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Related Concept Videos

Aliasing01:18

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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 Methods: Overview01:06

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A sample refers to a smaller subset representative of a larger population. In analytical chemistry, studying or analyzing an entire population is often impractical or impossible. Therefore, samples are used to draw inferences and generalize the whole population. The sampling method selects individuals or items from a population to create a sample. Standard sampling methods include random, judgemental, systematic, stratified, and cluster sampling. 
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Upsampling

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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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Cluster Sampling Method01:20

Cluster Sampling Method

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Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
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Sampling materials are classified into three main types: solid, liquid, and gas.
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Electroencephalographic Signal Acquisition Framework for Neurodiverse: A Case Study of Dolphin-Assisted Therapy
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Self-Contained High-SNR Underwater Acoustic Signal Acquisition Node and Synchronization Sampling Method for Multiple

Jiajia Jiang1, Han Liu2, Fajie Duan3

  • 1The State Key Lab of Precision Measuring Technology and Instruments, Tianjin University, 92 Wei Jin Road, Tianjin 300072, China. jiajiajiang@tju.edu.cn.

Sensors (Basel, Switzerland)
|November 6, 2019
PubMed
Summary

A new underwater acoustic signal acquisition node and a master-slave dual phase-locked loop (MSDPLL) method improve underwater monitoring. These advancements enhance signal acquisition and synchronization accuracy for marine applications.

Keywords:
large-capacity data storagemaster-slave dual phase-locked loopsself-contained acquisition nodesynchronization samplingunderwater acoustic signal acquisition

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Area of Science:

  • Acoustics
  • Oceanography
  • Signal Processing

Background:

  • Underwater acoustic signal acquisition is crucial for marine research and defense.
  • Existing systems face challenges in signal-to-noise ratio (SNR) and synchronization accuracy.
  • Applications include underwater noise monitoring, marine animal observation, and target localization.

Purpose of the Study:

  • To design a high-SNR underwater acoustic signal acquisition (UASA) node.
  • To develop a high-accuracy synchronization sampling method for distributed UASA nodes.
  • To enhance performance for underwater acoustic monitoring and target localization.

Main Methods:

  • Designed a UASA node integrating a self-contained acquisition system and floating platform.
  • Developed a master-slave dual phase-locked loop (MSDPLL) method for synchronization.
  • Incorporated low-noise signal conditioning and large-capacity data storage modules.

Main Results:

  • The designed UASA node demonstrated improved acquisition performance.
  • The MSDPLL method achieved high-accuracy synchronization sampling among multiple nodes.
  • Experimental results validated the effectiveness of the UASA node and synchronization technique.

Conclusions:

  • The developed UASA node and MSDPLL method meet the demands of underwater acoustic applications.
  • The system enhances SNR and synchronization accuracy for improved underwater monitoring.
  • This technology supports long-term monitoring and distributed target positioning.