Raytheon Company

Rockwell Collins, Inc.

Rtx Corporation

B/e Aerospace, Inc.

Ganesh Sundaramoorthi

Kin Gwn Lore

Kishore K Reddy

Peggy Wu

Justin R Urban

Brigid A Blakeslee

Andrew Radlbeck

Sudeepta Mondal

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

A system may include at least one processor configured to: obtain a trained neural network (NN); obtain or calculate at least one average feature information associated with the trained NN, each of the at least one average feature information including a given average feature information summarizing in-class statistics that each layer of the trained NN uses for a given class; receive an incoming observation influencing a given layer; calculate a corresponding feature information of the incoming observation, the corresponding feature information summarizing statistics of the incoming observation for the given layer; classify the incoming observation as being in the given class; calculate a distance score associated with a distance between the corresponding feature information of the incoming observation and the given average feature information; determine the incoming observation is an out-of-distribution (OOD) sample; and output an alert indicating the incoming observation is OOD and/or discard the incoming observation's classification.

System and method for determining incoming observation to be out-of-distribution (OOD) sample during neural network (NN) inference

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.

Object reconstruction with neuromorphic sensors

A computer system records eye tracking data and identifies movements in the eye tracking data to determine gaze and pupil dynamics. Eye tracking data is correlated with a current task and predetermined vigilance requirements. The system determines if the user is exhibiting an appropriate level of vigilance based on the task or is becoming fixated. When fixation is detected, the system may engage in remedial action. A task flow diagram represents the operator tasks. Interactions between the user and the instrumentation are used to estimate the probability distribution of the task the user is currently conducting. The system correlates eye tracking data and physiological data such as electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRs) to determine neuroactivity. Monitoring neuroactivity reduces the probability of a false positive for fixation.

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Duluth, GA, United States of America

Ganesh Sundaramoorthi