Zhejiang Lab

Zhejiang University

Hangzhou Hikvision Digital Technology Co., Ltd.

Huan Zhang

Yu Zhang

Zhichao Wang

Chaoliang Sun

Tianzi Jiang

Haotian Qian

Jingsong Li

Jianwei Yin

Jun Li

Shuiguang Deng

Bangpeng Zheng

Shengye Pang

Yucheng Guo

Maolin Zhang

Junyang Zhang

Peng Yin

Qiufang Liang

Disclosed are a disease prediction method, system and apparatus based on a multi-relation functional connectivity matrix. A Pearson correlation coefficient matrix and a DTW distance matrix are respectively calculated according to resting state functional magnetic resonance time series extracted from a brain atlas, the DTW distance matrix is converted in combination with the Pearson correlation coefficient matrix into a DTW' matrix which includes correlation degree and correlation direction information and whose numerical range is equivalent to the value range of a Pearson coefficient, and a functional connectivity matrix is obtained after weighted combination. The present disclosure combines DTW distance information to weaken the dynamic change of functional connectivity and the influence of asynchrony of functional signals in different brain regions on the functional connectivity matrix, so that the calculated functional connectivity matrix can better reflect the correlation between the functional signals in different brain regions.

Disease prediction system and apparatus based on multi-relation functional connectivity matrix

A system for classifying working memory task magnetoencephalography based on machine learning, including: the magnetoencephalography data acquisition module configured to acquire magnetoencephalography data of a subject in different working memory task states; the magnetoencephalography data preprocessing module configured to control the quality of magnetoencephalography data in different working memory tasks and separate noises and artifacts; the magnetoencephalography source reconstruction module configured for sensor signal analysis and source reconstruction analysis for the data processed by the magnetoencephalography data preprocessing module; and the machine learning classification module is configured to classify the working memory tasks to which the subjects belong by taking power time series as features. The present disclosure integrates the complete analysis pipeline from preprocessing to source reconstruction of the working memory magnetoencephalography data, classifies the working memory task magnetoencephalography data, and is of great significance to the study of working memory decoding and brain memory related mechanisms.

System for classifying working memory task magnetoencephalography based on machine learning

A method and a system for simulating magnetic resonance echo-planar imaging artifacts. Firstly, for K-space artifacts, K-space data are restored through normal magnetic resonance images, and the K-space data are modified pertinently, and then images with artifacts are reconstructed; for susceptibility artifacts, a susceptibility model is constructed through normal magnetic resonance images, and the magnetic field distribution is reconstructed, and then the images with distortion artifacts are reconstructed. According to the present disclosure, a large number of artifact data sets with different artifact types and artifact degrees can be quickly created through a small number of normal images, thus laying a foundation for the research of identifying artifacts, eliminating or weakening artifacts. A simulation algorithm is designed according to the principle of generation of EPI sequence artifacts, and the obtained images such as stripe artifacts, Moer artifacts, Nyquist artifacts, susceptibility artifacts and the like have good scientificity, accuracy and interpretability.

Method and system for simulating magnetic resonance echo-planar imaging artifact

A system for precisely locating abnormal areas of brain fiber bundles. The system extracts fiber connections of the whole brain from diffusion magnetic resonance data, and fiber bundle pathways extracts through self-defined fiber bundle pathways or based on brain fiber bundle templates. A selected fiber bundle pathway is projected on a fiber connection result of the whole brain and finely segmented. The imaging indexes such as fractional anisotropy, mean diffusivity, intra-neurite volume fraction and orientation dispersion index are calculated from diffusion magnetic resonance data, so as to obtain the imaging index of each node of each fiber bundle pathway. These imaging indexes are configured to classify the disease group and the healthy group by a machine learning method, and which nodes on which fiber bundle pathways have abnormal changes with different diseases can be precisely located.

System for precisely locating abnormal area of brain fiber bundle

A system for predicting disease with graph convolutional neural network based on multimodal magnetic resonance imaging, which extracts the radiomics information of multiple brain regions across modals as the features of nodes from multimodal magnetic resonance data, and extracts the connectomics information between brain regions to form an adjacency matrix. T1-weighted structural images extract cortical information through cortical reconstruction, and resting-state magnetic resonance data are used to calculate amplitude of low frequency fluctuations, regional homogeneity and functional connectivity. Through multimodal data preprocessing, image index extraction and structured data integration, multimodal unstructured magnetic resonance image data are integrated into unified graph-structured data, and the disease is predicted by a graph convolutional neural network method. The system can better integrate the cross-modal physiological indexes of multiple brain regions and the correlation between brain regions and improve prediction ability of the model and generalization ability of the model with different diseases.

System for predicting disease with graph convolutional neural network based on multimodal magnetic resonance imaging

The present invention discloses a service caching method for a cross-border service network, wherein the method includes: a cache space of a service switch node is divided into a resident area, a change area, a pre-reclaimed area and a maintenance index area; among them, a cache hit frequency is: a resident area&#x3e;a change area&#x3e;a pre-reclaimed area, and the maintenance index area is used for separate storage services call path. when a service call is generated, a cache content in the cache space is replaced according to a cache value of a missed cache or a hit cache; a service router and service switch nodes in the corresponding area jointly form a hierarchical cache mode. When the cache space of any node in the service switch node is insufficient, the service switch nodes in the same area perform collaborative cache and store them in other cache space of the service switch node through indexing. The method provided by the present invention can improve the cache utilization efficiency in the cross-border service network, thereby accelerating service invocation.

Service caching method for a cross-border service network

The present invention discloses a video signal switching circuit and switching method. The switching circuit includes: a regulating circuit connected to a first cable and configured to regulate the first cable to be in a pull-up state or a released state; a detection circuit connected to the first cable and configured to detect a first voltage value on the first cable when the first cable is in the pull-up state or detect a second voltage value on the first cable when the first cable is in the released state; and a controller configured to determine a change in the first voltage value or the second voltage value from the detection circuit, wherein the controller controls a second cable to transmit the video signal when determining that the second voltage value increases, or the controller controls the first cable to transmit the video signal when determining that the first voltage value decreases. The present invention solves the technical problem that the video signal cannot be switched adaptively due to the need of manually switching signal levels in the prior art.

Growing community of inventors

Hangzhou, China

Huan Zhang