Abstract Study electroencephalograph (EEG) of epileptic patients during different periods in order to do effective treatments. This paper studies wave forms and energy characteristics of the paroxysmal stage and the static epileptic EEG, adopts two different methods to classify. One is using Support Vector Machines (SVM) to rebuild and classify as to the characteristic signals. The other is app...

Hydrological time series forecasting remains a difficult task due to its complicated nonlinear, non-stationary and multi-scale characteristics. To solve this difficulty and improve the prediction accuracy, a novel four-stage hybrid model is proposed for hydrological time series forecasting based on the principle of 'denoising, decomposition and ensemble'. The proposed model has four stages, i.e...

The dynamics of modern interconnected power system is characterized by low frequency oscillations (LFOs) which are produced as results of various disturbances such as changes in loads, tripping of lines, faults, and other discrete events. A data driven empirical mode decomposition (EMD) method is applied to the detection of low frequency oscillation modes from disturbed trajectory with its stro...

Ship propeller beats have obvious amplitude modulation to the cavitation noise of its radiation. The DEMON (Detection of Envelope Modulation on Noise) spectrum analysis technology is to demodulate the high frequency components to get the low frequency modulation components. This paper studied the new method to combine the complementary ensemble empirical mode decomposition (CEEMD) with wavelet ...

In today’s era of digital revolution, electronic systems in the context of audio communication typically perform transmission, playback, analysis and synthesis of audio signals. So, from the perspective of electronic system/product design for any of these purposes, noise influences must be carefully considered. Various types of noise and distortion can be characterized and number of techniques ...

Empirical mode decomposition (EMD) is a signal processing method that produces data-driven time-frequency representation suited to characterize time-varying and nonlinear phenomena. In EMD, intrinsic functions (IMF) are sequentially estimated from the of interest represent different oscilation modes produce an orthogonal original information. Different algorithms have been proposed for EMD esti...

Radar emitter signal (RES) recognition is the important content in radar reconnaissance and signal processing. In order to study the problem of RES recognition, and to improve the RES recognition rate of the electronic warfare equipment, the empirical mode decomposition (EMD) theory and wavelet packet (WP) are introduced into RES feature extraction. A new RES recognition method is proposed base...

A novel electrocardiogram (ECG) signal de-noising and baseline wander correction method based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and wavelet threshold is proposed. Although CEEMDAN is based on empirical mode decomposition (EMD), it represents a significant improvement of the original EMD by overcoming the mode-mixing problem. However, there has been ...

Disc brake squeal, which is considered as a self-excited vibration induced by friction force, has received much attention over decades due to the high customer requirements of the NVH properties of vehicles. This paper presents a characteristic identifying method for the disc brake squeal by analysing the acoustic brake squeal signal obtained from experiment. The sound of brake squeal was recor...

Almost all processes that are quantified in neurobiology are stochastic and nonstationary. Conventional methods that characterize these processes to provide a meaningful and precise description of complex neurobiological phenomenon may be insufficient. Here, we report on the use of the data-driven empirical mode decomposition (EMD) method to study neuronal activity in visual cortical area V4 of...