针对计算机网络存在的一系列不安定因素问题采取的安全对策

随着信息产业的高速发展,计算机网络也迅速普及,它渗透到我们生活中的各个角落。然而,网络自身固有的脆弱性,加之中国网络信息技术起步较晚,给网络安全带来了诸多潜在的威胁。文章针对影响计算机网络系统安全的主要因素,再结合实际工作,就一系列问题提出的对策进行探讨。     

A data access framework for service-oriented rich clients

Facilitated by the SOA and new Web technologies, Service-Oriented Rich Clients (SORCs) compose various Web-delivered services in Web browser to create new applications. The SORCs support client-side data storage and manipulation and provide more features than traditional thin clients. However, the SORCs might suffer from data access issues, mainly due to both client-side incompatible data sources and server-side improper or even undesirable cache strategies. Addressing the data access issues, this paper proposes a data access framework for SORCs. The main contributions of this paper are as follows. First, the framework makes the SORCs accommodate heterogeneous local storage solutions and diverse Web browsers properly. The framework abstracts the underlying details of different local storages and selects the most proper data sources for current SORC in use. Secondly, the framework provides a cache mechanism, which supports client-side customized cache strategies. An adaptive technique for the strategies is also proposed to adjust cache strategies based on users?? historical actions to achieve better performance.     

曲线曲面小波变换的边界连续性保持

现有的小波分解边界连续性保持方法由于将去除的节点全部插回,摒弃了数据压缩特性.为此在曲线小波分解边界处理方面提出2种基于数据压缩的连续性保持方法:一是插入与Cr连续相关的节点,然后调整相应的控制顶点与高分辨曲线的控制顶点保持一致;二是不插入节点,直接调整与Cr连续相关的控制顶点.在曲面方面,提出了曲面分裂法和T曲面法:通过插入重节点把曲面分裂成边界部分和中心部分并对边界部分插入节点,再调整相应的控制顶点与高分辨曲面的重合;构造了T网格,使其边界部分与高分辨曲面一致,中心部分与低分辨曲面一致.最后把T曲面方法推广到周期曲面小波变换边界处理上.实验结果表明,文中方法既保证了小波分解后模型的边界连续,又保留了数据压缩特性.     

MEMS惯性传感器ADIS16355在姿态测量中的应用

设计了基于微电子机械系统(Microelectro mechanical system,MEMS)惯性传感器集成模块ADIS16355的姿态测量系统。该姿态测量系统采用ADIS16355作为惯性测量单元,利用加速度计对重力向量的观测来修正陀螺给出的姿态信息,卡尔曼滤波实现传感器信息融合以计算运动载体的姿态角。介绍了ADIS16355的基本功能模块,阐述了两种传感器融合测量实时姿态角的方法并给出了卡尔曼滤波算法迭代过程。基于ARMv7架构的Cotex-M3微处理器设计了姿态测量系统硬件。采用AHRS500GA对该姿态测量系统性能进行了测量姿态角的验证实验。测试结果表明,该姿态测量系统能在动态条件下准确地测定运动物体实时姿态角,其误差一般在±1°左右。     

《C语言程序设计》教学中问题和改进方法的探讨

《C语言程序设计》是计算机专业学习中的一门基础的课程．是培养学生对计算机学习兴趣的关键课程。提出C语言教学过程中存在的一些问题。对问题进行分析,提出解决问题的方法。根据笔者的多年的教学实践,阐述一些教学方法和教学经验,对教师在教学思路和方法上有一定的指导意义,能够更好地提高教学水平。     

基于地面先验的3D目标检测算法

为了提高自动驾驶汽车环境感知的性能,增强单目相机对障碍物三维和边界信息的感知能力,提出了一种基于地面先验的3D目标检测算法。基于优化的中心网络(CenterNet)模型,以DLA(deep layer aggregation)为主干网络,增加目标3D边沿框中心点冗余信息预测。根据自动驾驶场景的地面先验信息,结合针孔相机模型,获取目标3D中心深度信息,以优化深度网络学习效果。使用KITTI 3D数据集评测算法性能,结果表明:在保证2D目标检测准确性的基础上,该算法运行帧率约20 fps,满足自动驾驶感知实时性要求;同时相比于CenterNet模型,在平均方位角得分(average orientation score)和鸟视图平均准确率(bird eye view AP)上分别有4.4和4.4%的性能提升。因而,该算法可以提高自动驾驶汽车对障碍物三维和边界信息的感知能力。     

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.     

Correction to: Parallel Single-Pixel Imaging: A General Method for Direct–Global Separation and 3D Shape Reconstruction Under Strong Global Illumination

International Journal of Computer Vision - In the original publication of the article, the name of the last author should be Yang Xu, instead of Xu Yang. “Xu” is the family name, and...     

Bibliometric analysis on tendency and topics of artificial intelligence over last decade

Microsystem Technologies - Artificial intelligence (AI), together with its applications, has received world-wide attentions and is expected to exert force on the development of global economy and...     

Texture compensation with multi-scale dilated residual blocks for image denoising

Neural Computing and Applications - Deep convolutional neural networks have achieved great success for image denoising recently. However, increasing the depth of the neural network cannot...