Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Discrete-time Fourier transform01:26

Discrete-time Fourier transform

324
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...
324
Convolution: Math, Graphics, and Discrete Signals01:24

Convolution: Math, Graphics, and Discrete Signals

261
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...
261
Continuous -time Fourier Transform01:11

Continuous -time Fourier Transform

318
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...
318
Discrete Fourier Transform01:15

Discrete Fourier Transform

285
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...
285
Discrete-Time Fourier Series01:20

Discrete-Time Fourier Series

268
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]...
268
Properties of DTFT II01:24

Properties of DTFT II

199
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.
199

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Research on Lane-Changing Decision Making and Planning of Autonomous Vehicles Based on GCN and Multi-Segment Polynomial Curve Optimization.

Sensors (Basel, Switzerland)·2024
Same author

A Multi-Step CNN-Based Estimation of Aircraft Landing Gear Angles.

Sensors (Basel, Switzerland)·2021
Same author

"Reading Pictures Instead of Looking": RGB-D Image-Based Action Recognition via Capsule Network and Kalman Filter.

Sensors (Basel, Switzerland)·2021
Same author

Rapid and sensitive detection of vinorelbine in the urine of tumor patients by capillary electrophoresis with tris(2,2'-bipyridyl)ruthenium(II)-based electrochemiluminescence assay.

Analytical sciences : the international journal of the Japan Society for Analytical Chemistry·2013
Same author

The effects of external electric field: creating non-zero first hyperpolarizability for centrosymmetric benzene and strongly enhancing first hyperpolarizability for non-centrosymmetric edge-modified graphene ribbon H2N-(3,3)ZGNR-NO2.

Journal of molecular modeling·2013
Same author

Msx2 plays a critical role in lens epithelium cell cycle control.

International journal of ophthalmology·2013

相关实验视频

Updated: Jul 4, 2025

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
04:23

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

Published on: April 21, 2023

1.8K

DctViT:离散的等号变换与视觉变换器相遇

Keke Su1, Lihua Cao2, Botong Zhao3

  • 1Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun, 130033, Jilin, China; University of Chinese Academy of Sciences, Beijing, 100049, China.

Neural networks : the official journal of the International Neural Network Society
|February 1, 2024
PubMed
概括

本研究介绍了DctViT,这是一个混合网络,结合了CNN和视觉转换器 (ViT) 来改进图像识别. DctViT在ImageNet和COCO数据集上实现了最先进的准确性,并降低了计算成本.

关键词:
计算机视觉 计算机视觉 计算机视觉深度学习是一种深度学习.离散的等号变换.图像的分类图像的分类.视觉变压器 视觉变压器

更多相关视频

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

405
Author Spotlight: Insights into Visual Cortex Research Through Wide-View fMRI Mapping
07:11

Author Spotlight: Insights into Visual Cortex Research Through Wide-View fMRI Mapping

Published on: December 8, 2023

1.5K

相关实验视频

Last Updated: Jul 4, 2025

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
04:23

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

Published on: April 21, 2023

1.8K
Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

405
Author Spotlight: Insights into Visual Cortex Research Through Wide-View fMRI Mapping
07:11

Author Spotlight: Insights into Visual Cortex Research Through Wide-View fMRI Mapping

Published on: December 8, 2023

1.5K

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:

  • 视觉转换器 (ViT) 擅长通过自我注意力捕捉远程依赖.
  • 卷积神经网络 (CNN) 有效地提取本地特征.
  • 结合CNN和ViT的混合方法为平衡的性能和计算效率提供了潜力.

研究的目的:

  • 为增强视觉任务提出一种新的混合CNN-Transformer网络.
  • 为了引入一个新的功能地图分辨率降低技术,DCT-Attention Down-sample (DAD).
  • 在基准数据集上评估拟议的DctViT模型的性能.

主要方法:

  • 开发了一个混合网络,集成CNN用于本地特征提取和变压器用于远程依赖模型.
  • 提出了DCT-Attention Down-sample (DAD) 模块,利用离散等号变换和自我注意力来减少特征图.
  • 在ImageNet 1K上训练并评估了DctViT-L模型,并在COCO val2017上将DctViT-B作为RetinaNet的支柱.

主要成果:

  • 在ImageNet 1K上,DctViT-L实现了84.8%的top-1精度,超过了CMT和Next-ViT等现有最先进的模型.
  • 在COCO val2017上,DctViT-B骨干提高了RetinaNet的mAP到46.8%,超过了CMT-S和SpectFormer的表现.
  • 与基线模型相比,这两种配置都表现出优异的性能,计算成本较低.

结论:

  • 拟议的混合DctViT架构有效地利用了CNN和变压器的优势.
  • DAD模块提供了一种有效的方法来减少视觉模型中的特征图分辨率.
  • DctViT为开发高性能,计算效率高的视觉系统提供了一个有希望的方向.