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

Signal and System01:26

Signal and System

664
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...
664
Chunking and Rehearsal in Sensory Memory01:22

Chunking and Rehearsal in Sensory Memory

212
Improving short-term memory can be achieved through techniques like chunking and rehearsal. Chunking involves organizing information into larger, more manageable units. This technique is particularly useful for information that exceeds the typical memory span of between five and nine items. For instance, logging into an online account with a password like "ta89vq0179gz" involves grouping letters and numbers into three chunks—ta89, vq01, and 79gz. It makes large amounts of...
212
Sampling Continuous Time Signal01:11

Sampling Continuous Time Signal

246
In signal processing, a continuous-time signal can be sampled using an impulse-train sampling technique, followed by the zero-order hold method. Impulse-train sampling involves the use of a periodic impulse train, which consists of a series of delta functions spaced at regular intervals determined by the sampling period. When a continuous-time signal is multiplied by this impulse train, it generates impulses with amplitudes corresponding to the signal's values at the sampling points.
In the...
246
Classification of Signals01:30

Classification of Signals

461
In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
461
Assembly of Signaling Complexes01:30

Assembly of Signaling Complexes

5.8K
Multiprotein signaling complexes are formed in a dynamic process involving protein-protein interactions at the cytoplasmic domain of transmembrane receptors or enzymatic and non-enzymatic proteins associated with the receptor. These complexes ensure the activation and propagation of intracellular signals that regulate cell functions.
Interaction domains in cell signaling
Interaction domains recognize exposed features of their binding partners containing post-translationally modified sequences,...
5.8K
Signal Sequences and Sorting Receptors01:41

Signal Sequences and Sorting Receptors

5.4K
Signal sequences are short amino acid sequences that guide newly synthesized proteins to their proper location within the cell. Classical signal sequences are fifteen to sixty amino acids long and present at the N-terminus of a polypeptide chain. Each signal sequence has a conserved segment of basic residues towards their N terminus, a hydrophobic core, and a C-terminus rich in polar residues. The C-terminus also contains a signal cleavage site and features a -3 -1 sequence motif. The -3-1...
5.4K

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

Proteome Profiling of <i>S. cerevisiae</i> Strains Lacking the Ubiquitin-Conjugating Enzymes Ubc4 and Ubc5 During Exponential Growth and After Heat Shock Treatment.

Microorganisms·2024
Same author

Pesticide-induced metabolic disruptions in crops: A global perspective at the molecular level.

The Science of the total environment·2024
Same author

A C156 Molecular Nanocarbon: Planar/Rippled Nanosheets Hybridization.

Angewandte Chemie (International ed. in English)·2024
Same author

Protein aggregation behavior during highland barley dough formation induced by different hordein/glutelin ratio.

Food chemistry·2024
Same author

Leveraging AI technology for distinguishing Eucommiae Cortex processing levels and evaluating anti-fatigue potential.

Computers in biology and medicine·2024
Same author

Temporal trends in the burden of musculoskeletal diseases in China from 1990 to 2021 and predictions for 2021 to 2030.

Bone·2024

相关实验视频

Updated: Jul 3, 2025

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

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

402

美国有线电视新闻网 (CNN) 和基于注意力的联合源道编码,用于WSN中的语义通信.

Xinyue Liu1, Zhen Huang1, Yulu Zhang1

  • 1School of Microelectronics and Communication Engineering, Chongqing University, Chongqing 401331, China.

Sensors (Basel, Switzerland)
|February 10, 2024
PubMed
概括

本研究引入了基于注意力的自适应编码 (AAC) 模块,用于无线传感器网络 (WSN),以提高通信稳定性和降低带宽压力. 语义通信方法有效地适应不同信号条件,提高数据传输效率.

关键词:
注意力机制注意力机制深度神经网络是一个神经网络.共同源通道编码 共同源通道编码移动边缘计算移动边缘计算语义通信的语义通信无线传感器网络是无线传感器网络.

更多相关视频

Simultaneously Capturing Real-time Images in Two Emission Channels Using a Dual Camera Emission Splitting System: Applications to Cell Adhesion
10:30

Simultaneously Capturing Real-time Images in Two Emission Channels Using a Dual Camera Emission Splitting System: Applications to Cell Adhesion

Published on: September 4, 2013

9.6K
Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

539

相关实验视频

Last Updated: Jul 3, 2025

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

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

402
Simultaneously Capturing Real-time Images in Two Emission Channels Using a Dual Camera Emission Splitting System: Applications to Cell Adhesion
10:30

Simultaneously Capturing Real-time Images in Two Emission Channels Using a Dual Camera Emission Splitting System: Applications to Cell Adhesion

Published on: September 4, 2013

9.6K
Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

539

科学领域:

  • 无线传感器网络 无线传感器网络
  • 通信工程 通信工程
  • 人工智能的人工智能

背景情况:

  • 无线传感器网络 (WSN) 对于实时应用至关重要,但由于5G和移动边缘计算 (MEC) 而面临数据扩散和动态环境的挑战.
  • 确保可靠的通信和管理带宽压力是大规模WSN部署的关键问题.

研究的目的:

  • 提出一个语义通信解决方案,解决WSN的通信挑战.
  • 开发一个灵活的基于注意力的自适应编码 (AAC) 模块,适用于资源有限的WSN设备.

主要方法:

  • 提出了一个灵活的基于注意力的自适应编码 (AAC) 模块,集成窗口和通道注意力机制.
  • 开发了一个端到端的联合源道编码 (JSCC) 方案,用于使用AAC模块进行图像语义通信.
  • 训练了一个单一的模型,可以适应各种信号噪声比 (SNR) 环境.

主要成果:

  • 拟议的JSCC方案与AAC模块在各种图像数据集上优于现有的深度JSCC方案.
  • 实验结果验证了AAC模块在基于通道状态的语义信息动态调整方面的有效性.
  • 该模型证明了成功的培训和在一系列SNR中提高了性能.

结论:

  • 提出的语义通信方法,包括AAC模块,有效地提高了WSN通信的稳定性和带宽效率.
  • AAC模块的自适应能力使得单个模型能够在各种通信环境中发挥最佳性能.
  • 这项研究为先进的WSN应用中可靠和高效的数据传输提供了有希望的解决方案.