Intel Corporation

Apple Inc.

Yiting Liao

Vallabhajosyula S Somayazulu

Omesh Tickoo

Shao-Wen Yang

Yen-kuang Chen

Dave A Cavalcanti

Srikathyayani Srikanteswara

Venkatesan Nallampatti Ekambaram

Nageen Himayat

Feng Xue

Maruti Gupta Hyde

Thomas J Kenney

Hyung-Nam Choi

Bernhard Raaf

Markus Dominik Mueck

Shahrnaz Azizi

Shilpa Talwar

Marta Martinez Tarradell

Christian Drewes

Zhibin Yu

Thorsten Clevorn

John Joseph Browne

Robert Zaus

Biljana Badic

Simon R Hunt

Ingolf Karls

Ozgur Oyman

Paul S Diefenbaugh

Kristoffer Fleming

Krishnan Rajamani

Jaroslaw Sydir

Hassnaa Moustafa

Alexander W Min

Maruti Gupta

Srenivas Varadarajan

Ibrahima J Ndiour

Jeffrey R Foerster

Javier Perez-Ramirez

Juergen H Kreuchauf

Stefan Franz

Ralph Hasholzner

Reinhold Schneider

Christopher MacNamara

Sarabjot Singh

Niall Power

Ashish Singh

Uwe Kliemann

Ravikumar Balakrishnan

Roya Doostnejad

Ajay G Gupta

Lark Kwon Choi

James M Holland

Mohammad Mamunur Rashid

Vijay Sarathi Kesavan

Gabriel Arrobo Vidal

Barry A O'Mahony

Lei Shao

Vered Bar Bracha

Inching Chen

Mikhail T Galeev

Maria E Ramirez Loaiza

Mi Sun Park

Mohamed M Rehan

Audrey C Younkin

Tal Azogui

Segev Ravgad

Ajay Gupta

Chris Macnamara

Yomna Hassan

Vallabhajosyula Zulu Somayazulu

Chinh T Cao

Mohamed Badawi

Nageen Himatat

A circuit arrangement includes a preprocessing circuit configured to obtain context information related to a user location, a learning circuit configured to determine a predicted user movement based on context information related to a user location to obtain a predicted route and to determine predicted radio conditions along the predicted route, and a decision circuit configured to, based on the predicted radio conditions, identify one or more first areas expected to have a first type of radio conditions and one or more second areas expected to have a second type of radio conditions different from the first type of radio conditions and to control radio activity while traveling on the predicted route according to the one or more first areas and the one or more second areas.

Methods and devices for radio communications

A method can be performed by a first node for determining a parameter of physical (PHY) layer circuitry of a second node. The method can include implementing a cascaded hierarchy of techniques to determine, based on an electrical signal from a second node, a parameter of the PHY layer circuitry of the second node, and causing an antenna of the first node to transmit an electromagnetic wave consistent with the determined parameter.

Feature detection in collaborative radio networks

In one embodiment, an apparatus comprises a memory and a processor. The memory is to store visual data associated with a visual representation captured by one or more sensors. The processor is to: obtain the visual data associated with the visual representation captured by the one or more sensors, wherein the visual data comprises uncompressed visual data or compressed visual data; process the visual data using a convolutional neural network (CNN), wherein the CNN comprises a plurality of layers, wherein the plurality of layers comprises a plurality of filters, and wherein the plurality of filters comprises one or more pixel-domain filters to perform processing associated with uncompressed data and one or more compressed-domain filters to perform processing associated with compressed data; and classify the visual data based on an output of the CNN.

Frequency-domain convolutional neural network

A wireless communication device includes a processor configured to select an offload processing task for performance by an edge computing device; cause a baseband modem to establish a direct wireless connection between the wireless communication device and the edge computing device; cause the baseband modem to send first data to the edge computing device via the direct wireless connection; and receive second data from the edge computing device, wherein the second data comprise a result of the offload processing task performed on the first data. The edge computing device includes a processor configured to receive, from a user device, offloaded data to be processed according to an offload processing task; execute the offload processing task on the offloaded data; and cause the radio via the interface to wirelessly send a result of the executed offload processing task via a direct wireless connection with the user device.

Systems and methods of using machine-learning to improve communications across different networks are described. A CIRN node identifies whether it is within range of a source and destination node in a different network using explicit information or a machine-learning classification model. A neural network is trained to avoid interference using rewards associated with reduced interference or retransmission levels in each network or improved throughput at the CIRN node. A machine-learning scheduling algorithm determines a relay mode of the CIRN node for source and destination node transmissions. The scheduling algorithm is based on the probability of successful transmission between the source and destination nodes multiplied by a collaboration score for successful transmission and the probability of unsuccessful transmission of the particular packet multiplied by a collaboration score for unsuccessful transmission.

System and method using collaborative learning of interference environment and network topology for autonomous spectrum sharing

In one embodiment, an apparatus comprises processing circuitry to: receive, via a communication interface, a compressed video stream captured by a camera, wherein the compressed video stream comprises: a first compressed frame; and a second compressed frame, wherein the second compressed frame is compressed based at least in part on the first compressed frame, and wherein the second compressed frame comprises a plurality of motion vectors; decompress the first compressed frame into a first decompressed frame; perform pixel-domain object detection to detect an object at a first position in the first decompressed frame; and perform compressed-domain object detection to detect the object at a second position in the second compressed frame, wherein the object is detected at the second position in the second compressed frame based on: the first position of the object in the first decompressed frame; and the plurality of motion vectors from the second compressed frame.

Hybrid pixel-domain and compressed-domain video analytics framework

Multi-domain convolutional neural network

In one embodiment, an apparatus comprises a storage device and a processor. The storage device may store a plurality of compressed images comprising one or more compressed master images and one or more compressed slave images. The processor may: identify an uncompressed image; access context information associated with the uncompressed image and the one or more compressed master images; determine, based on the context information, whether the uncompressed image is associated with a corresponding master image; upon a determination that the uncompressed image is associated with the corresponding master image, compress the uncompressed image into a corresponding compressed image with reference to the corresponding master image; upon a determination that the uncompressed image is not associated with the corresponding master image, compress the uncompressed image into the corresponding compressed image without reference to the one or more compressed master images; and store the corresponding compressed image on the storage device.

Context-aware image compression

In one embodiment, an apparatus comprises a communication interface and a processor. The communication interface is to communicate with a plurality of devices. The processor is to: receive compressed data from a first device, wherein the compressed data is associated with visual data captured by sensor(s); perform a current stage of processing on the compressed data using a current CNN, wherein the current stage of processing corresponds to one of a plurality of processing stages associated with the visual data, and wherein the current CNN corresponds to one of a plurality of CNNs associated with the plurality of processing stages; obtain an output associated with the current stage of processing; determine, based on the output, whether processing associated with the visual data is complete; if the processing is complete, output a result associated with the visual data; if the processing is incomplete, transmit the compressed data to a second device.

Cascade convolutional neural network

A wireless network device to support quality-aware adaptive media streaming includes a radio-frequency transceiver, a processor operably coupled to the radio-frequency transceiver, and a memory device operably coupled to the processor. The memory storing instructions that configure the processor to parse a manifest file to read information characterizing media content available for hypertext transfer protocol (HTTP) adaptive streaming, obtain quality information of the media content based on a quality attribute parsed from the manifest file, dynamically switch streaming between different encoded portions of the media content in response to the quality information for an encoded portion of the media content deviating from a desired quality value, and generate a quality of experience (QoE) report including media presentation description information indicating a video quality metric.

Methods for quality-aware adaptive streaming over hypertext transfer protocol and reporting quality of experience

Techniques related to applying computer vision to decompressed video are discussed. Such techniques may include generating a region of interest in an individual video frame by translating spatial indicators of a first detected computer vision result from a reference video frame to the individual video frame and applying a greater threshold within the region of interest than outside of the region of interest for computer vision evaluation in the individual frame.

Adaptive thresholding for computer vision on low bitrate compressed video streams

Multi-domain cascade convolutional neural network

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Hillsboro, OR, United States of America

Yiting Liao