Rolling bearing is an important part in mechanical system and faults occur frequently with vibration noise. Empirical mode decomposition (EMD) is a tool for nonlinear and non-stationary signals analysis. However, the major drawbacks of EMD are mode mixing problem, ensemble empirical mode decomposition (EEMD) provides a new tool for signal analysis, and it is an improved technique of EMD. In ord...

Electroencephalography (EEG) from preterm infant monitoring systems is usually contaminated by several sources of noise that have to be removed in order to correctly interpret signals and perform automated analysis reliably. Band-pass and adaptive filters (AF) continue to be systematically applied, but their efficacy may be decreased facing preterm EEG patterns such as the tracé alternant and s...

In the analysis of nonlinear, non-stationary signal is better than the traditional wavelet signal analysis method, commonly used in this kind of signal singular value point detection. Empirical mode decomposition (EMD) is the core of the analysis method, but EMD in the original HHT method exists end effect in decomposition process, the decomposition of intrinsic mode functions IMF serious disto...

Cameras mounted on vehicles frequently suffer from image shake due to the vehicles’ motions. To remove jitter motions and preserve intentional motions, a hybrid digital image stabilization method is proposed that uses variational mode decomposition (VMD) and relative entropy (RE). In this paper, the global motion vector (GMV) is initially decomposed into several narrow-banded modes by VMD. REs,...

The paper proposes an application of Empirical Mode Decomposition in technical analysis. The EMD-candlestick is designed to replace the traditional candlestick as the signal generators in technical trading strategies to improve the profitability. We investigate a representative set of technical trading strategies, including moving average, trading range break-out, relative strength index, and i...

One of the most important techniques of ultrasonic flaw classification is feature extraction of flaw signals,which directly affects the accuracy and reliability of flaw classification.Based on the non-stationary characteristic of ultrasonic flaw signals, a new feature extraction method of ultrasonic signals based on empirical mode decomposition (EMD) is put forward in the paper. Firstly, the or...

This paper presents a detail analysis on the Electrocardiogram (ECG) denoising approaches based on noise reduction algorithms in Empirical Mode Decomposition (EMD) and Discrete Wavelet Transform (DWT) domains. Compared to other denoising methods such as; filtering, independent and principle component analysis, neural networks, and adaptive filtering, EMD and wavelet domain denoising algorithms ...

Empirical mode decomposition (EMD) is a newly developed tool to analyze nonlinear and non-stationary signals. It is used to decompose any signal into a finite number of time varying subband signals termed as intrinsic mode functions (IMFs). Such data adaptive decomposition is recently used in speech enhancement. This study presents the concept of EMD and its application to advanced speech signa...

Combing back-propagation neural network (BPNN) and empirical mode decomposition (EMD) techniques, this study proposes EMD-BPNN model for forecasting. In the first stage, the original exchange rate series were first decomposed into a finite, and often small, number of intrinsic mode functions (IMFs). In the second stage, kernel predictors such as BPNN were constructed for forecasting. Compared w...