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

Convolution Properties II01:17

Convolution Properties II

600
The important convolution properties include width, area, differentiation, and integration properties.
The width property indicates that if the durations of input signals are T1 and T2, then the width of the output response equals the sum of both durations, irrespective of the shapes of the two functions. For instance, convolving two rectangular pulses with durations of 2 seconds and 1 second results in a function with a width of 3 seconds.
The area property asserts that the area under the...
600
Convolution Properties I01:20

Convolution Properties I

627
Convolution computations can be simplified by utilizing their inherent properties.
The commutative property reveals that the input and the impulse response of an LTI (Linear Time-Invariant) system can be interchanged without affecting the output:
627
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
Gas Exchange and Transport01:20

Gas Exchange and Transport

77.2K
Gas exchange, the intake of molecular oxygen (O2) from the environment and the outflow of carbon dioxide (CO2) into the environment, is necessary for cellular function. Gas exchange during respiration occurs largely via the movement of gas molecules along pressure gradients. Gas travels from areas of higher partial pressure to areas of lower partial pressure. In mammals, gas exchange occurs in the alveoli of the lungs, which are adjacent to capillaries and share a membrane with them.
77.2K
Kinetic Molecular Theory and Gas Laws Explain Properties of Gas Molecules02:34

Kinetic Molecular Theory and Gas Laws Explain Properties of Gas Molecules

37.6K
The test of the kinetic molecular theory (KMT) and its postulates is its ability to explain and describe the behavior of a gas. The various gas laws (Boyle’s, Charles’s, Gay-Lussac’s, Avogadro’s, and Dalton’s laws) can be derived from the assumptions of the KMT, which have led chemists to believe that the assumptions of the theory accurately represent the properties of gas molecules.
37.6K
Network Covalent Solids02:18

Network Covalent Solids

16.3K
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...
16.3K

You might also read

Related Articles

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

Sort by
Same author

A novel model (SED-GNDE) integrating graph neural differential equations with selective electrodialysis process for nutrient prediction from synthetic wastewater.

Water research·2026
Same author

CCL17-neutralizing and esterase-responsive core-shell microgels for endogenous Tregs recruitment and functional enhancement in myocardial infarction.

Bioactive materials·2026
Same author

Programmable mRNA 3'UTR engineering restores MHC-I and overcomes immune evasion in prostate cancer.

Nature biomedical engineering·2026
Same author

DFB laser arrays utilizing CPM-based sampled gratings for precise wavelength control and enhanced single-mode stability.

Optics express·2026
Same author

Ultra-High Dielectric Acceptor Enables 21% Efficiency and Thickness-Insensitive Organic Solar Cells.

Advanced materials (Deerfield Beach, Fla.)·2026
Same author

Differential fouling behaviors of standard and monovalent-selective membranes by digestate-derived dissolved organic matter in selectrodialysis.

Water research·2026
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

Related Experiment Video

Updated: Feb 15, 2026

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

1.1K

Gas Classification Using Deep Convolutional Neural Networks.

Pai Peng1,2, Xiaojin Zhao3, Xiaofang Pan4

  • 1School of Electronic Science and Technology, Shenzhen University, Shenzhen 518060, China. pengpai_sh@szu.edu.cn.

Sensors (Basel, Switzerland)
|January 11, 2018
PubMed
Summary
This summary is machine-generated.

A novel Deep Convolutional Neural Network (DCNN), GasNet, effectively classifies electronic nose data. This DCNN model achieves higher accuracy than Support Vector Machine (SVM) and Multiple Layer Perceptron (MLP) methods.

Keywords:
deep convolutional neural networkselectronic nosegas classification

More Related Videos

Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

10.0K
Integration of Animal Behavioral Assessment and Convolutional Neural Network to Study Wasabi-Alcohol Taste-Smell Interaction
06:19

Integration of Animal Behavioral Assessment and Convolutional Neural Network to Study Wasabi-Alcohol Taste-Smell Interaction

Published on: August 16, 2024

892

Related Experiment Videos

Last Updated: Feb 15, 2026

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

1.1K
Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

10.0K
Integration of Animal Behavioral Assessment and Convolutional Neural Network to Study Wasabi-Alcohol Taste-Smell Interaction
06:19

Integration of Animal Behavioral Assessment and Convolutional Neural Network to Study Wasabi-Alcohol Taste-Smell Interaction

Published on: August 16, 2024

892

Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Sensor Technology

Background:

  • Gas classification is crucial for various applications.
  • Traditional methods like SVM and MLP have limitations in accuracy.
  • Deep Convolutional Neural Networks (DCNNs) show promise in pattern recognition.

Purpose of the Study:

  • To develop a novel DCNN model for enhanced gas classification.
  • To evaluate the performance of the proposed DCNN against existing methods.
  • To demonstrate the effectiveness of GasNet for electronic nose data.

Main Methods:

  • A 38-layer DCNN, named GasNet, was designed.
  • GasNet architecture includes convolutional blocks, pooling, and fully-connected layers.
  • The model was trained and tested on electronic nose data.

Main Results:

  • GasNet achieved high classification accuracy on electronic nose data.
  • The DCNN method outperformed Support Vector Machine (SVM) methods.
  • The DCNN method demonstrated superior performance compared to Multiple Layer Perceptron (MLP).

Conclusions:

  • The proposed GasNet DCNN is an effective technique for gas classification.
  • DCNNs offer a powerful approach for analyzing electronic nose data.
  • GasNet provides a more accurate alternative to traditional classification methods.