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公开(公告)号:US20210279379A1
公开(公告)日:2021-09-09
申请号:US17029087
申请日:2020-09-23
Inventor: Yuehang XU , Shuman MAO , Yunqiu WU , Ruimin XU , Bo YAN
IPC: G06F30/20
Abstract: A parameter extraction method for quasi-physical large-signal model for microwave gallium nitride high-electron-mobility transistors (GaN HEMTs). The method includes: 1) acquiring a data set of parameters for a large-signal model for a plurality of different microwave transistors GaN HEMTs having the same size; 2) performing statistical analysis of physical parameters of the large-signal model and sub-models thereof: 3) characterizing the correlation between the physical parameters by factor analysis; and 4) predicting the output characteristics of the GaN HEMTs.
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公开(公告)号:US20210217901A1
公开(公告)日:2021-07-15
申请号:US17078109
申请日:2020-10-23
Inventor: Yong ZHANG , Chengkai WU , Han WANG , Haomiao WEI , Ruimin XU , Bo YAN
IPC: H01L29/872 , H01L29/66
Abstract: A double Schottky-barrier diode includes a semi-insulating substrate, a left mesa formed by growth and etching on the semi-insulating substrate, a middle mesa formed by growth and etching on the semi-insulating substrate, a right mesa formed by growth and etching on the semi-insulating substrate, two anode probes and two air-bridge fingers. The two Schottky contacts are closely fabricated on the same mesa (middle mesa) in a back-to-back manner to obtain even symmetric C-V characteristics and odd symmetric I-V characteristics from the device level. The output of a frequency multiplier fabricated using the double Schottky-barrier diode only has odd harmonics, but no even harmonics, which is suitable for the production of high-order frequency multipliers. The cathodes of the two Schottky contacts are connected by the buffer layer without ohmic contact.
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公开(公告)号:US20210080261A1
公开(公告)日:2021-03-18
申请号:US16816267
申请日:2020-03-12
Inventor: Zhuoling XIAO , Xinguo YU , Yi HE , Bo YAN
Abstract: A pedestrian adaptive zero-velocity update point selection method based on a neural network, including the following steps: S1, collecting inertial navigation data of different pedestrians in different motion modes; S2, preprocessing the inertial navigation data collected in the step S1, labeling the preprocessed data, and obtaining a training data set, a validation data set, and a test data set according to the preprocessed data and a label corresponding to the preprocessed data; S3, inputting the training data set to a convolutional neural network for training, obtaining a pedestrian adaptive zero-velocity update point selection model based on the convolutional neural network, and using the validation data set to validate the pedestrian adaptive zero-velocity update point selection model; and S4, inputting the test data set into the pedestrian adaptive zero-velocity update point selection model based on the convolutional neural network, and obtaining a selection result of pedestrian zero-velocity update points.
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