HIGH FREQUENCY SIGNAL AMPLIFIER INCLUDING BALUN

    公开(公告)号:US20190207567A1

    公开(公告)日:2019-07-04

    申请号:US16201754

    申请日:2018-11-27

    Abstract: High frequency signal amplifier including balun is disclosed. The amplifier comprises an input terminal (Vin) through which the high frequency signal is input; a balun which is connected to the input terminal and outputs a first differential signal and a second differential signal based on the high frequency signal; a transistor (M) which is connected to the balun and outputs an amplified high frequency signal based on the first differential signal and the second differential signal; an output terminal which is connected to the transistor (M) and through which the amplified high frequency signal is acquired and the amplified high frequency signal is output. Therefore, performance of the amplifier can be enhanced.

    DEVICE AND METHOD FOR INCREMENTAL MACHINE LEARNING WITH VARYING FEATURE SPACES

    公开(公告)号:US20230214697A1

    公开(公告)日:2023-07-06

    申请号:US17938014

    申请日:2022-10-04

    CPC classification number: G06N7/005 G06N20/00

    Abstract: The present invention relates to a device and method for incremental machine learning in a varying feature space. A device for machine learning according to the present invention includes a probability table generator configured to generate a probability table for a target feature of a dataset and a conditional probability table for each input feature of the dataset, based on the received dataset, a correlation extractor configured to extract relevance between the target feature and each of the input features and redundancy between the input features, based on the dataset, and a feature weight extraction and model generator configured to extract weights for each of the input features based on the relevance and the redundancy, and generate a prediction model based on the probability table for the target feature, the conditional probability table, and the weight.

    SEMICONDUCTOR PLASMA ANTENNA APPARATUS
    5.
    发明申请

    公开(公告)号:US20170018400A1

    公开(公告)日:2017-01-19

    申请号:US15195214

    申请日:2016-06-28

    CPC classification number: H01J37/243 H01J37/147 H01Q1/366 H01Q19/12 H01Q19/18

    Abstract: Provided is a semiconductor plasma antenna apparatus. The apparatus includes: a cell array unit in which a plurality of PIN diode cells are arranged, and in which a cell pattern is formed by using a predefined PIN diode cell among the plurality of PIN diode cells; and a driver circuit unit configured to control a drive of the predefined PIN diode cell, wherein the driver circuit unit comprises: a direct-current conversion unit equipped with a DC-DC converter configured to drive a diode load of the cell pattern by applying an output voltage to a PIN diode cell corresponding to the cell patterns formed in the cell array unit; and a constant current controller configured to controlling a plasma concentration of the PIN diode cell by controlling a constant current for the diode load of the cell pattern.

    Abstract translation: 提供了一种半导体等离子体天线装置。 该装置包括:单元阵列单元,其中布置有多个PIN二极管单元,并且其中通过在多个PIN二极管单元中使用预定义的PIN二极管单元形成单元图案; 以及驱动器电路单元,被配置为控制所述预定义PIN二极管单元的驱动,其中所述驱动器电路单元包括:配备有DC-DC转换器的直流转换单元,所述DC-DC转换器被配置为通过施加所述DC二极管单元驱动所述单元图案的二极管负载 对与形成在单元阵列单元中的单元图案对应的PIN二极管单元的输出电压; 以及恒流控制器,其被配置为通过控制所述电池图案的二极管负载的恒定电流来控制所述PIN二极管单元的等离子体浓度。

    PREPROCESSING DEVICE AND METHOD FOR INCREMENTAL LEARNING OF CLASSIFIER WITH VARYING FEATURE SPACE

    公开(公告)号:US20230214673A1

    公开(公告)日:2023-07-06

    申请号:US17938018

    申请日:2022-10-04

    CPC classification number: G06N5/022

    Abstract: Provided are a preprocessing device and method for learning data of a classifier for improving stability and robustness of learning and prediction of a classifier in a varying feature space. The preprocessing device for learning data of a classifier according to the present invention includes a variable spatial information extractor configured to receive data for learning of the classifier, calculate an appearance frequency of each variable combination based on the data, and calculate a cumulative appearance frequency of each variable combination by cumulating and summing the appearance frequency of each variable combination, and a data preprocessor configured to generate a prediction variable—target variable frequency matrix based on the appearance frequencies of each variable combination, apply Laplace smoothing to the frequency matrix according to the cumulative appearance frequency to calculate a probability value of each variable combination, and provide the probability value of each variable combination to the classifier.

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