命令行中运行程序随我愿

通常我们在执行程序时。需要先进入该程序所在的目录。然后运行它。显得非常麻烦。为了方便。在桌面上建立一个快捷方式。但带来的结果却是满屏的程序快捷方式。前几天。看见朋友在“运行”对话框中输入Foxmail．exe后即可运行Foxmail。感到挺奇怪的。在我机器上一试。却提示找不到该文件。这是为什么。该问题该如何解决。下面提供两种解决办法。至于为什么。在第二种方法中已说明。     

金属外壳家甩电器“麻电”该咋办？

许多人在接触家用电器的金属外壳时。曾遇到过有“麻电”的感觉。严重时甚至有刺痛的感觉。“麻电”是一种危险的征兆。轻则引起使用者精神紧张。重则危及使用者的生命安全．发生触电伤亡事故。因此。当发生“麻电”现象时。必须暂停使用。找出“麻电”的原因。及时排除电器的故障。同时也应采取必要的技术措施避免“麻电”现象的再次产生。     

被咬了一口的苹果——苹果编年史

她是个人电脑的始祖。是那么的卓尔不群。气质不凡。如今似乎被排挤在我们视线的边缘。却仍是我们事实上的潮流引领者。每一款新产品的问世。都可以引起无数人的欢呼。再昂贵的价格也无法阻挡人们对这些艺术品的热爱。但是这是个商业化的社会．尊崇着利益至上的原则。因此她作为一个艺术家．一直遭受着各种各样的磨难。无数未来佳作被迫胎死腹中或是半路夭折。但是他并没有因此倒下。反而变得越来越成熟。今天的她不但更善于创造．同时也更善于生存。世界尚未终结。她的成败功过也无法断定。我们只好藉着过去的回忆。让大家自己来思考……     

职场心理

如果你有鸭梨,把它放冰箱里,它就会变成冻梨!!做个向日葵族:1.善于发现微小幸福。2.没有太大野心。3.对负面情绪不敏感。4.生活标准较低。5.选择喜欢的职业。6抗压力耐打击。7.感恩的心态。8.张弛有度的生活节奏。9.相互赞美。10.充满热情。11.适当健忘。12.善于自嘲。13.适当的精神胜利法。     

感受最真实的音乐 思铂睿音响改装

我会听很多很多的歌,以至于有点乱七八糟。要知道,我的CD多得无法摆上书架。音乐是我的一切。有时侯我会觉得自己是为音乐而生的。音乐就是我的耳朵。这感觉来得如此真实,如此强烈,以至我有些自恋。我就像是一个歌者,台下是已成黑点的观众。而我,从不曾倒下,经常会这样子近乎发呆地想。     

Windows XP与开发者

2001年2月13日。星期二。西雅图。BillGates终于揭开了Windows下一代操作系统的面纱。Gates宣称：“这将是自Windows95问世以来。最重要的一种Windows操作系统。它将再次激发市场对PC的需求。”     

20岁的虚拟现实开拓者

“我是一个自学成才的工程师、黑客、狂热电子迷。我来自加州长滩,我喜欢做东西。我爸是汽车销售员,我妈是个家庭主妇。很长时间以来我都是把家当学校。大概十四十五岁的时候我开始念社区大学。2010年我考上了加州州立大学。专业是新闻系。”     

按流量计费——互联网将走向何方？

提到上网收费。大家理所当然地想到当前最为普遍的包月方式。通常我们每个月只要付出60—100元左右的费用，我们就可以无限制地使用网络；也有的用户使用网络比较少。因而使用的是按时间计费的方式。通常可以50元包50个小时。70元包100个小时等。然而。你可曾听说宽带将按流量进行计费。这意味着你在网络上一切导致流量的行为都要收费。包括浏览时事新闻、查阅资料、下载歌曲、软件、电影等。你所浏览的每一个字符，每一bit流量。都需要你买单。     

闲侃

临近五一,气温日渐升高。说实话虽从小生在北京,但我并不习惯北京的天气。尤其换季时,气温总反复无常,易骤变。前天还身着春装,两日后便换短袖,完全没有过渡的时间。虽不习惯,但骨子里还总期盼换季,特别是春夏两季。两点原因:其一,喜欢春夏来临时,万物复苏所散发出的味道,深吸一口感觉一切都是那么新鲜。其二,喜欢窈寐淑女。工作前,每逢春夏就与三、两好友同去西单。坐在中友百货二层天台,打望人流密集之处,窃窃私议。这其二本想细谈,但又怕被误为低级趣味,所以就此作罢。     

用增强型51实验板控制液晶显示器

上期我们已经对增强型51实验板中按键、蜂鸣器、继电器的工作原理及使用方法有了一定的了解。也感受到了增强型51实验板的易用、易学和强大的功能。现在我们趁热打铁。再向上跨一步。进一步了解增强型51实验板还能做哪些更酷。更有趣味性的实验。当然。在你决定学习单片机之前。请做好如下准备工作：计算机一台，编程器一只。实验板一块。再准备一只仿真器。如果你想学单片机。这是不可少的不必要配置。好在这些投资并不算多。     

Cluster analysis of results of ultrasonic testing

The methods of cluster and correlation analysis are employed to solve various problems related to the processing of results obtained using the computerized ultrasonic testing of welded joints. In particular, the principal component analysis, K-means algorithm, and support vector machine are considered. A detailed analysis of the methods and the main results are presented. Aleksandr O. Skomorokhov. Born 1951. Graduated from the Faculty of Radio Physics, Nizhni Novgorod State University in 1973. Received candidate’s degree in 1984. Associate Professor of the Obninsk State Technical University of Atomic Energy. Scientific interests: development and application of modern methods for data analysis, technical diagnostics, and matrix programming languages. Author of 30 papers. Member of SIGAPL, SIGKDD, ACM, BCS, and the Russian Association of Artificial Intelligence. Pavel A. Belousov. Born 1979. Graduated from the Obninsk Institute of Atomic Power Engineering in 2002. PhD student of the Obninsk State Technical University of Atomic Energy. Author of three papers. Winner of the Obninsk Stipend for Students, PhD Students, and Young Lecturers. Aleksandr V. Nakhabov. Born 1979. Graduated from the Obninsk Institute of Atomic Power Engineering in 2002. PhD student of the Obninsk State Technical University of Atomic Energy. Author of three papers. Winner of the Federal Stipend of the Potanin Foundation.     

Choice of the objective function for pseudogradient measurement of image parameters

Approaches to the choice of objective functions and the form of the pseudogradient are considered for pseudogradient evaluation of interframe geometrical image deformations. The possibilities of reducing the computational cost of calculation of the pseudogradient are analyzed. Minkina Galina Leonidovna. Born in 1983. Fifth-year student at Ulyanovsk State Technical University, specialty applied mathematics. Scientific interests: optimization of parameters of algorithms for estimating interframe geometrical image deformations. Samoilov Mikhail Yurievich. Born in 1981. Graduated from Moscow State University in 2003. PhD student at Ulyanovsk State Technical University. Scientific interests: minimization of the computational cost in estimation of interframe geometrical image deformations. Tashlinskii Alexander Grigorievich. Born in 1954. Graduated from the Ulyanovsk Polytechnical Institute in 1977. Received doctoral degree in 2000. Professor in the Department of Systems of Computer Aided Design. Scientific interests: statistical image processing, in particular, estimation of spatiotemporal deformations of sequences of dynamical images. Author of 200 publications, including more than 60 papers and a monograph on estimation of parameters of spatial deformations of sequences of images. Full member of the International Academy of Authors of Scientific Discoveries and Investigations and of the Russian Academy of Natural Sciences. Awarded medals from these academies.     

Representation of epileptic discharge dynamics in EEGs on the basis of multiscale correlative signal dynamics analysis

This paper presents the latest results in the application of developed multiscale correlative signal dynamics analysis to epileptic discharge investigations. The basic principle of the approach proposed is in detecting the epileptic discharge time intervals and controlling the signal dynamics in such intervals by means of some signal quasi-periodicity features. These features are determined by the structure of a measure of self-similarity calculated for different scales. The graphical temporal representation based on this measure is at the center of the discussion. Interpretations of typical epileptic discharge dynamics patterns in the representation proposed are put into practice; typical patterns are illustrated by experimental representation examples. The text was submitted by the authors in English. Vyacheslav E. Antciperov. Born 1959. Graduated from the Moscow Institute of Physics and Technology in 1982. Received his PhD (Kandidat Nauk degree) in 1986. Senior Researcher at the Institute of Radio Engineering and Electronics, Russian Academy of Sciences. Scientific interests: information theory, recognition and identification theory, and computer modeling of biological aspects of human activities, including speech and image recognition and modeling of other functions of the central nervous system. Author of more than 30 papers. Yurii V. Obukhov. Born 1950. Graduated from the Moscow Institute of Physics and Technology in 1974. Obtained his Doctoral (Doctor Nauk) degree in Physics and Mathematics in 1992. Head of the Laboratory and Deputy Director of the Institute of Radio Engineering and Electronics of the Russian Academy of Sciences. Current research interests: data visualizing, image analysis and processing, biomedical information science, and information systems. Author of more than 80 publications. Member of the Editors Board of Biomedical Radioelectronics journal. Vladimir A. Morozov. Born 1932. Graduated from the Moscow Institute of Energetics in 1956. Received his PhD (Kandidat Nauk degree) in 1964. Chief of the Statistical Radiophysics Department of the Institute of Radio Engineering and Electronics, Russian Academy of Sciences. Scientific interests: information theory, weak signal detection, signal processing, stochastic recognition, and speech recognition. Author of more than 80 papers.     

Identification of inorganic salts and determination of their concentrations in aqueous solutions based on the valence Raman band of water using artificial neural networks

The characteristic features of the valence Raman band of water in the solutions of electrolytes are revealed. These features allow the noncontact recognition of the type of salt and the determination of its concentration in aqueous solutions using artificial neural networks. Viktor V. Fadeev. Born 1935. Graduated from the Physics Faculty of Moscow State University in 1959. Received candidate’s degree in 1967 and doctoral degree in 1983. Professor of the Physics Faculty, Moscow State University. Scientific interests: optics, laser physics, spectroscopy, and inverse problems. Author of more than 300 papers and a discovery diploma. Laureate of the USSR State Prize (1983). Tat’yana A. Dolenko. Born 1961. Graduated from the Physics Faculty of Moscow State University in 1983. Received candidate’s degree in 1987. Senior Researcher of the Physics Faculty, Moscow State University. Scientific interests: laser spectroscopy, Raman spectroscopy of aqueous media, inverse problems, and artificial neural networks. Author of 57 papers and an invention certificate. Laureate of the Lenin Komsomol Prize (1985). Sergei A. Burikov. Born 1978. Graduated from the Physics Faculty of Moscow State University in 2002. Junior Researcher of the Physics Faculty, Moscow State University. Scientific interests: optics, Raman spectroscopy of aqueous media, inverse problems, and artificial neural networks. Author of 14 papers. Aleksandr V. Sugonyaev. Born 1982. Graduated from the Physics Faculty of Moscow State University in 2005. Scientific interests: Raman spectroscopy of aqueous media, inverse problems, and artificial neural networks. Author of 5 papers.     

Using adaptive and intelligent methods in constructing of a model of seismic noise

In the paper, an attempt is made to develop an approach to analysis of seismic signals with the use of Data Mining techniques. A system for registering of signals of seismic noise is described. Algorithms for signal segmentation are proposed. Geppener Vladimir Vladimirovich. Born 1940. Graduated from the Leningrad Electrotechnical Institute in 1964. Received candidate’s degree (in Engineering) in 1969 and Doctoral degree (in Engineering) in 2000. Professor at the Chair of Mathematical Software and Computer Applications of the St. Petersburg Electrotechnical University. Scientific interests: systems of signal processing, methods of artificial intelligence, and pattern recognition theory. Author and coauthor of more than 150 scientific publications. Member of the Russian Association for Pattern Recognition and Image Analysis. Tristanov Aleksandr Borisovich. Born 1981. Graduated with honors from the Kamchatka State Technical University in 2003. Post-graduate student at the Kamchatka State Pedagogical University. Works as an Assistant Professor at the Kamchatka State Pedagogical University. Scientific interests: systems of digital signal processing, methods of artificial intelligence, and frequency-time analysis of signals. Author of 10 scientific publications. Firstov Pavel Pavlovich. Born 1941. Graduated with honors from Polzunov Altai State Polytechnical Institute in 1963. Candidate of Sciences in Physics and Mathematics. Since 1965 works at the Institute of Volcanology and Seismology of the Far East Division of the Russian Academy of Sciences; Head of a laboratory. Scientific interest: volcanic acoustics, nature of earthquake predecessors. Author and coauthor of more than 100 papers and one monograph. Rulenko Oleg Petrovich. Born 1946. Graduated with honors from the Department of Physics and Mathematics of the Kamchatka State Pedagogical Institute in 1968. Received candidate’s degree (in Physics and Mathematics) in 1994. Senior Researcher at the Institute of Volcanology and Seismology of the Far East Division of the Russian Academy of Sciences. Scientific interests: atmospheric electricity, interaction of lithosphere and atmosphere, physics of earthquake predecessors. Author of 29 scientific publications.     

Automation of parallel programming in modeling of multiphase media. Optimal parallelization

A flexible approach to automation of parallel programming of the problems of mechanics of the multiphase media was proposed. It is based on the object-event model of program generation. The model structure was described. The principles of interpretation and the technology of model translation into the program code were formulated. For solution of the aforementioned mechanical problems with spatial parallelization, strategies of reduction and optimization of data exchange were proposed. Part of exchanges was eliminated owing to periodic extrapolation. The least squares method with adjustable weight coefficients in the objective function was used. For optimization of the rest of exchanges, a special problem of nonlinear Boolean optimization was solved.     

Contour recognition based on spectral methods. Solution of the problem of choice of the start-point

The paper is devoted to the use of spectral methods in problems of visual pattern recognition. The main idea is to associate a two-dimensional closed line, treated as a univariate function, with the contour of each object. On the basis of analysis of expansion coefficients of these functions, we propose adequate quantitative estimates for similarity of objects, which are invariant under affine transformations of the plane. A particular result is the invariance of the spectral representation with respect to the choice of the start-point. This invariance is obtained on the basis of the sine-cosine decomposition of arbitrary periodic functions. Tetouev (Titùlany) Ruslan Kurmanbievich. Born 1976. Graduated from the Kabardino-Balkarskii State University in 1999. Works as a researcher at the Institute of Mathematical Problems of Biology of the Russian Academy of Sciences. Scientific interests: transformations in the space of expansion coefficients, numerical-analytical methods for determining parameters of models, extraction of “random” component of signals, prediction of dynamics of synergetic systems, investigation of genetic sequences, and prediction of genes. Author of one tutorial and three papers.     

Autonomous longitudinal motion of a paraglider. Mathematical simulation, synthesis of control

Mathematical model of motion of a paraglider in the longitudinal plane is constructed. The vehicle consists of a sail and a gondola. Both bodies are assumed to be perfectly rigid. They are connected by slings which are assumed to be perfectly rigid rods. Thus, the considered model of the paraglider represents one rigid body with three degrees of freedom. An engine, which develops thrust using a propeller, is mounted rigidly on the gondola of the vehicle. The orientation of the thrust vector with respect to the gondola is constant. The steady-state regimes of motion of the paraglider for the constant thrust are found. The law of automatic thrust control for which the flight of the vehicle is stabilized at the given altitude is designed. The domains of asymptotic stability of the paraglider motion at a constant altitude, including with account of delay, are constructed in the plane of the feedback coefficients. In this plane the domains in which a given stability factor is ensured are constructed. Some results of numerical simulation of the flight of the vehicle are presented.     

An easy measure of compactness for 2D and 3D shapes

An easy measure of compactness for 2D (two dimensional) and 3D (three dimensional) shapes composed of pixels and voxels, respectively, is presented. The work proposed here is based on the two previous works of the measure of discrete compactness [E. Bribiesca, Measuring 2-D shape compactness using the contact perimeter, Comput. Math. Appl. 33 (1997) 1–9; E. Bribiesca, A measure of compactness for 3D shapes, Comput. Math. Appl. 40 (2000) 1275–1284]. The measure of compactness proposed here improves and simplifies the previous measure of discrete compactness. Now, using this proposed measure of compactness, it is possible to compute measures for any kind of object including porous and fragmented objects. Also, the computation of the measures is very simple by means of the use of only one equation. The measure of compactness proposed here depends in large part on the sum of the contact perimeters of the side-connected pixels for 2D shapes or on the sum of the contact surface areas of the face-connected voxels for 3D shapes. Relations between the perimeter and the contact perimeter for 2D shapes and between the area of the surface enclosing the volume and the contact surface area, are presented.The measure presented here of compactness is invariant under translation, rotation, and scaling. In this work, the term of compactness does not refer to point-set topology, but is related to intrinsic properties of objects. Finally, in order to prove our measure of compactness, we calculate the measures of discrete compactness of different objects. Also, we present an important application for brain structures quantification by means of the use of the new proposed measure of discrete compactness.