A Bayesian Approach to Multi-Drone Source Localization Methods

From abandoned Soviet reactors to lost submarines and stolen medical materials, stewardship of the world’s nuclear materials throughout the nuclear age is not what one might hope it to be. The International Atomic Energy Agency (IAEA) estimates around 3000 incidents of illicit trafficking, theft, or loss of radioactive materials have occurred since 1993 [1]. Locating lost or stolen materials is no simple task, particularly when there is little information about the type of source or its activity, whether or not the source is stationary or being transported, and at large distances the signal-to-noise ratio is a limiting factor. Since the USS Scorpion, USS Thresher, and Palomares B-52 searches throughout the 1960’s [2], Bayesian inference techniques and Bayesian search methods have become a more commonly embraced approach to complex search missions. The semi-autonomous wide-area radiological measurements (SWARM) system presented in this work utilizes multiple Unmanned Aircraft System (UAS) devices, connected via a central data repository (swarm theory), to more effectively survey a search space and locate missing radioactive sources. Coupling swarm theory with Bayesian inference techniques, SWARM shows great potential in overcoming the challenges of large search spaces and potentially low-count rate contributions from missing radiological sources. Preliminary results prove the search algorithms ability to quickly filter out low probability areas. In simulation, three drones reduced the area of interest by 91.7% after each surveying three lengths of the area at an altitude of 100 meters. The SWARM Bayesian algorithm presented is designed to be a simple and efficient approach to aerial-based Bayesian search localization, applied to a multi-drone search format

POWERLIB: SAS/IML Software for Computing Power in Multivariate Linear Models

The POWERLIB SAS/IML software provides convenient power calculations for a wide range of multivariate linear models with Gaussian errors. The software includes the Box, Geisser-Greenhouse, Huynh-Feldt, and uncorrected tests in the "univariate" approach to repeated measures (UNIREP), the Hotelling Lawley Trace, Pillai-Bartlett Trace, and Wilks Lambda tests in "multivariate" approach (MULTIREP), as well as a limited but useful range of mixed models. The familiar univariate linear model with Gaussian errors is an important special case. For estimated covariance, the software provides confidence limits for the resulting estimated power. All power and confidence limits values can be output to a SAS dataset, which can be used to easily produce plots and tables for manuscripts.

POWERLIB: SAS/IML Software for Computing Power in Multivariate Linear Models

The POWERLIB SAS/IML software provides convenient power calculations for a wide range of multivariate linear models with Gaussian errors. The software includes the Box, Geisser-Greenhouse, Huynh-Feldt, and uncorrected tests in the "univariate" approach to repeated measures (UNIREP), the Hotelling Lawley Trace, Pillai-Bartlett Trace, and Wilks Lambda tests in "multivariate" approach (MULTIREP), as well as a limited but useful range of mixed models. The familiar univariate linear model with Gaussian errors is an important special case. For estimated covariance, the software provides confidence limits for the resulting estimated power. All power and confidence limits values can be output to a SAS dataset, which can be used to easily produce plots and tables for manuscripts

Satellite Proving Ground for the GOES-R Geostationary Lightning Mapper (GLM)

The key mission of the Satellite Proving Ground is to demonstrate new satellite observing data, products and capabilities in the operational environment to be ready on Day 1 to use the GOES-R suite of measurements. Algorithms, tools, and techniques must be tested, validated, and assessed by end users for their utility before they are finalized and incorporated into forecast operations. The GOES-R Proving Ground for the Geostationary Lightning Mapper (GLM) focuses on evaluating how the infusion of the new technology, algorithms, decision aids, or tailored products integrate with other available tools (weather radar and ground strike networks; nowcasting systems, mesoscale analysis, and numerical weather prediction models) in the hands of the forecaster responsible for issuing forecasts and warning products. Additionally, the testing concept fosters operation and development staff interactions which will improve training materials and support documentation development. Real-time proxy total lightning data from regional VHF lightning mapping arrays (LMA) in Northern Alabama, Central Oklahoma, Cape Canaveral Florida, and the Washington, DC Greater Metropolitan Area are the cornerstone for the GLM Proving Ground. The proxy data will simulate the 8 km Event, Group and Flash data that will be generated by GLM. Tailored products such as total flash density at 1-2 minute intervals will be provided for display in AWIPS-2 to select NWS forecast offices and national centers such as the Storm Prediction Center. Additional temporal / spatial combinations are being investigated in coordination with operational needs and case-study proxy data and prototype visualizations may also be generated from the NASA heritage Lightning Imaging Sensor and Optical Transient Detector data. End users will provide feedback on the utility of products in their operational environment, identify use cases and spatial/temporal scales of interest, and provide feedback to the developers for adjusted or new products

POWERLIB : SAS/IML Software for Computing Power in Multivariate Linear Models

The POWERLIB SAS/IML software provides convenient power calculations for a wide range of multivariate linear models with Gaussian errors. The software includes the Box, Geisser-Greenhouse, Huynh-Feldt, and uncorrected tests in the “univariate” approach to repeated measures (UNIREP), the Hotelling Lawley Trace, Pillai-Bartlett Trace, and Wilks Lambda tests in “multivariate” approach (MULTIREP), as well as a limited but useful range of mixed models. The familiar univariate linear model with Gaussian errors is an important special case. For estimated covariance, the software provides confidence limits for the resulting estimated power. All power and confidence limits values can be output to a SAS dataset, which can be used to easily produce plots and tables for manuscripts

POWERLIB

The POWERLIB SAS/IML software provides convenient power calculations for a widerange of multivariate linear models with Gaussian errors. The software includes the Box,Geisser-Greenhouse, Huynh-Feldt, and uncorrected tests in the univariate" approach torepeated measures (UNIREP), the Hotelling Lawley Trace, Pillai-Bartlett Trace, andWilks Lambda tests in multivariate" approach (MULTIREP), as well as a limited butuseful range of mixed models. The familiar univariate linear model with Gaussian errorsis an important special case. For estimated covariance, the software provides condencelimits for the resulting estimated power. All power and condence limits values canbe output to a SAS dataset, which can be used to easily produce plots and tables formanuscripts

Predicting cognitive outcome following mild traumatic brain injury: Applying neural reserve theory

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