Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

IR Spectrum01:19

IR Spectrum

863
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%...
863
Infrared (IR) Spectroscopy: Overview01:09

Infrared (IR) Spectroscopy: Overview

1.4K
When electromagnetic radiation passes through a material, atoms or molecules transition from a lower to a higher energy state by absorbing radiation corresponding to the energy difference between the two states. The absorption of infrared (IR) radiation causes transitions between vibrational energy levels in a molecule. Therefore, IR spectroscopy is a useful analytical tool for determining the molecular structure of molecules.
Different compounds display unique properties due to their...
1.4K
Applications of IR Spectroscopy: Overview01:11

Applications of IR Spectroscopy: Overview

435
The non-destructive nature and ability to provide valuable chemical information make IR spectroscopy a versatile technique with broad applications in various scientific and industrial fields. IR spectroscopy is commonly used to identify and characterize organic and inorganic compounds. It provides information about the functional groups present in a molecule and the bonding between atoms. This helps in the structural elucidation of compounds during organic synthesis, pharmaceutical research,...
435
Classification of Signals01:30

Classification of Signals

363
In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
363
Aliasing01:18

Aliasing

103
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...
103
The Electromagnetic Spectrum02:37

The Electromagnetic Spectrum

52.3K
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...
52.3K

You might also read

Related Articles

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

Sort by
Same author

Standalone RFID Access Control System with Data-Integrity Verification Capabilities.

Sensors (Basel, Switzerland)·2026
Same author

AI-Native PHY-Layer in 6G Orchestrated Spectrum-Aware Networks.

Sensors (Basel, Switzerland)·2025
Same author

Patient Health Record Smart Network Challenges and Trends for a Smarter World.

Sensors (Basel, Switzerland)·2025
Same author

A Blockchain Framework for Scalable, High-Density IoT Networks of the Future.

Sensors (Basel, Switzerland)·2025
Same author

A Comprehensive Survey on Emerging Assistive Technologies for Visually Impaired Persons: Lighting the Path with Visible Light Communications and Artificial Intelligence Innovations.

Sensors (Basel, Switzerland)·2024
Same author

The Design and Implementation of a Phased Antenna Array System for LEO Satellite Communications.

Sensors (Basel, Switzerland)·2024

Related Experiment Video

Updated: May 17, 2025

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

Fiber Optic Distributed Sensors for High-resolution Temperature Field Mapping

Published on: November 7, 2016

11.9K

LoRa Communications Spectrum Sensing Based on Artificial Intelligence: IoT Sensing.

Partemie-Marian Mutescu1, Valentin Popa1, Alexandru Lavric1

  • 1Faculty of Electrical Engineering and Computer Science, Ștefan Cel Mare University of Suceava, 720229 Suceava, Romania.

Sensors (Basel, Switzerland)
|May 14, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a hybrid spectrum sensing framework for the Internet of Things (IoT) using LoRa modulation. It effectively identifies multiple LoRa transmissions, improving spectrum management for growing IoT networks.

Keywords:
LPWANLoRaartificial intelligenceinternet of thingsspectrum sensing

More Related Videos

A Silicon-tipped Fiber-optic Sensing Platform with High Resolution and Fast Response
09:03

A Silicon-tipped Fiber-optic Sensing Platform with High Resolution and Fast Response

Published on: January 7, 2019

7.0K
Early Detection of Cyanobacterial Blooms and Associated Cyanotoxins using Fast Detection Strategy
07:13

Early Detection of Cyanobacterial Blooms and Associated Cyanotoxins using Fast Detection Strategy

Published on: February 25, 2021

3.7K

Related Experiment Videos

Last Updated: May 17, 2025

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

Fiber Optic Distributed Sensors for High-resolution Temperature Field Mapping

Published on: November 7, 2016

11.9K
A Silicon-tipped Fiber-optic Sensing Platform with High Resolution and Fast Response
09:03

A Silicon-tipped Fiber-optic Sensing Platform with High Resolution and Fast Response

Published on: January 7, 2019

7.0K
Early Detection of Cyanobacterial Blooms and Associated Cyanotoxins using Fast Detection Strategy
07:13

Early Detection of Cyanobacterial Blooms and Associated Cyanotoxins using Fast Detection Strategy

Published on: February 25, 2021

3.7K

Area of Science:

  • Wireless Communication
  • Signal Processing
  • Artificial Intelligence

Background:

  • Internet of Things (IoT) relies on wireless sensor networks and LoRa modulation for long-range, energy-efficient communication.
  • Exponential growth in IoT devices (projected 41 billion by 2034) strains limited radio spectrum, causing interference and degrading service quality.
  • Existing network capacity solutions are insufficient for managing increasing spectral demands.

Purpose of the Study:

  • To propose a novel hybrid spectrum sensing framework for LoRa transmissions.
  • To enhance spectrum management and network capacity in dense IoT environments.
  • To develop a flexible framework adaptable to various communication protocols.

Main Methods:

  • Developed a hybrid framework combining signal processing and artificial intelligence (AI).
  • Processed wideband signals directly from IQ samples for analysis.
  • Implemented LoRa spreading factor detection and communication channel analytics.

Main Results:

  • Achieved high detection accuracy (96.2%), precision (99.16%), and recall (95.4%) in identifying concurrent LoRa transmissions.
  • Demonstrated the framework's effectiveness in analyzing communication channels.
  • Validated the framework's ability to identify and classify multiple LoRa signals.

Conclusions:

  • The proposed hybrid framework offers a robust solution for managing spectrum resources in IoT networks.
  • Its flexible architecture allows for adaptation to diverse communication protocols beyond LoRa.
  • This advancement is crucial for maintaining quality of service as IoT deployments scale.