Determining Car Positions
    1.
    发明申请

    公开(公告)号:US20190035271A1

    公开(公告)日:2019-01-31

    申请号:US15664357

    申请日:2017-07-31

    CPC classification number: G08G1/123 G06K9/00791 G06K2209/27

    Abstract: Examples provided herein describe a method for determining car positions. For example, a physical processor of an edge computing device may receive position data for a legacy car and information about a make and model of the legacy car. The first edge device may also receive, from a sensor-rich car, a set of sensor data about a set of observed cars in the vicinity of the sensor-rich car, a set of position data for the set of observed cars, and a set of visual data of the set of observed cars, wherein the set of observed cars includes the legacy car and the sensor-rich car. The edge device may then determine an updated position for the legacy car based on the set of position data for the set of observed cars, the set of visual data, and the set of sensor data and provide the updated position of the legacy car.

    DETECTING CAMERA ACCESS BREACHES
    2.
    发明申请

    公开(公告)号:US20190050606A1

    公开(公告)日:2019-02-14

    申请号:US15675568

    申请日:2017-08-11

    CPC classification number: G06F21/83 G06F21/316 G06F21/554

    Abstract: Examples disclosed herein relate to detecting camera access breaches by an application running on a computing device. The examples enable determining, by a computing device comprising a physical processor that implements machine readable instructions, that a type of camera access of a camera on a computing device is requested by an application running on the computing device, wherein the type of camera access comprises a photo, a video, a facial recognition, a bar code scanning, or object detection; determining, by the computing device and based on a set of camera access types associated with the application, whether the requested type of camera access is permitted; and responsive to determining that the requested type of camera access is not permitted, remediating the unpermitted camera access request by causing display, by the computing device, of an alert on the computing device, where the alert comprises information about an improper access of the camera by the application.

    IMAGE COMPRESSION WITH BOUNDED DEEP NEURAL NETWORK PERCEPTION LOSS

    公开(公告)号:US20210035330A1

    公开(公告)日:2021-02-04

    申请号:US16526335

    申请日:2019-07-30

    Abstract: Example method includes: transmit a plurality of probe images from an Internet of Things (IoT) device at an edge network to a server hosting a target deep neural network (DNN), wherein the plurality of images are injected with a limited amount of noise; receive a feedback comprising a plurality of discrete cosine transform (DCT) coefficients from the server hosting the target DNN, wherein the plurality of DCT coefficients are unique to the target DNN; generate a quantization table based on the feedback received from the server hosting the target DNN; compress a set of real-time images using the generated quantization table by the IoT device at the edge network; and transmit the compressed set of real-time images to the server hosting the target DNN for DNN inferences.

    Deterrence of User Equipment Device Location Tracking

    公开(公告)号:US20180160258A1

    公开(公告)日:2018-06-07

    申请号:US15369508

    申请日:2016-12-05

    CPC classification number: H04W4/02 H04W12/02 H04W12/10

    Abstract: Examples include deterrence of user equipment (UE) device location tracking. Some examples include a core network device of a telecommunication network having a processing resource and a machine-readable storage medium with instructions executable by the processing resource to receive a first service request message from the UE device that includes a pseudo-Globally Unique Temporary Identifier (p-GUTI), to send a paging message that includes the p-GUTI, and to receive a second service request message from the UE device that includes a new p-GUTI based on the p-GUTI of the first service request message matching the p-GUTI of the paging message.

    DEEP NEURAL NETWORK COLOR SPACE OPTIMIZATION

    公开(公告)号:US20210035331A1

    公开(公告)日:2021-02-04

    申请号:US16527954

    申请日:2019-07-31

    Abstract: Example method includes: transmitting a plurality of probe images from an IoT device at an edge network to a server hosting a target DNN, wherein the plurality of images are injected with a limited amount of noise to probe sensitivities of the target DNN to the red, green, and blue colors; receiving a feedback comprising a plurality of DCT coefficients unique to target DNN from the server hosting the target DNN; computing a plurality of color conversion weights based on the feedback received from the server; converting a set of real-time images from RGB color space to YUV color space using the plurality of color conversion weights unique to the target DNN; compressing the set of real-time images using a quantization table unique to the target DNN by the IoT device; and transmitting the compressed set of real-time images to the server hosting the target DNN for DNN inferences.

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