Block Compressive Sensing (BCS) Based Low Complexity, Energy Efficient Visual Sensor Platform with Joint Multi-Phase Decoder (JMD)
1
Faculty of Engineering, Sciences and Technology, Iqra University, Karachi 75500, Pakistan
2
Faculty of Sciences and Technology, Sunway University, Bandar Sunway 47500, Malaysia
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(10), 2309; https://doi.org/10.3390/s19102309
Received: 13 March 2019
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Revised: 9 May 2019
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Accepted: 10 May 2019
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Published: 19 May 2019
(This article belongs to the Special Issue Visual Sensor Networks and Related Applications)
Devices in a visual sensor network (VSN) are mostly powered by batteries, and in such a network, energy consumption and bandwidth utilization are the most critical issues that need to be taken into consideration. The most suitable solution to such issues is to compress the captured visual data before transmission takes place. Compressive sensing (CS) has emerged as an efficient sampling mechanism for VSN. CS reduces the total amount of data to be processed such that it recreates the signal by using only fewer sampling values than that of the Nyquist rate. However, there are few open issues related to the reconstruction quality and practical implementation of CS. The current studies of CS are more concentrated on hypothetical characteristics with simulated results, rather than on the understanding the potential issues in the practical implementation of CS and its computational validation. In this paper, a low power, low cost, visual sensor platform is developed using an Arduino Due microcontroller board, XBee transmitter, and uCAM-II camera. Block compressive sensing (BCS) is implemented on the developed platform to validate the characteristics of compressive sensing in a real-world scenario. The reconstruction is performed by using the joint multi-phase decoding (JMD) framework. To the best of our knowledge, no such practical implementation using off the shelf components has yet been conducted for CS.
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Keywords:
visual sensor networks; multi-camera nodes; image processing; compressive sensing; computational complexity; low-cost image sensors; practical model; image reconstruction; joint multi-phase decoding (JMD)
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MDPI and ACS Style
Ebrahim, M.; Chia, W.C.; Adil, S.H.; Raza, K. Block Compressive Sensing (BCS) Based Low Complexity, Energy Efficient Visual Sensor Platform with Joint Multi-Phase Decoder (JMD). Sensors 2019, 19, 2309. https://doi.org/10.3390/s19102309
AMA Style
Ebrahim M, Chia WC, Adil SH, Raza K. Block Compressive Sensing (BCS) Based Low Complexity, Energy Efficient Visual Sensor Platform with Joint Multi-Phase Decoder (JMD). Sensors. 2019; 19(10):2309. https://doi.org/10.3390/s19102309
Chicago/Turabian StyleEbrahim, Mansoor, Wai C. Chia, Syed H. Adil, and Kamran Raza. 2019. "Block Compressive Sensing (BCS) Based Low Complexity, Energy Efficient Visual Sensor Platform with Joint Multi-Phase Decoder (JMD)" Sensors 19, no. 10: 2309. https://doi.org/10.3390/s19102309
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