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The research on virus propagation process and control method in wireless sensor networks (WSNs) is one of the essential challenges of network security. This paper proposes a virus control mechanism of degree, betweenness centrality, and k-core-based analytical hierarchy process (DBC-AHP) for WSNs. According to the topology of WSNs, the virus control mechanism uses the DBC-AHP to identify the crucial nodes of the network. It uses the way of crucial nodes’ self-disconnection to suppress the spread of the virus, to improve the network security. In this paper, the effectiveness of the virus control mechanism based on the DBC-AHP is verified by comparing and analyzing the effect of four different crucial node recognition algorithms. With the research of virus control mechanisms in various network environments, it is found that the average degree of nodes, the communication radius of nodes, and the probability of virus infection can affect the inhibition effect of the virus control mechanism. Furthermore, the inhibition effect of virus control mechanisms is studied under the condition with/without MAC mechanism.

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Geographic origins play a vital role in traditional Chinese medicinal materials. Using the geo-authentic crude drug can improve the curative effect. The main producing areas of Chinese wolfberry are Ningxia, Gansu, Qinghai, and so on. The geographic origin of Chinese wolfberry can affect its texture, shape, color, smell, nutrients, etc. However, the traditional method for identifying the geographic origin of Chinese wolfberries is still based on human eyes. To efficiently identify Chinese wolfberries from different origins, this paper presents an intelligent identification method for Chinese wolfberries based on color space transformation and texture morphological features. The first step is to prepare the Chinese wolfberry samples and collect the image data. Then the images are preprocessed, and the texture and morphology features of single wolfberry images are extracted. Finally, the random forest algorithm is employed to establish a model of the geographic origin of Chinese wolfberries. The proposed method can accurately predict the origin information of a single wolfberry image and has the advantages of low cost, fast recognition speed, high recognition accuracy, and no damage to the sample.

Defect detection is a critical element in the PCB manufacturing process. Different from surface mount PCB, the components on the plug-in PCB are usually installed manually, resulting in significant errors. Furthermore , because plug-in components have many different types and irregularities, it is difficult to detect their flaws. We make contributions in the following two aspects: (1) a framework and measurement method of a light source and make a cheap and efficient lighting system ; (2) a fusion algorithm based on machine learning and morphology for polarity detection of plug-in capacitors. The capacitor is detected using SVM and fused with the polar coordinate expansion method. The AOI system and the proposed fusion algorithm have been applied to the production line, with an accuracy of 99.73% and a missed detection rate of only 0.12%, according to the field test of the production line.