USA As Represented by Secretary of the Navy

Daniel J Gebhardt

Benjamin J Migliori

Michael W Walton

Logan Straatemeier

Daniel Carl Grady

Iryna P Dzieciuch

Maurice R Ayache

Riley Zeller-Townson

Keyur N Parikh

A platform is positioned within an environment. The platform includes an image capture system connected to a controller implementing a neural network. The neural network is trained to associate visual features within the environment with a target object utilizing a known set of input data examples and labels. The image capture system captures input images from the environment and the neural network recognizes features of one or more of the input images that at least partially match one or more of the visual features within the environment associated with the target object. The input images that contain the visual features within the environment that at least partially match the target object are labeled, a geospatial position of the target object is determined based upon pixels within the labeled input images, and a class activation map is generated, which is then communicated to a supervisory system for action.

Machine learning based automated object recognition for unmanned autonomous vehicles

An asynchronous convolutional neural network (CNN) can interpret a sequence of input data. An input value representing a sample of the sequence of input data is received by a computational unit (CU) in a layer of the asynchronous CNN. The CU calculates a dot product of the input value and a weight assigned to the CU to produce an activation value. A change detector (CD) associated with the CU detects a difference between the activation value and previous activation values. The CD determines whether the detected difference is significant, indicating that the sample of the sequence of input data includes a significant change. If the detected difference is significant, the activation value is supplied to at least one subsequent CU included in a subsequent layer of the asynchronous CNN.

Asynchronous artificial neural network architecture

Time-varying input signals are denoised by a neural network. The neural network learns features associated with noise added to reference signals. The neural network recognizes features of noisy time-varying input signals mixed with the noise that at least partially match at least some of the features associated with the noise. The neural network predicts denoised time-varying output signals that correspond to the time-varying input signals based on the recognized features of the noisy time-varying input signals that at least partially match at least some of the features associated with the noise.

Methods and systems for performing radio-frequency signal noise reduction in the absence of noise models

Class types of input signals having unknown class types are automatically classified using a neural network. The neural network learns features associated with a plurality of different observed signals having respective different known class types. The neural network then recognizes features of the input signals having unknown class types that at least partially match at least some of the features associated with the plurality of different observed signals having respective different known class types. The neural network determines probabilities that each of the input signals has each of the known class types based on strengths of the matches between the recognized features of the input signals and the features associated with plurality of different observed signals. The neural network classifies each of the input signals as having one of the respective different known class types based on a highest determined probability.

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San Diego, CA, United States of America

Daniel J Gebhardt