De-Noising of Magnetotelluric Signals by Discrete Wavelet Transform and SVD Decomposition

Magnetotelluric (MT) sounding data can easily be damaged by various types of noise, especially in industrial areas, where the quality of measured data is poor. Most traditional de-noising methods are ineffective to the low signal-to-noise ratio of data. To solve the above problem, we propose the use of a de-noising method for the detection of noise in MT data based on discrete wavelet transform and singular value decomposition (SVD), with multiscale dispersion entropy and phase space reconstruction carried out for pretreatment. No “over processing” takes place in the proposed method. Compared with wavelet transform and SVD decomposition in synthetic tests, the proposed method removes the profile of noise more completely, including large-scale noise and impulse noise. For high levels or low levels of noise, the proposed method can increase the signal-to-noise ratio of data more obviously. Moreover, application to the field MT data can prove the performance of the proposed method. The proposed method is a feasible method for the elimination of various noise types and can improve MT data with high noise levels, obtaining a recovery in the response. It can improve abrupt points and distortion in MT response curves more effectively than the robust method can.

Statistical analysis and characterization of Landsat 8 satellite images of forest wildfires regions

The increase in the increase in wildfires throughout the world is largely due to increases in temperature and even to an increase in the carelessness of the population in leaving a large amount of the garbage in forests. Using Python and Matlab programs were as working medium. We performed the preprocessing on multispectral images obtained by the Landsat 8 satellite with and without wildfires, which consists of three steps: alignment, characterization and normalization, with the intention of standardization the images. From obtaining the spectral signatures of wildfires and metallic structures, boxes and whiskers diagrams, Shannon entropy and mutual information from the images, there are similar behavior in bands 6, 7, 8, 10 and 11, with more relevant information, taking into account that each image is formed by 11 bands, and in bands 1, 2, 3, 4, 5, 8 and 9 there is less information, SVD decomposition allows to have the best k-rank approximation to the original data matrix. The purpose of this analysis is to reduce the computational complexity.

Research on forage hyperspectral image recognition based on F-SVD and XGBoost

Aiming at the high time complexity and poor accuracy of traditional SVD in hyperspectral recognition. we proposed F-SVD, which introduces the latent factors(F) into the SVD decomposition strategy and uses the correlation between the latent variable and the original variable to improve the singular matrix. Firstly, we used F-SVD to reduce the dimension of visible-near infrared hyperspectral image, and consequently designed a forage recognition model based on XGBoost. When the test set sets 40%, the OA of F-SVD-XGBoost is 91.67%, which takes 0.601s. Compared with the traditional FA-XGBoost and SVD-XGBoost, OA increases 1.98% and 1.67%, and the time consumption decreases 1.369s and 0.522s, respectively. The results show that our model not only effectively extracts the essential features of forage hyperspectral and improves the accuracy of classification, but also has a faster processing speed, so that can efficiently and quickly realize the identification of forage hyperspectral images.

A novel time domain polarization filter based on a correlation matrix analysis

Polarization filters are widely used for denoising seismic data. These filters are applied in the field of seismology, microseismic monitoring, vertical seismic profiling and subsurface imaging. They are primarily useful to suppress ground-roll in seismic reflection data and enhance P and S wave arrivals. Traditional implementations of the polarization filters involved the analysis of the covariance matrix or the SVD decomposition of a three-component seismogram matrix. The linear polarization level, known as rectilinearity, is expressed as a function of the covariance matrix eigenvalues or by the data matrix singular values. Wavefield records that are linearly polarized are amplified while others are attenuated. Besides the described implementations, we introduced a new time domain polarization filter based on the analysis of the seismic data correlation matrix. The principal idea is to extend the notion of the correlation coefficient in 3D space. This new filter avoids the need for diagonalization of the covariance matrix or SVD decomposition of data matrix, which are often time consuming. The new implementation facilitates the choice of the rectilinearity threshold: we demonstrate that linear polarization in 3D can be represented as three classic 2D correlations. A good linear polarization is detected when a high linear correlation between the three seismogram components is mutually observed. The tuning parameters of the new filter are the length of the time window, the filter order, and the rectilinearity threshold. Tests using synthetic seismograms show that optimal results are reached with a filter order that spans between 2 and 4, a rectilinearity threshold between 0.3 and 0.7, and a window length that is equivalent to one to three times the period of the signal wavelet. Compared to covariance-based filters, the new filter can enhance the signal-to-noise ratio by 6 to 20 dB and reduces computational costs by 25%.

Singular value automata and approximate minimization

The present paper uses spectral theory of linear operators to construct approximatelyminimal realizations of weighted languages. Our new contributions are: (i) a new algorithm for the singular value decomposition (SVD) decomposition of finite-rank infinite Hankel matrices based on their representation in terms of weighted automata, (ii) a new canonical form for weighted automata arising from the SVD of its corresponding Hankelmatrix, and (iii) an algorithmto construct approximateminimizations of given weighted automata by truncating the canonical form.We give bounds on the quality of our approximation.

Research on Digital Watermarking Algorithm for Anti-geometric Attack

For the problem that the traditional digital watermarking algorithm is less robustness against geometric attacks, this paper introduces the related content of digital watermarking technology, which combs digital watermarking technology, digital watermarking attack technology and digital watermarking evaluation method, and summarizes the improved algorithms proposed in recent years. Next, the traditional wavelet transform algorithm and the improved algorithm based on DCT transform are selected for comparison experiments. The latter combines Arnold scrambling and SVD decomposition, which has better shear resistance. Finally, combined with the research status, the future research focus of digital watermarking algorithm is prospected.