Hrl Laboratories, LLC

The Boeing Company

Hughes Electronics Corporation

Gm Global Technolgoy Operations, Inc.

Kevin R Martin

Deepak Khosla

Yang Chen

Michael J Daily

David J Huber

Michael D Howard

Kang-Yu Ni

Vincent De Sapio

Ryan M Uhlenbrock

Matthew E Phillips

Matthew S Keegan

Brian L Burns

Darrel J VanBuer

Michael O'Neal

Henry Lu

Narayan Srinivasa

Heiko Hoffmann

Rajan Bhattacharyya

David W Payton

Charles E Martin

Qin Jiang

Suhas E Chelian

Hankyu Moon

Lei Zhang

Matthias Ziegler

James Benvenuto

Mike Daily

Craig A Lee

Michael A Warren

Sean Soleyman

Joshua G Fadaie

Michael J O'Brien

Cheryl Hein

Charles Richard Tullock

Robert Belvin

Ignacio M Soriano

Timothy Hirzel

Rachel Millin

Calvin Chung

Robert L Seliger

Nicanor P Demesa, Iii

Kenneth S Herberger

John R Wright

Shawn Moffit

Described is a system for detecting adversarial activities. During operation, the system generates a multi-layer temporal graph tensor (MTGT) representation based on an input tag stream of activities. The MTGT representation is decomposed to identify normal activities and abnormal activities, with the abnormal activities being designated as adversarial activities. A device can then be controlled based on the designation of the adversarial activities.

System and method for detecting adversarial activities using a compact graph representation

A device includes a control input generator and a neural network trainer. A flight simulator is configured to generate first state data responsive to a first control input from the control input generator and to provide the first state data to a first neural network to generate a candidate second control input. The control input generator is also configured to select, based on a random value, a second control input from between the candidate second control input and a randomized offset control input that is based on a random offset applied to the first control input. The flight simulator is configured to generate second state data responsive to the second control input from the control input generator. The neural network trainer is configured to update weights of the first neural network based, at least in part, on the first state data and the second state data.

Systems and methods for training a neural network to control an aircraft

Apparatus and methods for training a machine learning algorithm (MLA) to control a first aircraft in an environment that comprises the first aircraft and a second aircraft are described. Training of the MLA can include: the MLA determining a first-aircraft action for the first aircraft to take within the environment; sending the first-aircraft action from the MLA; after sending the first-aircraft action, receiving an observation of the environment and a reward signal at the MLA, the observation including information about the environment after the first aircraft has taken the first-aircraft action and the second aircraft has taken a second-aircraft action, the reward signal indicating a score of performance of the first-aircraft action based on dynamic and kinematic properties of the second aircraft; and updating the MLA based on the observation of the environment and the reward signal.

Autonomous behavior generation for aircraft

Described is a system for neural decoding of neural activity. Using at least one neural feature extraction method, neural data that is correlated with a set of behavioral data is transformed into sparse neural representations. Semantic features are extracted from a set of semantic data. Using a combination of distinct classification modes, the set of semantic data is mapped to the sparse neural representations, and new input neural data can be interpreted.

System for mapping extracted Neural activity into Neuroceptual graphs

The present invention relates to a surveillance system and, more particularly, to a graphical display and user interface system that provides high-speed triage of potential items of interest in imagery. The system receives at least one image of a scene from a sensor. The image is pre-processed to identify a plurality of potential objects of interest (OI) in the image. The potential OI are presented to the user as a series of chips on a threat chip display (TCD), where each chip is a region extracted from the image that corresponds to a potential OI. Finally, the system allows the user to designate, via the TCD, any one of the chips as an actual OI.

Graphical display and user-interface for high-speed triage of potential items of interest in imagery

A system includes a processor configured to generate a registered first 3D point cloud based on a first 3D point cloud and a second 3D point cloud. The processor is configured to generate a registered second 3D point cloud based on the first 3D point cloud and a third 3D point cloud. The processor is configured to generate a combined 3D point cloud based on the registered first 3D point cloud and the registered second 3D point cloud. The processor is configured to compare the combined 3D point cloud with a mesh model of the object. The processor is configured to generate, based on the comparison, output data indicating differences between the object as represented by the combined 3D point cloud and the object as represented by the 3D model. The system includes a display configured to display a graphical display of the differences.

Monitoring object shape and deviation from design

Described is a system for system for gait intervention and fall prevention. The system is incorporated into a body suit having a plurality of distributed sensors and a vestibulo-muscular biostim array. The sensors are operable for providing biosensor data to the analytics module, while the vestibulo-muscular biostim array includes a plurality of distributed effectors. The analytics module is connected with the body suit and sensors and is operable for receiving biosensor data and analyzing a particular user's gait and predicting falls. Finally, a closed-loop biostim control module is included for activating the vestibulo-muscular biostim array to compensate for a risk of a predicted fall.

System and method for assistive gait intervention and fall prevention

Described is system for surveillance that integrates radar with a panoramic staring sensor. The system captures image frames of a field-of-view of a scene using a multi-camera panoramic staring sensor. The field-of-view is scanned with a radar sensor to detect an object of interest. A radar detection is received when the radar sensor detects the object of interest. A radar message indicating the presence of the object of interest is generated. Each image frame is marked with a timestamp. The image frames are stored in a frame storage database. The set of radar-based coordinates from the radar message is converted into a set of multi-camera panoramic sensor coordinates. A video clip comprising a sequence of image frames corresponding in time to the radar message is created. Finally, the video clip is displayed, showing the object of interest.

System for surveillance by integrating radar with a panoramic staring sensor

Described is a system for detecting objects of interest in imagery. The system is configured to receive an input video and generate an attention map. The attention map represents features found in the input video that represent potential objects-of-interest (OI). An eye-fixation map is generated based on a subject's eye fixations. The eye-fixation map also represents features found in the input video that are potential OI. A brain-enhanced synergistic attention map is generated by fusing the attention map with the eye-fixation map. The potential OI in the brain-enhanced synergistic attention map are scored, with scores that cross a predetermined threshold being used to designate potential OI as actual or final OI.

System and method for detection of objects of interest in imagery

Described is system for optimizing rapid serial visual presentation (RSVP) spacing and fusion. The system receives a sequence of a plurality of rapid serial visual presentation (RSVP) image chips. The plurality of RSVP image chips are generated from an image via a pre-processing step and have a high probability of containing a target of interest. The system alters the order of the sequence of the plurality of RSVP image chips to increase the probability of detection of a true target of interest when presented to a human subject.

Optimal rapid serial visual presentation (RSVP) spacing and fusion for electroencephalography (EEG)-based brain computer interface (BCI)

Described is a system for improved electroencephalograph (EEG) rapid serial visual presentation (RSVP) target stimuli detection through visual attention distractor insertion. A first RSVP sequence is created comprising a set of image chips. The image chips are a combination of target images containing target events and non-target images containing comment events. A number of visual attention distractors to optimize target event detection is determined, and the determined number of visual attention distractors is inserted into the first RSVP sequence to generate a second RSVP sequence. The second RSVP sequence is reordered to generate a third RSVP sequence. The third RSVP sequence is presented to a user, and an EEG signal is received from the user. Finally, the EEG signal is decoded to identify a true target event via a P300 detection in the EEG signal.

Visual attention distractor insertion for improved EEG RSVP target stimuli detection

A three dimensional audio playback system in which the audio clips are determined by location. The audio playback system being located within a vehicle to aid in navigation or for entertainment or informational or safety purposes.

Method and apparatus for sharing geographically significant information

Geo-coded spatialized audio in vehicles

A system is disclosed for receiving, categorically storing, and supplying different types of location-specific audio information to either fixed or mobile users. The audio information is categorically stored by geographical location. As users travel through different environments (i.e., cities, freeway systems, airports) they can access audio information that is specific to their geographical location. Users may also annotate locations with real-time audio information. This audio information is accessible to mobile users using cellular phones, or other transceivers, and by fixed users using phones or computers.

Audio on location

Described is a navigation system. The navigation system comprises a route planning module and a route guidance module. The route planning module is configured to receive a request from a user for guidance to a particular destination. Based on a starting point, the route planning module determines a route from the starting point to the particular destination. The route guidance module is configured to receive the route, and based on the route and current location of the user, provide location-specific instructions to the user. The location-specific instructions include reference to specific visible objects within the vicinity of the user.

System and method for using context in navigation dialog

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Oak Park, CA, United States of America

Kevin R Martin