Convergence analysis of a data echo canceller with a stochasticgradient adaptive FIR filter using the sign algorithm

Under the assumption that the “errors at the taps” are Gaussian distributed, a new set of recurrence formulae is derived for calculating theoretical convergence process of a data echo canceller equipped with an FIR filter that is adaptively controlled by using the “stochastic gradient sign algorithm” with a binary and white process as the filter input. Convergence curves for the mean squared residual echo based on the recurrence formulae show an excellent agreement with those obtained by simulation. Approximate recurrence formulae that yield a useful, though less accurate, estimation of the residual echo convergence are also proposed. Furthermore, a closed-form solution to the approximate recurrence formulae is derived. And finally, probability density function of the residual echo is depicted as it changes its shape as the echo canceller converges     

Convergence of the sign algorithm for adaptive filtering withcorrelated data

Convergence of a decreasing gain sign algorithm (SA) for adaptive filtering is analyzed. The presence of the hard limiter in the algorithm makes a rigorous analysis difficult. Therefore, there are few results available. Such results normally include restrictive assumptions such as the assumptions that successive observation vectors are independent and the new error signal of the adaptive filter has a time invariant probability density function. The former assumption is not valid in the context of adaptive filtering since two successive observation vectors share most of their components, while the latter assumption is a restriction on the adaptive weights whose evolution is a priori unknown. In lieu of using these assumptions, an almost-sure convergence of the SA is proved under the assumption that the sequence of observation vectors is M-dependent. This assumption allows strong correlation between successive observations     

延迟效应重构网络算法研究

大量调控网络重构的模型和方法被研究,但是数据大小对算法精度的影响问题涉及很少。通过研究数据尺寸大小对延迟信息论方法构建基因调控网络精度的影响,表明基因调控网络构建的精度会在一定数据尺寸规模下达到一个稳态。为了克服互信息的一些缺点,引入了多时延互信息值计算两个基因之间的调控关系,构建的基因调控网络取得了很好的查全率和查准率。并用真实的生物分子网络进行测试,实验表明,基于多时延的策略在构建基因调控网络中扮演了一个重要角色。     

Analysis of a repairable redundant system with delayed replacement

The paper deals with a redundant system with two types of spare units—a warm standby unit for instantaneous replacement at the time of failure of the active unit and a cold standby (stock) unit which can be replaced after a random amount of time. Failure time distributions of operative and standby units are exponential whereas all repair times follow arbitrary distributions. The system has been studied in detail by applying the results from the theory of semi-Markov process and mean-time-to-system-failure, steady-state availability, expected number of visits to a state, second moment of time in an up-state and expected profit of the system have been obtained.     

A blind equalization with the sign algorithm for broadband access

This paper presents an optimal reduced constellation point of sign reduced constellation algorithm (SRCA) for square and nonsquare carrierless amplitude and phase (CAP)/QAM signal constellations. Convergence characteristics of the SRCA algorithm are analyzed and compared to those of the RCA algorithm     

Leaky delayed LMS algorithm: stochastic analysis for Gaussian data and delay modeling error

This paper presents a stochastic analysis of the delayed least-mean-square (DLMS) adaptive algorithm with leakage. This analysis is obtained taking into account that mismatches between the system delay and its estimate may occur. Such an approach is not considered in previous models. In addition, it is shown that the introduction of a leakage factor into the adaptive algorithm keeps the adaptive algorithm stable under an imperfect delay estimate condition. Recursive difference equations for the first and second moments of the adaptive filter weights are derived. An expression for the critical value of the step size is also determined. Results of Monte Carlo simulations present excellent agreement with the proposed model for both white and colored Gaussian inputs.     

适用于紫外光通信的延迟判决均衡算法

为了消除紫外光通信过程中强烈散射所引起的码间干扰，采用一种带信道估计的最小均方误差-最大似然估计(LMS-MLE)延迟判决均衡算法进行了理论分析和仿真验证。通过选取合适的判决延迟深度来调整LMS自适应滤波器抽头系数进行信道跟踪，获取新的信道估计向量，最后利用MLE均衡算法得到最优序列输出。结果表明, 该算法可以明显提升紫外光通信系统的性能，在没有提高复杂度的情况下，性能接近最优MLE均衡算法，并且可以实现信道跟踪, 紫外光通信中算法的最佳延迟量取值为20。这一结果对紫外光通信性能提升以及MLE均衡器的工程实现是有帮助的。     

Analysis of a delayed delta modulator

While delta modulation (DM) simply compares the current predictive estimate of the input with the current sample, delayed delta modulation (DDM) also compares with the upcoming sample so as to detect and anticipate slope overloading. Since this future sample must be available before the present output is determined and the estimate updated, delay is introduced at the encoding. The performance of DDM with perfect integration and step-function reconstruction is analyzed for each of three random input signals. In every case, the stochastic stability of the system is established. For a discrete time, independent and identically distributed input, the (limiting) joint distribution of input and output is derived, and the (asymptotic) mean-square sample point error mse(SP) is computed when the input is Gaussian. For a Wiener input, the joint distribution of the sample point and prediction errors is derived, and mse(SP) and the time-averaged mse (mse(TA)) are computed. For a stationary first-order Gauss-Markov input, the joint distribution of input and output is derived and mse(SP) and mse(TA) computed. Graphs of the mse's illustrate the improvement attainable by using DDM instead of DM. With optimal setting of parameters, mse(SP) (mse(TA)) is reduced about15percent (35percent).     

基于划分的数据挖掘K-means聚类算法分析

为提升数据挖掘中聚类分析的效果,在分析数据挖掘、聚类分析、传统K-means算法的基础上,提出一种改进的K-means算法。首先将整体数据集分为k类,然后设定一个密度参数为■,该密度参数反映数据库中数据所处区域的密度大小,■值与密度大小成正比,通过密度参数优化k个样本数据的聚类中心点选取;依据欧几里得距离公式对未选取的其他数据到各个聚类中心之间的距离进行计算,同时以此距离为判别标准,对各个数据进行种类划分,从而得到初始的聚类分布;初始聚类分布得到之后,对每一个分布簇进行再一次的中心点计算,并判断与之前所取中心点是否相同,直到其聚类收敛达到最优效果。最后通过葡萄酒数据集对改进算法进行验证分析,改进算法比传统K-means算法的聚类效果更优,能够更好地在数据挖掘当中进行聚类。     

Convergence of the delayed normalized LMS algorithm with decreasingstep size

In several practical applications of the LMS algorithm, including certain VLSI implementations, the coefficient adaptation can be performed only after some fixed delay. The resulting algorithm is known as the delayed LMS (DLMS) algorithm in the literature. Previous published analyses of this algorithm are based on mean and moment convergence under the independence assumption between successive input vectors. These analyses are interesting and give valuable insights into the convergence properties but, from a practical viewpoint, they do not guarantee the correct performance of the particular realization with which the user must live. We consider a normalized version of this algorithm with a decreasing step size μ(n) and prove the almost sure convergence of the nonhomogeneous algorithm, assuming a mixing input condition and the satisfaction of a certain law of large numbers     

基于大数据挖掘的多维数据去重聚类算法分析

数据产生的渠道越来越多,速度越来越快,大量的数据为数据分析和处理带来了较大的难度,云平台中的数据种类和规模也在不断扩大,超大的数据规模给数据的存储、管理、分析等带来了前所未有的挑战。数据量剧增会导致数据的可靠性不足,如何有效地处理数据之间的关系,降低冗余数据,建立多维数据去重聚类模型是业界共同努力的方向。文中最先介绍了在大数据挖掘下的多维聚类算法,通过分析大数据内部之间的关系,建立一种适合大数据处理的多维数据去重聚类算法分析模型,对该算法进行改进和实验分析,得到该算法在采样时所存在的复杂度较低,数据分析的结果准确,有利于实现数据的分析和处理,减少数据的冗余,增加数据分析的效率,具有良好的判定效果。     

Performance analysis of the sign algorithm for a constrained adaptive IIR notch filter

Adaptive infinite impulse response (IIR) notch filters are very attractive in terms of their reasonable performances and low computational requirements. Generally, it is very difficult to assess their performances analytically due to their IIR nature. This paper analyzes in detail the steady-state performance of the sign algorithm (SA) for a well-known adaptive IIR notch filter with constrained poles and zeros. Slow adaptation and Gaussianity of the notch filter output are assumed for the sake of analysis. Two difference equations are first established for the convergences in the mean and mean square in the vicinity of the steady state of the algorithm. Steady-state estimation error or bias and mean square error (MSE) of the SA are then derived in closed forms. A coarse stability bound is also derived for the algorithm. Theory-based comparison between the algorithm and the plain gradient (PG) algorithm is done in some detail. Extensive simulations are conducted to demonstrate the validity of the analytical results for both slow and relatively fast adaptations.     

A simple gradient sign algorithm for transmit antenna weight adaptation with feedback

In this paper, a simple algorithm for adaptation of the complex baseband weights of a transmit antenna array using feedback from the receiver is proposed and analyzed. The system utilizes stochastic gradient adaptation to maximize the power delivered to the receiver for a constrained transmission power, which provides both fading diversity and beam steering gain. Dual perturbed transmission weight vectors are time multiplexed onto the pilot signal, and the receiver generates feedback selecting the perturbed weight vector which delivers greater power. This feedback is used to provide weight adaptation at the transmitter, and this adaptation is shown to be an update by a coarse estimate of the gradient of the delivered power. The performance of the algorithm is analyzed in terms of convergence and tracking of an AR1 fading channel, with simulations confirming the analysis. Bit error rate (BER) simulations in a dynamic fading channel show that the algorithm outperforms previously proposed vector selection feedback, and in slower fading, the algorithm substantially outperforms diversity space time coding.     

一种基于SURF的交通标志识别算法

提出了一种基于SURF的交通标志识别算法。算法首先对从视频中抽取的图像进行预处理,之后使用交通标志的颜色与形状特征信息来检测与分割交通标志。然后使用SURF特征提取算法来抽取和描述已经定位的交通标志的特征值。最后,使用基于加权欧几里德距离的最近邻搜索方法在经过粗分类的特征模板库中进行搜索匹配。实验结果显示该算法具有较好的识别精度和速度。     

Convergence analysis of the sign algorithm for adaptive filtering

We consider the convergence analysis of the sign algorithm for adaptive filtering when the input processes are uncorrelated and Gaussian and a fixed step size μ>0 is used. Exact recursive equations for the covariance matrix of the deviation error are established for any step size μ>0. Asymptotic time-averaged convergence for the mean-absolute deviation error, mean-square deviation error, and for the signal mean-square estimation error are established. These results are shown to hold for arbitrary step size μ>0     

Application of the Viterbi algorithm to adaptive delta modulation with delayed decision

The Viterbi algorithm is applied to adaptive delta modulation with delayed decision. The resulting coder is more efficient, while the search time involved is reduced by a factor equal to the decision delay expressed in input pulse epochs.     

一种基于新型符号函数的小波阈值图像去噪算法

在现有阈值去噪算法的基础上提出了一种基于新型符号函数的小波阈值图像去噪算法,该算法提出的新阈值函数具有连续可导、小波系数偏差小、阈值自适应性强等优势.不仅保留了分解后的低频小波系数,还有效滤除了高频系数中的噪声系数,使得重构后的图像更接近原始图像.对高斯白噪声的Bridge图像、Lena图像及含“斑点噪声”的B超Fetus图像进行仿真,实验的结果表明,无论是新阈值函数的视觉效果,还是定量指标PSNR和MSE,均优于现有的阈值图像去噪算法.其边缘及细节信息能得到较好的保护,无明显振荡,图像更平滑、均匀,且在复杂噪声背景下,该方法具有较好的顽健性.     

Analysis of an algorithm that resolves oscillatory data into smoothcomponents

An algorithm for extracting smooth functions from oscillatory data by D.A. Ksienski (1985) was tested for accuracy. It was generally found that the algorithm does not accurately reproduce smooth input functions. Yet, when the smooth functions produced from this algorithm were used for interpolation, satisfactory results were obtained. It is concluded that this algorithm can therefore be utilized for some applications requiring smooth functions     

基于DOCSIS标准的HFC数据加密算法的分析

基于DOCSIS标准的增强型基线保密性接口（BPI＋）采用56bit DES加密分组数据，采用二密钥三重DES加密通信量加密密钥。这种多重加密技术的应用为HFC网络的数据传输提供了相当于或更好于专线级的网络安全与加密性能。     

基于改进SSD算法的实时交通标示牌检测

由于当前交通标示牌检测模型体积太大,检测速度太慢,无法在嵌入式设备上使用,为提高交通标示牌的检测速度,在SSD算法的基础上使用MobileNet作为主体网络进行特征提取,极大地减小了模型的体积,降低了模型计算量和硬件消耗。模型使用反卷积和跨层连接,不仅丰富了上下文信息,而且提高了检测精度。实验结果表明,这种技术在保证检测交通标示牌检测精度的同时,大大提高了检测速度,为模型在嵌入式设备上进行实时的交通标示牌检测提供可能。