Engineering Commons^™

Ground Target Recognition And Damage Assessment Of Patrol Missiles Based On Multi-Source Information Fusion, Yibo Xu, Qinghua Yu, Yanjuan Wang, Ce Guo, Shiru Feng, Huimin Lu

Journal of System Simulation

Abstract: For the multiple patrol missiles to attack the high defense capacity targets, a mobile ground target detection and damage assessment method based on multi-source information fusion is proposed. The multi-source information fusion of infrared images and RGB images is carried out by using IoU determination. A novel two-stage tightly coupled damage assessment method based on YOLO-VGGNet of patrol missiles to mobile ground targets is proposed. This method can fully use the advantage of deep semantic information extraction of CNNs and introduce the infrared damaging information simultaneously to achieve the online and real-time damage assessment of mobile ground targets. The …

Urban Flood Extent Segmentation And Evaluation From Real-World Surveillance Camera Images Using Deep Convolutional Neural Network, Yidi Wang, Yawen Shen, Behrouz Salahshour, Mecit Cetin, Khan Iftekharuddin, Navid Tahvildari, Guoping Huang, Devin K. Harris, Kwame Ampofo, Jonathan L. Goodall

Civil & Environmental Engineering Faculty Publications

This study explores the use of Deep Convolutional Neural Network (DCNN) for semantic segmentation of flood images. Imagery datasets of urban flooding were used to train two DCNN-based models, and camera images were used to test the application of the models with real-world data. Validation results show that both models extracted flood extent with a mean F1-score over 0.9. The factors that affected the performance included still water surface with specular reflection, wet road surface, and low illumination. In testing, reduced visibility during a storm and raindrops on surveillance cameras were major problems that affected the segmentation of flood extent. …

Inverse Engineering Of Absorption And Scattering In Nanoparticles: A Machine Learning Approach, Alex Vallone, Nooshin M. Estakhri, Nasim Mohammadi Estrakhri

Engineering Faculty Articles and Research

We use a region-specified machine learning approach to inverse design highly absorptive multilayer plasmonic nanoparticles. We demonstrate the design of particles with a wide range of absorption to scattering ratios (i.e., cloaked absorbers and bright absorbers) and for different visible wavelengths.

Infrared Imaging Segmentation Employing An Explainable Deep Neural Network, Xinfei Liao, Dan Wang, Zairan Li, Nilanjan Dey, Rs Simon, Fuqian Shi

Turkish Journal of Electrical Engineering and Computer Sciences

Explainable AI (XAI) improved by a deep neural network (DNN) of a residual neural network (ResNet) and long short-term memory networks (LSTMs), termed XAIRL, is proposed for segmenting foot infrared imaging datasets. First, an infrared sensor imaging dataset is acquired by a foot infrared sensor imaging device and preprocessed. The infrared sensor image features are then defined and extracted with XAIRL being applied to segment the dataset. This paper compares and discusses our results with XAIRL. Evaluation indices are applied to perform various measurements for foot infrared image segmentation including accuracy, precision, recall, F1 score, intersection over union (IoU), Dice …

Precision Spraying Using Variable Time Delays And Vision-Based Velocity Estimation, Paolo Rommel Sanchez, Hong Zhang

Henry M. Rowan College of Engineering Faculty Scholarship

Traditionally, precision farm equipment often relies on real-time kinematics and global positioning systems (RTK-GPS) for accurate position and velocity estimates. This approach proved effective and widely adopted in developed regions where RTK-GPS satellite and base station availability and visibility are not limited. However, RTK-GPS signal can be limited in farm areas due to topographic and economic constraints. Thus, this study developed a precision sprayer that estimated the travel velocity locally by tracking the relative motion of plants using a deep-learning-based machine vision system. Sprayer valves were then controlled by variable time delay (VTD) queuing and dynamic filtering. The proposed velocity …

Short-Term Vehicle Speed Prediction With Spatiotemporal Convolution Fused With Variational Modal Decomposition, Kai Zhang, Haipeng Lu, Ying Han, Lingyun Zhang, Yujie Ding

Journal of System Simulation

Abstract: Accurate short-term vehicle speed prediction helps to resolve city traffic congestion problems. Focusing on the defect that CNN cannot process non-Euclidean geometric data, GCN and BiLSTM are combined to fully process the spatiotemporal characteristics of road network information, in which the advantages of GCN integrating global features and the ability of BiLSTM to extract temporal features are considered. In order to reduce the interference of noise to the data, variational modal decomposition (VMD) is introduced and short-term vehicle speed prediction model based on VMD-GCN-BiLSTM (VGBLSTM) is proposed . Simulation results show that the prediction accuracy of VGBLSTM model is …

Automatic Classification And Segmentation Of Patterned Martian Ground Using Deep Learning Techniques, Ruthy Brito

Electronic Thesis and Dissertation Repository

Science autonomy onboard spacecraft can optimize image return by prioritizing downlink of meaningful data. Martian polygonally cracked ground is actively studied by planetary geologists and may be indicative of subsurface water. Filtering images containing these polygonal features can be used as a case study for science autonomy and to reduce the overhead associated with parsing through Martian surface images. This thesis demonstrates the use of deep learning techniques in the classification of Martian polygonally patterned ground from HiRISE images. Three tasks are considered, a binary classification to identify images containing polygons, multiclass classification distinguishing different polygon types and semantic segmentation …

Detecting Alzheimer's Disease Using Artificial Neural Networks, Sally Lee, Mia Keegan

Electrical Engineering

This project aims to use artificial neural networks (ANN) in order to detect Alzheimer’s disease. More specifically, convolutional neural networks (CNN) will be utilized as this is the most common ANN and has been used in many different image processing applications. The purpose of using artificial neural networks as a detect method is so that an intelligent way for image and signal analysis can be used. A software that implements CNN will be developed so that users in medical settings can utilize this software to detect Alzheimer’s in patients. The input for this software will be the patient’s MRI scans. …

An Efficient Deep Learning Architecture For Turkish Lira Recognition And Counterfeit Detection, Burak İyi̇kesi̇ci̇, Ergun Erçelebi̇

Turkish Journal of Electrical Engineering and Computer Sciences

Banknote counterfeiting is a common practice worldwide. Due to the recent developments in technology, banknote imitation has become easier than before. There are different kinds of algorithms developed for the detection of counterfeit banknotes for different countries in the literature. The earlier algorithms utilized classical image processing techniques where the implementations of machine learning and deep learning algorithms appeared with the developments in the artificial intelligence field as well as the computer hardware. In this study, a novel convolutional neural networks-based deep learning algorithm has been developed that detects counterfeit Turkish Lira banknotes and their denominations using the banknote images …

Transfer Learning, Model Interpretation, And Dataset Bias Analysis For Automated Violence Detection From Video, Erik Clemens

Master's Theses (2009 -)

Many communities have installed surveillance cameras in an effort to deter and respond to violence.Due to the difficulty of constantly monitoring such camera feeds, these systems are rarely used to provide real-time information. To enable rapid alerts and information for first responders, this thesis develops a proof-of-concept system capable of automatically detecting violence from video footage. This system is developed by fine-tuning a convolutional neural network that has previously demonstrated success on general action recognition tasks. This thesis explores two new techniques to improve the accuracy of the fine-tuned model. The first is a data augmentation technique that generates aspect …

Tempnet – Temporal Super-Resolution Of Radar Rainfall Products With Residual Cnns, Muhammed Ali Sit, Bongchul Seo, Ibrahim Demir

Civil, Architectural and Environmental Engineering Faculty Research & Creative Works

The temporal and spatial resolution of rainfall data is crucial for environmental modeling studies in which its variability in space and time is considered as a primary factor. Rainfall products from different remote sensing instruments (e.g., radar, satellite) have different space-time resolutions because of the differences in their sensing capabilities and post-processing methods. In this study, we developed a deep-learning approach that augments rainfall data with increased time resolutions to complement relatively lower-resolution products. We propose a neural network architecture based on Convolutional Neural Networks (CNNs), namely TempNet, to improve the temporal resolution of radar-based rainfall products and compare the …

Nipuna: A Novel Optimizer Activation Function For Deep Neural Networks, Golla Madhu, Sandeep Kautish, Khalid Abdulaziz Alnowibet, Hossam Zawbaa, Ali Wagdy Mohamed

Articles

In recent years, various deep neural networks with different learning paradigms have been widely employed in various applications, including medical diagnosis, image analysis, self-driving vehicles and others. The activation functions employed in deep neural networks have a huge impact on the training model and the reliability of the model. The Rectified Linear Unit (ReLU) has recently emerged as the most popular and extensively utilized activation function. ReLU has some flaws, such as the fact that it is only active when the units are positive during back-propagation and zero otherwise. This causes neurons to die (dying ReLU) and a shift in …

Convolutional-Neural-Network-Based Des-Level Aerodynamic Flow Field Generation From Urans Data, John P. Romano, Oktay Baysal, Alec C. Brodeur

Mechanical & Aerospace Engineering Faculty Publications

The present paper culminates several investigations into the use of convolutional neural networks (CNNs) as a post-processing step to improve the accuracy of unsteady Reynolds-averaged Navier–Stokes (URANS) simulations for subsonic flows over airfoils at low angles of attack. Time-averaged detached eddy simulation (DES)-generated flow fields serve as the target data for creating and training CNN models. CNN post-processing generates flow-field data comparable to DES resolution, but after using only URANS-level resources and properly training CNN models. This document outlines the underlying theory and progress toward the goal of improving URANS simulations by looking at flow predictions for a class of …

Setransformer: A Transformer-Based Code Semantic Parser For Code Comment Generation, Zheng Li, Yonghao Wu, Bin Peng, Xiang Chen, Zeyu Sun, Yong Liu, Paul Doyle

Conference Papers

Automated code comment generation technologies can help developers understand code intent, which can significantly reduce the cost of software maintenance and revision. The latest studies in this field mainly depend on deep neural networks, such as convolutional neural networks and recurrent neural network. However, these methods may not generate high-quality and readable code comments due to the long-term dependence problem, which means that the code blocks used to summarize information are far from each other. Owing to the long-term dependence problem, these methods forget the previous input data’s feature information during the training process. In this article, to solve the …

Integrating The Spatial Pyramid Pooling Into 3d Convolutional Neural Networks For Cerebral Microbleeds Detection, Andre Accioly Veira

CCE Theses and Dissertations

Cerebral microbleeds (CMB) are small foci of chronic blood products in brain tissues that are critical markers for cerebral amyloid angiopathy. CMB increases the risk of symptomatic intracerebral hemorrhage and ischemic stroke. CMB can also cause structural damage to brain tissues resulting in neurologic dysfunction, cognitive impairment, and dementia. Due to the paramagnetic properties of blood degradation products, CMB can be better visualized via susceptibility-weighted imaging (SWI) than magnetic resonance imaging (MRI).CMB identification and classification have been based mainly on human visual identification of SWI features via shape, size, and intensity information. However, manual interpretation can be biased. Visual screening …

Gated Deep Reinforcement Learning With Red Deer Optimization For Medical Image Classification, Narayanan Ganesh, Sambandan Jayalakshmi, Rama Chandran Narayanan, Miroslav Mahdal, Hossam Zawbaa, Ali Wagdy Mohamed

Articles

The brain is one of the most important and complex organs in the body, consisting of billions of individual cells. Uncontrolled growth and expansion of aberrant cell populations within or around the brain are the main causes of brain tumors. These cells have the potential to harm healthy cells and impair brain function [1]. Tumors can be detected using medical imaging techniques, which are considered the most popular and accurate way to classify different types of cancer, and this procedure is even more crucial as it is noninvasive [2]. Magnetic resonance imaging (MRI) is one such medical imaging technique that …

Light Auditor: Power Measurement Can Tell Private Data Leakage Through Iot Covert Channels, Woosub Jung, Kailai Cui, Kenneth Koltermann, Junjie Wang, Chunsheng Xin, Gang Zhou

Electrical & Computer Engineering Faculty Publications

Despite many conveniences of using IoT devices, they have suffered from various attacks due to their weak security. Besides well-known botnet attacks, IoT devices are vulnerable to recent covert-channel attacks. However, no study to date has considered these IoT covert-channel attacks. Among these attacks, researchers have demonstrated exfiltrating users' private data by exploiting the smart bulb's capability of infrared emission.

In this paper, we propose a power-auditing-based system that defends the data exfiltration attack on the smart bulb as a case study. We first implement this infrared-based attack in a lab environment. With a newly-collected power consumption dataset, we pre-process …

Generation Of Phase Transitions Boundaries Via Convolutional Neural Networks, Christopher Alexis Ibarra

Open Access Theses & Dissertations

Accurate mapping of phase transitions boundaries is crucial in accurately modeling the equation of state of materials. The phase transitions can be structural (solid-solid) driven by temperature or pressure or a phase change like melting which defines the solid-liquid melt line. There exist many computational methods for evaluating the phase diagram at a particular point in temperature (T) and pressure (P). Most of these methods involve evaluation of a single (P,T) point at a time. The present work partially automates the search for phase boundaries lines utilizing a machine learning method based on convolutional neural networks and an efficient search …

Machine Learning For Aiding Blood Flow Velocity Estimation Based On Angiography, Swati Padhee, Mark Johnson, Hang Yi, Tanvi Banerjee, Zifeng Yang

Computer Science and Engineering Faculty Publications

Computational fluid dynamics (CFD) is widely employed to predict hemodynamic characteristics in arterial models, while not friendly to clinical applications due to the complexity of numerical simulations. Alternatively, this work proposed a framework to estimate hemodynamics in vessels based on angiography images using machine learning (ML) algorithms. First, the iodine contrast perfusion in blood was mimicked by a flow of dye diffusing into water in the experimentally validated CFD modeling. The generated projective images from simulations imitated the counterpart of light passing through the flow field as an analogy of X-ray imaging. Thus, the CFD simulation provides both the ground …

Natural Language Processing For Novel Writing, Leqing Qu, Okan Ersoy

Department of Electrical and Computer Engineering Technical Reports

No abstract provided.

Digital Image Forensics Via Meta-Learning And Few-Shot Learning, Yuxi Shi

Dissertations

Digital images are a substantial portion of the information conveyed by social media, the Internet, and television in our daily life. In recent years, digital images have become not only one of the public information carriers, but also a crucial piece of evidence. The widespread availability of low-cost, user-friendly, and potent image editing software and mobile phone applications facilitates altering images without professional expertise. Consequently, safeguarding the originality and integrity of digital images has become a difficulty. Forgers commonly use digital image manipulation to transmit misleading information. Digital image forensics investigates the irregular patterns that might result from image alteration. …

A New Automatic Bearing Fault Size Diagnosis Using Time-Frequency Images Of Cwt And Deep Transfer Learning Methods, Yilmaz Kaya, Fatma Kuncan, Hüseyi̇n Meti̇n Ertunç

Turkish Journal of Electrical Engineering and Computer Sciences

Bearings are generally used as bearings or turning elements. Bearings are subjected to high loads and rapid speeds. Furthermore, metal-to-metal contact within the bearing makes it sensitive. In today?s machines, bearing failures disrupt the operation of the system or completely stop the system. Bearing failures that can occur can cause enormous damage to the entire system. Therefore, it is necessary to anticipate bearing failures and to carry out a regular diagnostic examination. Various systems have been developed for fault diagnosis. In recent years, deep transfer learning (DTL) methods are often preferred in current bearing diagnosis models, as they provide time …

Large-Scale Analysis And Automated Detection Of Trunnion Corrosion On Hip Arthroplasty Devices, Anastasia M. Codirenzi

Electronic Thesis and Dissertation Repository

Corrosion at the modular head-neck taper interface of total and hemiarthroplasty hip implants (trunnionosis) is a cause of implant failure and thus a clinical concern. Patient and device factors contributing to the occurrence of trunnionosis have been investigated in prior implant retrieval studies. The Goldberg corrosion scoring method is considered the gold standard for observing trunnionosis, but it is labour-intensive. As a result, previous studies have generally looked at under 250 implants for analysis. The purpose of this thesis was to do a large-scale analysis of trunnionosis and explore its relationship to device and patient factors and compare to previously …

Machine Learning Based Surrogate Model For Hurricane Storm Surge Forecasting In The Laguna Madre, Cesar E. Davila Hernandez

Theses and Dissertations

Texas coastal communities are at constant risk of hurricane impacts every storm season. It is especially important to model and predict storm surge variations during hurricane and storm events. Traditionally, hurricane storm surge predictions have been the result of numerical hydrodynamics based simulations. This type of simulations often requires high amounts of computational resources and complex ocean modelling efforts. Recently, machine learning techniques are being explored and are gaining popularity in hydrologic and ocean engineering modelling fields based on their performance to model nonlinear relationships and low computational requirements for prediction. Advances in machine learning and artificial intelligence (A.I.) demand …

Multi-Modality Breast Mri Segmentation Using Nn-Unet For Preoperative Planning Of Robotic Surgery Navigation, Motaz Alqaoud, John Plemmons Md, Eric Feliberti Md, Facs, Krishnanand Kaipa, Siqin Dong, Gabor Fichtinger, Yimming Xiao, Michel Audette

Modeling, Simulation and Visualization Student Capstone Conference

Segmentation of the chest region and breast tissues is essential for surgery planning and navigation. This paper proposes the foundation for preoperative segmentation based on two cascaded architectures of deep neural networks (DNN) based on the state-of-the-art nnU-Net. Additionally, this study introduces a polyvinyl alcohol cryogel (PVA-C) breast phantom based on the segmentation of the DNN automated approach, enabling the experiments of navigation system for robotic breast surgery. Multi-modality breast MRI datasets of T2W and STIR images were acquired from 10 patients. Segmentation evaluation utilized the Dice Similarity Coefficient (DSC), segmentation accuracy, sensitivity, and specificity. First, a single class labeling …

Plant Identification In A Combined-Imbalanced Leaf Dataset, Viraj K. Gajjar, Anand K. Nambisan, Kurt Louis Kosbar

Electrical and Computer Engineering Faculty Research & Creative Works

Plant identification has applications in ethnopharmacology and agriculture. Since leaves are one of a distinguishable feature of a plant, they are routinely used for identification. Recent developments in deep learning have made it possible to accurately identify the majority of samples in five publicly available leaf datasets. However, each dataset captures the images in a highly controlled environment. This paper evaluates the performance of EfficientNet and several other convolutional neural network (CNN) architectures when applied to a combination of the LeafSnap, Middle European Woody Plants 2014, Flavia, Swedish, and Folio datasets. To normalize the impact of imbalance resulting from combining …

Malware Binary Image Classification Using Convolutional Neural Networks, John Kiger, Shen-Shyang Ho, Vahid Heydari

Faculty Scholarship for the College of Science & Mathematics

The persistent shortage of cybersecurity professionals combined with enterprise networks tasked with processing more data than ever before has led many cybersecurity experts to consider automating some of the most common and time-consuming security tasks using machine learning. One of these cybersecurity tasks where machine learning may prove advantageous is malware analysis and classification. To evade traditional detection techniques, malware developers are creating more complex malware. This is achieved through more advanced methods of code obfuscation and conducting more sophisticated attacks. This can make the manual process of analyzing malware an infinitely more complex task. Furthermore, the proliferation of malicious …

An Ensemble Approach For Patient Prognosis Of Head And Neck Tumor Using Multimodal Data, Numan Saeed, Roba Al Majzoub, Ikboljon Sobirov, Mohammad Yaqub

Computer Vision Faculty Publications

Accurate prognosis of a tumor can help doctors provide a proper course of treatment and, therefore, save the lives of many. Tradi-tional machine learning algorithms have been eminently useful in crafting prognostic models in the last few decades. Recently, deep learning algorithms have shown significant improvement when developing diag-nosis and prognosis solutions to different healthcare problems. However, most of these solutions rely solely on either imaging or clinical data. Utilizing patient tabular data such as demographics and patient med-ical history alongside imaging data in a multimodal approach to solve a prognosis task has started to gain more interest recently and …

A Survey Of Blind Modulation Classification Techniques For Ofdm Signals, Anand Kumar, Sudhan Majhi, Guan Gui, Hsiao-Chun Wu, Chau Yuen

Faculty Publications

Blind modulation classification (MC) is an integral part of designing an adaptive or intelligent transceiver for future wireless communications. Blind MC has several applications in the adaptive and automated systems of sixth generation (6G) communications to improve spectral efficiency and power efficiency, and reduce latency. It will become a integral part of intelligent software-defined radios (SDR) for future communication. In this paper, we provide various MC techniques for orthogonal frequency division multiplexing (OFDM) signals in a systematic way. We focus on the most widely used statistical and machine learning (ML) models and emphasize their advantages and limitations. The statistical-based blind …

Performance Prediction Of Underwater Acoustic Communications Based On Channel Impulse Responses, Evan Lucas, Zhaohui Wang

Michigan Tech Publications

Featured Application: Convolutional neural networks are used on the channel impulse response data to predict the performance of underwater acoustic communications. Abstract: Predicting the channel quality for an underwater acoustic communication link is not a straightforward task. Previous approaches have focused on either physical observations of weather or engineered signal features, some of which require substantial processing to obtain. This work applies a convolutional neural network to the channel impulse responses, allowing the network to learn the features that are useful in predicting the channel quality. Results obtained are comparable or better than conventional supervised learning models, depending on the …