将物理层网络编码引入光网络中,研究了在3Gb/s的传输速率下基于差分相移键控(DPSK)调制的物理层网络安全编码的光网络.将两路同频信号通过光纤传输到中继,在中继处混合在一起后同时再通过光纤传给两个用户,在接收端将混合信号通过解码出来得到两个用户信号比特的异或,最后通过与其中一个用户的信息进行异或就可以得到另一个用户的信息.由于两路信号为同频信号,混合后必会发生干涉,利用DPSK将两路信号占空比分别调制为33％与67％,解决了信号混合后的干涉问题.由于在中继处两个信号混合后,发出的是两个用户的混合信号,具有一次一密效果,在利用物理层网络编码提高网络的吞吐量的同时,也增加了从中继到用户的链路的安全性.
The physical layer network coding (PNC) technology is introduced into the optical network in this article. The proposed scheme employs all-optical XOR logic gates that rely on cross-phase modulation-based saganac interferometer fiber optic rings to perform encoding and decoding. The encoding and decoding operations are respectively applied to the coupler and the server of the data center passive optical interconnect structure, which increases the throughput of information exchange. Complex signal processing operations are replaced by optical XOR, which effectively reduces the delay. The scheme has been tested for error-free simulation of synchronization and asynchronous in transmission rate of 20-Gb/s.
In order to overcome the effects of posture, illumination, expression and other factors on face recognition, this paper proposes an algorithm which is based on local binary pattern (LBP) and convolutional neural network (CNN). LBP is a texture description method which describes the local texture features of an image. It has good robustness of illumination and posture. CNN can effectively extract the spatial features of images and reduce the dimensions of features. This paper combines the advantages of LBP and CNN to improve the accuracy of face recognition. The CNN in this algorithm has four convolution layers, two max-pooling layers, one activation layer, one fully connected layer, and one output layer. In order to optimize the network structure, batch normalization layer is added after the convolution layer. We get the local binary pattern coded images and put the images as the input of the CNN and train the network. Hence, we can use the well-trained CNN for classification and identification. The Experiments on the CMU-PIE face database show that our algorithm can effectively improve the rate of face recognition.
