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Aliasing01:18

Aliasing

209
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...
209
Bandpass Sampling01:17

Bandpass Sampling

248
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....
248
IR Spectrum01:19

IR Spectrum

1.2K
When infrared (IR) radiation passes through a molecule, the bonds stretch or bend by absorbing the radiation. This absorption creates the molecule's absorption spectrum, which is the plot of its percentage transmittance versus wavenumber.
Transmittance is defined as the ratio of the radiant power passing through a sample to that from the radiation's source. Multiplying the transmittance by 100 gives the percent transmittance (%T), which varies between 100% (no absorption) and 0%...
1.2K
UV–Vis Spectrometers01:14

UV–Vis Spectrometers

1.5K
The absorbance of UV and visible (UV–visible) radiations is measured using a UV–visible spectrophotometer. Deuterium lamps, which emit UV radiation, and tungsten lamps, which produce radiation in the visible region, are used as light sources in UV–visible spectrophotometers. A monochromator or prism is used for diffraction grating, i.e., to split the incoming radiation into different wavelengths. A system of slits is used to focus the desired wavelength on the sample cell.
1.5K
IR Frequency Region: Fingerprint Region01:03

IR Frequency Region: Fingerprint Region

1.1K
IR spectra are divided into two main regions: the diagnostic region and the fingerprint region. The diagnostic region of the spectrum lies above 1500 cm−1. The absorptions resulting from single-bond vibrations of the N–H, C–H, and O–H stretch at higher wavenumbers and appear on the left side of the spectrum. The stretching absorptions of the C≡C and C≡N occur between 2100–2300 cm−1. In contrast, those arising from stretching absorptions of the...
1.1K
The Electromagnetic Spectrum01:24

The Electromagnetic Spectrum

28.2K
Electromagnetic waves are categorized according to their wavelengths and frequencies, giving the electromagnetic spectrum. These waves are classified as radio, infrared, ultraviolet, etc. Radio waves refer to electromagnetic radiation with wavelengths ranging from millimeters to kilometers. Radio waves are commonly used for audio communications (i.e., radios) and typically result from an alternating current in the wires of a broadcast antenna. They cover a broad wavelength range and are used...
28.2K

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Related Experiment Video

Updated: Sep 2, 2025

Sampling and Analysis of Animal Scent Signals
14:59

Sampling and Analysis of Animal Scent Signals

Published on: February 13, 2021

4.8K

Signal detection with spectrum windows.

Harri Saarnisaari1, Johanna Vartiainen1

  • 1Centre for Wireless Communications, University of Oulu, Oulu, Finland.

Heliyon
|August 1, 2022
PubMed
Summary

This study on cognitive radio systems shows that simple rectangular windows are effective for spectrum sensing. Optimized bandwidth and threshold schemes ensure high sensitivity and accurate detection, even with varying signal conditions.

Area of Science:

  • Electrical Engineering
  • Signal Processing
  • Cognitive Radio

Background:

  • Spectrum sensing is crucial for frequency agile cognitive radio, cognitive radar, and electronic warfare systems.
  • Window-based detectors are employed for spectrum sensing, with customizable window shapes.

Purpose of the Study:

  • To analyze window-based detectors for spectrum sensing in cognitive radio and related systems.
  • To evaluate the impact of window shape and bandwidth on detection performance.
  • To compare constant false alarm rate (CFAR) threshold setting schemes.

Main Methods:

  • Analytical derivation of false alarm and detection probabilities for arbitrary window shapes.
  • Analysis of window shape and bandwidth mismatch effects.
  • Comparison of different CFAR threshold setting schemes.
Keywords:
Cognitive radioEnergy detectionRadiometer

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Main Results:

  • Analytical results for detection and false alarm probabilities are valid for any window shape.
  • Rectangular windows are often sufficient for spectrum sensing when bandwidth is optimized.
  • Analysis provides guidance on the number of rectangular windows needed to cover receiver bandwidth without significant sensitivity loss.
  • Nearly ideal CFAR threshold setting schemes were identified.

Conclusions:

  • Optimized rectangular windows offer a practical and effective solution for spectrum sensing.
  • Careful selection of window bandwidth and CFAR schemes enhances cognitive radio performance.
  • The findings contribute to the efficient design of spectrum-agile wireless systems.