To solve the problems of strong coupling between users and clients and the vulnerability of privacy security due to the plaintext storage of Verifiable Credentials （VCs） in the off-chain clients in the existing Decentralized IDentity （DID） authentication schemes， an on-demand disclosure blockchain digital identity authentication mechanism was proposed， namely DCIdentity. Firstly， based on the World Wide Web Consortium Decentralized IDentifier （W3C DID）， user identities’ VCs were encrypted and stored on the blockchain， which reduced users’ dependency on clients and realized loose coupling between the authentication process and the clients. Secondly， a hierarchical encryption mechanism for VCs was designed to support on-demand disclosure of user information， which enhanced efficiency in multi-party authentication and reduced the associated overhead. Experimental results show that compared with the off-chain storage scheme， the proposed mechanism reduces the degree of coupling between the clients and the user authentication process effectively， and achieves the on-demand disclosure of user identity information； compared with the Ciphertext-Policy Attribute-Based Encryption （CP-ABE） scheme， the proposed mechanism has the encryption processing delay and the on-chain storage overhead decreased by 91.5% and 84.1%， respectively. It can be seen that the proposed mechanism provides an efficient solution for unified identity authentication in multi-domain multi-application scenarios， which improves the authentication efficiency significantly while ensuring the privacy of user information， and can support the landing application of DID in actual scenarios strongly.

Concerning the contradiction between edge-preserving and noise-suppressing in the process of image denoising, a patch similarity anisotropic diffusion algorithm based on variable exponent for image denoising was proposed. The algorithm combined adaptive Perona-Malik (PM) model based on variable exponent for image denoising and the idea of patch similarity, constructed a new edge indicator and a new diffusion coefficient function. The traditional anisotropic diffusion algorithms for image denoising based on the intensity similarity of each single pixel (or gradient information) to detect edge cannot effectively preserve weak edges and details such as texture. However, the proposed algorithm can preserve more detail information while removing the noise, since the algorithm utilizes the intensity similarity of neighbor pixels. The simulation results show that, compared with the traditional image denoising algorithms based on Partial Differential Equation (PDE), the proposed algorithm improves Signal-to-Noise ratio (SNR) and Peak-Signal-to-Noise Ratio (PSNR) to 16.602480dB and 31.284672dB respectively, and enhances anti-noise capability. At the same time, the filtered image preserves more detail features such as weak edges and textures and has good visual effects. Therefore, the algorithm achieves a good balance between noise reduction and edge maintenance.

A reinforcement learning controller based on CMAC neural networks using triangulation was studied and applied to the learning control of intercepting a ball in the RoboCup. By utilizing Kuhn triangulation based on simplex interpolation in the continuous state space of Markov Decision Processes (MDPs), the value functions of MDPs were approximated with linear triangulation so that the generalization ability of the CMACbased reinforcement learning controller could be improved. Simulation results on the learning control of intercepting a ball show that the CMACbased learning controller using triangulation is much more efficient than the learning controller based on CMAC uniform coding.