A Novel Neighborhood-Weighted Sampling Method for Imbalanced Datasets

The weighted sampling methods based on k-nearest neighbors have been demonstrated to be effective in solving the class imbalance problem. However,they usually ignore the positional relationship between a sample and the heterogeneous samples in its neighborhood when calculating sample weight. This paper proposes a novel neighborhood-weighted based sampling method named NWBBagging to improve the Bagging algorithm’s performance on imbalanced datasets. It considers the positional relationship betwee...     

Imbalanced SVM‐Based Anomaly Detection Algorithm for Imbalanced Training Datasets

Abnormal samples are usually difficult to obtain in production systems, resulting in imbalanced training sample sets. Namely, the number of positive samples is far less than the number of negative samples. Traditional Support Vector Machine (SVM)‐based anomaly detection algorithms perform poorly for highly imbalanced datasets: the learned classification hyperplane skews toward the positive samples, resulting in a high false‐negative rate. This article proposes a new imbalanced SVM (termed ImSVM)‐based anomaly detection algorithm, which assigns a different weight for each positive support vector in the decision function. ImSVM adjusts the learned classification hyperplane to make the decision function achieve a maximum GMean measure value on the dataset. The above problem is converted into an unconstrained optimization problem to search the optimal weight vector. Experiments are carried out on both Cloud datasets and Knowledge Discovery and Data Mining datasets to evaluate ImSVM. Highly imbalanced training sample sets are constructed. The experimental results show that ImSVM outperforms over‐sampling techniques and several existing imbalanced SVM‐based techniques.     

一种抑制同步开关噪声的改进总线翻转编码方法

提出了一种改进总线翻转编码方法,用于抑制高速集成电路系统中的同步开关噪声.该方法可以显著减少总线平均翻转比特位数,同时尽可能使总线相邻位保持奇模传输,有利于改善信号完整性.利用FPGA实现了提出的改进总线翻转编码模块.物理实验表明,相对总线翻转编码,该方法以功耗稍有增加的代价,可取得平均翻转数量减少15.17%、电源波动噪声减小22.34%的编码增益.     

Noise Estimation in Magnitude MR Datasets

Estimating the noise parameter in magnitude magnetic resonance (MR) images is important in a wide range of applications. We propose an automatic noise estimation method that does not rely on a substantial proportion of voxels being from the background. Specifically, we model the magnitude of the observed signal as a mixture of Rice distributions with common noise parameter. The expectation-maximization (EM) algorithm is used to estimate all parameters, including the common noise parameter. The algorithm needs initializing values for which we provide some strategies that work well. The number of components in the mixture model also needs to be estimated en route to noise estimation and we provide a novel approach to doing so. Our methodology performs very well on a range of simulation experiments and physical phantom data. Finally, the methodology is demonstrated on four clinical datasets.     

一种图像去噪的小波相位滤波改进算法

大多数的小波去噪方法都是基于图像小波幅度信息的,但对于低SNR图像来说,其小波域中的图像边缘信息被噪声掩盖,所以有人提出了对幅度不敏感的小波相位滤波算法,利用含噪图像分解后的相位信息来恢复图像,本文对这种算法作出了一些改进。在相位滤波的基础上,考虑Laplace邻域,试验结果表明比原算法效果好。     

基于单边选择链和样本分布密度融合机制的非平衡数据挖掘方法

非平衡数据集分类问题是机器学习领域的重大挑战性难题.针对该难题,传统的少数类样本合成技术（Synthetic Minority Over-Sampling Technique,SMOTE）已成为一种有力手段并得到广泛采用.但在新样本生成过程中,SMOTE利用所有少数类样本合成新样本,由此产生过拟合瓶颈.为更好地解决该问题,提出了一种基于单边选择链和样本分布密度的非平衡数据挖掘新方法（One-Sided Link & Distribution Density-SMOTE,OSLDD-SMOTE）.OSLDD-SMOTE通过单边选择链遴选出处于分类边界的少数类样本,根据这些样本的动态分布密度生成新样本.进而分析了样本合成度对节点数目和对少数类精度的影响;基于G-mean、F-measure和AUC三个指标综合比较了OSLDD-SMOTE与其他同类方法的分类性能.实验结果表明,OSLDD-SMOTE有效提高了少数类样本的分类准确率.     

基于改进型GIW算法的TerraSAR-X图像斑点噪声抑制

针对TerraSAR-X图像中的斑点噪声抑制问题,分析了噪声的基本数学模型,并考察了经典处理方法,进而结合Kuan滤波的基本思想,给出了一种改进型梯度倒数加权(GIW)滤波算法,利用邻域像素和中心像素的灰度梯度倒数及其邻域统计信息构造滤波加权模板,然后通过卷积操作实现斑点噪声的抑制。对TerraSAR-X图像处理的结果表明该算法原理简单,实现方便,可有效抑制图像中的斑点噪声,同时具有优良的边缘细节保持能力,非常适合边缘提取、目标检测等后续处理。     

一种改进的LMS算法在噪声对消中的应用

给出了一种改进的LMS算法，该算法解决了算法收敛时间和稳态误差间的矛盾，为实际应用提供了更大的灵活性。它应用误差信号的相关值去调节步长，同时实现了均方误差小和收敛速度快，并且降低了LMS算法对噪声的敏感性。     

一种对噪声混沌信号的滤波方法

本文研究噪声混沌信号的滤波问题。组合信号建模技术,提出了一种基于无先导变换(Unscented Transform,UT)的滤波方法。仿真结果表明,不管混沌系统的参数如何变化,这种算法能都能有效地抑制噪声对混沌系统的影响。与基于扩展卡尔曼滤波器(Extended Kalman filter,EKF)算法相比,在低信噪比的情况下,基于UT的滤波方法都有较好的滤波性能;而在高信噪比的情况下,它有与扩展卡尔曼滤波器基本相同的性能。     

利用匹配相位谱减法降低噪声

本文首先利用谱减法推导出一种匹配相位降噪算法,再对其进行计算机仿真,并推出工程公式,使用实际数据验证其可使低信噪比信号减少干扰的影响。     

一种粗糙集属性约简的改进算法

通过时属性的重要性以及属性提供的信息量进行研究，提出一种综合考虑属性重要性和属性的信息量的属性加权平均重要度的概念，并构造在决策表中的基于此重要度的一种新的属性约简方法。通过实例计算表明，该算法简单有效。     

Audition对音频的降噪处理

由于各种原因,录制的音频(无论语音还是歌唱)出现一定响度的噪声是经常发生的.当噪声太明显时就会影响听觉效果,降噪处理就是消除这种噪声的基本方法.阐述了采用Audition软件处理常见音频噪声的方法,简单实用.     

消除噪声的一种变步长自适应滤波方法

在电子系统中不可避免地会受到噪声的干扰。用固定参数的滤波器进行消除噪声有其缺陷,它对信号与噪声的先验知识需要得较多。本文讨论了用一种变步长自适应滤波器消除噪声的方法。实验仿真证明这种方法能有效地去除弱信号中的噪声。     

多用户降噪差分混沌键控通信方案

传统多用户差分混沌键控主要缺点是误码率差,该文提出一种多用户降噪差分混沌键控(MU-NRDCSK)通信方案。在发射端,发送M/P长度混沌序列,复制P次后作为参考信号,所有用户共用同一参考信号,信息信号延迟不同的时间来区分用户。在接收端,将接收到的信号通过滑动平均滤波器平均,再与其不同时间延迟后的信号进行相关。该方案通过降低噪声项的方差来提高系统误码性能。文中推导了该方案在加性高斯白噪声(AWGN)信道和Rayleigh信道下的理论误码率公式并进行了蒙特卡洛仿真。理论分析和仿真结果表明,理论公式与仿真结果能较好地吻合,MU-NRDCSK方案能较好地提高系统误码性能,在混沌通信领域具有很好的发展前景与研究价值。

     

消除噪声的一种变步长自适应滤波方法

在电子系统中不可避免地会受到噪声的干扰．用固定参数的滤波器进行消除噪声有其缺陷，它对信号与噪声的先验知识需要得较多．本文讨论了用一种变步长自适应滤波器消除噪声的方法．实验仿真证明这种方法能有效地去除弱信号中的噪声．     

A Fast Adaptive Salt and Pepper Noise Reduction Method in Images

In this paper, an effective filtering method is proposed to remove impulse noise from images. In this two-stage method, detected noise-free pixels remain unchanged. Afterwards, a Gaussian filter with adaptive variances according to the image noise level is applied on the noisy pixels. Experimental results show that the proposed method outperforms recent impulse denoising methods in terms of PSNR, MAE, IEF, and SSIM. Moreover, the speed of the method is comparable with them, and it can be used effectively in real-time applications.     

脉搏信号去噪

分析了脉搏信号特性与研究现状,探究了脉搏信号的小波变换去噪与数学形态学去噪并给出了相应解决方案与注意事项,提出将两种去噪方法相结合.     

Noise Reduction for Images with Non-uniform Noise Using Adaptive Block Matching 3D Filtering

Noise reduction is a very important topic in image processing. We propose a new method to deal with the case where the noisy image has different noise levels in different regions. The main idea is to segment automatically the noisy image into several sub-images so that each sub-image has approximately the same noise level. We perform Block matching 3D filtering (BM3D) to these subimages in order to obtain denoised sub-images. We then merge sub-images together and enhance the discontinuous regions between the sub-images by performing BM3D again on small image patches. Our experimental results show the effectiveness of this proposed method in terms of Peak signal to noise ratio (PSNR) when compared with the bivariate wavelet shrinkage and the standard BM3D method. In addition to Gaussian white noise, our method performs better than the bivariate wavelet shrinkage and the standard BM3D method even for signal dependent noise.     

基于新型组合算法的语音降噪系统

在研究VSLMS和子空间降噪原理的基础上,分析了两种算法的优缺点,提出了一种组合降噪算法,并进行了硬件实现。组合降噪算法试图寻找一个语音信号的分解点,利用子空间算法分离出带噪信号,作为自适应算法的输入,最后由VSLMS算法对带噪信号进行增强处理。硬件实现在该组合降噪算法的基础上,利用SOPC技术在FPGA芯片中搭建嵌入式语音降噪系统。实验结果表明,该方法能够优于传统降噪算法,使用硬件结构加快了算法的收敛速度。     

A Noise Reduction Amplify-and-Forward Relay Protocol for Distributed Spatial Diversity

Amplify-and-forward transmission has been shown to be a viable transmission protocol for wireless networks incorporating distributed spatial diversity. A drawback of this relatively simple transmission protocol is identified. In addition to signal amplification, perpendicular noise at the relay is also amplified. A method to reduce the noise at the relay prior to amplification is proposed. To demonstrate the improvement of this protocol, we consider single-user single-relay transmission and show that by eliminating perpendicular noise at the relay, the outage region boundaries are reduced. Perpendicular noise elimination is inherent in decode-and-forward relaying. Hence, Fair comparisons of amplify-and-forward relaying with decode-and-forward relaying are based on noise reduced amplify-and-forward relaying