基于支持向量机递归特征消除和特征聚类的致癌基因选择方法

癌症通常由基因发生突变引起,因此从大量基因中有效地识别出少量致癌基因具有重要意义.针对基因表达谱数据高维小样本的特点,将支持向量机递归特征消除(SVM-RFE)和特征聚类算法相结合,提出一种新的基因选择方法:K类别SVM-RFE(K-SVM-RFE).该算法通过特征排序算法去除大量无关基因,利用K均值聚类算法将相似基因聚为一类,并通过两次SVM-RFE算法精选致癌基因.随后将K-SVM-RFE算法应用于多个基因表达谱数据集,并对其中的关键参数设置进行了讨论.实验结果表明K-SVM-RFE算法所选基因较已有方法在分类准确率上有显著提高,特别是在选择少量致癌基因上效果提升更为明显.国家自然科学基金(61771331

Screening of knee-joint vibroarthrographic signals using statistical parameters and radial basis functions

Externally detected vibroarthrographic (VAG) signals bear diagnostic information related to the roughness, softening, breakdown, or the state of lubrication of the articular cartilage surfaces of the knee joint. Analysis of VAG signals could provide quantitative indices for noninvasive diagnosis of articular cartilage breakdown and staging of osteoarthritis. We propose the use of statistical parameters of VAG signals, including the form factor involving the variance of the signal and its derivatives, skewness, kurtosis, and entropy, to classify VAG signals as normal or abnormal. With a database of 89 VAG signals, screening efficiency of up to 0.82 was achieved, in terms of the area under the receiver operating characteristics curve, using a neural network classifier based on radial basis functions

基于自适应权重AD-Census变换的双目立体匹配

针对AD-Census变换采用固定权重将AD变换代价与Census变换代价合成的双目立体匹配代价无法体现像素点区域特征的问题,提出一种基于自适应权重AD-Census变换的双目立体匹配算法。算法首先通过增加相邻像素点的灰度差阈值条件改善十字支撑自适应窗口;然后以每个像素点的十字支撑自适应窗口的最短臂长为自变量,利用指数形式的函数,进行AD变换代价与Census变换代价合成权重的自适应设置。由于像素点十字支撑自适应窗口的最短臂长能够反映像素点的区域特性,因此自适应设置的权重大小与像素点的区域特性直接相关,计算图像边缘区域像素点的匹配代价时,AD变换的权重大;计算平滑区域像素点的匹配代价时,Census变换的权重大。Middlebury第3代双目立体匹配评估平台的结果显示,基于自适应权重AD-Census变换的双目立体匹配性能与基于AD-Census变换的双目立体匹配性能相比,所有图像集的全部像素点的视差平均误差减小了25%,非遮挡像素点的视差平均误差减小了20%,性能得到了提升;平台上包括Adir在内的多个图像集的匹配结果表明基于自适应权重AD-Census变换的双目立体匹配更适合含纹理丰富、存在重复区域的图像。国家自然科学基金资助项目(61274133

Loop Closure Detection Algorithm Based on Greedy Strategy for Visual SLAM

动态环境与视觉混淆严重影响视觉闭环检测性能.基于贪心策略,提出了一种在线构建视觉词典的闭环检测算法.算法优先处理Surf描述与已有单词Surf描述欧式距离最大的特征点,改进特征点与单词Surf描述最近邻的约束条件,生成了表征性能强、量化误差小的视觉词典,算法具备实时性,并在动态环境图像集与视觉混淆多发生的图像集上,在确保100%,准确率的条件下,最大召回率分别提升了5%,与4%,.The performance of loop closure detection is seriously affected by dynamic objects and perceptual aliasing in the environment. Based on greedy strategy, a real-time loop closure detection approach using online visual dictionary is proposed. The process of dictionary construction gives priority to dealing with Surf feature that has the maximum Euclidean distance from the closest vocabulary word. A more discriminative and representative visual vocabulary is produced through adding constraint condition to the nearest neighbor distance. This visual vocabulary guarantees a small quantization error. The proposed approach meets real-time constraints. Experiments based on datasets from dynamic environments and visually repetitive environments demonstrated that the largest recall rate increased by 5% and 4% respectively at 100% precision. © 2017, Editorial Board of Journal of Tianjin University(Science and Technology). All right reserved.国家自然科学基金资助项目(61274133). Supported by the National Natural Science Foundation of China(61274133

Loop Closure Detection Algorithm Based on Greedy Strategy for Visual SLAM

动态环境与视觉混淆严重影响视觉闭环检测性能.基于贪心策略,提出了一种在线构建视觉词典的闭环检测算法.算法优先处理Surf描述与已有单词Surf描; 述欧式距离最大的特征点,改进特征点与单词Surf描述最近邻的约束条件,生成了表征性能强、量化误差小的视觉词典,算法具备实时性,并在动态环境图像集; 与视觉混淆多发生的图像集上,在确保100%,准确率的条件下,最大召回率分别提升了5%,与4%,.The performance of loop closure detection is seriously affected by; dynamic objects and perceptual aliasing in the environment.Based on; greedy strategy,a real-time loop closure detection approach using online; visual dictionary is proposed.The process of dictionary construction; gives priority to dealing with Surf feature that has the maximum; Euclidean distance from the closest vocabulary word.A more; discriminative and representative visual vocabulary is produced through; adding constraint condition to the nearest neighbor distance.This visual; vocabulary guarantees a small quantization error.The proposed approach; meets real-time constraints.Experiments based on datasets from dynamic; environments and visually repetitive environments demonstrated that the; largest recall rate increased by 5%, and 4%, respectively at 100%,; precision.国家自然科学基金资助项

CLASSIFICATION OF KNEE-JOINT VIBROARTHROGRAPHIC SIGNALS USING TIME-DOMAIN AND TIME-FREQUENCY DOMAIN FEATURES AND LEAST-SQUARES SUPPORT VECTOR MACHINE

Analysis of knee-joint vibration sounds, also known as vibroarthrographic (VAG) signals, could lead to a noninvasive clinical tool for early detection of knee-joint pathology. In this paper, we employed the wavelet matching pursuit (MP) decomposition and signal variability for time-frequency domain and time-domain analysis of VAG signals. The number of wavelet MP atoms and the number of significant turns detected with the fixed threshold from signal variability analysis were extracted as prominent features for the classification over the data set of 89 VAG signals. Compared with the Fisher linear discriminant analysis, the nonlinear least-squares support vector machine (LS-SVM) is able to achieve higher overall accuracy of 73.03%, and the area of 0.7307 under the receiver operating characteristic curve

BME education program at the EMBS student club of Beijing university of posts and telecommunications

An extended Biomedical Engineering (BME) education program is designed to help students enhance both technical expertise and communication skills at the EMBS Student Club of Beijing University of Posts and Telecommunications (BUPT). The specific steps of the program cover: raising funds for activity purpose; holding an introductory seminar and a panel discussion; inviting guest speakers; seeking team-based research opportunities; joining in IEEE-EMBS community events; and writing a code of ethics for a student club. The program addressed sets a good example to the EMBS student chapters and clubs all over the world

Screening of knee-joint vibroarthrographic signals using probability density functions estimated with Parzen windows

Pathological conditions of knee joints have been observed to cause changes in the characteristics of vibroarthrographic (VAG) signals. Several studies have proposed many parameters for the analysis and classification of VAG signals; however, no statistical modeling methods have been explored to analyze the distinctions in the probability density functions (PDFs) between normal and abnormal VAG signals. In the present work, models of PDFs were derived using the Parzen-window approach to represent the statistical characteristics of normal and abnormal VAG signals. The Kullback-Leibler distance was computed between the PDF of the signal to be classified and the PDF models for normal and abnormal VAG signals. Additional statistical measures, including the mean, standard deviation, coefficient of variation, skewness, kurtosis, and entropy, were also derived from the PDFs obtained. An overall classification accuracy of 77.53%, sensitivity of 71.05%, and specificity of 82.35% were obtained with a database of 89 VAG signals using a neural network with radial basis functions with the leave-one-out procedure for cross validation. The screening efficiency was derived to be 0.8322, in terms of the area under the receiver operating characteristics curve. (C) 2009 Elsevier Ltd. All rights reserved

Breast tissue classification based on unbiased linear fusion of neural networks with normalized weighted average algorithm

The diagnosis of breast cancer is performed based on informed interpretation of representative histological tissue sections. Tissue distribution detected from cytologic examinations is useful for tumor staging and appropriate treatment. In this paper, we propose a normalized weighted average (Normwave) algorithm for the unbiased linear fusion, and also construct the multiple classifier system that includes a group of Radial Basis Function (RBF) neural classifiers for the classification of breast tissue samples. The empirical results show that the proposed Normwave algorithm may improve the performance of the RBF-based multiple classifier system, and also reliably outperforms some widely used fusion methods, in particular the simple average and adaptive mixture of experts

An algorithm for evaluating the performance of adaptive filters for the removal of artifacts in ECG signals

Filtering electrocardiogram (ECG) signals calls for a filter whose impulse response can be automatically adjusted according to the varying characteristics of the signal and artifacts. In order to eliminate effectively the artifacts in ECG signals, we propose the unbiased linear artificial neural network (ULANN) as a new type of adaptive filter. This paper compares the performance of the ULANN filter with the prevailing least-mean-squares (LMS) and recursive-least-squares (RLS) adaptive filters, for the removal of artifacts in noisy ECG signals. The measures of performance include the root-mean-squared error, a normalized correlation coefficient (NCC), and entropy. A template derived from each ECG signal is used as a reference to derive the measures. The NCC values for the ULANN, LMS, and RLS filter, averaged over 22 ECG signals, are 0.9956 +/- 0.0022, 0.9948 +/- 0.0020, and 0.9940 +/- 0.0026, respectively. The results indicate that the ULANN filter provides filtered signals with the highest waveshape fidelity among the three filters studied