Statistical analysis of state-space approach in harmonic retrieval

The author presents a statistical analysis of the performance of the state-variable balancing for estimating the parameters of exponential signals in the presence of additive noise. The case of frequency estimation for a single damped sinusoid is carried out in detail because the analysis can be presented more clearly; the formulas are simpler and provide insight; and the results are applicable in radar, sonar, communication, and data modelling. Analytical expressions for the variances of the frequency estimate at high signal-to-noise ratios is derived. Both analytical and experimental results are presented to illustrate the performance of the state-variable balancing method     

Cumulant-based approach to harmonic retrieval and related problems

A frequently encountered problem in signal processing is that of estimating the frequencies and amplitudes of harmonics observed in additive colored Gaussian noise. In practice, the observed signals are contaminated with spatially and temporally colored noise of unknown power spectral density. A cumulant-based approach to these problems is proposed. The cumulants of complex processes are defined, and it is shown that specific 1-D slices of the fourth-order cumulant of the noisy signal for the direction of arrival (DOA) and retrieval of harmonics in noise (RHN) problems are identical to the autocorrelation of a related noiseless signal. Hence correlation-based high-resolution methods may be used with fourth-order cumulants as well. The effectiveness of the proposed methods is demonstrated through standard simulation examples     

NEW APPROACH TO HARMONIC RETRIEVAL IN NOISE

This paper addresses the harmonic retrieval problem in non-Gaussian ARMA noise. A hybrid ESPRIT approach using second-and third-order statistics is proposed. First, third-order statistics are used to identify the AR part of the non-Gaussian noise process, then the noisy measurements are filtered by AR polynomial, finally, the harmonic signal parameters are estimated. Simulation examples show the effectiveness and high resolution of the new approach.     

检测强混沌中微弱谐波信号的神经网络方法

提出了一种提取强混沌中微弱谐波信号的方法。该方法根据嵌入定理，利用混沌系统的单变量观测值对混沌背景重构相空间，并利用径向基函数神经网络(RBFNN)建立混沌噪声的一步预测模型，使其与混沌噪声具有相同的基本动力学特征。并结合一个梳状滤波器对预测误差进行滤波，从而检测出湮没在混沌中的感兴趣的微弱谐波信号。该方法在信噪比(SNR)为-46dB时仍可检测出强混沌中微弱谐波信号。     

粒子群优化RBF神经网络谐波电流检测方法研究

谐波污染问题,众所周知是使用了很多的电力电子器件导致进一步加重,所以谐波治理就变得愈来愈紧要。而瞬时无功功率的检测方法不能满足目前谐波检测对其检测精度的要求,故文中提出一种用粒子群优化RBF神经网络权重的谐波检测方法来检测谐波。改善的方法措施是以瞬时无功功率检测的输入作为RBF神经网络的输入,其输出作为RBF神经网络的期望输出,使用MATLAB软件对RBF神经网络模型进行搭建并进行仿真分析。从实验结果可以得出:使用粒子群优化RBF神经网络权重的谐波检测方法,克服了RBF神经网络检测方法精度不足的欠缺,提高了RBF神经网络的收敛速度和谐波检测的精度。     

Improved Newton-type algorithm for adaptive implementation ofPisarenko's harmonic retrieval method and its convergence analysis

Pisarenko's harmonic retrieval (PHR) method is probably the first eigenstructure based algorithm for estimating the frequencies of sinusoids corrupted by additive white noise. To develop an adaptive implementation of the PHR method, one group of authors has proposed a least-squares type recursive algorithm. In their algorithm, they made approximations for both gradient and Hessian. The authors derive an improved algorithm, where they use exact gradient and a different approximation for the Hessian and analyze its convergence rigorously. Specifically, they provide a proof for the local convergence and detailed arguments supporting the local instability of undesired stationary points. Computer simulations are used to verify the convergence performance of the new algorithm. Its performance is substantially better than that exhibited by its counterpart, especially at low SNR's     

A hybrid approach to harmonic retrieval in non-Gaussian ARMA noise

Addresses the harmonic retrieval problem in colored noise. As contrasted to the reported studies in which Gaussian noise was assumed, this paper focuses on additive non-Gaussian ARMA noise. Our approach is hybrid in the sense that third-order cumulants are first used to identify the AR part of the non-Gaussian noise process, and then correlation-based high-resolution methods are used for the filtered process to estimate the number of harmonics and their frequencies. Simulation examples are presented to demonstrate the high resolution of this approach     

基于卷积神经网络和注意力机制的图像检索

当前先进的图像检索方法中,存在着不能很好地分辨图像中不同区域和内容的重要性的问题,导致计算资源分配不合理、检索正确率较低等一系列结果.为了解决这些问题,提出了一种基于卷积神经网络(Convolutional Neural Network,CNN)和注意力机制的图像检索方法.首先使用卷积神经网络提取特征,然后使用注意力机...     

基于主成分分析与BP神经网络的识别方法研究

利用BP神经网络对红外目标进行识别之前,若不对原始样本数据进行预处理与特征提取,一方面使识别结果准确性降低,另一方面使BP神经网络的结构复杂化,采用主成分分析法可解决这些问题。主成分分析法能较好地提取表征样本的少数几个主分量,由该方法的特点可知,这几个主分量彼此不相关,非常符合特征优化的要求。研究结果表明,用该方法处理后的结果数据输入BP神经网络．提高了识别正确率,减少了训练时间,同时也简化了网络结构。将两种常见的模式识别方法结合用于红外目标识别：先由主成分分析法对原始样本数据进行精简处理,然后再由BP神经网络法进行分类识别,与传统的单一识别方法相比,准确度得到提高,计算量大为减少。     

Computational analysis of rotational search methods for adaptivePisarenko harmonic retrieval

The rotational search method (RSM), which reduces the computational burden of the Pisarenko method for identifying undamped sinusoids in additive noise, is discussed. A brief review of previous adaptive algorithms for implementing the method is presented. Two simulations are presented which show the inherent sensitivity of the RSM algorithm. The sensitivity is shown to depend upon the Rayleigh-Ritz eigenvector estimate and the value of the minimum eigenvalue estimate. The errors in their tracking result in a loss of search direction orthogonality to the estimated minimum eigenvector. A higher precision for the enforced search direction orthogonality which minimizes the added computation is proposed, resulting in the altered RSM (ARSM)     

基于人工神经网络的图像检索平台的设计与开发

用户描述图像的高层抽象语义与图像内在的底层特征之间存在差异,此时仅依靠图像内容特征进行检索的系统无法准确完成用户的检索任务。针对以上问题,提出了使用神经网络进行图像的匹配计算方法,通过样例自动学习和用户反馈学习两种学习方式,形成图像底层特征到图像分类的正确映射,学习后的神经网络可以进行图像的自动分类及检索。该方法结合了图像的底层特征描述及用户的高层语义反馈,有效地弥补了语义鸿沟。最后,系统通过整合Web前端、图像提取模块、神经网络模块及数据库模块,实现了神经网络学习及图像检索的完整流程。     

A novel traffic identification approach based on multifractal analysis and combined neural network

An accurate identification of Internet traffic of different applications is highly relevant for a broad range of network management and measurement tasks, including traffic engineering, service differentiation, performance monitoring, and security. Traditional traffic identification approaches have become increasingly inaccurate due to restrictions of port numbers, protocol signatures, traffic encryption, and etc. In this paper, a new traffic identification approach based on multifractal analysis of wavelet energy spectrum and classification of combined neural network models is proposed. The proposed approach is able to achieve the identification of different Internet application traffic by performing classification over the wavelet energy spectrum coefficients that were inferred from the original traffic. Without using any payload information, the proposed approach has more advantages over traditional methods. The experiment results illustrate that the proposed approach has satisfactory identification results.     

Spectroscopy and hybrid neural network analysis

This paper reviews the current use of spectroscopy and related instrumentation in chemical analysis. Advancements in digital signal processing technology are making it possible to improve the sensitivity and accuracy of analytical instruments without expensive upgrading of instrument hardware. A hybrid neural network (HNN) is described that can perform nonlinear signal analysis. The HNN approach combines the simple data reduction capability of conventional linear signal processing algorithms with the adaptive learning and recognition ability of a multilayer nonlinear neural network architecture. A number of examples show the rise of the HNN for environmental monitoring and real-time process control     

Almost-sure identifiability of multidimensional harmonic retrieval

Two-dimensional (2-D) and, more generally, multidimensional harmonic retrieval is of interest in a variety of applications, including transmitter localization and joint time and frequency offset estimation in wireless communications. The associated identifiability problem is key in understanding the fundamental limitations of parametric methods in terms of the number of harmonics that can be resolved for a given sample size. Consider a mixture of 2-D exponentials, each parameterized by amplitude, phase, and decay rate plus frequency in each dimension. Suppose that I equispaced samples are taken along one dimension and, likewise, J along the other dimension. We prove that if the number of exponentials is less than or equal to roughly IJ/4, then, assuming sampling at the Nyquist rate or above, the parameterization is almost surely identifiable. This is significant because the best previously known achievable bound was roughly (I+J)/2. For example, consider I=J=32; our result yields 256 versus 32 identifiable exponentials. We also generalize the result to N dimensions, proving that the number of exponentials that can be resolved is proportional to total sample size     

基于相关分析和神经网络的激光焊接稳态识别

为了准确识别激光焊接的稳态类型, 采用了图像处理、相关分析和神经网络的方法, 增加对准稳态的研究, 以信号特征的相关系数作为神经网络模型的输入, 进行了理论分析和实验验证, 得出了光学、视觉信号的相关性对激光焊接稳态类型的影响规律。结果表明, 匙孔面积和金属蒸汽面积的相关性区分稳态类型的效果最好, 其相关系数为0.2~0.3时为稳态, 0.4~0.5时为准稳态, 0.6~0.7时为非稳态; 训练完成的神经网络模型在测试集上达到了98.76%的预测准确率, 可满足准确识别焊缝稳态类型的需求。该研究为自动化生产中预防出现激光焊接缺陷提供了参考。     

一种融合MeanShift聚类分析和卷积神经网络的Vibe++背景分割方法

针对传统Vibe+算法存在噪点和拖影分割错误率较高的问题,提出了一种改进的Vibe+运动目标分割算法(Vibe++)。首先,通过对视频帧采用传统Vibe+算法处理获取二值图像,基于区域生长算法对结果图中各连通域标记,依据边界面积块差异获取面积筛选阈值,将低于阈值的连通区域视为噪点并删除;然后,引入5种不同核函数优化传统MeanShift聚类算法,并与卷积神经网络(CNN)进行顺序组合;最后,采用组合模型对已消除噪点图像中的拖影区、非拖影区和拖影边缘区分类,计算拖影区中每个像素点的坐标,定位拖影区并快速删除,获取分割结果。所提算法用于公开数据集的实验结果表明,其可取得98%以上的分割准确率,具有较好的应用效果和较高的实用价值。     

自适应线性神经网络LMM算法的谐波辨识技术研究

介绍了组合适应线性神经网络最小平均值评估法(Adaline-LMM)对脉冲控制信号的拟合分析方法,用于对电力控制系统中的信号评估。通过对系统信号中的各个谐波分量的幅值和相位进行谐波辨识,并对Adaline的权重向量进行更新,同时对目标函数进行技术估计。其中,自适应神经网络中的权重向量由LMM算法进行迭代更新,通过最小平均值估计算法的引入,减小由于脉冲噪声引起的暂时波动的影响。通过对给定脉冲信号进行拟合,可以发现所提方法具有较高的计算精度。     

Solder joints inspection using a neural network and fuzzyrule-based classification method

In this paper, we described an approach in automation, the visual inspection of solder joint defects of surface mounted components on a printed circuit board, using a neural network with fuzzy rule-based classification method. Inherently, the solder joints have a curved, tiny, and specular reflective surface. This presents the difficulty in taking good images of the solder joints. Furthermore, the shapes of the solder joints tend to greatly vary with their soldering conditions, and are not identical with each other, even though some of the solder joints belong to a set of the same soldering quality. This problem makes it difficult to classify the solder joints according to their properties. To solve this intricate problem, a new classification method is here proposed which consists of two modules: one based upon an unsupervised neural network, and the other based upon a fuzzy set theory. The novel idea of this approach is that a fuzzy rule table reflecting the knowledge of criteria of a human inspector, is utilized in order to correct any possible misclassification made by the neural network module. The performance of the proposed approach was tested on numerous samples of printed circuit boards in commercially available computers, and then compared with that of a human inspector. Experimental results reveal that the proposed method is superior to the neural network classification method alone, in terms of its accuracy of classification