2results about How to "Reduce expertise" patented technology

The utility model discloses an electronic sphygmomanometer with cuff wearing state detection function belongs to electronic medical instrument technical field, and the sphygmomanometer aims at solving the problem of inaccurate measurement result caused by improper wearing position or unsuitable tightness of user's cuff in prior art. The utility model discloses a sphygmomanometer main part, cuff, the position sensor for detecting wearing position set on the cuff, the tightness sensor for detecting wearing tightness, the microcontroller for analyzing signal and judging wearing state with sensor connection, and the prompt unit for sending prompt information when wearing improperly with microcontroller connection. The utility model discloses through real -time detection the position and tightness of wearing cuff, and timely provides adjustment guidance to the user, can effectively improve the accuracy and reliability of blood pressure measurement, and promotes user experience.

This invention proposes a reconfigurable metasurface beamforming design method based on CGAN and Gumbel-Sinkhorn. This method constructs a conditional generative adversarial network (GAN) as the inverse design network. The generator takes the target beam pattern as a conditional input to generate the corresponding encoding matrix. The Gumbel-Sinkhorn operator is introduced to discretize this continuous matrix, transforming it into an approximately doubly random permutation matrix. The encoding is sorted and selected by multiplying this permutation matrix with a predefined structure vector (SV), outputting a discrete 0 / 1 metasurface encoding array. A noisy forward prediction network is trained to predict the beam pattern corresponding to a given encoding array. In the training of the inverse design network, the discretized encoding matrix output by the generator is input into the noisy-trained forward prediction network. The mean square error between the predicted beam pattern and the target beam pattern is calculated as the target loss term to optimize the network parameters and accurately learn the complex mapping relationship from the beam pattern to the encoding array.