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公开(公告)号：US20170224246A1
公开(公告)日：2017-08-10
申请号：US15501978
申请日：2014-08-06
Inventor： Tianzi Jiang , Xin Zhang , Nianming Zuo , Juanning Si , Ruirui Zhao , Jian Yu
IPC: A61B5/0478 , A61B5/00 , A61B5/145
CPC classification number: A61B5/0478 , A61B5/0476 , A61B5/14542 , A61B5/14553 , A61B5/6803 , A61B5/7203 , A61B5/7225 , A61B5/7246 , A61B5/725 , A61B2562/066
Abstract: Disclosed are a method and system for brain activity detection. The method is: performing multi-channel synchronous collections of brain electrical signals and cerebral cortex blood oxygen signals simultaneously, and ensuring synchronicity of the collected signals among channels, and collecting said brain electrical signals and said cerebral cortex blood oxygen signals of all locations at the same time. The system comprises: a functional near-infrared light source emission module (2) which employs the frequency division multiplexing technique, wherein the light source is modulated by carrier of different frequencies, said signal is accessed from the multi-functional joint collection helmet (1) through a transmission optical fiber to irradiate the scalp, and after being scattered and absorbed by the brain, the attenuated light signal is processed by the functional near-infrared detection module (3); the functional near-infrared detection module (3) is used for detecting weak optical signals of the scalp; the brain electricity detection module (4) is used for detecting weak electrical signals of the scalp; the central control unit (5) is used for synchronizing and fusing data flows, sending control commands to each functional module, and uploading data to the host computer (6). The method and system can control the interference to be the minimum and have good time scale consistency.
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公开(公告)号：US20160162290A1
公开(公告)日：2016-06-09
申请号：US14785385
申请日：2013-04-19
Inventor： Donglin Wang , Shaolin Xie , Yongyong Yang , Leizu Yin , Lei Wang , Zijun Liu , Tao Wang , Xing Zhang
IPC: G06F9/30
CPC classification number: G06F9/30
Abstract: The present disclosure provides a processor having polymorphic instruction set architecture. The processor comprises a scalar processing unit, at least one polymorphic instruction processing unit, at least one multi-granularity parallel memory and a DMA controller. The polymorphic instruction processing unit comprises at least one functional unit. The polymorphic instruction processing unit is configured to interpret and execute a polymorphic instruction and the functional unit is configured to perform specific data operation tasks. The scalar processing unit is configured to invoke the polymorphic instruction and inquire an execution state of the polymorphic instruction. The DMA controller is configured to transmit configuration information for the polymorphic instruction and transmit data required by the polymorphic instruction to the multi-granularity parallel memory. With the present disclosure, programmers can redefine a processor instruction set based on algorithm characteristics of applications after tape-out of a processor.
Abstract translation: 本公开提供了具有多态指令集架构的处理器。 处理器包括标量处理单元，至少一个多态指令处理单元，至少一个多粒度并行存储器和DMA控制器。 多态指令处理单元包括至少一个功能单元。 多态指令处理单元被配置为解释并执行多态指令，并且功能单元被配置为执行特定的数据操作任务。 标量处理单元被配置为调用多态指令并查询多态指令的执行状态。 DMA控制器被配置为发送多态指令的配置信息并将多态指令所需的数据发送到多粒度并行存储器。 利用本公开，程序员可以在处理器输出之后基于应用的算法特性来重新定义处理器指令集。
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103.发明授权公开(公告)号：US12117508B1
公开(公告)日：2024-10-15
申请号：US18744685
申请日：2024-06-16
Inventor： Jie Tian , Zechen Wei , Hui Hui , Liwen Zhang , Xin Yang , Tao Zhu
IPC: G01R33/12 , A61B5/0515 , G06T11/00
CPC classification number: G01R33/1276 , A61B5/0515 , G06T11/006 , G06T2211/441
Abstract: A system for reconstructing a magnetic particle image based on adaptive optimization of regularization terms includes: a MPI device for scanning an imaging object to acquire a voltage response signal; a signal processor for constructing a system matrix; and a control processor for reconstructing the magnetic particle image based on an arbitrarily selected regularization term, inputting the reconstructed magnetic particle image to a regularization-term adaptive optimization neural network model for enhancement processing, taking the enhanced magnetic particle image as a first image, and calculating a loss value between the first image and an initial image to acquire a final reconstructed magnetic particle image. The system adopts a neural network model-based automatic learning approach, instead of the approach of manually selecting regularization terms and adjusting parameters, to improve the reconstruction efficiency and quality of the magnetic particle image.
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104.发明公开公开(公告)号：US20240320513A1
公开(公告)日：2024-09-26
申请号：US18731260
申请日：2024-06-01
Inventor： Zhenan SUN , Yunlong WANG , Zhengquan LUO , Kunbo ZHANG , Qi LI , Yong HE
IPC: G06N3/098
CPC classification number: G06N3/098
Abstract: Disclosed is a disentangled personalized federated learning method via consensus representation extraction and diversity propagation provided by embodiments of the present application. The method includes: receiving, by a current node, local consensus representation extraction models and unique representation extraction models corresponding to other nodes, respectively; extracting, by the current node, the representations of the data of the current node by using the unique representation extraction models of other nodes respectively, and calculating first mutual information between different sets of representation distributions, determining similarity of the data distributions between the nodes based on the size of the first mutual information, and determining aggregation weights corresponding to the other nodes based on the first mutual information; the current node obtains the global consensus representation aggregation model corresponding to the current node.
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105.发明授权公开(公告)号：US11908240B2
公开(公告)日：2024-02-20
申请号：US17471384
申请日：2021-09-10
Inventor： Jianhua Tao , Hao Zhang , Bin Liu , Wenxiang She
IPC: G06V40/16 , G06N3/049 , G06F18/214
CPC classification number: G06V40/176 , G06F18/2148 , G06N3/049 , G06V40/168 , G06V40/172
Abstract: Disclosed is a micro-expression recognition method based on a multi-scale spatiotemporal feature neural network, in which spatial features and temporal features of micro-expression are obtained from micro-expression video frames, and combined together to form more robust micro-expression features, at the same time, since the micro-expression occurs in local areas of a face, active local areas of the face during occurrence of the micro-expression and an overall area of the face are combined together for micro-expression recognition.
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Patent Agency Ranking
公开(公告)号：US20230417847A1
公开(公告)日：2023-12-28
申请号：US18144256
申请日：2023-05-08
Inventor： Yang DU , Jie TIAN , Zhengyao PENG , Lin YIN , Qian LIANG
CPC classification number: G01R33/1276 , G06N3/091 , G06T11/003
Abstract: An MPI reconstruction method, device, and system based on a RecNet model include obtaining a one-dimensional (1D) MPI signal on which imaging reconstruction is to be performed, taking the 1D MPI signal as an input signal, and inputting the input signal and a velocity signal of an FFP corresponding to the input signal into a trained magnetic particle reconstruction model RecNet for image reconstruction to obtain a two-dimensional (2D) MPI image, where the magnetic particle reconstruction model RecNet is constructed based on a domain conversion network and an improved UNet network. The MPI reconstruction method, device, and system obtain a high-quality and clear magnetic particle distribution image without obtaining the system matrix.
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公开(公告)号：US11776576B2
公开(公告)日：2023-10-03
申请号：US17623247
申请日：2021-08-09
Inventor： Qi Li , Zhenan Sun , Yuhao Zhu
IPC: G11B27/031 , G06V10/80 , G06V20/70 , G06V40/16 , G06V20/40 , G06V10/26 , G06V10/774
CPC classification number: G11B27/031 , G06V10/26 , G06V10/774 , G06V10/806 , G06V20/46 , G06V20/70 , G06V40/168
Abstract: A video generation method includes: obtaining a target face image and a source face image; extracting a feature of each of the source face image and the target face image through a face feature encoder, to obtain corresponding source feature codes and target feature codes; generating swapped face feature codes through a face feature exchanger according to the source feature codes and the target feature codes; generating an initial swapped face image through a face generator according to the swapped face feature codes; and fusing the initial swapped face image with the target face image through a face fuser, to obtain a final swapped face image. The face feature encoder performs hierarchical encoding on the face feature to reserve semantic details of a face, and the face feature exchanger performs further processing based on the hierarchical encoding, to obtain hierarchical encoding of a swapped face feature with semantic details.
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公开(公告)号：US20230086756A1
公开(公告)日：2023-03-23
申请号：US17597984
申请日：2020-05-25
Applicant: Institute of Automation, Chinese Academy of Sciences , Guangdong Institute of Artificial Intelligence and Advanced Computing
Inventor： Zhifeng Lv , Jie Hao , Jun Liang , Lin Shu , Meiting Zhao , Yafang Song , Qiuxiang Fan
IPC: G06F9/50
Abstract: The field of high-speed data acquisition and network data processing, and particularly relates to an Ethernet data stream recording method, an Ethernet data stream recording system, and an Ethernet data stream recording device for a high-speed data acquisition system. It is intended to solve problems such as a low utilization rate of CPU, poor system compatibility, difficulty in packaging and deployment and low reliability of system transmission of the traditional high-speed data acquisition system. The method of the present disclosure includes: isolating a preset number of CPU cores after a Linux operating system is booted; uninstalling a kernel network card driver of the operating system and creating a hugepage memory pool; for each 10-gigabit network card, allocating a corresponding data-receiving buffer pool and a corresponding lock-free FIFO buffer, and initializing a PCIE register of each 10 gigabit network card such that each 10-gigabit network card enters into an acquisition state; and continuously receiving packets acquired by each 10 gigabit network card in a driving manner of user space polling and performing disk recording. According to the present disclosure, the utilization rate of CPU, system compatibility and transmission reliability are improved and the difficulty in packaging and deployment is decreased.
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公开(公告)号：US20230076251A1
公开(公告)日：2023-03-09
申请号：US17667212
申请日：2022-02-08
Inventor： Jianhua TAO , Shan LIANG , Shuai NIE , Jiangyan YI
Abstract: Disclosed are a method, an electronic apparatus for detecting tampering audio and a storage medium. The method includes: acquiring a signal to be detected, and performing a wavelet transform of a first preset order on the signal to be detected so as to obtain a first low-frequency coefficient and a first high-frequency coefficient corresponding to the signal to be detected, the number of which is equal to that of the first preset order; performing an inverse wavelet transform on the first high-frequency coefficient having an order greater than or equal to a second preset order so as to obtain a first high-frequency component signal corresponding to the signal to be detected; calculating a first Mel cepstrum feature of the first high-frequency component signal in units of frame, and concatenating the first Mel cepstrum features of a current frame signal and a preset number of frame signals.
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公开(公告)号：US11580957B1
公开(公告)日：2023-02-14
申请号：US17836791
申请日：2022-06-09
Inventor： Jianhua Tao , Zhengkun Tian , Jiangyan Yi
IPC: G10L15/06 , G10L15/02 , G06F40/284 , G10L15/197 , G10L15/22
Abstract: Disclosed are a method for training speech recognition model, a method and a system for speech recognition. The disclosure relates to field of speech recognition and includes: inputting an audio training sample into the acoustic encoder to represent acoustic features of the audio training sample in an encoded way and determine an acoustic encoded state vector; inputting a preset vocabulary into the language predictor to determine text prediction vector; inputting the text prediction vector into the text mapping layer to obtain a text output probability distribution; calculating a first loss function according to a target text sequence corresponding to the audio training sample and the text output probability distribution; inputting the text prediction vector and the acoustic encoded state vector into the joint network to calculate a second loss function, and performing iterative optimization according to the first loss function and the second loss function.
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