Shenzhen Institutes of Advanced Technology

Huawei Technologies Co., Limited

Chinese Academy of Sciences

Tencent Technology (shenzhen) Company Limited

Launch Tech Co., Ltd.

Zte Corporation

Future Wei Technologies, Inc.

Sanechips Technology Co., Ltd.

Huawei Digital Power Technologies Co., Ltd.

Shenzhen Skyworth-rgb Electronic Co., Ltd.

Shanghai United Imaging Healthcare Co., Ltd.

Shenzhen University

Mgi Tech Co., Ltd.

Harbin Institute of Technology, Shenzhen

Honor Device Co., Ltd.

Xin Liu

Hairong Zheng

Dong Liang

Peiyun Di

Ye Li

Chao Luo

Na Zhang

Xin Ru Wang

Alexander Giladi

Shaobo Zhang

Shanshan Wang

Ke Xu

Zheng Liu

Yong Yang

Jun Liu

Nan Li

Qiang He

Xi Peng

Bin Li

Yong Li

Dong Wang

Jin Liu

Ke Zhang

Wei Su

Fan Zhang

Wei Yang

Xiao Zhang

Jing Liu

Qiang Zhang

Liang Chen

Min Wang

Yan Wang

Jian Li

Yi Lin

Hongyun Qu

Jiangwei Li

Jun Yao

Guobin Li

Xun Xu

Ling Xu

Yuanyuan Liu

Xing Li

Jie Chen

Jin Wang

Lei Zhang

Liang Wu

Tingfang Tang

Zhanli Hu

Yangpo Xu

Yongfeng Yang

Chao Zou

Lieqing Nie

Jo Lee

Guanjun Long

Shaoqi Shi

Yong Xu

Dehui Kong

Haifeng Wang

Changqi Hu

Ning Wang

Yu Tang

Li Chu

Huameng Fang

Yameng Wei

Qiaoyan Chen

Changquan Ai

Zixuan Zou

Chuanli Cheng

Fang Zhu

Jing You

Haotian Niu

Jisong Ai

Sheng Li

Xiaohui Wang

Wensheng Hong

Feng Du

Qi Yang

Lin Gao

Peng Nie

Bin Han

Xuan Rao

Rijun Xiao

Rongjian Huang

Hui Xiao

Jianquan Song

Xiaoqing Hu

Weixiong Chen

Yudong Chen

Xiaoxing Zhu

Kun Wu

Sen Jia

Xing Yang

Hongyu Sun

Dekai Li

Disclosed are an optical component, a backlight module and a display apparatus. The optical component includes: a lens body, a light distribution film and a light source; a light-emitting surface is provided at a top of the lens body, and an accommodating groove is provided at a bottom of the lens body; the light distribution film is provided on the light-emitting surface, at least one bubble layer is formed in the light distribution film, and the bubble layer is provided with a bubble structure; the light source is provided in the accommodating groove, and a luminous surface of the light source is configured to face towards a wall of the accommodating groove.

Optical component, backlight module and display apparatus

A low-dose PET image restoration method and system, a device, and a medium are provided. The method includes: S1, performing blocking processing on a training image comprising a low-dose PET image, an MR image, and a standard-dose PET image to obtain a first patch, and performing first preprocessing on the first patch to obtain a second patch; S2, obtaining, according to the second patch, a first joint dictionary by means of sparse coding and dictionary updating; and S3, restoring the low-dose PET image to a restored image of the standard-dose PET image according to the first joint dictionary.

Low-dose PET image restoration method and system, device, and medium

This application provides a fault handling system of a solid-state transformer, including a first power unit and a second power unit that are cascaded and connected. The first power unit includes a first auxiliary supply, a first control module, and a first communication module. The first auxiliary supply and the first control module are both electrically connected to two ends of a first busbar capacitor. The first control module is configured to detect a voltage of the first busbar capacitor. The second power unit includes a second auxiliary supply and a second control module. The second auxiliary supply and the second control module are both electrically connected to two ends of a second busbar capacitor. The first communication module outputs fault information to the second control module when the first control module detects that the voltage of the first busbar capacitor is greater than a threshold.

Fault handling system of solid-state transformer

A medical image noise reduction method, system, terminal and storage medium are disclosed. The method includes: obtaining a standard-dose PET image and a constant-value image; inputting the standard-dose PET image and the constant-value image into a decay function to obtain a corresponding low-dose noisy PET image and a noisy constant-value image, respectively; assembling the low-dose noisy PET image and the noisy constant-value image in a width dimension or a height dimension, and then inputting into a trained conjugate generative adversarial network, and outputting a denoised PET image and constant-value image output by the conjugate generative adversarial network.

Medical image noise reduction method, system, terminal, and storage medium

This application discloses a wireless signal propagation prediction method, and the method includes: obtaining S first sampling points in prediction space, and obtaining a first parameter of the prediction space through calculation based on location information of the S first sampling points and corresponding wireless signal received strength; obtaining a target algorithm based on the first parameter, and generating a wireless signal propagation model of the prediction space based on the target algorithm, the location information of the S first sampling points, and the corresponding wireless signal received strength; and obtaining wireless signal received strength of a terminal at any location in the prediction space based on the wireless signal propagation model of the prediction space. This application further discloses a wireless signal propagation prediction apparatus.

Wireless signal propagation prediction method and apparatus

A switch-mode power supply includes a first switch circuit, at least one second switch circuit, a switch control circuit, a first inductor switching circuit, a coupled inductor, a voltage input end, and a voltage output end. The coupled inductor in the switch-mode power supply may be connected to the first switch circuit, to implement signal transmission between the coupled inductor and the first switch circuit. Signal transmission between the coupled inductor and the second switch circuit may be implemented under control of the first inductor switching circuit. Alternatively, the signal transmission between the coupled inductor and the second switch circuit may be disconnected under the control of the first inductor switching circuit.

Switch-mode power supply

An encoding and decoding method are disclosed. The method includes: Each of multiple viewpoint bitstreams includes a first type bitstream and a second type bitstream; when a first image frame in a first type bitstream of a first viewpoint bitstream is played, receiving a switching request that indicates to play a second viewpoint bitstream; determining a first random access frame that is in a first type bitstream and/or a second type bitstream of the second viewpoint bitstream and has a play moment after and closest to the first image frame; decoding to obtain the first random access frame based on encoded data of the first random access frame in the second viewpoint bitstream, and decoding to obtain and play an image frame that is in the first type bitstream of the second viewpoint bitstream by using the first random access frame as a reference frame.

Encoding and decoding method, apparatus, and system

This disclosure discloses a multiplayer somatosensory system, method, and apparatus combining virtual and reality, and a medium, which belongs to the field of human-computer interaction. The system includes: a computer device, and a simulation carrier, n simulation firearms and a display apparatus; where the simulation carrier includes a driver's seat and n passenger seats; the display apparatus is configured to display a virtual environment picture and n aiming points provided by the computer device; and the computer device is configured to control a virtual carrier to change a driving direction in the virtual environment; control the virtual carrier to change a driving speed in the virtual environment; and shoot a virtual object aimed at an iaiming point in the virtual environment in response to a shooting operation on an isimulation firearm in the n simulation firearms.

Multiplayer somatosensory system, method, and apparatus combining virtual and reality, and medium

Provided is a quad-core radio-frequency coil circuit. The quad-core radio-frequency coil circuit includes a coil module () and a front-end module (). The coil module () is configured to receive a nuclear magnetic test signal and, according to the nuclear magnetic test signal, generate an induction signal. The front-end module () is connected to the coil module () and configured to generate the nuclear magnetic test signal and collect the induction signal.

Quad-core radio-frequency coil circuit

In a real-time video transmission system for a cellular-connected unmanned aerial vehicle (UAV) based on an adaptive bitrate provided by the present disclosure, an on-board computer is loaded and configured on the UAV, such that the UAV can provide multiple video streams with different bitrates for retrieval. A client with a bitrate adaptation unit is set up, allowing the system to adjust the bitrate of the video stream according to network conditions in each time interval. Intranet penetration and port mapping are implemented on the cloud server, enabling the client to obtain, from the cloud server, the real-time video stream captured by the UAV. The present disclosure allows a remote user to watch, through a cellular network, the real-time video stream captured by the UAV.

Real-time video transmission system for cellular-connected UAV based on adaptive bitrate

The present application provides an image processing method, an image processing device, a computer storage medium and a terminal, the image processing method includes: determining convolution kernels of at least two sizes for feature extraction; performing sparsity constraint for the determined convolution kernels of at least two sizes for feature extraction through a preset objective function; and performing feature extraction on an image based on the convolution kernels subjected to the sparsity constraint.

Image processing method and device, computer storage medium and terminal

Air purifier

A method for dynamically acquiring maximal oxygen consumption includes acquiring user's age, gender, weight, resting heart rate and maximum heart rate; detecting basic heart rate data comprising a plurality of exercise heart rates and basic speed data comprising a plurality of exercise speeds; calculating a characteristic heart rate and a characteristic speed based on the basic heart rate data and the basic speed data; and calculating the user's maximal oxygen consumption according to the user's age, gender, weight, resting heart rate and maximum heart rate, the characteristic heart rate and the characteristic speed. The maximal oxygen consumption for each exercise can be dynamically calculated and the result is accurate.

Method and electronic device for dynamically acquiring maximal oxygen consumption

A medical image processing method and processing apparatus, and a computer readable storage medium. The method includes: obtaining a to-be-processed image; performing a feature extraction on the to-be-processed image to obtain a corresponding feature image; and re-determining a pixel value of each pixel in the to-be-processed image based on first information and second information of a corresponding pixel in the feature image, and processing the to-be-processed image; wherein the first information is information of a pixel adjacent to the corresponding pixel in the features image, and the second information is information of a pixel that is not adjacent to and is similar to the corresponding pixel in the features image.

Medical image processing method, processing apparatus, and computer-readable storage medium

Methods and apparatuses for training a magnetic resonance imaging model, electronic devices and computer readable storage media are provided. A method may include: acquiring a magnetic resonance image data set; constructing a ring deep neural network to be trained; inputting an under-sampled magnetic resonance image and a full-sampled magnetic resonance image respectively to two neural networks included in the ring deep neural network, to generate respective simulated magnetic resonance images; inputting a first simulated full-sampled magnetic resonance image and the full-sampled magnetic resonance image to a pre-constructed first simulated magnetic resonance image class discrimination model, to obtain a first discrimination result indicating whether or not the first simulated full-sampled magnetic resonance image is of a simulated magnetic resonance image class; and adjusting a network parameter of the ring deep neural network based on a preset loss function, to obtain a trained magnetic resonance imaging model.

Methods and apparatuses for training magnetic resonance imaging model

Growing community of inventors

Shenzhen, China

Xin Liu