使用NI公司的PXI控制硬件平台结合NI的图形化编程软件LabVIEW快速并成功的开发构建出一个经济、灵活的PCB板功能测试系统(FCT测试系统)。该系统采用的PXI 控制板卡可以实现对音频、视频以及各种静态参数(电压、电流、频率)的综合性全自动测试，并且通过LabVIEW软件编程可以实现兼容GPIB,I2C,Modbus,TCT/IP等多种协议，通过VISA模块库，可以实现对串口随意方式的数据处理，和数据交互显示。

线性泛函非局部边界条件的奇异半正问题正解存在性，赵增勤，王丽君，我们利用不动点指数方法,研究了一类线性泛函边界条件下的非线性二阶奇异半正微分方程,得到了C[0,1] 正解的存在性,然后给出具体例子.

完全耦合的正倒向随机微分方程系统在非Lipschitz代价泛函下最优控制的存在性，孟庆欣，张奇，运用凸分析中的最优存在定理,本文研究了最优随机控制的存在性。而所研究的随机系统是完全耦合的线性正倒向随机微分方程,且其代价�

含电子传输功能基的新型铕配合物的光电性能研究，刘煜，王亚飞，本文设计合成了一种含有电子传输性能的噁二唑功能基的邻菲啰啉中性配体及其铕配合物[Eu(DBM)3(BuOXD-Phen)]，对其分子结构进行了表征。�

泛函分析讲义(MIT辅助教材)Functional analysis lecture notes by T.B. Ward，英文非扫描版

概述 EggNOG-mapper是一种用于对新序列进行快速功能注释的工具。 它使用来自eggNOG数据库（ ）的预先计算的直系同源基因组和系统发育树，仅从细粒度直系同源基因中转移功能信息。 eggNOG-mapper的常见用途包括注释新的基因组，转录组甚至宏基因组基因目录。 使用正交预测作为功能注释的方法比传统的同源搜索（即BLAST搜索）具有更高的精度，因为它避免了从紧密的旁系同源物转移注释（重复的基因更有可能参与功能差异）。 将不同的eggNOG-mapper选项与BLAST和InterProScan进行比较的基准。 EggNOG-mapper也可以作为公共在线资源获得： ://eggnog-mapper.embl.de 文献资料 引文 如果您使用此软件，请引用： [1] Fast genome-wide functional annotation through orth

Most books on data structures assume an imperative language such as C or C++. However, data structures for these languages do not always translate well to functional languages such as Standard ML, Haskell, or Scheme. This book describes data structures from the point of view of functional languages, with examples, and presents design techniques that allow programmers to develop their own functional data structures. The author includes both classical data structures, such as red-black trees and binomial queues, and a host of new data structures developed exclusively for functional languages. All source code is given in Standard ML and Haskell, and most of the programs are easily adaptable to other functional languages. This handy reference for professional programmers working with functional languages can also be used as a tutorial or for self-study.

matlab代码影响Functional-Multiplex-PageRank ％++++++++功能复用页面等级++++++++++++++++++++++++++++ 此文件夹包含4个MATLAB代码，用于计算双工网络和任意层数的网络的功能多路复用PageRank： functionalPageRank_duplex.m 给定影响向量z = [z ^（1,0），z ^（0,1），z ^（1,1）]，计算双工网络的功能多路复用PageRank。 fPR.m 计算影响向量的所有值的双工网络的功能复用PageRank。 该代码使用了functionalPageRank_duplex.m代码 functionalPageRank_multiplicity.m 计算具有任意数量的层并具有特定影响向量的多路复用网络的功能多路复用PageRank，而这些影响向量仅取决于链路重叠的多重性。 fPRm.m 计算具有任意数量的层且影响参数不同的多路复用网络的功能多路复用PageRank。 该代码使用了functionalPageRank_multiplicity.m代码 这些程序是在作者中分发的，希望

Functional magnetic resonance imaging (fMRI) has become the most popular method for imaging brain function. Handbook of Functional MRI Data Analysis provides a comprehensive and practical introduction to the methods used for fMRI data analysis. Using minimal jargon, this book explains the concepts behind processing fMRI data, focusing on the techniques that are most commonly used in the field. This book provides background about the methods employed by common data analysis packages including FSL, SPM and AFNI. Some of the newest cutting-edge techniques, including pattern classification analysis, connectivity modeling and resting state network analysis, are also discussed. Readers of this book, whether newcomers to the field or experienced researchers, will obtain a deep and effective knowledge of how to employ fMRI analysis to ask scientific questions and become more sophisticated users of fMRI analysis software.

Scientists today collect samples of curves and other functional observations. This monograph presents many ideas and techniques for such data. Included are expressions in the functional domain of such classics as linear regression, principal components analysis, linear modelling, and canonical correlation analysis, as well as specifically functional techniques such as curve registration and principal differential analysis. Data arising in real applications are used throughout for both motivation and illustration, showing how functional approaches allow us to see new things, especially by exploiting the smoothness of the processes generating the data. The data sets exemplify the wide scope of functional data analysis; they are drwan from growth analysis, meterology, biomechanics, equine science, economics, and medicine.The book presents novel statistical technology while keeping the mathematical level widely accessible. It is designed to appeal to students, to applied data analysts, and to experienced researchers; it will have value both within statistics and across a broad spectrum of other fields. Much of the material is based on the authors' own work, some of which appears here for the first time.Jim Ramsay is Professor of Psychology at McGill University and is an international authority on many aspects of multivariate analysis. He draws on his collaboration with researchers in speech articulation, motor control, meteorology, psychology, and human physiology to illustrate his technical contributions to functional data analysis in a wide range of statistical and application journals.Bernard Silverman, author of the highly regarded "Density Estimation for Statistics and Data Analysis," and coauthor of "Nonparametric Regression and Generalized Linear Models: A Roughness Penalty Approach," is Professor of Statistics at Bristol University. His published work on smoothing methods and other aspects of applied, computational, and theoretical statistics has been recognized by the Presidents' Award of the Committee of Presidents of Statistical Societies, and the award of two Guy Medals by the Royal Statistical Society.