Randy C Paffenroth

20130159263, Jun 20, 2013

Dec 18, 2011

13/329290

Randy C. Paffenroth - Loveland CO, US
Ryan Nong - Monterey Park CA, US
Woody D. Leed - Loveland CO, US
Scott M. Lundberg - Fort Collins CO, US

Numerica Corporation - Loveland CO

G06F 17/30

707693, 707E17002

A method for compressing a cloud of points with imposed error constraints at each point is disclosed. Surfaces are constructed that approach each point to within the constraint specified at that point, and from the plurality of surfaces that satisfy the constraints at all points, a surface is chosen which minimizes the amount of memory required to store the surface on a digital computer.

20200029806, Jan 30, 2020

Jul 26, 2019

16/523147

- WORCESTER MA, US
Soussan Djamasbi - Natick MA, US
Randy C. Paffenroth - Worcester MA, US
Andrew C. Trapp - Worcester MA, US

WORCESTER POLYTECHNIC INSTITUTE - WORCESTER MA

A61B 3/113
A61B 5/16
G06F 3/01
G06T 7/20
G06N 20/00
G06K 9/62
G06F 9/54

A visual tracking system, comprises an eye-tracking device and a cognitive load detection device disposed in electrical communication with the eye-tracking device, the cognitive load detection device comprising a controller having a memory and a processor. The controller is configured to receive eye-movement data from the eye-tracking device, the eye-movement data comprising pupil dilation event data and at least one of saccade event data and fixation event data, apply a classification function to the eye-movement data to detect a cognitive load associated with the eye-movement data and corresponding to a visual location of a field of view of the user, and output a notification regarding the cognitive load associated with the eye-movement data.

20160156652, Jun 2, 2016

Aug 1, 2012

13/564335

RANDY PAFFENROTH - Loveland CO, US
Philip Du Toit - Berthoud CO, US
Louis Scharf - Fort Collins CO, US

Numerica Corporaition - Loveland CO

H04L 29/06

A method of detecting an anomaly in a sensor network for diagnosing a network attack may include receiving a data set comprising a plurality of vector-valued measurements from a plurality of sensors, and decomposing the data set into a low-rank component L and a sparse component S using an Augmented Lagrange Multiplier (ALM) method. In one embodiment, at least one of L or S can be determined using an exact minimizer of a Lagrangian in the ALM method, L can represent patterns that occur in a relatively large number of the plurality of sensors, and S can represent patterns that occur in a relatively small number of the plurality of sensors. The method may also include ascertaining, using the computer system, the anomaly in the data set based on the patterns in the sparse component S.

20150081258, Mar 19, 2015

Sep 18, 2013

14/030514

- Loveland CO, US
RANDY C. PAFFENROTH - LOVELAND CO, US

NUMERICA CORP. - Loveland CO

G06F 17/50

703 2, 703 11

A method of tracking a plurality of tagged molecules in a cell in two or three dimensions may include receiving a plurality of unambiguous track segments, where each of the plurality of unambiguous track segments may include a plurality of time-valued observations of individual tagged molecules. The method may also include separating the plurality of unambiguous track segments into a plurality of time windows. The method may additionally include, for each of the plurality of unambiguous track segments, deriving one or more data sets representing features of the unambiguous track segment. The method may further include associating a first unambiguous track segment from a first time window with a second unambiguous track segment from a second time window using the one or more data sets.