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

Search research articles

Related Experiment Video

Updated: Jun 23, 2026

Deep Neural Networks for Image-Based Dietary Assessment

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

Drift-Robust Lightweight Deep Learning on Open Gas Sensor Benchmarks: A Reproducible Architecture Study with CBRN

Soohwan Kim¹, Myeongsik Shin¹, Ku Kang¹

¹CBRN Defense Research Institute, Seoul 06796, Republic of Korea.

Molecules (Basel, Switzerland)

|June 12, 2026

Summary

Related Concept Videos

You might also read

Related Articles

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

Sort by

Same author

Integrated multi-endpoint hazard prioritization of unregulated industrial contaminants: A safety-first screening framework for emerging environmental threats.

Ecotoxicology and environmental safety·2026

Same author

Benzyl-Spacer-Engineered Prodrug: Enabling NIR Fluorescent Activation for Cancer Therapy in a Zebrafish Gallbladder Model.

Journal of medicinal chemistry·2026

Same author

Experimental evidence of a liquid-liquid critical point in supercooled water.

Science (New York, N.Y.)·2026

Same author

Whitening fruit by CRISPR/Cas9-mediated homoeolog-specific gene editing of <i>MYB10-1B</i> in strawberry (<i>F</i>. × <i>ananassa</i>).

Horticulture research·2026

Same author

Genomic architecture of the resistance to Phytophthora cactorum 2 (RPc2) locus in strawberry (Fragaria × ananassa).

The plant genome·2026

Same author

The conference landscape of selenium research: A brief history, global structure, and stewardship.

Archives of biochemistry and biophysics·2025

Same journal

Correction: Chen et al. Chemical Composition of <i>Litsea pungens</i> Essential Oil and Its Potential Antioxidant and Antimicrobial Activities. <i>Molecules</i> 2023, <i>28</i>, 6835.

Molecules (Basel, Switzerland)·2026

Same journal

Correction: Ruan et al. Comparison of Extraction, Isolation, Purification, Structural Characterization and Immunomodulatory Activity of Polysaccharides from Two Species of <i>Cistanche</i>. <i>Molecules</i> 2025, <i>30</i>, 4754.

Molecules (Basel, Switzerland)·2026

Same journal

Correction: Li et al. Gastrodin Ameliorates Cognitive Dysfunction in Vascular Dementia Rats by Suppressing Ferroptosis via the Regulation of the Nrf2/Keap1-GPx4 Signaling Pathway. <i>Molecules</i> 2022, <i>27</i>, 6311.

Molecules (Basel, Switzerland)·2026

Same journal

Correction: Zueva et al. Steady-State Kinetics of Enzyme-Catalyzed Hydrolysis of Echothiophate, a P-S Bonded Organophosphorus as Monitored by Spectrofluorimetry. <i>Molecules</i> 2020, <i>25</i>, 1371.

Molecules (Basel, Switzerland)·2026

Same journal

1,4-Diazatriphenylene and Its Hetero-Fused Analogs: Synthesis and Applications.

Molecules (Basel, Switzerland)·2026

Same journal

Comparative Phytochemical Studies on the Aerial Parts of <i>Teucrium davaeanum</i> Coss. and <i>Teucrium zanonii</i> Pamp.

Molecules (Basel, Switzerland)·2026

See all related articles

This summary is machine-generated.

This study introduces LiteSensor-Net, a compact AI model for edge gas classification, significantly improving accuracy and reducing drift effects for environmental monitoring and CBRN scenarios.

Area of Science:

Edge AI
Gas Sensor Technology
Machine Learning for Environmental Monitoring

Background:

Resource-constrained edge processors on UAVs and wearables need robust gas classification.
Existing methods are server-grade or degrade under sensor drift.
CBRN (Chemical, Biological, Radiological, and Nuclear) scenarios require reliable, compact detection.

Purpose of the Study:

Develop an end-to-end pipeline for drift-robust edge gas classification.
Address limitations of existing server-grade and drift-prone models.
Create a standardized benchmark for evaluating edge AI gas classifiers.

Main Methods:

Proposed LiteSensor-Net: a 1D CNN with depth-wise separable convolutions.
Integrated INT8 quantization and structured magnitude pruning for model compression.

Keywords:

CBRN detection TinyML edge AI gas sensor array knowledge distillation sensor drift compensation

More Related Videos

Deep-Learning Based Multi-Joint Synchronous Tracking for Objective Quantification of Hindlimb Locomotor Kinematics in Rats

Deep-Learning Based Multi-Joint Synchronous Tracking for Objective Quantification of Hindlimb Locomotor Kinematics in Rats

Published on: April 3, 2026

Related Experiment Videos

Last Updated: Jun 23, 2026

Deep Neural Networks for Image-Based Dietary Assessment

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

Deep-Learning Based Multi-Joint Synchronous Tracking for Objective Quantification of Hindlimb Locomotor Kinematics in Rats

Deep-Learning Based Multi-Joint Synchronous Tracking for Objective Quantification of Hindlimb Locomotor Kinematics in Rats

Published on: April 3, 2026

Employed knowledge distillation domain adaptation (KD-DM) for sensor drift compensation.

Mapped UCI gas classes to CBRN behavioral categories using physicochemical analogies.

Main Results:

LiteSensor-Net achieved 92.63% accuracy and 0.898 macro-F1 on the UCI Gas Sensor Array Drift Dataset.
Optimized model size: 5.99 kB (INT8 pruned), inference latency: 6.3 ms, energy: 0.04 mJ.
KD-DM-20 improved accuracy by +9.25 pp over uncompensated models under chronological evaluation.
Introduced a six-metric framework for standardized edge AI gas classifier evaluation.

Conclusions:

The proposed pipeline offers an open-source, deployable foundation for edge-class gas classification.
LiteSensor-Net and KD-DM effectively address model size, latency, and sensor drift challenges.
The system is suitable for environmental monitoring and CBRN detection applications on edge devices.