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Regarding to the problems of low rate of convergence and fault saturation for neural network classifier based on the algorithm of error back propagation during the signal recognition, bee colony algorithm is applied in this paper so as to extract combined feature module of signal and suggest three different algorithms including algorithm with rapidly support, super self-adaption error back propagation and conjugate gradient. These three algorithms are respectively applied in multilayer perception neural network classifier, and help achieve automatic recognition for communication signals and higher recognition rate compared with error back propagation. The simulation result shows that the algorithms put forward in this paper can overcome the drawbacks of error back propagation algorithm. Meanwhile, under the condition that nerve cell has only 20, SNR is 4dB in the hidden layer, the recognition rate of three algorithms are all higher than 95%, the system is easy to implement and has wide range of application prospect in the signal recognition.

In the current E-healthcare scenarios, medical institutions are used to encrypt the information and store it in an Electronic Health Record (EHR) system in order to ensure the privacy of medical information. To realize data sharing, a Public-key Encryption with Keyword Search (PEKS) scheme is indispensable, ensuring doctors search for medical information in the state of ciphertext. However, the traditional PEKS scheme cannot resist the keyword guessing quantum computing attacks, and its security depends on the confidentiality of the secret key. In addition, classical PEKS hand over the search process to a third party, affecting the search results’ accuracy. Therefore, we proposed a postquantum Public-key Searchable Encryption scheme on Blockchain (PPSEB) for E-healthcare scenarios. Firstly, we utilized a lattice-based cryptographic primitive to ensure the security of the search process and achieve forward security to avoid key leakage of medical information. Secondly, we introduced blockchain technology to solve the problem of third-party untrustworthiness in the search process. Finally, through security analysis, we prove the correctness and forward security of the solution in the E-healthcare scenarios, and the comprehensive performance evaluation demonstrates the efficiency of our scheme compared with other existing schemes.

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Abstract-For the modulation recognition of the wireless communication signal, when extracting Eigenvalues, it is necessary to improve modulation recognition rate in order to achieve the optimized effect. In this article, combination eigenvalue module of the signal is extracted by applying bee colony algorithm and automatic recognition of the communication signal is achieved through the classifier which has multi-layer sensor neural network. The simulation results show that the proposed algorithm in this paper can result the communication signal modulation recognition rate is higher than the corresponding rate in using the conventional method under the conditions that the number of neurons is only 20 in the hidden layers, and the system is easy to realize, it has wide application prospect in signal recognition.