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- 저자 : Jiping Xiong (검색결과 2 건)
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An Improved Two-Factor Mutual Authentication Scheme with Key Agreement in Wireless Sensor Networks
( Jiping Li ),( Yaoming Ding ),( Zenggang Xiong ),( Shouyin Liu ) 한국인터넷정보학회 2017 KSII Transactions on Internet and Information Syst Vol.11 No.11
As a main component of Internet of Things (IoTs), the wireless sensor networks (WSNs) have been widely applied to various areas, including environment monitoring, health monitoring of human body, farming, commercial manufacture, reconnaissance mission in military, and calamity alert etc. Meanwhile, the privacy concerns also arise when the users are required to get the real-time data from the sensor nodes directly. To solve this problem, several user authentication and key agreement schemes with a smart card and a password have been proposed in the past years. However, these schemes are vulnerable to some attacks such as offline password guessing attack, user impersonation attack by using attacker’s own smart card, sensor node impersonation attack and gateway node bypassing attack. In this paper, we propose an improved scheme which can resist a wide variety of attacks in WSNs. Cryptanalysis and performance analysis show that our scheme can solve the weaknesses of previously proposed schemes and enhance security requirements while maintaining low computational cost.
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Analysis-Based Nonlocal-Approximate Sparsity Representation in Image Processing
Xiaowei He,Li Zhang,Jiping Xiong 보안공학연구지원센터 2016 International Journal of Signal Processing, Image Vol.9 No.9
1l norm is a popular regularizer in various linear inverse problems including image processing, compressed sensing and machine learning. But the non-zero entries of the sparsity solution obtained by 1l are independent with each other, which always leads to biased result to real solution. Actually, there always exist some different correlations among those non-zero entries in an image signal domain or various analysis domains. In this paper, based on a simple observation that the non-zero entries of the sparsity vector in various image analysis domains should be also approximate when the relevant signal values are proximate, we proposed a nonlocal-approximate sparsity regularizer in analysis domains by minimizing the sum of the 2l norms of those vectors with the same nonzero pattern like signal vectors. This regularizer is applied to image denoising, edge detecting, inpainting and decomposition models successively. The numerical experiments demonstrate the effectiveness of our method in terms of PSNR, visual effect and edge preserving.
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