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Related Concept Videos

The Electromagnetic Spectrum02:37

The Electromagnetic Spectrum

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The electromagnetic spectrum consists of all the types of electromagnetic radiation arranged according to their frequency and wavelength. Each of the various colors of visible light has specific frequencies and wavelengths associated with them, and you can see that visible light makes up only a small portion of the electromagnetic spectrum. Because the technologies developed to work in various parts of the electromagnetic spectrum are different, for reasons of convenience and historical...
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The Electromagnetic Spectrum01:24

The Electromagnetic Spectrum

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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...
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IR Spectrum01:19

IR Spectrum

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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%...
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Mass Spectrum01:23

Mass Spectrum

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A mass spectrum is the graphical representation of the relative abundance of the charged fragments in an analyte plotted against their mass-to-charge ratio (m/z). The plot's x-axis represents the ratio of the mass of the charged fragment to the number of charges it carries. The y axis of the plot represents the relative abundance of each charged species. The relative abundance is calculated from the signal intensity of each charged species recorded at the detector. The most intense signal (the...
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UV–Vis Spectrum01:30

UV–Vis Spectrum

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When light passes through a substance, a portion of the light is absorbed while the remaining light is reflected or transmitted. If the molecule absorbs light between the wavelengths of 180–400 nm range, the UV spectrum is obtained, and if it absorbs light in the 400–780 nm wavelength range, the visible spectrum is obtained.     
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Mass Spectrum: Interpretation01:24

Mass Spectrum: Interpretation

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An unknown compound can be established by identifying the molecular ion peak in the mass spectrum. The molecular ion peak is often weak or absent due to the predominance of fragmentation in high-energy electron beams. In such cases, a soft-energy electron beam can be used to scan the spectrum to enhance the intensity of the molecular ion peak. Additionally, chemical ionization, field ionization, and desorption ionization spectra are used to obtain a relatively intense molecular ion peak.To...
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Testing Sensory and Multisensory Function in Children with Autism Spectrum Disorder
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Compressive Multispectral Spectrum Sensing for Spectrum Cartography.

Jeison Marín Alfonso1, Jose Ignacio Martínez Torre2, Henry Arguello Fuentes3

  • 1GIDATI Research Group, Universidad Pontificia Bolivariana, 050031 Medellín, Colombia. jeison.marin@upb.edu.co.

Sensors (Basel, Switzerland)
|February 1, 2018
PubMed
Summary
This summary is machine-generated.

This study introduces compressed multispectral sampling for spectrum sensing, significantly reducing data needs for creating interference maps. This method enables efficient spectrum management by cutting data storage requirements by over 90%.

Keywords:
compressive sensing image (CSI)multispectral modelspectrum cartography

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Area of Science:

  • Wireless Communications
  • Signal Processing
  • Data Compression

Background:

  • Spectrum sensing is vital for wireless communication management, generating large datasets for interference mapping.
  • High data volumes lead to transmission delays and memory issues, hindering efficient spectrum analysis.
  • Compressive sensing (CS) reconstructs sparse signals from fewer samples than Nyquist criterion requires.

Purpose of the Study:

  • To present a novel model using compressed multispectral sampling for spectrum sensing.
  • To decrease data requirements for storing and constructing geo-referenced power spectral maps.
  • To enhance spectrum management decisions through efficient data processing.

Main Methods:

  • Utilized compressive sensing architectures adapted for multispectral image analysis.
  • Implemented a centralized manager selecting sensor power data via binary patterns.
  • Reconstructed a multispectral data cube of transmitted power and frequency from sampled data.

Main Results:

  • Successfully built a multispectral data cube using only 50% of the original device-generated samples.
  • Achieved a significant reduction in spectrum cartography data storage, requiring only 6.25% of original data.
  • Demonstrated the feasibility of efficient geo-referenced spectrum mapping.

Conclusions:

  • Compressed multispectral sampling offers a viable solution for reducing data complexity in spectrum sensing.
  • The proposed model significantly enhances the efficiency of spectrum cartography and management.
  • This approach paves the way for more effective utilization of the radio frequency spectrum.