Journal
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
Volume 71, Issue -, Pages -Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TIM.2021.3135319
Keywords
Matching pursuit algorithms; Dictionaries; Noise reduction; Discrete cosine transforms; Transforms; Sparse matrices; Discrete Fourier transforms; Discrete cosine transform (DCT); discrete sine transform (DST); electrical signal; filtering; noise reduction; orthogonal matching pursuit (OMP) algorithm; sparse representation (SR); white noise
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This article proposes a noise reduction methodology based on the orthogonal matching pursuit algorithm with dictionaries constructed from kernel functions of the discrete cosine transform and discrete sine transform. The methodology is proven to be effective in reducing noise and recovering signals through testing on synthetic and real electrical signals.
The noise is the most common issue present in all signals. Depending on the process or application, the reduction of the noise is mandatory mainly in areas such as signal classification, pattern recognition, and training process, among others. In recent years, diverse methodologies for noise reduction have been proposed, but they have left some issues with no solution. Such are the noise reduction in signals immersed in white noise without changes in their amplitude or phase or the high performance of recovering components of the desire signals distributed among the frequency spectrum. Also, filtering techniques without significant changes in signal phase and amplitude are necessary. This article proposes a methodology for noise reduction based on the orthogonal matching pursuit algorithm with dictionaries constructed from kernel functions of the discrete cosine transform and discrete sine transform. The methodology is proved in synthetic signals and real electrical signals, reaching noise reduction of white noise (& x007E;34 dB in electrical signals) and recovering without distortion.
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