Kind Of Non-stationary Signal Research Articles

Extracting the nonlinear features of motor imagery EEG using parametric t-SNE

When performing studies on brain computer interface based rehabilitation problems, researchers frequently encounter difficulty due to the curse of dimensionality and the nonlinear nature of Motor Imagery Electroencephalography (MI-EEG). Though many approaches have been proposed recently to address the feature extraction problem and have shown surprising performance, unfortunately, most of them are non-parametric or linear dimension reduction techniques, which are limited in utility for out-of-sample extension for MI-EEG classification. To address the problem and obtain accurate MI-EEG features, a new unsupervised nonlinear dimensionality reduction technique termed parametric t-Distributed Stochastic Neighbor Embedding (P. t-SNE) is employed to extract the nonlinear features from MI-EEG. Considering that MI-EEG is a kind of non-stationary signal with remarkable time-frequency rhythmic distribution characteristics, Discrete Wavelet Transform (DWT) is used to extract the time-frequency features of MI-EEG. Furthermore, P. t-SNE is applied to selected wavelet components to get the nonlinear features. They are then combined serially to construct the feature vector. Experiments are conducted on a publicly available dataset, and the experimental results show that the nonlinear features have great visualization performance with obvious clustering distribution, and the feature extraction method indicates excellent classification performance as evaluated by a support vector machine classifier. This paper suggests a manifold based technique for further analysis and classification research of MI-EEG.

Analysis Tire Tread Patterns Noise Based on Wavelet Transform

Pattern noise is mainly one of tire noise types, which is generated by impacting of tires block on the road. Generally, traditional Fourier Transform is used to process noise signals. However, the pattern noise is a kind of non-stationary signal and is related to pitch arrangement of a block, FFT is unsuitable. This paper proposes wavelet transform to analyze tire noise signals at different speeds. The causes of the frequency components of tire noise are interpreted. The results show that using wavelet transform method can effectively and intuitively analyze tire tread noise.

Cylindrical cyclic acoustic imaging with a Bayesian approach for cyclostationary sources reconstruction

Standard acoustic imaging techniques, such as beamforming or near acoustical holography, are now widely used in engineering contexts. However, large arrays of microphones are sometimes required to have a good resolution. Besides new challenges arise, particularly in the field of non stationary sources, which need to be identified and solved. Cyclostationary sound sources, a specific kind of non stationary signals, are characterized by statistical properties evolving periodically in time. In practice, the first-order statistical properties contain some periodic components while the second orders may be random with a periodic flow of energy. The present work tackles the acoustic imaging of cyclostationary sources with a scanning microphone, i.e., without any array. Cylindrical surfaces, adapted to standard rotating machines, are considered. The reconstruction difficulty of acoustic sources from discrete measurements is addressed here thanks to the cyclostationary properties. A cyclic sound field is hence extracted from the discrete measurements. Finally, a Bayesian formulation, gathering both physical and probabilistic information on this inverse problem, is used to back propagate the sound over the radiating surface.

The Study of Tire Pattern Noise by Using Wavelet Transform

Tire noise is classified by pattern noise and road noise in a vehicle. Especially pattern noise has impulsive characteristics since it is generated by impacting of tire’s block on the road. Therefore, a special signal process is needed other than traditional Fourier Transform, because the characteristic of signal is varying with time. On the other hand, the pattern noise is a kind of non-stationary signal and is related to the impulsive train of pitch sequence of a block. In this paper, Wavelet Transform is applied to verify the impulse signal caused by impact of block and groove and to verify the relationship between the pattern noise and the train of pitch sequence.

A Study on Holographic Reconstruction of Cyclostationary Sound Field Based on Boundary Element Method

The cyclostationary vibrational signal modulated by other signals can be considered as a special kind of nonstationary signal, which is found frequently in some rotating machinery. The modulation frequency component can be extracted from the total signal using second-order cyclic statistics at the appropriate cyclic frequency. By using the cyclic spectral density functions instead of power spectrum or spectrum as the variables, the cyclostationary nearfield acoustic holography (CYNAH) technique is used to reconstruct acoustic physical quantities in the cyclostationary sound field. In this presented work the CYNAH method is combined with the boundary element method (BEM) to overcome the limitations on the geometries of the planar CYNAH method. The methods used in the conventional BEM-based NAH to overcome nonuniqueness and the ill-posed nature should be also implemented here. The results of a simulation show satisfactory agreement between the computed values and the analytical ones.

Extraction of MHD Signal Based on Wavelet Transform

Mirnov signals mixed with interferences are a kind of non-stationary signal. It cannot obtain satisfactory effects to extract MHD signals from mirnov signals by Fourier Transform. This paper suggests that the wavelet transform can be used to treat mirnov signals. Theoretical analysis and experimental result have indicated that using the time-frequency analysis characteristics of the wavelet transform to filter mirnov signals can remove effectively interferences and extract useful MHD signals.