10Gbit/s甚短距离传输系统接收模块研究

本文研究了符合OIF—VSR4—01．0规范的甚短距离（VSR）并行光传输系统转换器集成电路，实现了接收部分转换器集成电路中帧同步、8B／10B解码、12路通道去斜移、检错纠错等模块的设计。在Altera的Stratix GX系列的FPGA上实现了接收部分转换器集成电路。仿真分析的结果表明所设计的各个模块能正确的实现接收部分转换器集成电路的功能，给出了仿真结果。     

Monolithic Catalysts with Low Noble-Metal Content for Exhaust Purification of Small Gasoline Engines

A series of low noble-metal coment monolithic catalysts for exhaust purification of small gasoline engines was investigated, and it was found that the Pt/Rh-OSM/Al2O3 （where OSM was oxygen storage material） catalyst with Ce0.5Zr0.5-MnOx（3%MnOx） OSM held low light-off temperature for CO, HC, and NO; quite wide three-way window, and outstanding thermal stability. The catalyst could efficiently comrol exhaust emission of small gasoline engines.     

FeSiB薄膜的巨磁阻抗和应力阻抗效应研究

软磁材料中存在巨磁阻抗 (giantmagneto impedance ,GMI)效应以及与之相同来源的应力阻抗 (stress impedance ,SI)效应 ,利用这两种效应可以制成具有高灵敏度的微型化的磁场和应力 应变传感器。本文基于传感器的实际应用 ,对图形化的、较大磁致伸缩的FeSiB单层和多层薄膜的巨磁阻抗和应力阻抗效应中频率和退火的影响进行了研究。结果表明 ,对于两种效应 ,经过退火处理的单层和多层膜均可在较低的频率下得到较高的灵敏度 ,而多层膜中的应力阻抗效应将为新型高灵敏传感器的设计和研制开辟一条崭新的途径     

数字水印攻击及对策分析

数字水印技术作为数字媒体版权保护的重要手段越来越引起人们的重视。文章讨论了数字水印的概念、特征,重点介绍了数字水印的鲁棒性及影响数字水印鲁棒性的因素,总结和分析了数字水印主要攻击方式并提出相应的应对措施。     

LDAP认证技术在油田生产系统中的设计与实现

根据采用单一用户登录应用系统的要求,提出一种基于LDAP的双重认证方式。以用户认证为背景,首先对LDAP基本原理及体系结构进行简单描述,然后设计并实现基于LDAP验证与本地验证相结合的双重认证方式。本文对认证方法的特点进行阐述,并成功应用于青海油田油气水井远程计量传输发布系统。     

图形图像制作软件中的色彩模式分析

色彩模式是在数字处理过程中处理色彩的基本标准,其是以颜色模型为基础,利用数字定量对颜色进行描述的方法。针对图形图像制作软件中的色彩模式进行分析。     

稀疏差分网络和多监督哈希用于高效图像检索

针对基于深度哈希的图像检索中卷积神经网络（CNN）特征提取效率较低和特征相关性利用不充分的问题,提出一种融合稀疏差分网络和多监督哈希的新方法SDNMSH（sparse difference networks and multi-supervised hashing）,并将其用于高效图像检索。SDNMSH以成对的图像作为训练输入,通过精心设计的稀疏差分卷积神经网络和一个监督哈希函数来指导哈希码学习。稀疏差分卷积神经网络由稀疏差分卷积层和普通卷积层组成。稀疏差分卷积层能够快速提取丰富的特征信息,从而实现整个网络的高效特征提取。同时,为了更加充分地利用语义信息和特征的成对相关性,以促进网络提取的特征信息能够更加有效地转换为具有区分性的哈希码、进而实现SDNMSH的高效图像检索,采用一种多监督哈希（MSH）函数,并为此设计了一个目标函数。在MNIST、CIFAR-10和NUS-WIDE三个广泛使用的数据集上进行了大量的对比实验,实验结果表明,与其他先进的深度哈希方法相比,SDNMSH取得了较好的检索性能。     

Semisupervised Encrypted Traffic Identification Based on Auxiliary Classification Generative Adversarial Network

The rapidly increasing popularity of mobile devices has changed the methods with which people access various network services and increased network traffic markedly. Over the past few decades, network traffic identification has been a research hotspot in the field of network management and security monitoring. However, as more network services use encryption technology, network traffic identification faces many challenges. Although classic machine learning methods can solve many problems that cannot be solved by port- and payload-based methods, manually extract features that are frequently updated is time-consuming and labor-intensive. Deep learning has good automatic feature learning capabilities and is an ideal method for network traffic identification, particularly encrypted traffic identification; Existing recognition methods based on deep learning primarily use supervised learning methods and rely on many labeled samples. However, in real scenarios, labeled samples are often difficult to obtain. This paper adjusts the structure of the auxiliary classification generation adversarial network (ACGAN) so that it can use unlabeled samples for training, and use the wasserstein distance instead of the original cross entropy as the loss function to achieve semisupervised learning. Experimental results show that the identification accuracy of ISCX and USTC data sets using the proposed method yields markedly better performance when the number of labeled samples is small compared to that of convolutional neural network (CNN) based classifier.     

Cross lingual speech emotion recognition via triple attentive asymmetric convolutional neural network

The application of cross‐corpus for speech emotion recognition (SER) via domain adaptation methods have gain high acknowledgment for developing good robust emotion recognition systems using different corpora or datasets. However, the issue of cross‐lingual still remains a challenge in SER and needs more attention to resolve the scenario of applying different language types in both training and testing. In this paper, we propose a triple attentive asymmetric convolutional neural network to address the recognition of emotions for cross‐lingual and cross‐corpus speech in an unsupervised approach. The proposed method adopts the joint supervision of softmax loss and center loss to learn high power discriminative feature representations for target domain via the use of high quality pseudo‐labels. The proposed model uses three attentive convolutional neural networks asymmetrically, where two of the networks are used to artificially label unlabeled target samples as a result of their predictions from training on source labeled samples and the other network is used to obtain salient target discriminative features from the pseudo‐labeled target samples. We evaluate our proposed method on three different language types (i.e., English, German, and Italian) data sets. The experimental results indicate that, our proposed method achieves higher prediction accuracy over other state‐of‐the‐art methods.