基于小波分析的车型识别

本文论述了用小波分析提取车辆图像边缘信号的方法，得到了连续、光滑的边缘图像，并将其应用于公路车辆车型的检测与识别系统中，实验表明识别过程准确、速度快，具有较好的应用前景。     

Planar and axially symmetric slow shearing granular flows: a comparative evaluation of assembly evolution incorporating wavelet transforms

We demonstrate the enhanced capacities of our analysis toolset for three-dimensional data. In particular, we provide supporting evidence for some of the conclusions reached in our previous studies of two-dimensional avalanching heaps. Segregation by self-diffusion is shown to take place in three-dimensional assemblies and self-diffusion velocities are shown to be of a comparable order of magnitude to those found in plane-strain situations. The effect on assembly evolution by discrete avalanching of the availability of a third dimension for translation is investigated. The discrete wavelet transform is again shown to be a useful component of the toolset in coupling process variables in the context of the discrete defining events associated with assembly evolution. In particular, we move towards the determination of time constants by correlating wavelet coefficients with a time shift.     

Applications of wavelet data reduction in a recommender system

Memory-based collaborative filtering (CF) recommender systems have emerged as an effective technique for information filtering. CF recommenders are being widely adopted for e-commerce applications to assist users in finding and selecting items of interest. As a result, the scalability of CF recommenders presents a significant challenge; one that is particularly resilient because the volume of data these systems utilize will continue to increase over time. This paper examines the impact of discrete wavelet transformation (DWT) as an approach to enhance the scalability of memory-based collaborative filtering recommender systems. In particular, a wavelet transformation methodology is proposed and applied to both synthetic and real-world recommender ratings. For experimental purposes, the DWT methodology’s effect on predictive accuracy and calculation speed is evaluated to compare recommendation quality and performance.     

Rolling element bearing fault diagnosis using wavelet transform

This paper is focused on fault diagnosis of ball bearings having localized defects (spalls) on the various bearing components using wavelet-based feature extraction. The statistical features required for the training and testing of artificial intelligence techniques are calculated by the implementation of a wavelet based methodology developed using Minimum Shannon Entropy Criterion. Seven different base wavelets are considered for the study and Complex Morlet wavelet is selected based on minimum Shannon Entropy Criterion to extract statistical features from wavelet coefficients of raw vibration signals. In the methodology, firstly a wavelet theory based feature extraction methodology is developed that demonstrates the information of fault from the raw signals and then the potential of various artificial intelligence techniques to predict the type of defect in bearings is investigated. Three artificial intelligence techniques are used for faults classifications, out of which two are supervised machine learning techniques i.e. support vector machine, learning vector quantization and other one is an unsupervised machine learning technique i.e. self-organizing maps. The fault classification results show that the support vector machine identified the fault categories of rolling element bearing more accurately and has a better diagnosis performance as compared to the learning vector quantization and self-organizing maps.     

An efficient VLSI architecture and FPGA implementation of the Finite Ridgelet Transform

In this paper, an efficient architecture for the Finite Ridgelet Transform (FRIT) suitable for VLSI implementation based on a parallel, systolic Finite Radon Transform (FRAT) and a Haar Discrete Wavelet Transform (DWT) sub-block, respectively is presented. The FRAT sub-block is a novel parametrisable, scalable and high performance core with a time complexity of O(p ²), where p is the block size. Field Programmable Gate Array (FPGA) and Application Specific Integrated Circuit (ASIC) implementations are carried out to analyse the performance of the FRIT core developed.

一类局部支撑的无限光滑小波与快速边缘检测

按照Ｄａｕｂｅｃｈｉｅｓ的理论,对于规范正交小波基,它的正则性阶数是随其支集宽度线性增长的,本文的结果表明,如果放松正交性要求,则母小波可以同时具备局部支撑性和无限光滑性。     

应用小波分析进行短期负荷预测

小波分析是一种新兴的数学工具，它能任意地提取短期负荷序列的细节。通过使用小波分析，可以在任何水平上分析短期负荷序列，它对信息成分采取逐渐精细的时域与频域处理，尤其对突发与短时的信息分析具有明显的优势。本文将小波分析引入了短期负荷预测，针对电力系统本身具有的负荷以天，周，年为周期发生波动的特点，使用周期自回归模型有选择的对分解序列进行预测，并对直接使用周期自回归(PAR)模型的预测结果及先使用小波分析处理的预测结果进行了比较，实例显示这种方法提高了预测的精度。     

基于小波算法的二阶线性定常分布参数系统的辨识

本文基于正交函数逼近方法,借助于小波变换,并利用其运算矩阵及其运算性质,研究了分布参数系统的辨识问题。将Haar小波正交基应用于分布参数系统的辨识中,经正交小波逼近变换,将原偏微分描述的分布参数系统转化为代数矩阵方程,并且,考虑了初始条件和边界条件,获得了算法简单、计算方便、具有较高精度的辨识算法,简化了分布参数系统辨识的求解过程,应用在分布参数系统辨识中不失为一种有效的分析方法。仿真实例表明了本文所提出的算法的有效性。     

用条件预优加速小波矩量法的计算

本文在小波矩量法中利用阻抗矩阵稀疏的特点,用条件预优法快速求解电磁散射问题,讨论了矩量法的采样密度对预优方法的影响。通过二维理想导电柱体散射的例子,表明该方法十分有效地加速了小波矩量法的计算,并且比文献（1）的方法更显著地减少了计算时间。     

An efficient numerical algorithm for multi-dimensional time dependent partial differential equations

An efficient and robust numerical scheme based on Haar wavelets and finite differences is suggested for the solution of two-dimensional time dependent linear and nonlinear partial differential equations (PDEs). Excellent feature of the scheme is the conversion of linear and non-linear PDEs to algebraic equations which are comparatively easy to handle. Convergence of the scheme, which guarantees small error norm as the resolution level increases, is also an important part of this work. Different error norms are computed to check efficiency of the technique. Computations verify accuracy, flexibility and low computational cost of the method.