Battery-free touch-aware user input using RFID tags

    公开(公告)号:US10346655B2

    公开(公告)日:2019-07-09

    申请号:US15833995

    申请日:2017-12-06

    Abstract: Aspects of the present disclosure describe a battery-free touch sensing user interface (UI) for Internet of Things (IoT) and other smart spaces employing Radio Frequency Identification (RFID) readers and tags that we call RIO. With RIO, any surface may be a touch-aware surface by attaching RFID tag(s) thereto. RIO advantageously supports custom-designed RFID tags and therefore facilitates customized UIs to be easily deployed in a real-world environment. RIO employs impedance tracking that results from a human finger—or other appendage—touching a surface of an RFID tag, thereby changing a characteristic impedance of the RFID tag antenna. This change manifests as a change in the phase of the RFID backscattered signal and is advantageously used by RIO to track fine-grained touch movement over the RFID tag. Disclosed further are multi-tag environments in which RIO operates and demonstrations including continuous tracking of finger movement during a swipe to within 3 mm of its actual position.

    Ultra-fast pattern generation algorithm for the heterogeneous logs

    公开(公告)号:US10333805B2

    公开(公告)日:2019-06-25

    申请号:US15956392

    申请日:2018-04-18

    Abstract: A computer-implemented method for generating patterns from a set of heterogeneous log messages is presented. The method includes collecting the set of heterogenous log messages from arbitrary or unknown systems or applications or sensors or instruments, splitting the log messages into tokens based on a set of delimiters, identifying datatypes of the tokens, identifying a log structure of the log messages by generating pattern-signatures of all the tokens and the datatypes based on predefined pattern settings, generating a pattern for each of the log structures and enabling users to edit the pattern for each of the log structures based on user requirements.

    Deep network flow for multi-object tracking

    公开(公告)号:US10332264B2

    公开(公告)日:2019-06-25

    申请号:US15695565

    申请日:2017-09-05

    Abstract: A multi-object tracking system and method are provided. The multi-object tracking system includes at least one camera configured to capture a set of input images of a set of objects to be tracked. The multi-object tracking system further includes a memory storing a learning model configured to perform multi-object tracking by jointly learning arbitrarily parameterized and differentiable cost functions for all variables in a linear program that associates object detections with bounding boxes to form trajectories. The multi-object tracking system also includes a processor configured to (i) detect the objects and track locations of the objects by applying the learning model to the set of input images in a multi-object tracking task, and (ii), provide a listing of the objects and the locations of the objects for the multi-object tracking task. A bi-level optimization is used to minimize a loss defined on a solution of the linear program.

    Fast distributed nonnegative matrix factorization and completion for big data analytics

    公开(公告)号:US10304008B2

    公开(公告)日:2019-05-28

    申请号:US15063236

    申请日:2016-03-07

    Abstract: Systems and methods are disclosed for operating a machine, by receiving training data from one or more sensors; training a machine learning module with the training data by: partitioning a data matrix into smaller submatrices to process in parallel and optimized for each processing node; for each submatrix, performing a greedy search for rank-one solutions; using alternating direction method of multipliers (ADMM) to ensure consistency over different data blocks; and controlling one or more actuators using live data and the learned module during operation.

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