Amazon Technologies, Inc.

Intel Corporation

Deere + Company

Blue River Technology Inc.

Rahul Agrawal

Ning Yao

Sowmya Gopalan

Rajesh Radhakrishnan

Brent William West

Christopher J Hughes

Krishnakanth Venkata Sistla

Alexander Gendler

Vivek Garg

Dean A Mulla

Edward Dietz Crump

Wai Chung Chu

Yue Liu

Eric J DeHaemer

Ashish V Choubal

Samuel Henry Chang

Kavitha Velusamy

Qiang Liu

Samantika S Sury

Guy G Sotomayor

Matthew Alan Townsend

Arvind Raman

Vijay Kamarshi

Manika Puri

Daniel Christopher Bay

Deon Poncini

Jon Lewis Lindskog

Robert Ramsey Flenniken

Lingxiang Xiang

Carl J Beckmann

Helder Jesus Ramalho

Paul Matthew Bombach

A control system of a farming machine is configured to identify obstruction in a field from image data of the field. The control system accesses an obstruction model configured to identify obstructions in a field from image data of the field. The obstruction model is generated by accessing image data of obstructions in a training field, each obstruction corresponding to a prescribed action occurring at a prescribed time, labelling the image data of the obstructions, and training the obstruction model based on the labelled image data. The control system captures image data of the field including an obstruction and inputs the image data into the obstruction model. Responsive to identifying the obstruction in the field, the control system modifies treatment instructions of the farming machine such that the farming machine performs a implements a farming objective while avoiding the obstruction in the field.

Machine-learned obstruction detection in a farming machine

Disclosed embodiments relate to atomic memory operations. In one example, an apparatus includes multiple processor cores, a cache hierarchy, a local execution unit, and a remote execution unit, and an adaptive remote atomic operation unit. The cache hierarchy includes a local cache at a first level and a shared cache at a second level. The local execution unit is to perform an atomic operation at the first level if the local cache is a storing a cache line including data for the atomic operation. The remote execution unit is to perform the atomic operation at the second level. The adaptive remote atomic operation unit is to determine whether to perform the first atomic operation at the first level or at the second level and whether to copy the cache line from the shared cache to the local cache.

Adaptive remote atomics

Detection of human hands within an environment is used for gesture recognition for input to a computing system. Detecting a hand is non-trivial, however, because the appearances of a hand vary widely with many independent digits and moving parts. A feature-based similarity measurement is used to detect two specific hand positions&#x2014;an open hand and a closed hand.

Open and closed hand detection

In one embodiment, a processor includes cores to execute instructions. At least some of the cores include a telemetry data control logic to send a first telemetry data packet to a power controller according to a stagger schedule to prevent data collisions, and a global alignment counter to count a stagger alignment period. Other embodiments are described and claimed.

Controlling telemetry data communication in a processor

The present disclosure relates to routers and quality of service (QoS) systems and methods that base decisions on the identification of one or more users of computing devices within the environment. Profiles and/or attributes associated with the users may be created and dynamically updated to optimize user experience. For example, the routers may dynamically adapt QoS settings to regulate bandwidth, latency and other parameters to prioritize users and/or optimize a specific user's experience based on the user's priority, personal profile, and/or other attributes.

Routers with personalized quality of service

A system may utilize sound localization techniques, such as time-difference-of-arrival techniques, to estimate an audio-based sound source position from which a sound originates. An optical image or depth map of an area containing the sound source location may then captured and analyzed to detect an object that is known or expected to have produced the sound. The position of the object may also be determined based on the analysis of the optical image or depth map. The position of the sound source may then be determined based at least in part on the position of the detected object or on a combination of the audio-based sound source position and the determined position of the object.

Audio and depth based sound source localization

A system may utilize a projector, a camera, and a depth sensor to produce images within the environment of a user and to detect and respond to user actions. Depth data from the depth sensor may be analyzed to detect and identify items within the environment. A coordinate transformation may then be used to identify corresponding color values from camera data, which can in turn be analyzed to determine the colors of detected items. A similar coordinate transformation may be used to identify color values of a projected image that correspond to the detected items. In some cases, camera color values corresponding to an item may be corrected based on the corresponding color values of a projected image. In other cases, projected color values corresponding to an item may be corrected based on the corresponding camera color values.

Object-based color detection and correction

Growing community of inventors

Santa Clara, CA, United States of America

Rahul Agrawal