Indirect test of M-S circuits using multiple specification band guarding

Testing analog and mixed-signal circuits is a costly task due to the required test time targets and high end technical resources. Indirect testing methods partially address these issues providing an efficient solution using easy to measure CUT information that correlates with circuit performances. In this work, a multiple specification band guarding technique is proposed as a method to achieve a test target of misclassified circuits. The acceptance/rejection test regions are encoded using octrees in the measurement space, where the band guarding factors precisely tune the test decision boundary according to the required test yield targets. The generated octree data structure serves to cluster the forthcoming circuits in the production testing phase by solely relying on indirect measurements. The combined use of octree based encoding and multiple specification band guarding makes the testing procedure fast, efficient and highly tunable. The proposed band guarding methodology has been applied to test a band-pass Butterworth filter under parametric variations. Promising simulation results are reported showing remarkable improvements when the multiple specification band guarding criterion is used.     

Recognizing machines with parametric and nonparametric learning methods using contextual information

Pattern recognition systems using information from the pattern which follows the present pattern are discussed. Parametric learning methods in the supervised and unsupervised machines are proposed and compare favorably with conventional methods. Furthermore, a semilinear machine with a nonparametric learning method is considered. The results of computer experiments with artificially generated data and with handprinted alphanumeric characters are given to show that the approach we adopt is quite useful for recognition of Markovian patterns.     

混合模式垃圾邮件检测

机器学习和数据挖掘可以有效的应用于各种各样的数据模式识别中,其中包括对电子邮件进行的模式识别、分类和知识发现。现在使用的垃圾邮件检测系统可以精确的进行邮件判别。但是,现实是总有一些误判,而这些误判有时会带来很大的损失。现提出的混合模式分类器分析了两个单个分类器的混合,降低了误判率,提高了检测的准确率。     

支持向量机分类器在医疗诊断中的应用研究

在医疗诊断中，常根据病人的多项病理检测结果进行诊断，由于存在个体的差异和数据本身的噪声，所以要准确的诊断是困难的，支持向量机是统计学习理论基础上发展而来的一种新的通用学习方法，具有很多独特的优点。该文介绍了支持向量机非线性分类算法，选取径向基核函数，构造了支持向量机非线性分类器，并将其应用于心脏病诊断，所用数据来自UCI benchmark数据集，与其它方法相比，取得了较高的准确率，结果表明支持向量机在医疗诊断中有很大的应用潜力。     

Arabic handwritten digit recognition

In this paper, we fill a gap in the literature by studying the problem of Arabic handwritten digit recognition. The performances of different classification and feature extraction techniques on recognizing Arabic digits are going to be reported to serve as a benchmark for future work on the problem. The performance of well known classifiers and feature extraction techniques will be reported in addition to a novel feature extraction technique we present in this paper that gives a high accuracy and competes with the state-of-the-art techniques. A total of 54 different classifier/features combinations will be evaluated on Arabic digits in terms of accuracy and classification time. The results are analyzed and the problem of the digit ‘0’ is identified with a proposed method to solve it. Moreover, we propose a strategy to select and design an optimal two-stage system out of our study and, hence, we suggest a fast two-stage classification system for Arabic digits which achieves as high accuracy as the highest classifier/features combination but with much less recognition time.     

基于集成学习的离子通道药物靶点预测

新药研制成功的关键在于药物靶点的发现和准确定位.在已知的药物靶点中,离子通道蛋白是一类广受欢迎的靶点,它与免疫系统、心血管等疾病密切相关.对于靶点的发现,传统生物方法成本高、耗时久.因此,探讨了基于机器学习的离子通道蛋白药物靶点的挖掘,以加快药物靶点发现过程,节约经费.由于药物靶点相关序列的长度不一致,考虑了蛋白质序列编码的13种特征,它们能将不等长的蛋白质序列转化成等长序列.通过数值实验筛选能够较好地区分靶点和非靶点的特征子集,并采用集成学习的方法整合特征得到预测模型.通过与已有工作的比较表明,提出的集成模型能得到较高的准确率,具有很好的应用前景.     

Automated breast cancer detection and classification using ultrasound images: A survey

Breast cancer is the second leading cause of death for women all over the world. Since the cause of the disease remains unknown, early detection and diagnosis is the key for breast cancer control, and it can increase the success of treatment, save lives and reduce cost. Ultrasound imaging is one of the most frequently used diagnosis tools to detect and classify abnormalities of the breast. In order to eliminate the operator dependency and improve the diagnostic accuracy, computer-aided diagnosis (CAD) system is a valuable and beneficial means for breast cancer detection and classification. Generally, a CAD system consists of four stages: preprocessing, segmentation, feature extraction and selection, and classification. In this paper, the approaches used in these stages are summarized and their advantages and disadvantages are discussed. The performance evaluation of CAD system is investigated as well.     

多距离分类器组合试验在人脸识别中的应用

先通过PCA特征脸或插值降维,并利用Fisher鉴别矢量集,获得人脸鉴别特征;然后采用常见距离分类器及其组合形式进行识别分析,分类器组合使用多数投票规则和最大值规则等;最后对计算结果进行分析。该文研究思路和方法简洁,结果令人满意,对基于生物特征鉴别分析的工程应用具有较大价值。     

Cultivar discrimination of stored apple seeds based on geometric features determined using image analysis

Digital image analysis can allow determining the features of seeds and their interpretation in a fast, inexpensive, and non-destructive way. The research aimed to develop discriminative models based on geometric features to distinguish seeds belonging to different apple cultivars. Images of seeds of apples ‘Gala’, ‘Jonagold’ and ‘Idared’ were acquired using a flatbed scanner. The linear dimensions and shape factors were calculated, and after attribute selection, they were used for the discriminant analyzes of seeds for ‘Gala’ with ‘Idared’, ‘Gala’ with ‘Jonagold’, ‘Idared’ with ‘Jonagold’ and ‘Gala’ with ‘Idared’ with ‘Jonagold’ with the use of discriminative classifiers from Bayes, Function, Meta, Rules and Decision Trees groups. In the case of models build based on selected linear dimensions, the accuracy of discrimination was equal up to 84% for distinguishing seeds of all three apple cultivars for the J48 classifier from Decision Trees and 93% for analysis of ‘Gala’ and ‘Idared’ for the J48 from Decision Trees. The slightly lower correctness of up to 82% for discrimination of ‘Gala’, ‘Idared’ and ‘Jonagold’, Multi Class Classifier) and 90% (‘Gala’ and ‘Jonagold’, J48) were obtained for models build based on selected shape factors. The analyzes performed based on sets including selected combined linear dimensions and shape factors of seeds provided the correctness of up to 86% for the discrimination of three apple seed cultivars (J48) and 91% (‘Gala’ and ‘Jonagold’, J48). The results can be used in practice for the assessment of the authentication of seeds of apple cultivars with high probability.     

基于KPCA＋CCA的人脸性别分类

KPCA＋CCA方法在人脸特征的提取、分类和分析中的有效性已受到有关研究人员的重视,用该法可从一张原始灰度人脸图像中直接识别出一个人的性别。将核方法引入到主分量分析中,由于CCA（Canonical Correlation Analysis）用到了KPCA（KernelPrincipal Component Analysis）变换后样本的全部核主分量,在分析中没有丢失任何鉴别信息,因而在不同光照、表情、姿态和脸部细节的原始灰度人脸图像中鲁棒性更高。在ORL人脸数据库中用基于核的最近邻特征分类器进行实验,取得了96%的平均准确率。