JOHN EDWARD WAGNER
Pilots at Kalgan Rd, Albuquerque, NM
License number
New Mexico A1981102
Issued Date
Oct 2016
Expiration Date
Oct 2017
Category
Airmen
Type
Authorized Aircraft Instructor
Address
Address
6520 Kalgan Rd NE, Albuquerque, NM 87144
Professional information
Owner, John Wagner Recording Studios, Inc
Position:
Owner at John Wagner Recording Studios, Inc
Location:
Albuquerque, New Mexico Area
Industry:
Nanotechnology
Work:
John Wagner Recording Studios, Inc
-
Owner
Education:
The University of New Mexico 1961 - 1967
BS Electrical Engineering, math and science
BS Electrical Engineering, math and science
Independent Hospital &Amp; Health Care Professional
Location:
Albuquerque, New Mexico Area
Industry:
Hospital & Health Care
John Wagner
Specialties:
Anthropology
Work:
University of New Mexico
Particle Analysis Using Laser Ablation Mass Spectroscopy
US Patent:
6618712, Sep 9, 2003
Filed:
May 28, 1999
Appl. No.:
09/321906
Inventors:
Eric P. Parker - Albuquerque NM
Stephen E. Rosenthal - Tijeras NM
Michael W. Trahan - Albuquerque NM
John S. Wagner - Albuquerque NM
Stephen E. Rosenthal - Tijeras NM
Michael W. Trahan - Albuquerque NM
John S. Wagner - Albuquerque NM
Assignee:
Sandia Corporation - Albuquerque NM
International Classification:
G06N 302
US Classification:
706 15, 356336, 422 91
Abstract:
The present invention provides a method of quickly identifying bioaerosols by class, even if the subject bioaerosol has not been previously encountered. The method begins by collecting laser ablation mass spectra from known particles. The spectra are correlated with the known particles, including the species of particle and the classification (e. g. , bacteria). The spectra can then be used to train a neural network, for example using genetic algorithm-based training, to recognize each spectra and to recognize characteristics of the classifications. The spectra can also be used in a multivariate patch algorithm. Laser ablation mass specta from unknown particles can be presented as inputs to the trained neural net for identification as to classification. The description below first describes suitable intelligent algorithms and multivariate patch algorithms, then presents an example of the present invention including results.
System For Identifying Known Materials Within A Mixture Of Unknowns
US Patent:
5926773, Jul 20, 1999
Filed:
Oct 27, 1997
Appl. No.:
8/958099
Inventors:
John S. Wagner - Albuquerque NM
Assignee:
Sandia Corporation - Albuquerque NM
International Classification:
G01N 2129
US Classification:
702 22
Abstract:
One or both of two methods and systems are used to determine concentration of a known material in an unknown mixture on the basis of the measured interaction of electromagnetic waves upon the mixture. One technique is to utilize a multivariate analysis patch technique to develop a library of optimized patches of spectral signatures of known materials containing only those pixels most descriptive of the known materials by an evolutionary algorithm. Identity and concentration of the known materials within the unknown mixture is then determined by minimizing the residuals between the measurements from the library of optimized patches and the measurements from the same pixels from the unknown mixture. Another technique is to train a neural network by the genetic algorithm to determine the identity and concentration of known materials in the unknown mixture. The two techniques may be combined into an expert system providing cross checks for accuracy.
Method For Identifying Known Materials Within A Mixture Of Unknowns
US Patent:
6035246, Mar 7, 2000
Filed:
May 15, 1997
Appl. No.:
8/773748
Inventors:
John S. Wagner - Albuquerque NM
Assignee:
Sandia Corporation - Albuquerque NM
International Classification:
G06F 1900
US Classification:
700266
Abstract:
One or both of two methods and systems are used to determine concentration of a known material in an unknown mixture on the basis of the measured interaction of electromagnetic waves upon the mixture. One technique is to utilize a multivariate analysis patch technique to develop a library of optimized patches of spectral signatures of known materials containing only those pixels most descriptive of the known materials by an evolutionary algorithm. Identity and concentration of the known materials within the unknown mixture is then determined by minimizing the residuals between the measurements from the library of optimized patches and the measurements from the same pixels from the unknown mixture. Another technique is to train a neural network by the genetic algorithm to determine the identity and concentration of known materials in the unknown mixture. The two techniques may be combined into an expert system providing cross checks for accuracy.