The empirical mode decomposition (EMD) was recently proposed as a new time-frequency analysis tool for nonstationary and nonlinear signals. Although the EMD is able to find the intrinsic modes of a signal and is completely self-adaptive, it does not have any implication on reconstruction optimality. In some situations, when a specified optimality is desired for signal reconstruction, a more fle...

The phenomenon of mode-mixing caused by intermittence signals is an annoying problem in Empirical Mode Decomposition (EMD) method. The noise assisted method of Ensemble EMD (EEMD) has not only effectively resolved this problem but also generated a new one, which tolerates the residue noise in the signal reconstruction. Of course, the relative magnitude of the residue noise could be reduced with...

This is due to the fact that, the human audio system is far more complex and sensitive than the human visual system. In this paper, we describe an imperceptible, robust and a new adaptive audio watermarking algorithm based on Empirical Mode Decomposition (EMD) is introduced. The audio signal is divided into frames and each one is decomposed adaptively, by EMD, into intrinsic oscillatory compone...

The New MAC mechanism plays a key role in achieving the needed requirements of B5G/6G radio technology and helps to avoid high-speed frequency issues limitations. With help ns-3 simulator, we generated 42 different cases for purpose analyzing impact network load on overall effective transmission rate. Therefore, use data-adaptive decomposition method Empirical Mode Decomposition (EMD) our non-s...

The vibration based signal processing technique is one of the principal tools for diagnosing faults of rotating machinery. Empirical mode decomposition (EMD), as a time-frequency analysis technique, has been widely used to process vibration signals of rotating machinery. But it has the shortcoming of mode mixing in decomposing signals. To overcome this shortcoming, ensemble empirical mode decom...

Empirical mode decomposition (EMD) has developed into a prominent tool for adaptive, scale-based signal analysis in various fields like robotics, security and biomedical engineering. Since the dramatic increase amount of data puts forward higher requirements capability real-time analysis, it is difficult existing EMD its variants to trade off growth dimension speed analysis. In order decompose ...

The record of human brain neural activities, namely electroencephalogram (EEG), is generally known as a non-stationary and nonlinear signal. In many applications, it is useful to divide the EEGs into segments within which the signals can be considered stationary. Combination of empirical mode decomposition (EMD) and Hilbert transform, called Hilbert-Huang transform (HHT), is a new and powerful ...

Empirical Mode Decomposition (EMD) is a signal decomposition technique particularly suitable for non-stationary and non-linear signals. In this paper, two target detection methods with improved accuracy in side scan sonar images are proposed. In the first method, target detection is based on morphological operations; the second method combines Empirical Mode Decomposition (EMD) with morphologic...