Hrl Laboratories, LLC

Gm Global Technolgoy Operations, Inc.

Iman Mohammadrezazadeh

Rajan Bhattacharyya

Yuri Owechko

Heiko Hoffmann

Michael D Howard

Michael J Daily

Praveen K Pilly

Charles E Martin

Kang-Yu Ni

Vincent De Sapio

Jaehoon Choe

Patricia M Laskowsky

Jeffrey M Hutmacher

Tiffany J Hwu

Ruggero Scorcioni

Jin Shen

Dana J Suttman

Vincent DeSapio

An ego vehicle includes decider modules and a grader module coupled to a resolver module. The decider modules generate trajectory decisions at a current time, generate a current two-dimensional slice of a flat space around the ego vehicle, generate future two-dimensional slices of the flat space by projecting the current two-dimensional slice of the flat space forward in time, and generate a three-dimensional state space by stacking the current two-dimensional slice and the future two-dimensional slices. The grader module generates rewards for the trajectory decisions based on a recent behavior of an ego vehicle. The resolver module selects a final trajectory decision for the ego vehicle from the trajectory decisions based on the three-dimensional state space and the rewards. The current two-dimensional slice includes a current ego vehicle location and current neighboring vehicle locations. The future two-dimensional slices include future ego vehicle locations and future neighboring vehicle locations.

Navigation trajectory using reinforcement learning for an ego vehicle in a navigation network

Described is a system for health assessment. The system is implemented on a mobile device having at least one of an accelerometer, a geographic location sensor, and a camera. In operation, the system obtains sensor data related to an operator of the mobile device from one of the sensors. A network of networks (NoN) is generated based on the sensor data, the NoN having a plurality of layers with linked nodes. Tuples are thereafter generated. Each tuple contains a node from each layer that optimizes importance, diversity, and coherence. Storylines are created based on the tuples that solves a longest path problem for each tuple. The storylines track multiple symptom progressions of the operator. Finally, a disease prediction of the operator is provided based on the storylines.

System and method for health assessment on smartphones

Methods and systems are provided for monitoring a fuel injector of an internal combustion engine. In one embodiment, a method includes: receiving a set of feature data, the feature data sensed from a fuel injector during a fuel injection event; processing, by a processor, the set of feature data with a decision tree model to generate a prediction of a fault status; and selectively generating, by the processor, a notification signal based on the prediction.

Diagnostic methods and systems

A system for closed-loop pulsed transcranial stimulation for cognitive enhancement. During operation, the system identifies a region of interest (ROI) in a subject's brain and then estimates ROI source activations based on the estimated source of the ROI. It is then determined if a subject is in a bad encoding state based on the ROI source activations. Finally, one or more electrodes are activated to apply a pulsed transcranial stimulation (tPS) therapy when the subject is in a bad encoding state, a predefined external event or behavior occurs, or the subject is in a consolidation state during sleep.

Personalized closed-loop pulsed transcranial stimulation system for cognitive enhancement

Described is a system for personalizing a human-machine interface (HMI) device based on a mental and physical state of a user. During performance of a task in a simulation environment, the system extracts biometric features from data collected from body sensors, and extracts brain entropy features from electroencephalogram (EEG) signals. The brain entropy features are correlated with the biometric features to generate a mental-state model. The mental-state model is deployed in a HMI device during performance of the task in an operational environment for continuous adaptation of the HMI device to its user's mental and physical states.

Neuro-adaptive body sensing for user states framework (NABSUS)

Growing community of inventors

Los Angeles, CA, United States of America

Iman Mohammadrezazadeh