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Full-Text Articles in Engineering
Accelerating Machine Learning Inference For Satellite Component Feature Extraction Using Fpgas., Andrew Ekblad
Accelerating Machine Learning Inference For Satellite Component Feature Extraction Using Fpgas., Andrew Ekblad
Theses and Dissertations
Running computer vision algorithms requires complex devices with lots of computing power, these types of devices are not well suited for space deployment. The harsh radiation environment and limited power budgets have hindered the ability of running advanced computer vision algorithms in space. This problem makes running an on-orbit servicing detection algorithm very difficult. This work proposes using a low powered FPGA to accelerate the computer vision algorithms that enable satellite component feature extraction. This work uses AMD/Xilinx’s Zynq SoC and DPU IP to run model inference. Experiments in this work centered around improving model post processing by creating implementations …
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 …
Achieving High Renewable Energy Integration In Smart Grids With Machine Learning, Yaze Li
Achieving High Renewable Energy Integration In Smart Grids With Machine Learning, Yaze Li
Graduate Theses and Dissertations
The integration of high levels of renewable energy into smart grids is crucial for achieving a sustainable and efficient energy infrastructure. However, this integration presents significant technical and operational challenges due to the intermittent nature and inherent uncertainty of renewable energy sources (RES). Therefore, the energy storage system (ESS) has always been bound to renewable energy, and its charge and discharge control has become an important part of the integration. The addition of RES and ESS comes with their complex control, communication, and monitor capabilities, which also makes the grid more vulnerable to attacks, brings new challenges to the cybersecurity. …
Efficient Scopeformer: Towards Scalable And Rich Feature Extraction For Intracranial Hemorrhage Detection Using Hybrid Convolution And Vision Transformer Networks, Yassine Barhoumi
Efficient Scopeformer: Towards Scalable And Rich Feature Extraction For Intracranial Hemorrhage Detection Using Hybrid Convolution And Vision Transformer Networks, Yassine Barhoumi
Theses and Dissertations
The field of medical imaging has seen significant advancements through the use of artificial intelligence (AI) techniques. The success of deep learning models in this area has led to the need for further research. This study aims to explore the use of various deep learning algorithms and emerging modeling techniques to improve training paradigms in medical imaging. Convolutional neural networks (CNNs) are the go-to architecture for computer vision problems, but they have limitations in mapping long-term dependencies within images. To address these limitations, the study explores the use of techniques such as global average pooling and self-attention mechanisms. Additionally, the …
Computer Vision-Based Hand Tracking And 3d Reconstruction As A Human-Computer Input Modality With Clinical Application, Tania Banerjee
Computer Vision-Based Hand Tracking And 3d Reconstruction As A Human-Computer Input Modality With Clinical Application, Tania Banerjee
Electronic Thesis and Dissertation Repository
The recent pandemic has impeded patients with hand injuries from connecting in person with their therapists. To address this challenge and improve hand telerehabilitation, we propose two computer vision-based technologies, photogrammetry and augmented reality as alternative and affordable solutions for visualization and remote monitoring of hand trauma without costly equipment. In this thesis, we extend the application of 3D rendering and virtual reality-based user interface to hand therapy. We compare the performance of four popular photogrammetry software in reconstructing a 3D model of a synthetic human hand from videos captured through a smartphone. The visual quality, reconstruction time and geometric …
Optimizing Constraint Selection In A Design Verification Environment For Efficient Coverage Closure, Vanessa Cooper
Optimizing Constraint Selection In A Design Verification Environment For Efficient Coverage Closure, Vanessa Cooper
CCE Theses and Dissertations
No abstract provided.
Eeg-Based Spanish Language Proficiency Classification: An Eeg Power Spectrum And Cross-Spectrum Analysis, Blaise Xavier O'Mara, Skyler Baumer
Eeg-Based Spanish Language Proficiency Classification: An Eeg Power Spectrum And Cross-Spectrum Analysis, Blaise Xavier O'Mara, Skyler Baumer
Honors Theses and Capstones
Second language proficiency may be predicted with electrophysiological techniques. In a machine learning application, this electrophysiological data may be used for language instructors and language students to assess their language learning. This study identifies how electroencephalogram (EEG) power spectrum and cross spectrum data of the brain cortex relates to Spanish second language (L2) proficiency of 20 Spanish language students of varying proficiency levels at the University of New Hampshire. The two metrics for assessing cortical power and processing were event-related desynchronization (ERD)—a measure of relative change in power—of the alpha (8-12 Hz) brain frequency band, and alpha and beta (13-30Hz) …