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相关概念视频

Convolution: Math, Graphics, and Discrete Signals01:24

Convolution: Math, Graphics, and Discrete Signals

823
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...
823
Uniform Depth Channel Flow: Problem Solving01:18

Uniform Depth Channel Flow: Problem Solving

426
To calculate the flow rate for a trapezoidal channel, first, identify the bottom width, side slope, and flow depth of the channel. The cross-sectional area (A) corresponding to the depth of flow (y), channel bottom width (B), and side slope (θ) is determined by:Next, calculate the wetted perimeter, which includes the bottom width and the sloped side lengths in contact with the water. Using the values of the cross-sectional area and the wetted perimeter, determine the hydraulic radius by...
426
Deconvolution01:20

Deconvolution

541
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...
541
Reconstruction of Signal using Interpolation01:10

Reconstruction of Signal using Interpolation

686
Signal processing techniques are essential for accurately converting continuous signals to digital formats and vice versa. When a continuous signal is sampled with a period T, the resulting sampled signal exhibits replicas of the original spectrum in the frequency domain, spaced at intervals equal to the sampling frequency. To handle this sampled signal, a zero-order hold method can be applied, which creates a piecewise constant signal by retaining each sample's value until the next...
686
Residuals and Least-Squares Property01:11

Residuals and Least-Squares Property

9.1K
The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
9.1K
Uniform Depth Channel Flow01:27

Uniform Depth Channel Flow

525
Uniform depth channel flow keeps fluid depth consistent along channels such as irrigation canals. In natural channels, such as rivers, approximate uniform flow is often assumed. This condition occurs when the channel’s bottom slope matches the energy slope, balancing potential energy lost from gravity with head loss due to shear stress. This balance prevents depth changes along the channel length, resulting in a steady, uniform flow.Uniform flow in open channels with a constant cross-section...
525

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相关实验视频

Updated: Jan 15, 2026

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

1.0K

一个卷积变压器剩余网络用于智能反射表面的通道估计,辅助MIMO系统.

Qingying Wu1, Junqi Bao1, Hui Xu1

  • 1Faculty of Applied Sciences, Macao Polytechnic University, Macao SAR, China.

Sensors (Basel, Switzerland)
|October 16, 2025
PubMed
概括

本研究介绍了一种混合深度学习框架,用于在智能反射表面 (IRS) 辅助的MIMO系统中高效地估算通道. 拟议的ConvTrans-ResNet模型显著提高了未来无线通信的准确性和效率.

关键词:
频道估计 频道估计多个输入多个输出.可重新配置的智能表面.

相关实验视频

Last Updated: Jan 15, 2026

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

1.0K

科学领域:

  • 无线通信无线通信
  • 信号处理 信号处理
  • 人工智能的人工智能

背景情况:

  • 智能反射表面 (IRS) 辅助的多输入多输出 (MIMO) 系统为无线网络提供了增强的频谱和能源效率.
  • 精确的道估计至关重要,但由于IRS道的被动性和高维度,具有挑战性.

研究的目的:

  • 提出一种轻量化混合框架,用于在IRS辅助的MIMO系统中高效的级联通道估计.
  • 与现有方法相比,提高道估计的准确性和效率.

主要方法:

  • 一个混合框架,将基于物理的双线交替最小平方 (BALS) 算法与深度神经网络 (ConvTrans-ResNet) 结合起来.
  • ConvTrans-ResNet将卷积嵌入和变压器模块集成到剩余学习架构中.
  • 进行了除研究,以优化网络架构,以减少复杂性和参数数量.

主要成果:

  • 拟议的ConvTrans-ResNet方法在标准化平均平方误差 (NMSE) 中显著优于最先进的神经模型 (HA02,ReEsNet,InterpResNet).
  • 在各种信号噪声比 (SNR) 级别和 IRS 元素大小中实现了卓越的估计准确性和效率.
  • 展示了具有较低计算复杂性的紧网络配置.

结论:

  • 混合BALS和ConvTrans-ResNet框架为IRS辅助的MIMO系统中的通道估计提供了实用和高效的解决方案.
  • 优化的轻量级网络适合于现实世界的部署,推进未来的无线通信技术.