公开(公告)号：US11653292B2

公开(公告)日：2023-05-16

申请号：US16455793

申请日：2019-06-28

Applicant: Intel Corporation

Inventor： Shahrnaz Azizi ,  Biljana Badic ,  John Browne ,  Dave Cavalcanti ,  Hyung-Nam Choi ,  Thorsten Clevorn ,  Ajay Gupta ,  Maruti Gupta Hyde ,  Ralph Hasholzner ,  Nageen Himayat ,  Simon Hunt ,  Ingolf Karls ,  Thomas Kenney ,  Yiting Liao ,  Christopher Macnamara ,  Marta Martinez Tarradell ,  Markus Dominik Mueck ,  Venkatesan Nallampatti Ekambaram ,  Niall Power ,  Bernhard Raaf ,  Reinhold Schneider ,  Ashish Singh ,  Sarabjot Singh ,  Srikathyayani Srikanteswara ,  Shilpa Talwar ,  Feng Xue ,  Zhibin Yu ,  Robert Zaus ,  Stefan Franz ,  Uwe Kliemann ,  Christian Drewes ,  Juergen Kreuchauf

IPC: H04W48/16 ,  H04W4/029 ,  H04W24/08 ,  H04W48/10 ,  H04W68/00 ,  H04W92/04

CPC classification number: H04W48/16 ,  H04W4/029 ,  H04W24/08 ,  H04W48/10 ,  H04W68/005 ,  H04W92/045

Abstract: 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.

公开(公告)号：US11570466B2

公开(公告)日：2023-01-31

申请号：US17509246

申请日：2021-10-25

Applicant: Intel Corporation

Inventor： Yiting Liao ,  Yen-Kuang Chen ,  Shao-Wen Yang ,  Vallabhajosyula S. Somayazulu ,  Srenivas Varadarajan ,  Omesh Tickoo ,  Ibrahima J. Ndiour

IPC: H04N19/52 ,  H04N19/523 ,  H04N19/172 ,  G06V10/20 ,  G06N3/04 ,  G06K9/62

Abstract: 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.

公开(公告)号：US20210258988A1

公开(公告)日：2021-08-19

申请号：US17251117

申请日：2018-09-28

Applicant: Intel Corporation

Inventor： Ravikumar Balakrishnan ,  Nageen Himayat ,  Yiting Liao ,  Gabriel Arrobo Vidal ,  Roya Doostnejad ,  Vijay Sarathi Kesavan ,  Venkatesan Nallampatti Ekambaram ,  Maria Ramirez Loaiza ,  Vallabbajosyula S. Somayazulu ,  Srikathyayani Srikanteswara

IPC: H04W72/12 ,  H04W24/10 ,  H04W24/02 ,  H04W88/04 ,  H04W72/04 ,  H04W16/14 ,  G06K9/62 ,  G06N3/08

Abstract: 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.

公开(公告)号：US20210194674A1

公开(公告)日：2021-06-24

申请号：US16947590

申请日：2020-08-07

Applicant: Intel Corporation

Inventor： Yen-Kuang Chen ,  Shao-Wen Yang ,  Ibrahima J. Ndiour ,  Yiting Liao ,  Vallabhajosyula S. Somayazulu ,  Omesh Tickoo ,  Srenivas Varadarajan

IPC: H04L9/06 ,  G06F21/64 ,  G06F21/53 ,  G06N5/02 ,  G06K9/00 ,  G06N3/04 ,  H04L29/08 ,  G06F21/45 ,  H04L9/32 ,  H04W4/70 ,  G06F21/44 ,  G06K9/46 ,  G06K9/62 ,  G06F16/538 ,  G06F16/535 ,  G06F16/54 ,  G06F21/62 ,  G06F9/50 ,  G06N3/063 ,  G06N3/08 ,  H04N19/80 ,  G06F16/951 ,  G06K9/36 ,  H04N19/46 ,  G06T7/70 ,  G06K9/64 ,  G06K9/72

Abstract: 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.

公开(公告)号：US10887614B2

公开(公告)日：2021-01-05

申请号：US16450743

申请日：2019-06-24

Applicant: Intel Corporation

Inventor： Srenivas Varadarajan ,  Omesh Tickoo ,  Vallabhajosyula Somayazulu ,  Yiting Liao ,  Ibrahima Ndiour ,  Shao-Wen Yang ,  Yen-Kuang Chen

IPC: H04N19/42 ,  H04N19/176 ,  H04N19/167 ,  H04N19/70 ,  H04N19/513 ,  H04N19/119 ,  H04N19/115 ,  H04N19/137 ,  H04N19/17

Abstract: 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.

公开(公告)号：US10420101B2

公开(公告)日：2019-09-17

申请号：US15721298

申请日：2017-09-29

Applicant: Intel Corporation

Inventor： Jaroslaw J. Sydir ,  Yiting Liao ,  Dave A. Cavalcanti ,  Vallabhajosyula S. Somayazulu

IPC: H04L12/43 ,  H04W72/04 ,  H04L12/24 ,  H04W4/70

Abstract: Aspects of traffic aware slot assignment are described, for example, in a multi-hop wireless network comprising a plurality of nodes. In some aspects, an apparatus of a wireless device is configured to decode signaling, received from a node of the multi-hop network, to determine an indication of a change to a topology of the multi-hop network. The apparatus is further configured to, in response to a determination, from the decoded signaling, of an addition of a second node to the multi-hop network topology, increment a total of a number of descendant nodes, and allocate one or more transmission slots to a number of unused slots in one or more transmission opportunity regions of a slotframe, wherein the slotframe includes a repeating pattern of one or more transmission opportunity periods for a plurality of nodes in the network.

公开(公告)号：US10349060B2

公开(公告)日：2019-07-09

申请号：US15640202

申请日：2017-06-30

Applicant: INTEL CORPORATION

Inventor： Srenivas Varadarajan ,  Yiting Liao ,  Vallabhajosyula S. Somayazulu ,  Omesh Tickoo ,  Ibrahima J. Ndiour ,  Javier Perez-Ramirez

IPC: H04N19/167 ,  H04N19/17 ,  H04N19/176

Abstract: An example apparatus for encoding video frames includes a receiver to receive video frames and a heat map from a camera and expected object regions from a video database. The apparatus also includes a region of interest (ROI) map generator to detect a region of interest in a video frame based on the expected object regions. The ROI map generator can also detect a region of interest in the video frame based on the heat map. The ROI map generator can then generate an ROI map based on the detected regions of interest. The apparatus further includes a parameter adjuster to adjust an encoding parameter based on the ROI map. The apparatus also further includes a video encoder to encode the video frame using the adjusted encoding parameter.

公开(公告)号：US20230237144A1

公开(公告)日：2023-07-27

申请号：US18157154

申请日：2023-01-20

Applicant: Intel Corporation

Inventor： Yen-Kuang Chen ,  Shao-Wen Yang ,  Ibrahima J. Ndiour ,  Yiting Liao ,  Vallabhajosyula S. Somayazulu ,  Omesh Tickoo ,  Srenivas Varadarajan

IPC: G06F21/44 ,  H04L9/06 ,  G06F21/64 ,  G06F21/53 ,  G06N5/022 ,  G06F21/45 ,  H04L9/32 ,  H04W4/70 ,  G06F16/538 ,  G06F16/535 ,  G06F16/54 ,  G06F21/62 ,  G06F9/50 ,  G06N3/04 ,  G06N3/063 ,  G06V10/44 ,  G06V20/00 ,  G06V40/20 ,  G06V40/16 ,  G06F9/48 ,  H04L67/51 ,  G06T7/11 ,  G06V10/96 ,  G06V30/262 ,  G06K15/02 ,  G06F18/24 ,  G06F18/21 ,  G06F18/22 ,  G06F18/211 ,  G06F18/213 ,  G06F18/2413 ,  G06N3/045 ,  G06V30/19 ,  G06V10/82 ,  G06V10/94 ,  G06V10/75 ,  G06V10/20 ,  G06V10/40 ,  G06N3/08 ,  H04L67/12 ,  H04N19/80 ,  G06F16/951 ,  H04N19/46 ,  G06T7/70

CPC classification number: G06F21/44 ,  H04L9/0643 ,  G06F21/64 ,  G06F21/53 ,  G06N5/022 ,  G06F21/45 ,  H04L9/3239 ,  H04W4/70 ,  G06F16/538 ,  G06F16/535 ,  G06F16/54 ,  G06F21/6254 ,  G06F9/5044 ,  G06F9/5072 ,  G06N3/04 ,  G06N3/063 ,  G06V10/454 ,  G06V20/00 ,  G06V40/20 ,  G06V40/161 ,  G06F9/4881 ,  G06F9/5066 ,  H04L67/51 ,  G06T7/11 ,  G06V10/96 ,  G06V30/274 ,  G06K15/1886 ,  G06F18/24 ,  G06F18/21 ,  G06F18/22 ,  G06F18/211 ,  G06F18/213 ,  G06F18/2163 ,  G06F18/24143 ,  G06N3/045 ,  G06V30/19173 ,  G06V10/82 ,  G06V10/95 ,  G06V10/75 ,  G06V10/20 ,  G06V10/40 ,  G06N3/08 ,  H04L67/12 ,  H04N19/80 ,  G06F16/951 ,  H04N19/46 ,  G06T7/70 ,  H04W12/02

Abstract: 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.

公开(公告)号：US20230185895A1

公开(公告)日：2023-06-15

申请号：US18060207

申请日：2022-11-30

Applicant: Intel Corporation

Inventor： Yen-Kuang Chen ,  Shao-Wen Yang ,  Ibrahima J. Ndiour ,  Yiting Liao ,  Vallabhajosyula S. Somayazulu ,  Omesh Tickoo ,  Srenivas Varadarajan

IPC: G06F21/44 ,  H04L9/06 ,  G06F21/64 ,  G06F21/53 ,  G06N5/022 ,  G06F21/45 ,  H04L9/32 ,  H04W4/70 ,  G06F16/538 ,  G06F16/535 ,  G06F16/54 ,  G06F21/62 ,  G06F9/50 ,  G06N3/04 ,  G06N3/063 ,  G06V10/44 ,  G06V20/00 ,  G06V40/20 ,  G06V40/16 ,  G06F9/48 ,  H04L67/51 ,  G06T7/11 ,  G06V10/96 ,  G06V30/262 ,  G06K15/02 ,  G06F18/24 ,  G06F18/21 ,  G06F18/22 ,  G06F18/211 ,  G06F18/213 ,  G06F18/2413 ,  G06N3/045 ,  G06V30/19 ,  G06V10/82 ,  G06V10/94 ,  G06V10/75 ,  G06V10/20 ,  G06V10/40 ,  G06N3/08 ,  H04L67/12 ,  H04N19/80 ,  G06F16/951 ,  H04N19/46 ,  G06T7/70

CPC classification number: G06F21/44 ,  G06F9/4881 ,  G06F9/5044 ,  G06F9/5066 ,  G06F9/5072 ,  G06F16/54 ,  G06F16/535 ,  G06F16/538 ,  G06F16/951 ,  G06F18/21 ,  G06F18/22 ,  G06F18/24 ,  G06F18/211 ,  G06F18/213 ,  G06F18/2163 ,  G06F18/24143 ,  G06F21/45 ,  G06F21/53 ,  G06F21/64 ,  G06F21/6254 ,  G06K15/1886 ,  G06N3/04 ,  G06N3/08 ,  G06N3/045 ,  G06N3/063 ,  G06N5/022 ,  G06T7/11 ,  G06T7/70 ,  G06V10/20 ,  G06V10/40 ,  G06V10/75 ,  G06V10/82 ,  G06V10/95 ,  G06V10/96 ,  G06V10/454 ,  G06V20/00 ,  G06V30/274 ,  G06V30/19173 ,  G06V40/20 ,  G06V40/161 ,  H04L9/0643 ,  H04L9/3239 ,  H04L67/12 ,  H04L67/51 ,  H04N19/46 ,  H04N19/80 ,  H04W4/70 ,  H04W12/02

Abstract: 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.

公开(公告)号：US11665777B2

公开(公告)日：2023-05-30

申请号：US17251117

申请日：2018-09-28

Applicant: Intel Corporation

Inventor： Ravikumar Balakrishnan ,  Nageen Himayat ,  Yiting Liao ,  Gabriel Arrobo Vidal ,  Roya Doostnejad ,  Vijay Sarathi Kesavan ,  Venkatesan Nallampatti Ekambaram ,  Maria Ramirez Loaiza ,  Vallabhajosyula S. Somayazulu ,  Srikathyayani Srikanteswara

IPC: H04W72/12 ,  H04W72/1263 ,  G06K9/62 ,  G06N3/08 ,  H04W16/14 ,  H04W24/02 ,  H04W24/10 ,  H04W72/0446 ,  H04W88/04

CPC classification number: H04W72/1263 ,  G06K9/6262 ,  G06N3/08 ,  H04W16/14 ,  H04W24/02 ,  H04W24/10 ,  H04W72/0446 ,  H04W72/1231 ,  H04W88/04

Abstract: 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.