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Related Concept Videos

Upsampling01:22

Upsampling

294
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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Aliasing01:18

Aliasing

200
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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Downsampling01:20

Downsampling

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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...
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Bandpass Sampling01:17

Bandpass Sampling

241
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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Sampling Theorem01:15

Sampling Theorem

720
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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Sampling Methods: Overview01:06

Sampling Methods: Overview

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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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Related Experiment Video

Updated: Aug 23, 2025

Fiber Optic Distributed Sensors for High-resolution Temperature Field Mapping
09:48

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Spatio-temporal joint oversampling-downsampling technique for ultra-high resolution fiber optic distributed acoustic

Hao Li, Cunzheng Fan, Zhengxuan Shi

    Optics Express
    |October 27, 2022
    PubMed
    Summary

    A novel spatio-temporal oversampling-downsampling technique significantly reduces noise in coherent fiber distributed acoustic sensing (DAS) systems. This method enhances strain resolution and signal-to-noise ratio for high-precision applications.

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    Area of Science:

    • Physics
    • Optical Engineering
    • Signal Processing

    Background:

    • Coherent fiber distributed acoustic sensing (DAS) systems are susceptible to noise, limiting their performance.
    • Existing noise suppression methods may increase system complexity or introduce crosstalk.

    Purpose of the Study:

    • To propose and validate a spatio-temporal joint oversampling-downsampling technique for noise suppression in coherent fiber DAS.
    • To improve the strain resolution and signal-to-noise ratio (SNR) of DAS systems.

    Main Methods:

    • Spatial oversampling for artificially dense sampling, followed by spatial downsampling using averaging of differential sub-vectors.
    • Temporal oversampling to expand noise bandwidth, ensuring correct noise frequency quantization.
    • Temporal downsampling of differential phase reconstruction to recover target bandwidth and reduce out-of-band noise.

    Main Results:

    • Noise floor inversely correlated with spatiotemporal downsampling factors.
    • Achieved strain resolution of 2.58pε/√Hz@100Hz-500Hz and 9.47pε/√Hz@10Hz.
    • Reduced probability of large-noise channels from 44.32% to 0% and improved dynamic signal SNR by 20.8dB.
    • Optimized noise floor 8dB lower than averaging technique without crosstalk.

    Conclusions:

    • The proposed technique effectively suppresses noise in coherent fiber DAS systems without increasing complexity or introducing crosstalk.
    • The method significantly enhances strain resolution and SNR, making DAS highly competitive for high-precision applications like seismic imaging.