Visible to the public Biblio

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Wang, Hui, Yan, Qiurong, Li, Bing, Yuan, Chenglong, Wang, Yuhao.  2019.  Sampling Time Adaptive Single-Photon Compressive Imaging. IEEE Photonics Journal. 11:1–10.
We propose a time-adaptive sampling method and demonstrate a sampling-time-adaptive single-photon compressive imaging system. In order to achieve self-adapting adjustment of sampling time, the theory of threshold of light intensity estimation accuracy is deduced. According to this threshold, a sampling control module, based on field-programmable gate array, is developed. Finally, the advantage of the time-adaptive sampling method is proved experimentally. Imaging performance experiments show that the time-adaptive sampling method can automatically adjust the sampling time for the change of light intensity of image object to obtain an image with better quality and avoid speculative selection of sampling time.
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Zhang, Yan, Li, Bing, Wang, Yazhou, Wu, Jiaxin, Yuan, Pengwei.  2020.  A Blockchain-based User Remote Autentication Scheme in IoT Systems Using Physical Unclonable Functions. 2020 IEEE 5th International Conference on Signal and Image Processing (ICSIP). :1100—1105.
Achieving efficient and secure accesses to real-time information from the designated IoT node is the fundamental key requirement for the applications of the Internet of Things. However, IoT nodes are prone to physical attacks, public channels reveal the sensitive information, and gateways that manage the IoT nodes suffer from the single-point failure, thereby causing the security and privacy problems. In this paper, a blockchain-based user remote authentication scheme using physical unclonable functions (PUFs) is proposed to overcome these problems. The PUFs provide physically secure identities for the IoT nodes and the blockchain acts as a distributed database to manage the key materials reliably for gateways. The security analysis is conducted and shows that our scheme realizes reliable security features and resists various attacks. Furthermore, a prototype was implemented to prove our scheme is efficient, scalable, and suitable for IoT scenarios.
Wang, Yazhou, Li, Bing, Zhang, Yan, Wu, Jiaxin, Yuan, Pengwei, Liu, Guimiao.  2020.  A Biometric Key Generation Mechanism for Authentication Based on Face Image. 2020 IEEE 5th International Conference on Signal and Image Processing (ICSIP). :231—235.
Facial biometrics have the advantages of high reliability, strong distinguishability and easily acquired for authentication. Therefore, it is becoming wildly used in identity authentication filed. However, there are stability, security and privacy issues in generating face key, which brings great challenges to face biometric authentication. In this paper, we propose a biometric key generation scheme based on face image. On the one hand, a deep neural network model for feature extraction is used to improve the stability of identity authentication. On the other hand, a key generation mechanism is designed to generate random biometric key while hiding original facial biometrics to enhance security and privacy of user authentication. The results show the FAR reach to 0.53% and the FRR reach to 0.57% in LFW face database, which achieves the better performance of biometric identification, and the proposed method is able to realize randomness of the generated biometric keys by NIST statistical test suite.