportation decisions are inextricably linked to health, work, and housing, EV adoption must also address multifaceted human needs. To avoid the transportation mistakes of the past, it is essential that people in communities are consulted in the adoption process and have opportunities so all may actively benefit from the infrastructures and economic growth caused by electrification. “If you don't know the space …

Global Variation Of Mesospheric Gravity Waves Observed By Awe, Yucheng Zhao, Jiarong Zhang, Pierre-Dominique Pautet, Ludger Scherliess, Michael J. Taylor

Space Dynamics Laboratory Publications

USU Presentation on the Global Variation of Mesospheric Gravity Waves Observed by AWE

Awe Data Status, P.-Dominique Pautet, Anh Phan, Ludger Scherliess, Yucheng Zhao, Jiarong Zhang, Dallin Tucker, Connor Waite, Pedro Sevilla, Russell Kirkham, Harri Latvakoski, Jacob Adams, Keith Paskett

Space Dynamics Laboratory Publications

SDL Presentation on the AWE data status.

Board # 271: Nsf Iuse 2315777: Training Engineering Students To Be Better Learners: A Course-Integrated Approach, Huihui Qi, C. Pilegard, Minju Kim, Saharnaz Baghdadchi, Curt Schurgers, Alex M. Phan, Marko Lubarda

Psychology Faculty Articles and Research

Learning is a lifelong process exercised within and beyond the classroom, and a vital skill in almost all technical professions. Engineers, in particular, are impacted by rapidly evolving technologies and practices that require continuous learning and adaptation long after their training and the initial transition into their professional careers. However, despite the critical role of learning in their academic success and profession, engineering students experience academically rigorous and challenging courses with minimal emphasis or conscious focus on learning strategies that power effective learning. Often-used learning strategies such as rereading, highlighting, repetition, and memorization are intuitive for many students, yet do …

Empowering Engineering Students To Become More Effective And Self-Regulated Learners Through Course-Integrated Learning Strategies Intervention: A Pilot Study In A Solid Mechanics Course, Huihui Qi, Richard Eugene Vallejo Jr., Changkai Chen, Minju Kim, Alex M. Phan, Marko Lubarda, Celeste Pilegard, Curt Schurgers

Psychology Faculty Articles and Research

Learning is a lifelong process exercised within and beyond the classroom and a vital skill in almost all technical professions. Engineers, in particular, are impacted by rapidly evolving technologies and practices that require continuous learning and adaptation long after their training and the initial transition into their professional careers. However, despite the critical role of learning in their academic success and profession, engineering students experience academically rigorous and challenging courses with very little emphasis or conscious focus on learning strategies that power effective learning. Often-used learning strategies such as rereading, highlighting, repetition, and memorization are intuitive for many students, yet …

Context-Switch Attacks: Understanding And Mitigating The Threat To Llm Applications, Sydney Holder, Bivin Sadler

SMU Data Science Review

Large Language Models (LLMs) are transforming conversational AI, yet their dependence on prompt-supplied context exposes them to context-switch attacks that covertly steer dialogue toward sensitive or malicious ends. A 70 one-sided conversation transcript evaluation set was constructed spanning various fraudulent scenarios. Each transcript embeds adversarial patterns drawn while preserving natural conversational flow. We introduce a hybrid defense that pairs a BERT-based semantic-drift detector (cosine-similarity threshold = 0.70) with a curated keyword and hack-phrase scanner to counter these threats. In aggregate, the system delivered 100 % recall, intercepting every simulated phishing or data-harvesting attempt. The keyword layer achieved perfect precision, generating …

Leveraging Genai For Biometric Voice Print Authentication, Erica Brooks, Lijo Jacob, Lani Lewis, Gaurav Mittal, Shivam Negi, Faizan Javed

SMU Data Science Review

This paper presents the development of a secure voice authentication system that delivers an inclusive solution for all users, including those with disabilities. Leveraging a Text-Dependent Active Verification process, the system combines a spoken passphrase with voice biometric coefficients and audio vector embeddings for reliable user verification. A vector database is used to efficiently store data and perform similarity retrieval. Initially, the system achieves a 71% spoof detection accuracy, ensuring that only genuine samples proceed to the embedding stage, where it attains a 55.21% accuracy in vector embedding and similarity retrieval. Furthermore, this approach paves the way for user-specific voice-controlled …

Emt Vision, Logan Calder, Grant Johnson, John Alvarado, Jack Landers

Computer Science and Engineering Senior Theses

Augmented Reality (AR) has demonstrated considerable promise for future mobile technologies, offering the ability to overlay crucial information within a user’s vision while they can still maintain awareness of the surrounding environment. Similarly, Artificial Intelligence (AI) is an increasingly influential technology with significant potential to revolutionize the medical field. Its ability to rapidly learn and adapt to specific tasks makes it particularly promising for supporting paramedics during emergency calls. AI can efficiently analyze real-time data and present it in a concise, actionable format, enhancing decision making in critical situations.

Given the potential of these technologies, we have developed a smart …

Simulation Study On Optimizing Microgrid Scheduling With Electric Vehicle Participation Under V2g Mode, Zhongan Yu, Hongliang Xiao, Qiangwei Xia, Jiawei Liu

Journal of System Simulation

Abstract: To address the negative impact of source-load uncertainty on the stable operation of the grid, a two-stage optimization scheduling strategy for the microgrid participation of electric vehicles based on the vehicle-to-grid (V2G) mode is proposed. In the first stage, the charging and discharging costs of electric vehicles as well as the load fluctuation target are determined taking into account the battery losses. Through a zero-sum game, we objectively weigh the interests of both vehicle owners and the microgrid, utilizing the mobile energy storage characteristics of electric vehicles to optimize the load curve and integrate renewable energy; in the second …

Aerial Target Detection Algorithm Fused With Multi-Scale Features, Lu Yang, Junying Pei

Journal of System Simulation

Abstract: In order to solve the problem that UAV aerial images have a large number of small target samples but little extractable feature information, which is not conducive to improving the accuracy of aerial target detection, an improved small target detection algorithm for aerial photography based on YOLOv8s is proposed. The algorithm applies deformable convolution to the feature extraction module of the backbone network to adaptively capture the details of the target at different locations and scales. The feature information at different scales of the backbone network is extracted and enhanced by the feature collection module in the multilevel information …

Enhancing Fault Tolerance In Tmr Soft Risc-V Fpga Socs Through Failure-Driven Mitigation Strategies, Andrew Elbert Wilson

Theses and Dissertations

Field-Programmable Gate Arrays (FPGAs) leveraging soft processors, particularly those implementing the open-standard RISC-V Instruction Set Architecture (ISA), are increasingly important for space missions due to their adaptability and reconfigurability. However, their susceptibility to radiation-induced Single Event Upsets (SEUs) presents significant reliability challenges, necessitating robust fault-tolerant strategies such as Triple Modular Redundancy (TMR). This dissertation evaluates the effectiveness of TMR-based mitigation techniques for Linux-capable soft RISC-V System-on-Chip (SoC) implementations deployed on SRAM-based FPGAs operating in high-radiation environments. Using a combination of deterministic fault injection and neutron radiation testing, this work identifies critical residual single-point failure modes that persist in TMR-protected designs. …

Development Of A Near Terahertz Backward Wave Oscillator Using Standard Waveguide, Alexander Glick

Electrical and Computer Engineering ETDs

There is a demand for terahertz (THz) frequency radiation sources. Applications include, but are not limited to, imaging for medical and security purposes, biochemical and organic spectroscopy, and velocimetry. Historically, there was a limited supply of THz devices due to technological limitations. In recent years much progress has been made to reduce this “gap” in supply and demand for THz sources. This work proposes a vacuum electronic device that produces high power, extremely high frequency radiation in the G-band, by utilizing a backward wave oscillator (BWO) based on WR3 standard waveguide. This device is compact, fundamentally simple, and has great …

Hierarchical Reinforcement Learning (Hrl) In Multi-Goal Spatial Navigation With Autonomous Mobile Robots, Brendon Johnson

USF Tampa Graduate Theses and Dissertations

Hierarchical reinforcement learning (HRL) is hypothesized to be able to take advantage of the inherent hierarchy in robot learning tasks with sparse reward schemes, in contrast to more traditional reinforcement learning algorithms. In this research, hierarchical reinforcement learning is evaluated and contrasted with standard reinforcement learning in complex navigation tasks. We evaluate unique characteristics of HRL, including their ability to create sub-goals and the termination function. We constructed experiments to test the differences between PPO and HRL, different ways of creating sub-goals, manual vs automatic sub-goal creation, and the effects of the frequency of termination on performance. These experiments highlight …

Integrated Decision Support System For Optimizing Time And Cost Tradeoffs In Linear Repetitive Construction Projects, Ahmed Gouda Mohamed, Ali Hassan Ali, Ahmed Adel Abdelhady

Civil Engineering

No abstract provided.

Integrated Decision Support System For Optimizing Time And Cost Trade Offs In Linear Repetitive Construction Projects, Ahmed Gouda Mohamed

Civil Engineering

time and cost performance. Traditional scheduling techniques often struggle to effectively address these complexities. This paper aims to enhance project optimization by introducing a metaheuristicbased Time-Cost Trade-off (TCT) framework specifically designed for repetitive project environments. Unlike previous studies that focus solely on single-algorithm applications, this research evaluates two metaheuristic optimization strategies—Genetic Algorithm (GA) and Particle Swarm Optimization (PSO)—within a consistent problem setting. The framework employs both algorithms, which are independently assessed for their effectiveness in tackling the Linear Repetitive Project Time-Cost Trade-off (LRPTCT) problem. The methodology utilizes task decomposition alongside the Line of Balance (LOB) scheduling technique, facilitating a more …

Finite-Time Robust Anti-Disturbance Control For Steer-By-Wire System, Jingyi Zhang, Xin Chen, Jingang Ding, Jianguo Luo, Shuo Feng

Journal of System Simulation

Abstract: To eliminate the influence of parameter perturbations and external disturbances on the wheel angle tracking control performance of steer-by-wire (SbW) system, a fractional-order integral terminal sliding mode control scheme based on a finite-time disturbance observer is proposed. A sliding modebased second order finite-time disturbance observer (FDO) is designed to precisely estimate the total disturbance of the SbW system, and the estimated total disturbance is compensated into the system control input to reduce the wheel angle tracking error. A fractional-order fast integral terminal sliding mode control (FOFITSMC) scheme is designed to ensure fast convergence of the wheel angle tracking error …

Modeling And Simulation Of Hybrid Traffic Flow Considering The Inherent Dynamics Of Cacc Vehicular Platoons, Xiujian Yang, Jingjing Huang, Xi Wang

Journal of System Simulation

Abstract: To investigate the characteristics of single-lane mixed traffic flow with the presence of cooperative adaptive cruise control (CACC) vehicle platoons, a modeling approach based on cellular automata is proposed. This method distinguishes between the car-following strategies of human-driven vehicles and CACC vehicles, incorporating dynamic inter-vehicle spacing within the platoon and actual control behaviors to construct a mixed traffic flow model with inherent dynamic properties. The model enables an in-depth analysis of the influence of platoon features, such as geometric formation, carfollowing control strategies, and platoon size, on the characteristics of mixed traffic flow. It also allows us to study …

Research On Obstacle Avoidance Of Substation Robot Based On Spatiotemporal Networks, Chong Cheng, Lixia Wang, Songtao Duan, Xiaoguang Xiong, Xianjun Ge

Journal of System Simulation

Abstract: In order to improve the visual obstacle avoidance ability of substation robots in complex environments, a robot visual obstacle avoidance method based on spatiotemporal networks is proposed. The method utilizes traditional image processing techniques to enhance road information and designs a lightweight deep convolutional neural network structure to extract road features from a spatial domain perspective; based on the spatial characteristics of the road, a long short-term memory network is introduced to mine the changes in the road from a temporal perspective, and a classification regression prediction structure is used to predict the robot's obstacle avoidance direction and angle; …

Operation System For Simulation Roadheader Based On Visual Motion Capture, Yongling Li, Lingzhi Liu, Baishun Zhou, Jingfa Lei, Miao Zhang, Ruhai Zhao

Journal of System Simulation

Abstract: To enhance the natural human-machine interaction in simulation roadheader environment, a vision-based simulation roadheader operation system is proposed. The visual motion capture unit is based on the MediaPipe framework, which captures hand gestures through cameras and creates a correspondence between the physical world and virtual space. An improved Kalman filter algorithm is proposed by setting a weighted centroid to address the issue of unreasonable jumps in hand keypoint data during large-scale movements. The operator's gestures are discerned and the corresponding commands are conveyed. The results show that the improved method has significant advantages over the control group in terms …

Research On Behavior Control Techniques For Autonomous Vehicles Based On Parallel Behavior Tree Architecture, Jianchao Yuan, Shuo Yang, Qi Zhang, Ge Li

Journal of System Simulation

Abstract: Aiming at the problem of high collision rate and low efficiency of traditional serial behavior tree in autonomous vehicle control, a solution based on improved parallel behavior tree architecture is discussed to achieve safe behavior control. A safety behavior control strategy under dynamic road conditions is proposed, and behavior models for observation, decision-making, and movement are constructed, as well as their temporal constraint relationships; an improved parallel behavior tree control architecture is proposed, which achieves parallel execution and real-time interaction of behaviors through parallel control nodes, improving the real-time performance of decision control. The results show that compared with …

Research On Improving Design Efficiency Of Coaxial Magnetic Gear Based On Linear Model, Shuguang Zhao, Ce Chen, Xiaochang Xie, Fuping Li, Jin Han

Journal of System Simulation

Abstract: To address the issues of large model computation load and cumbersome magnetization direction setting during the simulation design of coaxial magnetic field modulation type magnetic gears, a simplified design method is proposed, which uses a linear model to replace the original conventional circular ring model. Based on the periodicity of the structure and magnetic field of each part of the magnetic gear, the modeling work is simplified and the computational load of the simulation analysis is reduced. The results show that compared with the circular ring structure, the number of magnetization coordinate system settings for the linear structure is …

Direct Arp2/3-Vinculin Binding Is Required For Pseudopod Extension, But Only On Compliant Substrates And In 3d, Tadamoto Isogai, Kevin M. Dean, Philippe Roudot, Evgenia V. Azarova, Kushal Bhatt, Meghan K. Driscoll, Shaina P. Royer, Nikhil Mittal, Bo Jui Chang, Sangyoon J. Han, Reto Fiolka, Gaudenz Danuser

Michigan Tech Publications

A critical step in cell morphogenesis is the extension of actin-dense pseudopods, controlled by actin-binding proteins (ABPs). While this process is well-understood on glass coverslips, it is less so in compliant three-dimensional environments. Here, we knocked out a series of ABPs in osteosarcoma cells and evaluated their effect on pseudopod extension on glass surfaces (2D) and in collagen gels (3D). Cells lacking the longest Arp3 gene variant, or with attenuated Arp2/3 activity, had the strongest reduction in pseudopod formation between 2D and 3D. This was largely due to reduced activity of the hybrid Arp2/3-vinculin complex, which was dispensable on glass. …

Ex Vivo And Simulation Comparison Of Leakage In End-To-End Versus End-To-Side Anastomosed Porcine Large Intestine, Youssef Fahmy, Mohamed Trabia, Brian Ward, Lucas Gallup, Whitney Elks

Mechanical Engineering Faculty Research

Anastomotic leaks after colorectal resection are serious surgical complications. We have compared the integrity of two common colorectal anastomosis techniques, end-to-side (ES) and end-to-end (EE), to control specimens using a novel experimental setup that mimics anastomotic air leak tests, which are typically performed during surgeries. Freshly harvested porcine colonic sections from 23 F1 cross-species pigs were used. Pressure measurements and video imaging were used to monitor the ex vivo experiments on EE, ES, and Control specimens. Using EE (n = 16), ES (n = 12), and Control (n = 22) specimens, leak pressure was 282.6 ± 3.0 mm Hg for …

Simulation Study On Adaptive Signal Control Of Deformed Intersection Based On Lstm-Gnn, Kun Chen, Liang Chen, Jiming Xie, Fengbo Liu, Taixiong Chen, Lukuan Wei

Journal of System Simulation

Abstract: Aiming at the traffic congestion at deformed intersections, an improved adaptive traffic signal control scheme based on deep learning is designed, the scheme integrates the adaptive signal control of LSTM and GNN at deformed intersections. LSTM is used to capture the dependence between time series traffic data, while GNN is used to construct a spatial interaction model between lanes. By integrating the information of time and space dimensions, the model can dynamically adjust the phase duration of signal lights according to real-time traffic conditions. The results indicate that the LSTM-GNN adaptive control scheme improves overall traffic throughput efficiency by …