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Related Concept Videos

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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Bandpass Sampling01:17

Bandpass Sampling

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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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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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Raman Spectroscopy Instrumentation: Overview01:26

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A conventional Raman spectrophotometer includes a laser source, a sample holding system, a wavelength selector, and a detector.
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Identical bonds within a polyatomic group can stretch symmetrically (in-phase) or asymmetrically (out-of-phase). Similar to hydrogen bonding, these vibrations also influence the shape of the IR peak. Generally, asymmetric stretching frequencies are higher than symmetric stretching frequencies. For example, primary amines exhibit two distinct IR peaks between 3300–3500 cm−1 corresponding to the symmetric and asymmetric N-H stretching, while secondary amines exhibit a single...
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Inductively Coupled Plasma Atomic Emission Spectroscopy: Instrumentation01:26

Inductively Coupled Plasma Atomic Emission Spectroscopy: Instrumentation

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Inductively coupled plasma (ICP) is the common plasma source used in atomic emission spectroscopy (AES), a technique that detects and analyzes various elements in a sample. This method is often called inductively coupled plasma atomic emission spectroscopy (ICP-AES).
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Multiplex Chemical Imaging Based on Broadband Stimulated Raman Scattering Microscopy
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A SARS Method for Reliable Spectrum Sensing in Multiband Communication Systems.

Bashar I Ahmad1, Andrzej Tarczynski1

  • 1University of Westminster 115 New Cavendish StreetLondon U.K. W1W 6UW.

IEEE Transactions on Signal Processing : a Publication of the IEEE Signal Processing Society
|May 12, 2020
PubMed
Summary
This summary is machine-generated.

This study presents spectral analysis for randomized sampling (SARS), a novel method for efficient spectrum sensing. SARS uses nonuniform sampling to significantly reduce sampling rates and processed samples compared to traditional methods.

Keywords:
Fourier transformhypothesis testingnonuniform samplingspectral analysisspectrum sensing

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Area of Science:

  • Electrical Engineering
  • Signal Processing
  • Wireless Communication

Background:

  • Traditional spectrum sensing relies on uniform sampling, demanding high sampling rates.
  • Efficient spectrum sensing is crucial for cognitive radio and dynamic spectrum access.

Purpose of the Study:

  • Introduce a novel spectrum sensing method using nonuniform sampling.
  • Reduce sampling rate requirements and computational load in spectrum sensing.

Main Methods:

  • Developed Spectral Analysis for Randomized Sampling (SARS) technique.
  • Utilized nonuniform sampling with periodogram-type spectral analysis.
  • Analyzed the impact of signal cyclostationarity and noise on performance.

Main Results:

  • SARS achieves spectrum sensing with significantly lower sampling rates than uniform sampling methods.
  • Provided reliability conditions for sensing time and sampling rate trade-offs.
  • Evaluated the influence of noise and interfering signals on system dependability.

Conclusions:

  • SARS offers a new framework for efficient spectrum sensing.
  • Demonstrated considerable savings in sampling rate and processed samples.
  • The method is robust to noise and varying transmission power levels.