查询词典 wavelet

The basic property of image wavelet coefficient and classical wavelet domain statistical models are discussed.

探讨了图像小波系数的基本统计特征和几个典型的小波域图像统计模型。

Besides, as to inpainting problem in wavelet domain, we introduce a new iterative model. In this model low frequency and high frequency will carry on processing separately, enormous speed up the image repair convergence rate, and even if as high as 90% wavelet coefficient have been lost, this model will also be able to have the very good repair effect. In the old model, to compute the curvature we have to transform the coefficients to the pixel domain, and then transform back to the coefficient domain for several times and this method can solve this problem and speed up the iterative convergence.

其次针对小波域中图像修复的问题，本文提出了一种新的小波域图像修复模型，该模型根据小波变换后系数相关性的特点，将低频和高频分别进行处理，极大的加快了图像修复的收敛速度，并且即使丢失高达90%的小波系数，该模型也会有很好的修复效果，有效的解决了传统的TV- Wavelet模型在计算图像的平均曲率WCURV时，需要反复进行小波分解与重构的计算，迭代速度慢的问题。

Considering some period of time geomagnetic data recorded at the Observatory Surlari (colatitude 45.3°, longitude 26.3°) and comparing them with data recorded at other observatories, such as Ottawa (colatitude 44.6°, longitude 284.5°), Canberra (colatitude 125.3°, longitude 149.3°), Kakioka (colatitude 53.8°, longitude 140.2°) and Vernadsky (colatitude 155.3°, longitude 295.7°), including comparison to the windowed Fourier transform, the choice of an appropriate wavelet basis function, edge effects due to finite-length time series, and the relationship between wavelet scale and Fourier frequency.

我们分析了Surlari观测台（余纬45.3°，经度26.3°）的地磁时间序列，并与Ottawa（余纬44.6°，〖JP2〗经度284.5°），Canberra（余纬125.3°，经度149.3°）， Kakioka（余纬53.8°，经度140.2°），Vernadsky〖JP〗（余纬155.3°，经度295.7°）等台站的结果进行了对比分析，讨论了Fourier变换时间窗和小波基函数选取的影响，有限长时间序列引起的边缘效应，以及小波尺度和Fourier频率的关系等问题。

It is defined that the two integrating ways' characteristics of the wavelet NN and theirs applying on the equipments' intelligence diagnose. Wavelet analysis technology has the ability of analyzing strongly the non-placidity signals' time-frequency, self-studying the NN, of dealing with data distributed and collaterally, of mapping non-linear ability, and it is fit for diagnosing failures.

利用小波分析对非平稳信号强的时频分析能力和神经网络的自学习、分布式并行处理数据及非线性映射能力，以及它们适合于故障诊断的特点，说明小波与神经网络的松散型和和紧致型结合途径下产生的两种小波神经网络的各自特点及其在设备智能诊断中的应用。

In this paper, we construct a kind of wavelet, each of which has compact support in the frequency domain, and Fourier transform of the wavelet has analytic representation.

本文构造了一种频谱支集紧且有解析表达式的小波，利用它得到一种计算信号瞬时参数的能抗噪声干扰的快速、局部算法。

With a combination of wavelet transformation and regularization method we convert the ill-posed problem into a posed problem in coarse space, which takes full advantage of compact support of wavelet.

将小波变换和正则化方法相结合，利用小波函数的紧支撑性，将原不适定问题转化为粗子空间上的适定问题。

We come to the conclusionthat the physical wavelet is the conjugate of the wavelet operator group.

我们还以声波为例，给出了几种不同参数下声波的共轭小波算子族的图形。

The results show that the frequency characteristic corresponds with vortex structures; the detail and approximate characteristics of signal are reflected by wavelet coefficient at different scales; the coefficient map of continuous wavelet transformation reveals the self-similarity of the signal.

采用傅里叶变换和小波变换技术，实现了混合层密度脉动信号的频谱与多分辨率分析，结果表明：信号的频域特性与流场的涡结构相对应，不同层面的离散小波系数反映了不同尺度下信号与图像的细节和平滑近似特征，连续小波变换系数图定性的反映了信号的自相似特征。

On the basis of continuous wavelet transform, a signal analysis method based on extracting the energy spectrum of the signal on wavelet scale was proposed.

通过对连续小波变换的分析研究，提出了一种提取信号在小波尺度上的能量谱的信号分析方法。

In the applications of wavelet, it is the most difficult and cumbrous to select the suitable wavelet bases.

摘要在有关小波的各种应用中，合适小波基的选取是一个极为重要和棘手的问题。

There are different types of personal loans available.

有许多不同类型的个人贷款优惠。

The above mentioned teaching model of spiral rising can be used to avoid rigidness in teaching.

上述螺旋式上升的教学模式，可以避免教学形成模式後的僵化现象。