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相关概念视频

Sampling Continuous Time Signal01:11

Sampling Continuous Time Signal

357
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...
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Reconstruction of Signal using Interpolation01:10

Reconstruction of Signal using Interpolation

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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...
351
Basic Continuous Time Signals01:22

Basic Continuous Time Signals

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Basic continuous-time signals include the unit step function, unit impulse function, and unit ramp function, collectively referred to as singularity functions. Singularity functions are characterized by discontinuities or discontinuous derivatives.
The unit step function, denoted u(t), is zero for negative time values and one for positive time values, exhibiting a discontinuity at t=0. This function often represents abrupt changes, such as the step voltage introduced when turning a car's...
384
Continuous -time Fourier Transform01:11

Continuous -time Fourier Transform

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The Fourier series is instrumental in representing periodic functions, offering a powerful method to decompose such functions into a sum of sinusoids. This technique, however, necessitates modification when applied to nonperiodic functions. Consider a pulse-train waveform consisting of a series of rectangular pulses. When these pulses have a finite period, they can be accurately represented by a Fourier series. Yet, as the period approaches infinity, resulting in a single, isolated pulse, the...
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Pulse amplitude and quality01:17

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Pulse amplitude is a crucial indicator of cardiac health because it provides valuable insights into the strength of left ventricular contractions and the overall uniformity of blood circulation within the vasculature. The strength of the pulse is directly related to the force with which the heart contracts and the volume of blood being pumped.
A weak or absent pulse may indicate reduced cardiac output or poor left ventricular contraction, which can be signs of cardiovascular dysfunction or...
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NMR Spectrometers: Radiofrequency Pulses and Pulse Sequences01:17

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A pulse is a short burst of radio waves distributed over a range of frequencies that simultaneously excites all the nuclei in the sample. Upon passing a radio frequency pulse along the x-axis, the nuclei absorb energy corresponding to their Larmor frequencies and achieve resonance. This shifts the net magnetization vector from the z-axis toward the transverse plane. This angle of rotation of the magnetization vector, or the flip angle, is proportional to the duration and intensity of the pulse.
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连续波泥脉冲数据传输方法基于连续梯度频率键调和卷积神经网络解调.

Yingzhong Zhu1, Zhenhua Xia2, Yue Yang3

  • 1School of Electronic Information and Electrical Engineering, Yangtze University, JingZhou, 434022, China.

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概括

一种新的连续分级频率调节 (CGFK) 调制与卷积神经网络 (CNN) 调节有效地减少测量在钻井 (MWD) 系统中的符号间干扰 (ISI). 与传统技术相比,这种先进的泥脉冲数据传输方法可以提高数据速率和比特错误率.

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科学领域:

  • 石油工程是石油工程中的一个.
  • 信号处理 信号处理
  • 机器学习 机器学习

背景情况:

  • 钻井过程中的测量 (MWD) 对于复杂的井口导航和地下评估至关重要.
  • 由于电机惯性,MWD中的高速数据传输面临符号间干扰 (ISI),限制了像频率转换键 (FSK) 和相位转换键 (PSK) 这样的传统方法.

研究的目的:

  • 引入和评估一种新的连续分级频率调制 (CGFK) 调制与卷积神经网络 (CNN) 调制相结合,用于连续的泥脉冲数据传输.
  • 为了解决和减轻高速MWD数据传输中引擎惯性引起的ISI.

主要方法:

  • 开发了一种CGFK调制技术,在符号周期内控制波形频率坡道.
  • 使用卷积神经网络 (CNN) 来调节CGFK信号,利用频率变化的速度作为关键特征.
  • 通过模拟和物理测试将CGFK-CNN性能与传统方法 (匹配波器,SVM,LSTM) 进行比较.

主要成果:

  • 与其他机器学习模型和传统方法相比,CNN在识别CGFK信号方面表现优异.
  • 使用CNN解调的CGFK有效避免或减少因电机惯性引起的ISI.
  • 实现了有利的信息传输速率和比特错误率 (BER),超过了传统FSK和PSK.

结论:

  • CGFK调制与CNN调制相结合,为MWD系统中的高速,可靠的数据传输提供了强大的解决方案.
  • 这种创新方法通过克服传统调制技术的局限性,提高了MWD在具有挑战性的钻井环境中的能力.