基于小波变换的玉米撞击声信号滤波方法

根据玉米撞击声信号的特点,将信号分解成一系列小波函数,用母小波函数经过平移与尺度伸缩获得小波函数,对信号的分解系数进行阈值量化,然后再用处理后的分解系数进行信号重构,从而得到满意的处理结果,成功地将小波变换理论应用于玉米撞击声信号的滤波处理中。     

基于小波变换实现脉搏信号降噪处理

脉搏波形复杂而且测量过程中易受各种噪声影响,因此降噪处理是提取清晰、有效的脉搏信息的关键步骤。在去噪过程中,由于脉搏信号具有非平稳特性且污染噪声分布范围大,限制了传统线性滤波器的使用。这里利用小波变换的多层分解将脉搏信号分成具有不同尺度的小波系数,然后根据小波分解的底层低频系数和各层高频系数进行一维小波重构,在重构的过程中可以剔除与噪声相关的系数,保留信号中的有用成分。     

采用小波能量的光纤周界报警信号辨识

采用小波变换法对光纤周界报警系统的采样信号进行阈值滤波,根据滤波后信号的特征,采用db8小波基对信号进行4尺度小波分解,并统计各尺度下的小波细节系数能量,获取原始信号各频带的信号特征,并基于信号特征进行信号识别,能可靠的对蓄意入侵信号进行报警。实践证明,采用这种识别法可使该系统的误报率降低。     

一种基于小波分析的阈值去噪算法

利用信号和噪声在小波变换中不同尺度上具有不同的特性,提出了基于小波变换的去噪方法。经过小波变换后的信号,在其小波系数中包含了实际信号的重要信息特征,表现为幅值较大的小波系数,而噪声产生的小波系数幅值较小。通过在不同尺度上选取适当的阈值,对大于和小于该阈值的小波系数进行相应的处理,以得到去噪后的信号。     

具有平移和尺度不变性的自适应小波变换

本文提出了一种具有平移和尺度不变性的自适应小波分解新方法,该方法利用信号的一阶、二阶矩及正交小波尺度函数,先对信号进行自适应小波"重整".然后再对重整后的信号进行普通小波变换.本文证明这种自适应小波变换是平移和尺度不变的,并给出了计算自适应小波变换系数(称为小波不变矩)的一种有效算法.对二维数字信号(图像)的实验证实了我们的结论.     

基于小波分析的迎角传感器信号预处理

针对基于四象限压力传感器阵列的迎角测量方法中,传感器信号受到干扰的问题,讨论了在其预处理中应用小波变换进行信号分离提取的方法.根据小波分解理论,对信号进行多尺度的小波变换,信号中频率不同的部分落在不同的尺度上,剔除反映干扰的小波变换尺度,提取出有用信息.结果表明,该方法可较好地提取信号的主要特征信息,有效的去除了确定性干扰和随机噪声,与传统的信号滤波方法相比较具有明显的优点,同时为后续的信号处理奠定了基础.     

基于多尺度阈值技术的小波去噪

多尺度阈值技术的小波去噪是根据小波变换下信号和噪声在多尺度空间中的不同特性,用软阈值函数确定的阈值对不同尺度上的小波系数进行处理,进而达到去除噪声的目的。运用这种方法对信号进行处理时去噪效果良好,并且能够很好地保留原信号的细节信息。     

调制信号小波变换的尺度特征

本文根据小波变换的定义和通信调制信号的一般表示形式,分析了典型调制信号的小波系数在时间-尺度平面上的特征,详细推导了小波系数达到最大时所对应的最佳尺度因子与调制类型的关系,并给出了仿真结果.理论分析和实验结果均表明,不同调制信号的小波系数有着不同特征,最佳尺度因子的变化规律与调制类型一一对应,并且与被调制的基带信号存在确定关系.最佳尺度因子的这种特征可用来构造基于小波变换的调制信号分类器和解调器.     

基于AR模型的小波变换在脑电信号消噪中的应用

提出了基于自回归模型(ARM)与小波变换的脑电信号分析方法,并利用他来消除脑电信号中的噪声干扰。小波变换是一种多分辨率的时间尺度分析方法,他能够将信号划分为不同频段的子带信号。根据小波变换的这一特性,对采样获得的脑电信号进行各尺度分解及消噪分析,并给出了各尺度分解结果及消噪结果。利用小波变换能有效去除脑电信号中的噪声干扰。     

基于尺度间和尺度内相关性的平稳小波红外图像去噪

提出了一种基于尺度间和尺度内相关性的平稳小波变换红外图像去噪方法.首先对红外图像进行离散平稳小波变换,分别对各个分解层的高频子带,利用不同尺度小波系数形成的系数向量,通过线性最小均方误差估计小波系数,获得各个高频子带的估计系数,再利用小波系数尺度内的邻域相关性对小波系数进行修正,然后通过小波反变换得到去噪图像.仿真结果表明,考虑尺度间和尺度内相关性的平稳小波红外图像去噪算法能有效地去除红外图像噪声,在信噪比和视觉质量上要优于单纯考虑尺度间相关性的去噪方法.     

Multiple scale correlation of signals by shift-invariant discrete wavelet transform

Correlation of signals at multiple scales of observation is useful for multiresolution interpretation of image, data and target signature analysis. Multiresolution analysis is inherent in the discrete wavelet transform (DWT), but shift-variance of the coefficients of the transform in dyadic orthogonal and biorthogonal basis spaces is the problem associated with it. Shift-variance of the transform and absence of a direct transform domain relationship make correlation of signals by the DWT inconvenient at multiple scales. The circulant shift property of the DWT coefficients is used in a novel way to produce correlation of signals at multiple scales with the critically sampled DWT only. The algorithm is derived in both discrete time and z-domain for signal vectors of finite duration. The algorithm is independent of signal waveform and wavelet kernel and is applied particularly for multiple scale correlation of radar signals, namely linear frequency modulated (LFM) chirp signals.     

原子钟相位一时间起伏周期的小波分析

文中根据小波变换的奇异性检测原理，分析了环境温度变化对原子钟特性的影响。基于小波变换的信号重建原理，将温度变化引起原子钟相位-时间起伏进行时-频域分离。用小波变换理论分析了由于昼夜温度变化引起原子钟周期性波动的原因，结合传统的港分析方法，认证了原子钟相位－时间起伏的周期性。结果表明：在有环境温度调节的环境中，氢原子钟的相位－时间起伏标准差41ns左右，在一般环境中，她原子钟的相位一时间起伏标准差21us左右。改善环境条件可以提高原子钟的频率稳定度。     

Wavelet representations of stochastic processes and multiresolutionstochastic models

Deterministic signal analysis in a multiresolution framework through the use of wavelets has been extensively studied very successfully in recent years. In the context of stochastic processes, the use of wavelet bases has not yet been fully investigated. We use compactly supported wavelets to obtain multiresolution representations of stochastic processes with paths in L² defined in the time domain. We derive the correlation structure of the discrete wavelet coefficients of a stochastic process and give new results on how and when to obtain strong decay in correlation along time as well as across scales. We study the relation between the wavelet representation of a stochastic process and multiresolution stochastic models on trees proposed by Basseville et al. (see IEEE Trans. Inform. Theory, vol.38, p.766-784, Mar. 1992). We propose multiresolution stochastic models of the discrete wavelet coefficients as approximations to the original time process. These models are simple due to the strong decorrelation of the wavelet transform. Experiments show that these models significantly improve the approximation in comparison with the often used assumption that the wavelet coefficients are completely uncorrelated     

原子钟噪声的多尺度分形特征

将小波分析理论和分形理论结合起来，讨论原子钟噪声的多尺度分形特征。文中研究结果表明，在大尺度上，原子钟噪声具有某种以周期变化；在小尺度上，原子钟噪声是完全随机的变化特征。     

Multiresolution FIR neural-network-based learning algorithm applied to network traffic prediction

In this paper, a multiresolution finite-impulse-response (FIR) neural-network-based learning algorithm using the maximal overlap discrete wavelet transform (MODWT) is proposed. The multiresolution learning algorithm employs the analysis framework of wavelet theory, which decomposes a signal into wavelet coefficients and scaling coefficients. The translation-invariant property of the MODWT allows alignment of events in a multiresolution analysis with respect to the original time series and, therefore, preserving the integrity of some transient events. A learning algorithm is also derived for adapting the gain of the activation functions at each level of resolution. The proposed multiresolution FIR neural-network-based learning algorithm is applied to network traffic prediction (real-world aggregate Ethernet traffic data) with comparable results. These results indicate that the generalization ability of the FIR neural network is improved by the proposed multiresolution learning algorithm.     

Image denoising based on wavelets and multifractals for singularity detection.

This paper presents a very efficient algorithm for image denoising based on wavelets and multifractals for singularity detection. A challenge of image denoising is how to preserve the edges of an image when reducing noise. By modeling the intensity surface of a noisy image as statistically self-similar multifractal processes and taking advantage of the multiresolution analysis with wavelet transform to exploit the local statistical self-similarity at different scales, the pointwise singularity strength value characterizing the local singularity at each scale was calculated. By thresholding the singularity strength, wavelet coefficients at each scale were classified into two categories: the edge-related and regular wavelet coefficients and the irregular coefficients. The irregular coefficients were denoised using an approximate minimum mean-squared error (MMSE) estimation method, while the edge-related and regular wavelet coefficients were smoothed using the fuzzy weighted mean (FWM) filter aiming at preserving the edges and details when reducing noise. Furthermore, to make the FWM-based filtering more efficient for noise reduction at the lowest decomposition level, the MMSE-based filtering was performed as the first pass of denoising followed by performing the FWM-based filtering. Experimental results demonstrated that this algorithm could achieve both good visual quality and high PSNR for the denoised images.     

光纤周界报警信号自适应压缩感知

在光纤周界报警系统中，对光纤振动信号进行分析与辨识时，针对高频、大规模信号在采样、存储、传输与信号处理过程中存在网络宽带、存储容量、计算速度等一系列限制问题，提出基于小波包的光纤周界报警信号自适应压缩感知方法。对光纤振动信号进行多尺度小波包分解，通过计算各尺度下小波包系数高频部分的数学期望作为阈值，对小波包系数进行置零处理，自适应地选择小波包分解尺度，使信号在频域得到更高的稀疏度；根据小波包系数块的数学期望和信息熵对小波包系数块进行分类，并针对不同系数块的类型设计对应的处理方法，提高信号的传输与处理速度。结果表明，该方法能够有效减少光纤振动信号的观测数据，并在相同采样率前提下，能够提高信号的重构精度和重构速度。     

基于经验模分解的小波阈值滤波方法研究

信号的多分辨经验模分解方法可以解释为以信号极值特征尺度为度量的时空滤波过程。这种时空滤波器充 分保留了信号本身的非线性和非平稳特征,在信号的滤波和去噪中具有较大的优势。本文提出了一种基于经验模分解的小 波阈值滤波去噪方法,并和小波阈值去噪、多尺度EMD滤波效果相比较。实验结果表明了基于经验模分解的小波阈值去 噪具有广泛的适用性和独特的去除非平稳信号的有色噪声的优势。     

一种基于子波变换的信号滤噪改进方法

对子波变换多尺度下信号与噪声的不同性质进行了研究,提出了一种在子波域不同尺度上选取不同的滤噪方法,该方法将经典的软阈值滤噪与子波变换的模极值传播特性在一定尺度上有机结合起来处理信号.在改善信噪比的同时,也尽可能地保持原信号的边缘信息和精细特征.通过仿真验证了该方法的实用性和优越性.