实验曲线的自动分段拟合

实验曲线的自动分段拟合陈金友（酒泉卫星发射中心）ＡＵＴＯＭＡＴＩＣＳＥＣＴＩＯＮＡＬＦＩＴＴＩＮＧＯＦＴＨＥＭＥＡＳＵＲＥＤＤＡＴＡ￥ＣｈｅｎＪｉｎ－ｙｏｕ（ＣｈｉｎａＪｉｕｑｕａｎＳａｔｅｌｌｉｔｅＬａｕｎｃｋＣｅｎｔｅｒ）Ａｂｓｔｒａｃｔ：Ｔｈｉ...     

Feature-based Integrated CAD/CAPP/CAM System For Concurrent Engineering

Ｆｅａｔｕｒｅ＿ｂａｓｅｄＩｎｔｅｇｒａｔｅｄＣＡＤ／ＣＡＰＰ／ＣＡＭＳｙｓｔｅｍＦｏｒＣｏｎｃｕｒｅｎｔＥｎｇｉｎｅｒｉｎｇＷａｎｇＨｕｉｃｈｅｎｇＺｈａｎｇＸｉｎｆａｎｇＺｈｏｕＪｉ（ＣＡＤＣｅｎｔｅｒｏｆＨ．Ｕ．Ｓ．Ｔ）ＡｂｓｔｒａｃｔＴｈｉｓ...     

关于并行FFT算法

关于并行ＦＦＴ算法魏公毅，蔡中善（中国科学院计算中心）ＯＮＰＡＲＡＬＬＥＬＦＦＴＡＬＧＯＲＩＴＨＭＳ￥ＷｅｉＧｏｎｇ－ｙｉ；ＣａｉＺｈｏｎｇ－ｓｈａｎ（ＣｏｍｐｕｔｉｎｇＣｅｎｔｅｒ．ＡｃａｄｅｍｉａＳｉｎｉｃａ）Ａｂｓｔｒａｃｔ：Ｔｈｅｐａｒａｌｌ...     

计算机辅助考试系统

计算机辅助考试系统（ＣＡＥＳ），是一个应用它以计算机理论考试试题库为基础，在学校机房的网络环境下运行，能进行计算基础知识及ＢａｓｉｃＦｏｒｔｒａｎＰａｓｉｃａｌＣＦｏｘｂａｓｅｔ等高级语言的理论考试，具有自动出题，自动计时，自动判卷，自动统计分析等功能，能大幅度减轻教师工作量，有效提高考试的公平性和科学性。     

怎样防止他人使用你的计算机

假设某某在你的机器上,利用ＣＭＯＳ密码破译软件或口令计算软件（从网上下载：／／ｗｗｗ．．ｎｅｗｈｕａ．ｃｏｍ．ｃｎ或ｗｗｗ．ｌｏｓｔｐａｓｓｗａｒｄ．ｃｏｍ／）,或通过重写软件Ａｗｄｆｌａｓｈ．ｅｘｅ,或用ＤＥＢＵＧ命令的一Ｏ ７０ １０;－Ｏ７１００。利用ＣＭＯＳＲＡＭ在加电自检时发现奇偶校验错而进入ＣＭＯＳ,从而更改其相关选项：例如更改了 Ｂｏｏｔ Ｓｑｕｅｎｃｅ： Ｃ：, Ａ：（驱动器启动顺序,有的机器是Ｆｉｒｓｔ Ｓｑｕｅｎｃｅ项）,而使系统从软驱进行引导。并在ＭＳＤＯＳ． ＳＹＳ或ＣＯＮＦＩＧ…     

CE公司炉膛安全监控系统的分析和比较

ＣＥ公司炉膛安全监控系统的分析和比较王啸，王志祥，崔大勇，包龙海ＡｎａｌｙｓｉｓａｎｄＣｏｍｐａｒｉｓｏｎｏｆＦｕｒｎａｃｅＳａｆｅｇｕａｒｄＳｕｐｅｒｖｉｓｏｒｙＳｙｓｔｅｍｏｆＣＥＣｏｍｐａｙ（ＵＳＡ）￥Ｗａｎｇｘｉａｏ；ＷａｎｇＺｈｉｘｉａｎｇ...     

基于MFC封装的Windows通信API的研究

本文介绍 Ｗｉｎｄｏｗｓ环境下网络编程的多种通信 ＡＰＩ及 ＭＦＣ（微软基础类库）对它们的封装类,重点阐述封装 ＷｉｎＳｏｃｋ ＡＰＩ的 ＭＦＣ类 ＣＡＳｙｎｃＳｏｃｋｅｔ和Ｃｓｏｃｋｅｔ的编程模型。     

概念形成学习系统中数值属性的表示与聚类

本文在对ＣＯＢＷＥＢ、ＣＬＡＳＳＩＴ等概念聚类系统研究的基础上，提出了一种用数值属性的聚类分划来表示数值属性的方法．这种表示的核心是基于数值属性的取值分布．对于在这种表示下数值属性聚类的相关问题及性质，本文进行了较为详细的讨论．在此基础上，本文给出了一个能统一处理数值属性和符号属性的聚类评价函数．一个基于聚类分划表示方法的概念形成学习系统ＣＦＬＳ（ｃｏｎｃｅｐｔｆｏｒｍａｔｉｏｎｌｅａｒｎｉｎｇｓｙｓｔｅｍ）已在微机上实现，并被应用于地质学领域的三叶虫分类问题．本文对ＣＦＬＳ的设计和实现进行了介绍.     

含磷类血管紧张素转化酶抑制剂的3D—QSAR研究

文章利用限制性构象搜寻（Ｃｏｎｓｔｒａｉｎｔｓ Ｓｅａｒｃｈ）,确定了血管紧张素转化酶（ＡＣＥ）掏剂的药效团模型（活性构象或重叠规则）。用比较分子场分析法（Ｃｏｍｐａｒａｔｉｖｅ ＭｏｌｅｃｕｌａｒＦｉｄｌｄ Ａｎａｌｙｓｉｓ,ＣｏＭＦＡ）建立了含磷类ＡＣＥ掏剂的３Ｄ－ＱＳＡＲ模型。较高的交叉验证回归系数（q＾２＝０． ７２１）及此模型对不同类型抑制剂活性的预测结果,说明了其可靠性和较好的活性预测能     

功能强大的POWER TOY

ＰＯＷＥＲＴＯＹ这个软件包 ,包括了１５个“玩具”！它们是ＴｗｅａｋＵＩ,ＦｉｎｄＸ,ＳｅｎｄｔｏＸ,ＳｈｏｒｔｃｕｔＴａｒｇｅｔＭｅｎｕ,ＥｘｐｌｏｒｅｒＦｒｏｍＨｅｒｅ,ＤＯＳＰｒｏｍｐｔＨｅｒｅ,ＲｏｕｎｄＣｌｏｃｋ,ＤｅｓｋＭｅｎｕ,ＣａｂＦｉｌｅＶｉｅｗｅｒ,ＦｌｅｘｉＣＤ,ＦａｓｔＦｏｌｄｅｒＣｏｎｔｅｎｔｓ,ＴａｐｉＴＮＡ（ＴｅｌｅｐｈｏｎｙＴａｓｋｂａｒＮｏｔｉｆｉ ｃａｔｉｏｎＡｐｐｌｉｃａｔｉｏｎ）,Ｑｕｉｃｋｅｒ,ＸＭｏｕｓｅ,Ａｕｔｏｐｌａｙ。由ＭｉｃｒｏｓｏｆｔＷｉｎ９５…     

Fingerprint classification based on decision tree from singular points and orientation field

In this study, a high accuracy fingerprint classification method is proposed to enhance the performance in terms of efficiency for fingerprint recognition system. The recognition system has been considered as a reliable mechanism for criminal identification and forensic for its invariance property, yet the huge database is the key issue to make the system obtuse. In former works, the pre-classifying manner is an effective way to speed up the process, yet the accuracy of the classification dominates the further recognition rate and processing speed. In this paper, a rule-based fingerprint classification method is proposed, wherein the two features, including the types of singular points and the number of each type of point are adopted to distinguish different fingerprints. Moreover, when fingerprints are indistinguishable, the proposed Center-to-Delta Flow (CDF) and Balance Arm Flow (BAF) are catered for further classification. As documented in the experimental results, a good accuracy rate can be achieved, which endorses the effectiveness of the fingerprint classification scheme for the further fingerprint recognition system.     

一种基于网络的大型指纹数据库检索方法

随着指纹识别技术的广泛应用和网络技术的发展,基于网络的指纹自动识别系统面临着如何提高检索速度和准确度的问题。本文根据指纹数据库自身的特点,提出了一种新的检索和索引网络大型指纹数据库的方法。该算法利用指纹的类别和局部特征建立了三级索引,有利于缩小检索空间,提高检索速度。在算法中提出了一种新的指纹分类方法,将指纹分为八类：弧形、尖拱形、左旋形、右旋形、正涡形、左涡形、右涡形、混合形。该算法利用core点和delta点的位置、数目和方向判断指纹的类别,有效地解决了采集时对指纹的旋转、平移和形变不变性的要求,并有助于进一步缩小检索空间,提高检索速度。实验证明,效果良好。     

Modelling fingerprint ridge orientation using Legendre polynomials

The estimation of fingerprint ridge orientation is an essential step in every automatic fingerprint verification system. The importance of ridge orientation can be deflected from the fact that it is inevitably used for detecting, describing and matching fingerprint features such as minutiae and singular points. In this paper we propose a novel method for fingerprint ridge orientation modelling using Legendre polynomials. One of the main problems it addresses is smoothing orientation data while preserving details in high curvature areas, especially singular points. We show that singular points, which result in a discontinuous orientation field, can be modelled by the zero-poles of Legendre polynomials. The models parameters are obtained in a two staged optimization procedure. Another advantage of the proposed method is a very compact representation of the orientation field, using only 56 coefficients. We have carried out extensive experiments using a state-of-the-art fingerprint matcher and a singular point detector. Moreover, we compared the proposed method with other state-of-the-art fingerprint orientation estimation algorithms. We can report significant improvements in both singular point detection and matching rates.     

指纹门票系统的设计与实现

作为一种电子识别方法,作者设计和开发的指纹门票系统提供了指纹采集、识别和自动控制于一体的管理手段,将指纹采集识别芯片集成到IC卡读写器中,从而实现指纹采集识别和通道门闸控制于一体的连贯智能控制手段。指纹门票系统的实现采用了三层客户机/服务器体系结构,已经成功地应用在多个旅游风景区中,提高了旅游风景区的整体管理能力和管理工作效率。     

基于大尺度方向场描述子的指纹分类算法

指纹分类通过将指纹划分到一系列预定义的类别之中以极大降低指纹匹配的工作量,是指纹识别系统中一项非常关键的技术。受FingerCode分类特征启发,提出了一种称为大尺度方向场描述子的新的分类特征,该特征以指纹核心点(core点)为中心构造大尺度环形网状结构,通过抽取网状结构中节点处的方向来形成特征向量,以达到近似描述核心点周围的方向模式的目的。大量实验结果表明:相较于FingerCode特征,新特征在保证分类准确率的同时,由于特征提取方式更为简单、高效,分类速度也提高了近20倍。     

Combining flat and structured representations for fingerprint classification with recursive neural networks and support vector machines

We present new fingerprint classification algorithms based on two machine learning approaches: support vector machines (SVMs) and recursive neural networks (RNNs). RNNs are trained on a structured representation of the fingerprint image. They are also used to extract a set of distributed features of the fingerprint which can be integrated in the SVM. SVMs are combined with a new error-correcting code scheme. This approach has two main advantages: (a) It can tolerate the presence of ambiguous fingerprint images in the training set and (b) it can effectively identify the most difficult fingerprint images in the test set. By rejecting these images the accuracy of the system improves significantly. We report experiments on the fingerprint database NIST-4. Our best classification accuracy is of 95.6 percent at 20 percent rejection rate and is obtained by training SVMs on both FingerCode and RNN-extracted features. This result indicates the benefit of integrating global and structured representations and suggests that SVMs are a promising approach for fingerprint classification.     

基于方向滤波分割的指纹自动识别系统算法

为适应当前个人身份鉴定迫切需要，提出了一种基于方向滤波分割的指纹自动识别系统的完整方案。该系统主要分指纹预处理及识别两大部分，包括方向图的计算、平滑、方向滤波、二值化、去噪、特征提取、特征编码及识别的方法。利用该系统的预处理及特征编码和识别方法，指纹图象得到了较好的增强，而且，在识别过程中能克服一定范围内指纹旋转及平移因素的影响。本系统具有高效识别和很强的抗干扰能力，对于残缺或模糊的指纹图象也有很好的识别效果，有较高的使用价值。     

Fingerprint classification: a review

Biometrics is the automatic identification of an individual that is based on physiological or behavioural characteristics. Due to its security-related applications and the current world political climate, biometrics is currently the subject of intense research by both private and academic institutions. Fingerprints are emerging as the most common and trusted biometric for personal identification. The main objective of this paper is to review the extensive research that has been done on fingerprint classification over the last four decades. In particular, it discusses the fingerprint features that are useful for distinguishing fingerprint classes and reviews the methods of classification that have been applied to the problem. Finally, it presents empirical results from the state of the art fingerprint classification systems that have been tested using the NIST Special Database 4.     

一种新的指纹图像细化算法

在自动指纹识别系统中，指纹细化占有重要地位，本文讨论一种新的细化算法，将方向图引入指纹图像的细化，结合形态学细化算法，改进了传统细化算法不尽如人意的方向，取得了良好的细化效果。     

Fingerprint classification based on Adaboost learning from singularity features

Fingerprint classification is an important indexing scheme to narrow down the search of fingerprint database for efficient large-scale identification. It is still a challenging problem due to the intrinsic class ambiguity and the difficulty for poor quality fingerprints. In this paper, we presents a fingerprint classification algorithm that uses Adaboost learning method to model multiple types of singularity features. Firstly, complex filters are used to detect the singularities. For powerful representation, we compute the complex filter responses of the detected singularities at multiple scales and a feature vector is constructed for each scale that consists of the relative position and direction and the certainties of the singularities. Adaboost learning method is then applied on decision trees to design a classifier for fingerprint classification. Finally, fingerprint class is determined by the ensemble of the classification results at multiple scales. The experimental results and comparisons on NIST-4 database have shown the effectiveness and superiority of the fingerprint classification algorithm.