Dynamic compensation of nonlinear sensors by a learning-from-examples approach

IEEE Transactions on Instrumentation and Measurement

Volumn 57, Issue 8, 2008, Pages 1689-1694

  Marconato, Anna ^a   Hu, Mingqing ^b   Boni, Andrea ^a   Petri, Dario ^a  

^a UNIVERSITY OF TRENTO   (Italy)

^b INSTITUTE OF COMPUTING TECHNOLOGY   (China)

Author keywords

Dynamic compensation; Low power; Micro controller implementation; Reduced set methods; Support vector machines for regression (SVRs); Wireless sensor networks (WSNs)

Indexed keywords

DETECTORS; LEARNING ALGORITHMS; MICROCONTROLLERS; SENSOR NETWORKS; SENSORS; WIRELESS TELECOMMUNICATION SYSTEMS;

8-BIT MICROCONTROLLERS; DYNAMIC COMPENSATION; NONLINEAR SENSORS; REDUCED-COMPLEXITY; RESOURCE CONSTRAINED DEVICES; SENSOR DYNAMIC; SENSOR MODELLING; WIRELESS SENSORS;

WIRELESS SENSOR NETWORKS;

EID: 48749093090     PISSN: 00189456     EISSN: None     Source Type: Journal    
DOI: 10.1109/TIM.2008.922074     Document Type: Article

References (21)

1. D. Puccinelli and M. Haenggi, "Wireless sensor networks: Applications and challenges of ubiquitous sensing," IEEE Circuits Syst. Mag., vol. 5, no. 3, pp. 19-31, 2005.
2. M. Weiser, "Ubiquitous computing," Computer, vol. 26, no. 10, pp. 71-72, Oct. 1993.
3. A. Boni, M. Hu, F. Pianegiani, and D. Petri, "Energy-aware signal classification in wireless sensor networks," in Proc. Instrum. Meas. Technol. Conf., Ottawa, ON, Canada, 2005, pp. 1912-1916.
4. R. Pintelon and J. Schoukens, System Identification: A Frequency Domain Approach. Piscataway, NJ: IEEE Press, 2001.
5. T. Daboczi and I. Kollar, "Multiparameter optimization of inverse filtering algorithms," IEEE Trans. Instrum. Meas., vol. 45, no. 2, pp. 417-421, Apr. 1996.
6. P. B. Crilly, "A quantitative evaluation of various iterative deconvolution algorithms," IEEE Trans. Instrum. Meas., vol. 40, no. 3, pp. 558-562, Jun. 1991.
7. M. Bertocco, C. Narduzzi, C. Offelli, and D. Petri, "An improved method for iterative identification of band-limited linear systems," in Proc. IEEE Instrum. Meas. Technol. Conf., Atlanta, GA, 1991, pp. 368-372.
8. P. Arpaia, P. Daponte, D. Grimaldi, and L. Michaeli, "ANN-based error reduction for experimentally modeled sensors," IEEE Trans. Instrum. Meas., vol. 51, no. 1, pp. 23-30, Feb. 2002.
9. J. C. Patra, A. C. Kot, and G. Panda, "An intelligent pressure sensor using neural networks," IEEE Trans. Instrum. Meas., vol. 49, no. 4, pp. 829-834, Aug. 2000.
10. A. Marconato, M. Hu, C. Marzadro, A. Boni, and D. Petri, "A resource-constrained sensor dynamic compensation using a learning-from-examples approach," in Proc. Instrum. Meas. Technol. Conf., Warsaw, Poland, 2007, pp. 1-6.
11. V. Vapnik, Statistical Learning Theory. Hoboken, NJ: Wiley, 1998.
12. B. Schölkopf and A. Smola, Learning With Kernels. Cambridge, MA: MIT Press, 2002.
13. Y.-J. Lee and O. L. Mangasarian, "RSVM: Reduced support vector machines," in Proc. 1st SIAM Int. Conf. Data Mining, Chicago, IL, Apr. 2001.
14. S. S. Keerthi, O. Chapelle, and D. De Coste, "Building support vector machines with reduced classifier complexity," J. Mach. Learn. Res. vol. 7, pp. 1493-1515, Jul. 2006.
15. M. Wu, B. Schölkopf, and G. Bakir, "A direct method for building sparse kernel learning algorithms," J. Mach. Learn. Res., vol. 7, pp. 603-624, Apr. 2006.
16. O. Chapelle, "Training a support vector machine in the primal," Neural Comput., vol. 19, no. 5, pp. 1155-1178, May 2007.
17. A. Boni, F. Pianegiani, and D. Petri, "Low-power and low-cost implementation of SVMs for smart sensors," IEEE Trans. Instrum. Meas., vol. 56, no. 1, pp. 39-44, Feb. 2007.
18. R. Andraka, "A survey of CORDIC algorithms for FPGAs," in Proc. ACM/SIGDA 6th Int. Symp. Field Program. Gate Arrays, Monterey, CA, 1998.
19. M. Espinoza, J. A. K. Suykens, and B. de Moor, "Kernel based partially linear models and nonlinear identification," IEEE Trans. Autom. Control, vol. 50, no. 10, pp. 1602-1606, Oct. 2005.
20. O. Nelles, Nonlinear System Identification. Berlin, Germany: Springer-Verlag, 2001.
21. A. Marconato, A. Boni, B. Caprile, and D. Petri, "Model selection for power efficient analysis of measurement data," in Proc. Instrum. Meas. Technol. Conf., Sorrento, Italy, 2006, pp. 1524-1529.