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相关概念视频

Downsampling01:20

Downsampling

133
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...
133
Upsampling01:22

Upsampling

206
Managing signal sampling rates is essential in digital signal processing to maintain signal integrity. A decimated signal, characterized by a reduced frequency range due to its lower sampling rate, can be upsampled by inserting zeros between each sample. This upsampling process expands the original spectrum and introduces repeated spectral replicas at intervals dictated by the new Nyquist frequency. To refine this zero-inserted sequence, it is passed through a lowpass filter with a cutoff...
206
Reconstruction of Signal using Interpolation01:10

Reconstruction of Signal using Interpolation

177
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...
177
Deconvolution01:20

Deconvolution

137
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...
137
Sampling Continuous Time Signal01:11

Sampling Continuous Time Signal

211
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...
211
Bandpass Sampling01:17

Bandpass Sampling

164
In signal processing, bandpass sampling is an effective technique for sampling signals that have most of their energy concentrated within a narrow frequency band. This type of signal is known as a bandpass signal. The key principle of bandpass sampling involves sampling the signal at a rate that is greater than twice the signal's bandwidth to prevent aliasing.
A bandpass signal has a spectrum with a lower frequency limit, denoted as ω1, and an upper frequency limit, denoted as ω2....
164

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Functional Near-Infrared Spectroscopy Hyperscanning Study in Psychological Counseling
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最佳的波段选择,以消除信号的破坏.

Gyana Ranjan Sahoo1, Jack H Freed1,2, Madhur Srivastava1,2,3

  • 1Department of Chemistry and Chemical Biology, Cornell University, Ithaca, NY 14853, USA.

IEEE access : practical innovations, open solutions
|October 18, 2024
PubMed
概括
此摘要是机器生成的。

选择正确的波段对于有效的信号消噪至关重要. 这项研究引入了一种新的实证方法,通过分析信号组件的稀疏性来客观地识别最佳波段,提高准确性和效率.

关键词:
波段选择波段的选择.分解水平的选择,分解水平的选择.细节组件 细节组件 细节组件信号无声化 信号无声化稀缺性是一种稀缺性.波浪式无声化 波浪式无声化波形变换波形变换波形变换.

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科学领域:

  • 信号处理 信号处理
  • 数据分析 数据分析
  • 频谱学是一种光谱学.

背景情况:

  • 波段消噪对于各种应用中的信号降噪至关重要.
  • 当前的母波列选择方法往往是启发式的,耗时的,容易产生人类偏见.
  • 最佳波段选择最大限度地提高噪声和信号系数的分离,以实现有效的值.

研究的目的:

  • 引入一种普遍的,经验性的方法来选择最优的母波小组用于信号消噪.
  • 为了解决当前启发式和试错波段选择方法的局限性.
  • 提供基于信号特征的波段选择的客观和有效方法.

主要方法:

  • 一个新的参数,稀疏度变化的平均值 (MSC),被定义来量化噪音细节组件的变化.
  • 该方法分析了波形域中的Detail组件的稀疏性.
  • 使用模拟和实验电子自旋共振 (ESR) 光谱数据在不同的信号与噪声比率 (SNR) 上验证MSC参数的有效性.

主要成果:

  • 信号组件在不同的波段中显示MSC值的突然变化,而噪声组件显示类似的MSC值.
  • 在最高值和第二高值之间的MSC变化为低SNR数据约为8-10%,高SNR数据约为5%.
  • 随着信号SNR的增加,MSC增加,这表明更多的波段适合拒绝高SNR信号,而低SNR信号受益于有限的选择.

结论:

  • 拟议的经验方法提供了一种通用方法,用于为无声化进行最佳波段选择.
  • 选择具有最高MSC值的波段,单独或作为一个组 (前五个),可以确保有效的降噪.
  • 这种方法提高了无色化效率和客观性,特别是对于像ESR光谱学中的信号.