高精度智能转速测量模板的设计

介绍了一种利用高性能的16位单片机80C196KC进行转速实时高精度的测量方法，阐述了如何通过CAN总线实现与微机通讯；详细描述了硬件电路的设计，并给出了软件程序框图。     

Plasma properties of laser-ablated St target in air

Ｐｌａｓｍａｐｒｏｐｅｒｔｉｅｓｏｆｌａｓｅｒ－ａｂｌａｔｅｄＳｔｔａｒｇｅｔｉｎａｉｒＷａｎｇＸｉａｎｇ－Ｔａｉ（王象泰）；ＭａｎＢａｏ－Ｙｕａｎ（满宝元）；ＷａｎｇＧｏｎｇ－Ｔａｎｇ（王公堂）；ＦａｎＸｉ－Ｊｕｎ（樊锡君）；ＷａｎｇＪｕｎ（王军）...     

一幅画

从吃饱到吃好、吃精至吃的环境,人们对生活的要求逐渐趋向艺术化,餐馆老板会把环境设计与经营放在同等地位,所以好的餐饮空间层出不穷,也是社会发展的必然产物。位于宁波老外滩的瀚珍舫餐馆,建筑一层的面积较小,二、三层呈狭长形,做为餐馆这样的建筑条件并不理想,设计师把包厢和雅座设置在两边,这样走廊显得更幽深,柱子阵列得更有气势。拆除原先的楼梯,做了一部上下错位且不规则楼梯,曲折向上,增强了空间的层次。通顶的储酒柜,从三楼悬下的八米长吊灯,把一至三楼不落痕迹地融为一体。要设计一个好作品,设计师的构想需要不停地游走。碰撞的激情仿佛是一幅画,渐渐地,它的轮廓清晰了,色彩明朗了,它的主题神秘而遥不可及,每个人的心扉都有不同的暗香飘逸。掩映在现代手法中的精致、深沉从骨子里透出古典优雅     

Self-aligned diffusion technique for n-InP JFETs

A self-aligned diffusion process has been demonstrated in the fabrication of n-InP JFETs on semi-insulating InP. This process utilises the anisotropic and selective etching properties of InP/GaInAs materials. Using this technique, sub-micrometre gate lengths can be achieved even with conventional photolithography. Devices with trans-conductance of >35mS/mm, leakage current of < 10nA/mm and gate capacitance of <0.7pF/mm have been fabricated with a cutoff frequency of 7 GHz.     

A simplified dispersion limit formula for IM/DD systems and itscomparison with experimental results

A simplified formula for the dispersion limit of single-mode-fiber intensity modulation/direct detection (IM/DD) systems is derived for arbitrary given normalized pulse width U, eye opening penalty X and source linewidth enhancement factor α. From the comparison with published theoretical analyses, computer simulation and experimental data, its validity and the limits of existing formulae are shown. Using this simple and general formula, one can easily obtain the dispersion limit of various fiber types under different working conditions in IM/DD systems     

基于特征线匹配的城市建筑物识别方法研究

随着手机GPS位置测定、导航以及摄像功能的普及,对于移动状态下的位置定位以及都市空间建筑物实时搜索等应用功能存在日益增长的需求。介绍了图像处理方式的城市建筑物识别方法,通过对两种图像匹配算法--SIFT和Local Search的比较分析表明,构造物轮廓线及其组合是一种相对稳定的几何特征,在匹配时受图像的仿射变换、画质变化以及镜头畸变等因素干扰较小,此外,基于轮廓的图像匹配指数还能反映摄影位置的变化。因此,对Local Search进行了改进,利用实时图的建筑物的特征轮廓线与从三维数据库中提取的建筑群特征轮廓线进行匹配,然后选择匹配指数高的记录作为识别结果。结果表明基于Local Search算法的建筑物识别技术可以很好地适应移动条件下建筑物快速识别的要求。     

从光合作用的倒掉说起

席殊书屋、第三级书局走了,风入松、纸老虎世纪城店关了,我没太在意。2010年10月底,有着36家连锁店的光合作用竟然也宣告倒闭了!我终于淡定不起来了。清华大学出版社零售产品经理王川（简称川）清华大学出版社图书编辑刘青（简称刘）记者Justdo（简称J）新华书店＋网购是必然趋势J：光合作用倒闭了,你们惊讶吗？刘：其实我挺惊讶,因为我常去,特别是五道口那家店,从刚开业起就经常去逛。     

Stock Price Forecasting: An Echo State Network Approach

Forecasting stock prices using deep learning models suffers from problems such as low accuracy, slow convergence, and complex network structures. This study developed an echo state network (ESN) model to mitigate such problems. We compared our ESN with a long short-term memory (LSTM) network by forecasting the stock data of Kweichow Moutai, a leading enterprise in China’s liquor industry. By analyzing data for 120, 240, and 300 days, we generated forecast data for the next 40, 80, and 100 days, respectively, using both ESN and LSTM. In terms of accuracy, ESN had the unique advantage of capturing nonlinear data. Mean absolute error (MAE) was used to present the accuracy results. The MAEs of the data forecast by ESN were 0.024, 0.024, and 0.025, which were, respectively, 0.065, 0.007, and 0.009 less than those of LSTM. In terms of convergence, ESN has a reservoir state-space structure, which makes it perform faster than other models. Root-mean-square error (RMSE) was used to present the convergence time. In our experiment, the RMSEs of ESN were 0.22, 0.27, and 0.26, which were, respectively, 0.08, 0.01, and 0.12 less than those of LSTM. In terms of network structure, ESN consists only of input, reservoir, and output spaces, making it a much simpler model than the others. The proposed ESN was found to be an effective model that, compared to others, converges faster, forecasts more accurately, and builds time-series analyses more easily.     

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.