Hewlett Packard Enterprise Development LP

Hewlett-packard Development Company, L.p.

University of California

Kyu Han Kim

Puneet Jain

Sujata Banerjee

Souvik Sen

Yurong Jiang

Parameswaran Ramanathan

Prasant Mohapatra

Jeremy Gummeson

Jindan Zhu

Nathaniel Stone

Theodore A Stone

Eduardo Alberto Cuervo Laffaye

Xiaochen Liu

James Scott Hiscock

David D Lee

Daniel George Gelb

Paul T Congdon

Ran-Fun Chiu

Sangki Yun

Martin Robert Fink

S M Shahriar Nirjon

Kai Zeng

Examples include detecting objects and determining a set of features for the objects. Examples include receiving a first image input, generating a number of feature maps from the first image input using a number of convolution filters, generating a first number of fully connected layers directly based on the number of feature maps, and detecting a number of objects in the first image and determining a set of features for each object from the first number of fully connected layers.

Objects and features neural network

A system described herein provides real-time wireless video delivery using a multi-channel communications link. A method of employing elements of the system includes generating a first set of video data and generating a second set of video data. Further, encoding the first set of video data such that the second set of video data is a higher resolution version of the first set of video data and the encoded first set of video data is to supplement the second set of video data in response to a data drop.

Real-time wireless video delivery system using a multi-channel communications link

An example system comprising: a processing resource; and a memory resource storing machine readable instructions executable to cause the processing resource to: receive a Bluetooth Low Energy (BLE) signal transmitted from a user device; generate, from the BLE signal, a BLE moving pattern of the user device, wherein the BLE moving pattern is generated at a different entity than an entity that transmits the BLE signal; track an object carrying the user device via visual information of the object such that a visual moving pattern of the object is generated from the tracking; determine the visual moving pattern matches the BLE moving pattern; and assign, responsive to the determination, an identity obtained from the user device to the object being tracked via the visual information.

Match bluetooth low energy (BLE) moving patterns

Examples disclosed herein relate to locating and tracking a wireless beacon from a wireless device. A data collection module in the wireless device collects and processes sensory data from the wireless beacon. A location estimation module classifies an environment surrounding the wireless beacon based on the sensory data, determines a movement of the wireless device and a movement of the wireless beacon, and estimates a location of the wireless beacon based on the sensory data, the classified environment, the determined movement of the wireless device and the determined movement of the wireless beacon. A calibration module refines the location of the wireless beacon based on neighboring wireless beacons.

Locating and tracking a wireless beacon from a wireless device

Examples herein disclose capturing a wifi signal from a computing device corresponding to a computing accessory and harvesting energy from the captured wifi signal. The examples power the computing accessory based on the harvested energy.

Supplying power to a computer accessory from a captured wifi signal

Match Bluetooth low energy (BLE) moving patterns

A method is provided for real-time processing of IoT data. For example, a first physical processor of an edge computing device may receive a set of data from a first IoT device communicably coupled to the edge device. The first physical processor may split the set of data into a set of individual data packets. A second physical processor of the edge device process the set of individual data packets by: concurrently applying, by a plurality of instances of the second physical processor of the edge computing device, a learning model to each of a corresponding plurality of data packets from the set of individual data packets; and annotating, by a subset of the plurality of instances of the second physical processor, a corresponding subset of the plurality of data packets with a corresponding output from the concurrent application of the learning model.

Real-time processing of IoT data

Examples relate to determining finger movements. In one example, a computing device may: receive input from at least one of: a first proximity sensor coupled to the frame at a first position and facing a first direction; or a second proximity sensor coupled to the frame at a second position and facing a second direction; determine, based on the input, that a finger action occurred, the finger action being one of: a first movement of a first finger, the first movement being detected by the first proximity sensor; a second movement of a second finger, the second movement being detected by the second proximity sensor; generate, based on the finger action, output that includes data defining an event that corresponds to the finger action; and provide the output to another computing device.

Wearable devices for detecting finger movements and related methods

A method for power management of a mobile device. The method includes evaluating content of a plurality of applications received at a mobile device operated by a user and determining latency information for each of the plurality of applications. The method further includes dynamically determining a priority of the plurality of applications based on the latency information for each application, and dynamically adjusting the mobile device between at least two wireless power modes based on the priority of the plurality of applications.

Mobile device power control

An electronic device has a plurality of environments including respective communication stacks. The environments correspond to respective different user personas. Data associated with the different user personas are communicated in corresponding separate transport flows over the network.

Communicating data associated with different personas of a user

A system, method, and non-transitory computer readable medium for virtualizing battery in a personal mobile device or across a group of personal mobile devices controlled by a user are provided. The user specifies a set of power management policies for applications running in the personal mobile device(s). Battery usage per application is monitored and resources for the applications are scheduled based on the monitored battery usage and the power management policies.

Virtualizing battery across a group of personal mobile devices

Systems and methods of scheduling data in background services on mobile devices are disclosed. An example method includes identifying data consumption patterns on a mobile device. The method also includes determining sensitivity of data arriving at the mobile device based on the data consumption patterns. The method also includes aggregating network access by background services on the mobile device according to a schedule based on the sensitivity of the data arriving at the mobile device.

Scheduling data in background services on mobile devices

According to an example, a signal space based navigation apparatus may receive traces of wireless signal observations and corresponding pedestrian deadreckoning (PDR) traces between different ones of the wireless signal observations from a plurality of user devices. A plurality of wireless clusters may be generated based on the traces of wireless signal observations. A PDR displacement vector may be generated between two or more of the wireless clusters based on the two or more of the wireless clusters and a subset of the PDR traces corresponding to the two or more of the wireless clusters. A sensing map may be generated based on the plurality of wireless clusters and a plurality of PDR displacement vectors including the PDR displacement vector.

Growing community of inventors

Palo Alto, CA, United States of America

Kyu Han Kim