Engineering Commons^™

Robustness Of Fuzzy Artmap To Adversarial Attacks And Progressive Adversarial Training For Streaming Learning, Shane Cairns

Miners Solving for Tomorrow Research Conference

Incremental learners deployed on streaming data must remain robust to evolving adversarial perturbations, yet most adversarial-robustness studies assume offline multi-epoch training with repeated access to historical data. We investigate adversarial robustness in Fuzzy ARTMAP, a prototype-based Adaptive Resonance Theory model that supports single-pass learning without replay. We propose WB-Softmax, a differentiable relaxation that aggregates category-level activations into class-level scores for gradient-based attacks. WB-Softmax PGD achieves 89–100% attack success on vanilla models, exceeding transfer and query-based baselines. We then study adversarial training under true streaming constraints by comparing offline versus online adversarial example generation and standard versus selective updates. Offline adversarial …

Analysis Of Autonomous Vehicle Survivability To 5g Communication Errors, Sydney Clark

Miners Solving for Tomorrow Research Conference

Autonomous vehicles rely on low-latency, high-reliability data exchange for real-time perception and control. Disruptions such as packet loss, latency variation, protocol-level errors, and malicious interference can pose significant safety risks to both passengers and surrounding environments. This project aims to evaluate, quantify, and predict the survivability of autonomous vehicle systems to communication errors, with focus on 5G network environments. The impact of these communication impairments on vehicle stability and control will be investigated through high-fidelity cyber-physical simulation of the vehicle and its surrounding environment. Experiments designed to capture varying network conditions will be used to assess a broad range of …

A Wearable Mxene-Based Sweat Sensor For Real-Time Monitoring Of Inflammatory Biomarkers, Ariel Pilger

Miners Solving for Tomorrow Research Conference

Many conventional biosensing approaches rely on invasive sampling or bulky benchtop instrumentation, limiting their use in continuous and portable applications. This project focuses on the development of wearable sweat-based biosensors that enable non-invasive, continuous, and portable monitoring of physical, chemical, and biological markers. The system will be designed to target markers present in sweat and transduce the biochemical interactions into measurable electrical signals. These signals will be processed through integrated electronics to produce clear, interpretable outputs for users and medical professionals. Supporting circuitry including filters, amplifiers, and an independent power supply will be implemented as necessary to ensure signal accuracy, …

A Comparative Study On Performance Of Iot-Driven Ml-Enabled Forecasting Models For Efficient Air Quality Monitoring, Bara Ksiksi

Theses

Air pollution is one of the most critical environmental challenges affecting public health globally, responsible for approximately 4.2 million premature deaths annually according to the World Health Organisation. This thesis presents a comparative study of IoT-driven machine learning forecasting models for air quality monitoring in Abu Dhabi, UAE, introducing a zonal approach combined with satellite-based spatial validation. The primary objective is to evaluate forecasting performance across three distinct activity zones using ground station data from the Environment Agency Abu Dhabi (EAD), and to incorporate a spatial validation component using satellite imagery to assess the consistency of ground-based predictions at a …

Study Of Output And Behavior Of Llms Using Confidence Framing In Prompt Engineering, Micah Parrilla

Doctoral Dissertations and Master's Theses

While prompt engineering is pivotal for shaping Large Language Model (LLM) outputs, the impact of confidence framing on behavioral calibration remains underexplored. This study investigates the ways in which psychological framing, utilizing techniques such as capability praise, role amplification, and doubt induction, affects linguistic tone, objective accuracy, and internal calibration. A 1,080-trial experimental matrix evaluated six diverse models across factual, logical, coding, and cyber security domains. Analysis using the Kruskal-Wallis H-test revealed highly significant behavioral shifts across all measured dimensions, providing conclusive evidence that the applied frames exert a substantial influence on model performance.

The findings identify a distinct cognitive …

Developing A Multi-Band Gnss Remote Sensing Station To Measure Precipitable Water Vapor For Flash Flood Nowcasting, Madison Gleydura

Doctoral Dissertations and Master's Theses

Flash flood nowcasting in Central and Southern Appalachia is particularly challenging due to steep terrain, narrow valleys, highly localized rainfall patterns, and limited measurement coverage. Traditional remote sensing methods, such as Doppler radar and microwave radiometry, suffer from reduced resolution at long range and signal blockage by mountains. GNSS-meteorology offers an established alternative for measuring precipitable water vapor and is currently integrated into several numerical weather models. Recent research demonstrates that commercial-grade GNSS receivers can produce tropospheric products comparable to those from geodetic-grade equipment. The gaps in mountain coverage can be addressed by developing a low-cost, self-contained embedded system that …

Aiw26s: Machine Learning Of Structured Data, Moumita Saha

Paul English Applied Artificial Intelligence (AI) Institute Publications

This workshop introduces the fundamentals of machine learning for structured data, focusing on tabular datasets and real-world applications. Participants explore key concepts such as data types, data preprocessing, feature engineering, and supervised learning methods. The session covers commonly used models, including linear regression, logistic regression, decision trees, and neural networks, along with evaluation metrics such as RMSE, accuracy, and confusion matrices. By the end of the workshop, participants will have gained a practical understanding of how to build, interpret, and evaluate machine learning models for structured data.

The Psychology Behind Ai-Generated Phishing And Social Engineering Attacks, A’Shya Reynolds

School of Cybersecurity Master's Level Projects and Papers

Cybercrime has evolved significantly with the integration of artificial intelligence (AI), transforming traditional phishing and social engineering attacks into highly sophisticated and personalized threats. While early phishing attempts relied on generic messaging and low success rates, modern AI-driven attacks leverage advanced data analytics, natural language processing, and behavioral prediction to manipulate victims more effectively.

This research examines how cybercriminals utilize AI to enhance psychological manipulation techniques in phishing and social engineering attacks, increasing victim susceptibility. Drawing from interdisciplinary literature in cybersecurity and psychology, this study explores key psychological mechanisms, including cognitive biases, emotional triggers, and decision-making processes that influence victim …

Optimization Of Niobium Film For Particle Accelerators And Quantum Applications, Bektur Abdisatarov

Electrical & Computer Engineering Theses & Dissertations

Niobium (Nb) films play a central role in superconducting technologies used in particle accelerators and superconducting quantum circuits. Optimizing the physical properties of Nb films is therefore critical for improving both radiofrequency (RF) performance in superconducting radiofrequency (SRF) cavities and coherence in superconducting qubits. This thesis investigates the relationship between Nb film microstructure, impurity content, and electromagnetic response across these two application domains.

For particle accelerator applications, we studied Nb films deposited using high-power impulse magnetron sputtering (HiPIMS) with DC bias onto a 1.3 GHz elliptical SRF cavity. Nb film cavities exhibit a pronounced medium-field Q-slope, limiting their achievable accelerating …

Neutrosophic Sets In Neural Networks: Theory, Applications, And Challenges, Vladimir Simic, Dragan Pamucar, Hafiz Muhammad Athar Farid

Neutrosophic Systems with Applications

The integration of neutrosophic sets into neural networks presents a novel approach to handling uncertainty, indeterminacy, and falsity in data. Traditional neural networks typically operate under the assumption of precise and complete data, but real-world applications often involve noisy, incomplete, or ambiguous information. Neutrosophic sets extend fuzzy logic by incorporating three components: truth, indeterminacy, and falsity, allowing for a more nuanced representation of uncertain data. This paper explores the theoretical foundations of neutrosophic sets and their integration with neural networks, highlighting the challenges in computational complexity, training, and optimization. The paper also discusses the potential applications of neutrosophic neural networks …

Neutrosophic Probability With Dynamic Temporal Uncertainty (Nptu), Bhimraj Basumatary, Ashoke Kumar Brahma

Neutrosophic Systems with Applications

This paper introduces Neutrosophic Probability with Dynamic Temporal Uncertainty (NPTU), an extension of classical neutrosophic probability that incorporates the dimension of time. In classical neutrosophic probability, the degrees of truth, indeterminacy, and falsity are considered static. However, real-world uncertainties evolve, and their degrees change as new information becomes available. NPTU models these uncertainties dynamically, allowing for more accurate decision-making in time-varying environments. The paper explores key mathematical properties of NPTU, including entropy, distance measures, similarity measures, and Kullback-Leibler (KL) divergence, to quantify and compare temporal uncertainty states. The proposed framework is demonstrated through a case study on stock price prediction, …

Emergent Operator Logic: A Foundational Framework For Dynamic Reasoning And Generative Intelligence, Mona Gharib, Abduallah Gamal, Muhammad Nawaz, Basma Nasir

Neutrosophic Systems with Applications

This paper introduces Emergent Operator Logic (EOL), a framework that treats propositions as continuous operators $F_p:X \rightarrow X$ on a complete metric state space $( X,d )$ and evaluates truth after action via a continuous valuation $V:X \rightarrow [ 0,1 ]$. Logical composition is realized by three operator-level connectives: sequential $p \circ q$(causal order), parallel $p\parallel q$(1-Lipschitz cooperative blend), and the emergent synthesis $E( p,q ) = \frac12( F_p \circ F_q + F_q \circ F_p )$, which symmetrizes non-commuting actions. We provide a Hilbert-style proof system (sound), an algebraic semantics via E-algebras, and show that the category of E-algebras is …

Double-Valued Complex Neutrosophic Graphs, Suriyakumar G, V. J. Sudhakar, Takaaki Fujita

Neutrosophic Systems with Applications

This paper introduces a novel graph-theoretic framework, called the double-valued complex neutrosophic graph, as an extension of double-valued neutrosophic set theory. Within this framework, we investigate several important classes of such graphs, including self-complementary, strong, and full double-valued complex neutrosophic graphs, and establish a number of their fundamental properties. To clarify the proposed concepts and demonstrate their structural behavior, several relevant illustrative examples are also provided.

A Hybrid Multi-Criteria Decision-Making Approach For Sustainable Forest Fire Monitoring Using Unmanned Aerial Vehicles, Mohamed Eassa, Ahmed Abdelhafeez, Ahmad M. Nagm

Neutrosophic Systems with Applications

Unmanned aerial vehicles (UAVs) have become an effective tool for forest fire monitoring. This study evaluates UAVs for forest fire management, addressing the challenges posed by ambiguous and uncertain factors. Single-valued neutrosophic sets (SVNSs) are employed to model complex uncertainties, as they incorporate three distinct membership values: false, true, and indeterminate. The evaluation of UAVs is a multifaceted task due to the variety of factors involved. To address this complexity, multi-criteria decision-making (MCDM) methods are used. Specifically, the Analytic Hierarchy Process (AHP) and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) are integrated with SVNS to …

Development Of Deep Fused Neural Architecture For Ancient Tamil Palm-Leaf Manuscript Recognition, Hariharan P Mr

Theses and Dissertations

Digitizing Tamil palm-leaf manuscripts is important for education, communication, and the preservation of cultural heritage. The complex structure of the Tamil script, the wide range of handwriting styles, and the degradation seen in ancient Tamil palm-leaf manuscripts make these texts very difficult to read and understand. Digital Image Processing (DIP), document analysis techniques, and traditional Optical Character Recognition (OCR) are unable to handle noise, background interference, faded ink, and limited labelled data, motivating the need for robust, effective Deep Learning (DL)- based solutions.

As a prerequisite to understanding and designing effective recognition systems for ancient manuscripts, this thesis first examines …

The Waldo Dataset, Mary E. Koone, Rosie Kallie, Vassilis Athisos, Laurel S. Stvan

Computer Science and Engineering Datasets - Archive

Distinct from the task of predicting the author of a document (authorship attribution), we focus on addressing the issue of how to estimate the similarity between the written language styles of authors. To do so, we present a dataset of metadata derived by asking human annotators, who were presented with three documents, to identify which two were written by the same author and which was written by a different author. The dataset has over 400 such annotations, creating a companion to the Amazon Web Services (AWS) customer review dataset, laying the groundwork for crowdsourcing applications to other natural language processing …

Multi-Modal Tensor Fusion For Alzheimer’S Disease Recognition, Mason Li, Tiffany Le, Jiajing Huang, Yuxin Wen

Engineering Faculty Articles and Research

Accurate and early diagnosis of Alzheimer’s disease (AD) is critical for effective intervention, disease monitoring, and patient care. Traditional diagnostic approaches rely on a single modality, such as clinical assessments, neuroimaging, or genetic markers, which may fail to capture the complex, multifaceted nature of AD. Multimodal learning has therefore been explored to integrate complementary information across data sources. However, conventional fusion strategies, including early feature concatenation and late decision-level fusion, often model modalities independently and fail to capture high-order cross-modal interactions. To address these limitations, we propose a multimodal tensor fusion network (MTFN) that integrates heterogeneous data sources, including visual …

Marine Vehicle Dynamics Using Koopman Operator Theory With Hybrid Observables, Mikhalib A L Green

LSU Master's Theses

Accurate modeling of marine vehicle dynamics remains challenging due to strong nonlinear hydrodynamic effects, environmental disturbances, and sensitivity to configuration changes, particularly for small-scale platforms. Classical physics-based models require extensive parameter identification and often exhibit degraded performance outside narrow operating regimes, while purely data-driven approaches may lack structure or impose high computational cost. This thesis presents a data-driven Koopman operator framework with hybrid observables for modeling the dynamics of unmanned marine vehicles. The proposed approach combines structured monomial observables with a learned neural network embedding to construct a lifted state representation in which the nonlinear vehicle dynamics are approximated by …

Large-Scale File Fragment Classification Via Multi-View Learning, Samuel Hildebrand

LSU Master's Theses

File reassembly is one of the most fundamental tasks in digital forensics, enabling recovery of data from potentially damaged storage media even when file system metadata is unavailable. This thesis reviews more than two decades of work in the realm of file carving, with a particular focus on fragmented file carving, which remains a focus of research, and file fragment classification, a principal component of fragmented file carving. This thesis serves a literature review of both file carving and fragmented file carving, surveys the massive amounts of data needed for the task of fragment classification and the datasets that serve …

Analogy2kg: An Automatic Pipeline For Deriving Knowledge Graphs From Long-Text Analogies, Kara Combs, Lance E. Champagne, Bruce A. Cox, Christine M. Schubert Kabban, Trevor Bihl, Grace Lemming

Faculty Publications

Analogical reasoning is an increasingly popular, lightweight solution to enable large language model (LLM)-level reasoning without computational complexity. Still, it has yet to be adopted due to its reliance on strictly hand-formatted data. Therefore, we propose Analogy2KG (“Analogy to Knowledge Graph”), as an automatic pipeline that transforms text into a KG format via a fine-tuned version of information extraction (IE) algorithms for long-text analogies. The need to verify that the complex underlying analogical structure of the data is maintained was done via paired samples tests in the creation and validation of this pipeline. Graph density was used to evaluate the …

Research On Inter-Satellite Topology Design And Simulation Of Giant Leo Constellation Network With Consistent Pattern, Zhicheng Li, Shuaijun Liu, Lixiang Liu

Journal of System Simulation

Abstract: The giant low earth orbit (LEO) constellation network uses inter-satellite links to form an intersatellite topology, realizing the transmission of data between satellites. In order to adapt to the nature of uniform and symmetrical distribution of satellites in the constellation, this paper used a consistent connection pattern between satellites to construct an inter-satellite topology, and by analyzing the arrangement of non-mirror links in the constellation, it was found that the connection method of each link of the satellite itself could be independent of each other, which reduced the simulation complexity and the solution space of the inter-satellite topology. …

Design And Verification Of Manned-Unmanned Collaborative Combat Capability System Based On Mbse, Fangbo Wang, Jian Guo, Chenglie Du, Yifan Liu, Pengpeng Zhang

Journal of System Simulation

Abstract: The traditional model-based systems engineering (MBSE) method has problems of failing to fully exhibit complex combat logics in manned-unmanned collaborative combat system modeling, neglecting the scenario constraints in interface modeling, and requiring long-term and costly algorithm verification. In order to solve the problems, a methodology and design tool based on MBSE was proposed. An integrated verification method of a system's operational logic, interface design, and algorithmic design was constructed, thus providing a digital and rapidly iterative verification approach for system simulation. A verification environment for multiple key algorithm simulations was established, effectively reducing the economic cost of building verification …

Key Problems Of Intent Recognition Research: A Survey On Activity, Plan And Goal Recognition, Yi Zhang, Kai Xu, Shuilin Li, Dejun Chen, Yunxiu Zeng, Yong Peng

Journal of System Simulation

Abstract: With the development of artificial intelligence technology, realizing intent recognition in human-computer interaction has become one of the key challenges. In this paper, the current research status of three fields was systematically sorted out, namely activity recognition, plan recognition, and goal recognition, and the progress from the problem proposal to the current development was analyzed. The main research approaches in each field were reviewed, and a survey of research on activity recognition, a development overview of plan recognition, and a retrospective analysis of hotspots in goal recognition were conducted. This general view of the problem helped to clarify …

Review Of 3d Human Reconstruction Methods Empowering Vr/Ar, Lisha Zhang, Yuchi Huo, Qi Ye, Anjun Chen, Shihui Guo, Jiming Chen

Journal of System Simulation

Abstract: 3D human reconstruction is critical for VR/AR. Early methods relied on multi-view cameras and depth sensors but were costly. Mid-term approaches using parametric human models enabled efficient single-image reconstruction, while implicit neural representations improved fidelity yet suffered from low efficiency. Currently, 3D Gaussian Splatting achieves high accuracy and real-time rendering as a new paradigm. Challenges include detail distortion and limited generalization, and future development will focus on VR/AR integration.

Neural Radiance Fields Based On Explicit Feature Matching And Scaled Dot-Product Attention, Mingwei Cao, Fengna Wang, Zilong Wang, Haifeng Zhao

Journal of System Simulation

Abstract: To address the problems that neural radiance fields(NeRF) are prone to artifacts and texture blurring in novel view synthesis under sparse view input and complex scenes, this paper proposed neural radiance fields based on explicit feature matching and scaled dot-product attention(EMD-NeRF). A multiscale feature extraction network was used to extract multi-scale feature information from the input sparse views. A fusion dot-product module was utilized to calculate view interaction information as a shared branch. Cosine similarity was adopted as a matching clue for similarity embedding volume rendering. A regularization loss function was used to enhance the quality of the scene …

Analytical Reentry Guidance Method Based On Lift-To-Drag Ratio-Velocity Profile, Weibo Sun, Ping Ma, Yuxuan Wang, Songyan Wang, Tao Chao

Journal of System Simulation

Abstract: An analytical reentry guidance method based on the lift-to-drag ratio and velocity profile was proposed to address the challenges of precise flight time control and prohibited area avoidance for the hypersonic glide flight vehicle during the reentry phase. A reduced-order reentry motion model was established; the Chebyshev series was used to approximate its nonlinear integral terms and differential equations; analytical expressions for the glide trajectory were yielded. The analytical relationships among flight time, remaining range, and the lift-to-drag ratio and velocity profile were derived. The optimization problem of lift-to-drag ratio and velocity profile was converted into a segmented parameter …

Research And Analysis Of Algorithm For Detecting Surface Defects On Automotive Wheel Hubs Based On Ccl-Yolov8, Yanjun Chen, Min Zhou, Meng Zha, Meizhou Zhang

Journal of System Simulation

Abstract: To address the challenges such as low detection efficiency, difficulties in identifying small defects, and poor accuracy in detecting surface defects on automotive wheel hubs, a lightweight neural network called CCL-YOLOv8 was proposed based on an improved YOLOv8n architecture. A synergistic improvement in both detection accuracy and efficiency was achieved through a three-stage model optimization strategy. A convolutional attention fusion module was introduced, which integrated convolution operations with self-attention mechanisms, thereby enhancing the model's ability to capture local features of small defects while perceiving global context under low signal-to-noise ratio conditions.A C2f-Star module was constructed to reduce computational overhead …

Simulation And Multi-Perspective Recognition Algorithm For Typical Trajectory Shapes, Xuejian Feng, Han Ding, Yiqi Tong, Chaoying Huo, Yanjin Zhang

Journal of System Simulation

Abstract: Current trajectory simulation methods inadequately address geometric shape features and kinematic properties of the target trajectory. To bridge this gap, a target trajectory shape simulation algorithm based on kinematic laws was proposed. The polar coordinate equations and curvature equations of multiple trajectories were integrated. The aircraft state parameters were solved by combining kinematic equations. Angular Gaussian noise was introduced to enhance trajectory diversity and authenticity. Additionally, a multi-perspective trajectory shape recognition algorithm was designed, which could effectively integrate image and sequential multi-modal features by adopting a multilayer perceptron, enabling precise trajectory shape recognition. Experimental results demonstrate that the proposed …

Research On Performance Evaluation Of P300 Brain-Computer Interface Under Environment Modeling And Simulation, Xiaofei Ge, Jinling Lian, Jin Han, Xin'an Fan, Danmei Luo, Yuxiang Hua, Hao Liu, Lijian Zhang

Journal of System Simulation

Abstract: In the process of brain-computer interface(BCI) technology stepping from laboratory to practical application scenarios, it is difficult to make an accurate prediction and evaluation of the effect of real environment factors on the system performance. Therefore, a research method based on a multifactor simulation experiment was proposed. A controllable simulation experiment environment was constructed, and the parametric modeling of two key physical environmental factors, namely noise and light, was carried out. By taking the number of electroencephalogram channels as the system parameter, this paper systematically studied the influence mechanism of the aforementioned factors on the decoding performance of P300-BCI. …

Design And Application Of Bds Visual Simulation Teaching Platform Based On Cesium, Liguo Lu, Yulin Cai, Tangting Wu, Canhui Lin

Journal of System Simulation

Abstract: The deep development of the BeiDou navigation satellite system(BDS) cannot be separated from the support of corresponding talent education, especially the development of new digital resources and tools. A visual simulation teaching platform for BDS was designed based on open source Cesium engine library to address issues such as the large and complex size of the BDS project, the unreachable space environment, and the difficult understanding of principles and concepts. The platform adopted a B/S architecture to achieve visual simulation of course content such as orbit cognition, orbit calculation, constellation simulation, service performance, and satellite links. Teaching applications have …