In a secure data sharing system, users can selectively retrieve encrypted files by performing keyword search over the ciphertext of data. Most of the existing searchable encryption schemes can provide security protection for both data owner and users. Nevertheless, three pivotal issues need to be addressed. Firstly, the cloud might return a wrong result or incomplete result for some reasons, e.g., saving the computing resources. Secondly, users need to store massive keys to generate trapdoors and decrypt the ciphertext of data, which brings great challenges to users' key management. Thirdly, when users perform keyword search over a large number of files, they need to generate and submit massive trapdoors, which is unrealistic. Proceeding from these points, in this paper, we propose an efficient verifiable key-aggregate keyword searchable encryption (EVKAKSE) scheme. In this scheme, the data owner distributes only one aggregate key to users for keyword search, decryption and verification, who can use the aggregate key to generate a single trapdoor for keyword search over shared files. Generally, we define the requirements of the scheme, analyze the threat models and give a valid construction. Furthermore, our security analysis and experimental evaluation demonstrate that the scheme is efficient and secure.

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Accurate crop insect pest identification in fields is useful to control pests and beneficial to agricultural yield and quality. However, it is a difficult and challenging problem due to the crop insect pests being small with various sizes, postures, shapes, and disorganized backgrounds. Multi-scale convolution-capsule network (MSCCN) is constructed for crop insect pest identification. It consists of a multi-scale convolution module, capsule network (CapsNet) module, and SoftMax classification module. Multi-scale convolution is used to extract the multi-scale discriminative features, CapsNet is employed to encode the hierarchical structure of the size-variant insect pests in the crop images, and Softmax is adopted for insect pest identification. MSCCN combines the advantages of convolutional neural network (CNN), CapsNet, and multi-scale CNN, and can learn multi-scale robust features from pest images of different shapes and sizes for pest recognition and identify various morphed pests. Experimental results on the crop pest image dataset show that this method has a good recognition rate of 91.4%.

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Complex permittivity measurement using metamaterial split ring resonators Journal of Applied Physics 121, 054101 (2017) Based on a traditional split-ring resonator, a new type of eight-split-ring resonator structure, capable of providing negative permeability, is proposed in the paper. A three-dimension structure, consisting of three orthogonal eight-split-ring resonators, is a kind of three-dimension isotropic negative permeability structure. Simulation results show that in a three-dimensional space, the magnetic resonance behavior of three-dimensional structure is independent of electromagnetic wave direction and can give negative permeability at the frequency around 4.1GHz. Also, it is demonstrated that the orthogonal pattern in such three-dimension structure does not affect magnetic resonance behavior, facilitating structural analysis and material preparation. The paper provides a reference to the development of metamaterials towards multiple dimensions and directions. © 2017 Author(s). All article content, except where otherwise noted, is licensed under a Creative Commons Attribution (CC BY) license

Barrier coverage is one of the research hot spots of wireless sensor network coverage control. In order to fix the defects and deficiencies of the network, which is composed of all isomorphic nodes in the barrier coverage, Voronoi diagram is introduced to divide the entire deployment area. According to the principle of the least square method, a mixed network deployment mechanism consisting of static nodes and dynamic nodes is established with static nodes working as the reference barrier line. By monitoring whether there is a barrier covering the blind spot in the deployed area, the monitoring area can be effectively covered by determining whether dynamic nodes need limited movement to redeploy the monitored area. And puts forward mixed node barrier covered in this algorithm, algorithm Voronoi diagram was used for continuous path problem domain discretization, and from different dimensions to compare coverage performance of each algorithm, the simulation experiments show that the algorithm improves the quality of monitoring area coverage, under the same conditions as other algorithms, a mobile node distance and low energy consumption, less expected to cover requirements and goals are met.

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