№2, 2024
The objective of this article is to create an algorithm of the sequence of artificial signals that can be used to compare and create methods for processing one- and two-dimensional signals. It will then be implemented to compare feature extraction methods that rely on discrete wavelet transforms. The discrete wavelet transform is superior to other signal processing techniques in several ways. Developing a feature set is a crucial step in using the discrete wavelet transform. Mean value and standard deviation are suggested as feature extraction techniques in this study. The mean value is the only option selected for the first feature extraction method; the mean value and standard deviation are selected for the second feature extraction method. To build any number of artificial signal sequences from a single, several conditions are taken into account, for example, their symmetry, they are supposed to be located at the same distance from each other, that is, with an equal step. Symmetrical signal sequences constructed in this way differ from common well-known signal sequences, such as Fourier series, in that they converge to a given signal in equal steps (pp.24-29).
Akin, M. (2002). Comparison of Wavelet Transform and FFT Methods in the Analysis of EEG Signals. Journal of Medical Systems, 26, 241-247.
Blatter, K. (2006) Wavelet analysis. basic theory. Translated from English, Tekhnosfera, Moscow.
Itakura, F. (1975). Minimum prediction residual principle applied to speech recognition, IEEE Transactions on Acoustics, Speech and Signal Processing, 23(1), 67-72.
Geler, Z., Kurbalija, V., Ivanović, M., Radovanović, M., Dai, W. (2019). Dynamic time warping: itakura vs sakoe-chiba, IEEE International Symposium On Innovations in Intelligent SysTems and Applications (INISTA).
https://en.wikipedia.org/wiki/rate_of_convergence
Keogh, E., Pazzani, M. (2017). Derivative dynamic time warping. In Proceedings, 2001th SIAM International Conference on Data Mining (SDM), https://doi.org/10.1137/1.9781611972719.1
Kerimov, A. (2022). Accuracy comparison of signal recognition methods on the example of a family of successively horizontally displaced curves. Informatics and Control Problems, 42(2), 80–91.
Kerimov, A. (2022). Comparison of some signal recognition methods for their adequacy. In Proceedings, 8th International Conference on Control and Optimization with Industrial Applications. 1, Baku, Azerbaijan.
Rzayev, R., Kerimov, A. (2023). Comparison of signal recognition methods by combined use of appropriate evaluation criteria within the additive convolution. Problems of Information Society, 14 (2), 24–31.
Rzayev, R., Kerimov, A. (2023). Signal recognition by using addictive convolution criteria. Proceedings of IAM, 12(1), 52–64.
Rzayev, R., Kerimov, A. (2023). Signal recognition using weighted additive convolution of evaluation criteria. The Springer Series “Lecture Notes in Net-works and Systems”, 758(2), 407–416.
Rzayev, R., Kerimov, A, Gurbanli, U., Salmanov, F. (2024). Criteria for assessing the adequacy of image recognition methods and their verification using examples of artificial series of signals. Problems of Information Society, 15(1), 10-17, http://doi.org/10.25045/jpis.v15.i1.02
Sakoe, H., Chiba, S. (1978). Dynamic programming algorithm optimization for spoken word recognition. IEEE Transactions on Acoustics, Speech, and Signal Processing, 26(1). https://ieeexplore.ieee.org/document/1163055
Santos, M., Morais, C., Nascimento, Y., Araujo, J., Lima, K. (2017). Spectroscopy with computational analysis in biological studies: a decade (2006–2016). TrAC Trends Anal Chem, 97, 244–256.
Saraswat, S., Srivastava, G., Sachchidanand, N. (2017). Wavelet transform based feature extraction and classification of atrial fibrillation arrhythmia. Biomed Pharmakoi Journal 10(4) http://biomedpharmajournal.org/?p=17470
Scholl, S. Fourier, Gabor, Morlet or Wigner (2021): Comparison of Time-Frequency Transforms, https://arxiv.org/abs/2101.06707
Yakovlev, A.N. (2024) Introduction to wavelet transforms, textbook, Novosibirsk, NSTU Publishing House (in Russian).
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