Engineering Commons^™

Port And Vessel Communication Traffic Intrusion Detection: A Variational Autoencoder‑Enhanced Multilayer Perceptron Approach, Chien-Lin Chiang, Hsien-Cheng Chou, Ming-Yuan Peng, Yi-Yuan Chiang, Yu-Shun Liu

Journal of Marine Science and Technology–Taiwan

Port and vessel networks increasingly operate on IP/Ethernet backbones with high‑noise, high‑dimensional traffic. We present a lightweight hybrid intrusion‑detection model that couples a variational autoencoder (VAE) with a multilayer perceptron (MLP) and augments training with a boundary‑oriented latent‑space mixup strategy. The VAE models the distribution of normal traffic and identifies anomalies through reconstruction errors. Subsequently, it generates robust latent vectors, enabling the MLP to perform highly accurate supervised classification. On the UNSW‑NB15 dataset, the proposed pipeline attains ≥97% accuracy and an outstanding recall of 99.56% in binary intrusion detection, and visualization of the latent space (PCA) together with reconstruction‑error analyses …

Comparative Performance Of Physiological Vital-Sign Forecasting Under Random And Patient-Wise Splitting Using Deep Learning, Lavanya Vasavi Chittem Reddy

Theses and Dissertations

Physiological vital-sign forecasting estimates future measurements based on recent temporal patterns and can support analysis of continuously recorded monitoring data. This study comparatively evaluated deep feedforward, recurrent, bidirectional recurrent, long short-term memory, and bidirectional long short-term memory architectures for one-step-ahead forecasting of peripheral oxygen saturation, heart rate, and pulse rate. Each model received consecutive observations of peripheral oxygen saturation, heart rate, pulse rate, respiratory rate, and age, while separate single-output models predicted the next value of the selected target.

Random and patient-wise data splitting were compared using identical input definitions, preprocessing procedures, model architectures, and training hyperparameters. The strongest architecture …

Scalable Quality Assessment Of Ground Motion Records Via Interpretable Deep Learning Architectures, Ali Montazeri Namin

All Graduate Theses and Dissertations, Fall 2023 to Present

Earthquake engineers rely on accurate recordings of ground shaking to design safe and resilient buildings. However, sorting through thousands of these recordings to find the reliable ones and throwing out those ruined by sensor errors or background noise is traditionally done by hand. Because modern seismic networks collect massive amounts of earthquake data every day, this manual checking process is much too slow. To fix this, researchers are turning to artificial intelligence to automatically check the quality of these recordings.

While artificial intelligence offers a fast solution, there are limitations. These computer models can become massive and expensive to run, …

A Data-Driven Koopman Framework For Station-Keeping On Near Rectilinear Halo Orbit, Anusha Sharma Malladi

Theses and Dissertations

Cislunar missions have gained significant attention in recent decades, motivating the need for efficient modeling and reliable control. In this work a Koopman operator based framework is developed for approximating the error dynamics around a reference Near Rectilinear Halo Orbit (NRHO) in the Earth-Moon Circular Restricted Three-Body Problem (CR3BP). A decoder free neural network is used to learn a lifted linear representation of the nonlinear CR3BP dynamics and a residual based approach is used to identify the corresponding control input matrix. The model is then implemented in a receding-horizon target point controller and compared with uncontrolled propagation and a State …

Lrq-Solver: A Transformer-Based Neural Operator For Fast And Accurate Solving Of Large-Scale 3d Pdes, Peijian Zeng, Guan Wang, Haohao Gu, Xiaoguang Hu, Tiezhu Gao, Zhuowei Wang, Aimin Yang, Xiaoyu Song

Electrical and Computer Engineering Faculty Publications and Presentations

Solving large-scale PDEs on complex three-dimensional geometries remains a central challenge in scientific and engineering computing, often due to expensive pre-processing stages and high computational overhead. We present Low-Rank Query-based PDE Solver (LRQ-Solver), a physics-integrated deep learning framework for efficient CAE simulations of complex three-dimensional geometries in CAD-driven design analysis. Built upon the Parameter-Conditioned Lagrangian Modeling (PCLM) that embeds physical consistency into the learning process and the Low-Rank Query Attention (LR-QA) module that reduces attention complexity from O(N2) to O(NC2+C3) via covariance decomposition, LRQ-Solver supports multi-configuration analysis within iterative design workflows. On two benchmark datasets, it achieves a 28.6% error …

Dynamic Focal Loss Adjustment For Railway Defect Detection, Mehmet Koç, Ridvan Özdemi̇r, Ömer Gerek

Turkish Journal of Electrical Engineering and Computer Sciences

Railway infrastructure is critical to the safe and efficient operation of transportation systems, and the early detection of defects is essential for preventing catastrophic failures. Automated defect detection methods are therefore crucial for maintaining continuous safety while reducing maintenance costs. Although Focal Loss is widely used in object detection under class-imbalanced conditions, its fixed α parameter may limit its effectiveness in detecting rare defects. In this study, we propose an adaptive α-tuned Focal Loss approach that dynamically adjusts class weights based on average precision (AP) values. By iteratively optimizing α without relying on gradient-based optimization, the proposed method improves the …

Comparing Principal Component Analysis And Sequential Fusion Deeponet With Varying Dataset Size For Design Space Exploration Of Hypersonic Flow, J. Dallan Trentman

Theses and Dissertations

Design space exploration and optimization of hypersonic vehicles is costly due to the difficulty of producing high fidelity CFD simulations in the hypersonic domain. Reduced order modeling can allow for design optimization at a fraction of the computational cost. This paper investigates the amount of training data required to produce an accurate Reduced Order Model. A PCA based ROM is developed and compared to a Sequential Fusion DeepONet by comparing model prediction accuracy, training time, and prediction time, across dataset sizes from 25 samples to 1024 samples. These models are applied to 2D hypersonic CFD simulations of the Orion reentry …

Integrating Ai-Based Electricity Demand Forecasting With Solar Grid Planning To Enhance Sustainability And Reliability, Anas Thamer Mustafa, Omar Sharaf Al-Deen Al-Yozbaky

AUIQ Technical Engineering Science

Proper electricity-demand forecasting is essential for reliable power-system planning, particularly in urban networks facing rapid demand growth and transformer overloading. However, many previous studies have treated load forecasting and renewable-energy integration as separate tasks, which limits their usefulness for practical planning. This study develops an integrated forecasting–planning framework that links AI-based electricity-demand forecasting with photovoltaic (PV) system design and transformer-loading assessment. The framework is applied to real daily data from the Al-Intisar 132/33 kV substation in Mosul, Iraq, covering electrical load, temperature, population, and date-related variables for the period 2022–2024. Fourteen forecasting models from four methodological categories were evaluated: machine-learning …

Eyolov8-Msaff: An Enhanced Object Detection Algorithm With Multi-Scale Attention And Feature Fusion For Small And Dense Object Detection In Uav Applications, Mohammed Hasan Mutar, Morteza Valizadeh, Alaa Hussein Abdulaal, Mehdi Chehel Amirani

Iraqi Journal for Computer Science and Mathematics

The proliferation of unmanned aerial vehicles (UAVs) has necessitated the development of sophisticated object detection algorithms capable of handling the unique challenges posed by aerial imagery. Traditional detection methods often struggle with small object sizes, dense distributions, and complex backgrounds characteristic of UAV-captured scenes. This research presents EYOLOv8-MSAFF (Enhanced YOLOv8 with Multi-Scale Attention and Feature Fusion), a novel deep learning architecture specifically engineered for superior performance in UAV-based object detection tasks. The proposed methodology integrates four innovative components: a Hybrid Spatial-Channel Attention Mechanism (HSCAM) that processes attention information in parallel rather than sequentially, an Adaptive Multi-Scale Feature Fusion Module (AMSFFM) …

A Novel Hierarchical Multi-Scale Attention Mechanism For Enhanced Yolov8 Performance In Unmanned Aerial Vehicle-Based Small Object Detection, Mohammed Hasan Mutar, Morteza Valizadeh, Alaa Hussein Abdulaal, Mehdi Chehel Amirani

Iraqi Journal for Computer Science and Mathematics

Object detection in Unmanned Aerial Vehicle (UAV) images presents significant challenges due to the prevalence of small and densely packed objects, as well as variations in scale, orientation, and lighting conditions. This paper introduces a novel object detection algorithm, Hierarchical Multi-Scale Attention YOLO (HMSA-YOLO), which is an improved version of YOLOv8 designed to address these challenges. The proposed method incorporates a novel Hierarchical Multi-Scale Attention (HMSA) module, a Bidirectional Feature Pyramid Network (BiFPN) for enhanced feature fusion, a modified loss function, and an adaptive anchor optimization technique. The HMSA module effectively captures both channel and spatial dependencies at multiple scales, …

Comparison Of Log-Based Coal Structure Identification Methods For Structurally Complex Areas: A Case Study Of The Huainan Mining Area, Zhang Zixin, Zhou Xiaozhi, Chen Xuehua

Coal Geology & Exploration

Objective CO₂ displacing coalbed methane (CBM) provides substantial environmental and energy benefits. However, differences in coal structures represent a key geological factor influencing the effectiveness of this technique. Constrained by the complex geological conditions for CBM occurrence and the impacts of multiple factors on the log responses of coal structures, traditional log-based methods for coal structure identification face challenges such as much human intervention, complex feature engineering, and poor adaptability. This study aims to achieve effective identification of complex coal structures within a single coal seam. Methods An asymmetric convolution kernel-based convolutional neural network (CNN) model for coal structure …

Radiation Effects On The Amd Versal Running An Automatic Modulation Classification Model, Allan D. Howe

Theses and Dissertations

With the recent increase of interest into artificial intelligence (AI), graphics processing units (GPUs) have been used as the main hardware architecture for AI computation. Because of their high power consumption and large size, it is necessary to consider other architectures to perform AI computing in outer-space where size, weight, power, and cost (SWaP-C) are tightly constrained. One such architecture is the Advanced Micro Devices (AMD) Versal Adaptive Compute Acceleration Platform (ACAP), which can achieve acceptable AI throughput at a lower power and size compared to a GPU. As the Versal ACAP gets used in space applications, it is necessary …

Scalefusion: Hierarchical Feature Aggregation For Unified Multiscale Object Detection, Ayşenur Yaylaci, Berru Kaya, Mehmet Kiliçarslan

Turkish Journal of Electrical Engineering and Computer Sciences

Detecting objects across a wide range of scales, particularly small ones, remains a significant challenge in computer vision. Existing methods often improve small object detection at the cost of performance on larger objects or introduce significant computational overhead through external techniques like image slicing. This paper introduces ScaleFusion, a novel, unified, end-to-end object detection architecture designed to provide robust performance across all scales within a single model. The core of our approach is a hierarchical feature aggregation strategy structured like a tree. ScaleFusion processes an image by running a shared backbone network only on fine-grained patches at the lowest level …

Video Compression Optimization Techniques Using Artificial Intelligence: Review/Review Article, Amal Abbas Kadhim, Wedad Abdul Khuder Naser, Nada Abdulkareem Hameed

Al-Esraa University College Journal for Engineering Sciences

Compressing video is an important and essential function in today's multimedia systems as it enables the efficient storage and transmission of large volumes of video data. The proliferation of high-resolution videos that are being used in various application areas such as video streaming, video conferencing, surveillance, and autonomous systems has caused the strong demand for more efficient compression algorithms. This paper presents an in-depth review of video compression techniques with particular focus on AI methods. It also discusses traditional video coding standards including H. 264/AVC, H. 265/HEVC, and AV1, including motion estimation, transform coding quantization entropy coding, and rate-distortion optimization. …

Key Technologies For Artificial Intelligence-Based Inversion Of The Borehole Transient Electromagnetic Method In Coal Mining Areas, Fan Tao, Hao Yue, Zhang Peng, Li Ping, Zhao Zhao, Zhao Rui, Liu Qiang, Yan Junsheng, Song Xianzhe, Liu Borui, Chen Changyuan, Li Bo

Coal Geology & Exploration

Background Using directional boreholes, the borehole transient electromagnetic (TEM) method enables near-field excitation and the simultaneous reception of three components within a borehole. This method can effectively avoid interference from ferromagnetic materials in roadways, thereby significantly enhancing the detection accuracy and range. Therefore, this method is widely applied to the detection of underground concealed water hazards in coal mines. However, there is a conflict between the accuracy and efficiency of current inversion methods for borehole TEM data, and existing technologies are insufficient to simultaneously meet the demands for high efficiency and high precision in the detection of concealed water hazards. …

Vibration Diagnostics For Industrial Machinery: From Signal Processing To Deep Learning, Walid M. Shewakh

Emirates Journal for Engineering Research

When machines break down unexpectedly, it costs factories a lot of money and can even put workers at risk. That is why vibration analysis has become such a big deal in predictive maintenance. By listening to how a machine vibrates, engineers can often tell something is wrong before the whole thing falls apart. This paper looks at how we have gotten better at this over the years. We started with fairly basic signal processing, things like Fourier transforms that have been around for ages, and now we are seeing some really interesting work with neural networks and deep learning. I …

Skinclusive Ai: Towards Equitable Skin Cancer Detection For Deployment On Edge Devices, Joseph Galicinao

Master's Theses

Skin cancer is one of the most prevalent cancers worldwide, yet existing deep-learning models exhibit significant racial disparities because many widely used datasets are heavily skewed toward lighter skin tones. In addition, many approaches are not designed for deployment on resource-constrained devices, which limits accessibility. This work presents a comprehensive evaluation of classical machine learning and deep-learning based models for binary skin lesion classification, identifying the Swin-Tiny transformer architecture as the most effective backbone. To address bias, we curate a skin-tone balanced dataset, and introduce fairness-aware training through adversarial training, and joint distribution oversampling, to improve performance across protected attributes. …

Toward Learning-Based Reconstruction And Part Decomposition Of Man-Made 3d Geometry: Neural Implicit Representations And Scalable Supervision, Shen Fan

Dissertations

Digital three-dimensional (3D) models are central to engineering design, analysis, and manufacturing, but learning pipelines for man-made geometry often operate on sampled carriers that do not preserve all of the structure present in exact CAD representations. This dissertation studies learning-based reconstruction and part decomposition for structured man-made 3D geometry, from general object benchmarks to CAD-derived datasets, with a focus on neural implicit representations trained from signed-distance samples, point clouds, and tessellated meshes. The goal is to make these models more accurate, more part-aware, and more consistently supervised.

First, signed distance function (SDF) reconstruction with implicit neural representations is improved through …

Deep Learning Approaches For Ctenophore Identification And Tracking, Anagha Bharadwaj

Theses

Ctenophores are translucent marine organisms with nearly invisible tentacles and pose significant challenges due to their transparent morphology and ambiguous structural features. This research addresses the classification and tracking of these organisms and evaluates the performance of current computer vision models under sparse-data environments.

A dataset from the NJIT Life History Lab consisting of microscopic laboratory videos and photographs of different growth stages is used to train and assess a number of convolutional neural network designs, including VGG16, ResNet, BioCLIP2, YOLO, and DeepLabCut. Additionally, a web-based interface is developed to evaluate expert-labeled ground truth with the model's performance.

The findings …

The Reliability Of Yolo Object Detection On The Amd Versal In A Proton Radiation Environment, Jacob D. Brown

Theses and Dissertations

Object detection is an important operation for satellites to be able to perform, and in outer-space missions it is crucial that machine learning models perform inference on satellite images accurately and reliably. Where size, weight, power, and other constraints exist, meeting this goal for accurate and reliable object detection is challenging. Additionally, soft errors caused by radiation further disrupt and degrade the operation of object detection in satellites. This thesis studies the performance of a deep learning model on an embedded device, the AMD Versal, in the presence of soft errors. The well-known and high-performing YOLO convolutional neural network was …

A Descriptive Analysis Of Plant Leaf Disease Detection Using Machine Learning And Deep Learning Models: A Systematic Review, Arzoo Chamoli, Anuj Kumar

Turkish Journal of Electrical Engineering and Computer Sciences

Plant leaf disease detection (PLDD) is a growing active research area with burgeoning practical applications across various sectors such as agricultural monitoring, food security, and environmental conservation. Accurate segmentation and classification of plant leaf diseases remains a key challenge in the field of plant leaf disease prediction. The challenge demands automated methods for the plant disease identification because it needs to develop better crop management systems, which will boost agricultural production. In this article, we provide a systematic review of various machine learning (ML) and deep learning (DL) methods extensively used for PLDD. The review strategy follows a formal protocol, …

Multimodal Aerial Image-Based Ground Object Detection And Classification Using Yolo, Erik Nielsen

All Theses

Object detection in unmanned aerial vehicles (UAVs) present a unique challenge due to small object sizes, varying viewpoints, and changing environmental conditions. These challenges are exacerbated when operating during daytime and nighttime scenarios where illumination differences can heavily impact detection performance. This work is motivated by military object detection applications where the ability to reliably identify small objects such as landmines or unexploded ordnance from aerial imagery presents a critical safety need and a significant technical challenge. In this paper, we investigate object detection using both visible (RGB) and infrared (IR) imagery to improve robustness and reliability across diverse operating …

Enhancing Lane Detection In Autonomous Vehicles Using Data Augmentation For Adverse Environmental Conditions, Rutvikkumar Dave

Electronic Theses and Dissertations

To make sure that self-driving and connected automobile technologies are safe and work well, it’s really important that they can correctly identify lanes. But lane detection Algorithms typically have a hard time working well when the weather is bad, such when it rains, fogs, or goes too fast. The circumstances cause visual distortions that make existing computer vision systems less reliable, which makes it harder requires autonomous navigation systems to work well. This paper introduces a comprehensive lane detection system that integrates synthetic Weather-informed data augmentation combined with a Weather-aware Temporal Lane Detection Network (WTLDNet) to make it easier for …

Modelling Method Of Unmanned Vehicle Dynamics Based On Neural Network, Jun Wang, Min Liu, Xiaochuan Zhang, Yishan Ding, Juhui Feng, Ye Zhuang

Journal of System Simulation

Abstract: To address the challenges of high data acquisition costs of test data on dynamic characteristics between tires and soft terrain and low speed of numerical calculation for unmanned vehicles in complex terrestrial environments, a modeling method of unmanned vehicle dynamics based on a neural network was proposed. Tire-terrain contact dynamics models were built by using discrete element method (DEM) simulations for tire-terrain contact and experimental data, thereby creating a dataset of tire contact forces for various tire materials in terrestrial environments. The neural network was applied to regressively learn the dataset, and a nonlinear neural network tire model was …

Toward Human-Like Robot Behavior In Co-Manipulation: Learning From Human Dyad Performance, Adam Kenneth Jackson

Theses and Dissertations

Co-manipulation requires two or more agents to coordinate through a shared object while exchanging information through motion and force. Although this capability is routine for humans, robotic systems still struggle to participate in such interaction naturally and effectively. This thesis addresses that gap by studying human co-manipulation behavior, developing methods to quantify performance, collecting a leader-follower dataset that isolates haptic communication, and training data-driven controllers from human demonstrations. First, cumulative and real-time metrics were developed to quantify human dyad performance. In particular, the behavioral modes "quickly" and "smoothly" were formulated as measurable performance objectives using completion time and squared jerk. …

A Platform For Acquiring And Classifying Low-Noise Electrocardiogram Signals For Applications In Cardiovascular Monitoring, Begmamat Berdimurodovich Dushanov, Narzullo Mamatov Dr.

Technical science and innovation

The early screening and continuous monitoring of cardiovascular diseases need effective acquisition and smart processing of electrocardiogram (ECG) signals. In this article, we introduce a compact platform designed for the acquisition of low-noise ECG signals and classification of the signals using a one-dimensional convolutional neural network (1D-CNN). Our compact platform consists of a low-noise analog front-end (AFE), including an instrumentation amplifier and a chain of analog filters, along with a data acquisition component designed to ensure effective suppression of baseline wander and high frequencies. Our compact platform consumes a low amount of power and can therefore be used for continuous …

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 …

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 …

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 Visual Place Recognition Algorithms For Complex Urban Environments, Peijin Liu, Minxin Zhang, Lin He, Yige Sun, Tingqi Su

Journal of System Simulation

Abstract: Dynamic factors such as traffic flow and crowd density in complex urban environments reduce the accuracy of visual place recognition (VPR) algorithms. To solve these problems, a semantic-guided visual place recognition (SG-VPR) algorithm was proposed. A semantic-guided feature suppression module was designed. A semantic-guided module and feature suppression layer were constructed to reduce the dynamic object interference and more accurately extract the key static features. An adaptive triplet margin loss function (ATML) was proposed by improving the traditional triplet margin loss. The margins were adaptively adjusted according to the sample distribution, solving the problem of suboptimal solution convergence …