Biblio

Eddy Current Pulsed Thermography (ECPT) is widely used in Nondestructive Testing (NDT) of metal defects where the defect information is sometimes affected by coil noise and edge noise, therefore, it is necessary to segment the ECPT image sequences to improve the detection effect, that is, segmenting the defect part from the background. At present, the methods widely used in ECPT are mostly based on matrix decomposition theory. In fact, tensor decomposition is a new hotspot in the field of image segmentation and has been widely used in many image segmentation scenes, but it is not a general method in ECPT. This paper analyzes the feasibility of the usage of tensor decomposition in ECPT and designs several experiments on different samples to verify the effects of two popular tensor decomposition algorithms in ECPT. This paper also compares the matrix decomposition methods and the tensor decomposition methods in terms of treatment effect, time cost, detection success rate, etc. Through the experimental results, this paper points out the advantages and disadvantages of tensor decomposition methods in ECPT and analyzes the suitable engineering application scenarios of tensor decomposition in ECPT.

It is hard to set up an end-to-end connection between source and destination in Opportunistic Networks, due to dynamic network topology and the lack of infrastructure. Instead, the store-carry-forward mechanism is used to achieve communication. Namely, communication in Opportunistic Networks relies on the cooperation among nodes. Correspondingly, Opportunistic Networks have some issues like long delays, packet loss and so on, which lead to many challenges in Opportunistic Networks. However, malicious nodes do not follow the routing rules, or refuse to cooperate with benign nodes. Some misbehaviors like black-hole attack, gray-hole attack may arbitrarily bloat their delivery competency to intercept and drop data. Selfishness in Opportunistic Networks will also drop some data from other nodes. These misbehaviors will seriously affect network performance like the delivery success ratio. In this paper, we design a Trust-based Routing Protocol (TRP), combined with various utility algorithms, to more comprehensively evaluate the competency of a candidate node and effectively reduce negative effects by malicious nodes. In simulation, we compare TRP with other protocols, and shows that our protocol is effective for misbehaviors.

In this paper, we propose a variant of searchable public-key encryption named hidden-token searchable public-key encryption with two new security properties: token anonymity and one-token-per-trapdoor. With the former security notion, the client can obtain the search token from the data owner without revealing any information about the underlying keyword. Meanwhile, the client cannot derive more than one token from one trapdoor generated by the data owner according to the latter security notion. Furthermore, we present a concrete hiddentoken searchable public-key encryption scheme together with the security proofs in the random oracle model.