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  1. Home
  3. Zynq-based Hardware-software Codesign Architecture For An Intelligent Hyperspectral Camera.
  1. Home
  3. Zynq-based Hardware-software Codesign Architecture For An Intelligent Hyperspectral Camera.

Related Experiment Video

Multimodal Nonlinear Hyperspectral Chemical Imaging Using Line-Scanning Vibrational Sum-Frequency Generation Microscopy
08:49

Multimodal Nonlinear Hyperspectral Chemical Imaging Using Line-Scanning Vibrational Sum-Frequency Generation Microscopy

Published on: December 1, 2023

Zynq-Based Hardware-Software Codesign Architecture for an Intelligent Hyperspectral Camera.

Lufan Xie1,2, Lijing Zhang1,2, Fan Yang1,2

  • 1Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou 310024, China.

Sensors (Basel, Switzerland)
|June 12, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

This study introduces an intelligent hyperspectral camera with onboard processing, significantly reducing data transmission for real-time edge sensing. The novel hardware-software design achieves substantial data compression while maintaining accurate spectral analysis.

Keywords:
ZYNQeuclidean distance spectral matchinghardware–software codesignhyperspectral imagingintelligent camerareal-time processing

Related Experiment Videos

Multimodal Nonlinear Hyperspectral Chemical Imaging Using Line-Scanning Vibrational Sum-Frequency Generation Microscopy
08:49

Multimodal Nonlinear Hyperspectral Chemical Imaging Using Line-Scanning Vibrational Sum-Frequency Generation Microscopy

Published on: December 1, 2023

Area of Science:

  • * Advanced sensor technology and embedded systems.
  • * Real-time data processing and edge computing.
  • * Hyperspectral imaging and spectral analysis.

Background:

  • * Traditional hyperspectral cameras generate large data cubes, causing bandwidth and storage issues.
  • * Impediments to real-time analysis hinder the application of hyperspectral imaging in high-throughput scenarios.
  • * Edge-sensing applications require efficient data handling and immediate actionable insights.

Purpose of the Study:

  • * To develop an intelligent hyperspectral camera with integrated on-board processing capabilities.
  • * To overcome the limitations of traditional hyperspectral data transmission.
  • * To enable real-time spectral analysis and information extraction at the edge.

Main Methods:

  • * Hardware-software codesign utilizing a Zynq-7035 System on Chip (SoC).
  • * Implementation of a row-parallel pipeline in programmable logic (PL) for image acquisition, preprocessing, and spectral matching.
  • * Processing System (PS) for command scheduling and a unified DDR3 interface for module decoupling and algorithm integration.

    • Main Results:

    • * Successful execution of Euclidean distance-based spectral matching entirely within the intelligent camera.
    • * Achieved a two-orders-of-magnitude reduction in data volume.
    • * Accurate identification of diseased regions in leaf samples via push-broom experiments.

    Conclusions:

    • * The developed intelligent camera architecture effectively enables real-time hyperspectral processing.
    • * On-board processing significantly reduces data bottlenecks, facilitating high-throughput edge-sensing tasks.
    • * The system demonstrates a viable solution for efficient spectral analysis and actionable data extraction in resource-constrained environments.