Journal of information and communication convergence engineering

  • 제16권2호
  • /
  • Pages.130-134
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  • 2018
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  • 2234-8255(pISSN)
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  • 2234-8883(eISSN)
한국정보통신학회 (The Korea Institute of Information and Commucation Engineering)
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Reducing Power Consumption of Wireless Capsule Endoscopy Utilizing Compressive Sensing Under Channel Constraint

  • 투고 : 2018.05.11
  • 심사 : 2018.06.12
  • 발행 : 2018.06.30
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초록

Wireless capsule endoscopy (WCE) is considered as recent technology for the detection cancer cells in the human digestive system. WCE sends the captured information from inside the body to a sensor on the skin surface through a wireless medium. In WCE, the design of low-power consumption devices is a challenging topic. In the Shannon-Nyquist sampling theorem, the number of samples should be at least twice the highest transmission frequency to reconstruct precise signals. The number of samples is proportional to the power consumption in wireless communication. This paper proposes compressive sensing as a method to reduce power consumption in WCE, by means of a trade-off between samples and reconstruction accuracy. The proposed scheme is validated under channel constraints, expressed as the realistic human body path loss. The results show that the proposed scheme achieves a significant reduction in WCE power consumption and achieves a faster computation time with low signal error reconstruction.

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참고문헌

  1. G. Ciuti, A. Menciassi, and P. Dario, "Capsule endoscopy: from current achievements to open challenges," IEEE Reviews in Biomedical Engineering, vol. 4, pp. 59-72, 2011. DOI: 10.1109/RBME.2011.2171182.
  2. M. A. Al-Rawhani, D. Chitnis, J. Beeley, S. Collins, and D. R. Cumming, "Design and implementation of a wireless capsule suitable for autofluorescence intensity detection in biological tissues," IEEE Transactions on Biomedical Engineering, vol. 60, no. 1, pp. 55-62, 2013. DOI: 10.1109/TBME.2012.2222641.
  3. D. L. Donoho, "Compressed sensing," IEEE Transactions on Information Theory, vol. 52, no. 4, pp. 1289-1306, 2006. DOI: 10.1109/TIT.2006.871582.
  4. L. Niu and M. X. Duan, "Fusion for medical images based on shearlet transform and compressive sensing model," International Journal of Signal Processing, Image Processing and Pattern Recognition, vol. 9, no. 4, pp. 1-10, 2016. DOI: 10.14257/ijsip.2016.9.4.01.
  5. K.Y. Yazdandoost and K. Sayrafian-Pour, "Channel model for body area network (BAN)," 2009 [Internet], Available: https://mentor. ieee.org/802.15/dcn/08/15-08-0780-09-0006-tg6-channel-model.pdf.
  6. E. J. Candes and J. Romberg, "l1-magic: recovery of sparse signals via convex programming," 2005 [Internet], Available: http://www.cs.bham.ac.uk/-axk/Sakinah/inspiring_readings/l1magic.pdf.