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Full-Text Articles in Electrical and Computer Engineering
Automated Segmentation Of The Inner Ear And Round Window In Computed Tomography Scans Using Convolutional Neural Networks, Kyle A. Rioux
Automated Segmentation Of The Inner Ear And Round Window In Computed Tomography Scans Using Convolutional Neural Networks, Kyle A. Rioux
Electronic Thesis and Dissertation Repository
Computed tomography (CT) scans are acquired prior to cochlear implant (CI) surgery. Three-dimensional segmentations of the inner ear (IE) and round window (RW) based on clinical CTs can improve the CI procedure. Software pipelines are presented here which employ convolutional neural networks to automatically segment the IE and RW. The first pipeline produces high resolution segmentations of the IE and RW in tightly cropped CTs. Mean IE Dice score and RW centroid error were 0.88, 0.57mm and 0.93, 0.18mm in implanted and non-implanted samples, respectively. The second pipeline automatically segments the IE in large field of view CTs of any …
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 …
Adaptation Of A Deep Learning Algorithm For Traffic Sign Detection, Jose Luis Masache Narvaez
Adaptation Of A Deep Learning Algorithm For Traffic Sign Detection, Jose Luis Masache Narvaez
Electronic Thesis and Dissertation Repository
Traffic signs detection is becoming increasingly important as various approaches for automation using computer vision are becoming widely used in the industry. Typical applications include autonomous driving systems, mapping and cataloging traffic signs by municipalities. Convolutional neural networks (CNNs) have shown state of the art performances in classification tasks, and as a result, object detection algorithms based on CNNs have become popular in computer vision tasks. Two-stage detection algorithms like region proposal methods (R-CNN and Faster R-CNN) have better performance in terms of localization and recognition accuracy. However, these methods require high computational power for training and inference that make …
Autonomous And Real Time Rock Image Classification Using Convolutional Neural Networks, Alexis David Pascual
Autonomous And Real Time Rock Image Classification Using Convolutional Neural Networks, Alexis David Pascual
Electronic Thesis and Dissertation Repository
Autonomous image recognition has numerous potential applications in the field of planetary science and geology. For instance, having the ability to classify images of rocks would allow geologists to have immediate feedback without having to bring back samples to the laboratory. Also, planetary rovers could classify rocks in remote places and even in other planets without needing human intervention. In 2017, Shu et. al. used a Support Vector Machine (SVM) classification algorithm to classify 9 different types of rock images using a with the image features extracted autonomously. Through this method, they achieved a test accuracy of 96.71%. Within the …