Huawei Technologies Co., Limited

Shenzhen Sibionics Technology Co., Ltd.

Shenzhen Sibright Technology Co., Ltd.

Marvell International Limited

Tencent Technology (shenzhen) Company Limited

Shanghai Microport Medical (group) Co., Ltd.

Shenzhen Sisensing Co., Ltd.

Bin Xia

Mingzeng Dai

Zhenfei Tang

Gang Yu

Yu Zhao

Juan Wang

Yujing Bai

Zhigang Hu

Xiaoxin Li

Li Chen

Min Huang

Yu Geng Lin

Zhi Chen

Yinghua Huang

Daowei Wang

Yuanda Huang

Xiaoyan Bi

Hao Li

Yan Peng

Yu Wang

Jianjun Wu

Xuan Zhou

Dageng Chen

Tizheng Wang

Ming Wu

Qiyi Luo

Qunfang Lou

Huawei Zhang

Zheng Shang

Bingguang Peng

Kexin Xiao

Weilin Gong

Shiyi Zhu

Xiaoyong Yu

Zhiyong Chen

Biao Liang

Haiquan Yang

Zhengfei Xiao

Zhilin Zhao

Guangzhi Li

Hao Tian

Xioabo Zhang

Yunfeng Li

Guohui Zou

Zhilong Li

Yuechen Li

Mingsong Han

Xiaohu Chen

Qingming Zhang

Chubo Huang

Kerong Yan

Feng Zeng

Xiaoshan Zhang

Runfa Zhou

Xianwen Fang

Tanyue Xu

Yinhua Huang

A cell selection method, related to the communications field, includes a terminal device sending a first request to a network device, where the first request is used to request to select a serving cell for the terminal device in an energy efficiency first manner. The terminal device sends first information to the network device, where the first information is used by the network device to select a cell in a first communications system or a cell in a second communications system for the terminal device. The terminal device receives configuration information of a serving cell from the network device, where the serving cell is selected by the network device from the cell in the first communications system or the cell in the second communications system in a manner that energy efficiency of the terminal device is first.

Cell selection method and apparatus

Some embodiments of the disclosure describe a batch coating apparatus. In some examples, the batch coating apparatus includes a fixing mechanism, a liquid holding mechanism, a liquid injection mechanism and a control device. The fixing mechanism includes a plurality of fixing portions for fixing work-pieces to be coated. The liquid holding mechanism includes a plurality of liquid tanks arranged side by side. The liquid injection mechanism includes a liquid storage portion and an infusion portion. The control device is configured to control at least one of a relative movement between the fixing mechanism and the liquid holding mechanism and a relative movement between the liquid holding mechanism and the liquid injection mechanism. The control device is configured to control the liquid injection mechanism to supply the coating liquid via the infusion portion.

Batch coating apparatus and batch coating method

The technology of this application relates to a transmission control method and apparatus. The method includes obtaining traffic information of a first node at M sample moments, where M is a positive integer greater than or equal to 1, determining first traffic information of the first node at a first moment based on the traffic information of the first node at the M sample moments, where the first moment does not belong to the M sample moments, determining first rate-limited traffic based on the first traffic information, where the first rate-limited traffic indicates maximum traffic that is allowed to pass through a first queue of the first node at the first moment, and limiting traffic of the first queue at the first moment based on the first rate-limited traffic.

Transmission control method and apparatus

Some embodiments of the disclosure provide a diabetic retinopathy recognition system (S) based on fundus image. According to an embodiment, the system includes an image acquisition apparatus () configured to collect fundus images. The fundus images include target fundus images and reference fundus images taken from a person. The system further includes an automatic recognition apparatus () configured to process the fundus images from the image acquisition apparatus by using a deep learning method. The automatic recognition apparatus automatically determines whether a fundus image has a lesion and outputs the diagnostic result. According to another embodiment, the diabetic retinopathy recognition system (S) utilizes a deep learning method to automatically determine the fundus images and output the diagnostic result.

Retinopathy recognition system

A pulse current generation circuit () for neural stimulation includes an analogue signal receiving device () for receiving an analogue signal; an analogue-to-digital converter () for converting the analogue signal into a digital control signal; a current signal controller () for producing, according to the digital control signal, pulse current parameters for generating bidirectional pulse current signals; and a current generator () for generating, according to the pulse current parameters, bidirectional pulse current signals for neural stimulation, and the current generator can generate pulse currents of different precisions according to the pulse current parameters. In addition, the present invention further relates to a charge compensation circuit, a charge compensation method, and an implantable electrical retina stimulator using the pulse current generation circuit or the charge compensation circuit.

Pulse current generation circuit

Charge compensation circuit for neural stimulation

Some embodiments of the disclosure provide an artificial neural network system for recognizing a lesion in a fundus image. The system includes a pre-processing module configured to pre-process a target fundus image and a reference fundus image taken from one person separately, a first neural network () configured to generate a first advanced feature set from the target fundus image, a second neural network () configured to generate a second advanced feature set from the reference fundus image, a feature combination module () configured to combine the first advanced feature set and the second advanced feature set to form a feature combination set, and a third neural network () configured to generate, according to the feature combination set, a judgmental result of lesions.

System for recognizing diabetic retinopathy

A method includes: determining, by the control device, that a site receives a first service; determining that a mapping wavelength of a first service is blocked on an original routing path, where the original routing path includes a first line board connected to a first local dimension, and a wavelength occupied by the first local dimension includes the mapping wavelength of the first service; and routing, by the control device, the first service to a second line board connected to a second local dimension, where the mapping wavelength of the first service is available in the second local dimension.

Service processing method, control device, and storage medium

A decoding method performed by a receive end device is disclosed. The decoding method includes: receiving a first bit signal; performing level-M forward error correction (FEC) decoding on the first bit signal to obtain a second bit signal, where M is a positive integer greater than zero; checking the second bit signal to obtain a first check result; performing level-(M+1) FEC decoding on the second bit signal based on the first check result to obtain a third bit signal; and, upon determining that M+1 reaches a first preset threshold, performing data processing on the third bit signal to obtain a fourth bit signal, where the fourth bit signal is used by the receive end device to obtain service data transmitted by a transmit end device.

Decoding method and decoding apparatus

The present disclosure relates to modulation methods. One example method includes performing distribution matching (DM) encoding on N1 first bits to obtain N2 first symbols, determining N4 to-be-phase-modulated symbols whose signal powers are equal to a preset signal power from the N2 first symbols, performing phase modulation on the N4 to-be-phase-modulated symbols based on N3 second bits to obtain N4 second symbols, performing binary labeling (BL) encoding on the N4 second symbols and N2-N4 first symbols to obtain N5 BL encoded output bits, performing forward error correction (FEC) encoding on the N5 BL encoded output bits to obtain N6 FEC redundant bits, and performing quadrature amplitude modulation (QAM) mapping based on the N6 FEC redundant bits and the N5 BL encoded output bits to obtain N2 target QAM signals.

Quadrature amplitude modulation QAM signal modulation method and apparatus, and quadrature amplitude modulation QAM signal demodulation method and apparatus

The present disclosure provides an artificial neural network system for identifying a lesion in a retinal fundus image that comprises a pre-processing module configured to separately pre-process a target retinal fundus image and a reference retinal fundus image taken from a same person; a first neural network () configured to generate a first advanced feature set from the target retinal fundus image; a second neural network () configured to generate a second advanced feature set from the reference retinal fundus image; a feature combination module () configured to combine the first advanced feature set and the second advanced feature set to form a feature combination set; and a third neural network () configured to generate, according to the feature combination set, a diagnosis result. By using a target retinal fundus image and a reference retinal fundus image as independent input information, the artificial neural network may simulate a doctor, determining lesions on the target retinal fundus image using other retinal fundus images from the same person as a reference, thereby enhancing the diagnosis accuracy.

Artificial neural network and system for identifying lesion in retinal fundus image

A stent is disclosed, which includes a stent body () and a single-radiopaque component () disposed at one or each of a proximal end and a distal end of the stent body (). The stent body () is composed of rings and struts, and one part of the single-radiopaque component () is received in a receptacle () of the stent body () and another part of the single-radiopaque component () protrudes out of a surface of the stent body (). The area of the protruding part () of the single-radiopaque component () is larger than an area of the embedded part (), the presence of the protruding part () allowing the single-radiopaque component () to appear wider and thicker in a radiologic image, enhancing the radiopacity of the stent during surgery.

Stent

Diabetic retinopathy recognition system based on fundus image

Pulse current generation circuit for neural stimulation, charge compensation circuit and method, and implantable electrical retina stimulator

The present disclosure provides a method for identifying a clique network using a Pregel graph computing framework. The method includes determining, according to a data transfer feature of a target clique, a transmitting direction of attribute values of nodes; transmitting, an edge vector of the edge and an attribute value of the first node to the second node along the transmitting direction in a constructed data transmission relationship network, the edge vector comprising a plurality of data transfer eigenvalues; performing, weighted calculation on the edge vector received by the second node to obtain an optimal weighted edge; iterating, the above operations; and determining, according to attribute values of the nodes after the one or more iterations, nodes in the target clique, and determining attributes of the nodes in the target clique.

Clique network identification method and apparatus, computer device, and computer-readable storage medium

Growing community of inventors

Shenzhen, China

Bin Xia