The Earth Mover’s Distance (EMD) is a distance measure between distributions with applications in image retrieval and matching. We consider the problem of computing a transformation of one distribution which minimizes its EMD to another. The applications discussed here include estimation of the size at which a color pattern occurs in an image, lighting-invariant object recognition, and point fe...

Empirical Mode Decomposition (EMD), recently proposed by Huang et al. [12], appears to be a novel data analysis method for nonlinear and non-stationary time series. By decomposing a time series into a small number of independent and concretely implicational intrinsic modes based on scale separation, EMD explains the generation of time series data from a novel perspective. This paper presents an...

Proceedings of the 1998 IEEE International Conference on Computer Vision, Bombay, India We introduce a new distance between two distributions that we call the Earth Mover’s Distance (EMD), which reflects the minimal amount of work that must be performed to transform one distribution into the other by moving “distribution mass” around. This is a special case of the transportation problem from li...

The recognition that periodontal regeneration can be achieved has resulted in increased efforts focused on understanding the mechanisms and factors required for restoring periodontal tissues so that clinical outcomes of such therapies are more predictable than those currently being used. In vitro models provide an excellent procedure for providing clues as to the mechanisms that may be required...

Accurate load forecasting is an important issue for the reliable and efficient operation of a power system. This study presents a hybrid algorithm that combines similar days (SD) selection, empirical mode decomposition (EMD), and long short-term memory (LSTM) neural networks to construct a prediction model (i.e., SD-EMD-LSTM) for short-term load forecasting. The extreme gradient boosting-based ...

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...

The thermal conductivity of molten NaCl and KCl was calculated through the Evans-Gillan nonequilibrium molecular dynamics (NEMD) algorithm and Green-Kubo equilibrium molecular dynamics (EMD) simulations. The EMD simulations were performed for a "binary" ionic mixture and the NEMD simulations assumed a pure system for reasons discussed in this work. The cross thermoelectric coefficient obtained ...

The empirical mode decomposition (EMD) is applied to violence data from Afghanistan between 2006 and 2012. Several key behaviours are identified at distinct time scales ranging from days, through weeks to months, through months to a year, and finally spanning multiple years. The identified behaviour was compared to the traditionally-used multiplicative seasonal decomposition. Unlike seasonal de...

We report here on image texture analysis and on numerical simulation of fractional Brownian textures based on the newly emerged Empirical Mode Decomposition (EMD). EMD introduced by N.E. Huang et al. is a promising tool to non-stationary signal representation as a sum of zero-mean AM-FM components called Intrinsic Mode Functions (IMF). Recent works published by P. Flandrin et al. relate that, i...

Sparse representation has long been studied and several dictionary learning methods have been proposed. The dictionary learning methods are widely used because they are adaptive. In this paper, a new dictionary learning method for audio is proposed. Signals are at first decomposed into different degrees of Intrinsic Mode Functions (IMF) using Empirical Mode Decomposition (EMD) technique. Then t...