IEICE TRANSACTIONS on Fundamentals
Fast Wavelet Transform and Its Application to Detecting Detonation
Summary :
Fast wavelet transform is presented for realtime processing of wavelet transforms. A processor for the fast wavelet transform is of the frequency sampling structure in architectural level. The fast wavelet transform owes its parallelism both to the frequency sampling structure and parallel tapping of a series of delay elements. Computational burden of the fast transform is hence independent of specific scale values in wavelets and the parallel processing of the fast transform is readily implemented for real-time applications. This point is quite different from the computation of wavelet transforms by convolution. We applied the fast wavelet transform to detecting detonation in a vehicle engine for precise real-time control of ignition advancement. The prototype wavelet for this experiment was the Gaussian wavelet (i.e. Gabor function) which is known to have the least spread both in time and in frequency. The number of complex multiplications needed to compute the fast wavelet transform over 51 scales is 714 in this experiment, which is less than one tenth of that required for the convolution method. Experimental results have shown that detonation is successfully detected from the acoustic vibration signal picked up by a single knock sensor embedded in the outer wall of a V/8 engine and is discriminated from other environmental mechanical vibrations.
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- IEICE TRANSACTIONS on Fundamentals Vol.E75-A No.8 pp.980-987
- Publication Date
- 1992/08/25
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- Special Section PAPER (Special Section on the 6th Digital Signal Processing Symposium)
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Hisakazu KIKUCHI, Makoto NAKASHIZUKA, Hiromichi WATANABE, Satoru WATANABE, Naoki TOMISAWA, "Fast Wavelet Transform and Its Application to Detecting Detonation" in IEICE TRANSACTIONS on Fundamentals,
vol. E75-A, no. 8, pp. 980-987, August 1992, doi: .
Abstract: Fast wavelet transform is presented for realtime processing of wavelet transforms. A processor for the fast wavelet transform is of the frequency sampling structure in architectural level. The fast wavelet transform owes its parallelism both to the frequency sampling structure and parallel tapping of a series of delay elements. Computational burden of the fast transform is hence independent of specific scale values in wavelets and the parallel processing of the fast transform is readily implemented for real-time applications. This point is quite different from the computation of wavelet transforms by convolution. We applied the fast wavelet transform to detecting detonation in a vehicle engine for precise real-time control of ignition advancement. The prototype wavelet for this experiment was the Gaussian wavelet (i.e. Gabor function) which is known to have the least spread both in time and in frequency. The number of complex multiplications needed to compute the fast wavelet transform over 51 scales is 714 in this experiment, which is less than one tenth of that required for the convolution method. Experimental results have shown that detonation is successfully detected from the acoustic vibration signal picked up by a single knock sensor embedded in the outer wall of a V/8 engine and is discriminated from other environmental mechanical vibrations.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/e75-a_8_980/_p
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@ARTICLE{e75-a_8_980,
author={Hisakazu KIKUCHI, Makoto NAKASHIZUKA, Hiromichi WATANABE, Satoru WATANABE, Naoki TOMISAWA, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Fast Wavelet Transform and Its Application to Detecting Detonation},
year={1992},
volume={E75-A},
number={8},
pages={980-987},
abstract={Fast wavelet transform is presented for realtime processing of wavelet transforms. A processor for the fast wavelet transform is of the frequency sampling structure in architectural level. The fast wavelet transform owes its parallelism both to the frequency sampling structure and parallel tapping of a series of delay elements. Computational burden of the fast transform is hence independent of specific scale values in wavelets and the parallel processing of the fast transform is readily implemented for real-time applications. This point is quite different from the computation of wavelet transforms by convolution. We applied the fast wavelet transform to detecting detonation in a vehicle engine for precise real-time control of ignition advancement. The prototype wavelet for this experiment was the Gaussian wavelet (i.e. Gabor function) which is known to have the least spread both in time and in frequency. The number of complex multiplications needed to compute the fast wavelet transform over 51 scales is 714 in this experiment, which is less than one tenth of that required for the convolution method. Experimental results have shown that detonation is successfully detected from the acoustic vibration signal picked up by a single knock sensor embedded in the outer wall of a V/8 engine and is discriminated from other environmental mechanical vibrations.},
keywords={},
doi={},
ISSN={},
month={August},}
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TY - JOUR
TI - Fast Wavelet Transform and Its Application to Detecting Detonation
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 980
EP - 987
AU - Hisakazu KIKUCHI
AU - Makoto NAKASHIZUKA
AU - Hiromichi WATANABE
AU - Satoru WATANABE
AU - Naoki TOMISAWA
PY - 1992
DO -
JO - IEICE TRANSACTIONS on Fundamentals
SN -
VL - E75-A
IS - 8
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - August 1992
AB - Fast wavelet transform is presented for realtime processing of wavelet transforms. A processor for the fast wavelet transform is of the frequency sampling structure in architectural level. The fast wavelet transform owes its parallelism both to the frequency sampling structure and parallel tapping of a series of delay elements. Computational burden of the fast transform is hence independent of specific scale values in wavelets and the parallel processing of the fast transform is readily implemented for real-time applications. This point is quite different from the computation of wavelet transforms by convolution. We applied the fast wavelet transform to detecting detonation in a vehicle engine for precise real-time control of ignition advancement. The prototype wavelet for this experiment was the Gaussian wavelet (i.e. Gabor function) which is known to have the least spread both in time and in frequency. The number of complex multiplications needed to compute the fast wavelet transform over 51 scales is 714 in this experiment, which is less than one tenth of that required for the convolution method. Experimental results have shown that detonation is successfully detected from the acoustic vibration signal picked up by a single knock sensor embedded in the outer wall of a V/8 engine and is discriminated from other environmental mechanical vibrations.
ER -
English
