Abstract: A method for implementing fine-grained data access sharing in 6G network based on fog computing and cloud computing environment includes: (1) a security parameter is given for initialization; (2) each user is registered as a user with the said attribute authority AA; (3) a corresponding user private key is generated given a set of attributes S of a user; (4) an access policy is given by the owner of the data and data encryption is performed; (5) the user performs the data decryption process according to the embedded policy for the set of owned attributes in different environments; (6) the update key generation process is performed; (7) after receiving the private key update key from the attribute authority AA, the user updates the private key component associated with the revoked attribute in order to complete the private key update process; (8) upon receiving the ciphertext update key from the attribute authority AA, the cloud server updates the ciphertext component associated with the revocation attribute t

Abstract: The present disclosure relates to a difficult airway evaluation method and device based on a machine learning voice technology. The method includes the following steps: acquiring voice data of a patient; carrying out feature extraction on the voice data, obtaining a pitch period of pronunciations, and acquiring a voiced sound feature and unvoiced sound features based on the pitch period of pronunciations; and constructing a difficult airway evaluation classifier based on the machine learning voice technology, analyzing the received voiced sound feature and unvoiced sound features by the trained difficult airway evaluation classifier, and carrying out scoring on the severity of a difficult airway to obtain an evaluation result of the difficult airway.

Abstract: The present disclosure relates to a difficult airway evaluation method and device based on a machine learning voice technology. The method includes the following steps: acquiring voice data of a patient; carrying out feature extraction on the voice data, obtaining a pitch period of pronunciations, and acquiring a voiced sound feature and unvoiced sound features based on the pitch period of pronunciations; and constructing a difficult airway evaluation classifier based on the machine learning voice technology, analyzing the received voiced sound feature and unvoiced sound features by the trained difficult airway evaluation classifier, and carrying out scoring on the severity of a difficult airway to obtain an evaluation result of the difficult airway.