A noise constrained least mean fourth (NCLMF) adaptive algorithm

The learning speed of an adaptive algorithm can be improved by properly constraining the cost function of the adaptive algorithm. In this work, a noise-constrained least mean fourth (NCLMF) adaptive algorithm is proposed. The NCLMF algorithm is obtained by constraining the cost function of the standard LMF algorithm to the fourth-order moment of the additive noise. The NCLMF algorithm can be seen as a variable step-size LMF algorithm. The main aim of this work is to derive the NCLMF adaptive algorithm, analyze its convergence behavior, and assess its performance in different noise environments. Furthermore, the analysis of the proposed NCLMF algorithm is carried out using the concept of energy conservation. Finally, a number of simulation results are carried out to corroborate the theoretical findings, and as expected, improved performance is obtained through the use of this technique over the traditional LMF algorithm.     

Efficient sign based normalized adaptive filtering techniques for cancelation of artifacts in ECG signals: Application to wireless biotelemetry

In this paper, several simple and efficient sign based normalized adaptive filters, which are computationally superior having multiplier free weight update loops are used for cancelation of noise in electrocardiographic (ECG) signals. The proposed implementation is suitable for applications such as biotelemetry, where large signal to noise ratios with less computational complexity are required. These schemes mostly employ simple addition, shift operations and achieve considerable speed up over the other least mean square (LMS) based realizations. Simulation studies shows that the proposed realization gives better performance compared to existing realizations in terms of signal to noise ratio and computational complexity.     

Anisotropic diffusion for noise removal of band pass signals

A noise removal method for band pass signals based on the anisotropic diffusion algorithm originally put forward by Perona and Malik is proposed in this paper. The anisotropic smoothing algorithm proposed here is for band pass signals modulated with a constant carrier frequency. A partial differential equation to smooth band pass noisy signals is derived. The propagator of this differential equation is also analytically calculated in this paper. An appropriate linear operator is then considered here for such band pass signals to form an anisotropic diffusion algorithm. The algorithm proposed here demonstrates better performance for band pass noisy signals containing discontinuities in comparison with the traditional Perona-Malik (PM) algorithm and is robust in the presence of excessive noise with SNR less than unity.     

Low power finite state machine synthesis using power-gating

Power-gating turns off the power supply of a portion of the circuit completely, resulting in total elimination of power consumption for that part. However, it also necessitates that the sub-circuit to be activated should be charged for some time before its activation. This critical issue can influence the decomposition of a finite state machine (FSM) for its power gated implementation. In this paper we have presented a power-gating method that integrates FSM partitioning with state encoding, thus providing a total solution to the problem of power-aware FSM synthesis. It shows better results, in terms of dynamic and leakage power consumption, compared to the existing techniques reported in the literature.     

Self-tuning distributed measurement fusion Kalman estimator for the multi-channel ARMA signal

For the multisensor multi-channel autoregressive moving average (ARMA) signal with white measurement noises and a common disturbance measurement white noise, when the model parameters and the noise variances are all unknown, a multi-stage information fusion identification method is presented, where the consistent fused estimates of the model parameters and noise variances are obtained by the multi-dimension recursive instrumental variable (RIV) algorithm, correlation method and Gevers-Wouters algorithm with a dead band. Substituting these estimates into the optimal distributed measurement fusion Kalman signal estimator, a self-tuning distributed measurement fusion Kalman signal estimator is presented. Its convergence is proved by the dynamic error system analysis (DESA) method, so that it has asymptotical global optimality. In order to reduce computational load, a fast recursive inversion algorithm for a high-dimension matrix is presented by the inversion formula of partitioned matrix. Especially, when the process and measurement noise variance matrices are all diagonal matrices, the inversion formula of a high-dimension matrix is presented, which extends the formula of the inverse of Pei-Radman matrix. Applying the proposed inversion algorithm, the computation of the fused measurement and fused noise variance is simplified and their computational burden is reduced. A simulation example shows effectiveness of the proposed method.     

Phishing web image segmentation based on improving spectral clustering

This paper proposes a novel phishing web image segmentation algorithm which based on improving spectral clustering. Firstly, we construct a set of points which are composed of spatial lo-cation pixels and gray levels from a given image. Secondly, the data is clustered in spectral space of the similar matrix of the set points, in order to avoid the drawbacks of K-means algorithm in the con-ventional spectral clustering method that is sensitive to initial clustering centroids and convergence to local optimal solution, we introduce the clone operator, Cauthy mutation to enlarge the scale of clustering centers, quantum-inspired evolutionary algorithm to find the global optimal clustering centroids. Compared with phishing web image segmentation based on K-means, experimental results show that the segmentation performance of our method gains much improvement. Moreover, our method can convergence to global optimal solution and is better in accuracy of phishing web seg-mentation.     

Unsupervised Quick Reduct Algorithm Using Rough Set Theory

Feature selection (FS) is a process to select features which are more informative.It is one of the important steps in knowledge discovery.The problem is that not all features are important.Some of the features may be redundant,and others may be irrelevant and noisy.The conventional supervised FS methods evaluate various feature subsets using an evaluation function or metric to select only those features which are related to the decision classes of the data under consideration.However,for many data mining applications,decision class labels are often unknown or incomplete,thus indicating the significance of unsupervised feature selection.However,in unsupervised learning,decision class labels are not provided.In this paper,we propose a new unsupervised quick reduct (QR) algorithm using rough set theory.The quality of the reduced data is measured by the classification performance and it is evaluated using WEKA classifier tool.The method is compared with existing supervised methods and the result demonstrates the efficiency of the proposed algorithm.     

物体变形测量的相移数字散斑剪切干涉术

相移数字散斑剪切干涉技术可以提高检测精度,利用该方法对圆形铝板试件进行测量。为了实现全场相位的精确求解,运用等四步相移Carré算法定量计算相位。将该非接触测量技术与弹性力学理论分析相结合,对四周固支,中心受集中载荷试件的离面位移导数进行测量与分析,另外采用有限元方法进行模拟。最后将实验结果分别和理论结果、有限元结果进...     

分层协同进化免疫算法及其在TSP问题中的应用

为提高人工免疫算法求解TSP问题的效率,借鉴分层和协同进化的思想,构造了一种基于多子种群免疫进化的两层框架模型,在此模型的基础上提出了一种基于竞争一合作的分层协同进化免疫算法(Hierarchical Co-evolution Immune Algorithm,HCIA).HCIA通过对若干个子种群进行低层免疫操作:局...     

扇束CT几何伪影的校正方法

实际工业CT系统很难满足经典CT图像重建公式所要求的射线源焦点、转台中心及线阵探测器之间严格的对准关系.不满足对准关系将导致经典CT图像重建公式所重建的图像中出现几何伪影.本文引入可以描述一般情况下CT扫描几何关系的一组几何参数,给出了由金属丝的投影数据间接估计这些几何参数的方法,还给出了基于这些几何参数的图像重建公式...