Analyzing the non-linear trend and multiscale teleconnections of regional monsoon indices using empirical mode decomposition

2017 ◽  
Vol 3 (2) ◽  
pp. 669-682 ◽  
Author(s):  
S. Adarsh ◽  
M. Shyma
Keyword(s):  
Empirical Mode Decomposition ◽  
Linear Trend ◽  
Mode Decomposition ◽  
Non Linear ◽  
Monsoon Indices ◽  
Regional Monsoon

scholarly journals Correction to: Emotion classification from speech signal based on empirical mode decomposition and non-linear features

2021 ◽  
Author(s):  
Palani Thanaraj Krishnan ◽  
Alex Noel Joseph Raj ◽  
Vijayarajan Rajangam
Keyword(s):  
Empirical Mode Decomposition ◽  
Speech Signal ◽  
Emotion Classification ◽  
Linear Features ◽  
Mode Decomposition ◽  
Non Linear

A correction to this paper has been published: https://doi.org/10.1007/s40747-021-00377-y

EEG Classification of Imaginary Left and Right Leg Movements based on Multivariate Empirical Mode Decomposition and Non-Linear Support Vector Machine

2021 ◽  
Vol 39 (11) ◽  
Author(s):  
Sahar Zolfaghari ◽  
Mohammad Hamiruce Marhaban ◽  
Siti Anom Ahmad ◽  
Asnor Juraiza Ishak ◽  
Pegah Khosropanah ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Empirical Mode Decomposition ◽  
Motor Imagery ◽  
Classification Model ◽  
Support Vector ◽  
Linear Support Vector Machine ◽  
Multivariate Empirical Mode Decomposition ◽  
Mode Decomposition ◽  
Non Linear ◽  
Left And Right

Motor-imagery brain-computer interfaces, as rehabilitation tools for motor-disabled individuals, could inherently enrich neuroplasticity and subsequently restore mobility. However, this endeavour's significant challenge is classifying left and right leg motor imagery tasks from non-stationary EEG signals. A subject-independent feature extraction method is essential in a BCI system, and this work involves developing a subject-independent algorithm to classify left/right leg motion intention. The Multivariate Empirical Mode Decomposition was used to decompose EEG during left and right foot movements during imagery tasks. We validated our proposed algorithm using open-access motor imagery data to detect the user's mental intention from EEG. Five subjects of various performance categories with almost 150 trials for each left/right leg MI of hand/leg/tongue, HaLT Paradigm, utilizing C3, C4, and Cz channels were examined to generalize this study to all subjects. A set of statistical features were extracted from the intrinsic mode functions, and the most relevant features were selected for classification using Sequential Floating Feature Selection. Different classifiers were trained using extracted features, and their performances' were evaluated. The findings suggest that the non-linear support vector machine is the best classification model, resulting in the mean classification sensitivity, specificity, precision, negative predictive value, F-measure, 98.15%, 90.74%, 91.97%, 98.33%, 94.72%, 94.44%, respectively. The proposed subject-independent signal processing method significantly improved the offline calibration mode by eliminating the frequency selection step, making it the common-used method for different types of MI-based BCI participants. Offline evaluations suggest that it can lead to significant increases in classification accuracy in comparison to current approaches.

scholarly journals (min,+)-wavelets for non-linear analysis

2014 ◽  
Vol 15 (3) ◽  
pp. 261 ◽  
Author(s):  
Abdelouahab KENOUFI ◽  
Michel GONDRAN
Keyword(s):  
Empirical Mode Decomposition ◽  
Linear Analysis ◽  
Theoretical Level ◽  
Mode Decomposition ◽  
Multi Resolution Analysis ◽  
Non Linear ◽  
Resolution Analysis ◽  
Hölder Exponents ◽  
Weierstrass Functions ◽  
Linear Branch

<pre>For all function f : Rn to R one introduces (min; +)-wavelets which are lower and upper hulls build from (min; +) analysis.</pre><pre>One shows at theoretical level and on numerical applications for the Weierstrass functions, </pre><pre>that (min, +)-wavelets decomposition opens a non-linear branch to the multi-resolution analysis of a signal, </pre><pre>in particular for the Hölder exponents calculation and Empirical Mode Decomposition (EMD).<br /></pre>

Identification of MDOF Non-Linear Uncoupled Dynamical Systems Using Hilbert Transform and Empirical Mode Decomposition Method

2011 ◽  
Vol 255-260 ◽  
pp. 1676-1680
Author(s):  
Tian Li Huang ◽  
Wei Xin Ren ◽  
Meng Lin Lou
Keyword(s):  
Dynamical Systems ◽  
Empirical Mode Decomposition ◽  
Hilbert Transform ◽  
Degree Of Freedom ◽  
Intrinsic Mode Functions ◽  
Nonlinear Properties ◽  
Identification Method ◽  
Mode Decomposition ◽  
Non Linear ◽  
The Hilbert Transform

A non-linear dynamical system identification method using Hilbert transform (HT) and empirical mode decomposition (EMD) is proposed. For a single-degree-of-freedom (SDOF) nonlinear system, the Hilbert transform identification method is good at identifying the instantaneous modal parameters (natural frequencies, damping characteristics and their dependencies on a vibration amplitude and frequency). For the multi-degree-of-freedom (MDOF) non-linear uncoupled dynamical systems, the EMD method is attempting for the decomposition of response signals into a collection of mono-components signals, termed intrinsic mode functions (IMFs). Considering the IMFs admit a well-behaved Hilbert transform, the HT identification method has been applied for the identification of nonlinear properties. The numerical simulation of a 2-dof shear-beam building model with nonlinear stiffness illustrated the proposed technique.

scholarly journals Potato price analysis of Delhi market through ensemble empirical mode decomposition

2019 ◽  
Vol 34 (01) ◽  
Author(s):  
Kapil Choudhary ◽  
Girish Kumar Jha ◽  
Rajeev Ranjan Kumar
Keyword(s):  
Time Series ◽  
Empirical Mode Decomposition ◽  
Ensemble Empirical Mode Decomposition ◽  
Agricultural Commodities ◽  
Intrinsic Mode Functions ◽  
Price Series ◽  
Mode Decomposition ◽  
Non Linear ◽  
Agricultural Price ◽  
Commodities Prices

Agricultural commodities prices depends on production, unnecessary demand, production uncertainty, market flaws etc. Due to these factors agricultural price series are non-stationary and non-linear in nature. Therefore analyzing agricultural commodities prices is considered as a challenging task. The traditional stationary approach of time series is unable to capture non-stationary and non-linear properties of agricultural price series. Non-stationary and non-linear properties present in the price series may be accurately analyzed through empirical mode decongation (EMD). In this technique, the original time series decomposed into intrinsic mode functions and residue. One of the major limitation of EMD is the presence of the mode mixing. To overcome this limitation of the EMD, we use ensemble empirical mode decomposition (EEMD). Using this technique in this study, Delhi market potato prices have been analyzed.

scholarly journals Non-Linear Stability Analysis of Real Signals from Nuclear Power Plants (Boiling Water Reactors) based on Noise Assisted Empirical Mode Decomposition Variants and the Shannon Entropy

2017 ◽  
Author(s):  
Omar A. Olvera-Guerrero ◽  
Alfonso Prieto-Guerrero ◽  
Gilberto Espinosa-Paredes
Keyword(s):  
Empirical Mode Decomposition ◽  
Shannon Entropy ◽  
Nuclear Power ◽  
Power Plants ◽  
Nuclear Power Plants ◽  
Boiling Water ◽  
Boiling Water Reactors ◽  
Mode Decomposition ◽  
Non Linear ◽  
Water Reactors

There are currently around 78 Nuclear Power Plants (NPP) in the world based on Boiling Water Reactors (BWR). The current parameter to assess BWR instability issues is the linear Decay Ratio (DR). However, it is well known that BWRs are complex non-linear dynamical systems that may even exhibit chaotic dynamics that normally preclude the use of the DR when the BWR is working at a specific operating point during instability. In this work a novel methodology based on an adaptive Shannon Entropy estimator and on Noise Assisted Empirical Mode Decomposition variants is presented. This methodology was developed for real-time implementation of a stability monitor. This methodology was applied to a set of signals stemming from several NPPs reactors (Ringhals-Sweden, Forsmark-Sweden and Laguna Verde-Mexico) under commercial operating conditions, that experienced instabilities events, each one of a different nature

EMPIRICAL MODE DECOMPOSITION-BASED PROCESSING FOR AUTOMATED DETECTION OF EPILEPSY

2019 ◽  
Vol 19 (01) ◽  
pp. 1940003 ◽  
Author(s):  
G. MURALIDHAR BAIRY ◽  
YUKI HAGIWARA
Keyword(s):  
Empirical Mode Decomposition ◽  
Epileptic Seizures ◽  
Automated Detection ◽  
Learning Approaches ◽  
Eeg Signals ◽  
Small Magnitude ◽  
Cad System ◽  
Mode Decomposition ◽  
Non Linear ◽  
The Brain

Epilepsy is a chronic illness of the brain characterized by recurring seizure attacks. Electroencephalogram (EEG) can record the electrical activity of the brain and is extensively used to analyze and diagnose epileptic seizures. However, the EEG signals are highly non-linear and chaotic and are difficult to analyze due to their small magnitude. Hence, empirical mode decomposition (EMD), a non-linear technique, has been widely adopted to capture the subtle changes present in the EEG signals. Hence, it is an added advantage to develop an automated computer-aided diagnostic (CAD) system to detect the different brain activities from the EEG signals using machine learning approaches. In this paper, we focus on the previous works which have used the EMD technique in the automated detection of normal or epileptic EEG signals.

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