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相关概念视频

Properties of Fourier Transform II01:24

Properties of Fourier Transform II

320
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...
320
Continuous -time Fourier Transform01:11

Continuous -time Fourier Transform

412
The Fourier series is instrumental in representing periodic functions, offering a powerful method to decompose such functions into a sum of sinusoids. This technique, however, necessitates modification when applied to nonperiodic functions. Consider a pulse-train waveform consisting of a series of rectangular pulses. When these pulses have a finite period, they can be accurately represented by a Fourier series. Yet, as the period approaches infinity, resulting in a single, isolated pulse, the...
412
Discrete Fourier Transform01:15

Discrete Fourier Transform

413
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...
413
Discrete-time Fourier transform01:26

Discrete-time Fourier transform

486
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...
486
Properties of Fourier Transform I01:21

Properties of Fourier Transform I

246
The application of Fourier Transform properties in radio broadcasting is multifaceted, enabling significant advancements in the way signals are transmitted and received. Key areas where these properties are utilized include simultaneous multi-channel transmission, audio clip speed adjustments, live broadcast delays for different time zones, audio frequency adjustments, and signal demodulation.
In radio broadcasting, multiple audio signals often need to be transmitted simultaneously. The Fourier...
246
IR Spectrometers01:25

IR Spectrometers

1.5K
There are two main infrared (IR) spectrophotometers: dispersive IR spectrometers and Fourier transform infrared (FTIR) spectrometers. In a dispersive IR spectrometer, a beam of infrared radiation produced by a hot wire is divided into two parallel equal-intensity beams using mirrors. One beam passes through the sample, while another is a reference beam. The beams then move through the monochromator, which separates the radiations into a continuous spectrum of different frequencies. The...
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相关实验视频

Updated: Sep 13, 2025

Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters
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用于高光谱图像分类的光谱空间波和频率交互变压器.

Tahir Arshad1, Bo Peng1, Ali Rahman2

  • 1School of Computing and Artificial Intelligence, Southwest Jiaotong University, Chengdu, China.

Scientific reports
|July 27, 2025
PubMed
概括

这项研究引入了一种用于高光谱图像分类的新型变压器模型,有效地整合频率和相位信息,以获得卓越的光谱空间特征提取和更高的准确性.

关键词:
注意模块的注意力模块.卷积神经网络是一种卷积神经网络.频率域是一个频率域.超光谱图像分类的分类方法视觉变压器 视觉变压器

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相关实验视频

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科学领域:

  • 遥感 遥感 遥感 遥感
  • 计算机视觉 计算机视觉
  • 机器学习 机器学习

背景情况:

  • 准确的高光谱图像 (HSI) 分类需要高效的光谱空间特征提取.
  • 现有的方法往往忽略了区分频域组件,因为它们运行在原始输入上.
  • 卷积神经网络 (CNN) 和变压器分别在局部和全球依赖方面表现出色,但缺乏明确的频率分析.

研究的目的:

  • 开发一种新的光谱空间波和频率交互变压器,用于HSI分类.
  • 将频率感知和相位感知令牌表示集成到一个统一的变压器框架中.
  • 通过结合明确的频域分解来克服现有架构的局限性.

主要方法:

  • 利用CNN的骨干进行初始的光谱空间特征提取.
  • 开发了一种频域变压器编码器,配备了互补的光谱空间频率和波浪发生器.
  • 采用光谱空间交互模块和局部全球调制器来进行特征融合和改进.

主要成果:

  • 拟议的模型在五个基准HSI数据集上实现了最先进的分类性能.
  • 证明了高的整体准确率:98.49%,98.60%,99.07%,98.29%和97.97%.
  • 始终优于现有的HSI分类方法.

结论:

  • 频率和相位信息的整合显著提高了光谱空间特征表示.
  • 拟议的光谱空间波和频率交互变压器为HSI分类提供了一个强大的新方法.
  • 该模型的有效性通过其在多个数据集中的卓越性能来验证.