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Articles 1 - 5 of 5
Full-Text Articles in Data Science
Super-Resolution Imaging Of Remote Sensed Brightness Temperature Using A Convolutional Neural Network, Kellen A. Donahue
Super-Resolution Imaging Of Remote Sensed Brightness Temperature Using A Convolutional Neural Network, Kellen A. Donahue
Graduate Student Theses, Dissertations, & Professional Papers
Steady improvements to the instruments used in remote sensing has led to much higher resolution data, often contemporaneous with lower resolution instruments that continue to collect data. There is a clear opportunity to reconcile recent high resolution satellite data with the lower resolution data of the past. Super-resolution (SR) imaging is a technique that increases the spatial resolution of image data by training statistical methods on simultaneously occurring lower and higher resolution data sets. The special sensor microwave/imager (SSMI) and advanced microwave scanning radiometer (AMSR2) brightness temperature data products are well suited to super-resolution imaging, and SR can be used …
A Review And Evaluation Of Techniques For Improved Feature Detection In Mass Spectrometry Data, Annika R. Tostengard, Rob Smith
A Review And Evaluation Of Techniques For Improved Feature Detection In Mass Spectrometry Data, Annika R. Tostengard, Rob Smith
Graduate Student Theses, Dissertations, & Professional Papers
Mass spectrometry (MS) is used in analysis of chemical samples to identify the molecules present and their quantities. This analytical technique has applications in many fields, from pharmacology to space exploration. Its impacts on medicine are particularly significant, since MS aids in the identification of molecules associated with disease; for instance, in proteomics, MS allows researchers to identify proteins that are associated with autoimmune disorders, cancers, and other conditions. Since the applications are so wide-ranging and the tool is ubiquitous across so many fields, it is critical that the analytical methods used to collect data are sound.
Data analysis in …
Forecasting The Daily Percentage Of Delayed Flights Based On The National Weather Data, Parto Mahmoudi
Forecasting The Daily Percentage Of Delayed Flights Based On The National Weather Data, Parto Mahmoudi
Graduate Student Theses, Dissertations, & Professional Papers
Flight delays cost airlines and affect passenger’s satisfaction. In this research work, we predicted the daily percentage of delayed flights based on the national weather data using the multiple linear regression and the random forest models. We extracted the passenger flight on-time performance data from the Bureau of Transportation Statistics and the weather dataset from NOAA National Centers for Environmental Information for the years from 2015 to 2019. We used the flight dataset for Seattle airport as the origin. We predicted the daily percentage of delayed flights for the Seattle-originated flights based on the features such as weather conditions of …
Inference Of Surface Velocities From Oblique Time Lapse Photos And Terrestrial Based Lidar At The Helheim Glacier, Franklyn T. Dunbar Ii
Inference Of Surface Velocities From Oblique Time Lapse Photos And Terrestrial Based Lidar At The Helheim Glacier, Franklyn T. Dunbar Ii
Graduate Student Theses, Dissertations, & Professional Papers
Using time dependent observations derived from terrestrial LiDAR and oblique
time-lapse imagery, we demonstrate that a Bayesian approach to glacial motion es-
timation provides a concise way to incorporate multiple data products into a single
motion estimation procedure effectively producing surface velocity estimates with
an associated uncertainty. This approach brings both improved computational effi-
ciency, and greater scalability across observational time-frames when compared to
existing methods. To gauge efficacy, we apply these methods to a set of observa-
tions from the Helheim Glacier, a critical actor in contemporary mass loss trends
observed in the Greenland Ice Sheet. We find that …
Ensemble Protein Inference Evaluation, Kyle Lee Lucke
Ensemble Protein Inference Evaluation, Kyle Lee Lucke
Graduate Student Theses, Dissertations, & Professional Papers
The Protein inference problem is becoming an increasingly important tool that aids in the characterization of complex proteomes and analysis of complex protein samples. In bottom-up shotgun proteomics experiments the metrics for evaluation (like AUC and calibration error) are based on an often imperfect target-decoy database. These metrics make the inherent assumption that all of the proteins in the target set are present in the sample being analyzed. In general, this is not the case, they are typically a mix of present and absent proteins. To objectively evaluate inference methods, protein standard datasets are used. These datasets are special in …