Amazon Technologies, Inc.

Microsoft Technology Licensing, LLC

Gershon Parent

Shanon Isaac Drone

Scott Michael Wilcox

Brian C Beckman

Louis LeRoi LeGrand, Iii

Michael David Anderson

Jeffrey Scott Norris

Paul A Viola

Menashe Haskin

Karen Elaine Stevens

Troy David Barnes

Aerial vehicles may be outfitted with one or more ultrasonic anemometers, each having ultrasonic transducers embedded into external surfaces. The transducers may be aligned and configured to transmit acoustic signals to one another, and receive acoustic signals from one another, along one or more paths or axes. Elapsed times of signals transmitted and received by pairs of transducers may be used to determine air speeds along the paths or axes. Where two or more pairs of transducers are provided, a net vector may be derived based on air speeds determined along the paths or axes between the pairs of the transducers, and used to generate control signals for maintaining the aerial vehicle on a desired course, at a desired speed or altitude, or in a desired orientation. The transducers may be dedicated for use in an anemometer, or may serve multiple purposes, and may be reoriented or reconfigured as necessary.

Airframe-embedded ultrasonic transducers

A machine learning engine can be used to identify inconsistencies and errors in a plurality of bug reports and to glean new information from the bug reports. Bug data associated with a large number of bug reports from different bug categories may be processed and used by a machine learning model of the machine learning engine. The machine learning engine can extract bug attributes from the bug data of a first bug. The machine learning engine can then compare the attributes of the first bug to a machine learning model created using a plurality of second bug reports. Based on then similarity between the first bug report and the second bug reports, the machine learning engine can apply, or correct, various attributes of the first bug report. The machine learning model may be updated over time by the machine learning engine as data correlations evolve.

Automatic bug classification using machine learning

A passive control system can be implemented that converts an active control system on a multi-rotor unmanned aerial vehicle (UAV) to a passive control system. In the event that a system health of an active control system on the UAV is compromised, or in other instances, a passive control mechanism can be deployed to restore at least some in-flight stability. In various examples, the passive control system can monitor UAV attitude, velocity, and position to determine an active control system health. In other examples, the passive control system may receive an indication from the active control system itself. In some examples, the passive control mechanisms may include increasing an offset of a center of thrust and the center of gravity of the UAV. In other examples, the passive control mechanisms may include deploying control surfaces that induce a drag force that impedes translational and rotational movement.

Unmanned aerial vehicle (UAV) deployment of passive control stabilizers

Mobile tunnels may be provided on trailers or in association with one or more other vehicles. A mobile tunnel may be towed or otherwise accelerated to a sufficient velocity, thereby causing a fluid such as air or seawater to travel above, below and around the tunnel. The fluid may be diverted into the tunnel and caused to pass over a test object such as an unmanned aerial vehicle or unmanned undersea vehicle within the tunnel, thereby enabling aerodynamic testing or hydrodynamic testing of the test object to be performed. Additionally, one or more materials may be injected into the fluid, thereby enabling destructive testing of the test object to be performed.

Mobile tunnels for operational testing

This disclosure describes systems and methods for visually tracking a position of an unmanned aerial vehicle ('UAV') using reflected light. A light source at an origin location, such as the location of an operator of the UAV, is aligned and emitted toward the position of the UAV. The emitted light source reflects off a reflector coupled to the UAV toward a location of the operator or a visual observer working with the operator. The reflected light increases the visibility of the UAV, thereby extending the distance from an operator at which the UAV can be operated while maintaining visible contact between the operator and/or a visual observer working with the operator and the UAV.

Tracking unmanned aerial vehicles using reflected light

Automated testing of audio performance of applications across platforms is provided for via capture of audio data. The audio data can include, inter alia, output sounds from a sound card or pre-rendered buffer data. The audio data is processed to produce descriptive data including data describing the audio data at least a first resolution and a second resolution. This descriptive data is used to compare data samples and describe the degree of similarity of two or more data samples. This comparison enables a determination as to whether the audio performance is satisfactory.

Automated audio sub-band comparison

A system and method for determining whether a target display subsystem is compliant with a reference display subsystem is provided. A target display subsystem is selected for outputting image data. Drawing instructions are generated and applied to the target display subsystem. Image data generated by the target display subsystem, responsive to the applied drawing instructions, is periodically captured and recorded. The reference subsystem is selected, and the same drawing instructions are applied to the reference display subsystem. Image data generated by the reference display subsystem is periodically captured and recorded from the reference display subsystem. Thereafter, the captured image data from the target display subsystem and the reference display subsystem are compared to determine whether the target display subsystem is compliant with the reference display subsystem.

Determining display subsystem compliance

A system and method for testing the reliability of a target module is presented. A reliability testing module executes a testing script by directing the target module to perform various functions, and by further consuming system resources by executing test applications. The behaviors of the target module are recorded and evaluated, and a reliability score is generated for the target module. The reliability score enables a user to compare the reliability of a first target module to that of a second.

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Seattle, WA, United States of America

Gershon Parent