SEARCH WITHIN CONTENT
Citation Information : International Journal on Smart Sensing and Intelligent Systems. Volume 1, Issue 4, Pages 985-1,018, DOI: https://doi.org/10.21307/ijssis-2017-332
License : (CC BY-NC-ND 4.0)
Published Online: 02-November-2017
The paper describes the development of an FPGA based fuzzy processing system for pulmonary spirometry applications predicting the approaching obstructive or restrictive pulmonary disorder of the patient before criticality actually occurs. The system employs a
smart agent that accepts the Peak Expiratory Flow Rate (PEFR), Forced Expiratory Volume in 1 second (FEV1) and Forced Vital Capacity (FVC) data of patients. In order to speed up the computation process, hybrid parallel data processing architectures with dynamic scheduling mechanism have been employed leading to a speed up of approximately 12 times. The processor implemented on the FPGA can perform fuzzy inferencing at a speed of approximately 5.0 MFLIPS. The whole system is realized on Altera Cyclone EP1K6Q240C8 FPGA chip requiring 5,865 logic blocks. The system has been designed to be inexpensive, portable and user friendly for occupational health care applications in developing countries. Using the system, approaching pulmonary disorder of patients has been predicted with an accuracy of 95.83%.
 Long, R, Maycher, B, Dhar, A, et al Pulmonary tuberculosis treated with directly observed therapy: serial changes in lung structure and function. Chest 1998;113,933-943.
 Gaensler, EA, Lindgren, I Chronic bronchitis as an etiologic factor in obstructive emphysema, preliminary report. Am Rev Respir Dis 1959;80,185-193
 Snider, GL, Doctor, L, Demas, TA, et al Obstructive airways disease in patients with treated pulmonary tuberculosis. Am Rev Respir Dis 1971;103,625-640
 Wilcox, PA, Ferguson, AD Chronic obstructive airways disease following treated pulmonary tuberculosis. Respir Med 1989;83,195-198.
 Weiner, H Changes in employment status associated with hospitalization for tuberculosis: analysis of 163 consecutively admitted males. Am Rev Respir Dis 1963;87,17-22
 Hnzido, E, Singh, T, Churchyard, G Chronic pulmonary function impairment by initial and recurrent pulmonary tuberculosis following treatment. Thorax 2000;55,32-38.
 M. Togai and H. Watanabe, “Expert System on a Chip: An Engine for Real Time Approximate reasoning”, IEEE Expert Systems Magazine, Vol. 1, pp. 55-62, 1986.
 M.H. Lim, Y. Takefuji, “Implementing Fuzzy Rule Based Systems on Silicon Chips”, IEEE Expert Systems Magazine, Vol. 5, No. 1, pp. 31-45, 1990.
 L.A. Zadeh, “Outline of a New Approach to the Analysis of Complex Systems and Decision Processes”, IEEE Transactions on Systems, Man and Cybernetics,Vol. 3, No. 1, 1973.
 L.A.Zadeh, “Fuzzy logic, Neural Networks and Soft Computing”, ACM Transactions on Computing, Vol. 37, No. 3, 1994.
 H. Surmann, A. Ungering, “Fuzzy Rule Based Systems on General Purpose Processors”, IEEE Micro, Vol 15, no. 4, 1995.
 H. Watanabe, J.R. Symon, W.D. Detloff, K.E. Yount, “VLSI fuzzy chip and inference accelerator board system”, Fuzzy Logic for Management of Unceratinty, John Wiley & Sons, 1992.
 K.Nakamura, N.Sakashita, N.Nitta, K. Shimomura, T. Tokuda, “Fuzzy Inference and Fuzzy Inference Processor”, IEEE Micro, Vol. 13, No. 5, pp. 37-48, 1993.
 M.A. Manzoul, S. Tayal, “Systolic Array for multivariable Fuzzy Control Systems”, International Journal of Systems and Cybernetics, Vol. 21, No. 1, pp.27-42, 1990.
 A. Jaramillo-Botero, Y. Miyake, “A High Speed Parallel Architecture for Fuzzy Inference Control of Multiple Processes”, Proceedings of IEEE 1994 World Congress on Conputational Intelligence, Vol. 3, pp. 1765-1770, 1994.
 H. Orsila, T. Kangas, E. Salminen, T.D. Hamalainen, M. Hannikainen, “Automated memory-aware application distribution for Multi-processor System-on-Chips”, Journal of Systems Architecture, Vol. 53, No. 11, pp. 795-815, 2007.
 G. Aranguren, M. Barron, J.L. Arroyabe, G. Garcia-Carreira, “A pipeline fuzzy controller in FPGA”, Proceedings of IEEE Conference on Fuzzy systems, Vol. 2, pp 635-640, 1997.
 V. Samoladas, L. Petrou, “Special Purpose Architectures for Fuzzy Logic
Controllers”, Microprocessing and Microprogramming, Vol. 40, No. 4, pp. 275-
 L. de Salvador, J. Gutierrez, “A Multilevel Systolic Approach for Fuzzy Inference Hardware”, IEEE Micro, Vol. 15, No. 5, pp. 61-71, 1995.  R. Raychev, A. Mtibaa, M. Abid, “VHDL Modeling of a Fuzzy Coprocessor Architecture”, Proceedings of International Conference on Computer Systems and Technologies, pp. I. 2.1- I. 2.6, 2005.
 S. Roy Chowdhury, D. Chakrabarti, H. Saha, “FPGA realization of a Smart Processing System for Clinical Diagnostic Applications using Pipelined Datapath Architectures”, Microprocessors and Microsystems, Vol. 32, No. 2, pp. 107-120, 2008.
 S. Roy Chowdhury, D. Chakrabarti, H. Saha, “Medical diagnosis using Adaptive Perceptive Particle Swarm Optimization and its hardware realization using Field Programmable Gate Array”, Journal of Medical Systems, Available
 S. Roy Chowdhury, H. Saha, “A high performance generalized Fuzzy Processor Architecture and realization of its prototype on an FPGA”, IEEE Micro, Vol. 28,No. 5, pp. 38-52, 2008
 M. Figueiredo, F. Gomide, A. Rocha, R. Yager, “Comparison of Yager’s Level Set method for Logic Control with Mamdami’s and Larsen’s Methods”, IEEE Transactions on Fuzzy Systems, Vol. 1, No. 2, pp.156-159, 1993.
 M. Kukar, “Transductive reliability estimation in medical diagnosis”, Artificial Intelligence in Medicine, Vol. 29, pp 81-106, 2003.