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Reconstruction of Signal using Interpolation01:10

Reconstruction of Signal using Interpolation

183
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...
183
Downsampling01:20

Downsampling

144
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...
144
Energy and Power Signals01:17

Energy and Power Signals

274
In an electrical system with a resistor, voltage and current signals facilitate the measurement of power and energy across the resistor. For a continuous-time signal, the total energy over a time interval is defined as the integral of the square of the signal's magnitude over that interval. Mathematically, this is expressed as:
274
Traveling Waves: Lossless Lines01:27

Traveling Waves: Lossless Lines

128
The provided content explores the behavior of traveling waves on single-phase lossless transmission lines. It begins with a single-phase two-wire lossless transmission line of length Δx, characterized by a loop inductance LH/m and a line-to-line capacitance C F/m. These parameters result in a series inductance LΔx  and a shunt capacitance CΔx.
128
Deconvolution01:20

Deconvolution

143
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...
143
Energy Stored In A Coaxial Cable01:31

Energy Stored In A Coaxial Cable

1.4K
A coaxial cable consists of a central copper conductor used for transmitting signals, followed by an insulator shield, a metallic braided mesh that prevents signal interference, and a plastic layer that encases the entire assembly.
In the simplest form, a coaxial cable can be represented by two long hollow concentric cylinders in which the current flows in opposite directions. The magnetic field inside and outside the coaxial cable is determined by using Ampère's law. The magnetic...
1.4K

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相关实验视频

Updated: Jun 16, 2025

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

509

智能电网数据压缩和重建通过波段数据包转换.

Rakhi Jadhav1, Anurag Mahajan1

  • 1Electronics and Telecommunication Engineering, Symbiosis Institute of Technology, Symbiosis International (Deemed University), Pune 412115, India.

MethodsX
|August 20, 2024
PubMed
概括
此摘要是机器生成的。

智能电网产生了大量的数据,使网络变得紧张. 这项研究使用波形数据包转换来进行高效的数据压缩和无声化,从而提高智能电网的性能.

关键词:
压缩比是指压缩比.离散波量变换是离散波量变换.重建中的错误重建的错误波段数据包转换 波段数据包转换

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Quasi-light Storage for Optical Data Packets
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相关实验视频

Last Updated: Jun 16, 2025

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Published on: September 8, 2023

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科学领域:

  • 电气工程 电气工程
  • 计算机科学 计算机科学
  • 数据科学数据科学数据科学

背景情况:

  • 智能电网产生了大量的实时数据,这给存储和通信网络带来了挑战.
  • 现有的数据处理方法可能会导致扭曲和噪声,影响系统可靠性.
  • 随着数据量不断增加,需要高效的压缩和消噪技术.

研究的目的:

  • 开发一种有效的方法来压缩和删除智能电网数据.
  • 尽量减少存储和通信网络的压力.
  • 为了确保准确的数据再生和系统状态反射.

主要方法:

  • 使用低级别的不同波段来表示数据.
  • 应用波段数据包转换用于数据压缩和三级重建.
  • 专注于相位测量单元 (PMU) 的电流大小和电压下降信号.

主要成果:

  • 与现有方法相比,实现了更高的压缩比率.
  • 证明了低重建错误,保持数据完整性.
  • 拟议的设计是可访问的,系统的,有利可图的,和时间效率.

结论:

  • 波形数据包转换为智能电网数据压缩和无声化提供了一种高效的解决方案.
  • 该方法有效地平衡了数据压缩与准确的系统表示.
  • 这种方法提高了智能电网的运营效率和网络性能.