Intel Corporation

Guangdong Oppo Mobile Telecommunications Corp., Ltd.

Xue Yang

Lei Yang

Xingang Guo

Hsin-Yuo Liu

Jing Zhu

Eran Sudak

Lakshman Krishnamurthy

Stephanie Moyerman

Narayan Sundararajan

Zhen Yao

Ke Ding

Xintian Eddie Lin

Shao-Wen Yang

Xiangying Yang

Praveen Gopalakrishnan

Steven Thomas Holmes

Romit Roy Choudhury

Carlos Cordeiro

Songnan Yang

Sanjay Kumar

Sebastian Schoenberg

Jeffrey R Foerster

Jie Gao

Braxton L Lathrop

Changwen Liu

Suresh V Golwalkar

James Brian Hall

Xiaoyong Pan

Jeremy Parra

Leora Roth

Haibin Liu

Aveek Purohit

Ryan Shin

Mahanth Gowda

Emily H Qi

Yujian Zhang

Hujun Yin

Shilpa Talwar

Christian Maciocco

Ulun Karacaoglu

Arvind A Kumar

Linda L Hurd

Richard D Roberts

Srikathyayani Srikanteswara

Omesh Tickoo

Feng Xue

Maruti Gupta Hyde

Vijay Sarathi Kesavan

Anthony George LaMarca

Lama Nachman

Sridhar G Sharma

Kerstin Johnsson

Justin Lipman

Florence R Pon

Wei Yee Koay

Denica N Larsen

Chaitanya Sreerama

Jaideep Moses

Robert Colby

Masoud Sajadieh

Yongfang Guo

Meiyuan Zhao

Tzahi Weisman

Daniel Gurdan

Hongtao Xu

Sherry Chang

Shlomo Avital

Wen-Ling Huang

Chul Young Kim

Anthony G Lamarca

Alexander Essaian

Prasanna Singamsetty

Naissa Conde

Xiaochao Yang

Donnie H Kim

Martin Junkar

Minglei Wang

Yang Lei

Mathijs C Walma

Jun Li

Jared B Counts

Allen C Kim

Martin Moerth

Sheng Shen

Technologies for context-based management of wearable computing devices include a mobile computing device and a wearable computing device. The wearable computing device generates sensor data indicative of a location context of the wearable computing device and transmits the sensor data to the mobile computing device. The mobile computing device generates local sensor data indicative of a location context of the wearable computing device and fuses the local sensor data with the sensor data received from the wearable computing device. The mobile computing device determines a context of the wearable computing device based on the fused sensor data. The mobile computing device determines whether an adjustment to the functionality of the wearable computing device is required based on the determined context. The mobile computing device manages the functionality of the wearable computing device in response to determining that an adjustment to the functionality is required.

Context-based management of wearable computing devices

Edge devices utilizing personalized machine learning and methods of operating the same are disclosed. An example edge device includes a model accessor to access a first machine learning model from a cloud service provider. A local data interface is to collect local user data. A model trainer is to train the first machine learning model to create a second machine learning model using the local user data. A local permissions data store is to store permissions indicating constraints on the local user data with respect to sharing outside of the edge device. A permissions enforcer is to apply permissions to the local user data to create a sub-set of the local user data to be shared outside of the edge device. A transmitter is to provide the sub-set of the local user data to a public data repository.

Edge devices utilizing personalized machine learning and methods of operating the same

Methods and apparatus for high speed location determinations are disclosed. An example apparatus includes at least two coils arranged along a zone of interest to generate a magnetic field, and a sensor to measure a change in the magnetic field associated with the at least two coils as an object of interest moves within or into the zone of interest. The example apparatus also includes a processor to determine a position of the object of interest based on the measured change.

Methods and apparatus for high speed location determinations

Examples are disclosed to track sport implements and/or objects of interest. An example apparatus includes a first coil to generate a first magnetic field having a first vertical component with a zero magnitude along a first line of interest and a second coil partially overlapped with the first coil, where the second coil is to generate a second magnetic field. The example apparatus also includes a sensor to measure a magnitude of the first magnetic field in the first line of interest and a processor to determine an object of interest has crossed the first line of interest based on the magnitude of the first magnetic field measured by the sensor.

Apparatus and methods to track sport implements

Systems and techniques for sensor-derived object flight performance tracking are described herein. A set of magnetometer readings may be obtained from a magnetometer included with an object. A local rotation axis of the object may be determined at a time using the set of magnetometer readings. The local rotation axis may describe rotation of the object around a local magnetic target. A global rotation axis may be calculated based on an initial orientation of the object. The global rotation axis may describe a fixed rotation axis of the object during flight in a global coordinate frame, wherein an angle between the global rotation axis and magnetic north remains constant during the flight. An orientation of the object may be determined for the time using the global rotation axis and the local rotation axis of the object at the time.

Sensor-derived object flight performance tracking

Methods, apparatus, systems, and articles of manufacture to track movement of sports implements are disclosed herein. An example sensing unit disclosed herein is to be coupled to a sports implement. The sensing unit includes an inertial measurement unit to obtain movement data of said sports implement during a swing of said sports implement and a swing analyzer to determine a follow-through pattern of the swing of said sports implement based on the movement data.

Apparatus and methods to track movement of sports implements

Apparatuses, methods, and systems associated with safety-related decision making reporting and regulation of computer-assisted or autonomous driving (CA/AD) vehicles are disclosed herein. In some embodiments, an apparatus includes a safety-related decision making reporting unit, disposed in a CA/AD vehicle, to collect data about driving behavior of the CA/AD vehicle and to determine whether the collected data is related to a safety-related decision making rule. In embodiments, the collected data is to be reported to a remote organization associated with regulating the safety-related decision making rule. In some embodiments, a computing device or server associated with regulating safety-related decision making rules receives the collected data from the CA/AD vehicle and/or manufacturers of the CA/AD vehicle. In embodiments, the computing device analyzes the collected data to modify or generate a safety-decision making rule. Other embodiments are also described and claimed.

Computer-assisted or autonomous driving safety-related decision making system and apparatus

Methods, apparatus, systems, and articles of manufacture to track movement of sports implements are disclosed herein. An example sensing unit disclosed herein is to be coupled to a sports implement. The sensing unit includes an inertial measurement unit to obtain movement data of said sports implement during a swing of said sports implement and a swing analyzer to determine whether the swing is a horizontal shot or a vertical shot based on the movement data.

A controller comprises a communication interface to receive an anchor localization dataset comprising a plurality of anchor range measurements and a processing circuitry to identify a qualified subset of anchor range measurements from the anchor localization dataset, wherein the anchor range measurements in the qualified subset are consistent, select a first anchor range measurement in the anchor localization dataset from outside the qualified subset of anchor range measurements, and add the first anchor range measurement to the qualified subset of anchor range measurements when the first anchor range measurement is consistent with the anchor range measurements in the qualified subset of anchor range measurements.

Drone localization

Disclosed methods, systems, and storage media may track body movements and movement trajectories using internal measurement units (IMUs), where a first IMU may be attached to a first wrist of a user, a second IMU may be attached to a second wrist of the user, and a third IMU may be attached to a torso of the user. Upper body movements may be derived from sensor data produced by the three IMUs. IMUs are typically not used to detect fine levels of body movements and/or movement trajectory because most IMUs accumulate errors due to large amounts of measurement noise. Embodiments provide arm and torso movement models to which the sensor data is applied in order to derive the body movements and/or movement trajectory. Additionally, estimation errors may be mitigated using a hidden Markov Model (HMM) filter. Other embodiments may be described and/or claimed.

Body movement tracking

Certain embodiments herein are directed to managing wireless spectrum, which may include recommending or transmitting spectrum usage changes to one or more wireless devices. A spectrum management system comprising one or more computers may receive spectrum usage information associated with one or more wireless devices. The spectrum management system may generate a spectrum usage map based on the received information. Based on the spectrum usage map, a spectrum usage change is determined and transmitted to one or more wireless devices. The wireless devices may change their operation in accordance with the spectrum usage change.

Cloud based spectrum management

Growing community of inventors

Arcadia, CA, United States of America

Xue Yang