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相关概念视频

¹H NMR: Interpreting Distorted and Overlapping Signals01:02

¹H NMR: Interpreting Distorted and Overlapping Signals

1.0K
Spin systems where the difference in chemical shifts of the coupled nuclei is greater than ten times J are called first-order spin systems. These nuclei are weakly coupled, and their chemical shifts and coupling constant can generally be estimated from the well-separated signals in the spectrum.
As Δν decreases and the signals move closer, the doublets appear increasingly distorted. The intensities of the inner lines increase at the cost of those of the outer lines as the signals are...
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Downsampling01:20

Downsampling

135
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...
135
Aliasing01:18

Aliasing

123
Accurate signal sampling and reconstruction are crucial in various signal-processing applications. A time-domain signal's spectrum can be revealed using its Fourier transform. When this signal is sampled at a specific frequency, it results in multiple scaled replicas of the original spectrum in the frequency domain. The spacing of these replicas is determined by the sampling frequency.
If the sampling frequency is below the Nyquist rate, these replicas overlap, preventing the original...
123
Sampling Theorem01:15

Sampling Theorem

310
In signal processing, the analysis of continuous-time signals, denoted as x(t), often involves sampling techniques to convert these signals into discrete-time signals. This process is essential for digital representation and manipulation. A critical component in sampling is the train of impulses, characterized by the sampling interval and the sampling frequency. The relationship between these parameters and the original signal's properties dictates the success of the sampling process.
310
Upsampling01:22

Upsampling

214
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...
214
¹³C NMR: ¹H–¹³C Decoupling01:04

¹³C NMR: ¹H–¹³C Decoupling

1.0K
The probability of having two carbon-13 atoms next to each other is negligible because of the low natural abundance of carbon-13. Consequently, peak splitting due to carbon-carbon spin-spin coupling is not observed in spectra. However, protons up to three sigma bonds away split the carbon signal according to the n+1 rule, resulting in complicated spectra.
A broadband decoupling technique is used to simplify these complex, sometimes overlapping, signals. Broadband decoupling relies on a...
1.0K

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相关实验视频

Updated: Jun 12, 2025

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从碎片化量子化测量中进行节的系统识别.

Omar M Sleem1, Constantino M Lagoa1

  • 1Department of Electrical Engineering, Pennsylvania State University, State College, PA 16801, USA.

International journal of control
|September 23, 2024
PubMed
概括
此摘要是机器生成的。

本研究引入了一种新的方法,用于识别使用量化数据的线性时间不变系统. 该方法有效地处理杂和分散的观测,实现节的系统识别.

关键词:
在这个问题上,ADMMMM是ADMM.量子化是指量化过程中的一个过程.稀缺性 是一种稀缺性.系统识别 系统识别

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相关实验视频

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科学领域:

  • 信号处理 信号处理
  • 控制系统工程 控制系统工程
  • 系统识别系统识别系统

背景情况:

  • 量子化是一种非线性,不可逆转的过程,使传统系统识别复杂化.
  • 现有的方法与杂,碎片化和定量化的观测数据作斗争.

研究的目的:

  • 从量化观测中开发一种用于从量化观测中精简的线性时间不变 (LTI) 系统识别的方法.
  • 为应对噪音数据和潜在的数据碎片化所带来的挑战.
  • 根据可用的信息和先前知识,确定最低级系统.

主要方法:

  • 使用一个先验的信息关于系统极点在一个紧的集合.
  • 采用一个交替方向方法的乘法器 (ADMM) 算法.
  • 解决了一个混合的1,2准规范客观问题.

主要成果:

  • 提出的基于ADMM的方法成功地从量化,噪音和碎片化数据中识别了LTI系统.
  • 实现节的系统识别,偏好低级模型.
  • 在解决方案稀疏性方面,与l1最小化相比,表现出更高的性能.

结论:

  • 开发的ADMM方法为在量子化约束下系统识别提供了有效的解决方案.
  • 该方法提供了一种可靠的方式来处理在系统建模中不完美的观测数据.
  • 这项工作推动了系统识别领域的发展,通过使用有限的量化信息实现了准确的建模.