Papers by Keyword: EMD

Abstract: Multipath interference poses a significant challenge in satellite-based navigation systems, including NAVIC (Navigation with Indian Constellation), degrading the accuracy of position estimates. This study proposes a comprehensive approach to address multipath errors in NavIC receivers, combining multipath error calculation using the code minus carrier method with multipath reduction through mode decomposition techniques EMD- empirical mode decomposition, VMD-variational mode decomposition, and SVMD-successive variational mode decomposition. Data was collected from a NavIC receiver located at KLEF University in Guntur, India with latitude 16.44 N, and longitude 80.62 E during the period from April 12th to 14th, 2017. Initially, multipath errors are calculated by subtracting NavIC carrier phase measurements from code phase measurements, providing insights into the magnitude of multipath interference. Subsequently, the received signal is decomposed using EMD, VMD, and SVMD to extract intrinsic modes or oscillatory components representing different signal characteristics. The direct signal is reconstructed by selectively filtering or removing multipath-related modes, reducing multipath interference. To evaluate the effectiveness of each decomposition method, the SDE (standard deviation error) of the reconstructed multipath signal is computed. The decomposition method yielding the lowest SDE is identified as the optimal approach for multipath reduction in NavIC receivers. By integrating the code minus carrier method with mode decomposition techniques, significant enhancements in navigation performance can be achieved, facilitating reliable and precise positioning for various applications.

Abstract: Lung sound analysis plays an important role in the assessment and diagnosis of respiratory conditions and diseases. It can provide valuable information about the functioning of the respiratory system, including the airways, lungs, and associated structures. By analyzing the characteristics of lung sounds, healthcare professionals can gain insights into the presence of abnormalities, such as airway obstructions, lung diseases, and respiratory infections. In this paper, a multiple channel model for processing and classifying abnormalities in lung sound is proposed, which utilize the characteristics of Mel spectrogram and the Empirical Mode Decomposition (EMD). Unlike previous research which directly convert the lung sound into scalogram or spectrogram, the pre-processing of the original audio signal is considered and focused in this paper. This pre-processing step includes denoising, resampling, padding and augmentation, which incredibly increase the quality of the input signal. Finally, the multiple channel is put into the VGG16 deep learning model to classify the abnormalities in lung sound, including wheezes, crackles, and both. The model is trained and tested on the benchmark ICBHI dataset. The proposed model has shown better performance when compared with the state-of-the-art researches.

Abstract: The equipment operational reliability evaluation is very important for working safely. The traditional reliability evaluation methods couldn’t meet the demands. In order to consider the detection information during the operation from devices, the operational reliability assessment method based on information entropy and empirical mode decomposition (EMD) is proposed. First, the equipment operation detected signals’ multiple intrinsic mode functions (IMF) are obtained by using EMD ; then the relative energy of each intrinsic mode functions are calculated and their information entropy are normalized. Finally the operational reliability of equipment operating status can be judged according to the normalization information entropy value. The method is used for a rotor test analysis considering of the degree of injury. The results demonstrated the method effectively, which provided an effective method for assessing the running reliability of mechanical equipment with less sample data.

Abstract: Silicene is a two-dimensional (2D) allotrope of silicon known to have a lower thermal conductivity than graphene; thus, more suitable for thermoelectric applications. This paper investigates the effect of hydrogenation on the thermal conductivity of silicene nanoribbon (SiNR) using equilibrium molecular dynamics (EMD) simulations. The simulations were carried out in Large-scale Atomic/Molecular Massively Parallel Simulator (LAMMPS) using a modified Tersoff potential that considers both Si-Si and Si-H interactions. The thermal conductivity of fully hydrogenated silicene nanoribbon (H-SiNR), also known as silicane nanoribbon, was found to be higher than that of pristine SiNR in all the temperatures and dimensions considered here. This anomalous enhancement in the thermal conductivity is similar to that found in hydrogenated silicon nanowires (H-SiNWs). A mechanism for this anomalous effect has been proposed relating the hydrogenation of SiNR with the stiffening and increase of the acoustic out-of-plane flexural (ZA) phonon modes. Also, for both SiNR and H-SiNR, the thermal conductivities generally increase as the dimensions are increased while they generally decrease as the temperatures are increased, in agreement to other reports.

Abstract: In this paper, aimed at suppressing the noise amplitude modulation jamming for a novel combined modulation radio ranging system, which transmits the waves combining pseudo random binary phase code (PRBPC) with linear frequency modulation (LFM), a signal processing method based on the empirical mode decomposition (EMD) and reconstruction of the target correlation peak signal is presented. Firstly, the correlation detection output signal of the ranging system is theoretically derived and proved to meet the condition of the Intrinsic Mode Function (IMF). Then the EMD processing for the target correlation peak signal, completely covered by the noise amplitude modulation (AM) jamming, is done and decomposes the signal into several IMFs. The difference of certain main IMF between the target signal and the strong jamming is utilized to reconstruct the target correlation function as the input signal of the detecting part. It is proved by simulation that the processing gain to the noise AM jamming can be significantly improved and the main lobe of the reconstructed correlation peak signal is more obvious, therefore it can be more easily identified.

Abstract: This paper used the Empirical Mode Decomposition (EMD) ,solving the problem of the modulation type recognition of Orthogonal Frequency Division Multiplexing (OFDM) on blind signal Processing of communication .This method uses the characteristics of the OFDM' envelope nearly Gaussian .Using the EMD decomposition algorithm of signal decomposition, extract the intrinsic mode function (IMF) after signal decomposition and computes the correlation co-efficient of IMF and the original signal as the recognition feature .So as to achieve that identification the OFDM signals and single carrier linearly digitally modulated (SCLD) signals (MFSK(2FSK, 4FSK), MPSK(BPSK, QPSK)) without prior knowledge. Experiments show that this method has a good performance with less computation, and this method also has well Real-time and robustness compare with the methods of the wavelet transform and the higher order cumulants.

Abstract: Ocean front is a narrow transitional zone that the penetration of sea is obviously different between two or more waters there. It is an important feature of geophysical turbulence which plays an important role in ocean dynamics. Ocean fronts become visible on radar images because they are associated with a variable surface current which modulates the sea surface roughness and thus the backscattered radar power. This paper propose a new integrated method to extract ocean fronts based on two-dimensional Empirical Mode Decomposition (EMD), image edge detection and mathematical morphology processing. Experimental results show that this integrated method can be effective in ocean front feature extraction.

Abstract: Aiming at the de-noising of GPR echo signal, a de-noising method based on EEMD and wavelet is presented. First the echo signal data is processed with EEMD and yields IMF components. Then the IMF components which indicate noise are subtracted. Next, the high frequency IMF components of the remaining are subjected to wavelet threshold. Finally, the signal is reconstructed using the de-noising IMF and low frequency IMF to realize signal de-noising. Compared with other commonly used methods, EEMD-wavelet method has improvement on SNR. The experiment results show its effectiveness and feasibility in GPR de-noising.

Abstract: For the non-stationary vibration signals of wind tower, using a new method based on KPCA to eliminate the interference of the vibration signal. After cancellation noise according to the method based on EMD, the vibration signals are decomposed and the vibration characteristics of the signals are finally extracted. The experimental analysis indicates that this method can provide an effective reference for the health detection of wind tower.

Abstract: In order to improve the predictive accuracy of short-term wind power, a prediction model based on improved empirical mode decomposition (EMD) and support vector machine (SVM) is constructed. As to the problems of basic EMD, it is proposed to use the steady point meaning sifting method instead of spline envelope meaning sifting method, to improve the overshoots/undershoots caused by traditional cubic spline interpolation. Wind power series can be decomposed into different series by improved EMD, and then SVM is used to forecast power by each component. The total wind power prediction result is obtained through reconstructing at last. Case study shows that the predictive accuracy has significantly been improved by comparing with other models.