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    Method for establishing transistor statistical model based on artificial neural network system
    1.
    发明公开
    Method for establishing transistor statistical model based on artificial neural network system 审中-公开

    公开(公告)号：US20240273274A1

    公开(公告)日：2024-08-15

    申请号：US18427865

    申请日：2024-01-31

    申请人： Peking University Shenzhen Graduate School

    发明人： Lining ZHANG ,  Wu DAI ,  Yu LI ,  Runsheng WANG ,  Ru HUANG

    IPC分类号： G06F30/373 ,  G06F119/06

    CPC分类号： G06F30/373 ,  G06F2119/06

    摘要： A method of establishing a transistor statistical model based on an artificial neural network system comprising receiving a first data set and generating a nominal model of a baseline transistor by the artificial neural network system based on the first data set; screening neurons in the artificial neural network system based on the first data set and the nominal model to obtain final variational neurons; obtaining distribution of weights of the final variational neurons and distribution of threshold voltages based on variation of the nominal model with respect to weights of the final variational neurons, variation of the nominal model with respect to the threshold voltages, distribution of the drain-source current and distribution of the gate-source voltage in the first data set; and establishing the transistor statistical model based on the nominal model, the distribution of weights of the final variational neurons and the distribution of the threshold voltages.

    METHOD AND DEVICE FOR ENCODING OR DECODING BASED ON INTER-FRAME PREDICTION
    4.
    发明申请
    METHOD AND DEVICE FOR ENCODING OR DECODING BASED ON INTER-FRAME PREDICTION 有权

    公开(公告)号：US20230109825A1

    公开(公告)日：2023-04-13

    申请号：US17768212

    申请日：2019-10-25

    申请人： PEKING UNIVERSITY SHENZHEN GRADUATE SCHOOL

    发明人： Ronggang WANG ,  Xi XIE ,  Kui FAN ,  Wen GAO

    IPC分类号： H04N19/577 ,  H04N19/105 ,  H04N19/159 ,  H04N19/176

    摘要： A method and a device for encoding or decoding based on an inter-frame prediction. The method includes steps of: determining a temporal motion vector prediction value of a to-be-processed coding unit, where the temporal motion vector prediction value is a temporal motion vector prediction value of a sub-block, a temporal motion vector of which is obtainable through prediction, in sub-blocks adjacent to the to-be-processed coding unit and/or sub-blocks in the to-be-processed coding unit; determining a motion vector residual prediction value of the to-be-processed coding unit according to the temporal motion vector prediction value; determining a motion vector of a sub-block in the to-be-processed coding unit according to the temporal motion vector prediction value and the motion vector residual prediction value and performing a motion compensation according to the motion vector of the sub-block in the to-be-processed coding unit to determine a prediction block of the to-be-processed coding unit.

    ORGANIC FIELD-EFFECT TRANSISTOR AND FABRICATION METHOD THEREFOR
    5.
    发明申请
    ORGANIC FIELD-EFFECT TRANSISTOR AND FABRICATION METHOD THEREFOR 有权

    公开(公告)号：US20230093494A1

    公开(公告)日：2023-03-23

    申请号：US16960420

    申请日：2020-04-17

    申请人： PEKING UNIVERSITY SHENZHEN GRADUATE SCHOOL

    发明人： Hong MENG ,  Yuhao SHI ,  Xinwei WANG ,  Lin AI

    IPC分类号： H10K10/46 ,  H10K71/12

    摘要： An organic field-effect transistor and a fabrication method therefor, including: providing a gate; depositing polymer material onto the gate to form a dielectric layer; performing supercritical fluids treatment on the gate having the dielectric layer deposited; depositing organic semiconductor layer material on the dielectric layer having been processed, to form an organic semiconductor layer; depositing electrode layer material on the organic semiconductor layer and forming an electrode layer. The dielectric properties of the dielectric layer after adopting the supercritical fluids treatment have been significantly improved. While the hysteresis effect of the dielectric layers in the OFET devices has been basically eliminated, the sub-threshold slope of the OFET is also significantly reduced, the carrier mobility is effectively improved. Additionally, an OFET switching rate after being processed is improved, and, by connecting the LEDs in series, the switching rate of the LED is increased.

    MEMORY NETWORK METHOD BASED ON AUTOMATIC ADDRESSING AND RECURSIVE INFORMATION INTEGRATION
    7.
    发明申请
    MEMORY NETWORK METHOD BASED ON AUTOMATIC ADDRESSING AND RECURSIVE INFORMATION INTEGRATION 有权

    公开(公告)号：US20220138525A1

    公开(公告)日：2022-05-05

    申请号：US17423223

    申请日：2019-08-21

    申请人： Peking University Shenzhen Graduate School

    发明人： Ge LI ,  Zhangheng LI ,  Jiaxing ZHONG ,  Jingjia HUANG ,  Tao ZHANG

    IPC分类号： G06N3/04 ,  G06F12/06

    摘要： A memory network method based on automatic addressing and recursive information integration. The method is based on a memory neural network framework integrating automatic addressing and recursive information, and is an efficient and lightweight memory network method. A memory is read and written by means of an automatic addressing operation having low time and space complexity, and memory information is effectively utilized by a novel computing unit. The whole framework has the characteristics of high efficiency, high speed and high universality. The method is suitable for various time sequence processing tasks, and shows the performance superior to that of the conventional LSTM and the previous memory network.

    METHOD, SYSTEM, DEVICE, AND COMPUTER-READABLE STORAGE MEDIUM FOR INVERSE QUANTIZATION
    8.
    发明申请
    METHOD, SYSTEM, DEVICE, AND COMPUTER-READABLE STORAGE MEDIUM FOR INVERSE QUANTIZATION 有权

    公开(公告)号：US20220116607A1

    公开(公告)日：2022-04-14

    申请号：US16610467

    申请日：2019-03-06

    申请人： PEKING UNIVERSITY SHENZHEN GRADUATE SCHOOL

    发明人： Ronggang WANG ,  Zhenyu WANG ,  Wen GAO

    IPC分类号： H04N19/124 ,  H04N19/176 ,  H04N19/136 ,  H04N19/103 ,  H04N19/196

    摘要： The application discloses a method, system, device and computer-readable storage medium for inverse quantization, wherein, in some embodiments, determining an initial weighted inverse quantization matrix, wherein, the initial weighted inverse quantization matrix is the same as the quantized block in size; setting some matrix elements in the initial weighted inverse quantization matrix to zero to obtain a weighted inverse quantization matrix, wherein, determining the matrix elements that need to be zeroed according to the size of the quantized block; weighted inverse quantizing the quantized coefficients in the quantized block to generate corresponding inverse transform coefficients, wherein, the value of the matrix element corresponding to the position of the quantized coefficient in the weighted inverse quantization matrix is used as a weight coefficient of the weighted inverse quantization.

    Back-propagation image visual saliency detection method based on depth image mining
    9.
    发明授权
    Back-propagation image visual saliency detection method based on depth image mining 有权

    公开(公告)号：US11227178B2

    公开(公告)日：2022-01-18

    申请号：US16336737

    申请日：2017-11-24

    申请人： Peking University Shenzhen Graduate School

    发明人： Ge Li ,  Chunbiao Zhu ,  Wenmin Wang ,  Ronggang Wang ,  Tiejun Huang ,  Wen Gao

    IPC分类号： G06K9/46 ,  G06K9/62

    摘要： A back-propagation significance detection method based on depth map mining, comprising: for an input image Io, at a preprocessing phase, obtaining a depth image Id and an image Cb with four background corners removed of the image Io; at a first processing phase, carrying out positioning detection on a significant region of the image by means of the obtained image Cb with four background corners removed and the obtained depth image Id to obtain the preliminary detection result S1 of a significant object in the image; then carrying out depth mining on a plurality of processing phases of the depth image Id to obtain corresponding significance detection results; and then optimizing the significance detection result mined in each processing phase by means of a back-propagation mechanism to obtain a final significance detection result map. The method can improve the detection accuracy of the significance object.