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Related Concept Videos

Upsampling01:22

Upsampling

230
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...
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Sampling Continuous Time Signal01:11

Sampling Continuous Time Signal

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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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Aliasing01:18

Aliasing

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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...
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Sampling Theorem01:15

Sampling Theorem

329
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.
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Bandpass Sampling01:17

Bandpass Sampling

174
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....
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Pulse amplitude and quality01:17

Pulse amplitude and quality

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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.
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Pulse Compression Shape-Based ADC/DAC Chain Synchronization Measurement Algorithm with Sub-Sampling Resolution.

Xiangyu Hao1, Hongji Fang1, Wei Luo1

  • 1College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou 310027, China.

Sensors (Basel, Switzerland)
|May 11, 2024
PubMed
Summary
This summary is machine-generated.

This study presents a novel pulse compression algorithm for synchronizing analog-to-digital (ADC) and digital-to-analog (DAC) converter chains. The method achieves sub-sampling resolution for precise delay measurements in multi-channel systems.

Keywords:
delay parameter measurementmulti-channel system synchronizationpulse compressionsub-sampling resolution

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Area of Science:

  • Electrical Engineering
  • Signal Processing
  • Instrumentation

Background:

  • Synchronizing multiple analog-to-digital converter (ADC) and digital-to-analog converter (DAC) chains in multi-channel systems is challenging due to sampling frequency constraints and component inconsistencies.
  • Accurate synchronization is critical for the performance of complex signal processing systems.

Purpose of the Study:

  • To develop and validate a novel algorithm for measuring and compensating synchronization delays in ADC/DAC chains.
  • To achieve synchronization with sub-sampling resolution, improving precision in multi-channel systems.

Main Methods:

  • A pulse compression shape-based algorithm is proposed to measure the entire delay parameter of ADC/DAC chains.
  • The algorithm maps the discrete pulse compression peak's shape to the signal propagation delay, enabling sub-sampling resolution.
  • Matched filtering is employed within the pulse compression process for enhanced noise performance.

Main Results:

  • The proposed algorithm accurately measures synchronization differences with sub-sampling resolution.
  • The method demonstrates robust performance in scenarios with signal-to-noise ratios (SNR) greater than -10 dB.
  • The algorithm is suitable for wireless communication scenarios due to its noise performance.

Conclusions:

  • The pulse compression shape-based algorithm offers a precise and effective solution for synchronizing multi-channel ADC/DAC systems.
  • This approach overcomes limitations of traditional synchronization methods, particularly in noisy or complex environments.
  • The sub-sampling resolution achieved enhances the overall accuracy and reliability of multi-channel signal processing.