Raytheon Company

Rtx Corporation

B/e Aerospace, Inc.

Kin Gwn Lore

Ganesh Sundaramoorthi

Kishore K Reddy

Justin R Urban

Systems and methods are provided for producing aligned and aggregated aligned time surfaces. An exemplary method includes: receiving camera event data from a first location; computing, using the camera event data, a plurality of unaligned time surfaces; determining a plurality of optical flows corresponding to the unaligned time surfaces; determining, using the optical flows, an accumulated optical flow for mapping displacement between any two time periods; producing, using the optical flows, for a plurality of consecutive mapped time surfaces; performing, using the accumulated optical flow, warping of the mapped time surfaces producing corresponding aligned time surfaces; and aggregating the aligned time surfaces to produce an image having lowered blurring of object boundaries.

Aligned time surfaces via dense optical flows for event-based object detection, identification and tracking

Systems and methods for implementing neuromorphic sensor technology in aircraft cabin environments including smart devices to improve and/or expand the use and functionality of the smart devices. In some embodiments, neuromorphic sensors and smart devices are communicatively coupled to a controller operative to analyze object movements and instruct smart devices to operate according to preprogrammed instructions. Non-limiting examples include detecting passenger movements in passenger seating areas and lavatories associated with smart devices, detecting movements of smart devices configured to traverse aircraft aisles, etc. The neuromorphic sensors are incapable of capturing visually recognizable passenger identities and therefore satisfy privacy concerns.

Neuromorphic sensor implementation in aircraft cabin environments

A neuromorphic foreign object debris (FOD) detection system includes a FOD processing system and a neuromorphic sensor. The FOD processing system includes a FOD controller including a trained artificial intelligence machine learning (AIML) model representing an area of interest. The neuromorphic sensor has a field of view (FOV) containing the area of interest and is configured to output pixel data in response to FOD appearing in the FOV. The FOD controller detects the FOD is present in the area of interest in response to receiving the pixel data, and generates an alert signal indicating the presence of the FOD.

Neuromorphic foreign object detection

Systems and methods are provided for performing image processing. An exemplary method includes: receiving neuromorphic camera data from at least one neuromorphic camera; producing, using the neuromorphic camera data, a plurality of unaligned event data, where the unaligned event data are unaligned in time; aligning the unaligned event data in time producing aligned event data; generating, using a model and the aligned event data, a plurality of aligned event volumes; and aggregating the aligned event volumes to produce an image.

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Belmont, MA, United States of America

Kin Gwn Lore