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Energy Efficient VLSI Design on FPGA using Capacitance Scaling Technique

Affiliations

  • Department of Information and Computer Technology, Yanbu University College (YUC), Yanbu, Saudi Arabia
  • Faculty of Engineering, Sciences and Technology, Indus University, Karachi, Pakistan
  • Department of Computer Science, Muhammad Ali Jinnah University, Karachi, Pakistan

Abstract


In medical sciences and particular in cardiology related area, ECG machine is considered a basic equipment to get the fundamental knowledge about proper functioning of heart. In this work the aim is to make energy efficient ECG machine design on FPGA using capacitance scaling technique while the device is operating under various WLAN specific frequencies. Concept of internet of things is used in this work by adding additional 128-bit IPv6 address in the input of ECG machine that will use to control the device via internet. Kintex-7 is used from the FPGA family for this task. It is analyzed that 89.15%, 89.75% and 89.81% power reduction can be achieved under device operating frequencies 0.9 GHz, 2.4 GHz and 3.6 GHz respectively when the capacitance is taken 500 pF in place of 5000 pF.

Keywords

Capacitance Scaling, ECG Machine , FPGA, Internet of Things (IoT), Total Power.

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References


  • Kiran MPRS, Rajalakshmi P, Bharadwaj K, Acharyya A. Adaptive rule engine based iot enabled remote health care data acquisition and smart transmission system. IEEE World Forum on Internet of Things (WF-IoT) India; 2014.
  • Chandrakar B, Yadav OP, Chandra VK. A survey of noise removal techniques for ecg signals. International Journal of Advanced Research in Computer and Communication Engineering. 2013 Mar; 2(3).
  • Elmansouri K, Latif R, Nassiri B, Elouaham S. New electrocardiogram signal analysis in a research laboratory using labview. IJIRI. 2013; 1(1):15–21.
  • Jayant A, Singh T, Kaur M. Different techniques to remove baseline wander from ECG signal. International Journal of Emerging Research in Management and Technology. 2013; 2(6):16–9.
  • Mihel J, Magjarevic R. FPGA based two-channel ECG sensor node for wearable applications. 4th European Conference of the International Federation for Medical and Biological Engineering IFMBE Proceedings; 2009. p. 1208–11.
  • Jewajinda Y, Chongstitvatana P. FPGA-based online-learning using parallel genetic algorithm and neural network for ECG signal classification. International Conference on Electrical Engineering/Electronics Computer Telecommunications and Information Technology (ECTI-CON); Chaing Mai. 2010. p. 1050–4.
  • Desai V. Electrocardiogram (ECG/EKG) using FPGA [Master’s Projects Paper 238]. San Jose State University; 2012.
  • Halperin D, et al. Predictable 802.11 packet delivery from wireless channel measurements. ACM SIGCOMM Computer Communication Review. 2011; 41(4):159–70.
  • Tsao S-L, Huang C-H. A survey of energy efficient MAC protocols for IEEE 802.11 WLAN. Computer Communications. 2011; 34(1):54–67.
  • Ong EH, et al. IEEE 802.11 ac: Enhancements for very high throughput WLANs. IEEE 22nd International Symposium on Personal Indoor and Mobile Radio Communications (PIMRC); 2011.
  • IEEE 802.11™: Wireless LANs. IEEE Standard for Information technology--Telecommunications and Information Exchange. Available from: http://standards.ieee.org/about/get/802/802.11.html
  • Kumar T, Memon AK, Musavi SHA, Khan F, Kumar R. FPGA based energy efficient ECG machine design using different IO standard. 2nd International Conference on Computing for Sustainable Global Development (INDIACom); India. 2015. p. 1541–5.
  • Kumar T, Pandey B, Mussavi SHA, Zaman N. CTHS based energy efficient thermal aware image ALU design on FPGA. Wireless Personal Communications. 2015; 85(3):671–96.
  • Kumar T, Pandey B, Das T. LVCMOS I/O standard and drive strength based green design on ultra scale FPGA. IEEE International Conference on Green Computing, Communication and Conservation of Energy (ICGCE); India. 2013. p. 116–9.

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