scalogram

简明释义

英[ˈskæləˌɡræm]美[ˈskeɪləˌɡræm]

n. 由易到难的试题程序；量图

英英释义

单词用法

同义词

反义词

例句

1.The detection is based on the detection of the peaks of scalogram.

通过检测尺度谱的峰值实现对瞬态信号的检测。

2.Results show that the reassigned scalogram has the signal feature in the time-scale plane with the high resolution, the transient energy evaluation has the transient feature in a more distinct as…

结果表明，重分配的小波尺度图能够以较高的时频分辨率表示轴承振动中的非平稳特征，反映轴承的多种故障，瞬时能量估计可以作为分析轴承状态的依据。

3.Result Wavelet scalogram of blood flow ultrasonic signal had a higher distinguishability than STFT spectrogram in the low frequency portion, but had poorer decussation terms than spectrogram.

结果通过血流多普勒信号的小波尺度图研究科室，发现利用小波尺度图可以眼科提高血流多普勒信号低频段的频谱分辨率。

4.The singal transient feature detection represent method, and a transient energy evaluation method are used based on the reassigned scalogram for the vibration non-stationary feature representation.

提出基于小波尺度图重分配的信号瞬态特征检测方法和瞬时能量估计方法，并应用于轴承在多种轻微故障状况下的振动非平稳特征的检测与表示。

5.The singal transient feature detection represent method, and a transient energy evaluation method are used based on the reassigned scalogram for the vibration non-stationary feature representation.

提出基于小波尺度图重分配的信号瞬态特征检测方法和瞬时能量估计方法，并应用于轴承在多种轻微故障状况下的振动非平稳特征的检测与表示。

6.Based on the principle of modulus maximum, a new approach using scalogram of wavelet transform for transient signal, under noise free condition or noise condition separately, is described.

利用小波变换模极大值原理，提出了用小波变换尺度谱检测瞬态信号波至点的新方法，研究了其检测无噪声和有噪声条件下的瞬态信号的能力。

7.The team used a scalogram to visualize the effects of noise on the signal.

团队使用scalogram来可视化噪声对信号的影响。

8.By examining the scalogram, we could detect anomalies in the data more easily.

通过检查scalogram，我们可以更容易地检测到数据中的异常。

9.In wavelet analysis, a scalogram provides a visual representation of frequency components over time.

在小波分析中，scalogram提供了频率成分随时间变化的可视化表示。

10.The scalogram displayed the energy distribution of the signal across different scales.

scalogram展示了信号在不同尺度上的能量分布。

11.The researcher analyzed the scalogram to identify patterns in the time series data.

研究人员分析了scalogram以识别时间序列数据中的模式。

作文

In the realm of signal processing and time-frequency analysis, the term scalogram plays a crucial role. A scalogram is a visual representation that depicts the frequency content of a signal as it varies over time. This powerful tool allows researchers and engineers to analyze non-stationary signals, which are signals whose statistical properties change over time. Unlike traditional Fourier transforms that provide a global frequency representation, the scalogram offers a more localized view, revealing how different frequency components evolve throughout the duration of the signal.To understand the importance of the scalogram, one must first grasp the concept of wavelet transforms. Wavelet transforms decompose a signal into wavelets, which are small oscillatory functions. These wavelets can be stretched or compressed, allowing for a multi-resolution analysis of the signal. The scalogram is essentially a graphical depiction of this decomposition, where the x-axis represents time, the y-axis represents scale (or frequency), and the intensity of color indicates the strength of the signal at that particular scale and time.One of the primary advantages of using a scalogram is its ability to analyze transient phenomena. For instance, in biomedical engineering, when examining electroencephalogram (EEG) signals, researchers often encounter brief events like spikes or bursts that may carry significant information about brain activity. The scalogram helps in identifying these transient features by highlighting the frequency content during specific time intervals.Moreover, the scalogram has applications in various fields, including geophysics, finance, and even music analysis. In geophysics, for example, seismic signals can be analyzed using scalograms to detect patterns that indicate geological changes. In finance, traders might use scalograms to study market trends and fluctuations, thereby making informed decisions based on the temporal behavior of stock prices.The construction of a scalogram involves several steps, starting with the selection of an appropriate wavelet function. Commonly used wavelets include the Haar wavelet, Daubechies wavelet, and Morlet wavelet. Each type of wavelet has its characteristics and is suitable for different types of signals. Once the wavelet is chosen, the signal is convolved with the wavelet at various scales, resulting in a set of coefficients that represent the signal's frequency content. These coefficients are then plotted to create the scalogram.In conclusion, the scalogram serves as a vital tool in analyzing complex signals across various disciplines. Its ability to provide a time-frequency representation enables researchers to uncover hidden patterns and insights that would be difficult to discern using traditional methods. As technology continues to advance, the relevance of scalograms will likely increase, paving the way for new discoveries and innovations in signal analysis. Understanding and utilizing the scalogram can significantly enhance our ability to interpret dynamic systems, ultimately leading to better-informed decisions in both scientific research and practical applications.

在信号处理和时频分析领域，术语scalogram扮演着至关重要的角色。scalogram是一个视觉表示，描绘了信号随时间变化的频率内容。这一强大的工具使研究人员和工程师能够分析非平稳信号，即统计特性随时间变化的信号。与提供全局频率表示的传统傅里叶变换不同，scalogram提供了更局部的视图，揭示了不同频率成分在信号持续时间内的演变。要理解scalogram的重要性，首先必须掌握小波变换的概念。小波变换将信号分解为小波，这些小波是小的振荡函数。这些小波可以被拉伸或压缩，从而允许对信号进行多分辨率分析。scalogram本质上是这种分解的图形表示，其中x轴代表时间，y轴代表尺度（或频率），颜色的强度表示在特定尺度和时间下信号的强度。使用scalogram的主要优点之一是其分析瞬态现象的能力。例如，在生物医学工程中，当检查脑电图（EEG）信号时，研究人员常常遇到如尖峰或爆发等短暂事件，这些事件可能携带有关大脑活动的重要信息。scalogram通过突出特定时间间隔内的频率内容来帮助识别这些瞬态特征。此外，scalogram在地球物理学、金融甚至音乐分析等多个领域都有应用。在地球物理学中，例如，可以使用scalogram分析地震信号，以检测指示地质变化的模式。在金融领域，交易者可能会使用scalogram研究市场趋势和波动，从而根据股票价格的时间行为做出明智的决策。构建scalogram涉及几个步骤，首先是选择合适的小波函数。常用的小波包括Haar小波、Daubechies小波和Morlet小波。每种类型的小波都有其特性，适用于不同类型的信号。一旦选择了小波，就会在各种尺度上对信号进行卷积，得到一组表示信号频率内容的系数。这些系数随后被绘制以创建scalogram。总之，scalogram作为分析复杂信号的重要工具，服务于各个学科。它提供的时频表示使研究人员能够发现隐藏的模式和见解，而这些在传统方法中难以辨别。随着技术的不断进步，scalogram的相关性可能会增加，为信号分析中的新发现和创新铺平道路。理解和利用scalogram可以显著增强我们对动态系统的解读能力，最终在科学研究和实际应用中做出更明智的决策。