12 results about "Connectome" patented technology

The application discloses a physiological signal monitoring method and a wearable device, relates to the technical field of detection, and mainly aims to solve the problem of poor accuracy of existing electrocardiogram for heart state judgment. The method comprises the following steps: acquiring user basic information and a physiological signal collected in real time, wherein the physiological signal comprises a heart electrocardiogram signal, a muscle electrocardiogram signal and an acceleration signal, the physiological signal is collected based on a multi-modal sensor on a wearable device, and the wearable device is fixed on the user body through a belt type connection assembly; performing multi-modal fusion on the physiological signal based on a multi-modal fusion algorithm to obtain physiological parameter data, and determining a monitoring task, wherein the multi-modal fusion algorithm comprises one of parallel fusion, serial fusion and attention fusion; calling a monitoring prediction model matched with the monitoring task and having completed model training to perform monitoring processing on the physiological parameter and the user basic information, obtaining a monitoring result, and performing feature comparison based on the monitoring result.

The application discloses a magnetocardiogram gradient instrument and an electrocardio collection circuit packaging structure and a signal processing system, relates to the technical field of medical equipment, and realizes high-density integration of hardware and reliable protection through a rigid protection frame formed by a circuit chip, a layer plate structure and a connecting assembly, shortens a signal transmission path, reduces the influence of environmental vibration, dust, moisture and electromagnetic disturbance on a synchronous sampling link, and provides a stable data basis for multi-modal fusion imaging. The signal processing system comprises multi-modal synchronous collection, noise suppression, data fusion imaging and a diagnosis auxiliary module, generates a high-precision space-time dynamic imaging atlas by constructing a cardiac electromagnetic field joint forward model and applying time and space double constraints, and outputs a cardiovascular risk assessment, a cardiac arrhythmia positioning and a myocardial ischemia quantification result in combination with machine learning. The application solves the problems of low integration, poor anti-interference and insufficient single modal detection precision of existing equipment.

This invention provides a highly supportive mesh structure, comprising beam-column units and single expansion units. The beam-column units at both ends and the single expansion unit in the middle are connected to form a tubular mesh structure. Each single expansion unit is composed of single expansion grids axially connected by reinforcing bars, with the crests of radially adjacent single expansion grids arranged in an alternating pattern. This mesh structure exhibits strong radial support, low axial contraction, no sharp angles, and stable mechanical properties. It is adaptable to various cavities in the human body, enabling the establishment and maintenance of cavities blocked by various hard, sharp, and irregularly shaped foreign objects. This provides surgeons with a safer surgical procedure, increases their treatment options, and reduces patient pain and suffering during and after surgery.

This disclosure generally relates to a computer-implemented method for isolating the physical locations of components within a physical structure. This is consistent with the present disclosure, which involves performing a series of evaluations at evaluation points depending on the type of physical structure and combining the results of the performed evaluations. The solution according to this disclosure can be used in many different technical fields, for example, for identifying erroneous locations within connection components, such as wire harnesses. This disclosure also relates to corresponding computer systems and computer program products.

The application relates to the technical field of brain science image processing, and discloses a brain tissue neuron morphology three-dimensional reconstruction and connection group analysis method and system. The method comprises the following steps: feature point extraction and matching and optical flow registration are performed on continuous ultrathin section electron microscope images to obtain three-dimensional image body data; a three-dimensional deep convolutional neural network is used for semantic segmentation of cell bodies, dendrites, axons and dendritic spines, and secondary registration is driven through confidence feedback; synapses are detected and classified into excitatory or inhibitory types; topological perception skeletonization reconstruction is performed, and segmentation correction is constrained through topological anomaly feedback; and a synapse connection matrix is constructed, and graph theory analysis is performed.

This invention relates to the field of medical device technology, specifically to a multi-parameter adjustable multi-channel electrical stimulation waveform generation device, comprising a main unit, a multi-channel electrode unit, a multi-channel connection component mounted on the portable main unit, and electrode components mounted on the multi-channel connection component. By adopting a multi-channel integrated structural design and a portable wearable layout, the device improves ease of use and applicability. The use of independent multi-channel output and unified timing synchronization control enhances treatment and training efficiency. Independent and continuous adjustable control of the three key parameters—stimulation frequency, pulse width, and amplitude—improves treatment adaptability and effectiveness. Closed-loop negative feedback constant current output control enhances safety and stimulation reliability. The magnetic quick-connect structure and multi-channel signal distribution method ensure pure and stable stimulation output, further improving device safety and treatment efficacy.

This anchoring device (10) comprises:a support (14) to be held on the patient;a connecting assembly (18) mounted on the support (14);an introducer (12) receiving a clip (16) attached to the connecting assembly (18), the clip (16) defining a central passage (60) extending along a longitudinal axis (B) and a longitudinal opening (62) for inserting the introducer (12); anda holding system (20) for holding the introducer (12) in position in the dip (16).The holding system (20) includes at least one reversible locking pad (88) for immobilizing the introducer (12), the holding system (20) being able to change from a released configuration to a configuration in which the introducer (12) is locked in position.

This application proposes a method and application for constructing a thyroid nodule detection model based on mid-frequency feature interaction. The method includes: acquiring multiple training images, wherein the training images are thyroid ultrasound images annotated with thyroid nodules; constructing a thyroid nodule detection architecture consisting of a backbone network, a feature fusion network, and a classification network connected sequentially; and training the thyroid nodule detection architecture using multiple training images to obtain the constructed thyroid nodule detection model. This scheme uses dilated convolutional units with depthwise separable convolutions at different dilation rates for feature processing, which can aggregate mid-range context while preserving details of small nodules. Furthermore, the mid-frequency feature interaction unit achieves efficient coupling between local details and mid-range semantics, thereby enabling the thyroid nodule detection model to achieve better classification performance.

The invention relates to sampling training data for training a graph neural network for electronic design automation. A graph database stores (1) a plurality of graphs each representing a finished electronic design, with each graph containing components nodes with component features, and edges, wherein each component node represents a physical component. A graph clustering module assigns (3) a cluster from a set of clusters to each component node, wherein each cluster represents component nodes with common characteristics in topology and / or common component features. A cluster-based link sampling module samples (4) an edge from a sample space, wherein the sample space consists of all edges connecting clusters of component nodes in a selected graph currently selected among the graphs, and wherein a component node connected to the sampled edge is taken as a source component node, and wherein a subgraph is taken from the selected graph based on a receptive field around the source component node. Finally, a cluster-based receptive field sampling module picks (5) a cluster of component nodes that is connected to the source component node within the subgraph, reduces (6) the subgraph by dropping the picked cluster of component nodes, creates a corresponding reduced subgraph, and adds (7) the reduced subgraph along with the source component node and a target to a training set, wherein the target is a label of the picked cluster. These operations can be repeated iteratively (8), until every cluster has been picked, and / or recursively (9) within the reduced subgraphs, until only one cluster remains. Further embodiments provide a novel end-to-end training procedure for GNN-based recommender systems based on the efficient link sampling, which results in faster convergence during training, thus saving both compute resource costs and expert's time while waiting for new models. As a consequence, this enables the possibility to explore a wider range of models (e.g., a larger hyperparameter range) within given constraints.

The application discloses a multi-agent parameter sharing method and a multi-agent system, and belongs to the technical field of artificial intelligence. The method comprises the following steps: encoding time-varying contrast sequences of a plurality of agents, and determining first abstract features of the plurality of agents; connecting and combining the first abstract features of the plurality of agents by using a graph attention mechanism network, to obtain second abstract features of the plurality of agents; the second abstract features are higher-level abstract features compared with the first abstract features; selecting matched first Q networks for the plurality of agents respectively according to the first abstract features; wherein the same agent parameters of the matched first Q networks are shared; selecting matched second Q networks for the plurality of agents respectively according to the second abstract features; wherein the same agent parameters of the matched second Q networks are shared. The method can improve the cooperation ability of the multi-agent.

The application discloses a human brain function reconstruction method based on symbolic connection group characteristic patterns, belongs to the field of biological information, and is used for human brain function reconstruction. The method is used for the reconstruction of neural activity in the resting state and the task state, and a small number of connection group characteristic patterns can achieve high reconstruction precision, which is much better than the structural connection group characteristic pattern based on diffusion magnetic resonance imaging, and proves that dynamic cross-regional interaction and synchronization play a key role in shaping individual brain function.

The application discloses a kind of multi-modal perception adaptive modular home robots, it is related to robot technical field, the robot includes multi-modal perception component, central processing component, standardization quick-release connection component, mobile execution component, power component and wireless communication component.The application is integrated in the front end of robot body top by multi-modal perception component, and at least three of laser ranging, vision acquisition, ultrasonic detection, infrared detection are set, and constitute multi-sensor cooperative perception structure;The structure can collect environmental geometry, texture, ground material, heat source and dynamic target information, complete noise reduction, time synchronization, space alignment and format uniformity processing by data preprocessing module, compared with single sensor scheme, the accuracy and robustness of environmental perception are improved, and the incidence of jam or misoperation caused by sensing blind area is reduced.