J. M. Combes, A. Grossmann, and Ph. Tchamitchian, Eds., Wavelets: Time-Frequency Methods and Phase Space, 2nd edition, Springer-Verlag, New York, NY, 1989.  Home/Search   Document Not in Database   Summary   Related Articles   Check

.... developed for artificial neural networks [5,6,7,8] in particular stochastic neural networks [9, 10] to real world applications [11] The early literature survey outlined the problems of interfacing a neural network to the environment [12,13,14,15,16,17] The uneven distribution of signal energy [18,19] in the frequency domain, common in most practical signals, makes an interfacing technique used for one application useless in some other. It was realised that in order to interface analogue inputs to a neural network, the signals must be modified to facilitate the extraction of useful ....

.... [118] 119] 120] 121] Current Mode: 48] 49] 55] 56] 57] 59] 60] 61] 62] 63] 64] 65] 66] 67] 68] 69] Field Programmable Analogue Arrays: 51] 76] 77] 78] 79] 80] 82] 83] 84] 85] 86] 87] 88] 89] 90] 91] 95] 96] 97] 98] 99] 102] Literature: 8] 14] [19] [22] 35] 36] 37] 38] 39] 40] 41] 42] 43] 44] 46] 50] 58] Log Domain: 122] 123] 124] 125] 126] 127] 128] 129] 130] 131] 133] 134] 135] 140] 141] 142] Manuals: 73] 74] 75] 81] 92] 93] 94] 117] Matching: 69] 100] 105] 107] 108] 109] 110] 111] 112] ....

P. T. J.M. Combes, A. Grossmann, WAVELETS time-frequency methods and phase space. Spiegel, 1988.

....and go back to the context of subband filters, or more precisely quadrature mirror filters [36, 37, 41, 51, 52, 53, 54, 58, 56, 59] In mathematical analysis, wavelets were defined as translates and dilates of one fixed function and were used to both analyze and represent general functions. [15, 20, 25, 35, 24]. In the late eighties the introduction of multiresolution analysis and the fast wavelet transform by Mallat and Meyer provided the connection between subband filters and wavelets [33, 34, 35] this led to the first construction of smooth, orthogonal, and compactly supported wavelets in 1987 [18] ....

J. M. Combes, A. Grossmann, and Ph. Tchamitchian, editors. Wavelets: Time-Frequency Methods and Phase Space. Inverse problems and theoretical imaging. Springer-Verlag, New York, 1989.

....of the original curve. 4 Figure 2: Original curve and scaling coefficient curves (1 8 and 1 32 of points) 1. 4 Related work In other application areas, such as image and signal processing, wavelets have been used for several years to provide multiresolution data representation ( 22] [10]) Similarly, wavelet techniques for image and elevation surface compression have been studied in for instance [1] 14] Methods of hierarchical curve and surface representation based on subdivision (e.g. strip trees, quadtrees, spline subdivision) have been used extensively in geometric ....

J.M. Combes, A. Grossman, and Ph. Tchamitchian, Eds., Wavelets -- Time Frequency Methods and Phase Space, Proceedings of the Int. Conf., Marseille, 1987, Springer-Verlag, Berlin.

....can be found in Kolaczyk (1997) and Powell et al. 1995) albeit with different wavelet transforms. In the work described in this paper we employ thresholding in a data and noisedriven manner. 2. 3 Choice of Multiscale Transform Background texts on the wavelet transform include Meyer (1993) Combes et al. 1989), Mallat (1989) Shensa (1992) Bijaoui et al. 1994) Wickerhauser (1994) and Strang 8 and Nguyen (1996) Some important properties of the a trous wavelet transform are as follows. As already noted, the a trous transform is symmetric. Unlike it, Mallat s widelyused multiresolution algorithm ....

Combes, J.M., Grossmann, A. and Tchamitchian, Ph., Eds. (1989) Wavelets: TimeFrequency Methods and Phase Space, Springer-Verlag, Berlin.

....tool for multiresolution decomposition of different types of continuous time signals. In this section we present the wavelet transform as a signal analysis tool with capability of variable time frequency localization. In multiresolutional analysis, we often use the term local spectrum [6], where the signal is decomposed into a sum of complex exponentials, weighted by a function that plays a role of a localization window. This procedure is well suited for the identification of periodic structures in the signal, but it is not appropriate for the signals which contain short (mostly ....

.... This procedure is well suited for the identification of periodic structures in the signal, but it is not appropriate for the signals which contain short (mostly high frequency) disturbances which are delocalized, and their energy is distributed over a region determined by the width of the window [5, 6, 7]. At time frequency analysis we look for techniques that stress the time aspect at high frequencies and the frequency aspect at low frequencies [7] These requirements are satisfied by a technique of analysis and synthesis by wavelet transform, which is founded on basis functions formed by ....

J. M. Combes, A. Grossman, Ph. Tchamitchian (Eds.), "Wavelets - Time-Frequency Methods and Phase Space", Proceedings of the International Conference, Marseille, France, December 14-18, 1987.

....volume of work done more in the direction of approximation theory, and the efforts in the field of fractal functions and the more applied areas are left out almost entirely. Although wavelets are a relatively recent phenomenon, there are already several books on the subject, for example [14, 15, 33, 40, 60, 79, 88, 97]. Partially supported by DARPA Grant AFOSR 89 0455 and ONR Grant N00014 90 J 1343. y Research Assistant of the National Fund of Scientific Research Belgium, partially supported by ONR Grant N0001490 J 1343. 2 Notation and definitions Much of the notation will be presented as we go along. ....

J. M. Combes, A. Grossmann, and Ph. Tchamitchian, editors. Wavelets: Time-Frequency Methods and Phase Space. Inverse problems and theoretical imaging. Springer-Verlag, 1989.

....73 Table 6. 2: Description of Data for Feature Set 2 (FS2) Class Description Training Testing Porpoise Sound 142 284 Ice 175 175 Whale Sound 1 129 129 Whale Sound 2 118 235 Total 564 823 ffl 16 coefficients of Gabor wavelets a multiscale representation that does not assume signal stationarity [32]; ffl 1 value denoting signal duration; and ffl 8 other temporal descriptors and spectral measurements. For FS2, the 24 dimensional feature vectors extracted from the raw signals consist of: ffl 10 reflection coefficients corresponding to the maximum broadband energy segment, using a short term ....

J. Combes, A. Grossman, and P. T. (Eds.), Wavelets: TimeFrequency Methods and Phase Space, Springer-Verlag, 1989.

.... as well as the extraction of good feature vectors is crucial to the performance of the classifiers[1] The 25 dimensional feature vectors extracted from the raw signals consist of : 16 coefficients of Gabor wavelets a multiscale representation that does not assume signal stationarity[5], 1 value denoting signal duration, and 8 other temporal descriptors and spectral measurements. The preprocessing techniques and choice of the feature vector reflect the following two goals: i) all effects that vary, but not as a result of the events of interest, be removed or accounted for to the ....

J.M. Combes, A. Grossman, and Ph. Tchamitchian (Eds.). Wavelets: Time-Frequency Methods and Phase Space. Springer-Verlag, 1989.

....scene illumination, perspective transform, and view point change. This is unlike most current research on image invariants which concentrates on either geometric or illumination invariants exclusively. The formulations are widely applicable to many popular basis representations, such as wavelets [3, 4, 24, 25], short time Fourier analysis [13, 35] and splines [2, 5, 37] Exploiting formulations that examine information about shape and color at different resolution levels, the new approach is neither strictly global nor local. It enables a quasi localized, hierarchical shape analysis which is rarely ....

....perspective transform. 3. The proposed framework is applicable to many basis functions. We propose to use the framework with wavelet, short time Fourier analysis, and spline bases, which have been widely used in signal and speech processing, image analysis, computer vision, and computer graphics [3, 4, 5, 24, 25, 35, 37]. 4. It ameliorates some difficulties encountered in computing global or local image invariants. We employ basis functions of a compact support (wavelets, short time Fourier analysis, and splines) Although the invariant features computed capture local shape traits, it does not require ....

J. M. Combes, A. Grossman, and Ph. Tchamitchian (Eds.). Wavelets: Time-Frequency Methods and Phase Space, 2nd ed. Springer-Verlag, Berlin, 1990.

.... of applications of wavelets in image processing [1, 20, 21, 36] speech processing [13, 18, 32] wideband correlation processing [35] and numerical analysis [2, 3] The wavelet transform is an operation that transforms a function by integrating it with modified versions of some kernel function [5]. The kernel function is called the mother wavelet, and the modifications are translations and compressions (or dilations) of the mother wavelet. In this paper we consider parallel implementations of the 2 dimensional discrete wavelet transform (DWT) for analyzing the information content of ....

J. M. Combes, A. Grossman, and P. Tchamitchian, editors. Wavelets: Time-Frequency Methods and Phase Space. Springer-Verlag, New York, second edition, 1989.

.... representation for 1 D signals, e.g. for speech coding, for compression of 2 D images, and for 3 D analysis, e.g. for motion detection in video sequences [8] The wavelet transform is an operation that transforms a function by integrating it with modified versions of some kernel function [2]. The kernel function is called the mother wavelet, and the modifications are translations and compressions (or dilations) of This work was supported in part by ARPA contract No. DABT63 92 C 0022 and by NSF Parallel Infrastructure Grant No. CDA 9015696. The content of the information does not ....

J. M. Combes, A. Grossman, and P. Tchamitchian, editors. Wavelets: Time-Frequency Methods and Phase Space. Springer-Verlag, New York, second edition, 1989.

.... of applications of wavelets in image processing [1, 22, 23, 36] speech processing [15, 20, 32] wideband correlation processing [35] and numerical analysis [2, 3] The wavelet transform is an operation that transforms a function by integrating it with modified versions of some kernel function [5]. The kernel function is called the mother wavelet, and the modifications are translations and compressions (or dilations) of the mother wavelet. The boom in practical applications of wavelet transforms is greatly attributed to the discovery of a fast algorithm for computing a discrete wavelet ....

J. M. Combes, A. Grossman, and P. Tchamitchian, editors. Wavelets: Time-Frequency Methods and Phase Space. Springer-Verlag, New York, second edition, 1989.

....signals. A vector obtained from the power spectra needs to be augmented by other temporal and spectral descriptors such as signal duration, peak frequencies, bandwidths and possibly transformed AR model coefficients. Another technique is to use multiscale representations such as Gabor wavelets [25] that do not require assumptions of signal stationarity. ii) Each signal can be represented by a series of feature vectors sequenced in time [26] Each feature vector is a descriptor of the signal observed within a particular time window. A set of overlapping time windows is used to obtain the ....

....of impulsive sounds. At lower frequencies, the bandwidth is narrower and the duration longer. A good overview of wavelet transforms can be found in [27] The particular wavelet transform used in this paper represents a signal x(t) by shifted and dilated versions of an analyzing waveform [25]: T x ( a) a Gammam=2 Z 1 Gamma1 x(t)g ( t Gamma a m )dt (1) where the prototype wavelet is given by g(t) a Gammam=2 g( t Gamma a m )dt (2) Here a is a scaling factor, m the scaling index, and indicates the complex conjugate. The choice of the most appropriate ....

J.M. Combes, A. Grossman, and Ph. Tchamitchian (Eds.). Wavelets: Time-Frequency Methods and Phase Space. Springer-Verlag, 1989.

.... as the extraction of good feature vectors is crucial to the performance of the classifiers[BDSW91] For FS1, the 25 dimensional feature vectors extracted from the raw signals consist of : ffl 16 coefficients of Gabor wavelets a multiscale representation that does not assume signal stationarity[CGE89], ffl 1 value denoting signal duration, and ffl 8 other temporal descriptors and spectral measurements. For FS2, the 24 dimensional feature vectors extracted from the raw signals consist of: ffl 10 reflection coefficients corresponding to the maximum broadband energy segment, using a short term ....

J.M. Combes, A. Grossman, and Ph. Tchamitchian (Eds.). Wavelets: TimeFrequency Methods and Phase Space. Springer-Verlag, 1989.

.... and go back to the context of subband filters, or more precisely quadrature mirror filters [35, 36, 42, 50, 51, 52, 53, 57, 55, 59] In mathematical analysis, wavelets were defined as translates and dilates of one fixed function and were used to both analyze and represent general functions [13, 18, 22, 34, 21]. In the mid eighties the introduction of multiresolution analysis and the fast wavelet transform by Mallat and Meyer provided the connection between subband filters and wavelets [30, 31, 34] this led to new constructions, such as the smooth orthogonal, and compactly supported wavelets [16] ....

J. M. Combes, A. Grossmann, and Ph. Tchamitchian, editors. Wavelets: Time-Frequency Methods and Phase Space. Inverse problems and theoretical imaging. Springer-Verlag, New York, 1989.

....coefficients or spectral coefficients. A constant Q prototype wavelet with 24 coefficients is used, in addition to time duration to characterize the signals in our current work. The particular wavelet transform used represents a signal X(t) by shifted and dilated versions of an analyzing waveform [CoGr89]: T x t, a = a m 2 x(t) h t t a m dt where the prototype wavelet is given by h(t) a m 2 h t t a m . Here a m is a scaling factor and indicates the complex conjugate. The choice of the most appropriate prototype function is still an open research issue. To date, octave frequency ....

J.M. Combes, A. Grossman, Ph. Tchamitchian (Eds.), Wavelets: Time-Frequency Methods and Phase Space, Springer Verlag, 1989.

....Mathematical Imaging and Vision, and Optical Engineering. Also, an electronic information service exists on the Internet, the Wavelet Digest, with the address wavelet math.scarolina.edu. Last but not least, several books on the subject exist, monographs as well as edited volumes. The list includes [13, 20, 21, 43, 49, 74, 92, 96, 106, 108, 116, 119]. 2. Notation. Most of the notation will be presented as we go along. The space of square integrable functions, L 2 (R) is defined as the space of Lebesgue measurable functions for which kfk 2 = Z 1 Gamma1 jf(x)j 2 dx 1: The inner product of two functions f; g 2 L 2 (R) is given ....

J. M. Combes, A. Grossmann, and P. Tchamitchian, eds., Wavelets: Time-Frequency Methods and Phase Space, Inverse problems and theoretical imaging, Springer-Verlag, 1989.

....other . Moreover, as stressed before, the with holes dilation appears clearly here. b) The wavelet is real valued with only three nonzero values and P p Gamma1 n=0 (n) 0. The signal is a sine, and the corresponding wavelet transform has a structure similar to the continuous one (see e.g. [2]) Figure 2 The signal is an exponential f 1 (n) e 3 Delta2k=p . The wavelet is also complex valued (n) e ik 1 n e Gammak 2 n 2 , and it is centered around a certain frequency 0 . We have that jT f (b; a)j = j ( a)j where is the frequency of the signal. The modulus jT f (b; ....

Grossmann, A., Kronland-Martinet, R., Morlet, J. Reading and understanding continuous wavelet transforms in "Wavelets: time-frequency methods and phase space", Combes, Grossmann and Tchamitchian Eds, IPTI Springer (1989) pp.2-20.

No context found.

J. M. Combes, A. Grossmann, and Ph. Tchamitchian, Eds., Wavelets: Time-Frequency Methods and Phase Space, 2nd edition, Springer-Verlag, New York, NY, 1989.

No context found.

J. M. Combes, A. Grossman, and P. Tchamitchian, editors. Wavelets: Time-Frequency Methods and Phase Space. Springer-Verlag, New York, second edition, 1989.

Online articles have much greater impact   More about CiteSeer.IST   Add search form to your site   Submit documents   Feedback  

CiteSeer.IST - Copyright Penn State and NEC