WILLIAM LEO SMITH
Pilots at Crk Cir, Seaford, VA
License number
Virginia A2376666
Issued Date
Dec 2016
Expiration Date
Dec 2018
Category
Airmen
Type
Authorized Aircraft Instructor
Address
Address
117 Creek Cir, Seaford, VA 23696
Professional information
William Smith - Seaford, VA
Work:
Sodexo - Fort Eustis, VA
Cashier Wawa - Yorktown, VA
Customer Service Associate/Facilities Maintenance Associate McDonald's - Yorktown, VA
Trainee
Cashier Wawa - Yorktown, VA
Customer Service Associate/Facilities Maintenance Associate McDonald's - Yorktown, VA
Trainee
Education:
ECPI College of Technology - Newport News, VA
Bachelor's of Science in Network Security o Majoring New Horizons Regional Education Center - Hampton, VA
Computer Networking
Bachelor's of Science in Network Security o Majoring New Horizons Regional Education Center - Hampton, VA
Computer Networking
Skills:
Proficient in the use of the Microsoft Office Suite, proficient with Windows 2000, Windows XP, Windows 7, Windows 8, and Vista, advanced familiarity in computer hardware and networking, strong customer service skills, currency auditing and cash transactions made by customers or others, and ability to perform inventory stocking and organizing of merchandise.
Method And System For Determining Atmospheric Profiles Using A Physical Retrieval Algorithm
US Patent:
7558673, Jul 7, 2009
Filed:
Dec 22, 2006
Appl. No.:
11/644721
Inventors:
Yanni Qu - Fort Wayne IN, US
William L. Smith - Seaford VA, US
William L. Smith - Seaford VA, US
Assignee:
ITT Manufacturing Enterprises, Inc. - Wilmington DE
International Classification:
G01W 1/00
US Classification:
702 3, 702 2, 374109
Abstract:
The present invention provides a new approach for processing hyper-spectral radiance data. It uses a transformation matrix to convert an instrument radiance spectrum into a pseudo-monochromatic radiance spectrum. The pseudo-monochromatic radiance spectrum is produced by an empirical transform of the instrument channel spectrum to a monochromatic equivalent spectrum (i. e. , a pseudo-monochromatic spectrum). Eigenvector regression is used to produce the empirical transformation. Although the transformation does not produce the monochromatic radiance spectrum without error, the transformation error is generally well below nominal instrument noise levels for most spectral channels. The reduction in instrument noise results from a noise filtering effect of the eigenvector transformation. One of the advantages of the present invention is that it eliminates the need to build different fast radiative transfer models (RTMs) for different observing instruments, since the retrieval of geophysical parameters is based on an inversion of the monochromatic radiative transfer model. Although a different transformation matrix is required for different instrument spectral channel characteristics, the production of this transformation matrix is straightforward and simpler than the production of an accurate channel radiance fast model.