Engineering Commons^™

Machine Learning Security For Tactical Operations, Dr. Denaria Fields, Shakiya A. Friend, Andrew Hermansen, Dr. Tugba Erpek, Dr. Yalin E. Sagduyu

Military Cyber Affairs

Deep learning finds rich applications in the tactical domain by learning from diverse data sources and performing difficult tasks to support mission-critical applications. However, deep learning models are susceptible to various attacks and exploits. In this paper, we first discuss application areas of deep learning in the tactical domain. Next, we present adversarial machine learning as an emerging attack vector and discuss the impact of adversarial attacks on the deep learning performance. Finally, we discuss potential defense methods that can be applied against these attacks.

Statistical And Machine Learning Analysis In Brain-Imaging Genetics: A Review Of Methods, Connor L. Cheek, Peggy Lindner, Elena L. Grigorenko

Engineering Management and Systems Engineering Faculty Research & Creative Works

Brain-imaging-genetic analysis is an emerging field of research that aims at aggregating data from neuroimaging modalities, which characterize brain structure or function, and genetic data, which capture the structure and function of the genome, to explain or predict normal (or abnormal) brain performance. Brain-imaging-genetic studies offer great potential for understanding complex brain-related diseases/disorders of genetic etiology. Still, a combined brain-wide genome-wide analysis is difficult to perform as typical datasets fuse multiple modalities, each with high dimensionality, unique correlational landscapes, and often low statistical signal-to-noise ratios. In this review, we outline the progress in brain-imaging-genetic methodologies starting from early massive univariate …

Entropy-Infused Deep Learning Loss Function For Capturing Extreme Values In Wind Power Forecasting, Mucun Sun, Sergio Valdez, Juan M. Perez, Kevin Garcia, Gael Galvan, Cesar Cruz, Yifeng Gao, Li Zhang

Electrical and Computer Engineering Faculty Publications

Extreme scenarios in wind power generation occur with higher frequency and larger magnitude in the recent years due to the ever-increasing extreme meteorological factors. Accurate forecasting of the occurrence of extreme values in wind power generation is of great concern to ensure reliable power system operation. Recently, deep learning models have surged in popularity for wind power forecasting, with the mean squared error (MSE) loss function being commonly used. However, the MSE loss function, being sensitive to extreme values, disproportionately penalizes larger errors, cannot adequately capture the extreme values present in wind energy data, and novel loss functions have seldom …

Radio Frequency Interference (Rfi) Detection In Microwave Radiometry Using Multi-Dimensional Data Mining Techniques, Fathima Imara Mohamed Nazar

Legacy Theses & Dissertations (2009 - 2024)

Measurements of natural electromagnetic radiation from Earth using microwave radiometers provide deep insight into our planet and its environmental conditions. These insights are essential for quantifying, understanding, and predicting various geophysical processes, such as climate patterns, water cycles, carbon cycles, and more. These passive measurements are diverse and cover a wide range of frequencies, depending on the sensitivity of microwave radiation to changes in important geophysical parameters. However, it is important to note that the microwave spectrum is also utilized by active services, such as wireless communication networks and radars. As a result, Radio Frequency Interference (RFI) in the measurements …

Identifying Temporomandibular Disorder Morphological Risk Factors Via Explainable Deep Learning And Multiscale Biomechanical Modeling, Shuchun Sun

All Dissertations

Clarifying multifactorial musculoskeletal disorder etiologies supports risk analysis and development of targeted prevention and treatment modalities. Deep learning enables comprehensive risk factor identification through systematic analysis of disease datasets but does not provide sufficient context for mechanistic understanding, limiting clinical applicability for etiological investigations. Conversely, multiscale biomechanical modeling can evaluate mechanistic etiology within the relevant biomechanical and physiological context. We propose a hybrid approach combining 3D explainable deep learning and multiscale biomechanical modeling; we applied this approach to investigate temporomandibular joint (TMJ) disorder etiology by systematically identifying risk factors and elucidating mechanistic relationships between risk factors and TMJ biomechanics and …

Next-Generation Crop Monitoring Technologies: Case Studies About Edge Image Processing For Crop Monitoring And Soil Water Property Modeling Via Above-Ground Sensors, Nipuna Chamara

Dissertations and Doctoral Documents, University of Nebraska-Lincoln, 2023–

Artificial Intelligence (AI) has advanced rapidly in the past two decades. Internet of Things (IoT) technology has advanced rapidly during the last decade. Merging these two technologies has immense potential in several industries, including agriculture.

We have identified several research gaps in utilizing IoT technology in agriculture. One problem was the digital divide between rural, unconnected, or limited connected areas and urban areas for utilizing images for decision-making, which has advanced with the growth of AI. Another area for improvement was the farmers' demotivation to use in-situ soil moisture sensors for irrigation decision-making due to inherited installation difficulties. As Nebraska …

A Reinforcement Learning Framework For Powertrain Control Including Shared Learning Among A Fleet Of Vehicles, Lindsey Kerbel

All Dissertations

The transportation sector provides a significant opportunity to reduce global emissions, both through technological advancements and vehicular control strategies. Model-based control systems are popular methods for increasing the operating efficiency of vehicles. However, these systems often rely on models that require costly calibrations that still fail to capture the complexity of modern powertrain systems and the variations found in real-world driving. The recent availability of operational data through connected vehicle technology and/or edge devices has led to the emergence of data-driven control strategies that can learn optimal control policies through the interactions of the vehicle’s control system with the environment. …

Predicting Hospitalization Risk Of Schizophrenia Patients In Missouri, Arun Kumar

Theses

Understanding the factors that contribute to the risk of hospitalization among individuals with schizophrenia is crucial for optimizing treatment strategies and improving patient outcomes. In this study, we used recent Medicaid adminis trative claims data in Missouri (from 2016 to 2019) and predicted the risk of hospitalization of patients diagnosed with Schizophrenia (N=143446). We ana lyzed the association between previous hospitalizations and the likelihood of future hospitalizations. We developed machine learning techniques, including logistic re gression and decision tree classifiers, to predict the risk of hospitalization based on demographic factors, clinical variables, and historical hospitalization records. Our findings reveal that …

Emerging Technologies For Automation In Environmental Sensing: Review, Shekhar Suman Borah, Aaditya Khanal, Prabha Sundaravadivel

Electrical Engineering Faculty Publications and Presentations

This article explores the impact of automation on environmental sensing, focusing on advanced technologies that revolutionize data collection analysis and monitoring. The International Union of Pure and Applied Chemistry (IUPAC) defines automation as integrating hardware and software components into modern analytical systems. Advancements in electronics, computer science, and robotics drive the evolution of automated sensing systems, overcoming traditional limitations in manual data collection. Environmental sensor networks (ESNs) address challenges in weather constraints and cost considerations, providing high-quality time-series data, although issues in interoperability, calibration, communication, and longevity persist. Unmanned Aerial Systems (UASs), particularly unmanned aerial vehicles (UAVs), play an important …

Incremental Image Dehazing Algorithm Based On Multiple Transfer Attention, Jinyang Wei, Keping Wang, Yi Yang, Shumin Fei

Journal of System Simulation

Abstract: In order to improve the processing ability of the depth-neural network dehazing algorithm to the supplementary data set, and to make the network differently process the image features of different importance to improve the dehazing ability of the network, an incremental dehazing algorithm based on multiple migration of attention is proposed. The teacher's attention generation network in the form of Encoder-Decoder extracts the multiple attention of labels and haze, which is used it as the label of the characteristic migration media network to constrain the network training to form the migration media attention as close as possible to the …

Preserving Location Authenticity: Multi-Sensor System To Thwart Gps Spoofing In Self-Driving Vehicles, Peng Jiang

Electrical & Computer Engineering Theses & Dissertations

The ubiquity of the Global Positioning System (GPS) has cemented its role as the cornerstone for an array of location-based services and navigation systems, spanning applications from autonomous vehicles and drones to maritime vessels and wearable technology. Nonetheless, ensuring the integrity of reported geographical coordinates poses a formidable challenge, owing to the proliferation of diverse GPS spoofing tools. This predicament is compounded by the pervasive availability of tools like Fake GPS, Lockito, and software-defined radios, enabling even unsophisticated users to commandeer and disseminate counterfeit GPS coordinates. This dissertation undertakes the task of devising an encompassing and resilient framework, integrating a …

Convolutional Neural Network In Motion Detection For Physiotherapy Exercise Movement, Dika Fikri Laistulloh, Anik Nur Handayani, Rosa Andrie Asmara, Phillip Taw

Knowledge Engineering and Data Science

Physiotherapy focuses on movement and optimal utilization of the patient's potential. Exercise Therapy is a physiotherapy procedure that specifically focuses exercises on active and passive movements. Cerebral Palsy (CP) patients are one of the sufferers of motor disorders of the upper extremities. Cerebral Palsy (CP) patients suffer from disorders in motor functions of the upper extremities. Physiotherapy Exercise Movement has 4 categories of movement exercises for the therapy of people with upper extremity body disorders: Elbow flexor strengthening in sitting using free weights, lifting an object up, reaching diagonally in sitting, and reaching from a low surface to a high …

Computational Modeling And Analysis Of Facial Expressions And Gaze For Discovery Of Candidate Behavioral Biomarkers For Children And Young Adults With Autism Spectrum Disorder, Megan Anita Witherow

Electrical & Computer Engineering Theses & Dissertations

Facial expression production and perception in autism spectrum disorder (ASD) suggest the potential presence of behavioral biomarkers that may stratify individuals on the spectrum into prognostic or treatment subgroups. High-speed internet and the ease of technology have enabled remote, scalable, affordable, and timely access to medical care, such as measurements of ASDrelated behaviors in familiar environments to complement clinical observation. Machine and deep learning (DL)-based analysis of video tracking (VT) of expression production and eye tracking (ET) of expression perception may aid stratification biomarker discovery for children and young adults with ASD. However, there are open challenges in 1) facial …

Transformer-Based Deep Learning Model For Sign Language Recognition, Ganzorig Batnasan

Theses

Sign language recognition research aims to develop systems and tools that can interpret and translate sign language into text or spoken language. During the past two decades, the challenges faced in this domain are multifaceted. The first and foremost challenge is the complexity of sign language, which includes intricate hand gestures, facial expressions, and body movements. Recognizing and interpreting these components accurately is challenging. The second challenge is variability among different regions and communities, leading to variations in signs and gestures. This variability poses a challenge for developing universal recognition systems.
Limited data is another challenge which makes it difficult …

Expressway Traffic Flow Prediction Based On Data From Multiple Related Toll Stations, Zhang Yang, Yao Fangyu, Yang Shumin

Journal of China & Foreign Highway

The strong inter-economic connection makes a spatial correlation between traffic data from multiple related toll stations between urban cluster regions，and an accurate description of this connection can improve the accuracy of expressway traffic flow prediction.However，due to many uncertainties，the correlation is difficult to be captured and quantified.To solve this problem，an ATGCN-ResGRU deep learning-based expressway traffic flow prediction method was proposed.By combining attention mechanisms，three graph convolutional networks （GCN ） topological networks with high，medium，and low attention levels were constructed，and spatial learning data was obtained according to the weighted attention level of each network.The connection of multiple related toll stations was quantified and graded.At …

Anti-Phishing Approach For Iot System In Fog Networks Based On Machine Learning Algorithms, Mahmoud Gad Awwad, Mohamed M. Ashour, El Said A. Marzouk, Eman Abdelhalim

Mansoura Engineering Journal

As the Internet of Things (IoT) continues to expand, ensuring the security and privacyِ of IoT systems becomes increasingly critical. Phishing attacks pose a significant threat to IoT devices and can lead to unauthorized access, data breaches, and compromised functionality. In this paper, we propose an anti-phishing approach for IoT systems in fog networks that leverages machine learning algorithms, including a .fusion with deep learning techniques We explore the effectiveness of eleven traditional machine learning algorithms combined with deep learning in detecting and preventing phishing attacks in IoT systems. By utilizing a diverse range of algorithms, we aim to enhance …

A Deep Learning Convolutional Neural Network For Antenna Near-Field Prediction And Surrogate Modeling, Md Rayhan Khan, Constantinos L. Zekios, Shubhendu Bhardwaj, Stavros V. Georgakopoulos

Department of Electrical and Computer Engineering: Faculty Publications

This study investigates the use of deep learning techniques for building a generalized surrogate model that can accurately and very efficiently predict antenna performance parameters. Notably, we focus on applications where a substantial amount of simulation time is required and prior data is available for deep learning use. Specifically, for these applications, we introduce deep learning models that efficiently and reliably model the near-field of the antenna. These models, in turn, accurately predict far-field properties and essential antenna metrics, such as the reflection coefficient. To demonstrate the efficiency of our method, the widely used rectangular patch antenna is considered, encompassing …

Hybrid Features Extraction For The Online Mineral Grades Determination In The Flotation Froth Using Deep Learning, Ahmed Bendaouia, El Hassan Abdelwahed, Sara Qassimi, Abdelmalek Boussetta, Intissar Benzakour, Abderrahmane Benhayoun, Oumkeltoum Amar, François Bourzeix, Karim Baïna, Mouhamed Cherkaoui, Oussama Hasidi

Manufacturing & Industrial Engineering Faculty Publications

The control of the froth flotation process in the mineral industry is a challenging task due to its multiple impacting parameters. Accurate and convenient examination of the concentrate grade is a crucial step in realizing effective and real-time control of the flotation process. The goal of this study is to employ image processing techniques and CNN-based features extraction combined with machine learning and deep learning to predict the elemental composition of minerals in the flotation froth. A real world dataset has been collected and preprocessed from a differential flotation circuit at the industrial flotation site based in Guemassa, Morocco. …

Small Unmanned Aircraft System Detection And Tracking With Audio, Computer Vision, And Deep Learning Techniques, Anthony C. Brunson

Theses and Dissertations

sUAS present significant risks to local and federal agencies when under the control of negligent, reckless, or criminal operators. In the face of an escalating presence of sUAS in shared airspace with traditional aircraft, and their deployment in protected airspace as potential weapons, safeguarding personnel, facilities, and assets becomes paramount. This research seeks to address this emerging threat by investigating the efficacy of integrating low-cost distributed sensors and Machine learning (ML) models to enhance battlespace awareness and complement existing sensing platforms for real-time sUAS detection, classification, and localization. The thesis introduces the conceptualization and development of a Drone Detection Command …

The Use Of Deep Learning And Transfer Learning In Complex Problems, Jacob S. Lang

Theses and Dissertations

Deep neural networks and transfer learning show potential in addressing complex problems such as the Tower of Hanoi and knapsack problems. The primary aim is to examine how the use of deep neural networks and transfer learning can enhance the ability of artificial learning systems to generalize. Transfer learning plays a crucial role in machine learning, particularly in the domain of artificial neural networks, as it helps overcome the challenges associated with limited data, computational efficiency, and generalization. The methodology used in this research involves the creation of data sets for the Tower of Hanoi and knapsack problems. To predict …

Artificial Intelligence And Perception: An Empirical Study, Anthony J. Neil

Theses and Dissertations

This thesis investigates the impact of adjusting artificial intelligence explainability levels’ outputs on user perception. The overarching study extends within the Explainable Artificial Intelligence (XAI) domain. It examines a spectrum of variables, including performance, cognizance, familiarity, transparency, system bias, and the overall impact of AI, to understand their collective and individual effects that enable effective professional use in an organization. The study aims to illuminate the relationship between the degree of explainability provided by large language models such as ChatGPT, Bard, and Bing AI and the performance of these models when tasked with XAI adjustments.

Understanding The Impact Of Trade Policy Effect Uncertainty On Firm-Level Innovation Investment: A Deep Learning Approach, Daniel Chang, Nan Hu, Peng Liang, Morgan Swink

Research Collection School Of Computing and Information Systems

Integrating the real options perspective and resource dependence theory, this study examines how firms adjust their innovation investments to trade policy effect uncertainty (TPEU), a less studied type of firm specific, perceived environmental uncertainty in which managers have difficulty predicting how potential policy changes will affect business operations. To develop a text-based, context-dependent, time-varying measure of firm-level perceived TPEU, we apply Bidirectional Encoder Representations from Transformers (BERT), a state-of-the-art deep learning approach. We apply BERT to analyze the texts of mandatory Management Discussion and Analysis (MD&A) sections of annual reports for a sample of 22,669 firm-year observations from 3,181 unique …

Assessing Military Parking: A Deep Learning Approach To Evaluating Standards And Impacts, Ryan D. Lalonde

Theses and Dissertations

Current United States Department of Defense (DoD) standards require a minimum amount of parking for each building. This requirement defines how much off-street parking to construct. However, the impact of these requirements remains unclear. This study builds upon the emerging field of overhead imagery analytics by directly tying it to parking on military installations. Specifically, this study leverages a pretrained deep learning car detection model, Car Detection – USA, developed by Esri for use within ArcGIS, and couples it with open-access temporal imagery sourced from Google Earth Pro to assess selected parking lots across Area B, Wright-Patterson Air Force Base, …

Improving Rogue Radio Emitter Detection Using Siamese Networks, Mason Wright

Theses and Dissertations

Radio Frequency Fingerprinting (RFF) is the process of creating discerning signatures of emitted radio signals, most often with the goal of identifying specific devices again in the future. The security benefits of this task are intended to build upon current software-based authentication by making use of multi-factor authentication (MFA), but the related task of being able to reject unwanted emitters is limited. This paper presents a Siamese network trained on two different extracted fingerprints of raw Wi-Fi signals, along with a verifier to perform classification and rogue device detection. It was found that fingerprints using the Distortion Reconstruction (DR) technique …

Motion Magnification-Inspired Feature Manipulation For Deepfake Detection, Aydamir Mirzayev, Hamdi Di̇bekli̇oğlu

Turkish Journal of Electrical Engineering and Computer Sciences

Recent advances in deep learning, increased availability of large-scale datasets, and improvement of accelerated graphics processing units facilitated creation of an unprecedented amount of synthetically generated media content with impressive visual quality. Although such technology is used predominantly for entertainment, there is widespread practice of using deepfake technology for malevolent ends. This potential for malicious use necessitates the creation of detection methods capable of reliably distinguishing manipulated video content. In this work we aim to create a learning-based detection method for synthetically generated videos. To this end, we attempt to detect spatiotemporal inconsistencies by leveraging a learning-based magnification-inspired feature manipulation …

Automated Identification Of Vehicles In Very High-Resolution Uav Orthomosaics Using Yolov7 Deep Learning Model, Esra Yildirim, Umut Güneş Seferci̇k, Taşkın Kavzoğlu

Turkish Journal of Electrical Engineering and Computer Sciences

The utilization of remote sensing products for vehicle detection through deep learning has gained immense popularity, especially due to the advancement of unmanned aerial vehicles (UAVs). UAVs offer millimeter-level spatial resolution at low flight altitudes, which surpasses traditional airborne platforms. Detecting vehicles from very high-resolution UAV data is crucial in numerous applications, including parking lot and highway management, traffic monitoring, search and rescue missions, and military operations. Obtaining UAV data at desired periods allows the detection and tracking of target objects even several times during a day. Despite challenges such as diverse vehicle characteristics, traffic congestion, and hardware limitations, the …

Action Recognition Model Of Directed Attention Based On Cosine Similarity, Chen Li, Ming He, Chen Dong, Wei Li

Journal of System Simulation

Abstract: Aiming at the lack of directionality of traditional dot product attention, this paper proposes a directed attention model (DAM) based on cosine similarity. To effectively represent the direction relationship between the spatial and temporal features of video frames, the paper defines the relationship function in the attention mechanism using the cosine similarity theory, which can remove the absolute value of the relationship between features. To reduce the computational burden of the attention mechanism, the operation is decomposed from two dimensions of time and space. The computational complexity is further optimized by combining linear attention operation. The experiment is divided …

Deep Transfer Learning For Detection Of Upper And Lower Body Movements: Transformer With Convolutional Neural Network, Kyle Lacroix, Davoud Gholamiangonabadi, Ana Luisa Trejos, Katarina Grolinger

Electrical and Computer Engineering Publications

When humans repeat the same motion, the tendons, muscles, and nerves can be damaged, causing Repetitive Stress Injuries (RSI). If the repetitive motions that lead to RSI are recognized early, actions can be taken to prevent these injuries. As Human Activity Recognition (HAR) aims to identify activities employing wearable or environment sensors, HAR is the first step toward identifying repetitive motions. Deep learning models, such as Convolutional Neural Networks (CNNs), have seen great success in recognizing activities for participants whose data are used in the model training; however, their accuracy drops for new participants as people move in different ways. …

Classification Of Sow Postures Using Convolutional Neural Network And Depth Images, Md Towfiqur Rahman, Tami M. Brown-Brandl, Gary A. Rohrer, Sudhendu R. Sharma, Yeyin Shi

Department of Agricultural and Biological Systems Engineering: Faculty Publications

The United States swine industry reports an average preweaning mortality of approximately 16% where approximately 6% of them are attributed to piglets overlayed by sows. Detecting postural transitions and estimating sows’ time budgets for different postures are valuable information for breeders and engineering design of farrowing facilities to eventually reduce piglet death. Computer vision tools can help monitor changes in animal posture accurately and efficiently. To create a more robust system and eliminate varying lighting issues within a day including daytime/ nighttime differences, there is an advantage to using depth cameras over digital cameras. In this study, a computer vision …

Multimodal Fusion For Audio-Image And Video Action Recognition, Muhammad B. Shaikh, Douglas Chai, Syed M. S. Islam, Naveed Akhtar

Research outputs 2022 to 2026

Multimodal Human Action Recognition (MHAR) is an important research topic in computer vision and event recognition fields. In this work, we address the problem of MHAR by developing a novel audio-image and video fusion-based deep learning framework that we call Multimodal Audio-Image and Video Action Recognizer (MAiVAR). We extract temporal information using image representations of audio signals and spatial information from video modality with the help of Convolutional Neutral Networks (CNN)-based feature extractors and fuse these features to recognize respective action classes. We apply a high-level weights assignment algorithm for improving audio-visual interaction and convergence. This proposed fusion-based framework utilizes …