液滴传感器及其鉴别算法的设计

液滴传感器是基于液滴分析技术,利用光纤和电容测试技术制成特殊的液滴分析装置,通过获取液滴在生长过程中光纤信号与电容信号的变化规律,达到对不同液体进行鉴别的目的。介绍了液滴传感器的设计理论,给出了系统设计的总体框图,提出了基于液滴指纹图特征值的鉴别算法,并通过对部分样品进行测试实验,证明此算法可以作为鉴别液体的依据。     

中药制剂的光纤液滴指纹图分析

光纤液滴指纹图是以利用光学方法检测液体特征参量为目的,并通过计算机分析在液滴下降过程中的光强变化曲线.在实验中利用光纤液滴传感器测试了多种中药制剂的不同体积分数的液体试样,分析光纤液滴指纹图与液体物理、化学特征参量的对应关系,并得到2种样品的线性方程,为中药制剂的在线监测与鉴别提供了一种新方法.     

光纤液滴传感器实现汽油溶液的鉴别

光纤液滴传感器是利用光纤来监测液滴的增长过程,通过分析随液滴下降过程而变化的光强变化曲线,获得反映液体的物理、化学性质的“光纤液滴指纹图”的测试系统。本文利用光纤液滴传感器测量了不同型号的汽油溶液,同时测量了不同型号的汽油溶液按比例混合后的液滴指纹图,通过对比分析液滴指纹图可以实现汽油溶液的鉴别。     

光电式液滴分析技术定量检测水中的盐度

基于光纤-电容液滴分析方法的原理,研究了盐度的检测.由20种不同盐度溶液的实验结果得出:随着盐度的增加,光纤信号值和液滴指纹图曲线下的面积不断增大.利用1-12-1三层BP神经网络结构,分别以光纤信号的平均值和液滴指纹图曲线下的面积为输入向量,进行网络训练,并利用训练好的网络分别进行测试.比较测试结果得出:利用光纤信号的平均值作为BP网络的输入向量进行盐度检测效果较好,最大检测误差为0.14％.     

自动指纹识别系统的研究与实现

对指纹图像的预处理、特征提取和指纹匹配的算法进行了研究.在指纹图像预处理中,首先选取了一种简单的分割算法,即在获取指纹频率场和方向图前就可以完成,从而能够尽早获得有效区域;在脊线频率和方向图的基础上,实现了一种基于脊线方向分析的二值化算法;实现了利用数学形态学对二值化后的图像进行细化处理,构成了一个比较完整的预处理过程.在指纹图像特征提取和匹配中,每个指纹特征点的信息都以向量进行表示,向量构成了指纹的所有信息;为了提高指纹旋转后的匹配率,改进了指纹的特征提取和匹配的算法.最后,对研究的所有算法进行了测试,结果表明,利用这些算法能够很好的实现各自的功能,完成了预期的目标.     

温度对液滴指纹图水质识别的影响分析

研究水本身温度和环境温度的变化对液滴指纹图产生的影响,可为液滴分析仪仪器化设计提供参考。在环境温度保持在27℃左右时,对注射器中水的温度由57℃降至31.3℃期间,连续采集了16组液滴指纹图,比较各组液滴指纹图,光纤信号和电容信号的最大变化分别为0.17%和1.23%,与系统误差相当,说明水本身温度对液滴指纹图几乎无影响;以纯水在不同温度下的折射率、表面张力和介电常数值,计算了环境温度对纯水液滴测量信号的影响大小,计算结果表明:环境温度由18℃升至30℃时,光纤信号和电容信号的变化分别为0.64%和3%,对电容信号的影响较大。     

改进的软K段主曲线算法及其在指纹骨架提取中的应用

主曲线是一种基于非线性变换的特征提取方法,它是通过数 据分布“中间”并满足“自相合”的光滑曲线,能较好抽取出数据的结构特征。针对软K段 主曲线算法提取的指纹图像的骨架结构光滑度较差,而且提取的指纹图像骨架经常出现小圈 和短枝的现象,本文在对软K段主曲线算法和指纹图像数据特点分析的基础上,引入了一个 新的评判函数,并提出了改进的软K段主曲线算法,将该算法应用在提取指纹图像骨架上。 实验结果表明,改进的软K段主曲线算法在提取指纹图像骨架的效果和准确率上比原算法都 有明显提高。     

基于隐马尔可夫模型的躯感网心电图信号特征提取方法

为了解决躯感网的心电信号特征提取问题,结合心电图信号波形的特征区间,建立了面向心电图信号特征提取的离散隐马尔可夫模型;并面向该模型定制了专家标注选取、导联选取、观察数据归一化、三元组初始值选取以及训练数据量选取等方法.最后,采用Baum-Welch算法训练HMM模型的参数,并利用Viterbi算法提取心电图的信号特征.仿真结果表明,基于 HMM的心电图信号特征提取算法的复杂度较低、精确度较高、实时性较好,适合在线处理非线性、动态变化的心电图信号,能够满足基于躯感网的心电图信号特征提取的性能要求.     

改进的指纹细节特征提取算法

指纹细节特征（minutiae)提取是指纹自动识别的核心技术之一，常规的指纹细节特征提取算法需要先采用纹线跟踪的方法对细化后的指纹图象进行纹线修复，然后再实现细节特征提取，纹线修复不仅步骤繁琐，而且比较耗时，针对这一问题，提出了一种改进的指纹细节特征提取算法，该算法首先在细化后的指纹图象上直接提取原始细节特征点集，然后分析图象中存在的各类噪声及其特点，总结伪特征点的分布规律，最后，结合局部纹线方向信息，针对不同的噪声，采用针对性的算法，将各类噪声引起的伪特征点分别予以删除，最终保留下来的特征点集即视为真正的特征点集，为验证该算法的性能，将改进算法与常规算法进行了对比实验，实验结果表明，改进算法有效地减少了计算时间，细节特征提取准确率也基本可以满足应用的需要。     

一种改进的指纹图像增强算法

指纹识别一般包括指纹采集、图像预处理、特征提取及特征匹配等步骤.其中图像预处理中的指纹图像增强是最关键的步骤,直接影响特征提取和匹配.论文针对指纹采集中存在的指纹图像局部模糊的问题,提出了一种改进的基于Gabor函数的指纹增强算法.该算法首先对指纹图像进行归一化处理,然后对指纹图像分成奇异区和非奇异区分别进行点方向和块方向的求取,最后用改进的Gabor滤波器对指纹图像进行增强.实践表明,该方法能提高指纹图像增强的效果和速度.     

Fingerprint image analysis for automatic identification

Most of the papers on fingerprints deal with classification of fingerprint images. Fingerprint databases being large (in the range of millions), the effort in matching of fingerprints within a class or when the class is unknown, is very significant. This requires fingerprint image analysis and extraction of the “minutiae” features, which are used for matching FPs. In this paper a scheme of preprocessing and feature extraction of fingerprint images for automatic identification is presented, which works even if the pattern class is unknown. The identification of fingerprints is based on matching the minutiae features of a given finger-print against those stored in the database. The core and delta information is used for classification and for registration while matching. These algorithms have been tested for more than 10,000 fingerprint images of different qualities. The results are manually verified and found to be very good for practical application. A few sample results are presented.     

Fingerprint classification using a feedback-based line detector

We present a fingerprint classification algorithm in this paper. This algorithm classifies a fingerprint image into one of the five classes: Arch, Left loop, Right loop, Whorl, and Tented arch. We use a new low-dimensional feature vector obtained from the output of a novel oriented line detector presented here. Our line detector is a co-operative dynamical system that gives oriented lines and preserves multiple orientations at points where differently oriented lines meet. Our feature extraction process is based on characterizing the distribution of orientations around the fingerprint. We discuss three different classifiers: support vector machines, nearest-neighbor classifier, and neural network classifier. We present results obtained on a National Institute of Standards and Technology (NIST) fingerprint database and compare with other published results on NIST databases. All our classifiers perform equally well, and this suggests that our novel line detection and feature extraction process indeed captures all the crucial information needed for classification in this problem.     

Fingerprint recognition using mel-frequency cepstral coefficients

This paper presents a new fingerprint recognition method based on mel-frequency cepstral coefficients (MFCCs). In this method, cepstral features are extracted from a group of fingerprint images, which are transformed first to 1-D signals by lexicographic ordering. MFCCs and polynomial shape coefficients are extracted from these 1-D signals or their transforms to generate a database of features, which can be used to train a neural network. The fingerprint recognition can be performed by extracting features from any new fingerprint image with the same method used in the training phase. These features are tested with the neural network. The different domains are tested and compared for efficient feature extraction from the lexicographically ordered 1-D signals. Experimental results show the success of the proposed cepstral method for fingerprint recognition at low as well as high signal to noise ratios (SNRs). Results also show that the discrete cosine transform (DCT) is the most appropriate domain for feature extraction.     

航空三相静止变流器死区效应分析与补偿

为了解决航空三相静止变流器输出波形失真问题,详细分析了死区效应造成电压畸变的原因和由此带来的谐波成分.采用正弦脉宽调制(SPWM)控制技术,使变流器输出的波形更接近于正弦波,提出了设置死区与电压补偿相结合的控制方法,并通过SABER进行了仿真分析.同时,利用TMS320F2812数字芯片实现了相应的补偿策略.仿真和试验结果证明了该控制方法的有效性.     

Enhancement of feature extraction for low-quality fingerprint images using stochastic resonance

This paper presents a new approach to enhancing feature extraction for low-quality fingerprint images by adding noise to the original signal. Feature extraction often fails for low-quality fingerprint images obtained from excessively dry or wet fingers. In nonlinear signal processing systems, a moderate amount of noise can help amplify a faint signal while excessive amounts of noise can degrade the signal. Stochastic resonance (SR) refers to a phenomenon where an appropriate amount of noise added to the original signal can increase the signal-to-noise ratio. Experimental results show that Gaussian noise added to low-quality fingerprint images enables the extraction of useful features for biometric identification. SR was applied to 20 fingerprint images in the FVC2004 DB2 database that were rejected by a state-of-the-art fingerprint verification algorithm due to failures in feature extraction. SR enabled feature extraction from 10 out of 11 low-quality images with poor contrast. The remaining nine images were damaged fingerprints from which no meaningful features can be obtained. Improved feature extraction using SR decreases an equal error rate of fingerprint verification from 6.55% to 5.03%. The receiver operating characteristic curve shows that the genuine acceptance rates are improved for all false acceptance rates.     

多模态生物特征提取及相关性评价综述

生物特征识别是身份认证的重要手段,特征提取技术在其中扮演了关键角色,直接影响识别的结果。随着特征提取技术日趋成熟,学者们逐渐将目光投向了生物特征间的相关性问题。本文以单模态和多模态生物识别中的特征提取方法为研究对象,回顾了人脸与指纹的特征提取方法,分析了基于经验知识的特征分类提取方法以及基于深度学习的计算机逻辑采样提取方法,并从图像处理的角度对单模态与多模态方法进行对比。以当前多模态生物特征提取方法和DNA表达过程为引,提出了不同模态的生物特征之间存在相关性的猜想,以及对这一猜想进行建模的思路。在多模态生物特征提取的基础上,对今后可能有进展的各生物特征之间的相关性建模进行了展望。     

特征提取及其在电子鼻对可燃液体识别中的应用

利用6只TGS传感器组成的阵列对4种常见的易燃液体和3种不可燃饮料进行测试,并选用4种有代表性的特征提取方法,主元分析法(PCA)、Fisher判别法(FDA)、自组织映射(SOM)、Sammon映射法(Sammon map)作为数据预处理方法,并用3种模式识别方法对预处理后的数据进行识别。结果表明:在各种特征提取方法的处理下,可燃类和不可燃类样本都能被准确地区分,而只有在有导师的特征提取方法才能有效地识别各个可燃液体类子类和不可燃液体类子类的样本类别,最佳的投影维数与各特征提取方法有密切联系,而最优的模式识别方法则与数据的分布有关。     

基于小型雨水发电装置的电能转换电路设计

基于充分利用清洁能源、保护环境的理念,设计了小型雨水发电装置的电能转换电路。通过对小型雨水发电装置的输出电压值数据分析处理,为发电装置设计电能转换及收集电路。在搭建好的试验装置上,改变出水口与发电机扇叶的相对位置,测出不同因素不同水平(因素的不同状态)下的电压值作为试验数据。采用方差分析和统计分析等方法进行数据处理分析,得到最佳的输出电压值,作为转换电路的输入电压。设计转换电路,对发电机输出电压进行升压、逆变和滤波,以实现用电补给。在Matlab(Simulink)环境下搭建的转换电路模型,模拟交流侧的负载为感性条件下进行仿真。对所设计的电路模型进行两个方向的触发试验,转换电路输出了较为理想的波形。该电路设计为雨水发电的研究提供了依据。     

基于主曲线的指纹细节特征提取方法

要提高指纹识别的识别率,关键是指纹特征的提取。主曲线是主成份分析的非线性推广,它是通过数据分布“中间’’并满足“自相合”的光滑曲线,较好地反映了数据分布的结构特征。本文尝试使用主曲线这种新的方法来提取指纹的细节特征。实验结果表明利用主曲线来提取指纹的结构特征是可行的,它为指纹特征提取的研究提供了一条新途径。     

A comparative study on feature extraction for fingerprint classification and performance improvements using rank-level fusion

Fingerprint classification represents an important preprocessing step in fingerprint identification, which can be very helpful in reducing the cost of searching large fingerprint databases. Over the past years, several different approaches have been proposed for extracting distinguishable features and improving classification performance. In this paper, we present a comparative study involving four different feature extraction methods for fingerprint classification and propose a rank-based fusion scheme for improving classification performance. Specifically, we have compared two well-known feature extraction methods based on orientation maps (OMs) and Gabor filters with two new methods based on "minutiae maps" and "orientation collinearity". Each feature extraction method was compared with each other using the NIST-4 database in terms of accuracy and time. Moreover, we have investigated the issue of improving classification performance using rank-level fusion. When evaluating each feature extraction method individually, OMs performed the best. Gabor features fell behind OMs mainly because their computation is sensitive to errors in localizing the registration point. When fusing the rankings of different classifiers, we found that combinations involving OMs improve performance, demonstrating the importance of orientation information for classification purposes. Overall, the best classification results were obtained by fusing orientation map with orientation collinearity classifiers.