Jove
Visualize
Contact Us

Related Concept Videos

Discrete Fourier Transform01:15

Discrete Fourier Transform

913
The Discrete Fourier Transform (DFT) is a fundamental tool in signal processing, extending the discrete-time Fourier transform by evaluating discrete signals at uniformly spaced frequency intervals. This transformation converts a finite sequence of time-domain samples into frequency components, each representing complex sinusoids ordered by frequency. The DFT translates these sequences into the frequency domain, effectively indicating the magnitude and phase of each frequency component present...
913
Protein Networks02:26

Protein Networks

4.6K
An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
4.6K
Protein Networks02:26

Protein Networks

2.9K
2.9K
Discrete-time Fourier transform01:26

Discrete-time Fourier transform

1.1K
The Discrete-Time Fourier Transform (DTFT) is an essential mathematical tool for analyzing discrete-time signals, converting them from the time domain to the frequency domain. This transformation allows for examining the frequency components of discrete signals, providing insights into their spectral characteristics. In the DTFT, the continuous integral used in the continuous-time Fourier transform is replaced by a summation to accommodate the discrete nature of the signal.
One of the notable...
1.1K
Network Covalent Solids02:18

Network Covalent Solids

16.2K
Network covalent solids contain a three-dimensional network of covalently bonded atoms as found in the crystal structures of nonmetals like diamond, graphite, silicon, and some covalent compounds, such as silicon dioxide (sand) and silicon carbide (carborundum, the abrasive on sandpaper). Many minerals have networks of covalent bonds.
To break or to melt a covalent network solid, covalent bonds must be broken. Because covalent bonds are relatively strong, covalent network solids are typically...
16.2K
Basic Discrete Time Signals01:16

Basic Discrete Time Signals

731
The unit step sequence is defined as 1 for zero and positive values of the integer n. This sequence can be graphically displayed using a set of eight sample points, showing a step function starting from n=0 and remaining constant thereafter.
The unit impulse or sample sequence is mathematically expressed as zero for all n values except at n=0, where it is one. The unit impulse sequence, denoted by δ(n), is the first difference of the unit step sequence, while the unit step sequence u(n) is the...
731

You might also read

Related Articles

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

Sort by
Same journal

RETRACTED: Zhang et al. A Novel Framework for Reconstruction and Imaging of Target Scattering Centers via Wide-Angle Incidence in Radar Networks. <i>Sensors</i> 2025, <i>25</i>, 6802.

Sensors (Basel, Switzerland)·2026
Same journal

Enhancing Unsupervised Multi-Source Domain Adaptation for Person Re-Identification via Mixture of Experts and Graph-Based Relation.

Sensors (Basel, Switzerland)·2026
Same journal

Development of an Instrumented Glove for Palmar Pressure Assessment in Kayakers.

Sensors (Basel, Switzerland)·2026
Same journal

Development and Experimental Validation of an Autonomous IoT-Based Monitoring System for Real-Time Water Quality Assessment in the Amazon River.

Sensors (Basel, Switzerland)·2026
Same journal

Semi-Supervised Adversarial Learning Framework for Controller Area Network Bus Intrusion Detection.

Sensors (Basel, Switzerland)·2026
Same journal

Smart Optimization Method for Safety Signs in Innovative Manufacturing Environments Integrating Industrial Field IoT Sensors and Knowledge Graphs.

Sensors (Basel, Switzerland)·2026
See all related articles
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 Experiment Video

Updated: Feb 6, 2026

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

1.2K

Adaptive Discrete Vector Field in Sensor Networks.

Mengyi Zhang1, Alban Goupil2

  • 1College of Electrical Engineering and Control Science, Nanjing Tech University, Nanjing 210028, China. myzhang@njtech.edu.cn.

Sensors (Basel, Switzerland)
|August 15, 2018
PubMed
Summary
This summary is machine-generated.

We present a novel method using adaptive discrete vector fields and discrete Morse theory to compute homology groups for sensor networks. This approach efficiently detects network holes and optimizes sensor coverage.

Keywords:
algebraic topologycoveragediscrete Morse theorydiscrete vector fielddistributed algorithmsensor networkssensor selection

More Related Videos

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

12.4K
Construction of a Compact Low-Cost Radiation Shield for Air-Temperature Sensors in Ecological Field Studies
05:56

Construction of a Compact Low-Cost Radiation Shield for Air-Temperature Sensors in Ecological Field Studies

Published on: November 6, 2018

8.7K

Related Experiment Videos

Last Updated: Feb 6, 2026

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

1.2K
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

12.4K
Construction of a Compact Low-Cost Radiation Shield for Air-Temperature Sensors in Ecological Field Studies
05:56

Construction of a Compact Low-Cost Radiation Shield for Air-Temperature Sensors in Ecological Field Studies

Published on: November 6, 2018

8.7K

Area of Science:

  • Computational topology
  • Network science
  • Sensor networks

Background:

  • Homology groups measure network connectivity.
  • Distributed and adaptive computation is crucial for sensor networks.
  • Existing methods may lack efficiency or adaptability.

Purpose of the Study:

  • To propose a novel, distributed, and adaptive method for computing homology groups.
  • To leverage discrete Morse theory for efficient generator extraction.
  • To demonstrate the approach's utility in sensor network applications.

Main Methods:

  • Construction of an adaptive discrete vector field.
  • Application of discrete Morse theory to extract homology group generators.
  • Testing against applications in coverage hole detection and sensor selection.

Main Results:

  • The proposed method efficiently computes homology groups in a distributed and adaptive manner.
  • Successful detection and localization of coverage holes.
  • Effective selection of active sensors for complete coverage.

Conclusions:

  • The adaptive discrete vector field approach provides an efficient and adaptable solution for homology group computation in sensor networks.
  • This method has practical implications for network monitoring and optimization.
  • The approach facilitates robust sensor network management.