Predictive signal processing in instrumentation and measurement: A tutorial review

Conference Record - IEEE Instrumentation and Measurement Technology Conference

Volumn 1, Issue , 1997, Pages 48-53

  Ovaska, Seppo J ^a  

^a AALTO UNIVERSITY   (Finland)

Author keywords

[No Author keywords available]

Indexed keywords

PROCESSING; ALGORITHMS; FUZZY SETS; MEASUREMENT THEORY; NEURAL NETWORKS; POLYNOMIALS; STATISTICAL MECHANICS; TIME DOMAIN ANALYSIS;

INSTRUMENTATION AND MEASUREMENTS; PHASE DELAY; PREDICTIVE SIGNALS; REAL TIME SIGNAL; RESEARCH AND DEVELOPMENT; TIME-CRITICAL;

SIGNAL PROCESSING; SIGNAL FILTERING AND PREDICTION;

PREDICTIVE SIGNAL PROCESSING (PSP) ALGORITHMS;

EID: 0030674094     PISSN: 10915281     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IMTC.1997.603915     Document Type: Conference Paper

References (31)

1. V. V. Athani, "Microprocessor based data acquisition system," Micro-processors and Microsystems, vol. 3, pp. 359-364, Oct. 1979.
2. R. Carotenuto et al., "Non-linear system process prediction using neural networks," Proc. IEEE Int. Conf. Neural Networks, Washing-ton, D.C., June 1996, pp. 184-189.
3. X. M. Gao and S. J. Ovaska, "Velocity estimation in high-performance motor control using optimized neural networks," Proc. Int. Conf. Engineering Applications of Artificial Neural Networks, Espoo, Finland, Aug. 1995, pp. 171-174.
4. X. M. Gao et al., "Comparison of linear and neural network-based power prediction schemes for mobile DS/CDMA systems," Proc. 46th IEEE Vehicular Technology Conf., Atlanta, GA, Apr. 1996, pp. 61-65.
5. P. T. Harju, "Finite wordlength implementation of IIFt polynomial predictive filters," Proc. IEEE Instrum. Meas. Tech. Conf., Ottawa, Canada, May 1997, IN PRESS.
6. -, "Roundoff noise properties of MR polynomial predictive filters," Proc. IEEE Instrum. Meas. Tech. Conf, Ottawa, Canada, May 1997, IN PRESS.
7. P. T. Harju and S. J. Ovaska, "Optimization of IIR polynomial predictive filter magnitude response," Signal Processing, vol. 56, no. 3, 1997, IN PRESS.
8. P. Heinonen and Y. Neuvo, "FIR median hybrid filters with predictive FIR substructures," IEEE Trans. Acoust, Speech, Signal Proc, vol. 36, pp. 892-899, June 1988.
9. A. Kehagias and V. Petridis, "Predictive modular fuzzy systems for intelligent sensing," Proc. IEEE Int. Conf. Systems, Man and Cybernetics, Beijing, P. R. China, Oct. 1996, pp. 2816-2821.
10. S.-H. Kim et al., "A real-time adaptive lattice predictor using a digital signal processing chip," IEEE Trans. Instrum. Meas., vol. 38, pp. 1016-1019, Oct. 1989.
11. M. Lehtokangas et al., "Predictive minimum description length criterion for time series modeling with neural networks," Neural Computation, vol. 8, pp. 583-593, Apr. 1996.
12. J. Makhoul, "Linear prediction: a tutorial review," Proc. IEEE, vol. 63, pp. 561-580, Apr. 1975.
13. A. Nieminen and Y. Neuvo, "Filtering of nonstationary signals using predictor median hybrid structures," Signal Processing, vol. 13, pp. 165-176, Sept. 1987.
14. S. J. Orfanidis, Optimum Signal Processing: An Introduction. New York, NY: Macmillan, 1985.
15. S. J. Ovaska, "Improving the velocity sensing resolution of pulse en-coders by FIR prediction," IEEE Trans. Instrum. Meas., vol. 40, pp. 657-658, June 1991.
16. -, "Newton-type predictors-a signal processing oriented view-point," Signal Processing, vol. 25, pp. 251-257, Nov. 1991.
17. S. J. Ovaska and O. Vainio, "Recursive linear smoothed Newton predictors for polynomial extrapolation," IEEE Trans. Instrum. Meas., vol. 41, pp. 510-516, Aug. 1992.
18. S. J. Ovaska et al., "Design of predictive IIR filters with feedback ex-tension of FIR forward predictors," Proc. 38th Midwest Symp. Circ. Syst, Rio de Janeiro, Brazil, Aug. 1995, pp. 370-375.
19. J. Pasanen et al., "Predictive synchronization and restoration of corrupted velocity samples," Measurement, vol. 13, pp. 315-324, July 1994.
20. J. P. Ranta et al., "Adaptive polynomial predictor for velocity filtering," Applied Signal Processing, vol. 1, no. 2, pp. 94-106,1994.
21. R. Rape et al., "Comparison of neural networks to statistical techniques for prediction of time series generated by nonlinear dynamic systems," Proc. IEEE Instrumentation and Measurement Technology Conf, Waltham, MA, Apr. 1995, pp. 300-304.
22. J. Renko et al., "Neural network control system for DS/CDMA power signal prediction," Proc. IEEE Nordic Signal Processing Symp., Espoo, Finland, Sept. 1996, pp. 139-142.
23. U. Ruckert and D. Schroder, "Comparison of different prediction methods for arc furnace compensators," Proc. Int. Power Electronics Conf., Tokyo, Japan, Mar. 1983.
24. D. H. Ryu et al., "Motion search region prediction using neural net-work vector quantization," Proc. 3rd Int. Conf. Signal Processing, Beijing, P. R. China, Oct. 1996, pp. 1473-1476.
25. J. M. A. Tanskanen et al., "Prediction of received signal power in CDMA cellular systems," Proc. 45th IEEE Vehicular Technology Conference, Chicago, IL, July 1995, pp. 922-926.
26. O. Vainio and S. J. Ovaska, "Multirate polynomial prediction with un-evenly spaced samples," IEEE Trans. Instrum. Meas., vol. 41, pp. 506-509, Aug. 1992.
27. -, "Noise reduction in zero crossing detection by predictive digital filtering," IEEE Trans. Ind. Electron., vol. 42, pp. 58-62, Feb. 1995.
28. -, "Digital filtering for robust 50/60 Hz zero crossing detectors," IEEE Trans. Instrum. Meas., vol. 45, pp. 426-430, Apr. 1996.
29. -, "Multistage adaptive filters for in-phase processing of line-frequency signals," IEEE Trans. Ind. Electron., vol. 44, no. 2, April 1997 IN PRESS.
30. R. N. J. Veldhuis, Restoration of Lost Samples in Digital Signals. London, UK: Prentice-Hall International, 1990.
31. C. S. Williams, Designing Digital Filters. Englewood Cliffs, NJ: Prentice-Hall, 1986.