John A Malas, 6534 Enid Ave, Dayton, OH 45429

8350749, Jan 8, 2013

Apr 29, 2010

12/770211

John Malas - Centerville OH, US
Krishna Pasala - Centerville OH, US

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

G01S 7/41

342 90, 342195

A method for testing and/or validating the suitability of a multi-radar signature database to be used on radar systems having automatic target recognition. The database may include measured field data and/or modeled synthetic data. The technique allows field data to be compared to the synthetic data using modal mutual information.

2021007, Mar 11, 2021

Sep 14, 2020

17/019531

John A. Malas - Kettering OH, US
Patricia A. Ryan - Centerville OH, US
John A. Cortese - Reading MA, US

US Gov't as represented by Secretary of Air Force - Wright-Patterson AFB OH

G01S 7/38
G01S 7/02
G06K 9/00
G06K 9/62

Methods are provided for identifying and quantifying information loss in a system due to uncertainty and analyzing the impact on the reliability of system performance. Models and methods join Fano's equality with the Data Processing Inequality in a Markovian channel construct in order to characterize information flow within a multi-component nonlinear system and allow the determination of risk and characterization of system performance upper bounds based on the information loss attributed to each component. The present disclosure additionally includes methods for estimating the sampling requirements and for relating sampling uncertainty to sensing uncertainty. The present disclosure further includes methods for determining the optimal design of components of a nonlinear system in order to minimize information loss, while maximizing information flow and mutual information.

2020007, Mar 5, 2020

Oct 29, 2019

16/666516

John A. Malas - Kettering OH, US
Patricia A. Ryan - Centerville OH, US
John A. Cortese - Reading MA, US

U.S. Government as represented by Secretary of the Air Force - Wright-Patterson AFB OH

G06N 7/08
G06N 7/00
G06N 7/02

The present disclosure includes theoretical models and methods for identifying and quantifying information loss in a system due to uncertainty and analyzing the impact on the reliability of system performance. These models and methods join Fano's equality with the Data Processing Inequality in a Markovian channel construct in order to characterize information flow within a multi-component nonlinear system and allow the determination of risk and characterization of system performance upper bounds based on the information loss attributed to each component. The present disclosure additionally includes methods for estimating the sampling requirements and for relating sampling uncertainty to sensing uncertainty. The present disclosure further includes methods for determining the optimal design of components of a nonlinear system in order to minimize information loss, while maximizing information flow and mutual information.

2018016, Jun 14, 2018

Dec 7, 2017

15/834492

- Wright-Patterson AFB OH, US
John A. Malas - Kettering OH, US

Government of the United States as represented by the Secretary of the Air Force - Wright-Patterson AFB OH

G06K 9/03
G06T 5/00

A method of quantifying computer vision algorithm performance. The method includes receiving a first image and a second image from an imaging system. Each of the first and second images is characterized by an image intensity value. Iteratively, an evaluation value of a noise profile is applies to the first and second images to form respective first and second composite images, and algorithm performance using the first and second composite images is measured. The measured performances are compared and an operational range of the algorithm determined. The noise profile includes at least one source of noise inherent to the imaging system.