Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Bandpass Sampling

Bandpass Sampling

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....

Sampling Theorem

Sampling Theorem

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.

Aliasing

Aliasing

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...

IR Spectrum

IR Spectrum

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%...

Sampling Continuous Time Signal

Sampling Continuous Time Signal

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...

Upsampling

Upsampling

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...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Gramian Angular Field and Convolutional Neural Networks for Real-Time Multiband Spectrum Sensing in Cognitive Radio Networks.

Sensors (Basel, Switzerland)·2025

Same author

Cooperative Multiband Spectrum Sensing Using Radio Environment Maps and Neural Networks.

Sensors (Basel, Switzerland)·2023

Same author

Real-Time Implementation of Multiband Spectrum Sensing Using SDR Technology.

Sensors (Basel, Switzerland)·2021

Same author

Machine Learning Techniques Applied to Multiband Spectrum Sensing in Cognitive Radios.

Sensors (Basel, Switzerland)·2019

Same author

A Novel Multiband Spectrum Sensing Method Based on Wavelets and the Higuchi Fractal Dimension.

Sensors (Basel, Switzerland)·2019

Search research articles

Related Experiment Video

Updated: Sep 29, 2025

Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy

Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy

Published on: June 27, 2013

Multiband Spectrum Sensing Based on the Sample Entropy.

Yanqueleth Molina-Tenorio¹, Alfonso Prieto-Guerrero², Rafael Aguilar-Gonzalez³

¹Information Science and Technology, Metropolitan Autonomous University Iztapalapa, Mexico City 09360, Mexico.

Entropy (Basel, Switzerland)

|March 25, 2022

Summary

This summary is machine-generated.

Cognitive radios can detect unused radio spectrum gaps using sample entropy. This technique achieves high detection accuracy (0.99 probability of success) in real-time wireless environments.

Keywords:

cognitive radio multiband spectrum sensing real-time spectrum sensing sample entropy software-defined radios

More Related Videos

Computer-based Multitaper Spectrogram Program for Electroencephalographic Data

Computer-based Multitaper Spectrogram Program for Electroencephalographic Data

Published on: November 13, 2019

High Speed Sub-GHz Spectrometer for Brillouin Scattering Analysis

High Speed Sub-GHz Spectrometer for Brillouin Scattering Analysis

Published on: December 22, 2015

Related Experiment Videos

Last Updated: Sep 29, 2025

Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy

Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy

Published on: June 27, 2013

Computer-based Multitaper Spectrogram Program for Electroencephalographic Data

Computer-based Multitaper Spectrogram Program for Electroencephalographic Data

Published on: November 13, 2019

High Speed Sub-GHz Spectrometer for Brillouin Scattering Analysis

High Speed Sub-GHz Spectrometer for Brillouin Scattering Analysis

Published on: December 22, 2015

Area of Science:

Wireless Communications
Signal Processing
Spectrum Management

Background:

Radio spectrum scarcity necessitates efficient spectrum utilization.
Cognitive radios require precise spectrum sensing to identify available frequency bands.
Existing spectrum sensing methods face challenges in real-time implementation and accuracy.

Purpose of the Study:

To present a multiband spectrum sensing technique for cognitive radios.
To implement and validate the technique using sample entropy as a decision rule.
To evaluate the feasibility of real-time implementation in a practical wireless environment.

Main Methods:

Developed a multiband spectrum sensing technique for cognitive radios.
Implemented the technique using low-cost hardware and sample entropy.
Validated the method through simulations and real-time wireless communication tests.
Compared results with the Higuchi fractal dimension for performance benchmarking.

Main Results:

Sample entropy demonstrated high accuracy in detecting primary user transmissions and noise.
Achieved a probability of success around 0.99 for spectrum detection.
Minimized errors at transmission frequency edges, averaging only 12 samples.
Confirmed feasibility for real-time implementation in wireless environments.

Conclusions:

The proposed sample entropy-based spectrum sensing technique is effective for cognitive radios.
The method offers a viable, low-cost solution for real-time multiband spectrum sensing.
Achieved high precision and efficiency, addressing spectrum scarcity challenges.