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Articles 1 - 4 of 4
Full-Text Articles in Physics
Enhancing Gravitational-Wave Science With Machine Learning, Elena Cuoco, Jade Powell, Marco Cavaglia, Kendall Ackley, For Full List Of Authors, See Publisher's Website.
Enhancing Gravitational-Wave Science With Machine Learning, Elena Cuoco, Jade Powell, Marco Cavaglia, Kendall Ackley, For Full List Of Authors, See Publisher's Website.
Physics Faculty Research & Creative Works
Machine learning has emerged as a popular and powerful approach for solving problems in astrophysics. We review applications of machine learning techniques for the analysis of ground-based gravitational-wave (GW) detector data. Examples include techniques for improving the sensitivity of Advanced Laser Interferometer GW Observatory and Advanced Virgo GW searches, methods for fast measurements of the astrophysical parameters of GW sources, and algorithms for reduction and characterization of non-astrophysical detector noise. These applications demonstrate how machine learning techniques may be harnessed to enhance the science that is possible with current and future GW detectors.
Speckle Correlation Using A Deep Learning Algorithm On Newtonian Fluids, Sanskar Basnet
Speckle Correlation Using A Deep Learning Algorithm On Newtonian Fluids, Sanskar Basnet
College of Science and Health Theses and Dissertations
In soft-matter physics, Dynamic Light Scattering (DLS) stands out as the non-invasive technique designed to measure the size and distribution of particles using speckle correlation. Traditional DLS analysis performs a one-to-one mapping of the size with diffusion which is computationally costly, in addition to lacks in generalisation to seek deeper relationships amongst speckle patterns. A recent advancement in double-pulse speckle imaging allows for the temporal-correlation problem presented by DLS to be transformed into an image classification problem. In this prototype study, I probe the feasibility and efficacy of DLS data analysis of speckle correlation using Deep Learning (DL). To reduce …
Soarnet, Deep Learning Thermal Detection For Free Flight, Jake T. Tallman
Soarnet, Deep Learning Thermal Detection For Free Flight, Jake T. Tallman
Master's Theses
Thermals are regions of rising hot air formed on the ground through the warming of the surface by the sun. Thermals are commonly used by birds and glider pilots to extend flight duration, increase cross-country distance, and conserve energy. This kind of powerless flight using natural sources of lift is called soaring. Once a thermal is encountered, the pilot flies in circles to keep within the thermal, so gaining altitude before flying off to the next thermal and towards the destination. A single thermal can net a pilot thousands of feet of elevation gain, however estimating thermal locations is not …
Assessment And Diagnosis Of Human Colorectal And Ovarian Cancer Using Optical Imaging And Computer-Aided Diagnosis, Yifeng Zeng
Assessment And Diagnosis Of Human Colorectal And Ovarian Cancer Using Optical Imaging And Computer-Aided Diagnosis, Yifeng Zeng
McKelvey School of Engineering Theses & Dissertations
Tissue optical scattering has recently emerged as an important diagnosis parameter associated with early tumor development and progression. To characterize the differences between benign and malignant colorectal tissues, we have created an automated optical scattering coefficient mapping algorithm using an optical coherence tomography (OCT) system. A novel feature called the angular spectrum index quantifies the scattering coefficient distribution. In addition to scattering, subsurface morphological changes are also associated with the development of colorectal cancer. We have observed a specific mucosa structure indicating normal human colorectal tissue, and have developed a real-time pattern recognition neural network to localize this specific structure …