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Related Concept Videos

Convolution Properties II01:17

Convolution Properties II

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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.
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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.
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Real-Time Underwater Image Recognition with FPGA Embedded System for Convolutional Neural Network.

Minghao Zhao1, Chengquan Hu2, Fenglin Wei3

  • 1College of Computer Science and Technology, Jilin University, Changchun 130012, China. zhaomh17@mails.jlu.edu.cn.

Sensors (Basel, Switzerland)
|January 19, 2019
PubMed
Summary

This study introduces an embedded Field-Programmable Gate Array (FPGA) system for real-time underwater image recognition using Convolutional Neural Networks (CNNs). The FPGA system achieves high accuracy and frame rates for crucial underwater identification tasks.

Keywords:
CNNFPGAimage recognitionunderwater smart device

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Area of Science:

  • Robotics and Automation
  • Computer Vision
  • Marine Technology

Background:

  • Underwater environments remain largely unexplored, necessitating advanced data acquisition tools like high-definition cameras.
  • Current underwater submersible devices lack sufficient power for real-time image data analysis.

Purpose of the Study:

  • To design an embedded Field-Programmable Gate Array (FPGA) image recognition system utilizing Convolutional Neural Networks (CNNs).
  • To overcome power limitations in submersible devices for real-time underwater image analysis.

Main Methods:

  • Leveraging FPGA's low power consumption, computing capability, and flexibility.
  • Implementing parallelism and pipeline technologies for multi-depth convolution operations.
  • Collecting, segmenting, and tagging underwater images to create a training dataset.

Main Results:

  • The proposed FPGA system demonstrated accuracy comparable to a workstation.
  • Achieved a frame rate of 25 FPS at a resolution of 1920 × 1080.
  • Successfully met the requirements for underwater identification tasks.

Conclusions:

  • The developed embedded FPGA system offers an efficient solution for real-time underwater image recognition.
  • FPGA-based CNN acceleration is viable for power-constrained underwater applications.
  • This technology enhances the capability for exploring and analyzing underwater environments.