This paper investigates the ability of a new hybrid forecasting model based on empirical mode decomposition (EMD), cluster analysis and Autoregressive Integrated Moving Average (ARIMA) model to improve the accuracy of fishery landing forecasting. In the first step, the original fishery landing was decomposed into a finite number of Intrinsic Mode Functions (IMFs) and a residual by EMD. The seco...

This paper presents a pitch estimation method of noisy speech signal using empirical mode decomposition (EMD). The normalized autocorrelation function (NACF) of the noisy speech signal is decomposed into a finite set of band-limited signals termed as intrinsic mode functions (IMFs) using EMD. The periodicity of one IMF is supposed to be equal to the accurate pitch period. A conventional autocor...

Empirical mode decomposition (EMD) is a principally new technique, intended to process various types of non-stationary signals by means of decomposing them into a set of certain functions, called “Intrinsic mode functions” (IMFs) or Empirical modes. This paper is dedicated to a newly developed EMD application to Data Mining, namely, to segmentation and clustering problems. Two new algorithms of...

-Empirical Mode Decomposition (EMD) has recently been introduced as a local and fully data-driven technique aimed at analyzing nonstationary signals, by decomposing nonstationary signals into Intrinsic Mode Functions (IMFs). In this contribution, we employ it to process the signals of partial discharge, a typical type of nonstationary signal. Based on the IMFs extracted from the corrupted signa...

The Empirical mode decomposition (EMD) is an adaptive decomposition of the data, as is the Wavelet packet best basis decomposition. This work present the first attempt to examining the use of EMD for image compression purposes. The Intrinsic Mode Function (IMF) and their Hilbert spectra are compared to the wavelet basis and the wavelet packet decompositions expanded in each of its best bases on...

Accurate forecast of water demand is one of the main problems in developing management strategy for the optimal control of water supply system. In this paper, a hybrid model which combines empirical mode decomposition (EMD) and least square support vector machine (LSSVM) model is proposed to forecast water demand. This hybrid is formulated specifically to address in modelling water demand that ...

Marine Engineering faces certain challenges in recent times due to the prevalence of ambient conditions caused by imbalance in the ecosystem. Underwater ambient noise is primarily a background noise, which is a function of time, location and depth. It is of prime importance to detect the signals such as the sound of a submarine or an echo from a target, surpassing and surmounting this ambient n...

Ensemble empirical mode decomposition (EEMD) is a noiseassisted method and also a significant improvement on empirical mode decomposition (EMD). However, the EEMD method lacks a guide to choosing the appropriate amplitude of added noise and its computation efficiency is fairly low. To alleviate the problems of the EEMD method, the improved complementary EEMD method (ICEEMD) was proposed. Furthe...

Now a days Empirical Mode Decomposition (EMD) is an important tool for image analyzing. Optimizing threshold value of Bidimensional Intrinsic Mode Function (BIMF) is one of the important tasks in speckle noise reduction in the Bidimensional Empirical Mode Decomposition (BEMD) domain. Without proper selection of threshold value image information may be lost, which is unwanted. In this paper we p...

Automatic Audio Segmentation aims at extracting information about type of audio i.e. silence, clean speech or speech with noise, music etc. The aim of this thesis is to find and extract different features of audio, segment the audio and combine them to form single type of audio. In this work, the audio signal is initially decomposed into non-overlapping frames. Then these frames are decomposed ...