合成孔径雷达图像内波参数提取方法研究

给出了利用傅立叶谱分析和小波分析提取合成孔径雷达(SAR)图像中内波波长和波向参数的方法,并以台湾西南部附近海域1995年6月26日的ERS-1 SAR图像为例,作了内波参数提取实例研究。结果表明,两种方法能较好地提取所需参数,并有较好的一致性。同时还解决了谱分析法确定波向过程中的180°模糊问题。     

Log-Gabor小波和分数阶多项式KPCA的火焰图像状态识别

目的 为了进一步提高锅炉燃烧火焰图像状态识别的性能,提出了一种基于Log-Gabor小波和分数阶多项式核主成分分析(KPCA)的火焰图像状态识别方法。方法 首先利用Log-Gabor滤波器组对火焰图像进行滤波,提取滤波后图像的均值和标准差,并构成纹理特征向量。然后使用分数阶KPCA方法对纹理特征向量进行降维,并将降维后的纹理特征向量输入支持向量机进行分类。结果 本文与基于Log-Gabor小波特征提取以及2种基于Gabor小波特征提取的方法相比,本文方法的分类识别正确率更高,分类精度为76%。同时,第1主分量方差比重与核函数参数d之间满足递增关系。本文方法能够准确地提取火焰图像纹理特征。结论 本文提出一种对锅炉燃烧火焰图像进行状态识别的方法,对提取的火焰图像纹理特征向量进行降维并进行分类,可以获得较高的分类精度。实验结果表明,本文方法分类精度较高,运行时间较短,具有良好的实时性。     

基于小波包分析的滚动轴承故障特征提取

简述了小波包分析的基本原理及其用于特征提取的机理,利用小波包对滚动轴承振动加速度信号进行分解,求出各频率段的能量,并以此作为滚动轴承所发生故障的特征向量进行提取,从而识别出滚动轴承的故障,通过对于实测信号的分析证明了该方法的有效性,体现了小波包分析的优良性。     

基于荧光卟啉传感器阵列系统的肺癌标志物检测研究

针对肺癌呼出挥发性有机气体（VOCs）中的特定标志物,提出了一种新型的基于荧光卟啉传感器阵列检测系统,并对4种肺癌呼出标志物进行检测研究。通过小波分析等数学工具对测得的荧光光谱数据进行特征提取,然后采用层次聚类、主成分分析等统计学方法对特征向量进行分析。不同体积分数的各类标志物在聚类分析中能够完全正确的聚到一起。通过主成分分析得到的前3个主成分包含了标志物的88%的信息,便能对不同类别的标志物进行识别。研究表明：该荧光卟啉传感器阵列系统能够快速有效地对不同肺癌标志物进行识别,有望在临床中得到应用。     

基于加Blackman窗FFT和db44小波包变换的综合电力系统谐波分析

传统的傅里叶变换（FFT）主要适用于平稳信号的分析,确定信号的幅值和频率,但会丢失信号的局部信息,而小波包变换虽然可以准确得到信号局部细节的信息,但其分析精度不及傅里叶变换。将高分析精度的傅里叶变换和可以准确得到信号局部细节信息的小波包变换结合,提出结合两者优点的谐波分析方法。对平稳信号采用加Blackman窗傅里叶变换进行分析,得到信号的频率和幅值。对暂态信号采用db44小波包变换进行分解分析,得到信号局部细节的信息。通过 MATLAB仿真结果表明,该方法可以准确分析电力系统中的稳态谐波并准确定位暂态谐波。     

子波变换理论及其在信号处理中的应用

子波分析的形成是傅里叶分析发展史上里程碑式的进展，子波分析优于傅里叶交换的地方在于它在时域和频域同时具有良好的局部化性质，从而可以把分析的重点聚焦到任意的细节，被人们誉为数学显微镜，成为近年来在工具和方法上的重大突破。本文将子波理论中的主要定理、结论、变换特性和一些重要概念加以综述，以促进子波理论的应用。本文的重点在于介绍多分辨率分析和子波分析及其实现、子波变换及其算法、子波和滤波器组等的重要内容，并介绍其在信号处理中的应用及研究动态。     

子波分析及其在机械故障诊断中的应用

子波分析是一种日益获得广泛应用的信号处理新技术，具有良好的时－频局部化特性，尤其适用于时变及非平稳信号。本文简述了子波函数与子波展开的基本概念，利用展缩子波和调和子波将信号及信号均方值进行时－频两维分布。最后采用了三种子波分析方法对轴承故障进行诊断，即使对现有各种方法尚难以诊断的滚动体故障，也能获得满意的结果，说明子波分析的确为机械故障诊断提供了强有力的分析手段     

U-系统与 V-系统的理论及应用综述

传统的 Fourier 级数在逼近间断信号时因 Gibbs 现象的干扰,会产生比较大的误差。针对此问 题,国内学者齐东旭教授带领的课题组提出了非连续正交函数系的研究课题,其中 U-系统和 V-系统是两类典 型的非连续完备正交函数系。从数学理论上来说,U-系统和 V-系统分别是对著名的 Walsh 函数和 Haar 函数由 分段常数向分段 k 次多项式进行推广的结果,其最重要的特点是函数系中既有光滑函数又有各个层次的间断函 数。因此,U,V-系统可以处理连续和间断并存的信息,在一定程度上弥补了 Fourier 分析和连续小波的缺憾。 本文从理论与应用 2 个方面对 U,V-系统进行了综述。在理论方面,首先介绍了单变量 U-系统与 V-系统各自 的构造方法,其次介绍三角域上 U,V-系统的构造方法,最后介绍 U,V-系统的主要性质。在应用方面,介绍 了若干具有代表性的应用案例。     

基于小波模极大值的信号奇异性检测

小波分析近乎完美的数学特性受到各领域科学家和工程技术人员的青睐.文中讨论了基于小波模极大值的信号奇异性检测方法,该方法突破了傅立叶分析在时域和频域方面的局部化能力,信号的局部正则性可由其小波变换模随尺度参数的衰减特性来刻画,通过确定小波变换在细尺度下的局部模极大值来检测信号奇异性.实验表明,该方法能有效的、实用的.     

机载综合孔径辐射计图像和光学图像融合及评价

微波辐射计图像可以很直观的反映地表亮温,但其分辨率不高且图像地物的几何外形不明显等缺点给研究带来诸多不便。现有一些光学图像与之相比,具有分辨率高,几何外形更清晰等优点。如何有效利用光学图像的信息来研究辐射计图像是一件有意义的课题。采用IHS方法、PCA方法、小波方法对机载综合孔径微波辐射计图像和Landsat ETM+图像进行融合。从对实验结果定量和定性的分析表明小波融合方法更具潜力。融合结果中可以看出在多光谱图像中表达相似的地物,可 以很快的在图像中分辨出来。结果图像基本上保持了辐射计图像的辐射特性,并且融合了光学图像的不同地物的光谱信息,为在辐射计图像上对大区域地物的解译和分析带来方便。     

面向提高图像分辨率的遥感数据融合新算法

在遥感应用研究中,数据融合技术有着非常广泛的应用.主分量分析方法(principalcomponentanalysis,简称PCA)是一种经典的遥感数据融合技术,在主分量分析方法的基础上,将小波变换与其结合起来,提出了一种新的基于小波叠加的PCA融合算法(addingwaveletcoefficientsprincipalcomponentanalysis,简称AWPCA).实验证明,与原来的PCA和IHS方法相比,基于小波叠加的PCA融合算法进一步提高了融合信息的质量,并能应用于其他需要高分辨率图像的场合中.     

基于小波分析的变频调速系统故障诊断研究

讨论了交—交变频调速系统故障诊断的重要性,在当前的检测方法与故障诊断手段研究的基础上,提出了基于DSP和小波分析的变频调速系统故障诊断方法,建立了故障诊断系统;采用了基于小波能量的机电设备状态检测方法,充分利用了DSP强大的数据处理功能,以及小波分析所具有的对非平稳信号的分析处理能力和多分辨力的特性;建立了交—交变频调速系统的数学模型。经仿真实验证明:该方法适用于变频调速系统的故障诊断。     

A wavelet transform method to merge Landsat TM and SPOT panchromatic data

To take advantage of the high spectral resolution of Landsat TM images and the high spatial resolution of SPOT panchromatic images (SPOT PAN), we present a wavelet transform method to merge the two data types. In a pyramidal fashion, each TM reflective band or SPOT PAN image was decomposed into an orthogonal wavelet representation at a given coarser resolution, which consisted of a low frequency approximation image and a set of high frequency, spatially-oriented detail images. Band-by-band, the merged images were derived by performing an inverse wavelet transform using the approximation image from each TM band and detail images from SPOT PAN. The spectral and spatial features of the merged results of the wavelet methods were compared quantitatively with those of intensity-hue-saturation (IHS), principal component analysis (PCA), and the Brovey transform. It was found that multisensor data merging is a trade-off between the spectral information from a low spatial-high spectral resolution sensor and the spatial structure from a high spatial-low spectral resolution sensor. With the wavelet merging method, it is easy to control this trade-off. Experiments showed that the simultaneous best spectral and spatial quality can only be achieved with wavelet transform methods, compared with the three other approaches examined.     

利用小波分析检测航空发动机传感器故障

该文比较了傅立叶变换与小波分析的基本理论并研究了它们在航空发动机传感器故障检测应用中的特点,提出了一种基于小波变换的故障检测方法.该方法针对噪声和故障信号均具有呈现非平稳瞬态特性的特点,利用小波多分辨分析将量测信号分解到不同的频率通道中去,因此它就可以在一定的频率区间内,将故障信号成分和正常信号输出成分区分开来,提高传感器故障检测的准确度.仿真结果表明,该方法借助小波变换强大的时频分析能力,可以准确判定传感器软、硬故障,有效降低误报率和漏报率,具有良好的应用价值.     

Nonlinear identification of a gasoline HCCI engine using neural networks coupled with principal component analysis

Homogeneous charge compression ignition (HCCI) is a futuristic combustion technology that operates with high efficiency and reduced emissions. HCCI combustion is characterized by complex nonlinear dynamics which necessitates the use of a predictive model in controller design. Developing a physics based model for HCCI involves significant development times and associated costs arising from developing simulation models and calibration. In this paper, a neural networks (NN) based methodology is reported where black box type models are developed to predict HCCI combustion behavior during transient operation. The NN based approach can be considered a low cost and quick alternative to the traditional physics based modeling. A multi-input single-output model was developed each for indicated net mean effective pressure, combustion phasing, maximum in-cylinder pressure rise rate and equivalent air–fuel ratio. The two popular architectures namely multi-layer perceptron (MLP) and radial basis network (RBN) models were compared with respect to design, prediction performance and overall applicability to the transient HCCI modeling problem. A principal component analysis (PCA) is done as a pre-processing step to reduce input dimension thereby reducing memory requirements of the models. Also, PCA reduces the cross-validation time required to identify optimal model hyper-parameters. On comparing the model predictions with the experimental data, it was shown that neural networks can be a powerful approach for non-linear identification of a complex combustion system like the HCCI engine.     

Analysis and validation of wavelet transform based DC fault detection in HVDC system

Fault detection plays an important role in both conventional AC and upcoming DC power systems. This paper aims to study the application of discrete wavelet transform (WT) for detecting the DC fault in the high voltage DC (HVDC) system. The methods of choosing the mother wavelet suited for DC fault is presented, based on degree of correlation to the fault pattern and the time delay. The wavelet analysis is performed on a multi-terminal HVDC system, built in PSCAD/EMTDC software. Its performance is judged for critical parameter like the fault location, resistance and distance. The analysis is further extended to validation using results from experiment, which is obtained from a lab-scale DC hardware setup. Load change, one of the transient disturbances in power system, is carried out to understand the effectiveness of the wavelet transform to differentiate it from the DC fault. The noise in the experimental result gives rise to non-zero wavelet coefficient during the steady-state. This can be improved by removing the unwanted noise using right filter while still retaining the fault-induced transient. The wavelet transform is compared with short-time Fourier transform to highlight the issue with window size and noise.     

Comparison and improvement of wavelet‐based image fusion

The wavelets used in image fusion can be categorized into three general classes: orthogonal, biorthogonal, and non‐orthogonal. Although these wavelets share some common properties, each wavelet also has a unique image decomposition and reconstruction characteristic that leads to different fusion results. This paper focuses on the comparison of the image‐fusion methods that utilize the wavelet of the above three general classes, and theoretically analyses the factors that lead to different fusion results. Normally, when a wavelet transformation alone is used for image fusion, the fusion result is not good. However, if a wavelet transform and a traditional fusion method, such as an IHS transform or a PCA transform, are integrated, better fusion results may be achieved. Therefore, this paper also discusses methods to improve wavelet‐based fusion by integrating an IHS or a PCA transform. As the substitution in the IHS transform or the PCA transform is limited to only one component, the integration of the wavelet transform with the IHS or PCA to improve or modify the component, and the use of IHS or PCA transform to fuse the image, can make the fusion process simpler and faster. This integration can also better preserve colour information. IKONOS and QuickBird image data are used to evaluate the seven kinds of wavelet fusion methods (orthogonal wavelet fusion with decimation, orthogonal wavelet fusion without decimation, biorthogonal wavelet fusion with decimation, biorthogonal wavelet fusion without decimation, wavelet fusion based on the ‘à trous’, wavelet and IHS transformation integration, and wavelet and PCA transformation integration). The fusion results are compared graphically, visually, and statistically, and show that wavelet‐integrated methods can improve the fusion result, reduce the ringing or aliasing effects to some extent, and make the whole image smoother. Comparisons of the final results also show that the final result is affected by the type of wavelets (orthogonal, biorthogonal, and non‐orthogonal), decimation or undecimation, and wavelet‐decomposition levels.     

TM+SPOT图像的二次融合方法

提出了一种基于PCA变换和IHS变换的小波二次融合方法。首先将多光谱图像和高空间分辨率图像进行ISH变换融合得到第一次融合图像;再将此图像和原多光谱图像进行PCA变换,分别对第一主成分进行小波多尺度分解至适当层次并进行方向对比度融合,最后作PCA逆变换得到最终的融合图像。实验结果表明,文中方法在保留光谱信息的同时提高了空间分辨率,其客观性能指标均优于其它方法。     

应用电导探针技术识别气液两相流流型方法及电导波动信号噪声的辨识

针对垂直上升管的气液两相流流型的识别,提出了一种多电导探针测量系统,该测量系统由3个电导探头组成电导传感器,在此基础上利用INV306型数据采集卡实现了电导波动信号的数据采集。由于各种生产过程的参数进行测量时不可避免地信号中存在噪声信息,通过对信号的小波分解和自相关函数的分析发现电导波动信号为低频信号,且其频率一般不会超过128Hz。又通过对信号进行了小波去噪和傅立叶变换去噪之后发现利用小波分析进行信号的消噪可以很好的保存原信号中的有用部分,其有着傅立叶分析不可比拟的优点。     

Fractional dual tree complex wavelet transform and its application to biometric security during communication and transmission

In this paper, the dual tree complex wavelet transform, which is an important tool and recent advancement in signal and image processing, has been generalized by coalescing dual tree complex wavelet transform and fractional Fourier transform. The new transform, i.e. the fractional dual tree complex wavelet transform (FrDT-CWT) inherits the excellent mathematical properties of dual tree complex wavelet transform and fractional Fourier transform. Possible applications of the proposed transform are in biometrics, image compression, image transmission, transient signal processing etc. In this paper, biometric is chosen as the primary application and hence a new technique is proposed for securing biometrics during communication and transmission over insecure channel.