Peter M Kuhn

US Patent:

42661307, May 5, 1981

Filed:

Oct 13, 1978

Appl. No.:

5/950943

Inventors:

Peter M. Kuhn - Boulder CO

Assignee:

The United States of America as represented by the Secretary of Commerce - Washington DC

International Classification:

G01J 100

US Classification:

250339

Abstract:

An improved clear air turbulence (CAT) detector sensing infrared radiation in the 27-33 micrometer range of the water vapor rotational wavelength band. The improved clear air turbulence detector for use on an aircraft includes a forward looking and upwardly oriented between 8. degree. to 12. degree. optical assembly having a thallium bromoiodide infrared lens, a 27-33 micrometer wavelength interference filter and an infrared detector; a radiometer amplifier for amplifying the output of the optical assembly and for generating an analog voltage signal related to the received infrared intensity, a processing unit for analyzing the analog signal, and a display unit for generating a first signal when no clear air turbulence is detected, a second signal when a moderate turbulence is detected, and a third signal when extreme turbulence is detected. A method for determining the advance time for entering the clear air turbulence after detection is based upon the altitude of the aircraft.

US Patent:

20210090732, Mar 25, 2021

Filed:

Apr 27, 2018

Appl. No.:

16/499152

Inventors:

- Los Angeles CA, US
Peter Kuhn - Los Angeles CA, US
Yan Liu - Los Angeles CA, US
Paymaneh Malihi - Los Angeles CA, US
Sanjay Purushotham - Los Angeles CA, US

International Classification:

G16H 50/20
G06N 3/08
G06N 3/04
G16H 10/60
G16H 50/30
G16H 10/40
G16H 50/70
G16H 40/67

Abstract:

Methods, systems, and apparatus for a method that predicts an individual survival survival time of a patient. The method includes obtaining clinical data associated with health factors of the patient. The method includes obtaining liquid biopsy data associated with one or more attributes of diseased cells within the patient. The method includes predicting or determining a survival time of the patient using a deep learning model based on the clinical data and the liquid biopsy data. The method includes providing or outputting the survival time.