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Block Compressive Sensing (BCS) Based Low Complexity, Energy Efficient Visual Sensor Platform with Joint Multi-Phase

Mansoor Ebrahim1, Wai Chong Chia2, Syed Hasan Adil3

  • 1Faculty of Engineering, Sciences and Technology, Iqra University, Karachi 75500, Pakistan. mebrahim@iqra.edu.pk.

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|May 22, 2019
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Summary
This summary is machine-generated.

This study presents a practical implementation of compressive sensing (CS) for visual sensor networks (VSNs). A low-power hardware platform validates CS for efficient data compression and reduced energy consumption in real-world scenarios.

Keywords:
compressive sensingcomputational complexityimage processingimage reconstructionjoint multi-phase decoding (JMD)low-cost image sensorsmulti-camera nodespractical modelvisual sensor networks

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Area of Science:

  • Computer Engineering
  • Signal Processing
  • Wireless Sensor Networks

Background:

  • Visual sensor networks (VSNs) face critical challenges in energy consumption and bandwidth utilization due to battery-powered devices.
  • Compressive sensing (CS) offers an efficient data sampling method for VSNs, reducing data volume by acquiring fewer samples than the Nyquist rate.
  • Existing CS research often focuses on theoretical aspects and simulations, with limited exploration of practical implementation challenges and real-world validation.

Purpose of the Study:

  • To develop and validate a low-power, low-cost visual sensor platform for practical compressive sensing implementation.
  • To address the open issues in reconstruction quality and computational validation of CS in real-world VSN applications.
  • To demonstrate the feasibility of using off-the-shelf components for practical CS in VSNs.

Main Methods:

  • Development of a visual sensor platform using Arduino Due, XBee transmitter, and uCAM-II camera.
  • Implementation of Block Compressive Sensing (BCS) on the developed hardware platform.
  • Reconstruction of compressed visual data using the joint multi-phase decoding (JMD) framework.

Main Results:

  • Successful practical implementation of Block Compressive Sensing (BCS) on a low-power hardware platform.
  • Validation of compressive sensing characteristics in a real-world visual sensor network scenario.
  • Demonstration of a functional system using readily available components for efficient visual data acquisition and compression.

Conclusions:

  • The developed platform provides a viable solution for energy-efficient data compression in VSNs.
  • This work bridges the gap between theoretical CS concepts and practical, real-world applications.
  • The study highlights the potential of off-the-shelf components for implementing advanced signal processing techniques like CS in resource-constrained environments.