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Medical image classification is currently a challenging task that can be used to aid the diagnosis of different brain diseases. Thus, exploratory and discriminative analysis techniques aiming to obtain representative features from the images play a decisive role in the design of effective Computer Aided Diagnosis (CAD) systems, which is especially important in the early diagnosis of dementia. In this work, we present a technique that allows using specific time series analysis techniques with 3D images. This is achieved by sampling the image using a fractal-based method which preserves the spatial relationship among voxels. In addition, a method called Empirical functional PCA (EfPCA) is presented, which combines Empirical Mode Decomposition (EMD) with functional PCA to express an image in the space spanned by a basis of empirical functions, instead of using components computed by a predefined basis as in Fourier or Wavelet analysis. The devised technique has been used to classify images from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and the Parkinson Progression Markers Initiative (PPMI), achieving accuracies up to 93% and 92% differential diagnosis tasks (AD versus controls and PD versus Controls, respectively). The results obtained validate the method, proving that the information retrieved by our methodology is significantly linked to the diseases.

Analysis of heart sound is of great importance to the diagnosis of heart diseases. Most of the feature extraction methods about heart sound have focused on linear time-variant or time-invariant models. While heart sound is a kind of highly nonstationary and nonlinear vibration signal, traditional methods cannot fully reveal its essential properties. In this paper, a novel feature extraction approach is proposed for heart sound classification and recognition. The ensemble empirical mode decomposition (EEMD) method is used to decompose the heart sound into a finite number of intrinsic mode functions (IMFs), and the correlation dimensions of the main IMF components (IMF1~IMF4) are calculated as feature set. Then the classical Binary Tree Support Vector Machine (BT-SVM) classifier is employed to classify the heart sounds which include the normal heart sounds (NHSs) and three kinds of abnormal signals namely mitral stenosis (MT), ventricular septal defect (VSD) and aortic stenosis (AS). Finally, the performance of the new feature set is compared with the correlation dimensions of original signals and the main IMF components obtained by the EMD method. The results showed that, for NHSs, the feature set proposed in this paper performed the best with recognition rate of 98.67%. For the abnormal signals, the best recognition rate of 91.67% was obtained. Therefore, the proposed feature set is more superior to two comparative feature sets, which has potential application in the diagnosis of cardiovascular diseases.

The characteristics of nonlinear wave decay over a fluidized bed in coastal waters were investigated by applying the HHT method to analyze the measurements from wave flume experiments. The Ensemble EMD (EEMD) was adopted to decompose the wave and pore pressure data into the intrinsic mod functions (IMF). In a resonantly fluidized response, the results of Hilbert amplitude spectrum of waves derived by the HHT illustrate that wave decay obviously occurs mainly on the fundamental component. In following non-resonantly fluidized tests, the wave decay becomes relaxed with thinner fluidized-layer. Furthermore, the wave decay is immediately due to fluidized response, but the decay along propagation over a fluidized bed is relatively insignificant. The wave-decay ratio is found to be directly proportional to the magnitude of Ursell number (Ur).

In recent years, the impacts of climate change and human activities generate the change of hydrological conditions, leading that the hydrological frequency calculation data don't satisfy the premise of consistency. For the hydrological frequency calculation about inconsistency series, this paper proposed the inconsistency of hydrological frequency calculation method called TFPW-MK-Pettitt and EEMD. First, texting the hydrological series inconsistency used trend-off pre-whitening Mann Kendall-Pettitt methods; Second, the EEMD method was applied to revise uniformity hydrological series; Finally, calculating the hydrological frequency of correcting series. The following conclusions 1) the TFPW-MK-Pettitt method is suitable for our country hydrological series by statistical experiment; 2) the year of 1956 to 2012, runoff series of the Yichang station have downward trend by using the proposed method; 3) the design value of frequency calculation results is smaller 10% than before correction by using the EEMD method.

In order to analyze the effect of engine vibration on cab noise of construction machinery in multi-frequency bands, a new method based on ensemble empirical mode decomposition (EEMD) and spectral correlation analysis is proposed. Firstly, the intrinsic mode functions (IMFs) of vibration and noise signals were obtained by EEMD method, and then the IMFs which have the same frequency bands were selected. Secondly, we calculated the spectral correlation coefficients between the selected IMFs, getting the main frequency bands in which engine vibration has significant impact on cab noise. Thirdly, the dominated frequencies were picked out and analyzed by spectral analysis method. The study result shows that the main frequency bands and dominated frequencies in which engine vibration have serious impact on cab noise can be identified effectively by the proposed method, which provides effective guidance to noise reduction of construction machinery.