收视价值的迷失 高端人群在远离卫视 2008目睹中国电视之怪现状

近两个月来,本刊网站的论坛里又不太安静。有个叫"高清迷"的香港网友遭到了广东一些网友的质疑。彼此论战的焦点大部分在说香港电视好看或不好看。"高清迷"网友说大陆电视越来越好,水平已经超过香港的电视节目。广东网友们则是缅怀香港电视的辉煌历史。这原本是简单的我说你好你说我好,互相捧场的事情,但是网络的奇妙性在于事情并没有朝积极的方向发展,其结果是吵架不可避免的发生了。外来的和尚是不是会念经,我们先不去管他。重要的是我们要弄清楚自己是怎么回事。本文,是专业电视人对我国各省卫视台做的调查评估。让我们了解一下我国的电视台的现状吧。     

上海慕尼黑电子展暨同期展会见闻

2007年3月21日～23日的上海新国际博览中心是格外的热闹,超过2万名来自电子行业的专业观众汇聚与此.吸引他们来到这里的原因就是慕尼黑上海电子展,慕尼黑上海激光、光电展(LASER. World of Photonics China)、中国国际半导体设备、材料、生产和服务展览暨研讨会(SEMICON China),以及中国国际电子电路展览会(CPCA Show)四大电子业权威品牌展联合在此举行.     

ADSL服务

现在使用ADSL上宽带网的用户是越来越多了,对ADSL设备的维护也成了一个不容忽视的问题.只有维护保养好它,不但让你的"宽带之旅"省心又省力,还能延长其寿命.ADSL的维护可分为软件和硬件两个方面来分析,软件主要是PPPOE协议的问题,和计算机本身的气操作系统,和网卡方面的事情.硬件主要是局端的节点设备,和用户端猫的问题.还有通信的线路等一些因素.     

命题逻辑中代数学的影子

命题逻辑是一个以命题为基本研究对象的数学化的逻辑系统,命题逻辑是数理逻辑的基础,也是计算机科学与技术的理论基础.为了深入理解命题逻辑,将命题逻辑与一般的代数学进行比较,从6个方面简要总结和论述命题逻辑中代数学的一些思想和方法,使得读者能从中体会到代数学的一些思想和方法在命题逻辑中的应用.     

不一样的热插拔(hot swap)控制器

引言 热插拔(hot swapping)的定义是从一块正在通电运作中的背板(backplane)上插入或移除电路板.这项技术被广泛应用在电信服务器(telecom servers)、USB界面、火线(firewire)界面和 CompactPCI应用等[参考1].这种技术可在维持系统背板的电压下,更换发生故障的电路板,而同时系统中其它正常的电路板仍可保持运作.     

从跨国公司CEO扎堆访华说起

11月1日,似乎是个好日子!来自英特尔、EMC、思科的3位跨国公司CEO均选在这一天访华,并宣布其在中国市场大手笔的战略投入. 11月2日,来自全球最大显卡芯片厂商NVIDIA公司的联合创始人、总裁兼CEO黄仁勋借访华之际,在清华大学美术学院报告厅进行了"GPU--还原一个真世界"的演讲.     

FEW-NNN: A Fuzzy Entropy Weighted Natural Nearest Neighbor Method for Flow-Based Network Traffic Attack Detection

Attacks such as APT usually hide communication data in massive legitimate network traffic, and mining structurally complex and latent relationships among flow-based network traffic to detect attacks has become the focus of many initiatives. Effectively analyzing massive network security data with high dimensions for suspicious flow diagnosis is a huge challenge. In addition, the uneven distribution of network traffic does not fully reflect the differences of class sample features, resulting in the low accuracy of attack detection. To solve these problems, a novel approach called the fuzzy entropy weighted natural nearest neighbor(FEW-NNN) method is proposed to enhance the accuracy and efficiency of flowbased network traffic attack detection. First, the FEW-NNN method uses the Fisher score and deep graph feature learning algorithm to remove unimportant features and reduce the data dimension. Then, according to the proposed natural nearest neighbor searching algorithm(NNN_Searching), the density of data points, each class center and the smallest enclosing sphere radius are determined correspondingly. Finally, a fuzzy entropy weighted KNN classification method based on affinity is proposed, which mainly includes the following three steps: 1、 the feature weights of samples are calculated based on fuzzy entropy values, 2、 the fuzzy memberships of samples are determined based on affinity among samples, and 3、 K-neighbors are selected according to the class-conditional weighted Euclidean distance, the fuzzy membership value of the testing sample is calculated based on the membership of k-neighbors, and then all testing samples are classified according to the fuzzy membership value of the samples belonging to each class;that is, the attack type is determined. The method has been applied to the problem of attack detection and validated based on the famous KDD99 and CICIDS-2017 datasets. From the experimental results shown in this paper, it is observed that the FEW-NNN method improves the accuracy and efficiency of flow-based network traffic attack detection.     

追日

去年10月一个温暖的早晨,在澳大利亚内陆深处,14名麻省理工学院的学生和校友上午6点就爬出了他们的睡袋,准备车队,进行又一天的艰苦驾驶,在护卫队的护送下,他们驶过一段平坦、笔直、炎热、灰尘弥漫的道路.领头的车辆迎着其他车流,形成了一个缓冲带,一辆货车断后以监测整个队伍的进程.     

Design of Diode Type Un-Cooled Infrared Focal Plane Array Readout Circuit

The diode infrared focal plane array uses the silicon diodes as a sensitive device for infrared signal measurement. By the infrared radiation, the infrared focal plane can produces small voltage signals. For the traditional readout circuit structures are designed to process current signals, they cannot be applied to it. In this paper,a new readout circuit for the diode un-cooled infrared focal plane array is developed. The principle of detector array signal readout and small signal amplification is given in detail. The readout circuit is designed and simulated by using the Central Semiconductor Manufacturing Corporation (CSMC) 0.5 μm complementary metal-oxide-semiconductor transistor (CMOS) technology library. Cadence Spectre simulation results show that the scheme can be applied to the CMOS readout integrated circuit (ROIC) with a larger array, such as 320×240 size array.     

农村有线电视网络常见故障

（1）原来转播的中央1套节目变成了中央2套。检查机柜正常．调出菜单发现原设定的频率1290变为970，其他节目正常。重新调整频率，节目恢复正常，分析其原因为接收一体机内部集成块出现误码导致原储存的数据发生变化。     

基于深度卷积神经网络的图上半监督极化SAR图像分类算法

为实现在只有少量标记数据情况下的高质量的图像分类,本文提出了一种基于深度卷积神经网络的图上半监督极化SAR图像分类算法.该算法将极化SAR图像建模为无向图,并基于该无向图,定义了包含半监督项,卷积神经网络项和类标光滑项的能量函数.算法所采用的卷积神经网络提取抽象的数据驱动的极化特征.半监督项约束了有标记像素的类标在分类过程中保持不变.类标光滑项约束了像素间类标的光滑性.基于对PauliRGB图像进行超像素分割而产生的初始化类标图,交替迭代优化所定义的能量函数直至其收敛.在两幅真实极化SAR图像上的实验结果表明,该算法达到了优异的分类效果,其性能优于当前已有算法.     

（英）集成颜色特征和统计特征的极化SAR图像城区分类框架

在传统的利用极化合成孔径雷达(PolSAR)遥感图像分类中,除了近期一个有监督分类的工作,很少涉及颜色特征。与该工作不同,在本文中,针对城区分类,利用颜色特征构造一个新颖的无监督的分类框架。首先,基于最近提出的PolSAR数据的四分量分解模型,计算了常用的颜色空间：YUV,RGB,HSI和CIELab,通过引入颜色熵量化的选择颜色特征,然后,联合纹理特征和扩展的散射功率熵,用自适应的均值漂移算法分割PolSAR图像,最后,根据基于G0分布的距离测度合并聚簇为较为匀质的地物类别。通过L波段AIRSAR数据和C波段Radarsat-2的PolSAR数据验证了提出算法的有效性,分类正确率表明,相比于已有的工作,提出的算法对于城区有较好的区分能力。     

结合特征选择和大尺度谱聚类的极化SAR图像非监督分类

非监督分类是极化SAR图像解译的重要手段,但其分类结果易受到高维特征的影响。针对此问题,本文提出一种结合特征选择和大尺度谱聚类的极化SAR图像非监督分类方法。该方法首先深入分析并提取了极化SAR图像分类中常用的特征参数,包括基于测量数据及其简单线性变换的特征和极化目标分解的特征。然后通过聚类森林特征选择算法进行特征降维处理,去除冗余信息。最后利用过分割产生代表点并构建原始数据与代表点间的二分图,通过大尺度谱聚类算法完成图像的非监督分类。实验结果表明,该方法能够选取有效的特征组合,并得到较为满意的分类效果。      

Genetic Sequence Classification and its Application to Cross-Species Homology Detection

Although large-scale classification studies of genetic sequence data are in progress around the world, very few studies compare different classification approaches, e.g. unsupervised and supervised, in terms of objective criteria such as classification accuracy and computational complexity. In this paper, we study such criteria for both unsupervised and supervised classification of a relatively large sequence data set. The unsupervised approach involves use of different sequence alignment algorithms (e.g., Smith-Waterman, FASTA and BLAST) followed by clustering using the Maximin algorithm. The supervised approach uses a suitable numeric encoding (relative frequencies of tuples of nucleotides followed by principal component analysis) which is fed to a Multi-layer Backpropagation Neural Network. Classification experiments conducted on IBM-SP parallel computers show that FASTA with unsupervised Maximin leads to best trade-off between accuracy and speed among all methods, followed by supervised neural networks as the second best approach. Finally, the different classifiers are applied to the problem of cross-species homology detection.     

一种基于张量积扩散的非监督极化SAR图像地物分类方法

针对相似度表达的困难性以及极化SAR图像中固有的相干斑噪声问题,该文提出了一种基于张量积(TPG)扩散的非监督极化SAR图像地物分类算法。张量积扩散一般用于光学图像的分割或检索,目前研究表明,其已可用于极化SAR(PolSAR)图像地物分类。基于张量积扩散可以稳健地度量数据点之间的测地线距离,因此能够更好地挖掘数据点之间内在的相似度信息。首先,将极化SAR图像进行分割,生成许多超像素;其次,基于超像素提取7种特征并生成一个特征向量,进而利用高斯核构建相似度矩阵;再次,基于已构建的相似度矩阵,利用张量积扩散沿着数据点的内在流形结构进行相似度的传播,实现全局的相似性度量,从而获得一个具有更强判别能力的相似度矩阵;最后,基于此相似度矩阵进行谱聚类以得到地物分类结果。该文在仿真和实测极化SAR图像上均进行了大量实验,并与4种经典算法进行对比,结果表明该方法可以有效地结合空间邻域相似度信息并取得更高的分类精度。      

Clustering-based label estimation for network anomaly detection

A substantial body of work has been done to identify network anomalies using supervised and unsupervised learning techniques with their unique strengths and weaknesses. In this work, we propose a new approach that takes advantage of both worlds of unsupervised and supervised learnings. The main objective of the proposed approach is to enable supervised anomaly detection without the provision of the associated labels by users. To this end, we estimate the labels of each connection in the training phase using clustering. The “estimated” labels are then utilized to establish a supervised learning model for the subsequent classification of connections in the testing stage. We set up a new property that defines anomalies in the context of network anomaly detection to improve the quality of estimated labels. Through our extensive experiments with a public dataset (NSL-KDD), we will prove that the proposed method can achieve performance comparable to one with the “original” labels provided in the dataset. We also introduce two heuristic functions that minimize the impact of the randomness of clustering to improve the overall quality of the estimated labels.     

深度学习在极化SAR图像分类上的应用综述

极化合成孔径雷达（极化SAR）是当前最先进的遥感监测技术之一。它可以全天时、全天候地进行对地观测,并提供高分辨率、具有丰富地表信息的数据。极化SAR图像分类近年来被广泛研究和应用,而蓬勃发展的深度学习技术大大加速了其进展。基于此现状,本文对深度学习在极化SAR图像分类上的应用进行了综述。综述涵盖了不同类别的深度学习算法,包括监督、无监督、半监督和主动学习算法在此任务上的应用分析。另外,本文分析当前极化SAR图像分类所面临的挑战以及未来的发展趋势。     

基于马尔科夫判别谱聚类的极化SAR图像分类方法

该文针对现有的谱聚类方法用于极化SAR图像分类时精度较低的问题,提出一种基于马尔科夫的判别谱聚类方法(MDSC),具有低秩和稀疏分解的特点。该方法首先恢复一个真实的低秩概率转移矩阵,将其作为标准马尔科夫谱聚类方法的输入,以减少噪声对分类结果的影响;然后在目标函数中引入判别信息,使极化SAR图像的数据信息能够得到更加充分地利用;最后采用增广拉格朗日乘子法来解决低秩和概率单纯形约束下的目标函数优化问题。在荷兰小农田、德国、西安和荷兰大农田4个不同数据集上的实验证明,该方法具有较好的准确率,且参数敏感性较低,表现出了良好的分类性能。      

采用流形近邻传播聚类的极化SAR图像分类

针对传统近邻传播（Affinity Propagation, AP）聚类算法使用欧式距离构建相似度矩阵,不能有效描述极化SAR数据复杂分布的问题,本文提出一种新的基于联合流形距离的AP聚类算法(CMD-AP) 用于极化SAR图像分类。首先将待分类极化SAR图像分割成若干超像素,在相应的极化特征基础上加入图像纹理特征,利用拉普拉斯特征映射算法对特征降维;然后结合相干矩阵Wishart流形和特征矢量欧式流形作为流形距离测度,构造相似性矩阵;最后利用上述相似性矩阵,采用AP聚类算法,对极化SAR图像进行分类。该算法充分考虑了极化SAR数据集潜在的流形结构,将联合的流形距离测度引入AP算法中。实验表明,本文算法提高了极化SAR图像的分类精度,具有更优的区域一致性和边缘保持效果。      

Supervised and unsupervised learning in radial basis functionclassifiers

The paper considers a number of strategies for training radial basis function (RBF) classifiers. A benchmark problem is constructed using ten-dimensional input patterns which have to be classified into one of three classes. The RBF networks are trained using a two-phase approach (unsupervised clustering for the first layer followed by supervised learning for the second layer), error backpropagation (supervised learning for both layers) and a hybrid approach. It is shown that RBF classifiers trained with error backpropagation give results almost identical to those obtained with a multilayer perceptron. Although networks trained with the two-phase approach give slightly worse classification results, it is argued that the hidden-layer representation of such networks is much more powerful, especially if it is encoded in the form of a Gaussian mixture model. During training, the number of subclusters present within the training database can be estimated: during testing, the activities in the hidden layer of the classification network can be used to assess the novelty of input patterns and thereby help to validate network outputs