一种基于小波的三维扫描线数据处理算法

从三维点云数据中提取实物的边界特征点，在以计算机视觉为基础的数字化曲面重建过程中有非常重要的意义。为提高精度，重建之前，必须对通过各种方法获得的大量原始散乱数据进行除噪及精简处理。基于此，提出了一种基于小波变换的激光测量扫描边界特征点提取算法，我们通过严格的理论推导，构造了一种类似mexh小波的小波基来对两种边界特征点进行检测。多次实验结果显示：该算法有效地避免了噪声和冗余数据的干扰，较精确地定位到了边界特征点，通过重建原始数据，准确地提取了三维实体的外型轮廓，同时也为实现冗余数据的精简提供了一种新的思想。     

整数5/3小波变换的VLSI结构优化设计

小波变换已被广泛应用于各种工业控制系统的信号处理部分.对基于提升算法的整数5/3小波变换算法进行了研究,并提出一种优化VLSI结构,该结构内嵌边界数据处理部分,利用有限状态机技术控制各个模块的运行.体现了提升算法的优势,较大的提高了硬件效率和运算速度.     

小波变换在水声信号处理中的应用分析

小波变换是一种重要的信号处理方式,需要采取合适的变换形式,提高算法的有效性,保障信号的识别效果。基于此,将从小波变换、数据处理、变换解析、变换算法4个方面对小波变换在水声信号处理中的应用进行了分析,对小波变换使用方法进行了深入探讨,采取正确的小波变换算法,使水声信号的处理更加的精确,更具有实际意义。     

一种基于小波的轮廓特征提取算法

从大量含有噪声的3维点云数据中提取实物的边界特征,在以计算机视觉为基础的数字化曲面重建过程中有非常重要的意义。为提高重建精度,需要首先对大量原始散乱数据进行除噪及精简处理,但常规的数据处理方法由于没有区分噪声和特征点,因而使重建精度大大降低。为了准确的进行轮廓特征提取,提出了一种基于小波变换的激光测量扫描表面轮廓特征提取算法,并通过严格的理论推导,构造了一种类似m exh小波的小波基用来对两种边界特征点进行检测。多次实验结果显示,该算法不仅有效地避免了噪声和冗余数据的干扰,较精确地定位到了边界特征点,而且通过重建原始数据,较准确地提取了3维实体的外形轮廓,同时也为实现冗余数据的精简提供了一种新思想。     

小波变换在图像处理中的应用

数字图像处理是将图像信号转换成数字信号,并利用计算机对其进行处理的过程。小波变换技术已广泛地应用于数字信号处理领域。对小波变换在数字图像处理技术中的应用进行了研究,并对以后的发展进行了展望。     

小波分析及其应用

小波分析是传统傅里叶分析发展史上里程碑式的发展,近年来成为众多学科共同关注的热点.文章在小波变换的基础上,介绍了小波变换的一种快速算法-Mallat算法.在此基础上将其应用于信号分析与处理,对故障信号进行分解与重构,通过分析实验结果可知,高频部分能够清晰地反映信号的转折点,而这些点正是故障诊断所需的重要信息,通过对这些信息的分析可以得出故障点的位置.因此,可以推断小波分析在信号的奇异性位置检测方面是有效的.     

小波分析及其应用

小波分析是传统傅里叶分析发展史上里程碑式的发展，近年来成为众多学科共同关注的热点。文章在小波变换的基础上，介绍了小波变换的一种快速算法-Mallat算法。在此基础上将其应用于信号分析与处理，对故障信号进行分解与重构，通过分析实验结果可知，高频部分能够清晰地反映信号的转折点，而这些点正是故障诊断所需的重要信息，通过对这些信息的分析可以得出故障点的位置。因此，可以推断小波分析在信号的奇异性位置检测方面是有效的。     

基于DSP离散小波变换的信号不连续性检测技术

本文介绍了离散小波变换的原理及其在处理非平稳信号时的优势.通过使用离散小波变换对输入的带有间断点的信号进行分析,给出了经过小波变换的结果的图形并进行了分析.本实验硬件平台使用TMS320C5509APGE开发板.     

Fourier变换和小波变换一般形式 —线调频小波变换和多普勒小波变换

Fourier变换、窗口Fourier变换与小波变换在许多领域得到广泛的应用。该文回顾了Fourier变换和小波变换的发展;介绍了两种新的处理非平稳信号的方法,即线调频小波变换和多普勒小波变换;分析了线调频小波变换是短时Fourier变换和小波变换的时频分析的统一时频表示形式,Fourier变换、小波变换以及线调频小波变换都是多普勒小波变换的特殊情况。线调频小波变换和多普勒小波变换比Fourier变换和小波变换更具灵活性,为图像、信号处理提供了新的方法和工具。     

基于三维整数小波变换的图像压缩算法TDPIWT

介绍了整数小波变换的应用背景、特点及提升方法.着重分析了三维整数小波变换的分解过程,对其纵深方向的分解方法作了改进,讨论了选取最优整数小波变换的方法.并提出了一种基于三维整数小波变换的图像压缩新算法TDPIWT,该算法适用于压缩医学序列图像.     

2D-discontinuity detection from scattered data

We describe a numerical approach for the detection of discontinuities of a two dimensional function distorted by noise. This problem arises in many applications as computer vision, geology, signal processing. The method we propose is based on the two-dimensional continuous wavelet transform and follows partially the ideas developed in [2], [6] and [8]. It is well-known that the wavelet transform modulus maxima locate the discontinuity points and the sharp variation points as well. Here we propose a statistical test which, for a suitable scale value, allows us to decide if a wavelet transform modulus maximum corresponds to a function value discontinuity. Then we provide an algorithm to detect the discontinuity curves fromscattered and noisy data.     

Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet

Time-varying data is usually explored by animation or arrays of static images. Neither is particularly effective for classifying data by different temporal activities. Important temporal trends can be missed due to the lack of ability to find them with current visualization methods. In this paper, we propose a method to explore data at different temporal resolutions to discover and highlight data based upon time-varying trends. Using the wavelet transform along the time axis, we transform data points into multi-scale time series curve sets. The time curves are clustered so that data of similar activity are grouped together, at different temporal resolutions. The data are displayed to the user in a global time view spreadsheet where she is able to select temporal clusters of data points, and filter and brush data across temporal scales. With our method, a user can interact with data based on time activities and create expressive visualizations.     

基于小波变换自适应门限信号预处理

在测控系统中遥测数据野点的存在给数据的进一步处理带来严重的困难,由于传统的野值剔除方法无法完全保留信号的高频信息,提出一种基于小波变换和矩分析理论的野值剔除新方法。该方法以原信号小波分解后的低频成分作为原信号的估计并对残差进行自适应野值剔除,将剔除野值后的信号重构,得到野值剔除后的理想信号。与传统方法相比,该方法既保留了信号的高频成分又有效剔除了野值。对固定门限和自适应门限两种野值剔除方法进行Matlab仿真比较,结果表明,自适应野值剔除方法对数据野值剔除具有很好的效果。     

Wavelet transformation-based management of integrated summary data for distributed query processing

As the Internet technology evolves, there is growing need for Internet queries involving multiple information sources. Efficient processing of such queries necessitates the integrated summary data that compactly represents the data distribution of the entire database scattered over many information sources. We propose a new method based on wavelet transform that creates and maintains the integrated summary data by merging multiple instances of summary data, each of which is maintained in an information source. A wavelet-based summary data is easily converted to satisfy conditions for merging. Moreover, the merging process is very simple owing to the shifting and linearity properties of wavelet transform. We formally derive the upper bound of the absolute, square-root, and maximum errors in the integrated wavelet-based summary data. We also show that the integrated summary data can be used for optimizing Internet queries effectively.     

Interactive 3D visualization for wireless sensor networks

Wireless sensor networks open up a new realm of ubiquitous computing applications based on distributed large-scale data collection by embedded sensor nodes that are wirelessly connected and seamlessly integrated within the environment. 3D visualization of sensory data is a challenging issue, however, due to the large number of sensors used in typical deployments, continuous data streams, and constantly varying network topology. This paper describes a practical approach for interactive 3D visualization of wireless sensor network data. A regular 3D grid is reconstructed using scattered sensor data points and used to generate view-dependent 2D slices that are consequently rendered with commodity graphics hardware leading to smooth visualization over space and time. Furthermore, the use of efficient space partitioning data structures and the independent processing of sensor data points facilitates interactive rendering for large number of sensors while accommodating constantly changing network topology. The practical value of the proposed method is demonstrated and results obtained for visualizing time-varying temperature distributions in an urban area are presented.     

基于小波变换的图像镶嵌技术的研究

提出了一种基于小波变换的图像镶嵌的方法，小波变换具有多分辨分析特性，极适合解决图像镶嵌边缘灰度不连续问题。还提出了一种新的图像边界处理算法，对镶嵌图像边界作了精确处理，保证图像能够完全恢复，该边界处理算法适用于任何基于正交变换的小波数字滤波器，试验表明该方法具有良好的视觉效果。     

Faber-Schauder Wavelet Transform, Application to Edge Detection and Image Characterization

The Faber-Schauder wavelet transform is a simple multiscale transformation with many interesting properties in image processing. Some of these properties are: preservation of pixel ranges, arithmetic operations, non requirement of boundary processing, multiscale edge detection, elimination of the constant and the linear correlation, and the use of close neighboring information. In this study we describe this transformation and we propose a mixed scale visualization of the wavelet transform which makes it possible to show the transform result as an image. This visualization is used, with orientation information, to refine edge detection and image characterization by selecting regions with a high density of extrema wavelet coefficients.     

基于快速非抽样小波变换的岩屑多聚焦图像融合

针对传统非抽样小波变换算法较复杂的缺点,结合空、频域处理上的特点,提出了一种基于快速非抽样小波变换的多聚焦图像融合算法。与之前基于非抽样小波变换的融合算法不同,该算法取消了反变换,它根据高频小波系数绝对值和取大原则,融合图像像素值直接在对应源图像的相应位置取值,从而大大提高了图像处理的实时性,改善了融合效果。通过与六种非抽样小波变换融合算法的比较,以及快速非抽样小波变换与非抽样小波变换的融合时间对比,直观地给出了该算法的效果和时间优势。     

Temporary short circuit detection in induction motor winding using combination of wavelet transform and neural network

Monitoring system for induction motor is widely developed to detect the incipient fault. Such system is desirable to detect the fault at the running condition to avoid the motor stop running suddenly. In this paper, a new method for detection system is proposed that emphasizes the fault occurrences as temporary short circuit in induction motor winding. The investigation of fault detection is focused on the transient phenomena during starting and ending points of temporary short circuit. The proposed system utilizes the wavelet transform for processing the motor current signal. Energy level of high frequency signal from wavelet transform is used as the input variable of neural network which works as detection system. Three types of neural networks are developed and evaluated including feed forward neural network (FFNN), Elman neural network (ELMNN) and radial basis functions neural network (RBFNN). The results show that ELMNN is the most simply and accurate system that can recognize all of unseen data test. Laboratory based experimental setup is performed to provide real-time measurement data for this research.     

基于小波变换和脊波变换的自适应图像去噪算法

为了克服单纯小波变换或脊波变换的不足,提出了基于小波变换和脊波变换的自适应去噪算法。实验结果表明,在处理点奇异性和线奇异性的图像时,该方法比单纯小波变换或脊波变换的阈值去噪算法更具优越性,在实际应用中更为有效。