wavelet

简明释义

英[ˈweɪvlət]美[ˈweɪvlət]

n. 微波，小浪

复 数 w a v e l e t s

英英释义

单词用法

同义词

反义词

例句

1.That provides not only the theoretic bases for discussing the image space of wavelet transform, but also a new method to investigate the wavelet analysis theory further.

这不仅为小波变换像空间的讨论提供了理论基础，也为小波分析理论的进一步研究提供了新的途径。

2.EMD similars the wavelet transforms, the decomposition result is from high frequency to the low frequency distribution, namely the noise mainly concentrates in first several.

经验模式分解的主要思想类似小波变换，分解结果是由高频到低频分布的，即噪声主要集中在前几层。

3.Four component surface consistent wavelet deconvolution method well makes up for insufficiency of the above deconvolution methods.

四分量地表一致性子波反褶积方法较好地弥补了上述反褶积方法的不足。

4.For the image high-frequency part, use the new evaluation factor - wavelet neighborhood information to choose the ultimate wavelet high-frequency coefficients.

使用由方差和平均梯度构造的新的评价因子——小波邻域信息量作为融合规则选取小波高频系数。

5.A new effective image segmentation algorithm Based on Wavelet Operator is presented, which is tied tightly in Wavelet analysis with Fuzzy, information theory.

提出了一种新的有效的图像阈值分割算法。本算法将小波理论，模糊集理论和信息论三者有机的结合起来。

6.Therefore, a lossy Internet image transmission scheme based on wavelet transform is presented, by which Internet image transmission time can be decreased substantially.

因此，提出了一种基于小波变换的因特网图像传输方案，能大大缩短通过因特网的图像传输时间。

7.With the development of society, the increasing demands of the watermark, the watermark embedding algorithm based on wavelet transform everyone's accreditation.

跟着社会的发铺，对水印的请求越来越高，应用小波变换嵌入水印的算法显明得到大家的认可。

8.On the basis of analyzing time sequence concept, logic and application, puts forward the space sequence concept, applies the wavelet analysis on space sequence concept.

分析了时间序列的概念，在逻辑和应用的基础上，提出了空间序列概念，并将小波分析应用于空间序列概念。

9.By using wavelets 小波, we can achieve better feature extraction in machine learning tasks.

通过使用小波，我们可以在机器学习任务中实现更好的特征提取。

10.Researchers applied a wavelet 小波 analysis to detect anomalies in seismic data.

研究人员应用小波分析来检测地震数据中的异常。

11.In image compression, wavelets 小波 can effectively reduce file sizes without significant loss of quality.

在图像压缩中，小波可以有效减少文件大小而不会显著损失质量。

12.The wavelet 小波 transform is particularly useful for non-stationary signals.

对于非平稳信号，小波变换特别有用。

13.The signal processing algorithm utilizes a wavelet 小波 transform to analyze the frequency components of the audio.

该信号处理算法利用小波变换来分析音频的频率成分。

作文

In the field of signal processing, the concept of wavelet is crucial for analyzing and representing data. A wavelet is a mathematical function that can be used to break down complex signals into simpler components. This technique allows for the examination of various frequencies present in the signal at different scales, making it particularly useful in applications such as image compression, noise reduction, and feature extraction. The beauty of wavelets lies in their ability to provide both time and frequency localization, which is something traditional Fourier transforms cannot achieve. To understand how wavelets work, it is essential to grasp the idea of scale and translation. A wavelet can be stretched or compressed, allowing it to capture details at various resolutions. When analyzing a signal, one can slide the wavelet across the signal, observing how well it correlates with the signal at each position. This results in a set of coefficients that represent the signal's composition at different scales and locations.One of the most significant advantages of using wavelets is their adaptability to different types of signals. For instance, in image processing, wavelets can effectively handle images with varying levels of detail. By applying wavelet transformations, one can compress images without losing critical information, which is why they are widely used in JPEG 2000, a popular image compression standard.Moreover, wavelets have found applications in various fields beyond signal processing. In medical imaging, for example, wavelets are used to enhance the quality of images obtained from MRI scans. By reducing noise and improving the clarity of the images, wavelets contribute to better diagnosis and treatment planning.In addition to their practical applications, wavelets also play a role in theoretical research. They are used in the study of fractals and chaos theory, where understanding the underlying patterns of complex systems is essential. The versatility of wavelets makes them a powerful tool in both applied and theoretical aspects of science and engineering.In conclusion, wavelets are an indispensable part of modern signal processing and analysis. Their unique properties allow for efficient representation and manipulation of data across various domains. As technology continues to advance, the importance of wavelets will only grow, leading to further innovations and discoveries in how we process and understand information. The exploration of wavelets opens up new possibilities for researchers and practitioners alike, making it a topic worth studying and mastering in the ever-evolving landscape of technology.

在信号处理领域，wavelet的概念对于分析和表示数据至关重要。wavelet是一种数学函数，可用于将复杂信号分解为更简单的组件。这种技术允许在不同尺度上检查信号中存在的各种频率，使其在图像压缩、噪声减少和特征提取等应用中特别有用。wavelet的美在于它们能够提供时间和频率的局部化，这是传统的傅里叶变换无法实现的。要理解wavelet的工作原理，必须掌握尺度和位移的概念。wavelet可以被拉伸或压缩，使其能够捕捉到不同分辨率的细节。在分析信号时，可以将wavelet滑动过信号，观察其在每个位置与信号的相关程度。这会产生一组系数，表示信号在不同尺度和位置上的组成。使用wavelet的一个显著优势是它们对不同类型信号的适应性。例如，在图像处理中，wavelet能够有效处理具有不同细节级别的图像。通过应用wavelet变换，可以压缩图像而不丢失关键信息，这就是为什么它们广泛用于JPEG 2000这一流行的图像压缩标准。此外，wavelet还在信号处理以外的多个领域找到了应用。在医学成像中，例如，wavelet用于增强从MRI扫描获得的图像质量。通过减少噪声和改善图像的清晰度，wavelet有助于更好的诊断和治疗计划。除了实际应用外，wavelet在理论研究中也发挥着作用。它们被用于研究分形和混沌理论，在这些领域，理解复杂系统的基础模式至关重要。wavelet的多功能性使其成为科学和工程应用及理论方面的强大工具。总之，wavelet是现代信号处理和分析中不可或缺的一部分。它们独特的属性使得在各个领域高效表示和操作数据成为可能。随着技术的不断进步，wavelet的重要性只会增加，从而推动我们如何处理和理解信息的进一步创新和发现。对wavelet的探索为研究人员和从业者打开了新的可能性，这使其成为在不断发展的技术领域中值得学习和掌握的主题。