Method and system for repairing Reed-Solomon codes

    公开(公告)号:US11228323B2

    公开(公告)日:2022-01-18

    申请号:US16887804

    申请日:2020-05-29

    Abstract: Methods and devices are provided for error correction of distributed data in distributed systems using Reed-Solomon codes. In one embodiment, processes are provided for error correction that include receiving a first correction code for data fragments stored in storage nodes, constructing a second correction code responsive to an unavailable storage node of the storage nodes, performing erasure repair of the unavailable storage node, and outputting a corrected data fragment. The first correction code is a Reed-Solomon code represented as a polynomial and the second correction code is represented as a second polynomial with an increased subpacketization size. Processes are configured to account for repair bandwidth and sub-packetization size. Code constructions and repair schemes accommodate different sizes of evaluation points and provide a flexible tradeoff between the subpacketization size repair bandwidth of codes. In addition, schemes are provided to manage a single node failure and multiple node failures.

    Automated segmentation of organ chambers using deep learning methods from medical imaging

    公开(公告)号:US11182896B2

    公开(公告)日:2021-11-23

    申请号:US16711129

    申请日:2019-12-11

    Abstract: A method of detecting whether or not a body chamber has an abnormal structure or function including: (a) providing a stack of images as input to a system comprising one or more hardware processors configured to obtain a stack of medical images comprising at least a representation of the body chamber inside the patient; to obtain a region of interest using a convolutional network trained to locate the body chamber, wherein the region of interest corresponds to the body chamber from each of the medical images; and to infer a shape of the body chamber using a stacked auto-encoder (AE) network trained to delineate the body chamber, wherein the AE network segments the body chamber; (b) operating the system to detect the body chamber in the images using deep convolutional networks trained to locate the body chamber, to infer a shape of the body chamber using a stacked auto-encoder trained to delineate the body chamber, and to incorporate the inferred shape into a deformable model for segmentation; and (c) detecting whether or not the body chamber has an abnormal structure, wherein an abnormal structure is indicated by a body chamber clinical indicia that is different from a corresponding known standard clinical indicia for the body chamber.

    Interleaved Training and Limited Feedback for Multiple-Antenna Systems

    公开(公告)号:US20170346541A1

    公开(公告)日:2017-11-30

    申请号:US15604102

    申请日:2017-05-24

    CPC classification number: H04B7/0626 H04B7/0417 H04B7/061 H04B7/0684

    Abstract: A method includes the step of interleaving training and feedback stages in a transmitter and a multiplicity of antennas, wherein the transmitter trains the corresponding ones of the multiplicity of antennas one by one and receives feedback information after training each one of the corresponding ones of the multiplicity of antennas. An apparatus operating using the method includes a multiple-input single-output system with t transmitter antennas, a short-term power constraint P, and target data rate ρ where for any t, the same outage probability as a system with perfect transmitter and receiver channel state information is achieved with a feedback rate of R1 bits per channel state and via training R2 transmitter antennas on average, where R1 and R2 are independent of t, and depend only on ρ and P.

    FULLY AUTOMATED FOUR-CHAMBER SEGMENTATION OF ECHOCARDIOGRAMS

    公开(公告)号:US20210012885A1

    公开(公告)日:2021-01-14

    申请号:US16926517

    申请日:2020-07-10

    Abstract: Devices, systems and methods related to techniques for performing four-chamber segmentation of echocardiograms are disclosed. In one example aspect, a method for generating segmented image data based on an input echocardiogram includes receiving an input echocardiogram that includes information associated with four chambers of a heart, performing segmentation on the information associated with the four chambers using an adversarial model that comprises a first artificial neural network with multiple layers, and combining data from selected layers of the first artificial neural network to generate an output image that includes the segmented four chambers of the heart.

    Cost-efficient repair for storage systems using progressive engagement

    公开(公告)号:US10187088B2

    公开(公告)日:2019-01-22

    申请号:US14689870

    申请日:2015-04-17

    Abstract: An apparatus or method for minimizing the total accessing cost, such as minimizing repair bandwidth, delay or the number of hops including the steps of minimizing the number of nodes to be engaged for the recovery process using a polynomial-time solution that determines the optimal number of participating nodes and the optimal set of nodes to be engaged for recovering lost data, where in a distributed database storage system, for example a dynamic system, where the accessing cost or even the number of available nodes are subject to change results in different values for the optimal number of participating nodes. An MDS code is included which can be reused when the number of participating nodes varies without having to change the entire code structure and the content of the nodes.

    AUTOMATED SEGMENTATION OF ORGAN CHAMBERS USING DEEP LEARNING METHODS FROM MEDICAL IMAGING

    公开(公告)号:US20170109881A1

    公开(公告)日:2017-04-20

    申请号:US15294207

    申请日:2016-10-14

    Abstract: Systems and methods are disclosed for automatically segmenting a heart chamber from medical images of a patient. The system may include one or more hardware processors configured to: obtain image data including at least a representation of the patient's heart; obtain a region of interest from the image data; organize the region of interest into an input vector; apply the input vector through a trained graph; obtain an output vector representing a refined region of interest corresponding to the heart based on the application of the input vector through the trained graph; apply a deformable model on the obtained output vector representing the refined region of interest; and identify a segment of a heart chamber from the application of the deformable model on the obtained output vector.

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