3results about How to "Easy to decode" patented technology

An experimental platform for implantable brain-computer interface of limited movement, comprising: a cooperative control module, which extracts the brain neuron signals of a limited rat through a microelectrode, pre-processes and extracts features of the neuron signals, analyzes and processes the movement information contained therein, converts the movement information into appropriate control instructions, and delivers the control instructions from Raspberry Pi to Arduino to control the corresponding actions of a machine rat made by the limited rat, so as to realize the movement control and sensing feedback between the limited rat and the machine rat; and an external control module, which is installed on the machine rat, and is used for carrying out epidural spinal cord electrical stimulation on the limited rat with partial movement function after incomplete spinal cord injury and combining with external equipment for rehabilitation training. The application can analyze and record the neuron signals of specific regions when the limited rat performs actions, which is helpful for in-depth analysis of the neuron activity characteristics and regulation mechanism of the limited rat.

The present application relates to the technical field of communication, especially to a processing method of QC-LDPC decoder, the method comprising: obtaining soft information of QC-LDPC decoder; performing field splicing on the soft information to obtain spliced code block set; performing decoding on the spliced code block set to obtain decoding information field; deleting zero-puncturing bits in the decoding information field to obtain standard information field. The method improves the working efficiency of QC-LDPC decoder, makes QC-LDPC decoder compatible with different code length and code rate of QC-LDPC code, and further makes QC-LDPC decoder work flexibly and in real time according to different protocols.

The application discloses an event segmentation and extraction method and system in a text extraction task, comprising the following steps: adopting a data segmentation and labeling strategy of a Span fragment, building a neural network segmentation algorithm model based on a pre-training model, enabling the network to learn the first and last token positions of an event, segmenting the event, and reducing the number of events processed by the extraction model at a time; building a neural network extraction algorithm model based on a pre-training model, converting the event extraction task into three subtasks of argument recognition, event division and graph search. The above method can avoid overly complex decoding of the extraction model, improve the accuracy of text event extraction, and reduce extraction errors. In addition, the application is simple and easy to implement, adopts a segmentation and extraction Pipeline mode, greatly improves the accuracy of text event extraction, has high extraction efficiency, and theoretically does not have the exposure bias problem of a traditional autoregressive model.