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Deep Learning With Effective Hierarchical Attention Mechanisms In Perception Of Autonomous Vehicles, Qiuxiao Chen Dec 2023

Deep Learning With Effective Hierarchical Attention Mechanisms In Perception Of Autonomous Vehicles, Qiuxiao Chen

All Graduate Theses and Dissertations, Fall 2023 to Present

Autonomous vehicles need to gather and understand information from their surroundings to drive safely. Just like how we look around and understand what's happening on the road, these vehicles need to see and make sense of dynamic objects like other cars, pedestrians, and cyclists, and static objects like crosswalks, road barriers, and stop lines.

In this dissertation, we aim to figure out better ways for computers to understand their surroundings in the 3D object detection task and map segmentation task. The 3D object detection task automatically spots objects in 3D (like cars or cyclists) and the map segmentation task automatically …


Contemporary Art Authentication With Large-Scale Classification, Todd Dobbs, Abdullah-Al-Raihan Nayeem, Isaac Cho, Zbigniew Ras Oct 2023

Contemporary Art Authentication With Large-Scale Classification, Todd Dobbs, Abdullah-Al-Raihan Nayeem, Isaac Cho, Zbigniew Ras

Computer Science Faculty and Staff Publications

Art authentication is the process of identifying the artist who created a piece of artwork and is manifested through events of provenance, such as art gallery exhibitions and financial transactions. Art authentication has visual influence via the uniqueness of the artist’s style in contrast to the style of another artist. The significance of this contrast is proportional to the number of artists involved and the degree of uniqueness of an artist’s collection. This visual uniqueness of style can be captured in a mathematical model produced by a machine learning (ML) algorithm on painting images. Art authentication is not always possible …


Accuracy Vs. Energy: An Assessment Of Bee Object Inference In Videos From On-Hive Video Loggers With Yolov3, Yolov4-Tiny, And Yolov7-Tiny, Vladimir A. Kulyukin, Aleksey V. Kulyukin Jul 2023

Accuracy Vs. Energy: An Assessment Of Bee Object Inference In Videos From On-Hive Video Loggers With Yolov3, Yolov4-Tiny, And Yolov7-Tiny, Vladimir A. Kulyukin, Aleksey V. Kulyukin

Computer Science Faculty and Staff Publications

A continuing trend in precision apiculture is to use computer vision methods to quantify characteristics of bee traffic in managed colonies at the hive's entrance. Since traffic at the hive's entrance is a contributing factor to the hive's productivity and health, we assessed the potential of three open-source convolutional network models, YOLOv3, YOLOv4-tiny, and YOLOv7-tiny, to quantify omnidirectional traffic in videos from on-hive video loggers on regular, unmodified one- and two-super Langstroth hives and compared their accuracies, energy efficacies, and operational energy footprints. We trained and tested the models with a 70/30 split on a dataset of 23,173 flying bees …


Deep Learning With Attention Mechanisms In Breast Ultrasound Image Segmentation And Classification, Meng Xu May 2023

Deep Learning With Attention Mechanisms In Breast Ultrasound Image Segmentation And Classification, Meng Xu

All Graduate Theses and Dissertations, Spring 1920 to Summer 2023

Breast cancer is a great threat to women’s health. Breast ultrasound (BUS) imaging is commonly used in the early detection of breast cancer as a portable, valuable, and widely available diagnosis tool. Automated BUS image analysis can assist radiologists in making accurate and fast decisions. Generally, automated BUS image analysis includes BUS image segmentation and classification. BUS image segmentation automatically extracts tumor regions from a BUS image. BUS image classification automatically classifies breast tumors into benign or malignant categories. Multi-task learning accomplishes segmentation and classification simultaneously, which makes it more appealing and practical than an either individual task. Deep neural …


Generative Neural Network Approach To Designing And Optimizing Dynamic Inductive Power Transfer Systems, Andrew Pond Curtis May 2023

Generative Neural Network Approach To Designing And Optimizing Dynamic Inductive Power Transfer Systems, Andrew Pond Curtis

All Graduate Theses and Dissertations, Spring 1920 to Summer 2023

Electric vehicles (EVs) offer many improvements over traditional combustion engines including increasing efficiency, while decreasing cost of operation and emissions. There is a need for the development of cheap and efficient charging systems for the future success of EVs. Most EVs currently utilize static plug-in charging systems. An alternative charging method of significant interest is dynamic inductive power transfer systems (DIPT). These systems utilize two coils, one placed in the vehicle and one in the roadway to wirelessly charge the vehicle as it passes over. This method removes the current limitations on EVs where they must stop and statically charge …