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相关概念视频

Fast Fourier Transform01:10

Fast Fourier Transform

295
The Fast Fourier Transform (FFT) is a computational algorithm designed to compute the Discrete Fourier Transform (DFT) efficiently. By breaking down the calculations into smaller, manageable sections, the FFT significantly reduces the computational complexity involved. Direct computation of an N-point DFT requires N2 complex multiplications, whereas the FFT algorithm needs only (N/2)log⁡2N multiplications, offering a much faster performance.
The computational efficiency of the FFT becomes...
295
Discrete Fourier Transform01:15

Discrete Fourier Transform

245
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...
245
Convolution: Math, Graphics, and Discrete Signals01:24

Convolution: Math, Graphics, and Discrete Signals

240
In any LTI (Linear Time-Invariant) system, the convolution of two signals is denoted using a convolution operator, assuming all initial conditions are zero. The convolution integral can be divided into two parts: the zero-input or natural response and the zero-state or forced response, with t0 indicating the initial time.
To simplify the convolution integral, it is assumed that both the input signal and impulse response are zero for negative time values. The graphical convolution process...
240
Properties of DTFT II01:24

Properties of DTFT II

190
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.
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Discrete-time Fourier transform01:26

Discrete-time Fourier transform

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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...
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Deconvolution01:20

Deconvolution

146
Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...
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分_复合:一个雷达目标识别方法在FFT卷积加速上.

Xuanchao Li1, Yonghua He2, Weigang Zhu2

  • 1Graduate School, Space Engineering University, Beijing 101416, China.

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|July 27, 2024
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概括
此摘要是机器生成的。

一种新的Split_Composite方法通过优化频域卷积,通过使用合成孔径雷达 (SAR) 加快船舶目标识别. 这种技术可以提高大规模数据处理的推断速度和效率,而不会牺牲准确性.

关键词:
美国有线电视新闻网 (CNN)复合零填充的复合零填充快速的里埃转换是什么?推断速度的推断速度是什么输入区块分解输入区块分解船舶目标识别系统 船舶目标识别系统

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科学领域:

  • 遥感 遥感 遥感 遥感
  • 人工智能的人工智能
  • 信号处理 信号处理

背景情况:

  • 合成孔径雷达 (SAR) 提供全天候,全时成像,对于船舶目标识别至关重要.
  • 深度学习模型,特别是卷积神经网络 (CNN),由于嵌入式系统的内存和实时限制,在频率领域面临效率挑战.

研究的目的:

  • 引入一种创新的卷积加速技术,Split_ Composite,旨在克服用于SAR船舶目标识别的频域处理中CNNs的局限性.
  • 提高SAR船舶目标识别系统的推断速度和可扩展性.

主要方法:

  • 分_复合方法使用快速里埃变换 (FFT) 进行卷积加速.
  • 它采用输入块分解和复合零填充来优化内存带宽和计算复杂性.
  • 频域卷积和图像重建被简化,利用FFT周期性来改善频率分辨率和重量共享.

主要成果:

  • 在OpenSARShip-4数据集上的实验表明,Split_ Composite保持了高的识别精度.
  • 该方法显著提高了推断速度,特别是在大型SAR数据处理中.
  • 与Winograd和TensorRT.com等现有方法相比,Split_ Composite具有更高的可扩展性和效率.

结论:

  • 通过优化频域操作,Split_Composite方法有效地加速了SAR船舶目标识别.
  • 该技术为实时嵌入式系统提供了可扩展和高效的解决方案,而不会影响识别精度.
  • 在SAR图像分析和深度学习加速方面,Split_ Composite代表了重大进步.