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相关概念视频

Downsampling01:20

Downsampling

109
When considering a sampled sequence with zero values between sampling instants, one can replace it by taking every N-th value of the sequence. At these integer multiples of N, the original and sampled sequences coincide. This process, known as decimation, involves extracting every N-th sample from a sequence, thereby creating a more efficient sequence.
The Fourier transform of the decimated sequence reveals a combination of scaled and shifted versions of the original spectrum. This...
109
¹³C NMR: Distortionless Enhancement by Polarization Transfer (DEPT)01:20

¹³C NMR: Distortionless Enhancement by Polarization Transfer (DEPT)

949
When proton-coupled carbon-13 spectra are simplified by a broadband proton decoupling technique, structural information about the coupled protons is lost. Distortionless enhancement by polarization transfer (DEPT) is a technique that provides information on the number of hydrogens attached to each carbon in a molecule. While the DEPT experiment utilizes complex pulse sequences, the pulse delay and flip angle are specifically manipulated. The resulting signals have different phases depending on...
949
Upsampling01:22

Upsampling

161
Managing signal sampling rates is essential in digital signal processing to maintain signal integrity. A decimated signal, characterized by a reduced frequency range due to its lower sampling rate, can be upsampled by inserting zeros between each sample. This upsampling process expands the original spectrum and introduces repeated spectral replicas at intervals dictated by the new Nyquist frequency. To refine this zero-inserted sequence, it is passed through a lowpass filter with a cutoff...
161
Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

79
Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
In contrast, nonlinear systems do not inherently possess these properties. However, for small deviations around an operating point, a nonlinear system can often be approximated as linear....
79
Discrete-time Fourier transform01:26

Discrete-time Fourier transform

222
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...
222
Estimation of the Physical Quantities01:05

Estimation of the Physical Quantities

4.0K
On many occasions, physicists, other scientists, and engineers need to make estimates of a particular quantity. These are sometimes referred to as guesstimates, order-of-magnitude approximations, back-of-the-envelope calculations, or Fermi calculations. The physicist Enrico Fermi was famous for his ability to estimate various kinds of data with surprising precision. Estimating does not mean guessing a number or a formula at random. Instead, estimation means using prior experience and sound...
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相关实验视频

Updated: May 10, 2025

Quantifying Intermembrane Distances with Serial Image Dilations
07:45

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对于压缩的DETR模型的高效整数量化.

Peng Liu1, Congduan Li1, Nanfeng Zhang2

  • 1The School of Electronics and Communication Engineering, Sun Yat-sen University, Shenzhen 518000, China.

Entropy (Basel, Switzerland)
|April 26, 2025
PubMed
概括
此摘要是机器生成的。

本研究引入了一种高效,硬件友好的目标检测模型,通过将DETR的骨干替换为Swin-T并应用纯整数量子化. 这大大减少了计算和存储需求,精度损失最小.

关键词:
这就是DETR.边缘计算是一种边缘计算.只有整数的推理推理.对象检测检测对象检测对象检测

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Quantification of Information Encoded by Gene Expression Levels During Lifespan Modulation Under Broad-range Dietary Restriction in C. elegans
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Neutron Crystallography Data Collection and Processing for Modelling Hydrogen Atoms in Protein Structures
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Neutron Crystallography Data Collection and Processing for Modelling Hydrogen Atoms in Protein Structures

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相关实验视频

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科学领域:

  • 计算机视觉 计算机视觉
  • 深度学习 (Deep Learning) 是一种深度学习.
  • 人工智能的人工智能

背景情况:

  • DETR (检测转换器) 提供了强大的对象检测,但具有很高的计算和存储需求.
  • 由于DETR的广泛要求,资源有限的设备面临着部署挑战.

研究的目的:

  • 开发一个高效和硬件友好的目标检测模型,适合资源有限的环境.
  • 为了减少DETR模型的计算和存储复杂性.

主要方法:

  • 用Swin-T替换了DETR中的ResNet-50骨干,用于统一的变压器处理.
  • 除了线性层外,还为所有非线性层 (Sigmoid,Softmax,LayerNorm,GELU) 提出了一种全整数量子化方案.
  • 实施数据压缩方法以减少内存占用和计算复杂性.

主要成果:

  • 模型计算减少到6.3%,存储到原始DETR模型的25%.
  • 实现了最小的平均精度下降仅为1.1%.
  • 在所有操作层中证明了全整数量化量子化的有效性.

结论:

  • 拟议的方法显著提高了DETR在资源有限的设备上部署的效率.
  • 所有层的纯整数量化提供了一种可行的方法来减少模型大小和计算,而不会大幅降低准确性.
  • 这种方法为高效的基于硬件的目标检测提供了一个实际的解决方案.