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    Frequency multiplier based on a low dimensional semiconductor structure
    37.
    发明授权
    Frequency multiplier based on a low dimensional semiconductor structure 有权
    基于低维半导体结构的倍频器

    公开(公告)号：US09530845B2

    公开(公告)日：2016-12-27

    申请号：US14767301

    申请日：2014-03-25

    Applicant: SOUTH CHINA NORMAL UNIVERSITY

    Inventor： Kunyuan Xu

    IPC: H01L29/66 ,  H01L29/20 ,  H03B19/14 ,  H01L29/778

    CPC classification number: H01L29/2003 ,  H01L29/778 ,  H03B19/14

    Abstract: A frequency multiplier based on a low dimensional semiconductor structure, including an insulating substrate layer, a semiconductor conducting layer arranged on the surface of the insulating substrate layer, an insulating protective layer arranged on the surface of the semiconductor conducting layer, an insulating carving groove penetrating the semiconductor conducting layer, an inlet electrode arranged on the side surface of the semiconductor conducting layer, and an outlet electrode arranged on the side surface corresponding to the access electrode is provided. The semiconductor conducting layer comprises two two-dimensional, quasi-one-dimensional, or one-dimensional current carrying channels near to and parallel to each other. The frequency multiplier has advantages that the structure is simple, the process is easy to implement, no extra filter circuit needs to be added, dependence on material characteristics is little, and the selection range of materials is wide.

    Abstract translation: 基于低维半导体结构的倍频器，包括绝缘基板层，布置在绝缘基板层的表面上的半导体导电层，布置在半导体导电层的表面上的绝缘保护层，穿透 设置半导体导电层，布置在半导体导电层的侧表面上的入口电极和布置在与存取电极相对应的侧表面上的出口电极。 半导体导电层包括彼此靠近并且彼此平行的两个二维，准一维或一维载流通道。 倍频器具有结构简单，易于实现的优点，不需要添加额外的滤波电路，材料特性依赖性小，材料选择范围广。

    NUCLEIC ACID DETECTION KIT AND DETECTION METHOD BASED ON THE PHOTO-CONTROLLED CRISPR-CAS SYSTEM
    39.
    发明申请
    NUCLEIC ACID DETECTION KIT AND DETECTION METHOD BASED ON THE PHOTO-CONTROLLED CRISPR-CAS SYSTEM 有权

    公开(公告)号：US20240409986A1

    公开(公告)日：2024-12-12

    申请号：US18268964

    申请日：2022-05-06

    Applicant: South China Normal University

    Inventor： Xiaoming ZHOU ,  Menglu HU

    IPC: C12Q1/6844 ,  C12N9/22 ,  C12N15/11

    Abstract: The present invention discloses a nucleic acid detection kit and a detection method based on photo-controlled CRISPR-Cas, wherein the kit comprises silent guide RNA and Cas protein; the silent guide RNA is formed by annealing hybridization of silent nucleotide and guide RNA; the guide RNA, designed according to a target nucleic acid sequence, includes two regions, i.e. a repetitive region and a spacer region; the silent nucleotide is completely complementarily paired with a the of the guide RNA, or is completely paired with a the of the guide RNA; the bases of the silent nucleotide are linked by PC linker; and the Cas protein is Cas12 protein or Cas13 protein. Although this method separates the nucleic acid amplification from the CRISPR-Cas detection in time, it can allow them to be completed in the same closed reaction tube, thereby avoiding the transfer process of uncapped reagent, ensuring that the detection is not affected by aerosol pollution while ensuring high detection sensitivity.

    NONINVASIVE METHOD AND SYSTEM FOR SLEEP APNEA DETECTION
    40.
    发明公开
    NONINVASIVE METHOD AND SYSTEM FOR SLEEP APNEA DETECTION 审中-公开

    公开(公告)号：US20240023886A1

    公开(公告)日：2024-01-25

    申请号：US18480906

    申请日：2023-10-04

    Applicant: SOUTH CHINA NORMAL UNIVERSITY

    Inventor： Han ZHANG ,  Weiwei ZHU ,  Songbin YE ,  Baoxian YU

    IPC: A61B5/00 ,  G16H50/20 ,  G06N20/20 ,  A61B5/11

    CPC classification number: A61B5/4818 ,  G16H50/20 ,  G06N20/20 ,  A61B5/1102 ,  A61B5/7203 ,  A61B5/7267

    Abstract: A noninvasive method and system for sleep apnea detection. The noninvasive method for sleep apnea detection comprises: collecting vital sign signals of a sleeping user; performing structured processing on the vital sign signals of the sleeping user to remove invalid signals to obtain a set of valid vital sign signals; extracting multi-dimensional morphological features from a sleep respiratory signal and performing feature training on multiple initial models of a classifier by means of the multi-dimensional morphological features to obtain a sleep breathing detection model; and inputting the set of valid vital sign signals into the sleep breathing detection model and performing signal processing to obtain probability data of the sleeping user experiencing sleep apnea; inputting the set of valid vital sign signals into a sleep breathing detection model and performing signal processing to obtain predicted probability of the sleeping user suffering from sleep apnea.

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