Raytheon Company

Raytheon Bbn Technologies Corp.

Christopher R Eck

John A Cogliandro

Linda A Gee

Suzanne P Hassell

Jagannath Chirravuri

Carlos R Costas

Michael J Hirsch

Gary W Gleason

Paul F Beraud, Iii

Cassandra L Wellman

Brian J Mastropietro

Richard E Neese

Hector J Ortiz Pena

A computational accelerator architecture facilitates change management of works in a model-based system engineering (MBSE) system in which each of the MBSE works includes a plurality of separately-identifiable statements. A linkage data store stores statement-wise, variable-strength linkages between certain statements of the MBSE works, where the linkages are indicative of associations between those certain statements. A revision control engine detects changes made to statements of MBSE works, and selectively indicates calls for revision of other statements in response to those changes based on respective strengths of linkages associated with the changed statements.

Computational accelerator architecture for change control in model-based system engineering

Generally discussed herein are systems, devices, and methods for determining if a circuit is acting improperly. A system can include a module to receive proper performance values of a circuit, a module to receive improper performance values of the circuit, a module to compare actual circuit input characteristics (X) and actual circuit output characteristics (Y) to X, Y, D, and Z to determine if the circuit is more likely operating properly or more likely operating improperly, and an alert module to, in response to determining the circuit is operating improperly, provide an alert to personnel indicating that the circuit is operating improperly or providing one or more signals to the circuit that cause the circuit to alter its current operation.

Systems and methods for determining whether a circuit is operating properly

Generally discussed herein are systems, apparatuses, or processes to recognize that a cyber threat exists or predict a future track of a cyber threat in a network. According to an example, a process for recognizing a cyber threat can include (1) determining a network layout of a network based on received network layout data, (2) receiving cyber sensor data indicating actions performed on the network, (3) calculating a first score associated with the cyber sensor data indicating that a cyber threat is present in the network by comparing a cyber threat profile of the cyber threat that details actions performed by the cyber threat to actions indicated by the cyber sensor data, (4) determining whether the calculated first score is greater than a specified threshold, or (5) determining that the cyber threat is present in response to determining the calculated first score is greater than the specified threshold.

Probabilistic cyber threat recognition and prediction

Embodiments of a system and method for predicting a future state of a set of data are generally described herein. In some embodiments, a set of data in a first domain is obtained. The set of data in the first domain may be represented as a network graph. The set of data in the first domain is mapped into a set of data in a second domain. A plurality of prediction models are applied to the set of data in the second domain to produce a plurality of predicted sets of data. The predicted sets of data are combined to generate a combined predicted set of data having a best match. The combined predicted sets of data having the best match are reverse mapped to the first domain.

Predicting edges in temporal network graphs described by near-bipartite data sets

In certain embodiments, a computer-implemented method includes accessing first and second data associated with a semantic network, the first data indicating a first plurality of nodes within the semantic network and a first plurality of relationships between the first plurality of nodes at a first time, and the second data indicating a second plurality of nodes within the semantic network and a second plurality of relationships between the second plurality of nodes at a second time. The method further includes generating a first waveform from the first data and a second waveform from the second data. The waveforms indicate an activity level of each of the nodes within the semantic network. The method further includes analyzing the semantic network using the generated first and second waveforms.

Waveform mapping technique and process for tracking and estimating evolution of semantic networks

According to one embodiment, the disclosure provides a system for algal cell lysis. The system may include at least one algal cell, a plurality of metal nanoparticles, and an electromagnetic radiation generator. The generator may be able to generate radio frequency or microwave radiation that excites the plurality of metal nanoparticles, resulting in lysis of the algal cell. The disclosure also provides a system for recovery of a lipid from an algal cell similar to the above system but also including a separator. The disclosure further provides a method of recovering a lipid from an algal cell by supplying a plurality of metal nanoparticles to at least one algal cell, exciting the plurality of metal nanoparticles with radio frequency or microwave electromagnetic radiation, lysing the algal cell to release the lipid, and separating the lipid from the lysed algal cell.

Algal cell lysis and lipid extraction using electromagnetic radiation-excitable metallic nanoparticles

In certain embodiments, a method includes accessing first target information associated with a first target visible to a first UAV at a first time and accessing second target information, received from a neighboring UAV, associated with a second target visible to the neighboring UAV at the first time. The method further includes combining the first target information and the second target information to generate combined target information and determining, based on the combined target information, a predicted location for the first and second targets at a second time. The method further includes determining a planned route for the first UAV for the second time by solving an optimization problem, the optimization problem accounting for the determined predicted location for the first and second targets, kinematic constraints associated with the first UAV and the neighboring UAV, and line-of-sight constraints associated with the first UAV and the neighboring UAV.

Growing community of inventors

Dunedin, FL, United States of America

Christopher R Eck