As a new kind of cryptographic primitive, attribute‐based encryption (ABE) is widely used in various complex scenarios because it has the characteristic of access control while encrypting messages. However, the existing ciphertext policy ABE (CP‐ABE) encryption schemes have some inherent defects, such as the lack of privacy‐preserving and inefficient decryption. These weak points make them difficult to play a role in scenarios with high real‐time and high data confidentiality requirements, for example, vehicle ad hoc network (VANET). Therefore, we proposed a verifiable hidden policy CP‐ABE with decryption testing scheme (or VHPDT, for short). It has the following features: hidden policy to privacy‐preserving and outsourced decryption testing can verify the correctness of the decrypted result. Furthermore, we apply it into VANET.
Ultra-low-loss and large-effective-area fiber has been successfully applied in transoceanic transmission, which is considered as a promising candidate for 100 Gbit/s and beyond 100 Gbit/s coherent long-haul terrestrial optical networks. Several theoretical and experimental investigations have been reported, including provincial terrestrial field trial. To support long-haul terrestrial application, it is urgent to prove that the ultra-low-loss and large-effective-area fiber after terrestrial deployment can significantly enhance the performance of long-haul transmission over 1000 km compared with the conventional single mode fiber. In this paper, we extended our previous work and summarized design methods for complex terrestrial environment. To verify the fiber characteristics in long-haul terrestrial transmission, we installed the longest terrestrial ultra-low-loss and large-effective-area fiber link in the world with a total length of 1539.6 km. The results show that the transmission performances of wavelength-division-multiplexed signals with per-channel data rates of 100 Gbit/s, 200 Gbit/s, and 400 Gbit/s over the ultra-low-loss and large-effective-area fiber are all obviously improved, demonstrating that this fiber is more suitable for ultrahigh-speed long-haul terrestrial transmission.
It is well known that the Unet has been widely used in the area of medical image segmentation because of the cascade connection in the up-sampling process. However, it does not perform well in dealing with complex medical images, such as brain MRI. In order to achieve better segmentation performance by adopting the Unet, many researchers have paid more attention to stacking the Unet. However, the stacking process leads to a large increase in the number of parameters. This is not a good choice when considering the tradeoff between precision and efficiency. Another problem is that as the depth of the network increases, the excessive loss of information is also a tricky problem. To address those problems, in this paper, we are trying to improve the network structure of Unet to make it more suitable for brain tumor segmentation. We propose a novel framework called Stack Multi-Connection Simple Reducing_Net(SMCSRNet) that are stacked by our basic blocks called Simple Reducing_Net(SRNet). The basic block SRNet is improved from the original Unet, which consists of four downsampling and upsampling operations during the encoding and decoding. Only one convolution operation is performed before each downsampling process. The operation of copy and crop is preserved between encoding and decoding. The main advantage of the SRNet is that the amount of parameters is reduced by 4/5 by comparing with the original Unet. Except for the problem of parameters number, we also proposed a series of bridge connections among the stacked cascade network to improve the loss of information. More specifically, some bridge connections will be adopted before the pooling operation in each layer during the downsampling process. It means that each layer in one basic block has a bridge connection with the same feature size from the previous basic block before pooling, and it is worth noting that the training time of the proposed framework is much less than the original stacked Unet. Moreover, the performance of the proposed method is also improved compared to the stacked Unet. When further comparing with other state-of-the-art segmentation networks, it can be found that the performance is as good as the most popular DenseNet or ResNet. Overall, by evaluating the proposed framework on the BRAT2015, it can be proven that the proposed segmentation network has the ability to accurately extract the brain tumor boundary so as to obtain higher recognition quality with high efficiency.
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