METHOD AND APPARATUS FOR PROVIDING MULTICASTING SERVICE FOR SPECIFIC GROUP
    2.
    发明申请
    METHOD AND APPARATUS FOR PROVIDING MULTICASTING SERVICE FOR SPECIFIC GROUP 审中-公开
    用于提供特定组的多媒体服务的方法和装置

    公开(公告)号:US20140143808A1

    公开(公告)日:2014-05-22

    申请号:US13850657

    申请日:2013-03-26

    CPC classification number: H04N21/2381 H04N21/6162 H04N21/6405

    Abstract: The present invention discloses a multicasting broadcasting apparatus and method. In an aspect, a multicasting broadcasting method includes receiving content from a user, inspecting the content, performing IP encapsulation on the content, reconfiguring a broadcasting frame so that the content does not interfere with other broadcasting signals, generating a modulation signal comprising the content, and broadcasting the modulation signal to target receivers over a broadcasting network.

    Abstract translation: 本发明公开了一种组播广播装置及方法。 一方面,多播广播方法包括从用户接收内容,检查内容,对内容进行IP封装,重新配置广播帧,使得内容不干扰其他广播信号,生成包含内容的调制信号, 并且通过广播网络将调制信号广播到目标接收机。

    COMMUNICATION APPARATUS AND METHOD OF USING MASSIVE MULTIPLE INPUT MULTIPLE OUTPUT
    4.
    发明申请
    COMMUNICATION APPARATUS AND METHOD OF USING MASSIVE MULTIPLE INPUT MULTIPLE OUTPUT 审中-公开
    通信设备和使用大量多输入多路输出的方法

    公开(公告)号:US20140204785A1

    公开(公告)日:2014-07-24

    申请号:US14160897

    申请日:2014-01-22

    CPC classification number: H04W24/08

    Abstract: A communication apparatus and method using a massive multiple input multiple output (MIMO) is disclosed, the method including measuring a transmission capacity between terminals disposed in a cell and a base station when a number of terminals disposed in the cell of the base station is greater than a number of terminals to be supported by the base station, and selecting at least one terminal for maximizing a network capacity from among terminals disposed in the cell based on the transmission capacity and the number of terminals to be supported by the base station.

    Abstract translation: 公开了一种使用海量多输入多输出(MIMO)的通信装置和方法,所述方法包括当设置在基站的小区中的终端数量较大时,测量设置在小区中的终端和基站之间的传输容量 比由基站支持的多个终端,并且基于要由基站支持的传输容量和终端数量,从设置在小区中的终端中选择至少一个用于使网络容量最大化的终端。

    COMMUNICATION METHOD USING MULTIPLE INPUT MULTIPLE OUTPUT AND COMMUNICATION APPARATUS PERFORMING THE SAME
    9.
    发明申请
    COMMUNICATION METHOD USING MULTIPLE INPUT MULTIPLE OUTPUT AND COMMUNICATION APPARATUS PERFORMING THE SAME 有权
    使用多路输入多路输出的通信方法及其通信设备

    公开(公告)号:US20160295570A1

    公开(公告)日:2016-10-06

    申请号:US15089361

    申请日:2016-04-01

    CPC classification number: H04L5/005 H04B7/0413 H04L5/0048 H04L5/0064

    Abstract: Provided herein is a communication method using MIMO (Multiple-Input Multiple-Output) technology for communicating with a terminal included in each of a plurality of base station cells using a communication apparatus, the method including calculating, by the communication apparatus, the number of terminals included inside a base station cell; generating, by the communication apparatus, pilot signals corresponding to the calculated number of terminals; and allocating, by the communication apparatus, the pilot signals to a terminal that may maximize a network capacity based on the generated pilot signals.

    Abstract translation: 本文提供了一种使用MIMO(多输入多输出)技术的通信方法,用于使用通信装置与包括在多个基站小区中的每一个中的终端通信,该方法包括由通信装置计算 包括在基站小区内的终端; 由通信装置产生与计算出的终端数相对应的导频信号; 以及由所述通信装置将导频信号分配给可以基于所生成的导频信号来最大化网络容量的终端。

    ANOMALY DETECTION USING MACHINE-LEARNING BASED NORMAL SIGNAL REMOVING FILTER

    公开(公告)号:US20210271957A1

    公开(公告)日:2021-09-02

    申请号:US17174199

    申请日:2021-02-11

    Abstract: The invention relates to a technology for detecting an abnormal signal using a filter for removing normal sound (or normal signals) around a sensor at normal times. The filter is provided to remove normal sound based on a denoising autoencoder learning technique for removing noise and used to determine whether field sound is an abnormal signal different from that of normal times. The filter is trained to pass normal sound, regarded as noise, to output a value of 0 and pass an abnormal signal without change. The filter is retrained by collecting only normal sound rather than abnormal signals in the field and then adding the collected normal sound to the existing training data. Therefore, even machine-learning nonexperts may easily and conveniently retrain the filter.

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