We propose a method to enhance the quality and precision of prediction results using measurements in the context of radio network modelling. The proposed method involves the use of an Independent Component Analysis (ICA) block and a MultiLayer Perceptron (MLP) Artificial Neural Network (ANN). The role of the ICA block is to make the variables at the input of the ANN statistically independent so...

Abstract: Based on the fast kernel entropy optimization independent component analysis and the minimum conditional entropy, this paper proposes a harmonic source localization method which aims at accurately estimating harmonic currents and identifying harmonic sources. The injected harmonic currents are estimated by the fast kernel entropy optimization independent component analysis (FKEO-ICA) ...

When exploiting independent component analysis (ICA) to perform blind source separation (BSS), it is assumed that sources are mutually independent. However, in practice, the latent sources are usually dependent to some extent. Subband decomposition ICA (SDICA) is an extension of ICA to admit source dependence. It assumes that each source is represented as the sum of some independent sub-compone...

Principal component analysis (PCA) and independent component analysis (ICA) are both based on a linear model of multivariate data. They are often seen as complementary tools, PCA providing dimension reduction and ICA separating underlying components or sources. In practice, a two-stage approach is often followed, where first PCA and then ICA is applied. Here, we show how PCA and ICA can be seen...

Principal Component Analysis (PCA) is a classical technique in statistical data analysis, feature extraction and data reduction, aiming at explaining observed signals as a linear combination of orthogonal principal components. Independent Component Analysis (ICA) is a technique of array processing and data analysis, aiming at recovering unobserved signals or 'sources' from observed mixtures, ex...

HiPerSAT, a C++ library and associated tools, processes large EEG data sets with statistical data whitening and ICA (Independent Component Analysis) methods. The library uses BLAS, LAPACK, MPI and OpenMP to achieve a high performance solution that exploits available parallel hardware. ICA is a class of methods for analyzing a large set of data samples and deducing the independent components res...

The detection of transient events related to slow earthquakes in GNSS positional time series is key understanding seismogenic processes subduction zones. Here, we present a novel Principal and Independent Components Correlation Analysis (PICCA) method that allows for the temporal spatial signals. PICCA based on an optimal combination principal (PCA) independent component analysis (ICA) network....

In this paper, we propose a new population optimization algorithm called Univariate Marginal Distribution Algorithm with Independent Component Analysis(UMDA/ICA). Our main idea is to incorporate ICA into UMDA algorithm in order to tackle the interrelations among variables. We demonstrate that UMDA/ICA performs better than UMDA for a test function with highly correlated variables.

Independent Component Analysis (ICA) is a technique that attempts to separate data into maximally independent groups. Achieving maximal independence in space or time yields two varieties of ICA meaningful for functional MRI (fMRI) applications: spatial-ICA (SICA) and temporal-ICA (TICA). SICA has so far dominated the application of ICA to fMRI. The objective of these experiments was to study IC...

The process of analysing unaveraged interictal epileptic magnetoencephalographic (MEG) data with Magnetic Field Tomography (MFT) is discusses focusing on three of its associated image processing issues are considered, namely, multimodality image registration, averaging of images corresponding to single epoch recordings, and independent component analysis applied on MFT image sequences. A robust...