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相关概念视频

Discrete Fourier Transform01:15

Discrete Fourier Transform

234
The Discrete Fourier Transform (DFT) is a fundamental tool in signal processing, extending the discrete-time Fourier transform by evaluating discrete signals at uniformly spaced frequency intervals. This transformation converts a finite sequence of time-domain samples into frequency components, each representing complex sinusoids ordered by frequency. The DFT translates these sequences into the frequency domain, effectively indicating the magnitude and phase of each frequency component present...
234
Discrete-Time Fourier Series01:20

Discrete-Time Fourier Series

241
The Discrete-Time Fourier Series (DTFS) is a fundamental concept in signal processing, serving as the discrete-time counterpart to the continuous-time Fourier series. It allows for the representation and analysis of discrete-time periodic signals in terms of their frequency components. Unlike its continuous counterpart, which utilizes integrals, the calculation of DTFS expansion coefficients involves summations due to the discrete nature of the signal.
For a discrete-time periodic signal x[n]...
241
Properties of DTFT II01:24

Properties of DTFT II

188
In the study of discrete-time signal processing, understanding the properties of the Discrete-Time Fourier Transform (DTFT) is crucial for analyzing and manipulating signals in the frequency domain. Several properties, including frequency differentiation, convolution, accumulation, and Parseval's relation, offer powerful tools for signal analysis.
The frequency differentiation property is illustrated by considering a DTFT pair and differentiating both sides with respect to ω.
188
Properties of Fourier Transform II01:24

Properties of Fourier Transform II

190
The Fourier Transform (FT) is an essential mathematical tool in signal processing, transforming a time-domain signal into its frequency-domain representation. This transformation elucidates the relationship between time and frequency domains through several properties, each revealing unique aspects of signal behavior.
The Frequency Shifting property of Fourier Transforms highlights that a shift in the frequency domain corresponds to a phase shift in the time domain. Mathematically, if x(t) has...
190
Properties of DTFT I01:24

Properties of DTFT I

382
In signal processing, Discrete-Time Fourier Transforms (DTFTs) play a critical role in analyzing discrete-time signals in the frequency domain. Various properties of the DTFTs such as linearity, time-shifting, frequency-shifting, time reversal, conjugation, and time scaling help understand and manipulate these signals for different applications.
The linearity property of DTFTs is fundamental. If two discrete-time signals are multiplied by constants a and b respectively, and then combined to...
382
Discrete-time Fourier transform01:26

Discrete-time Fourier transform

288
The Discrete-Time Fourier Transform (DTFT) is an essential mathematical tool for analyzing discrete-time signals, converting them from the time domain to the frequency domain. This transformation allows for examining the frequency components of discrete signals, providing insights into their spectral characteristics. In the DTFT, the continuous integral used in the continuous-time Fourier transform is replaced by a summation to accommodate the discrete nature of the signal.
One of the notable...
288

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Using Microwave and Macroscopic Samples of Dielectric Solids to Study the Photonic Properties of Disordered Photonic Bandgap Materials
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空间域分散变换及其应用,提取水平波数结构.

Hongchen Zhang1,2, Shihong Zhou1,2, Changpeng Liu2

  • 1University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China.

The Journal of the Acoustical Society of America
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概括
此摘要是机器生成的。

这项研究引入了一种用于分析浅水声学的新方法,从复杂的声音场中提取关键的波导和源信息. 该技术增强了声学逆转能力,即使在有限的阵列大小和低信号质量的情况下也是如此.

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科学领域:

  • 水下声学 水下声学
  • 信号处理 信号处理
  • 地质物理勘探地质物理勘探

背景情况:

  • 浅水声场由于分散和多路径效应而复杂.
  • 这种复杂性包含了关于水下环境和声源的重要信息.
  • 提取水平波数和模式振幅是声学逆转的关键,但受到数组约束的限制.

研究的目的:

  • 开发一种方法来从浅水环境中提取声态信息.
  • 为了克服小型光圈阵列和低信号噪声比在声学反转中的局限性.
  • 验证一个新的空间域分散变换和频域积累方法.

主要方法:

  • 拟议的空间域分散变换和频域积累.
  • 具有已知或缓慢变化的相位光谱的杆宽带源.
  • 验证了双声水平阵列的方法,放松了信号噪声比限制.

主要成果:

  • 提供了算法性能的理论证明.
  • 对参数影响的分析:声源带宽,阵列元素数和光圈.
  • 通过模拟和实验数据验证的有效性.

结论:

  • 拟议的方法在具有挑战性的浅水条件下成功提取了关键的声学信息.
  • 这种技术为低光圈和低SNR阵列的声学倒置提供了可行的解决方案.
  • 经过验证的算法证明了对现实世界声学数据的稳定性和适用性.