Robust Independent Component Analysis Research Articles

Searching Reproducible Brain Features using NeuroMark: Templates for Different Age Populations and Imaging Modalities

A primary challenge to the data-driven analysis is the balance between poor generalizability of population-based research and characterizing more subject-, study- and population-specific variability. We previously introduced a fully automated spatially constrained independent component analysis (ICA) framework called NeuroMark and its functional MRI (fMRI) template. NeuroMark has been successfully applied in numerous studies, identifying brain markers reproducible across datasets and disorders. The first NeuroMark template was constructed based on young adult cohorts. We recently expanded on this initiative by creating a standardized normative multi-spatial-scale functional template using over 100,000 subjects, aiming to improve generalizability and comparability across studies involving diverse cohorts. While a unified template across the lifespan is desirable, a comprehensive investigation of the similarities and differences between components from different age populations might help systematically transform our understanding of the human brain by revealing the most well-replicated and variable network features throughout the lifespan. In this work, we introduced two significant expansions of NeuroMark templates first by generating replicable fMRI templates for infants, adolescents, and aging cohorts, and second by incorporating structural MRI (sMRI) and diffusion MRI (dMRI) modalities. Specifically, we built spatiotemporal fMRI templates based on 6,000 resting-state scans from four datasets. This is the first attempt to create robust ICA templates covering dynamic brain development across the lifespan. For the sMRI and dMRI data, we used two large publicly available datasets including more than 30,000 scans to build reliable templates. We employed a spatial similarity analysis to identify replicable templates and investigate the degree to which unique and similar patterns are reflective in different age populations. Our results suggest remarkably high similarity of the resulting adapted components, even across extreme age differences. With the new templates, the NeuroMark framework allows us to perform age-specific adaptations and to capture features adaptable to each modality, therefore facilitating biomarker identification across brain disorders. In sum, the present work demonstrates the generalizability of NeuroMark templates and suggests the potential of new templates to boost accuracy in mental health research and advance our understanding of lifespan and cross-modal alterations.

Identification of bridge modal parameters from GNSS data by integrating IEWT and robust ICA algorithm

Empirical wavelet transform is often used to process global navigation satellite system (GNSS) bridge deformation monitoring data, but it leads to inaccurate band division and too many spurious modes. To address these problems, this study proposes an improved algorithm to identify bridge modal parameters, which combines improved empirical wavelet transform and robust independent component analysis (ICA). The proposed method adopts the autoregressive power spectrum of an improved covariance algorithm, instead of the Fourier spectrum, for band division. Additionally, it performs noise reduction and reconstruction of multi-channel GNSS monitoring data. The reconstructed signal is inputted as multi-channel observation signal into robust ICA to extract features of the source signal. Finally, the natural excitation technique and Hilbert transform are used to solve the self-oscillation frequency and damping ratio of the structure. The proposed method is validated using both simulation data and the GNSS monitoring data of the Wilford suspension bridge. The results show that the proposed method can effectively reduce the measurement noise and successfully identify the first-order vibration frequencies and damping ratios of bridge. This algorithm can also be applied in the parameter identification of other engineering structures from GNSS data.

Robust Independent Component Analysis based EMG decomposition - a comparison study.

High density surface Electromyography (HD-sEMG) provides a high fidelity measurement of the myoelectric activity that can be leveraged by EMG decomposition methods to estimate the motor neuron discharges. Independent Component Analysis (ICA) methods are used as basis for many EMG decomposition algorithms, for the estimation of motor unit action potential signals. Accurate source separation is a non-trivial task in EMG decomposition. While FastICA is widely used for this purpose, other methods with attractive characteristics, such as RobustICA, remain relatively unexplored. The purpose of the current work is to compare three different ICA-based EMG decomposition methods (FastICA, RobustICA and RobustICALCH) in terms of decomposition accuracy and computation time. The evaluation was performed on simulated data using a decomposition algorithm inspired by previous studies. Our results demonstrate that RobustICA outperforms the other methods in terms of number of correctly identified motor units, high decomposition accuracy, and low computation time, across different muscle contraction levels.

Multifrequency Difference Magnetic Induction Tomography With Robust Independent Component Analysis for Hemorrhage Detection

Multifrequency Difference Magnetic Induction Tomography With Robust Independent Component Analysis for Hemorrhage Detection

Induction Motor Noise Source Separation and Identification Based on Adaptive Scale-Space Mode Extraction

Separating induction motor noise sources can provide an important reference basis for induction motor condition detection, noise reduction treatment, and fault diagnosis. Induction motors have different types of noise sources that partially overlap, and most radiate outward through the housing, so it is difficult to separate these noise sources. Therefore, a single-channel induction motor noise source separation and identification method, based on adaptive scale-space modal extraction (ASSME) is proposed. Firstly, the adaptive scale-space mode extraction method is proposed by constructing the electromagnetic feature scale space and the adaptive penalty factor. The simulation results show that this method solves over-decomposition problems in the classical scale-space variational mode decomposition and the difficulty in balancing the harmonic and shock modes. Secondly, motor noise experiments are conducted to construct blind source separation multi-channel inputs using the adaptive scale-space modal extraction method, judging the validity of the modal components using correlation and the variance contribution rate. Finally, robust independent component analysis (RobustICA) is used to extract independent noise components and identify these noise sources by power spectral density and envelope analysis. The results show that the multi-channel input signals obtained by the proposed method are more accurate and practical than those obtained by other methods. The independent components extracted through this noise source separation method are: electromagnetic noise of different orders, aerodynamic noise, and switching frequency noise.

Robustica: customizable robust independent component analysis

BackgroundIndependent Component Analysis (ICA) allows the dissection of omic datasets into modules that help to interpret global molecular signatures. The inherent randomness of this algorithm can be overcome by clustering many iterations of ICA together to obtain robust components. Existing algorithms for robust ICA are dependent on the choice of clustering method and on computing a potentially biased and large Pearson distance matrix.ResultsWe present robustica, a Python-based package to compute robust independent components with a fully customizable clustering algorithm and distance metric. Here, we exploited its customizability to revisit and optimize robust ICA systematically. Of the 6 popular clustering algorithms considered, DBSCAN performed the best at clustering independent components across ICA iterations. To enable using Euclidean distances, we created a subroutine that infers and corrects the components’ signs across ICA iterations. Our subroutine increased the resolution, robustness, and computational efficiency of the algorithm. Finally, we show the applicability of robustica by dissecting over 500 tumor samples from low-grade glioma (LGG) patients, where we define two new gene expression modules with key modulators of tumor progression upon IDH1 and TP53 mutagenesis.Conclusionrobustica brings precise, efficient, and customizable robust ICA into the Python toolbox. Through its customizability, we explored how different clustering algorithms and distance metrics can further optimize robust ICA. Then, we showcased how robustica can be used to discover gene modules associated with combinations of features of biological interest. Taken together, given the broad applicability of ICA for omic data analysis, we envision robustica will facilitate the seamless computation and integration of robust independent components in large pipelines.

A Novel Blind Image Source Separation Using Hybrid Firefly Particle Swarm Optimization Algorithm

Signal and image separation are extensively used in numerous imaging applications and communication systems. In this paper, a novel Blind Source Separation (BSS) approach, based on the Hybrid Firefly Particle Swarm Optimization (HFPSO), is proposed for separating mixed images. This approach processes the observed source without any prior knowledge about the model and the statistics of the source signal. The proposed method presents high robustness against local minima and converges quickly to the global minimum. Via numerical simulations, the proposed approach is tested and validated in comparison with standard Particle Swarm Optimization (PSO), Robust Independent Component Analysis (RobustICA), and Artificial Bee Colony (ABC) algorithms. The obtained results show that the presented technique outperforms the existing ones in terms of quality of image separation, the Signal-to-Noise Ratio (PSNR), and Structural Similarity Index Measure (SSIM). Moreover, the obtained results demonstrate that our approach provides also promising results in image separation from noisy mixtures.

Research on noise source separation and sound quality prediction for electric powertrain

The overall noise of the electric powertrain (EP) features the characteristics of high-frequency discreteness, abundant orders of harmonics, and serious howling. The consequent poor sound quality problems in the EP are frequently inevitable. In this paper, a complete set of sound quality evaluation system for EP noise signals is established, which closely combines the time-domain separation and sound quality evaluation. The EP noise source separation method in the time domain based on computational auditory scene analysis (CASA) and Robust-independent component analysis (ICA) is proposed to separate and identify EP independent noise sources. Five independent noise source signals in the time domain are then obtained, which could be played back for further psychoacoustic evaluation. The EP sound quality prediction method based on kernel principal component analysis (KPCA) and Lasso regression is proposed to develop the relationship between the subjective and objective quality for EP noise signals. Besides, the dimensionality of the objective evaluation parameters can be significantly reduced from nine to five. The experimental results indicate the superiority of the proposed prediction method to the traditional multiple linear regression approach on predicting the sound quality. A further deduction is made that the harmonic noise (HN) and switching noise (SN) can contribute more to sound quality of the EP overall noise and optimization on them can be a good choice.

Research on Fault Feature Extraction Method Based on Parameter Optimized Variational Mode Decomposition and Robust Independent Component Analysis

The variational mode decomposition mode (VMD) has a reliable mathematical derivation and can decompose signals adaptively. At present, it has been widely used in mechanical fault diagnosis, financial analysis and prediction, geological signal analysis, and other fields. However, VMD has the problems of insufficient decomposition and modal aliasing due to the unclear selection method of modal component k and penalty factor α. Therefore, it is difficult to ensure the accuracy of fault feature extraction and fault diagnosis. To effectively extract fault feature information from bearing vibration signals, a fault feature extraction method based on VMD optimized with information entropy, and robust independent component analysis (RobustICA) was proposed. Firstly, the modal component k and penalty factor α in VMD were optimized by the principle of minimum information entropy to improve the effect of signal decomposition. Secondly, the optimal parameters weresubstituted into VMD, and several intrinsic mode functions (IMFs) wereobtained by signal decomposition. Secondly, the kurtosis and cross-correlation coefficient criteria were comprehensively used to evaluate the advantages and disadvantages of each IMF.And then, the optimal IMFs were selected to construct the observation signal channel to realize the signal-to-noise separation based on RobustICA. Finally, the envelope demodulation analysis of the denoised signal was carried out to extract the fault characteristic frequency. Through the analysis of bearing simulation signal and actual data, it shows that this method can extract the weak characteristics of rolling bearing fault signal and realize the accurate identification of fault. Meanwhile, in the bearing simulation signal experiment, the results of kurtosis value, cross-correlation coefficient, root mean square error, and mean absolute error are 6.162, 0.681, 0.740, and 0.583, respectively. Compared with other traditional methods, better index evaluation value is obtained.

Identification of prominent noise components of an electric powertrain using a psychoacoustic model

Because of the electric power transmission system has no sound masking effect compared with the traditional internal combustion power transmission system, electric powertrain noise has become the prominent noise of electric vehicles, adversely affecting the sound quality of the vehicle interior. Because of the strong coupling of motor and transmission noise, it is difficult to separate and identify the compositions of the electric powertrain by experiments. A psychoacoustic model is used to separate and identify the noise sources of the electric powertrain of a vehicle, considering the masking effect of the human ear. The electric powertrain noise is tested in a semi-anechoic chamber and recorded by a high-precision noise sensor. The noise source compositions of the electric powertrain are analyzed by the computational auditory scene analysis and robust independent component analysis. Five independent noise sources are obtained, i.e., the fundamental frequency of the first gear mesh noise, fundamental frequency of the second gear mesh noise, double frequency of the second gear mesh noise, radial electromagnetic force noise and stator slot harmonic noise. The results provide a guide for the optimization of the sound quality of the electric powertrain and for the improvement of the sound quality of the vehicle interior.

Pseudo-channel matrix truncation based spatial correlation mitigation in massive MIMO

Massive multiple-input multiple-output (MIMO), a technique that can greatly increase spectral efficiency (SE) of cellular networks, has attracted significant interests in recent years. One of the major limitations of massive MIMO systems is pilot contamination, which will deteriorate the SE. The superimposed pilot-based scheme has been proved to be a viable method for pilot contamination reduction. However, it cannot break through another limitation of massive MIMO, i.e., spatial correlation. In addition, it will also lead to interference between the pilot and user data since they are imposed together. In this paper, we try to tackle these two issues, which will be described as follows. Firstly, a column-wise asymptotically orthogonal matrix, named as pseudo-channel matrix, is developed by orthogonalization of received signal. To recover the information about the large-scale fading (LSF) coefficients, the pseudo-channel matrix is truncated according to the cardinality of adjacent users set (CAUS). By this means, spatial correlation can be mitigated effectively. Secondly, robust independent component analysis (RobustICA) is used to reduce the interference caused by user data, and as a result the system performance can be further improved. Numerical simulation results demonstrate the effectiveness of the proposed method.

Time–frequency analysis of torsional vibration signals based on the improved complete ensemble empirical mode decomposition with adaptive noise, Robust independent component analysis, and Prony’s methods

The torsional vibration signals of rotating shafts are multimodal non-stationary noisy signals. The harmonics and attenuation characteristics of these non-stationary signals cannot be obtained effectively by the ordinary short-time Fourier transform algorithm. Although Prony analysis can accurately fit and identify the characteristic coefficients of such non-stationary signals, it is still sensitive to noise. In this article, we propose a system for denoising and identification of torsional vibration signals. In particular, the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), wavelet transform, and robust independent component analysis methods are used to denoise the torsional vibration signals, and then, Prony analysis is used to obtain the characteristic parameters of these signals. The proposed algorithm has good denoising performance and it can improve the identification accuracy and reduce the order of the Prony analysis.

Robust ICA based mode demultiplexing

In this paper, we present a mode demultiplexing algorithm based on robust independent component analysis (ICA) in electrical domain. Multi-mode fiber (MMF) based spatial division multiplexing system is modeled as a behavioral model. Different modes can carry the data to increase the system capacity with the proposed algorithm. In the system, two orthogonal frequency division multiplexing (OFDM) signals are modulated on two modes by two vertical cavity surface emitting lasers (VCSEL) and then coupled into the MMF with an optical coupler. Two channel signals are separated by the proposed robust ICA mode demultiplexing algorithm after photodetectors at the receiver side. The OFDM signals can be demodulated successfully after the separation. Compared with a single mode transmission in MMF, the transmission performance of two mode channels is not degraded significantly with the proposed mode demultiplexing algorithm. Our results show that the proposed algorithm can carry out the modes demultiplexing successfully and the cross-talk between modes can be reduced or be negligible after processing. It also demonstrates a new application of ICA algorithm in the electrical time-domain mode demultiplexing.

A RobustICA-based algorithmic system for blind separation of convolutive mixtures

In this paper, we propose a frequency-domain method employing robust independent component analysis (RICA) to address the multichannel Blind Source Separation (BSS) problem of convolutive speech mixtures in highly reverberant environments. We impose regularization processes to tackle the ill-conditioning problem of the covariance matrix and to mitigate the performance degradation. We evaluate the impact of several parameters on the performance of separation, e.g., windowing type and overlapping ratio of the frequency domain method. We then assess and compare different techniques to solve the frequency-domain permutation ambiguity. Furthermore, we develop an algorithm to separate the source signals in adverse conditions, i.e. high reverberation conditions when short observation signals are available. Finally, through extensive simulations and real-world experiments, we evaluate and demonstrate the superiority of the presented convolutive algorithmic system in comparison to other BSS algorithms, including recursive regularized ICA (RR-ICA) and independent vector analysis (IVA).

Unbalanced responsibility division considering renewable energy integration

A reasonable division of the unbalanced contribution is the premise of responsibility division and mitigation. As a large amount of renewable energy integrates into the power system, the disturbance of the system increases and the system impedance is no longer much smaller than the load impedance. The traditional unbalanced responsibility division method is difficult to calculate accurately. This study proposes a new method for calculating unbalanced contribution, which can effectively avoid the limitations of traditional methods. The robust independent component analysis is used to estimate the equivalent source signal. The sparse component analysis method is used to construct the screening criterion to screen the equivalent source signals to obtain estimated signals that are more in line with the real source signals. Then the equivalent negative sequence impedance of upstream and downstream is calculated, and the unbalanced responsibility is divided. This method is still effective when the system impedance is not much different from the load impedance. At the same time, the calculation accuracy is high and the anti-interference ability is strong. Simulation and field test verify the correctness and effectiveness of the proposed method.

Research on Fault Feature Extraction Method Based on FDM-RobustICA and MOMEDA

Aiming at the difficulty of extracting rolling bearing fault features under strong background noise conditions, a method based on the Fourier decomposition method (FDM), robust independent component analysis (RobustICA), and multipoint optimal minimum entropy deconvolution adjusted (MOMEDA) is proposed. Firstly, the FDM method is introduced to decompose the single-channel bearing fault signal into several Fourier intrinsic band functions (FIBF). Secondly, by setting the cross-correlation coefficient and kurtosis as a new selection criterion, it can effectively construct the virtual noise channel and the observation signal channel, which makes RobustICA complete the separation of the useful signal and noise well. Finally, MOMEDA is introduced to enhance the periodic impact components in the denoised signal, and then the filtered signal is analyzed by the Hilbert envelope spectrum to extract the fault characteristic frequency. Through the experimental analysis of the simulated signals and the actual bearing fault signals, the results show that the proposed method not only has the ability to suppress noise and accurately extract fault feature information but also has better performance than the traditional method of local mean decomposition (LMD) and intrinsic time-scale decomposition (ITD), highlighting its practicality in the fault diagnosis of rotating machinery.

Robust Regressive Feature Extraction Based Relevance Vector Margin Boosting For Aerial Images Scene Classification

Aerial image scene classification is a key problem to be resolved in image processing. Many research works have been designed for carried outing scene classification. But, accuracy of existing scene classification was lower. In order to overcome such limitation, a Robust Regressive Feature Extraction Based Relevance Vector Margin Boosting Scene Classification (RRFERVMBSC) Technique is proposed. The RRFE-RVMBSC technique is designed for improving the classification performance of aerial images with minimal time. The RRFERVMBSC technique comprises two main processes namely feature extraction and classification. Initially, RRFE-RVMBSC technique gets number of aerial images as input. After taking input, Robust Regressive Independent Component Analysis Based Feature Extraction process is performed in order to extract the features i.e. shape, color, texture and size from aerial image. After completing feature extraction process, RRFERVMBSC technique carried outs Ensembled Relevance Vector Margin Boosting Classification (ERVMBC) where all the input aerial images are classified into multiple classes with higher accuracy. The RRFE-RVMBSC technique constructs a strong classifier by reducing the training error of weak RVM classifier for effectual aerial images scene categorization. The RRFERVMBSC technique accomplishes simulation work using parameters such as feature extraction time classification accuracy and false positive rate with respect to number of aerial images.

Improved utility harmonic impedance measurement based on robust independent component analysis and bootstrap check

In this study, a new method for utility harmonic impedance measurement based on robust independent component analysis (RICA) and bootstrap check is proposed. The RICA approach which adopts exact line search to seek the optimal step-size in iteration has better performance for short data records. At the same time, a bootstrap check technique to remove the singular solutions of this sort of independent component analyses methods is presented as well. This verification technique is performed through judging the dispersion degree of the estimates of the bootstrap samples which is quantified by the coefficient of variation. Based on the results of computer simulation and filed test, it is proved that the combination of the RICA approach with the bootstrap check technique can reduce the adverse impact of the background harmonic fluctuation and the singular solutions on impedance estimation effectively, and satisfactory measurement results can be acquired.

Determining the Responsibility of Three-Phase Unbalanced Sources Based on RICA

Voltage unbalance is one of the main power quality problems, and the source of many negative effects on utilities that are experienced by customers. In this paper, a method based on robust independent component analysis (RICA) for responsibility division of unbalanced sources is proposed for voltage unbalance. According to the weak correlation between negative-sequence voltage and upstream and downstream current fluctuation at the point of common coupling (PCC), the independent component of the negative-sequence voltage and current fluctuation at the point of common coupling is obtained by RICA. The blind source mixing coefficient matrix can be obtained according to the least squares method. The equivalent negative-sequence impedance on both sides of the PCC can be obtained using the linear correlation between the mixing coefficients. Finally, according to the principle of partial pressure, the unbalance contribution of the upstream and downstream at the PCC is calculated. The method is accurate for upstream and downstream impedance estimation compared with the traditional method, and has a strong anti-interference ability. When the background noise or system fluctuation is large, the responsibility division result is still accurate. The correctness and effectiveness of the proposed method are proven by the simulation of the experimental circuit.

Noise Source Separation of an Internal Combustion Engine Based on a Single-Channel Algorithm

The separation and identification technology of noise sources is the focus and hot spot in the field of internal combustion engine noise research. Combustion noise and piston slap noise are the main noise sources of an internal combustion engine. However, both combustion noise and piston slap noise occur almost at the top dead center. They mix in the time domain and frequency domain. It is difficult to accurately and effectively separate them. A single-channel algorithm which combines time-varying filtering-based empirical mode decomposition (TVF-EMD) and robust independent component analysis (RobustICA) methods is proposed to separate them. Firstly, the TVF-EMD method is utilized to decompose the single-channel noise signal into several intrinsic mode functions (IMFs). Then, the RobustICA method is applied to extract the independent components. Finally, related prior knowledge and time-frequency analysis are employed to identify noise sources. Furthermore, the spectral filtering method and the calculation method of piston slap noise based on the dynamic model are further carried out to verify separation results. The simulation and experimental research results show the effectiveness of the proposed method.