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Articles 1 - 18 of 18
Full-Text Articles in Engineering
Deep Learning Strategies For Pool Boiling Heat Flux Prediction Using Image Sequences, Connor Heo
Deep Learning Strategies For Pool Boiling Heat Flux Prediction Using Image Sequences, Connor Heo
Graduate Theses and Dissertations
The understanding of bubble dynamics during boiling is critical to the design of advanced heater surfaces to improve the boiling heat transfer. The stochastic bubble nucleation, growth, and coalescence processes have made it challenging to obtain mechanistic models that can predict boiling heat flux based on the bubble dynamics. Traditional boiling image analysis relies on the extraction of the dominant physical quantities from the images and is thus limited to the existing knowledge of these quantities. Recently, machine-learning-aided analysis has shown success in boiling crisis detection, heat flux prediction, real-time image analysis, etc., whereas most of the existing studies are …
An Analysis Of Camera Configurations And Depth Estimation Algorithms For Triple-Camera Computer Vision Systems, Jared Peter-Contesse
An Analysis Of Camera Configurations And Depth Estimation Algorithms For Triple-Camera Computer Vision Systems, Jared Peter-Contesse
Master's Theses
The ability to accurately map and localize relevant objects surrounding a vehicle is an important task for autonomous vehicle systems. Currently, many of the environmental mapping approaches rely on the expensive LiDAR sensor. Researchers have been attempting to transition to cheaper sensors like the camera, but so far, the mapping accuracy of single-camera and dual-camera systems has not matched the accuracy of LiDAR systems. This thesis examines depth estimation algorithms and camera configurations of a triple-camera system to determine if sensor data from an additional perspective will improve the accuracy of camera-based systems. Using a synthetic dataset, the performance of …
Computer Vision-Based Automatic Railroad Crossing Monitoring And Track Inspection, Feng Guo
Computer Vision-Based Automatic Railroad Crossing Monitoring And Track Inspection, Feng Guo
Theses and Dissertations
Currently, there are many imminent challenges in the railroad infrastructure system of the United States, impacting the operation, safety, and management of railroad transportation. In this work, three major challenges which are overcrowded traffic congestion at the grade crossing, low-efficiency and accuracy on inspection of missing or broken rail track components, and dense rail surface defects without quantification, respectively are studied. The congested railroad grade crossing not only introduces significant traffic delays to travelers but also brings potential safety concerns to the first responders. However, limited studies have been devoted on developing an intelligent traffic monitoring system which is significant …
Using Computer Vision To Track Anatomical Structures During Cochlear Implant Surgery, Nicholas Bach
Using Computer Vision To Track Anatomical Structures During Cochlear Implant Surgery, Nicholas Bach
McKelvey School of Engineering Theses & Dissertations
There is a steep learning curve for surgeons performing cochlear implant surgeries. We aimed to use computer vision to track anatomical features with the goal of helping surgeons perform cochlear implant surgery without damaging the cochlea. We compared nine algorithms in total, seven object tracking algorithms and two optical flow algorithms utilizing the LucasKanade method, on manually created cochlear implant surgery videos to determine the accuracy associated with each. Compared with eight other algorithms, we observed that an iterative pyramidal implementation of the Lucas-Kanade (IPLK) method, implemented through OpenCV, performed the best. The IPLK method had the lowest error rate …
An Anomaly Detection System For Smart Manufacturing Using Deep Learning, Tareq Tayeh
An Anomaly Detection System For Smart Manufacturing Using Deep Learning, Tareq Tayeh
Electronic Thesis and Dissertation Repository
The smart manufacturing evolution enables financial and operational improvements across the manufacturing industry. However, smart manufacturing encompasses complex, interconnected systems which can fail at any time. To address this challenge, a novel, two-part anomaly detection system for robotic processes, with an application focus on robotic surface finishing, is presented. The first part proposes an unsupervised Attention-based Convolutional Long Short-Term Memory Autoencoder with Dynamic Thresholding (ACLAE-DT) framework for anomaly detection and diagnosis in multivariate time series of robotic surface finishing components. The second part proposes a deep residual Convolutional Neural Network-based triplet model for anomaly detection in the produced robotic surface …
Analysis Of Microscopic Objects Using Computer Vision Methods, Yuan Dao
Analysis Of Microscopic Objects Using Computer Vision Methods, Yuan Dao
UNLV Theses, Dissertations, Professional Papers, and Capstones
As an essential and powerful tool to observe living organisms, three-dimensional fluorescence microscopy is widely used in biological research and diagnosis. The 4D fluorescence microscopy data can be obtained using time-lapsed videos of 3D images. To analyze and extract useful information from the increasingly large and complex biological image dataset, efficient and effective computational tools are in need but still lagging behind. In analyzing biological data, two major challenges are faced. First, time-lapsed fluorescence microscopic images typically have a low SNR. Second, biological objects often change their morphology and internal structure frequently. As such, conventional image processing methods may not …
Forecasting Pedestrian Trajectory Using Deep Learning, Arsal Syed
Forecasting Pedestrian Trajectory Using Deep Learning, Arsal Syed
UNLV Theses, Dissertations, Professional Papers, and Capstones
In this dissertation we develop different methods for forecasting pedestrian trajectories. Complete understanding of pedestrian motion is essential for autonomous agents and social robots to make realistic and safe decisions. Current trajectory prediction methods rely on incorporating historic motion, scene features and social interaction to model pedestrian behaviors. Our focus is to accurately understand scene semantics to better forecast trajectories. In order to do so, we leverage semantic segmentation to encode static scene features such as walkable paths, entry/exits, static obstacles etc. We further evaluate the effectiveness of using semantic maps on different datasets and compare its performance with already …
Signal Processing And Data Analysis For Real-Time Intermodal Freight Classification Through A Multimodal Sensor System., Enrique J. Sanchez Headley
Signal Processing And Data Analysis For Real-Time Intermodal Freight Classification Through A Multimodal Sensor System., Enrique J. Sanchez Headley
Graduate Theses and Dissertations
Identifying freight patterns in transit is a common need among commercial and municipal entities. For example, the allocation of resources among Departments of Transportation is often predicated on an understanding of freight patterns along major highways. There exist multiple sensor systems to detect and count vehicles at areas of interest. Many of these sensors are limited in their ability to detect more specific features of vehicles in traffic or are unable to perform well in adverse weather conditions. Despite this limitation, to date there is little comparative analysis among Laser Imaging and Detection and Ranging (LIDAR) sensors for freight detection …
High-Speed Image Classification For Resource-Limited Systems Using Binary Values, Taylor Scott Simons
High-Speed Image Classification For Resource-Limited Systems Using Binary Values, Taylor Scott Simons
Theses and Dissertations
Image classification is a memory- and compute-intensive task. It is difficult to implement high-speed image classification algorithms on resource-limited systems like FPGAs and embedded computers. Most image classification algorithms require many fixed- and/or floating-point operations and values. In this work, we explore the use of binary values to reduce the memory and compute requirements of image classification algorithms. Our objective was to implement these algorithms on resource-limited systems while maintaining comparable accuracy and high speeds. By implementing high-speed image classification algorithms on resource-limited systems like embedded computers, FPGAs, and ASICs, automated visual inspection can be performed on small low-powered systems. …
Lecture Video Transformation Through An Intelligent Analysis And Post-Processing System, Xi Wang
Lecture Video Transformation Through An Intelligent Analysis And Post-Processing System, Xi Wang
Masters Theses
Lecture videos are good sources for people to learn new things. Students commonly use online videos to explore various domains. However, some recorded videos are posted on online platforms without being post-processed due to technology and resource limitations. In this work, we focus on the research of developing an intelligent system to automatically extract essential information, including the main instructor and screen, in a lecture video in several scenarios by using modern deep learning techniques. This thesis aims to combine the extracted essential information to render the videos and generate a new layout with a smaller file size than the …
Robot Object Detection And Locomotion Demonstration For Eecs Department Tours, Bryson Howell, Ethan Haworth, Chris Mobley, Ian Mulet
Robot Object Detection And Locomotion Demonstration For Eecs Department Tours, Bryson Howell, Ethan Haworth, Chris Mobley, Ian Mulet
Chancellor’s Honors Program Projects
No abstract provided.
Analysis Of Hardware Accelerated Deep Learning And The Effects Of Degradation On Performance, Samuel C. Leach
Analysis Of Hardware Accelerated Deep Learning And The Effects Of Degradation On Performance, Samuel C. Leach
Masters Theses
As convolutional neural networks become more prevalent in research and real world applications, the need for them to be faster and more robust will be a constant battle. This thesis investigates the effect of degradation being introduced to an image prior to object recognition with a convolutional neural network. As well as experimenting with methods to reduce the degradation and improve performance. Gaussian smoothing and additive Gaussian noise are both analyzed degradation models within this thesis and are reduced with Gaussian and Butterworth masks using unsharp masking and smoothing, respectively. The results show that each degradation is disruptive to the …
Cascaded Deep Learning Network For Postearthquake Bridge Serviceability Assessment, Youjeong Jang
Cascaded Deep Learning Network For Postearthquake Bridge Serviceability Assessment, Youjeong Jang
Electronic Theses and Dissertations
Damages assessment of bridges is important to derive immediate response after severe events to decide serviceability. Especially, past earthquakes have proven the vulnerability of bridges with insufficient detailing. Due to lack of a national and unified post-earthquake inspection procedure for bridges, conventional damage assessments are performed by sending professional personnel to the onsite, detecting visually and measuring the damage state. To get accurate and fast damage result of bridge condition is important to save not only lives but also costs.
There have been studies using image processing techniques to assess damage of bridge column without sending individual to onsite. Convolutional …
Uncertainty Estimation For Stereo Visual Odometry, Derek W. Ross
Uncertainty Estimation For Stereo Visual Odometry, Derek W. Ross
Graduate Theses, Dissertations, and Problem Reports
Over the past few decades, unmanned aerial vehicles (UAVs) have been increasingly popular for use in locations that are lacking, or have unreliable global navigation satellite system (GNSS) availability. One of the more popular localization techniques for quadrotors is the use of visual odometry (VO) through monocular, RGB-D, or stereo cameras. With primary applications in the context of Simultaneous Localization And Mapping (SLAM) and indoor navigation, VO is largely used in combination with other sensors through Bayesian filters, namely Extended Kalman Filter (EKF) or Particle Filter. This work investigates the accuracy of two standard covariance estimation techniques for a feature-based …
Self && Self, Shuang Cai
Self && Self, Shuang Cai
Senior Projects Spring 2021
Seldom before the COVID-19 pandemic have so many people simultaneously had their lifestyle drastically changed in the same way. The forced physical isolation is, ironically, a communal experience. The sickening quarantine left everyone nothing but time to confront and reconnect with themselves. Another inevitable result of corporal isolation is the predominant awakening awareness of digital existences and connections. Evoking the shared sensitivity and delicacy, studying the tectonic activity of the digital world, the project documents the endured contemplation in the upcoming resurgence.
Perceptually Improved Medical Image Translations Using Conditional Generative Adversarial Networks, Anurag Vaidya
Perceptually Improved Medical Image Translations Using Conditional Generative Adversarial Networks, Anurag Vaidya
Honors Theses
Magnetic resonance imaging (MRI) can help visualize various brain regions. Typical MRI sequences consist of T1-weighted sequence (favorable for observing large brain structures), T2-weighted sequence (useful for pathology), and T2-FLAIR scan (useful for pathology with suppression of signal from water). While these different scans provide complementary information, acquiring them leads to acquisition times of ~1 hour and an average cost of $2,600, presenting significant barriers. To reduce these costs associated with brain MRIs, we present pTransGAN, a generative adversarial network capable of translating both healthy and unhealthy T1 scans into T2 scans. We show that the addition of non-adversarial …
Ping Pong Ball Collecting Robot, Gina Lanese, Jason Colonna, Sarah Kuchcinski, Johndavid Rogers
Ping Pong Ball Collecting Robot, Gina Lanese, Jason Colonna, Sarah Kuchcinski, Johndavid Rogers
Williams Honors College, Honors Research Projects
The ping pong ball collecting robot will collect ping pong balls in a ping pong arena, such as after a game or practice session. Ball collection is now typically done by a human, collecting the balls either by hand or with a net. A robot, autonomously collecting the ping pong balls from the floor, would allow for the time spent playing or practicing to be maximized. The team will design the software and hardware for the robot, including the mechanical system, the software, and circuit boards. This robot will serve as a capstone project for the senior design team consisting …
Deep Models For Improving The Performance And Reliability Of Person Recognition, Sobhan Soleymani
Deep Models For Improving The Performance And Reliability Of Person Recognition, Sobhan Soleymani
Graduate Theses, Dissertations, and Problem Reports
Deep models have provided high accuracy for different applications such as person recognition, image segmentation, image captioning, scene description, and action recognition. In this dissertation, we study the deep learning models and their application in improving the performance and reliability of person recognition. This dissertation focuses on five aspects of person recognition: (1) multimodal person recognition, (2) quality-aware multi-sample person recognition, (3) text-independent speaker verification, (4) adversarial iris examples, and (5) morphed face images. First, we discuss the application of multimodal networks consisting of face, iris, fingerprint, and speech modalities in person recognition. We propose multi-stream convolutional neural network architectures …