Teledyne Scientific and Imaging, LLC

Mario Aguilar-Simon

Patrick M Connolly

Stephen Simons

Barry Ahrens

Karen Zachery

William D Reynolds, Jr

David Krnavek

Milind Mahajan

Mark Burton Anderson

Brian Wesley Gregory

Weiya Zhang

Renee Shimizu

Rolando Estrada

Mary Whatley

Ryan Charles Brown

Andrew Paul Brna

William Reynolds

Systems and methods for a machine learning system to learn a new skill without catastrophically forgetting an existing skill and to continually learn in a self-supervised manner during operation, without human intervention.

Adaptive continuous machine learning by uncertainty tracking

A brain machine interface system for use with an electroencephalogram to identify a behavioral intent of a person is disclosed. The system includes an electroencephalogram configured to sense electromagnetic signals generated by a brain of a person. The electromagnetic signals include a time component and a frequency component. A monitor monitors a response of the person to a stimulus and a characteristic of the stimulus. A synchronization module synchronizes the sensed electromagnetic signals with the response and the characteristic to determine a set of electromagnetic signals corresponding to the monitored response and the characteristic. A processor processes the set of electromagnetic signals and extracts feature vectors. The feature vectors define a class of behavioral intent. The processor determines the behavioral intent of the person based on the feature vectors. A brain machine interface and a method for identifying a behavioral intent of a person is also disclosed.

System and method for noninvasive identification of cognitive and behavioral goals

A vision-cued random-access LIDAR system and method which determines the location and/or navigation path of a moving platform. A vision system on a moving platform identifies a region of interest. The system classifies objects within the region of interest, and directs random-access LIDAR to ping one or more of the classified objects. The platform is located in three dimensions using data from the vision system and LIDAR. The steps of classifying, directing, and locating are preferably performed continuously while the platform is moving and/or the vision system's field-of-view (FOV) is changing. Objects are preferably classified using at least one smart-vision algorithm, such as a machine-learning algorithm.

Vision-cued random-access lidar system and method for localization and navigation

Provided is an apparatus, system, and method for targeted memory enhancement. A computer processing circuit receives a plurality of electroencephalography (EEG) signals from a plurality of spatially separated EEG sensors located on the head of a subject that is asleep. A first process of the computer processing system determines a sleep state of the subject and upon determining that the subject is in sleep stage 2 or 3 based on a specific EEG signal, the processing system triggers a second process of the computer processing system that determines a transition event in the specific EEG signal, and upon detecting the transition event delivers an intervention to the subject designed to evoke a specific neurophysiological change to the subject.

Apparatus, system, and methods for targeted memory enhancement during sleep

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Chapel Hill, NC, United States of America

Mario Aguilar-Simon