The genus Loxocephala Schaum, 1850 is reviewed. Three new species: L. rugosa sp. nov., L. verticalis sp. nov. and L. mangkangensis sp. nov. are described from China. L., perpunctata Jacobi, 1944 and L. sinica Chou & Huang, 1985 are given supplementary descriptions. In addition, according to the male genitalia, L. sinica sichuanensis Chou & Huang, 1985 is upgraded to species level: L. sichuanens...

The Hilbert-Huang transform (HHT) is a powerful method for nonlinear and non-stationary vibrations analysis. This approach consists of two basic parts of empirical mode decomposition (EMD) and Hilbert spectral analysis (HSA). To achieve the reliable results, Bedrosian and Nuttall theorems should be satisfied. Otherwise, the phase and amplitude functions are mixed together and consequently, the ...

Empirical mode decomposition (EMD) is an algorithm for signal analysis recently introduced by Huang. It is a completely datadriven non-linear method for the decomposition of a signal into AM FM components. In this paper two new EMD-based methods for the analysis and classification of pathological voices are presented. They are applied to speech signals corresponding to real and simulated sustai...

Recognizing vehicle license plate image captured in low illumination place is a difficult problem. To solve the problem, this paper proposes a new license plate image enhancement method using bidimensional empirical mode decomposition (BEMD) technique. BEMD is a 2D datadriven adaptive nonlinear signal decomposition approach derived from the 1D empirical mode decomposition (EMD). In the proposed...

There are some methods to decompose a signal into different components such as: Fourier decomposition and wavelet decomposition. But they have limitations in some aspects. Recently, there is a new signal decomposition algorithm called the Empirical Mode Decomposition (EMD) Algorithm which provides a powerful tool for adaptive multiscale analysis of nonstationary signals. Recent works have demon...

Prediction of nonlinear and nonstationary time series datasets can be achieved by using support vector regression. To improve the accuracy, we propose a new model ‘averaging intrinsic mode function’ which is a derivative of empirical mode decomposition to filter datasets of an exchange rate, followed by using a new algorithm of multiclass Support Vector Regression (SVR) for prediction. Simulati...

This work deals with the decomposition of a signal into a collection of intrinsic mode functions. More specifically, we aim to revisit Empirical Mode Decomposition (EMD) based on a sifting process step, which highly depends on the choice of an interpolation method, the number of inner iterations, and that does not have any convergence guarantees. The proposed alternative to the sifting process ...

Ensemble empirical mode decomposition (EEMD) is a noise-assisted data analysis method which decomposes a signal into a collection of intrinsic mode functions (IMFs). There nevertheless appears a multi-mode problem where signals with a similar timescale are decomposed into different IMF components. A possible solution to this problem is to recombine the multi-mode IMF components into a proper si...