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In this study, the authors propose the seismic trace noise reduction by wavelets and double threshold estimation method (STNRW), that is based on the discrete wavelet transform, estimates two thresholds instead of the one threshold estimation of the traditional methods. The authors verify the robustness of the method proving that the probability of classification error for a noisy wavelet coefficient decreases, as the length of the signal increases. The authors perform Monte Carlo simulations considering eight seismic traces obtained from astsa R package with different signal-noise-to-ratio (SNR) values in order to compare the performance of the new method with three denoising methods well-known in the literature. The results show that the STNRW method is efficient.

Separating signal from unwanted noise is a major problem when analysing biomedical data, such as electrocardiography. Electrocardiogram (ECG) data are typically a mixture of real signal and various sources of noise, including baseline wander, power line interference, and electromagnetic interference. Since ECG signals are non-stationary physiological signals, the wavelet transform has been proposed to be an effective tool for eliminating unwanted noise from the ECG data. Here, the authors proposed a new noise reduction method for ECG data based on the discrete wavelet transform and hidden Markov model. They performed Monte Carlo simulations to compare the performance of this new method with seven other wellknown denoising techniques.

The grinding efficiency evaluation can be performed through the comparison of the operational work index with the ore work index Wi. In this work, the development of an ore grindability softsensor (ESTMOL) is presented. The ore work index is estimated on the basis of its lithological composition. Also addressed is the experimental development of a lithological composition sensor (ACOLITO) for ores on a conveyor belt. The lithological composition is determined from image analysis on samples obtained by a color video camera. Finally, a global operational work index for a complete grinding section is defined here, and its on-line estimation (PREDIMOL) is addressed, including the required soft-sensors to overcome the measurement problems. The experimental work is done with samples obtained from the CODELCO -And& grinding plant. All the sensors have given up to now good results. 0

Purpose Separating or eliminating the noise from a biomedical signal is what allows the accuracy of a diagnosis. In particular, in the case of an electrocardiogram (ECG), it is necessary to reduce the distortions caused by several sources of noise. In this paper, we propose a new ECG denoising method called by noise reduction by genetic algorithm minimization of a new noise variation estimate (GAMNVE). Methods The GAMNVE method applies the discrete wavelet transform (DWT) in the noisy ECG signal and processes the wavelet coefficients by the minimization of a new noise variance estimate. This minimization was made by genetic algorithm. For the simulations, we consider eight real ECG signal corrupted by additive white Gaussian noise (AWGN), power line interference (PLI), and muscle artifact (MA). Results We compare the GAMNVE method with five well-known denoising methods. The simulations results show that the GAMNVE method presents a better performance for the considered cases. Conclusion Simulations have demonstrated that the GAMNVE method can be applied in noisy ECG signals with superior performance than other methods established in the literature.