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A signal decomposition is presented that disentangles the deterministic and stochastic components of a multivariate time series. The dynamical component analysis (DyCA) algorithm is based on the assumption that an unknown set of ordinary differential equations (ODEs) describes the dynamics of the deterministic part of the signal. The algorithm is thoroughly derived and accompanied by a link to the GitHub repository containing the algorithm. The method was applied to both simulated and real-world data sets and compared to the results of principal component analysis (PCA), independent component analysis (ICA), and dynamic mode decomposition (DMD). The results demonstrate that DyCA is capable of separating the deterministic and stochastic components of the signal. Furthermore, the algorithm is able to estimate the number of linear and non-linear differential equations and to extract the corresponding amplitudes. The results demonstrate that DyCA is an effective tool for signal decomposition and dimension reduction of multivariate time series. In this regard, DyCA outperforms PCA and ICA and is on par or slightly superior to the DMD algorithm in terms of performance. [ABSTRACT FROM AUTHOR]

Background/Objectives: Based on Blind Source Separation and the use of multispectral imaging, the new approach we propose in this paper aims to improve the estimation of the concentrations of the main skin chromophores (melanin, oxyhemoglobin and deoxyhemoglobin), while considering shading as a fully-fledged source. Methods: In this paper, we demonstrate that the use of the Infra-Red spectral band, in addition to the traditional RGB spectral bands of dermatological images, allows us to model the image provided by each spectral band as a mixture of the concentrations of the three chromophores in addition to that of the shading, which are estimated through four steps using Blind Source Separation. Results: We studied the performance of our new method on a database of real multispectral dermatological images of melanoma by proposing a new quantitative performances measurement criterion based on mutual information. We then validated these performances on a database of multispectral dermatological images that we simulated using our own new protocol. Conclusions: All the results obtained demonstrated the effectiveness of our new approach for estimating the concentrations of the skin chromophores from a multispectral dermatological image, compared to traditional approaches that consist of using only the RGB image by neglecting shading. [ABSTRACT FROM AUTHOR]

This article presents a new neural network-based model called SARCoorP-RBFNet for localizing synthetic aperture radar (SAR) images. The model addresses the limitations of traditional models in complex scenarios and has been tested on SAR images in China. It utilizes pairs of geodetic and image space coordinate points as input and output, respectively, and incorporates Gaussian functions as radial basis functions (RBFs). The model is trained using the generalized inverse matrix method and evaluated using the root mean square error function. The study found that the model performs well on single-scene imagery but has reduced accuracy with different-resolution images of the same area. However, it still achieves high fitting and generalization capabilities when trained with mixed samples from different regions. The study suggests that the proposed model can be a viable alternative to traditional geometric processing models for SAR images. [Extracted from the article]

This paper proposes novel methods for extracting a single Speech signal of Interest (SOI) from a multichannel observed signal in underdetermined situations, i.e., when the observed signal contains more speech signals than microphones. It focuses on extracting the SOI using prior knowledge of the SOI's Direction of Arrival (DOA). Conventional beamformers (BFs) and Blind Source Separation (BSS) with spatial regularization struggle to suppress interference speech signals in such situations. Although Switching Minimum Power Distortionless Response BF (Sw-MPDR) can handle underdetermined situations using a switching mechanism, its estimation accuracy significantly decreases when it relies on a steering vector determined by the SOI's DOA. Spatially-Regularized Independent Vector Extraction (SRIVE) can robustly enhance the SOI based solely on its DOA using spatial regularization, but its performance degrades in underdetermined situations. This paper extends these conventional methods to overcome their limitations. First, we introduce a time-varying Gaussian (TVG) source model to Sw-MPDR to effectively enhance the SOI based solely on the DOA. Second, we introduce the switching mechanism to SRIVE to improve its speech enhancement performance in underdetermined situations. These two proposed methods are called Switching weighted MPDR (Sw-wMPDR) and Switching SRIVE (Sw-SRIVE). We experimentally demonstrate that both surpass conventional methods in enhancing the SOI using the DOA in underdetermined situations. [ABSTRACT FROM AUTHOR]

The expansion of air traffic has led to an increase in mixed secondary surveillance radar (SSR) signal replies, which occur when multiple replies arrive at the receiver antenna simultaneously. These overlapping signals render the messages unrecoverable, leading to a loss of information. Additionally, the existing methods do not effectively address the issue of handling different types of signals with varying structures and characteristics. The authors validate the effectiveness of the disjoint component analysis (DCA) criterion for SSR signals and introduce a new DCA‐based algorithm designed to optimise the separation process. Through several simulations, the proposed algorithm demonstrates robust performance across various reception parameters. Additionally, it achieves good results when applied to real‐world data, showing its practical applicability and efficiency. [ABSTRACT FROM AUTHOR]

To address the issues of poor anti-noise performance of second-order statistics and low estimation accuracy in previous joint underdetermined blind source separation (JUBSS) methods, we propose a novel JUBSS method based on the dependence between different data sets and the advantages of cross third-order cumulant in resisting distributed noise. The method involves several steps. Firstly, we calculate the cross third-order cumulant of multiple whitening data sets with different delays. Then, we stack several third-order cumulants into fourth-order tensors. Next, we decompose the fourth-order tensor using Canonical Polyadic through weight nonlinear least squares, which allows us to estimate the mixed matrix. Finally, depending on the independence of source signals, we propose a matrix diagonalization method to recover the source signal. Experiments demonstrate that the method effectively suppresses the influence of Gaussian noise and performs well in underdetermined, positive and overdetermined cases and produces a better performance than various common approaches. Specifically, for the 3 × 4 mixed model with signal-to-noise ratio of 20 dB, the average relative error is − 14.48 dB, the average similarity coefficient is 0.92 and the signal-to-interference ratio is 24.84 dB. [ABSTRACT FROM AUTHOR]

Digital devices may leak electromagnetic signals containing important information during operation, posing a risk of information leakage. In order to evaluate the safety level of the equipment, it is necessary to detect electromagnetic leakage signals and separate important information. Traditional methods such as ICA and IVA have limited performance when the signal input is smaller than the output. In recent years, signal separation technology based on deep learning has developed rapidly, demonstrating effective separation performance and potential in signal separation. For the separation of electromagnetic signals, we propose a blind source separation method for electromagnetic signals based on Swish-Tasnet. This method improves the time convolutional network structure and introduces the Swish activation function to optimize the internal functions and stacking layers of the model, thereby achieving efficient blind source separation of electromagnetic signals. The experiment evaluated the separation performance through SISDR, SDR, and SAR, and the results showed that this method performs excellently in reducing background noise interference and accurately separating signal sources from mixed signals. Swish-Tasnet not only provides a practical and effective technical means for electromagnetic signal leakage separation, but also brings substantial progress to the field of equipment information security assessment research. This method has broad prospects in practical applications and is worth further research and promotion. [ABSTRACT FROM AUTHOR]

Fetal heart monitoring, as a crucial part of fetal monitoring, can accurately reflect the fetus's health status in a timely manner. To address the issues of high computational cost, inability to observe fetal heart morphology, and insufficient accuracy associated with the traditional method of calculating the fetal heart rate using a four-channel maternal electrocardiogram (ECG), a method for extracting fetal QRS complexes from a single-channel non-invasive fetal ECG based on a multi-feature fusion neural network is proposed. Firstly, a signal entropy data quality detection algorithm based on the blind source separation method is designed to select maternal ECG signals that meet the quality requirements from all channel ECG data, followed by data preprocessing operations such as denoising and normalization on the signals. After being segmented by the sliding window method, the maternal ECG signals are calculated as data in four modes: time domain, frequency domain, time–frequency domain, and data eigenvalues. Finally, the deep neural network using three multi-feature fusion strategies—feature-level fusion, decision-level fusion, and model-level fusion—achieves the effect of quickly identifying fetal QRS complexes. Among the proposed networks, the one with the best performance has an accuracy of 95.85% and sensitivity of 97%. [ABSTRACT FROM AUTHOR]

Civil aviation is an important part of public transportation. However, the wireless communication systems used in the approach and tower control phases of traffic control are susceptible to external interference, posing a threat to flight safety. Traditional communication interference solutions are time-consuming and require specialized technicians to troubleshoot. To solve this problem, we propose a real-time method for monitoring abnormal signals and detecting interference sources during aviation radio communications. The method consists of three steps: real-time blind source signal separation using cubic polynomial fitting, abnormal signal monitoring based on discriminative signal residence time, and using Pearson correlation coefficients to identify abnormal interference sources. This comprehensive approach effectively ensures the frequency safety of aviation radio communications. Experiments conducted at different locations in real airport environments demonstrate that this method can efficiently identify the signal bands and their interference sources. [ABSTRACT FROM AUTHOR]

As a critical component in industrial production, pipelines face the risk of failure due to long-term corrosion. In recent years, acoustic emission (AE) technology has demonstrated significant potential in online pipeline monitoring. However, the interference of flow-induced noise seriously hinders the application of acoustic emission technology in pipeline corrosion monitoring. Therefore, a pattern-recognition model for online pipeline AE monitoring signals based on blind source separation (BSS) and a convolutional neural network (CNN) is proposed. First, the singular spectrum analysis (SSA) was employed to transform the original AE signal into multiple observed signals. An independent component analysis (ICA) was then utilized to separate the source signals from the mixed signals. Subsequently, the Hilbert–Huang transform (HHT) was applied to each source signal to obtain a joint time–frequency domain map and to construct and compress it. Finally, the mapping relationship between the pipeline sources and AE signals was established based on the CNN for the precise identification of corrosion signals. The experimental data indicate that when the average amplitude of flow-induced noise signals is within three times that of corrosion signals, the separation of mixed signals is effective, and the overall recognition accuracy of the model exceeds 90%. [ABSTRACT FROM AUTHOR]

Featured Application: This work can be applied to estimate acceleration using measured inclination, withoutpartially measuring the target acceleration. Moreover, this is a finiteelement model-free method and can be conveniently applied to beam bridges. Data fusion is an important issue in bridge health monitoring. Through data fusion, specific unknown bridge responses can be estimated with measured responses. However, existing data fusion methods always require a precise finite element model of the bridge or partially measured target responses, which are hard to realize in actual engineering. In this study, we propose a novel data fusion method. Measured inclinations across multiple cross-sections of the target bridge and accelerations at a subset of these sections were used to estimate accelerations at the remaining sections. Theoretical analysis of a typical vehicle-bridge interaction (VBI) system has shown parallels with the blind source separation (BSS) problem. Based on this, Independent Component Analysis (ICA) was applied to derive surrogate inclination mode shapes. This was followed by calculating surrogate displacement mode shapes through numerical integration. Finally, a surrogate inter-section transfer matrix for both measured and unmeasured accelerations was constructed, enabling the estimation of the target accelerations. This paper presents three key principles involving the relationship between the surrogate and actual inter-section transfer matrices, the integration of mode shape functions, and the consistency of transfer matrices for low- and high-frequency responses, which form the basis of the proposed method. A series of numerical simulations and a large-scale laboratory experiment were proposed to validate the proposed method. Compared to existing approaches, our proposed method stands out as a purely data-driven technique, eliminating the need for finite element analysis assessment. By incorporating the ICA algorithm and surrogate mode shapes, this study addresses the challenges associated with obtaining accurate mode shape functions from low-frequency responses. Moreover, our method does not require partial measurements of the target responses, simplifying the data collection process. The validation results demonstrate the method's practicality and convenience for real-world engineering applications, showcasing its potential for broad adoption in the field. [ABSTRACT FROM AUTHOR]

This paper studied the problem of multi-user blind signal separation (BSS) in wireless communications. The existing separation algorithms work on quadrature phase shift keying (QPSK). Through our work, two proposed algorithms were presented to enhance the BSS performance. The first proposed algorithm uses wavelet denoising to remove noise from the received signals in time domain. It adopts different modulation techniques such as minimum shift keying (MSK), QPSK, and Gaussian minimum shift keying (GMSK). Then several BSS algorithms such as independent component analysis (ICA), principle component analysis (PCA), and multi-user kurtosis (MUK) algorithms were implemented. The second proposed algorithm transferred the problem of BSS to transform domain and used wavelet denoising to reduce noise effect on received mixture. The BSS with Discrete Sine Transform (DST) and Discrete Cosine Transform (DCT) were investigated and compared to time domain performance. Minimum square error (MSE) and signal to noise ratio (SNR) were used as the evaluating metrics for comparison. Simulation results proved that in time domain, MUK with QPSK gave best performance and wavelet denoising was found to enhance the performance of BSS under all conditions. Signal separation in transform domain was found to give better performance than that in time domain due to the energy compaction process of these transforms and noise reduction due to their averaging effect. [ABSTRACT FROM AUTHOR]

Copyright of Journal of South China Agricultural University is the property of Gai Kan Bian Wei Hui and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Data Acquisition & Processing / Shu Ju Cai Ji Yu Chu Li is the property of Editorial Department of Journal of Nanjing University of Aeronautics & Astronautics and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Functional time series model has been the subject of the most research in recent years, and since functional data is infinite dimensional, dimension reduction is essential for functional time series. However, the majority of the existing dimension reduction methods such as the functional principal component and fixed basis expansion are unsupervised and typically result in information loss. Then, the functional time series model has an urgent need for a supervised dimension reduction method. The functional sufficient dimension reduction method is a supervised technique that adequately exploits the regression structure information, resulting in minimal information loss. Functional sliced inverse regression (FSIR) is the most popular functional sufficient dimension reduction method, but it cannot be applied directly to functional time series model. In this paper, we examine a functional time series model in which the response is a scalar time series and the explanatory variable is functional time series. We propose a novel supervised dimension reduction technique for the regression model by combining the FSIR and blind source separation methods. Furthermore, we propose innovative strategies for selecting the dimensionality of dimension reduction space and the lags of the functional time series. Numerical studies, including simulation studies and a real data analysis are show the effectiveness of the proposed methods. [ABSTRACT FROM AUTHOR]

Abstract The expansion of air traffic has led to an increase in mixed secondary surveillance radar (SSR) signal replies, which occur when multiple replies arrive at the receiver antenna simultaneously. These overlapping signals render the messages unrecoverable, leading to a loss of information. Additionally, the existing methods do not effectively address the issue of handling different types of signals with varying structures and characteristics. The authors validate the effectiveness of the disjoint component analysis (DCA) criterion for SSR signals and introduce a new DCA‐based algorithm designed to optimise the separation process. Through several simulations, the proposed algorithm demonstrates robust performance across various reception parameters. Additionally, it achieves good results when applied to real‐world data, showing its practical applicability and efficiency.

Purpose – The safe operation of the metro power transformer directly relates to the safety and efficiency of the entire metro system. Through voiceprint technology, the sounds emitted by the transformer can be monitored in real-time, thereby achieving real-time monitoring of the transformer’s operational status. However, the environment surrounding power transformers is filled with various interfering sounds that intertwine with both the normal operational voiceprints and faulty voiceprints of the transformer, severely impacting the accuracy and reliability of voiceprint identification. Therefore, effective preprocessing steps are required to identify and separate the sound signals of transformer operation, which is a prerequisite for subsequent analysis. Design/methodology/approach – This paper proposes an Adaptive Threshold Repeating Pattern Extraction Technique (REPET) algorithm to separate and denoise the transformer operation sound signals. By analyzing the Short-Time Fourier Transform (STFT) amplitude spectrum, the algorithm identifies and utilizes the repeating periodic structures within the signal to automatically adjust the threshold, effectively distinguishing and extracting stable background signals from transient foreground events. The REPET algorithm first calculates the autocorrelation matrix of the signal to determine the repeating period, then constructs a repeating segment model. Through comparison with the amplitude spectrum of the original signal, repeating patterns are extracted and a soft time-frequency mask is generated. Findings – After adaptive thresholding processing, the target signal is separated. Experiments conducted on mixed sounds to separate background sounds from foreground sounds using this algorithm and comparing the results with those obtained using the FastICA algorithm demonstrate that the Adaptive Threshold REPET method achieves good separation effects. Originality/value – A REPET method with adaptive threshold is proposed, which adopts the dynamic threshold adjustment mechanism, adaptively calculates the threshold for blind source separation and improves the adaptability and robustness of the algorithm to the statistical characteristics of the signal. It also lays the foundation for transformer fault detection based on acoustic fingerprinting.

Local damage or stress concentration that forms during manufacturing and long-term use of ferromagnetic materials has a direct impact on the safety of engineering structures. Thus, accurately identifying damage and stress conditions in these materials is crucial. In this study, martensitic stainless steel, a type of ferromagnetic material, is chosen as the subject for investigation. A weak magnetic detection device is engineered specifically for this purpose, and tests are conducted on the material using this device. The stress value of the material is determined using X-ray diffraction, while magnetic induction intensity is simultaneously recorded with a weak magnetic detection device along the same path. The stress value and magnetic induction intensity are normalized, and the results are analyzed to establish a correlation between weak magnetic signals and stress. Then, a signal processing technique combining blind source separation and eigenvalue recognition is introduced to achieve stress concentration and microdefect location identification. This method is based on the correlation analysis results between weak magnetic signals and stress, as well as supporting evidence from prior studies. The experimental results demonstrate that the location of stress concentration can be accurately determined by identifying the peak or valley value of weak magnetic signals, with an error range of less than 30%. The algorithm of blind source separation and eigenvalue recognition can pinpoint the location of stress concentration and microdefects from the obtained signal. This study presents a novel nondestructive testing method for stress concentration and microdefect identification in ferromagnetic materials. [ABSTRACT FROM AUTHOR]

Machinery health monitoring techniques provide valuable insights into the performance and condition of machines. Acoustic sensor-based monitoring has emerged as a significant area of interest for the industry due to its ability to accurately capture fault signatures, thereby improving the detection accuracies of anomalies or deviations from regular operations. However, the collected sensor signals typically contain a complex mixture of sounds that relate to multiple fault conditions, environmental noise, and other unwanted sounds from the surroundings. Identifying the specific root causes of failures is a challenge in modeling without knowledge of the unique characteristics of failure conditions. This can ultimately degrade the model's performance or yield inaccurate failure estimations in condition monitoring, which is a consistent concern in the industry. Therefore, this study proposes a novel framework that enhances the accuracy of machinery fault diagnosis using audio source separation of a complex mixture of sound signals. The proposed approach employs a Deep Extractor for Music Source Separation (DEMUCS), a state-of-the-art music source separation approach consisting of an encoder-decoder architecture that uses bidirectional long short-term memory (LSTM) for industrial machine sound separation and enhancement. The proposed methodology comprises two steps. In the first step, the fault sound isolation and recovering individual fault sounds from a complex mixture of sound signals are enabled using DEMUCS. In the second step, the isolated fault sounds are fed through a 1D-convolutional neural network (1D-CNN) classifier for adequate classification. A machine fault simulator by Spectra Quest equipped with a condenser mic was employed to evaluate the proposed DEMUCS-CNN methodology for identifying multiple faults. The effectiveness of the DEMUCS-CNN method was also compared to the traditional approach of blind source separation (BSS). The outcomes of the comparison indicated that the suggested approach of fault isolation by DEMUCS led to enhanced fault classification accuracy, making it a more effective approach compared to conventional BSS. [ABSTRACT FROM AUTHOR]

Independent component analysis (ICA) and dictionary learning (DL) are the most successful blind source separation (BSS) methods for functional magnetic resonance imaging (fMRI) data analysis. However, ICA to higher and DL to lower extent may suffer from performance degradation by the presence of anomalous observations in the recovered time courses (TCs) and high overlaps among spatial maps (SMs). This paper addressed both problems using a novel three-layered sparse DL (TLSDL) algorithm that incorporated prior information in the dictionary update process and recovered full-rank outlier-free TCs from highly corrupted measurements. The associated sequential DL model involved factorizing each subject's data into a multi-subject (MS) dictionary and MS sparse code while imposing a low-rank and a sparse matrix decomposition restriction on the dictionary matrix. It is derived by solving three layers of feature extraction and component estimation. The first and second layers captured brain regions with low and moderate spatial overlaps, respectively. The third layer that segregated regions with significant spatial overlaps solved a sequence of vector decomposition problems using the proximal alternating linearized minimization (PALM) method and solved a decomposition restriction using the alternating directions method (ALM). It learned outlier-free dynamics that integrate spatiotemporal diversities across brains and external information. It differs from existing DL methods owing to its unique optimization model, which incorporates prior knowledge, subject-wise/multi-subject representation matrices, and outlier handling. The TLSDL algorithm was compared with existing dictionary learning algorithms using experimental and synthetic fMRI datasets to verify its performance. Overall, the mean correlation value was found to be 26 % higher for the TLSDL than for the state-of-the-art subject-wise sequential DL (swsDL) technique. [ABSTRACT FROM AUTHOR]

Modern neurophysiological recordings are performed using multichannel sensor arrays that are able to record activity in an increasingly high number of channels numbering in the 100s to 1000s. Often, underlying lower-dimensional patterns of activity are responsible for the observed dynamics, but these representations are difficult to reliably identify using existing methods that attempt to summarize multivariate relationships in a post hoc manner from univariate analyses or using current blind source separation methods. While such methods can reveal appealing patterns of activity, determining the number of components to include, assessing their statistical significance, and interpreting them requires extensive manual intervention and subjective judgment in practice. These difficulties with component selection and interpretation occur in large part because these methods lack a generative model for the underlying spatio-temporal dynamics. Here, we describe a novel component analysis method anchored by a generative model where each source is described by a bio-physically inspired state-space representation. The parameters governing this representation readily capture the oscillatory temporal dynamics of the components, so we refer to it as oscillation component analysis. These parameters – the oscillatory properties, the component mixing weights at the sensors, and the number of oscillations – all are inferred in a data-driven fashion within a Bayesian framework employing an instance of the expectation maximization algorithm. We analyze high-dimensional electroencephalography and magnetoencephalography recordings from human studies to illustrate the potential utility of this method for neuroscience data. [ABSTRACT FROM AUTHOR]

In order to solve the problem of difficult separation of audio signals collected in pig environments, this study proposes an underdetermined blind source separation (UBSS) method based on sparsification theory. The audio signals obtained by mixing the audio signals of pigs in different states with different coefficients are taken as observation signals, and the mixing matrix is first estimated from the observation signals using the improved AP clustering method based on the "two-step method" of sparse component analysis (SCA), and then the audio signals of pigs are reconstructed by L1-paradigm separation. Five different types of pig audio are selected for experiments to explore the effects of duration and mixing matrix on the blind source separation algorithm by controlling the audio duration and mixing matrix, respectively. With three source signals and two observed signals, the reconstructed signal metrics corresponding to different durations and different mixing matrices perform well. The similarity coefficient is above 0.8, the average recovered signal-to-noise ratio is above 8 dB, and the normalized mean square error is below 0.02. The experimental results show that different audio durations and different mixing matrices have certain effects on the UBSS algorithm, so the recording duration and the spatial location of the recording device need to be considered in practical applications. Compared with the classical UBSS algorithm, the proposed algorithm outperforms the classical blind source separation algorithm in estimating the mixing matrix and separating the mixed audio, which improves the reconstruction quality. [ABSTRACT FROM AUTHOR]

Particularly, environmental pollution, such as air pollution, is still a significant issue of concern all over the world and thus requires the identification of good models for prediction to enable management. Blind Source Separation (BSS), Copula functions, and Long Short-Term Memory (LSTM) network integrated with the Greylag Goose Optimization (GGO) algorithm have been adopted in this research work to improve air pollution forecasting. The proposed model involves preprocessed data from the urban air quality monitoring dataset containing complete environmental and pollutant data. The application of Noise Reduction and Isolation techniques involves the use of methods such as Blind Source Separation (BSS). Using copula functions affords an even better estimate of the dependence structure between the variables. Both the BSS and Copula parameters are then estimated using GGO, which notably enhances the performance of these parameters. Finally, the air pollution levels are forecasted using a time series employing LSTM networks optimized by GGO. The results reveal that GGO-LSTM optimization exhibits the lowest mean squared error (MSE) compared to other optimization methods of the proposed model. The results underscore that certain aspects, such as noise reduction, dependence modeling and optimization of parameters, provide much insight into air quality. Hence, this integrated framework enables a proper approach to monitoring the environment by offering planners and policymakers information to help in articulating efficient environment air quality management strategies. [ABSTRACT FROM AUTHOR]

The parietal P300 wave of event‐related potentials (ERPs) has been associated with various psychological operations in numerous laboratory tasks. This study aims to decompose the P3 wave of ERPs into subcomponents and link them with behavioral parameters, such as the strength of stimulus–response (S‐R) links and GO/NOGO responses. EEGs (31 channels), referenced to linked ears, were recorded from 172 healthy adults (107 women) who participated in two cued GO/NOGO tasks, where the strength of S‐R links was manipulated through instructions. P300 waves were observed in active conditions in response to cues, GO/NOGO stimuli, and in passive conditions when no manual response was required. Utilizing a combination of current source density transformation and blind source separation methods, we decomposed the P300 wave into two distinct components, purportedly originating from different parts of the parietal lobules. The amplitude of the parietal midline component (with current sources around Pz) closely mirrored the strength of the S‐R link across proactive, reactive, and passive conditions. The amplitude of the lateral parietal component (with current sources around P3 and P4) resembled the push–pull activity of the output nuclei of the basal ganglia in action selection‐inhibition operations. These findings provide insights into the neural mechanisms underlying action selection processes and the reactivation of S‐R links. We aimed to associate subcomponents of P3 waves in event‐related potentials with behavioral parameters in cued GO/NOGO tasks. The parietal midline component was shown to reflect the strength of stimulus–response (S‐R) links across proactive, reactive, and passive conditions. The lateral parietal component mirrored basal ganglia activity in action selection. These results illuminate neural mechanisms for S‐R links and action selection operations. [ABSTRACT FROM AUTHOR]

The electroencephalogram (EEG) is a crucial tool for studying brain activity; yet it frequently encounters artifacts that distort meaningful neural signals. This paper addresses the challenge of artifact removal through a unique hybrid method, combining Variational Mode Decomposition (VMD) techniques with Blind Source Separation (BSS) algorithms. VMD, recognized for its adaptability to non-linear and non-stationary EEG data, as well as its ability to alleviate mode mixing and the "endpoint effect," which serves as an effective preprocessing step. The paper evaluates the performance of two integrated BSS algorithms, AMICA and AMUSE, across various criteria. Comparisons across metrics such as Euclidean distance, Spearman correlation coefficient, and Root Mean Square Error reveal similar performance between AMICA and AMUSE. However, a distinct divergence is evident in the Signal to Artifact Ratio (SAR). When employed with VMD, AMICA demonstrates superiority in effectively discerning and segregating brain signals from artifacts, which gives a mean value of 1.0924. This study introduces a potent hybrid VMDBSS approach for enhancing EEG signal quality. The findings emphasize the notable impact of AMICA, particularly in achieving optimal results in artifact removal, as indicated by its superior performance in SAR. The abstract concludes by underlining the significance of these results, emphasizing AMICA's pivotal role in achieving the highest measurable evaluation value, making it a compelling choice for researchers and practitioners in EEG signal processing. [ABSTRACT FROM AUTHOR]

Copyright of Light Industry Machinery is the property of Light Industry Machinery Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This contribution conceptualizes a blind source separation (BSS) model to recover sources of geochemical signals such that multi-depth ore-related enrichment patterns in complex metallogenic systems can be recognized. The proposed BSS framework consists of two consecutive modules. The first module is for the spectral decomposition of elemental mixtures to obtain different frequency-related components of signals induced by various geological sources. The second module serves to recover the sources of spectrally filtered geochemical signals according to the statistical assumptions made for the transmission of the latter from the former. In a real case experiment on a multiphase mineralization system, the proposed model was applied to the surface geochemical signals of ore-forming elements to gauge the relevance of source-related signals in depicting subsurface ore-related enrichment patterns. Multifractal filtering according to the generalized scale invariance characteristics of the power spectral density plane was adopted to derive elemental images enhanced in different spectral bands. Assuming linear instantaneous transmission, the FastICA technique was employed to encode spectrally filtered representations of elemental mixtures and recover source-related geochemical signals corresponding to different geo-processes. Support vector machines were used to train classifiers to establish statistical links between the surface geochemical signals and the shallow/deep ore-related enrichment patterns within the study area. The classification accuracies demonstrated that shallow/deep ore-related enrichment patterns can be recognized and distinguished more effectively using recovered source-related signals than using elemental mixtures or spectrally filtered representations. The results indicated that the proposed BSS model can provide efficient source-related geochemical signals to identify robust ore-related enrichment patterns with integrated grade and depth resolution to guide further metal exploration. [ABSTRACT FROM AUTHOR]

Nonnegative tensor factorizations (NTF) have applications in statistics, computer vision, exploratory multi-way data analysis, and blind source separation. This paper studies randomized multiplicative updating algorithms for symmetric NTF via random projections and random samplings. For random projections, we consider two methods to generate the random matrix and analyze the computational complexity, while for random samplings the uniform sampling strategy and its variants are examined. The mixing of these two strategies is then considered. Some theoretical results are presented based on the bounds of the singular values of sub-Gaussian matrices and the fact that randomly sampling rows from an orthogonal matrix results in a well-conditioned matrix. These algorithms are easy to implement, and their efficiency is verified via test tensors from both synthetic and real datasets, such as for clustering facial images. [ABSTRACT FROM AUTHOR]

Aims. Recent simulations have shown that asymmetries in the ejecta distribution of supernova remnants (SNRs) may be a reflection of asymmetries left over from the initial supernova explosion. Thus, SNR studies provide a vital means for testing and constraining model predictions in relation to the distribution of heavy elements, which are key to improving our understanding of the explosion mechanisms in Type Ia supernovae. Methods. The use of a novel blind source separation method applied to the megasecond X-ray observations of the historic Kepler and Tycho supernova remnants has revealed maps of the ejecta distribution. These maps are endowed with an unprecedented level of detail and clear separations from the continuum emission. Our method also provides a three-dimensional (3D) view of the ejecta by individually disentangling red- and blueshifted spectral components associated with images of the Si, S, Ar, Ca, and Fe emission. This approach provides insights into the morphology of the ejecta distribution in those two remnants. Results. Those mappings have allowed us to thoroughly investigate the asymmetries in the intermediate-mass elements and Fe distribution in two Type Ia supernova remnants. We also compared the results with the core-collapse Cassiopeia A remnant, which we had studied previously. The images obtained confirm, as expected for Type Ia SNRs, that the Fe distribution is mostly closer to the core than that of intermediate-mass elements. They also highlight peculiar features in the ejecta distribution, such as the Fe-rich southeastern knot in Tycho. [ABSTRACT FROM AUTHOR]

In recent years, the incidence of cardiac arrhythmias has been on the rise because of changes in lifestyle and the aging population. Electrocardiograms (ECGs) are widely used for the automated diagnosis of cardiac arrhythmias. However, existing models possess poor noise robustness and complex structures, limiting their effectiveness. To solve these problems, this paper proposes an arrhythmia recognition system with excellent anti-noise performance: a convolutionally optimized broad learning system (COBLS). In the proposed COBLS method, the signal is convolved with blind source separation using a signal analysis method based on high-order-statistic independent component analysis (ICA). The constructed feature matrix is further feature-extracted and dimensionally reduced using principal component analysis (PCA), which reveals the essence of the signal. The linear feature correlation between the data can be effectively reduced, and redundant attributes can be eliminated to obtain a low-dimensional feature matrix that retains the essential features of the classification model. Then, arrhythmia recognition is realized by combining this matrix with the broad learning system (BLS). Subsequently, the model was evaluated using the MIT-BIH arrhythmia database and the MIT-BIH noise stress test database. The outcomes of the experiments demonstrate exceptional performance, with impressive achievements in terms of the overall accuracy, overall precision, overall sensitivity, and overall F1-score. Specifically, the results indicate outstanding performance, with figures reaching 99.11% for the overall accuracy, 96.95% for the overall precision, 89.71% for the overall sensitivity, and 93.01% for the overall F1-score across all four classification experiments. The model proposed in this paper shows excellent performance, with 24 dB, 18 dB, and 12 dB signal-to-noise ratios. [ABSTRACT FROM AUTHOR]

Since the invention of the microphone by Barina in 1876, there have been numerous applications of audio processing, such as phonographs, broadcasting stations, and public address systems, which merely capture and amplify sound and play it back. Nowadays, audio processing involves analysis and noise-filtering techniques. There are various methods for noise filtering, each employing unique algorithms, but they all require two or more microphones for signal processing and analysis. For instance, on mobile phones, two microphones located in different positions are utilized for active noise cancellation (one for primary audio capture and the other for capturing ambient noise). However, a drawback is that when the sound source is distant, it may lead to poor audio capture. To capture sound from distant sources, alternative methods, like blind signal separation and beamforming, are necessary. This paper proposes employing a beamforming algorithm with two microphones to enhance speech and implementing this algorithm on an embedded system. However, prior to beamforming, it is imperative to accurately detect the direction of the sound source to process and analyze the audio from that direction. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this study was to compare laterality in motor unit firing behavior between females and males. Methods: Twenty-seven subjects (14 females) were recruited for this study. The participants performed ramp up and hold isometric index finger abduction at 10, 30, and 60% of their maximum voluntary contraction (MVC). High-density surface electromyography (HD-sEMG) signals were recorded in the first dorsal interosseous (FDI) muscle and decomposed into individual motor unit (MU) firing behavior using a convolution blind source separation method. Results: In total, 769 MUs were detected (females, n = 318 and males, n = 451). Females had a significantly higher discharge rate than males at each relative torque level (10%: male dominant hand, 13.4 ± 2.7 pps vs. female dominant hand, 16.3 ± 3.4 pps; 30%: male dominant hand, 16.1 ± 3.9 pps vs. female dominant hand, 20.0 ± 5.0 pps; and 60%: male dominant hand, 19.3 ± 3.8 vs. female dominant hand, 25.3 ± 4.8 pps; p < 0.0001). The recruitment threshold was also significantly higher in females than in males at 30 and 60% MVC. Furthermore, males exhibited asymmetrical discharge rates at 30 and 60% MVC and recruitment thresholds at 30 and 60% MVC, whereas no asymmetry was observed in females. Conclusion: In the FDI muscle, compared to males, females exhibited different neuromuscular strategies with higher discharge rates and recruitment thresholds and no asymmetrical MU firing behavior. Notably, the findings that sex differences in neuromuscular activity also occur in healthy individuals provide important information for understanding the pathogenesis of various diseases. [ABSTRACT FROM AUTHOR]

To solve the separation of multi-source signals and detect their features from a single channel, a signal separation method using multi-constraint non-negative matrix factorization (NMF) is proposed. In view of the existing NMF algorithm not performing well in the underdetermined blind source separation, the β-divergence constraints and determinant constraints are introduced in the NMF algorithm, which can enhance local feature information and reduce redundant components by constraining the objective function. In addition, the Sine-bell window function is selected as the processing method for short-time Fourier transform (STFT), and it can preserve the overall feature distribution of the original signal. The original vibration signal is first transformed into time–frequency domain with the STFT, which describes the local characteristic of the signal from the time–frequency distribution. Then, the multi-constraint NMF is applied to reduce the dimensionality of the data and separate feature components in the low dimensional space. Meanwhile, the parameter WK is constructed to filter the reconstructed signal that recombined with the feature component in the time domain. Ultimately, the separated signals will be subjected to envelope spectrum analysis to detect fault features. The simulated and experimental results indicate the effectiveness of the proposed approach, which can realize the separation of multi-source signals and their fault diagnosis of bearings. In addition, it is also confirmed that the proposed method, juxtaposed with the NMF algorithm of the traditional objective function, is more applicable for compound fault diagnosis of the rotating machinery. [ABSTRACT FROM AUTHOR]

In view of the problem that source signals cannot be effectively separated in the process of blind source separation of similar nonstationary nonlinear signals, a spatiotemporal intrinsic mode decomposition method was proposed for bearing fault diagnosis. Spatiotemporal intrinsic mode decomposition can separate source signals and construct Fourier basis dictionaries and nonlinear signal models. The fault components can be separated by using this method in fault diagnosis. The proper initial phase function is selected for blind source separation of signals and signal decomposition components are obtained. By simulation analysis, spatiotemporal intrinsic mode decomposition than the fast independent component analysis method can more intuitive clearly separate the signals of admixed with large modulation components of correlation coefficient, and through the impact of component kurtosis index to judge fault, inherent modal decomposition method better prove time restore original bearing vibration signals and fault impact. Through the analysis and comparison of experimental data, the spatiotemporal intrinsic mode decomposition method has a significant effect on the fault diagnosis and analysis of rolling bearing outer ring, inner ring, and can intuitively express the fault characteristic frequency and frequency doubling through the analysis of envelope spectrum. In processing the industrial data, the spatiotemporal intrinsic mode decomposition method can also find the frequency characteristics of inner ring fault more clearly and accurately. Therefore, the spatiotemporal intrinsic mode decomposition method can solve the problem of blind source separation and realize fault diagnosis in rolling bearing fault field. [ABSTRACT FROM AUTHOR]

Aiming at the problem that the traditional density-based spatial clustering of applications with noise (DBSCAN) algorithm in the mixing matrix estimation algorithm needs to artificially set the neighborhood radius and the number of core points, a double constrained particle swarm optimization (DCPSO) algorithm is proposed. The neighborhood radius parameters of the DBSCAN algorithm are optimized, and the obtained optimal parameters are used as the parameter input of the DBSCAN algorithm, and then the clustering center is calculated to complete the mixing matrix estimation. Aiming at the problem that the source signal number estimation algorithm based on distance sorting relies on the selection of empirical parameters and does not have the ability to eliminate noise points, a maximum distance sorting algorithm is proposed. The experimental results show that the improved algorithm is improved. The accuracy of source signal number estimation is nearly 40% higher than that of the original algorithm. The error of mixing matrix estimation is more than 3 dB higher than that of the comparison algorithm. Moreover, the proposed algorithm has a better convergence speed than the original algorithm. [ABSTRACT FROM AUTHOR]

Independent component analysis (ICA), as a statistical and computational approach, has been successfully applied to digital signal processing. Performance analysis for the ICA approach is perceived as a challenging task to work on. This contribution concerns the complex-valued FastICA algorithm in the range of ICA over the complex number domain. The focus is on the robust and equivariant behavior analysis of the complex-valued FastICA estimator. Although the complex-valued FastICA algorithm as well as its derivatives have been widely used methods for approaching the complex blind signal separation problem, rigorous mathematical treatments of the robust measurement and equivariance for the complex-valued FastICA estimator are still missing. This paper strictly analyzes the robustness against outliers and separation performance depending on the global system. We begin with defining the influence function (IF) of complex-valued FastICA functional and followed by deriving its closed-form expression. Then, we prove that the complex-valued FastICA algorithm based on the optimizing cost function is linear-equivariant, depending only on the source signals. [ABSTRACT FROM AUTHOR]

Acoustic signal processing technology is a new discipline developed in recent years, which has been widely applied in various fields such as communication, radar, sonar, and so on. Due to the wide variety of sound sources and the high susceptibility of signals to various interferences, it is necessary to perform noise reduction processing on them. Blind source separation technology is an emerging signal processing method that can simultaneously separate signals from multiple sources, thus having significant advantages in noise reduction. On the basis of summarizing traditional noise reduction algorithms, this article proposes an improved adaptive filtering algorithm combined with blind source separation technology, and conducts simulation experiments on both the traditional algorithm and the improved algorithm. [ABSTRACT FROM AUTHOR]

Summary: Modal identification method based on blind source separation (BSS) technique has gained extensive attentions for civil structures. Developing the complex modes estimation method is important in practical applications because the assumption of proportional damping is not always satisfied. Sparse component analysis (SCA) performs well in underdetermined BSS problems. However, SCA is confined to the situation of proportional damping. In this study, a generalized SCA method is proposed to extend the original SCA method to both real and complex modes identification. First, the general formulation of complex modes is extended by the analytic form to eliminate the complex conjugate part in the BSS model. A new single‐source‐point detection method that is available to handle real and complex modes is proposed. Local outlier factor method is adopted to remove the outliers in single source points. Subsequently, complex‐valued modal matrix is calculated by the clustering technique. Then, modal responses are recovered using the complex version of smoothed zero norm method, where modal frequencies and damping ratios can be extracted. Finally, the effectiveness of the proposed method is demonstrated for identification of real and complex modes, close modes, and underdetermined problem. The application to a benchmark structure demonstrates the effectiveness for practical applications. [ABSTRACT FROM AUTHOR]

Feature mode decomposition (FMD) has advantages over the other newer time-frequency methods such as ensemble empirical mode decomposition (EEMD) and variational mode decomposition (VMD) in single channel blind source separation (SCBSS). However, FMD has the defect of needing to determine the precise number of fault sources manually. To solve the above defect of FMD, an adaptive method for determining the number of fault sources based on the shift invariant sparse code (SISC) is proposed. SISC was used to train a set of basis functions from the single channel signal, and the corresponding potential components were reconstructed firstly. Subsequently, the structural similarity of these potential components was used for clustering, and each of the obtained clustering signals represented one kind of fault. Then the number of clustering was determined by minimizing the structural correlation among the clustering signals. It was considered that the source separation had achieved the best effect when the structural difference among the clusters was the largest, and the number of clustering at this time was used as the optimal estimated value, which was used as the modal inputs number of FMD calculation model to realize SCBSS of rolling bearing. Simulation and experimental analysis were carried out to verify the effectiveness of the proposed method, and its superiority was also verified through comparison. [ABSTRACT FROM AUTHOR]

The extraction of weak acoustic signals under strong background noise is of great significance in the applications of target identification and localization. In this paper, the pulse signal with high randomness is set as the weak signal sound source, random noise and sine sound are used as the background noise. Under the condition of a signal-to-noise ratio of −20 dB, combined with blind source separation and neural network methods, the collected observation signals are subjected to weak sound signal separation and recognition research. The optimization method of centralization and scaling processing is used to eliminate the unfavorable influence of the uncertainty of the separated signal amplitude caused by the blind source separation method on the pattern recognition. The recognition result is verified by the combination of "weak impulse acoustic signal" and "random noise signal," and the output vector (0.99 0.01 0.01) approaches (1 0 0), which is recognized as impulse acoustic signal. By combining blind source separation and neural network methods, the separation and identification of weak pulse signals under the condition of a signal-to-noise ratio of −20 dB can be achieved. [ABSTRACT FROM AUTHOR]

Magnetometers are essential instruments in space physics, but their measurements are often contaminated by various external interference sources. In this work, we present a comprehensive review of existing magnetometer interference removal methods and introduce MAGPRIME (MAGnetic signal PRocessing, Interference Mitigation, and Enhancement), an open‐source Python library featuring a collection of state‐of‐the‐art interference removal algorithms. MAGPRIME streamlines the process of interference removal in magnetic field data by providing researchers with an integrated, easy‐to‐use platform. We detail the design, structure, and functionality of the library, as well as its potential to facilitate future research by enabling rapid testing and customization of interference removal methods. Using the MAGPRIME Library, we present two Monte Carlo benchmark results to compare the efficacy of interference removal algorithms in different magnetometer configurations. In Benchmark A, the Underdetermined Blind Source Separation (UBSS) and traditional gradiometry algorithms surpass the uncleaned boom‐mounted magnetometers, achieving improved correlation and reducing median error in each simulation. Benchmark B tests the efficacy of the suite of MAGPRIME algorithms in a boomless magnetometer configuration. In this configuration, the UBSS algorithm proves to significantly reduce median error, along with improvements in median correlation and signal to noise ratio. This study highlights MAGPRIME's potential in enhancing magnetic field measurement accuracy in various spacecraft designs, from traditional gradiometry setups to compact, cost‐effective alternatives like bus‐mounted CubeSat magnetometers, thus establishing it as a valuable tool for researchers and engineers in space exploration and magnetism studies. Plain Language Summary: Magnetometers, tools used to measure magnetic fields, are crucial for space exploration but often face interference from a spacecraft's own systems. This can distort measurements, making it hard to get accurate data. In order to address this, we introduce MAGPRIME, an open‐source software that helps clean up these magnetic field measurements. It is user‐friendly and integrates various techniques to reduce interference in the data. We tested MAGPRIME in two scenarios: one with magnetometers on a long mechanical arm or boom, and another without the boom. The results from these cases show that the use of MAGPRIME can make it easier and cheaper to get reliable magnetic field data, especially for smaller satellites like CubeSats. Key Points: MAGPRIME is an open‐source Python library with advanced interference removal algorithms for magnetic field measurements in space missionsThe MAGPRIME library is designed to be a community‐driven platform for magnetometer signal processing algorithmsMonte Carlo simulation results show UBSS achieves significant interference reduction in both boom and bus‐mounted magnetometer setups [ABSTRACT FROM AUTHOR]

Ship-radiated noise separation is critical in both military and economic domains. However, due to the complex underwater environments with multiple noise sources and reverberation, separating ship-radiated noise poses a significant challenge. Traditionally, underwater acoustic signal separation has employed blind source separation methods based on independent component analysis. Recently, the separation of underwater acoustic signals has been approached as a deep learning problem. This involves learning the features of ship-radiated noise from training data. This paper introduces a deep time-domain network for ship-radiated noise separation by leveraging the power of parallel dilated convolution and group convolution. The separation layer employs parallel dilated convolution operations with varying expansion factors to better extract low-frequency features from the signal envelope while preserving detailed information. Additionally, we use group convolution to reduce the expansion of network size caused by parallel convolution operations, enabling the network to maintain a smaller size and computational complexity while achieving good separation performance. The proposed approach is demonstrated to be superior to the other common networks in the DeepShip dataset through comprehensive comparisons. [ABSTRACT FROM AUTHOR]

In this paper, we introduce two structure constrained non-negative matrix factorization (NMF) approaches designed to address nonnegative blind source separation challenges. We develop corresponding iterative multiplicative update rules and investigate their convergence properties. To demonstrate the effectiveness of our proposed methods, we provide numerical experiments using spectral data and facial images. [ABSTRACT FROM AUTHOR]

Wideband Frequency Hopping Spread Spectrum (FHSS) communications are widely used in both military and commercial applications. In military applications, it is very important to investigate these communications, especially when frequency hopping signals are received simultaneously by a single antenna. This paper investigates the problem of estimating interfering wideband asynchronous frequency hopping (FH) signals parameters with the same hop rate, and using narrow-band receivers. Due to minimal knowledge about the transmitted signals, the problem is analyzed in semi-blind mode. For this purpose, time-frequency (TF) processing has been applied to the modulated wideband converter (MWC) output. The proposed method consists of two stages; In the first stage, frequency-hopping signals with different amplitudes are received by a single antenna. By passing through baseband receivers, the TF distribution of the converter's output signal is obtained. In the next stage, by computing instantaneous spectral entropy (SE), and finding the local maxima in the spectrum, the hop time of each signal is obtained. We use MWC for sub-Nyquist sampling and simultaneous extraction of time and frequency information from signals while eliminating irrelevant signals. The results obtained from estimating hop time parameters demonstrate improved performance compared to other traditional source separation methods such as sparse linear regression (SLR). Based on evaluation metrics such as root mean squared error (RMSE), in lower signal-to-noise ratio (SNR) values, the amount of error has been substantially reduced. [ABSTRACT FROM AUTHOR]

A signal decomposition is presented that disentangles the deterministic and stochastic components of a multivariate time series. The dynamical component analysis (DyCA) algorithm is based on the assumption that an unknown set of ordinary differential equations (ODEs) describes the dynamics of the deterministic part of the signal. The algorithm is thoroughly derived and accompanied by a link to the GitHub repository containing the algorithm. The method was applied to both simulated and real-world data sets and compared to the results of principal component analysis (PCA), independent component analysis (ICA), and dynamic mode decomposition (DMD). The results demonstrate that DyCA is capable of separating the deterministic and stochastic components of the signal. Furthermore, the algorithm is able to estimate the number of linear and non-linear differential equations and to extract the corresponding amplitudes. The results demonstrate that DyCA is an effective tool for signal decomposition and dimension reduction of multivariate time series. In this regard, DyCA outperforms PCA and ICA and is on par or slightly superior to the DMD algorithm in terms of performance.