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1
Asymmetric Key Pre-distribution Scheme for sensor networks

Zhihong Liu,Jianfeng Ma,Qiping Huang,SangJae Moon IEEE 2009 IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS Vol.8 No.3
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<P>A key pre-distribution scheme is a method by which initially an off-line trusted authority distributes pieces of information among a set of users. Later, each member of a group of users can compute a common key for secure communication. In this paper we present an asymmetric key pre-distribution scheme. Instead of assuming that the network is comprised entirely of identical users in conventional key pre-distribution schemes, the network now consists of a mix of users with different missions, i.e., ordinary users and keying material servers. A group of users, using secret keys preloaded in their memory and public keying material retrieved from one keying material server, can compute a session key. The properties of this method are that, the compromise of keying material servers does not reveal any information about users' secret keys and the session keys of privileged subset of users; if computational assumptions are considered, each user has very low storage requirement. These properties make it attractive for sensor networks. We first formally define the asymmetric key pre-distribution scheme in terms of the entropy and give lower bounds on user's storage requirement and the public keying material size. Then, we present its constructions and applications for sensor networks.</P>
2
A Novel Hyperspectral Microscopic Imaging System for Evaluating Fresh Degree of Pork

Yi Xu,Quansheng Chen,Yan Liu,Xin Sun,Qiping Huang,Qin Ouyang,Jiewen Zhao 한국축산식품학회 2018 한국축산식품학회지 Vol.38 No.2
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  ScienceON
  
  스콜라
This study proposed a rapid microscopic examination method for pork freshness evaluation by using the self-assembled hyperspectral microscopic imaging (HMI) system with the help of feature extraction algorithm and pattern recognition methods. Pork samples were stored for different days ranging from 0 to 5 days and the freshness of samples was divided into three levels which were determined by total volatile basic nitrogen (TVB-N) content. Meanwhile, hyperspectral microscopic images of samples were acquired by HMI system and processed by the following steps for the further analysis. Firstly, characteristic hyperspectral microscopic images were extracted by using principal component analysis (PCA) and then texture features were selected based on the gray level co-occurrence matrix (GLCM). Next, features data were reduced dimensionality by fisher discriminant analysis (FDA) for further building classification model. Finally, compared with linear discriminant analysis (LDA) model and support vector machine (SVM) model, good back propagation artificial neural network (BP-ANN) model obtained the best freshness classification with a 100 % accuracy rating based on the extracted data. The results confirm that the fabricated HMI system combined with multivariate algorithms has ability to evaluate the fresh degree of pork accurately in the microscopic level, which plays an important role in animal food quality control.