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Articles 1 - 30 of 202
Full-Text Articles in Engineering
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
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 …
Nonuniform Sampling-Based Breast Cancer Classification, Santiago Posso
Nonuniform Sampling-Based Breast Cancer Classification, Santiago Posso
Theses and Dissertations--Electrical and Computer Engineering
The emergence of deep learning models and their success in visual object recognition have fueled the medical imaging community's interest in integrating these algorithms to improve medical diagnosis. However, natural images, which have been the main focus of deep learning models and mammograms, exhibit fundamental differences. First, breast tissue abnormalities are often smaller than salient objects in natural images. Second, breast images have significantly higher resolutions but are generally heavily downsampled to fit these images to deep learning models. Models that handle high-resolution mammograms require many exams and complex architectures. Additionally, spatially resizing mammograms leads to losing discriminative details essential …
Integration Of Infrared Thermography And Deep Learning For Real-Time In-Situ Defect Detection And Rapid Elimination Of Defect Propagation In Material Extrusion, Asef Ishraq Sadaf
Integration Of Infrared Thermography And Deep Learning For Real-Time In-Situ Defect Detection And Rapid Elimination Of Defect Propagation In Material Extrusion, Asef Ishraq Sadaf
Electronic Theses and Dissertations
This study presents a novel approach to overcoming process reliability challenges in Material Extrusion (ME), a prominent additive manufacturing (AM) technique. Despite ME's advantages in cost, versatility, and rapid prototyping, it faces significant barriers to commercial-scale production, primarily due to quality issues such as overextrusion and underextrusion, which compromise final part performance. Traditional manual monitoring methods severely lack the capability to efficiently detect these defects and highlight the necessity for an efficient and real-time monitoring solution. Considering these challenges, an innovative and field-deployable infrared thermography-based in-situ real-time defect detection and feedback control system is proposed in this thesis. A novel …
Investigation Of Software Defined Radio And Deep Learning For Ground Penetrating Radar, Yan Zhang
Investigation Of Software Defined Radio And Deep Learning For Ground Penetrating Radar, Yan Zhang
Graduate College Dissertations and Theses
Ground Penetrating Radar (GPR) is a non-invasive geophysical method that uses radar pulses to image the subsurface. This technology is widely used to detect and map subsurface structures, utilities, and features without the need for physical excavation. Traditional GPR systems, which rely on fixed radio frequency electronics like Application-Specific Integrated Circuits (ASICs), have significant limitations in their flexibility and adaptability. Adjusting operational parameters such as waveform, frequency, and modulation schemes is challenging, which is crucial for tailoring performance to specific tasks or conditions. The considerable size and weight of these systems restrict their applicability in harsh or confined spaces where …
Melanoma Detection Based On Deep Learning Networks, Sanjay Devaraneni
Melanoma Detection Based On Deep Learning Networks, Sanjay Devaraneni
Electronic Theses, Projects, and Dissertations
Our main objective is to develop a method for identifying melanoma enabling accurate assessments of patient’s health. Skin cancer, such as melanoma can be extremely dangerous if not detected and treated early. Detecting skin cancer accurately and promptly can greatly increase the chances of survival. To achieve this, it is important to develop a computer-aided diagnostic support system. In this study a research team introduces a sophisticated transfer learning model that utilizes Resnet50 to classify melanoma. Transfer learning is a machine learning technique that takes advantage of trained models, for similar tasks resulting in time saving and enhanced accuracy by …
Spoken Language Processing And Modeling For Aviation Communications, Aaron Van De Brook
Spoken Language Processing And Modeling For Aviation Communications, Aaron Van De Brook
Doctoral Dissertations and Master's Theses
With recent advances in machine learning and deep learning technologies and the creation of larger aviation-specific corpora, applying natural language processing technologies, especially those based on transformer neural networks, to aviation communications is becoming increasingly feasible. Previous work has focused on machine learning applications to natural language processing, such as N-grams and word lattices. This thesis experiments with a process for pretraining transformer-based language models on aviation English corpora and compare the effectiveness and performance of language models transfer learned from pretrained checkpoints and those trained from their base weight initializations (trained from scratch). The results suggest that transformer language …
Faster, Cheaper, And Better Cfd: A Case For Machine Learning To Augment Reynolds-Averaged Navier-Stokes, John Peter Romano Ii
Faster, Cheaper, And Better Cfd: A Case For Machine Learning To Augment Reynolds-Averaged Navier-Stokes, John Peter Romano Ii
Mechanical & Aerospace Engineering Theses & Dissertations
In recent years, the field of machine learning (ML) has made significant advances, particularly through applying deep learning (DL) algorithms and artificial intelligence (AI). The literature shows several ways that ML may enhance the power of computational fluid dynamics (CFD) to improve its solution accuracy, reduce the needed computational resources and reduce overall simulation cost. ML techniques have also expanded the understanding of underlying flow physics and improved data capture from experimental fluid dynamics.
This dissertation presents an in-depth literature review and discusses ways the field of fluid dynamics has leveraged ML modeling to date. The author selects and describes …
Automatic Cardiac Mri Image Segmentation And Mesh Generation, Ziyuan Li
Automatic Cardiac Mri Image Segmentation And Mesh Generation, Ziyuan Li
McKelvey School of Engineering Theses & Dissertations
Segmenting and reconstructing cardiac anatomical structures from magnetic resonance (MR) images is essential for the quantitative measurement and automatic diagnosis of cardiovascular diseases [1]. However, manual evaluation of the time-series cardiac MRI (CMRI) obtained during routine clinical care are laborious, inefficient, and tends to produce biased and non-reproducible results [2]. This thesis proposes an end-to-end pipeline for automatically segmenting short-axis (SAX) CMRI images and generating high-quality 2D and 3D meshes suitable for finite element analysis. The main advantage of our approach is that it can not only work as a stand-alone pipeline for the automatic CMR image segmentation and mesh …
Non-Destructive Evaluation Of White Striping And Microbial Spoilage Of Broiler Breast Meat Using Structured-Illumination Reflectance Imaging, Ebenezer O. Olaniyi
Non-Destructive Evaluation Of White Striping And Microbial Spoilage Of Broiler Breast Meat Using Structured-Illumination Reflectance Imaging, Ebenezer O. Olaniyi
Theses and Dissertations
Manual inspection is a prevailing practice for quality assessment of poultry meat, but it is labor-intensive, tedious, and subjective. This thesis aims to assess the efficacy of an emerging structured illumination reflectance imaging (SIRI) technique with machine learning approaches for assessing WS and microbial spoilage in broiler breast meat. Broiler breast meat samples were imaged by an in house-assembled SIRI platform under sinusoidal illumination. In first experiment, handcrafted texture features were extracted from direct component (DC, corresponding to conventional uniform illumination) and amplitude component (AC, unique to the use of sinusoidal illumination) images retrieved from raw SIRI pattern images build …
Physics-Augmented Modeling And Optimization Of Complex Systems: Healthcare Applications, Jianxin Xie
Physics-Augmented Modeling And Optimization Of Complex Systems: Healthcare Applications, Jianxin Xie
Doctoral Dissertations
The rapid advances in sensing technology have created a data-rich environment that tremendously
benefits predictive modeling and decision-making for complex systems. Harnessing
the full potential of this complexly-structured sensing data requires the development of
novel and reliable analytical models and tools for system informatics. Such advancements in
sensing present unprecedented opportunities to investigate system dynamics and optimize
decision-making processes for smart health. Nevertheless, sensing data is typically
characterized by high dimensionality and intricate structures. To fully unlock the potential of
this data, we significantly rely on innovative analytical methods and tools that can effectively
process information.
The objective of this …
Deep Learning Based Power System Stability Assessment For Reduced Wecc System, Yinfeng Zhao
Deep Learning Based Power System Stability Assessment For Reduced Wecc System, Yinfeng Zhao
Doctoral Dissertations
Power system stability is the ability of power system, for a giving initial operating condition, to reach a new operation condition with most of the system variables bounded in normal range after subjecting to a short or long disturbance. Traditional power system stability mainly uses time-domain simulation which is very time consuming and only appropriate for offline assessment.
Nowadays, with increasing penetration of inverter based renewable, large-scale distributed energy storage integration and operation uncertainty brought by weather and electricity market, system dynamic and operating condition is more dramatic, and traditional power system stability assessment based on scheduling may not be …
Automatic Classification And Segmentation Of Patterned Martian Ground Using Deep Learning Techniques, Ruthy Brito
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 …
Patient Movement Monitoring Based On Imu And Deep Learning, Mohsen Sharifi Renani
Patient Movement Monitoring Based On Imu And Deep Learning, Mohsen Sharifi Renani
Electronic Theses and Dissertations
Osteoarthritis (OA) is the leading cause of disability among the aging population in the United States and is frequently treated by replacing deteriorated joints with metal and plastic components. Developing better quantitative measures of movement quality to track patients longitudinally in their own homes would enable personalized treatment plans and hasten the advancement of promising new interventions. Wearable sensors and machine learning used to quantify patient movement could revolutionize the diagnosis and treatment of movement disorders. The purpose of this dissertation was to overcome technical challenges associated with the use of wearable sensors, specifically Inertial Measurement Units (IMUs), as a …
Long-Term Human Video Activity Quantification In Collaborative Learning Environments, Venkatesh Jatla
Long-Term Human Video Activity Quantification In Collaborative Learning Environments, Venkatesh Jatla
Electrical and Computer Engineering ETDs
Research on video activity detection has mainly focused on identifying well-defined human activities in short video segments, often requiring large-parameter systems and extensive training datasets. This dissertation introduces a low-parameter, modular system with rapid inference capabilities, capable of being trained on limited datasets without transfer learning from large-parameter systems. The system accurately detects specific activities and associates them with students in real-life classroom videos. Additionally, an interactive web-based application is developed to visualize human activity maps over long classroom videos.
Long-term video activity detection in classrooms presents challenges, such as multiple simultaneous activities, rapid transitions, long-term occlusions, duration exceeding 15 …
Detection Of Crypto-Ransomware Attack Using Deep Learning, Muna Jemal
Detection Of Crypto-Ransomware Attack Using Deep Learning, Muna Jemal
Master of Science in Computer Science Theses
The number one threat to the digital world is the exponential increase in ransomware attacks. Ransomware is malware that prevents victims from accessing their resources by locking or encrypting the data until a ransom is paid. With individuals and businesses growing dependencies on technology and the Internet, researchers in the cyber security field are looking for different measures to prevent malicious attackers from having a successful campaign. A new ransomware variant is being introduced daily, thus behavior-based analysis of detecting ransomware attacks is more effective than the traditional static analysis. This paper proposes a multi-variant classification to detect ransomware I/O …
Deep Learning Of Semantic Image Labels On Hdr Imagery In A Maritime Environment, Charles Montagnoli
Deep Learning Of Semantic Image Labels On Hdr Imagery In A Maritime Environment, Charles Montagnoli
Doctoral Dissertations and Master's Theses
Situational awareness in the maritime environment can be extremely challenging. The maritime environment is highly dynamic and largely undefined, requiring the perception of many potential hazards in the shared maritime environment. One particular challenge is the effect of direct-sunlight exposure and specular reflection causing degradation of camera reliability. It is for this reason then, in this work, the use of High-Dynamic Range imagery for deep learning of semantic image labels is studied in a littoral environment. This study theorizes that the use HDR imagery may be extremely beneficial for the purpose of situational awareness in maritime environments due to the …
Tomato Flower Detection And Three-Dimensional Mapping For Precision Pollination, Kaitlyn Mckensie Nelms
Tomato Flower Detection And Three-Dimensional Mapping For Precision Pollination, Kaitlyn Mckensie Nelms
Masters Theses
It is estimated that nearly 75% of major crops have some level of reliance on pollination. Humans are reliant on fruit and vegetable crops for many vital nutrients. With the intensification of agricultural production in response to human demand, native pollinator species are not able to provide sufficient pollination services, and managed bee colonies are in decline due to colony collapse disorder, among other issues. Previous work addresses a few of these issues by designing pollination systems for greenhouse operations or other controlled production systems but fails to address the larger need for development in other agricultural settings with less …
A Long-Term Funds Predictor Based On Deep Learning, Shuiyi Kuang
A Long-Term Funds Predictor Based On Deep Learning, Shuiyi Kuang
Electronic Theses, Projects, and Dissertations
Numerous neural network models have been created to predict the rise or fall of stocks since deep learning has gained popularity, and many of them have performed quite well. However, since the share market is hugely influenced by various policy changes or unexpected news, it is challenging for investors to use such short-term predictions as a guide. In this paper, we try to find a suitable long-term predictor for the funds market by testing different kinds of neural network models, including the Long Short-Term Memory(LSTM) model with different layers, the Gated Recurrent Units(GRU) model with different layers, and the combination …
Enhanced Iot-Based Electrocardiogram Monitoring System With Deep Learning, Jian Ni
Enhanced Iot-Based Electrocardiogram Monitoring System With Deep Learning, Jian Ni
UNLV Theses, Dissertations, Professional Papers, and Capstones
Due to the rapid development of computing and sensing technologies, Internet of Things (IoT)-based cardiac monitoring plays a crucial role in providing patients with cost-efficient solutions for long-term, continuous, and pervasive electrocardiogram (ECG) monitoring outside a hospital setting. In a typical IoT-based ECG monitoring system, ECG signals are picked up by sensors located on the edge, and then uploaded to the remote cloud servers. ECG interpretation is performed for the collected ECGs in the cloud servers and the analysis results can be made instantly available to the patients as well as their healthcare providers.In this dissertation, we first examine the …
Wearable Sensor Gait Analysis For Fall Detection Using Deep Learning Methods, Haben Girmay Yhdego
Wearable Sensor Gait Analysis For Fall Detection Using Deep Learning Methods, Haben Girmay Yhdego
Electrical & Computer Engineering Theses & Dissertations
World Health Organization (WHO) data show that around 684,000 people die from falls yearly, making it the second-highest mortality rate after traffic accidents [1]. Early detection of falls, followed by pneumatic protection, is one of the most effective means of ensuring the safety of the elderly. In light of the recent widespread adoption of wearable sensors, it has become increasingly critical that fall detection models are developed that can effectively process large and sequential sensor signal data. Several researchers have recently developed fall detection algorithms based on wearable sensor data. However, real-time fall detection remains challenging because of the wide …
Transfer Learning, Model Interpretation, And Dataset Bias Analysis For Automated Violence Detection From Video, Erik Clemens
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 …
Utilizing Deep Learning Methods In The Identification And Synthesis Of Gene Regulations, Jiandong Wang
Utilizing Deep Learning Methods In The Identification And Synthesis Of Gene Regulations, Jiandong Wang
Theses and Dissertations
Gene expression is the fundamental differentiation and development process of life. Although all cells in an organism have essentially the same DNA, cell types and activities vary due to changes in gene expression. Gene expression can be influenced by many gene regulations. RNA editing contributes to the variety of RNA and proteins by allowing single nucleotide substitution. Reverse transcription can alter the expression status of genes by inducing genetic diversity and polymorphism via novel insertions, deletions, and recombination events. Gene regulation is critical to normal development because it enables cells to respond rapidly to environmental changes. However, identifying gene regulations …
Autonomous 3d Urban And Complex Terrain Geometry Generation And Micro-Climate Modelling Using Cfd And Deep Learning, Tewodros F. Alemayehu
Autonomous 3d Urban And Complex Terrain Geometry Generation And Micro-Climate Modelling Using Cfd And Deep Learning, Tewodros F. Alemayehu
Electronic Thesis and Dissertation Repository
Sustainable building design requires a clear understanding and realistic modelling of the complex interaction between climate and built environment to create safe and comfortable outdoor and indoor spaces. This necessitates unprecedented urban climate modelling at high temporal and spatial resolution. The interaction between complex urban geometries and the microclimate is characterized by complex transport mechanisms. The challenge to generate geometric and physics boundary conditions in an automated manner is hindering the progress of computational methods in urban design. Thus, the challenge of modelling realistic and pragmatic numerical urban micro-climate for wind engineering, environmental, and building energy simulation applications should address …
Inducing Sparsity Within High-Dimensional Remote Sensing Modalities For Lightning Prediction, Grace E. Metzgar
Inducing Sparsity Within High-Dimensional Remote Sensing Modalities For Lightning Prediction, Grace E. Metzgar
Theses and Dissertations
The uncertainty of lightning constantly threatens many weather-sensitive fields where the slightest presence of lightning can endanger valuable personnel and assets. The consequences of delaying operations have incited the research of methods that can accurately predict the location of future lightning strikes from the current weather conditions. High-dimensional remote sensing modalities contain information capable of detecting significant patterns and intensities within storms that could indicate the presence of lightning. This thesis induces sparsity into convolutional neural networks (CNNs) and remote sensing modalities through a combination of regularization and tensor decomposition techniques to call attention to sparse features that are most …
Deep Learning For Detection Of Upper And Lower Body Movements, Kyle B. Lacroix
Deep Learning For Detection Of Upper And Lower Body Movements, Kyle B. Lacroix
Electronic Thesis and Dissertation Repository
When humans repeat the same motion, the tendons, muscles, and nerves can be damaged, causing repetitive stress injuries (RSI). Symptoms usually begin slowly and become more intense and constant over time. If the motions that lead to RSI are recognized early, these injuries can be prevented. A preventative approach could be implemented in factories to warn workers about possible injuries. By detecting the movements that can cause RSI, the worker can be alerted to stop carrying out those movements. For this purpose, machine learning models can detect human motion with the human activity recognition (HAR) model. HAR models typically require …
Pt-Net: A Multi-Model Machine Learning Approach For Smarter Next-Generation Wearable Tremor Suppression Devices For Parkinson's Disease Tremor, Anas Ibrahim
Electronic Thesis and Dissertation Repository
According to the World Health Organization (WHO), Parkinson's Disease (PD) is the second most common neurodegenerative condition that can cause tremors and other motor and non motor related symptoms. Medication and deep brain stimulation (DBS) are often used to treat tremor; however, medication is not always effective and has adverse effects, and DBS is invasive and carries a significant risk of complications. Wearable tremor suppression devices (WTSDs) have been proposed as a possible alternative, but their effectiveness is limited by the tremor models they use, which introduce a phase delay that decreases the performance of the devices. Additionally, the availability …
Adversarial Training Of Deep Neural Networks, Anabetsy Termini
Adversarial Training Of Deep Neural Networks, Anabetsy Termini
CCE Theses and Dissertations
Deep neural networks used for image classification are highly susceptible to adversarial attacks. The de facto method to increase adversarial robustness is to train neural networks with a mixture of adversarial images and unperturbed images. However, this method leads to robust overfitting, where the network primarily learns to recognize one specific type of attack used to generate the images while remaining vulnerable to others after training. In this dissertation, we performed a rigorous study to understand whether combinations of state of the art data augmentation methods with Stochastic Weight Averaging improve adversarial robustness and diminish adversarial overfitting across a wide …
Machine Learning Assisted Framework For Advanced Subsurface Fracture Mapping And Well Interference Quantification, Mohammad Faiq Adenan
Machine Learning Assisted Framework For Advanced Subsurface Fracture Mapping And Well Interference Quantification, Mohammad Faiq Adenan
Graduate Theses, Dissertations, and Problem Reports
The oil and gas industry has historically spent significant amount of capital to acquire large volumes of analog and digital data often left unused due to lack of digital awareness. It has instead relied on individual expertise and numerical modelling for reservoir development, characterization, and simulation, which is extremely time consuming and expensive and inevitably invites significant human bias and error into the equation. One of the major questions that has significant impact in unconventional reservoir development (e.g., completion design, production, and well spacing optimization), CO2 sequestration in geological formations (e.g., well and reservoir integrity), and engineered geothermal systems (e.g., …
Intelligent Wide-Area Monitoring Systems Using Deep Learning, Mustafa Matar
Intelligent Wide-Area Monitoring Systems Using Deep Learning, Mustafa Matar
Graduate College Dissertations and Theses
Scientific advancements based on the wide-area measurements as a way to monitor systems, are fundamental in reliable operation of different types of complex networks. These advanced measurement units capable of real-time wide-area monitoring, which enables capture system dynamic behavior. Therefore, advanced technology is urgently necessary to analyze substantial streaming data from these networks and handle system uncertainties. As an example, uncertainties in power systems due to renewable energy and demand response. Power system operation, and planning have become more complex and vulnerable to extreme weather and natural disasters. Thus, increasing power system resilience has gained more attention.Machine Learning (ML), and …
Machine Learning Models To Automate Radiotherapy Structure Name Standardization, Priyankar Bose
Machine Learning Models To Automate Radiotherapy Structure Name Standardization, Priyankar Bose
Theses and Dissertations
Structure name standardization is a critical problem in Radiotherapy planning systems to correctly identify the various Organs-at-Risk, Planning Target Volumes and `Other' organs for monitoring present and future medications. Physicians often label anatomical structure sets in Digital Imaging and Communications in Medicine (DICOM) images with nonstandard random names. Hence, the standardization of these names for the Organs at Risk (OARs), Planning Target Volumes (PTVs), and `Other' organs is a vital problem. Prior works considered traditional machine learning approaches on structure sets with moderate success. We compare both traditional methods and deep neural network-based approaches on the multimodal vision-language prostate cancer …