By Matej Kseneman; Dušan Gleich
ISBN-10: 9535104942
ISBN-13: 9789535104940
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S. , Eds. New York: Springer-Verlag, pp. 571–578. , Zhou C. J. (2000) “DSP based partial discharge characterization by wavelet analysis”, IEEE 19th Int. Symp. On Discharge and Electrical Insultaion in Vacuum, Xi’an, China, pp. 780- 783. Mallat, S. (1999). A Wavelet Tour of Signal Processing, Second Edition (Wavelet Analysis & ItsApplications), Academic Press. Mallat, S. (1989). A theory for multiresolution signal decomposition: the wavelet representation, IEEE Transactions on Pattern Analysis and Machine Intelligence 11: 674–693.
11. The ME/MC results on the 129th frame of the “foreman” sequence. (a) The original image. The estimated frame: (b) ME/MC in the DCT domain, (c) ME/MC in the DWT domain, (d) with our method. The efficiency of our motion estimation method is well confirmed by the results, in the visual qualities of the reconstructed frames, reached by applying the ME/MC on the Tennis sequence conducted in several domains. 12 consolidate the fact that our motion estimation method outperforms other motion estimations methods conducted in different domains.
1991). 3. Wavelet based speech scrambler The analog scrambling process which employs a transformation of the input speech to facilitate encryption can best be described using matrix algebra. Let us consider the vector x which contains N speech time samples obtained from A/D conversion process, representing a frame of the original speech signal. Let this speech sample vector x be subject to an orthogonal transformation matrix F such that: u Fx (1) This transformation results in a new vector (u) made up of transform coefficients.
Information extraction and despeckling of SAR images with second generation of wavelet transform by Matej Kseneman; Dušan Gleich
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