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Related Concept Videos

State Space Representation01:27

State Space Representation

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The frequency-domain technique, commonly used in analyzing and designing feedback control systems, is effective for linear, time-invariant systems. However, it falls short when dealing with nonlinear, time-varying, and multiple-input multiple-output systems. The time-domain or state-space approach addresses these limitations by utilizing state variables to construct simultaneous, first-order differential equations, known as state equations, for an nth-order system.
Consider an RLC circuit, a...
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Related Experiment Video

Updated: Apr 6, 2026

Real-time Breath Analysis by Using Secondary Nanoelectrospray Ionization Coupled to High Resolution Mass Spectrometry
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Low-cost video-based air quality estimation system using structured deep learning with selective state space

Maqsood Ahmed1, Xiang Zhang1, Yonglin Shen1

  • 1National Engineering Research Center of Geographic Information System, School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China.

Environment International
|May 9, 2025
PubMed
Summary

This study introduces Air Quality Prediction-Mamba (AQP-Mamba), a novel video-based deep learning model for accurate air quality prediction. AQP-Mamba effectively estimates multiple pollutants and air quality index (AQI) from video data, outperforming existing methods.

Keywords:
Air quality index (AQI)ClassificationDeep learningPM(10)PM(2.5)Regression

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Area of Science:

  • Environmental Science and Engineering
  • Computer Science (Artificial Intelligence, Machine Learning)

Background:

  • Accurate air quality prediction is vital for public health and environmental sustainability.
  • Existing models often rely on static images, neglecting the dynamic, temporal nature of air pollution.
  • Video-based air quality estimation research is limited, especially for multi-pollutant prediction.

Purpose of the Study:

  • To propose Air Quality Prediction-Mamba (AQP-Mamba), a video-based deep learning model for estimating air quality.
  • To accurately predict multiple pollutants (PM2.5, PM10) and the Air Quality Index (AQI) using video data.
  • To address the limitations of static image analysis by incorporating spatiotemporal features from videos.

Main Methods:

  • Developed AQP-Mamba, integrating a structured Selective State Space Model (SSM) with a hybrid predictor.
  • Employed spatiotemporal SSM with selective scan and bidirectional processing for dynamic feature extraction.
  • Utilized the LMSAQV dataset, comprising 13,176 outdoor videos from Lahore, Pakistan, for training and validation.

Main Results:

  • AQP-Mamba achieved high regression performance: R² of 0.91 (PM2.5), 0.90 (PM10), and 0.92 (AQI).
  • Excellent classification metrics were obtained: 94.57% accuracy, 93.86% precision, 94.20% recall, and 93.44% F1-score for AQI.
  • The model significantly outperformed state-of-the-art video analysis models (VideoSwin-T, VideoMAE, I3D, VTHCL, TimeSformer).

Conclusions:

  • AQP-Mamba offers an efficient, scalable, and cost-effective solution for real-time, multi-pollutant air quality estimation.
  • The video-based approach captures dynamic air pollution variations, overcoming limitations of static image analysis.
  • This method has the potential to supplement data from expensive instruments globally, improving air quality monitoring.