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

Propagation Speed of Electromagnetic Waves01:30

Propagation Speed of Electromagnetic Waves

4.0K
Electromagnetic waves are consistent with Ampere's law. Assuming there is no conduction current Ampere's law is given as:
4.0K
Network Function of a Circuit01:25

Network Function of a Circuit

389
Frequency response analysis in electrical circuits provides vital insights into a circuit's behavior as the frequency of the input signal changes. The transfer function, a mathematical tool, is instrumental in understanding this behavior. It defines the relationship between phasor output and input and comes in four types: voltage gain, current gain, transfer impedance, and transfer admittance. The critical components of the transfer function are the poles and zeros.
389
Super-resolution Fluorescence Microscopy01:37

Super-resolution Fluorescence Microscopy

7.6K
Super-resolution fluorescence microscopy (SRFM) provides a better resolution than conventional fluorescence microscopy by reducing the point spread function (PSF). PSF is the light intensity distribution from a point that causes it to appear blurred. Due to PSF, each fluorescing point appears bigger than its actual size, and it is the PSF interference of nearby fluorophores that causes the blurred image. Various approaches to achieving higher resolution through SRFM have recently been...
7.6K
Generating Electromagnetic Radiations01:10

Generating Electromagnetic Radiations

3.9K
The German physicist Heinrich Hertz (1857–1894) was the first to generate and detect certain types of electromagnetic waves in the laboratory. Starting in 1887, he performed a series of experiments that confirmed the existence of electromagnetic waves and verified that they travel at the speed of light. Hertz used an alternating-current RLC (resistor-inductor-capacitor) circuit that resonated at a known frequency and connected it to a loop of wire. High voltages induced across the gap in...
3.9K
Signal and System01:26

Signal and System

1.1K
A signal x(t) is a set of data or a time function representing a variable of interest. Signals typically convey information about a phenomenon, such as atmospheric temperature, humidity, human voice, television images, a dog's bark, or birdsongs. More generally, a signal can be a function of more than one independent variable. For instance, images depend on horizontal and vertical positions and can be regarded as two-dimensional signals. However, this text will focus on one-dimensional...
1.1K
State Space to Transfer Function01:21

State Space to Transfer Function

302
The conversion of state-space representation to a transfer function is a fundamental process in system analysis. It provides a method for transitioning from a time-domain description to a frequency-domain representation, which is crucial for simplifying the analysis and design of control systems.
The transformation process begins with the state-space representation, characterized by the state equation and the output equation. These equations are typically represented as:
302

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

Kaempferide in Alpinia officinarum Hance Exerts Anti-Hepatocellular Carcinoma Activity by Inhibiting Mitophagy Through Downregulation of BNIP3L.

Journal of cellular and molecular medicine·2026
Same author

Optimizing Extraction and HPLC-ESI-QTOF-MS/MS Analysis of Bound Phenolics From Okra (<i>Abelmoschus esculentus</i>) and Their Biological Activity.

Food science & nutrition·2026
Same author

Recent advances in molecular representation methods and their applications in scaffold hopping.

npj drug discovery·2026
Same author

CircUBAP2 promotes intrahepatic cholangiocarcinoma progression via PRMT1-p65 methylation and the miR-642b-5p/IL-1β axis.

Biochimica et biophysica acta. Molecular basis of disease·2026
Same author

CME-KGDTI: integrating clustered mutations into knowledge graph embedding for drug-target interaction prediction.

BioData mining·2026
Same author

Multifunctional LWIR metalens integrating polarimetric detection, bright-field imaging, and edge enhancement.

Optics express·2026

相关实验视频

Updated: Sep 10, 2025

Transmission of Multiple Signals through an Optical Fiber Using Wavefront Shaping
09:43

Transmission of Multiple Signals through an Optical Fiber Using Wavefront Shaping

Published on: March 20, 2017

10.0K

一个用于无线图像语义传输的恒星调制网络

Xiangcheng Li1,2, Dongri Ban1, Zhaokai Ruan1

  • 1The School of Computer, Electronics and Information, Guangxi University, Nannning, 53004, China.

Scientific reports
|August 24, 2025
PubMed
概括

这项研究介绍了STARJSCC,一个用于高效无线图像语义传输的轻量级框架. 它提高了各种条件的性能和适应性,同时降低了计算复杂性和模型大小.

关键词:
频道带宽比率的调整道状态调整共同源通道编码轻量级的

更多相关视频

Quasi-light Storage for Optical Data Packets
07:45

Quasi-light Storage for Optical Data Packets

Published on: February 6, 2014

11.0K
Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

658

相关实验视频

Last Updated: Sep 10, 2025

Transmission of Multiple Signals through an Optical Fiber Using Wavefront Shaping
09:43

Transmission of Multiple Signals through an Optical Fiber Using Wavefront Shaping

Published on: March 20, 2017

10.0K
Quasi-light Storage for Optical Data Packets
07:45

Quasi-light Storage for Optical Data Packets

Published on: February 6, 2014

11.0K
Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

658

科学领域:

  • 无线通信
  • 深度学习
  • 图像处理

背景情况:

  • 深度联合源通道编码 (DEEPJSCC) 是用于语义通信的广泛研究.
  • 现有的DEEPJSCC方法面临效率,模型大小和计算复杂性的挑战.

研究的目的:

  • 为高效无线图像语义传输开发一个轻量级的DEEPJSCC框架.
  • 提高适应不同通道条件和传输速率的能力.

主要方法:

  • 推出了STARJSCC,一个新的轻量级DEEPJSCC框架.
  • 包含一个通道状态适应模块 (CSA Mod) 进行动态适应.
  • 使用解的静态语义压缩 (SC) 面具进行速率控制.

主要成果:

  • 与基线方案相比,STARJSCC表现出更高的性能和适应性.
  • 在高分辨率图像传输方面实现了2.73dB的改进.
  • 显著减少模型参数,计算复杂性和存储开销.

结论:

  • 在资源有限的环境中,STARJSCC提供了高质量的无线图像传输的可行解决方案.
  • 该框架为语义沟通提供了灵活性和效率.