IEEE Organizations related to Empirical Mode Decomposition

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Conferences related to Empirical Mode Decomposition

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2020 IEEE 23rd International Conference on Information Fusion (FUSION)

The International Conference on Information Fusion is the premier forum for interchange of the latest research in data and information fusion, and its impacts on our society. The conference brings together researchers and practitioners from academia and industry to report on the latest scientific and technical advances.


2020 IEEE International Instrumentation and Measurement Technology Conference (I2MTC)

The Conference focuses on all aspects of instrumentation and measurement science andtechnology research development and applications. The list of program topics includes but isnot limited to: Measurement Science & Education, Measurement Systems, Measurement DataAcquisition, Measurements of Physical Quantities, and Measurement Applications.


ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

The ICASSP meeting is the world's largest and most comprehensive technical conference focused on signal processing and its applications. The conference will feature world-class speakers, tutorials, exhibits, and over 50 lecture and poster sessions.


IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium

All fields of satellite, airborne and ground remote sensing.


2018 11th Biomedical Engineering International Conference (BMEiCON)

Biomedical signal processing, Biomedical imaging and image processing, Bioinstrumentation, Bio-robotics and biomechanics, Biosensors and Biomaterials, Cardiovascular and respiratory systems engineering, Cellular and Tissue Engineering, Healthcare information systems, Human machine/computer interface, Medical device design, Neural and rehabilitation engineering, Technology commercialization, industry, education, and society, Telemedicine, Therapeutic and diagnostics systems, Recent advancements in biomedical engineering

  • 2017 10th Biomedical Engineering International Conference (BMEiCON)

    Biomedical signal processing, Biomedical imaging and image processing, Bioinstrumentation, Bio-robotics and biomechanics, Biosensors and Biomaterials, Cardiovascular and respiratory systems engineering, Cellular and Tissue Engineering, Healthcare information systems, Human machine/computer interface, Medical device design, Neural and rehabilitation engineering, Technology commercialization, industry, education, and society, Telemedicine, Therapeutic anddiagnostics systems, Recent advancements in biomedical engineering

  • 2016 9th Biomedical Engineering International Conference (BMEiCON)

    Biomedical signal processing, Biomedical imaging and image processing, Bioinstrumentation, Bio-robotics and biomechanics, Biosensors and Biomaterials, Cardiovascular and respiratory systems engineering, Cellular and Tissue Engineering, Healthcare information systems, Human machine/computer interface, Medical device design, Neural and rehabilitation engineering, Technology commercialization, industry, education, and society, Telemedicine, Therapeutic and diagnostics systems, Recent advancements in biomedical engineering

  • 2015 8th Biomedical Engineering International Conference (BMEiCON)

    Bioinformatics, Medical Informatics, Biomechanics and Biomaterials, Biomedical Engineering Education, Health Care Technology, Medical Expert Systems, Image andSignal Processing, Medical Simulation, Rehabilitation and Clinical Eng., UWB-BAN

  • 2014 7th Biomedical Engineering International Conference (BMEiCON)

    Bioinformatics, Medical Informatics, Biomechanics and Biomaterials, Biomedical Engineering Education, Health Care Technology, Medical Expert Systems, Image andSignal Processing, Medical Simulation, Rehabilitation and Clinical Eng., UWB-BAN

  • 2013 6th Biomedical Engineering International Conference (BMEiCON)

    Bioinformatics, Medical Informatics, Biomechanics and Biomaterials, Biomedical Engineering Education, Health Care Technology, Medical Expert Systems, Image andSignal Processing, Medical Simulation, Rehabilitation and Clinical Eng., UWB-BAN

  • 2011 Biomedical Engineering International Conference (BMEiCON) - Conference postponed to 2012

    The BMEiCON is intended to provide an international forum where researchers, practitioners, and professionals interested in the advances in, and applications of, biomedical engineering can exchange the latest research, results, and ideas in these areas through presentation and discussion. The 2011 event will be held in ChiangMai, Thailand, during 9-11 November 2011. It is is synchronized with the marvelous and exotic November full-moon celebration, Loykrathong . The organizing committee is pleased to invite all engineers, physicians, scientists, technicians, and technologists to attend and help shape the future of biomedical technology.

  • 2012 5th Biomedical Engineering International Conference (BMEiCON)

    Bioinformatics, Medical Informatics, Biomechanics and Biomaterials, Biomedical Engineering Education, Health Care Technology, Medical Expert Systems, Image and Signal Processing, Medical Simulation, Rehabilitation and Clinical Eng., & UWB-BAN.


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Periodicals related to Empirical Mode Decomposition

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Most published Xplore authors for Empirical Mode Decomposition

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Xplore Articles related to Empirical Mode Decomposition

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Rolling bearing faults diagnosis based on empirical mode decomposition: Optimized threshold de-noising method

2016 8th International Conference on Modelling, Identification and Control (ICMIC), 2016

The faults of rolling bearings frequently occur in rotary machinery, therefore the rolling bearings fault diagnosis is a very important research project. The vibration signal is usually noisy and the information about the fault in the early stage of its development can be lost. A threshold de-noising method based on Empirical Mode Decomposition (EMD) is presented in this paper. Firstly, ...


Rolling bearing fault diagnosis based on Improved Complete Ensemble Empirical Mode Decomposition

2016 4th International Conference on Control Engineering & Information Technology (CEIT), 2016

In order to rolling bearing fault diagnosis using vibration signal analysis, this paper presents a new procedure based on the Improved Complete Ensemble Empirical Mode Decomposition ICEMD. In this procedure, firstly, in order to calculate the feature vector, we propose the use a combination of the Improved Complete Ensemble Empirical Mode Decomposition ICEMD and Entropy techniques for determining the entropy ...


Comparison between soft and hard Thresholding on selected intrinsic mode selection

2012 6th International Conference on Sciences of Electronics, Technologies of Information and Telecommunications (SETIT), 2012

This paper uses an improved speech enhancement approach based on Empirical Mode Decomposition (EMD), Mode Selection approach and Thresholding technique through hard and soft functions. At first, by using a time decomposition called sifting process, the noisy speech signal is decomposed adaptively into intrinsic oscillatory components called Intrinsic Mode Functions (IMFs). Basically, the Modes Selection idea implies that the lower ...


A sinusoidal-signal-assisted method of improving multivariate empirical mode decomposition

2016 13th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP), 2016

As the multivariate extension of empirical mode decomposition, multivariate empirical mode decomposition still suffers the problem of mode mixing. A noise-assisted method has been proposed to reduce mode mixing in multivariate empirical mode decomposition by using the dyadic filter bank property of multivariate empirical mode decomposition when applied to white Gaussian noise. However, the noise-assisted method generates redundant components that ...


Apply ensemble empirical mode decomposition to discover time variants of metro station passenger flow

2017 4th International Conference on Industrial Engineering and Applications (ICIEA), 2017

This paper applies both Empirical Mode Decomposition (EmD) and Ensemble Empirical Mode Decomposition (EEMD) to extract the EMD and EEMD components from a data set of passenger flows of a station in the metro system, and illustrates the time variants of short-term passenger flow for this data sets. The results indicate that the extracted meaningful EEMD components reveal a more ...


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Educational Resources on Empirical Mode Decomposition

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APEC 2012 - Dr. Fred Lee Plenary
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IEEE-USA E-Books

  • Rolling bearing faults diagnosis based on empirical mode decomposition: Optimized threshold de-noising method

    The faults of rolling bearings frequently occur in rotary machinery, therefore the rolling bearings fault diagnosis is a very important research project. The vibration signal is usually noisy and the information about the fault in the early stage of its development can be lost. A threshold de-noising method based on Empirical Mode Decomposition (EMD) is presented in this paper. Firstly, the signal is decomposed into a number of IMFs using the EMD decomposition. Secondly the algorithm based on the energy to determine the trip point is designed for IMF selection, then, by comparing the energy of the selected IMFs with excluded IMFs, singular selected IMFs are treated with soft threshold function, and finally the de-noised signal is obtained by summing up the selected IMFs, it is proved that the best IMFs can be summed up and properly de-noised by the proposed method. The results show the effectiveness of the proposed technique in revealing the bearing fault impulses and its periodicity and amelioration the sensibility of scalar indicator for real rolling bearing vibration signals.

  • Rolling bearing fault diagnosis based on Improved Complete Ensemble Empirical Mode Decomposition

    In order to rolling bearing fault diagnosis using vibration signal analysis, this paper presents a new procedure based on the Improved Complete Ensemble Empirical Mode Decomposition ICEMD. In this procedure, firstly, in order to calculate the feature vector, we propose the use a combination of the Improved Complete Ensemble Empirical Mode Decomposition ICEMD and Entropy techniques for determining the entropy values for each one of the five first intrinsic mode functions (IMFs) of the ICEMD. Lastly, using the calculated feature vector, the Adaptive-Network-based Fuzzy Inference System ANFIS algorithm is used as a classifier system. In the experimental step, twelve different health bearing conditions were introduced to provide that the proposed approach can be an effective and efficient method for processing bearing fault signals.

  • Comparison between soft and hard Thresholding on selected intrinsic mode selection

    This paper uses an improved speech enhancement approach based on Empirical Mode Decomposition (EMD), Mode Selection approach and Thresholding technique through hard and soft functions. At first, by using a time decomposition called sifting process, the noisy speech signal is decomposed adaptively into intrinsic oscillatory components called Intrinsic Mode Functions (IMFs). Basically, the Modes Selection idea implies that the lower order IMFs (high- frequency modes) is mostly dominated by noise and the last ones (low-frequency modes) represent the most structures of the signal. Therefore, the denoised signal is partially reconstructed only by the low-frequency modes. Secluding the first IMFs may introduce a signal distortion rather than reducing noise. Our upgraded approach consists of thresholding the lower order modes using the soft or hard thresholding algorithm and that are added to the rest of IMFs. The simulations results show that the denoising with the hard function is more effective in removing the noise components then the soft one.

  • A sinusoidal-signal-assisted method of improving multivariate empirical mode decomposition

    As the multivariate extension of empirical mode decomposition, multivariate empirical mode decomposition still suffers the problem of mode mixing. A noise-assisted method has been proposed to reduce mode mixing in multivariate empirical mode decomposition by using the dyadic filter bank property of multivariate empirical mode decomposition when applied to white Gaussian noise. However, the noise-assisted method generates redundant components that do not exist in original signals because the added noise occupies a broad range in the frequency spectrum. We propose a method of using sinusoidal signals, occupying the same frequency spectrum as the original signal, instead of white Gaussian noise to solve this problem. Results show that the new method not only solves the problem of redundant components successfully but also obtains purer modes.

  • Apply ensemble empirical mode decomposition to discover time variants of metro station passenger flow

    This paper applies both Empirical Mode Decomposition (EmD) and Ensemble Empirical Mode Decomposition (EEMD) to extract the EMD and EEMD components from a data set of passenger flows of a station in the metro system, and illustrates the time variants of short-term passenger flow for this data sets. The results indicate that the extracted meaningful EEMD components reveal a more unique pattern than the extracted meaningful EMD components. The patterns of these EEMD components of passenger flow in the metro system are more specific and can be explained more easily for management purposes.

  • EMD Endpoint Suppression Based on Waveform Means Continuation

    Aiming at the endpoint effect existing in the empirical mode decomposition at the current stage and the limitations of the existing methods to solve this problem, this paper proposes a waveform mean continuation method. The method mainly focuses on the calculation method of waveform difference degree and the waveform mean value extension method, which can suppress the signal edge distortion, and effectively solve the endpoint effect problem by performing signal mean value continuation. Extensive experimental results suggest that the proposed approach can achieve better suppression effect on the endpoint effect of signals with periodic characteristics.

  • Classification of motor imagery tasks using phase synchronization analysis of EEG based on multivariate empirical mode decomposition

    Phase synchronization has been employed to study brain networks and connectivity patterns. The phase locking value (PLV) is one of the most effective measures widely used for phase synchronization analysis. We first calculate the PLVs of the pair-wise intrinsic mode functions (IMFs) based on multivariate empirical mode decomposition (MEMD) method. Next, the average PLV of the prominent pairs relative to the rest duration is adopted for the classification of motor imagery (MI) tasks. Comparative analysis with the EMD- based PLV method, the proposed method has a significant increase in feature separability for most subjects. This paper demonstrates that MEMD-based PLV method can provide an effective feature in the MI task classification and the potential for BCI applications.

  • Prediction Model Selection with Frequency Check on Decomposed Time Series

    High prediction accuracies at time series modeling and forecasting is of the utmost importance for a variety of application domains. Various time series prediction methods exist that use linear and nonlinear models separately or combination of both. These methods highly increase prediction performance results when they are applied on a many number of stationary components obtained by more sophisticated decomposition techniques. Although these stationary components are easily predictable, they each have different characteristics. In this study, we have developed a hybrid method that aims to increase prediction performance in time series considering these differences. The developed method decomposes given time series into many stationary components with two-level decomposition using Movingaverage (MA) filter and the Empirical Mode Decomposition (EMD) techniques. Then, the obtained components are modeled separately by appropriate methods according to the frequency changing rates calculated by fourier analysis. The evaluation of the developed method is performed on three different publicly available benchmark dataset and achieved highly successful results as compared to existing well- accepted hybrid methods.

  • Fault diagnosis based on EEMD-IGSA-IPNN for motor bearing

    To improve the accuracy of fault diagnosis for motor bearing, an ensemble approach for fault diagnosis based on Ensemble Empirical Mode Decomposition (EEMD), Improved Gravitational Search Algorithm (GSA) and Incremental Probabilistic Neural Network (IPNN), called EEMD-IGSA-IPNN, is presented. Firstly, EEMD is used to extract the fault feature from the data of the motor bearing, then IGSA optimizes the thresholds of IPNN to classify the type of fault signal, and finally the goal of fault diagnosis is achieved. The experiment shows that the proposed method can improve the accuracy for motor bearing fault diagnosis.

  • Empirical mode decomposition of heart rate variability. A methodological study

    Aim of this study is to investigate advantages and disadvantages of empirical mode decomposition (EMD) approaches for the investigation of heart rate variability (HRV). Signal-adaptive approaches like EMD can be used to separate components of HRV which are associated with cardiovascular regulatory mechanisms. Two EMD approaches, standard EMD and complete empirical mode decomposition (CEMD) are used to decompose the HRV of children during temporal lobe epilepsy (TLE; 10 min recordings of 18 children). As nonlinear properties are preserved by EMD, analysis of nonlinear predictability of HRV components is applied resulting in a nonlinear, time-variant, frequency-selective examination of HRV. Especially mode mixing problems are investigated. Complementary analysis steps are suggested to detect their occurrence. CEMD is able to better separate defined HRV components and to reduce, but not completely solve, mode mixing. Nonlinear analysis of CEMD based HRV components results in more distinct differences between specific seizure-related states.



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