Fast Independent Component Analysis Algorithm Research Articles

A novel component separation method for deformation monitoring of rockfill dams

High dams are constructed in mountain valley areas with extremely complex geomorphologies, geologies, and operating environments. The dam body and foundation are subjected to large hydraulic loadings and high stress levels, where many factors affect the structural deformation of the dam, making dam safety monitoring and risk assessment difficult. Since dam deformation is critical for evaluating safe operation, a novel dam deformation monitoring model based on the component separation method is proposed, which can provide scientific significance and high application value for operational safety control. The deformation monitoring data include deformation caused by the water level, the temperature, and the time effect. The homologous monitoring sequence points are selected, and a safety monitoring model is proposed based on the physical causes and the independent component signal decomposition technology. First, the aging component can be extracted by using the correlation consistency of a specific measurement point, where the environment is almost the same at the same time of year, such as the temperature and the water level. Then, fast independent component analysis algorithm is applied to further extract the dam deformation induced by the water level and the temperature via correlation analysis. Finally, a deformation monitoring model based on the component separation can be successfully constructed by combining models of the three components. The proposed dam deformation model can overcome problems of large covariance of deformation component, large collinear interference of periodic component separation, and unstable separation. It is further applied to a high face rockfill dam, and the results shows that the multiple correlation coefficient is greater than 0.995 and the mean absolute percentage error is less than 1.37%, demonstrating the high accuracy and stable separation of the components produced by using this model.

Orthogonal extended infomax algorithm.

Objective.The extended infomax algorithm for independent component analysis (ICA) can separate sub- and super-Gaussian signals but converges slowly as it uses stochastic gradient optimization. In this paper, an improved extended infomax algorithm is presented that converges much faster.Approach.Accelerated convergence is achieved by replacing the natural gradient learning rule of extended infomax by a fully-multiplicative orthogonal-group based update scheme of the ICA unmixing matrix, leading to an orthogonal extended infomax algorithm (OgExtInf). The computational performance of OgExtInf was compared with original extended infomax and with two fast ICA algorithms: the popular FastICA and Picard, a preconditioned limited-memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) algorithm belonging to the family of quasi-Newton methods.Main results.OgExtInf converges much faster than original extended infomax. For small-size electroencephalogram (EEG) data segments, as used for example in online EEG processing, OgExtInf is also faster than FastICA and Picard.Significance.OgExtInf may be useful for fast and reliable ICA, e.g. in online systems for epileptic spike and seizure detection or brain-computer interfaces.

Joint Improved Fast Independent Component Analysis and Singular Value Decomposition for Fetal Electrocardiogram Extraction.

Combined the improved fast independent component analysis (FastICA) algorithm with the singular value decomposition algorithm, a single-channel fetal electrocardiogram (fECG) extraction method is proposed. First, the improved FastICA algorithm is used to estimate the maternal ECG component from a single-channel abdominal signal of pregnant women using an overrelaxation factor. Then, a preliminary estimate of the fECG signal is obtained by subtracting from the single-channel abdominal signal. Subsequently, the singular value decomposition algorithm is used to denoise the preliminarily estimated fECG signal to obtain a high signal-to-noise ratio. In addition, in the singular value decomposition algorithm for fetal arrhythmia, an improved method for constructing the ECG signal reconstruction matrix is proposed. Finally, the fECG extraction experiments on synthetic abdominal signals and actual abdominal signals (data from 49 abdominal channels sourced from DAISY database and the non-invasive fECG database in PhysioNet) are carried out. The experimental results show that the method in this paper can effectively improve the signal-to-noise ratio and the accuracy of fECG signal extraction, and is suitable for maternal or fetal arrhythmias. Compared with the FastICA algorithm, the signal-to-noise ratio of the fECG signal extracted by the method in this paper is improved by about 5 dB, and the accuracy of fECG extraction in the PhysioNet database can reach 96.54%.

EEG-BCI Features Discrimination between Executed and Imagined Movements Based on FastICA, Hjorth Parameters, and SVM

Brain–Computer Interfaces (BCIs) communicate between a given user and their nearest environment through brain signals. In the case of device handling, an accurate control-based BCI depends essentially on how the user performs corresponding mental tasks. In the BCI illiteracy-related literature, one subject could perform a defined paradigm better than another. Therefore, this work aims to identify recorded Electroencephalogram (EEG) signal segments related to the executed and imagined motor tasks for BCI system applications. The proposed approach implements pass-band filters and the Fast Independent Component Analysis (FastICA) algorithm to separate independent sources from raw EEG signals. Next, EEG features of selected channels are extracted using Hjorth parameters. Finally, a Support Vector Machines (SVMs)-based classifier identifies executed and imagined motor features. Concretely, the Physionet dataset, related to executed and imagined motor EEG signals, provided training, testing, and validating data. The numerical results let us discriminate between executed and imagined motor tasks accurately. Therefore, the proposed method offers a reliable alternative to extract EEG features for BCI based on executed and imagined movements.

A high-precision and efficient algorithm for space-based ADS-B signal separation

AbstractSpace-based automatic dependent surveillance-broadcast (ADS-B) receivers can cover thousands of aircraft, each transmitting 6 ⋅ 2 signals per second. As a result, ADS-B signals are very prone to overlap. When the number of aircraft covered by a receiver reaches 3,000, about 90 % of the signals will be overlapping. Overlapped signals can reduce the decoding accuracy of receivers, so that aircraft information cannot be accurately transmitted to the air traffic control (ATC) surveillance system, hence threatening aviation flight safety. It is necessary to propose signal separation algorithms for space-based ADS-B systems. An orthogonal projection linear constrained minimum variance (OPLCMV) algorithm is proposed, which can separate two signals simultaneously based on the linearly constrained minimum variance algorithm by exploiting the characteristics of overlapped signals. Compared with the state-of-the-art extended projection algorithm and the fast independent component analysis algorithm, the OPLCMV method has a higher decoding accuracy for multiple overlapping signals with a small direction difference of arrival or frequency shift. Moreover, the OPLCMV algorithm has a low computational complexity when the number of overlapped signal sources is less than seven.

Application of Underdetermined Blind Source Separation Algorithm on the Low-Frequency Oscillation in Power Systems

The timely discovery of low-frequency oscillations in power systems and accurate identification of their modal parameters is critical in numerous applications. Therefore, we investigated the feasibility of using multi-channel signals and established a relative theory. An algorithm based on the underdetermined blind source separation (UBSS) algorithm was proposed using this theory. First, the energy ratio function was used to determine the fault occurrence time. Then, the Bayesian information criterion was used to estimate the number of fault sources, and the boundary conditions were set to determine the number of fault sources. Next, the UBSS algorithm was used to analyze raw data, extract individual components that characterize faults, and subsequently measure low-frequency oscillation modal parameters through the Hilbert transform. Finally, the fast independent component analysis (FastICA) algorithm was used to separate noise signal from raw data. This separation considerably reduced noise disturbance and ensured the stability of the proposed method. Model simulation was conducted in MATLAB and experimental measurement revealed that the proposed method effectively reduced noise disturbance and could be applied to conditions with considerable disturbance.

Fetal electrocardiogram signal extraction and analysis method combining fast independent component analysis algorithm and convolutional neural network

Fetal electrocardiogram (ECG) signals provide important clinical information for early diagnosis and intervention of fetal abnormalities. In this paper, we propose a new method for fetal ECG signal extraction and analysis. Firstly, an improved fast independent component analysis method and singular value decomposition algorithm are combined to extract high-quality fetal ECG signals and solve the waveform missing problem. Secondly, a novel convolutional neural network model is applied to identify the QRS complex waves of fetal ECG signals and effectively solve the waveform overlap problem. Finally, high quality extraction of fetal ECG signals and intelligent recognition of fetal QRS complex waves are achieved. The method proposed in this paper was validated with the data from the PhysioNet computing in cardiology challenge 2013 database of the Complex Physiological Signals Research Resource Network. The results show that the average sensitivity and positive prediction values of the extraction algorithm are 98.21% and 99.52%, respectively, and the average sensitivity and positive prediction values of the QRS complex waves recognition algorithm are 94.14% and 95.80%, respectively, which are better than those of other research results. In conclusion, the algorithm and model proposed in this paper have some practical significance and may provide a theoretical basis for clinical medical decision making in the future.

Deep learning aided cyclostationary feature analysis for blind modulation recognition in massive MIMO systems

Blind modulation recognition (BMR) has been proposed as a promising approach for massive multiple-input multiple-output (M-MIMO) systems to support massive user equipment (UE). In this work, we devise a new uplink BMR algorithm based on a deep learning (DL) aided cyclostationary feature (CF) analysis approach. Firstly, we propose the so-called minimum description length (MDL) method aided complex fast independent component analysis (CFICA) algorithm, to separate the multi-user (MU) signals subjected to arbitrary, different modulation schemes with an unknown number of transmitters. Then, we tailor a convolutional deep belief network (CDBN) to analyze the relation between CF and modulation type (MT) for generating the BMR results. As the proposed BMR process is invoked blindly, it is particularly suitable for non-cooperative MU communication scenarios. Simulation results demonstrate the effectiveness of the proposed technique.

Anomaly Detection of UAV State Data Based on Single-Class Triangular Global Alignment Kernel Extreme Learning Machine

Unmanned Aerial Vehicles (UAVs) are widely used and meet many demands in military and civilian fields. With the continuous enrichment and extensive expansion of application scenarios, the safety of UAVs is constantly being challenged. To address this challenge, we propose algorithms to detect anomalous data collected from drones to improve drone safety. We deployed a one-class kernel extreme learning machine (OCKELM) to detect anomalies in drone data. By default, OCKELM uses the radial basis (RBF) kernel function as the kernel function of the model. To improve the performance of OCKELM, we choose a Triangular Global Alignment Kernel (TGAK) instead of an RBF Kernel and introduce the Fast Independent Component Analysis (FastICA) algorithm to reconstruct UAV data. Based on the above improvements, we create a novel anomaly detection strategy FastICA-TGAK-OCELM. The method is finally validated on the UCI dataset and detected on the Aeronautical Laboratory Failures and Anomalies (ALFA) dataset. The experimental results show that compared with other methods, the accuracy of this method is improved by more than 30%, and point anomalies are effectively detected.

Compressed spike-triggered averaging in iterative decomposition of surface EMG

Background and ObjectiveAnalysis of motor unit activity is important for assessing and treating diseases or injuries affecting natural movement. State-of-the-art decomposition translates high-density surface electromyography (HDsEMG) into motor unit activity. However, current decomposition methods offer far from complete separation of all motor units. MethodsThis paper proposes a peel-off approach to automatic decomposition of HDsEMG into motor unit action potential (MUAP) trains, based on the Fast Independent Component Analysis algorithm (FastICA). The novel steps include utilizing compression by means of Principal Component Analysis and spike-triggered averaging, to estimate surface MUAP distributions with less noise, which are iteratively subtracted from the HDsEMG dataset. Furthermore, motor unit spike trains are estimated by high-dimensional density-based clustering of peaks in the FastICA source output. And finally, a new reliability measure is used to discard poor motor unit estimates by comparing the variance of the FastICA source output before and after the peel-off step. The method was validated using reconstructed synthetic data at three different signal-to-noise levels and was compared to an established deflationary FastICA approach. ResultsBoth algorithms had very high recall and precision, over 90%, for spikes from matching motor units, referred to as matched performance. However, the peel-off algorithm correctly identified more motor units for all noise levels. When accounting for unidentified motor units, total recall was up to 33 percentage points higher; and when accounting for duplicate estimates, total precision was up to 24 percentage points higher, compared to the state-of-the-art reference. In addition, a comparison was done using experimental data where the proposed algorithm had a matched recall of 97% and precision of 85% with respect to the reference algorithm. ConclusionThese results show a substantial performance increase for decomposition of simulated HDsEMG data and serve to validate the proposed approach. This performance increase is an important step towards complete decomposition and extraction of information of motor unit activity.

Hybrid Algorithm To Classify The Lung Abnormalities Using The Stethoscope Recordings

The pandemic of 2020 brought a lot of changes to the health and medical industry where a lot of smart devices started flowing in to compensate the lack of hospitals for the less severe cases. This work primarily focuses on the lung abnormalities as the lungs were one of the first organs to break down when the virus affected the body. The lung abnormalities whilst not becoming severe but will act as a catalyst to spread the virus’s effect through the lung thus concluding in complete pulmonary breakdown or irreversible lung damage. This work focuses on hybridisation of Fast Independent Component Analysis (FAST-ICA) algorithm coupled with Mel-frequency cepstral coefficients (MFCC) for independent component analysis and is fed as a input to the classification algorithm of Keras library which is the sequential algorithm. The pre-processing of the data in FAST-ICA is mainly based on three components of sound which are frequency, amplitude and wavelength.

A novel modified long short term memory architecture for automatic liver disease prediction from patient records

SummaryThe liver is the second largest organ in the human body after the skin and liver disease mainly impacts the liver's functionality by properly separating the nutrients and waste into the digestive system and also causes scarring (cirrhosis) as time passes. The scarring over time affects the healthy liver tissue and also affects its proper functioning and if left untreated for a prolonged period it can also result in severe complications such as liver failure or liver cancer. The patients can be prevented from the severe complications if the disease is detected at an earlier stage and the existing research for liver disease prediction mainly encouraged the usage of intelligent machine learning‐based techniques. However, these techniques have several complexities such as low accuracy, overfitting, higher training time, poor feature extraction capabilities and so on. To overcome these problems, we present modified long short term emory (MLSTM) architecture for chronic liver disease prediction. The proposed methodology has three stages: information enhancement, feature extraction, and classification. The modified generative adversarial network uses an autoencoder system for sample augmentation which helps to enrich the diversity present in both the normal and abnormal classes. The outlier information is eliminated via the criminal search algorithm which captures the differences and correlation associated with multiple samples. The fast independent component analysis algorithm and enhanced whale optimization algorithm are used for feature extraction. This step mainly identifies the crucial features for liver disease prediction and leaves out the irrelevant and duplicate features thus enhancing the convergence, computational time, and prediction accuracy. The MLSTM architecture is used to classify the samples present in the liver disease datasets into normal and abnormal (liver disease) classes. The proposed methodology offers improved performance in terms of accuracy, recall, means square error, and F‐measure. The results show that the proposed methodology will be efficient for doctors to diagnose liver disease in the earlier stage.

60 Gb/s coherent optical secure communication over 100 km with hybrid chaotic encryption using one dual-polarization IQ modulator

We propose and experimentally study a coherent optical secure transmission system based on one dual-polarization in-phase and quadrature modulator (IQM). One beam of the polarized light is used to generate broadband chaos by configuring a nonlinear opto-electronic oscillator while the other beam carries the encrypted signal. The encrypted signal is obtained through sequential encryption of the analog and digital chaos. The mutual mask of the hybrid chaotic signals can effectively enhance the security performance. Moreover, by varying the encryption depth of analog and digital vectors, the transmission performance can be flexibly adjusted. A commercial dual-polarization IQM could simultaneously generate a chaotic signal and a load message, which provides a high-integration solution. A fast independent component analysis (ICA) algorithm is adopted to compensate for the rotation of state of polarization (RSOP). 60 Gb/s encrypted quadrature phase shift keying (QPSK) signal transmission over 100 km single-mode fiber is realized, and the decrypted bit error rate (BER) performance is below the 7% forward error correction (FEC) threshold (BER = 3.8 × 10-3).

Fetal Electrocardiogram Signal Extraction Based on Fast Independent Component Analysis and Singular Value Decomposition

Fetal electrocardiograms (FECGs) provide important clinical information for early diagnosis and intervention. However, FECG signals are extremely weak and are greatly influenced by noises. FECG signal extraction and detection are still challenging. In this work, we combined the fast independent component analysis (FastICA) algorithm with singular value decomposition (SVD) to extract FECG signals. The improved wavelet mode maximum method was applied to detect QRS waves and ST segments of FECG signals. We used the abdominal and direct fetal ECG database (ADFECGDB) and the Cardiology Challenge Database (PhysioNet2013) to verify the proposed algorithm. The signal-to-noise ratio of the best channel signal reached 45.028 dB and the issue of missing waveforms was addressed. The sensitivity, positive predictive value and F1 score of fetal QRS wave detection were 96.90%, 98.23%, and 95.24%, respectively. The proposed algorithm may be used as a new method for FECG signal extraction and detection.

Evaluation of decomposition parameters for high-density surface electromyogram using fast independent component analysis algorithm

The surface electromyogram (sEMG) signal reveals the electrical neuromuscular activities and offers theoretical and clinical information. Recently, exploring motoneuron discharge events at the microscopic level through decomposition has been proposed as a promising analysis approach, which can surpass traditional global sEMG-based analysis method in some aspects. However, computational efficiency is an essential issue when discharge events of individual motor unit are decomposed using blind source separation algorithms. Therefore, choosing proper parameters of decomposition algorithms for different research purposes is important. Accordingly, we have systematically investigated the influences between computation time and decomposition performance for fast independent component analysis (FastICA)-based sEMG decomposition algorithm under different value selections of five decomposition parameters, namely the percentage of eliminated channels, extension factor, the number of decomposition iteration loops, the number of maximum inner loops in each iteration and sampling frequency. We employed high-density sEMG signals from 14 intact subjects during muscle contractions of four-digit extension and flexion at different force levels (20% and 50% maximum voluntary contraction) and a public dataset for sEMG decomposition. According to obtained results, we offer four preference suggestions (less computation time, more motor units, higher accuracy and trade-off). Results show that the trade-off values with consideration of decomposition performance and computation time are recommended as 25% of channels with minimal root mean square, extension factor of 4, 200 iteration loop numbers of decomposition, 20 maximum inner loop numbers and 2048 sampling frequency. Overall, this paper provides a guide for researchers to determine proper decomposition parameters for sEMG decomposition-related works.

Fractional Order Butterworth Filter for Fetal Electrocardiographic Signal Feature Extraction

The non-invasive Fetal Electrocardiogram (FECG) signal has become a significant method for monitoring the fetus's physiological conditions, extracted from the Abdominal Electrocardiogram (AECG) during pregnancy. The current techniques are limited during delivery for detecting and analyzing fECG. The non - intrusive fECG recorded from the mother's abdomen is contaminated by a variety of noise sources, can be a more challenging task for removing the maternal ECG. These contaminated noises have become a major challenge during the extraction of fetal ECG is managed by uni-modal technique. In this research, a new method based on the combination of Wavelet Transform (WT) and Fast Independent Component Analysis (FICA) algorithm approach to extract fECG from AECG recordings of the pregnant woman is proposed. Initially, preprocessing of a signal is done by applying a Fractional Order Butterworth Filter (FBWF). To select the Direct ECG signal which is characterized as a reference signal and the abdominal signal which is characterized as an input signal to the WT, the cross-correlation technique is used to find the signal with greater similarity among the available four abdominal signals. The model performance of the proposed method shows the most frequent similarity of fetal heartbeat rate present in the database can be evaluated through MAE and MAPE is 0.6 and 0.041209 respectively. Thus the proposed methodology of de-noising and separation of fECG signals will act as the predominant one and assist in understanding the nature of the delivery on further analysis.

3D Geophysical Post-Inversion Feature Extraction for Mineral Exploration through Fast-ICA

A major problem in the post-inversion geophysical interpretation is the extraction of geological information from inverted physical property models, which do not necessarily represent all underlying geological features. No matter how accurate the inversions are, each inverted physical property model is sensitive to limited aspects of subsurface geology and is insensitive to other geological features that are otherwise detectable with complementary physical property models. Therefore, specific parts of the geological model can be reconstructed from different physical property models. To show how this reconstruction works, we simulated a complex geological system that comprised an original layered Earth model that has passed several geological deformations and alteration overprints. Linear combination of complex geological features comprised three physical property distributions: electrical resistivity, induced polarization chargeability, and magnetic susceptibility models. This study proposes a multivariate feature extraction approach to extract information about the underlying geological features comprising the bulk physical properties. We evaluated our method in numerical simulations and compared three feature extraction algorithms to see the tolerance of each method to the geological artifacts and noises. We show that the fast-independent component analysis (Fast-ICA) algorithm by negentropy maximization is a robust method in the geological feature extraction that can handle the added unknown geological noises. The post-inversion physical properties were also used to reconstruct the underlying geological sources. We show that the sharpness of the inverted images is an important constraint on the feature extraction process. Our method successfully separates geological features in multiple 3D physical property models. This methodology is reproducible for any number of lithologies and physical property combinations and can recover the latent geological features, including the background geological patterns from overprints of chemical alteration.

Random Forest (RF) based identification of rice powder mixture using terahertz spectroscopy

Adulteration is a severe problem in agriculture field. It may cause some dishonest traders in the marketing side. For their own wish, dishonest traders make adulteration products for higher profit. In consumer side, agro product adulterations provide some unhealthy situation. It may cause severe health problem and also some incurable disease. Its right time, need to find an adulteration various level of agro products. For detecting rice adulteration, to collect spectral data for mixed rice powder (low quality rice powder and high quality rice powder), and a Fast Independent Component Analysis (FICA) algorithm is used to extract valuable information. Then, apply Random Forest Classifier algorithm for classifying purpose, whether the mixture is low quality or high quality. It achieves high accuracy with a single model and also easy to implement.

Mechanical Compound Fault Analysis Method Based on Shift Invariant Dictionary Learning and Improved FastICA Algorithm

For mechanical compound fault, it is of great significance to employ the vibration signal of a single-channel compound fault to analyze and realize the separation of multiple fault sources, which is essentially the problem of single-channel blind source separation. Shift invariant K-means singular value decomposition (shift invariant K-SVD) dictionary learning is suitable to extract the periodic and repeated fault features of a rotating machinery fault, hence in this article a single-channel compound fault analysis method is put forward which combines shift invariant K-SVD with improved fast independent component analysis (improved FastICA) algorithm. Firstly, based on single-channel compound fault signal, the shift invariant K-SVD algorithm can be used for learning multiple latent components that can be constructed as a virtual multi-channel signal. Then the improved FastICA algorithm is utilized to realize the separation of multiple fault source signals. With regard to the FastICA algorithm, the third-order convergence Newton iteration method is adopted to improve convergence speed. Moreover, in order to address the problem that FastICA is very sensitive to initialization, a steepest descent method can be applied. The experimental analysis of the compound fault of rolling bearing verifies that the presented method is effective to separate multiple fault source signals and the improved FastICA algorithm can increase convergence rate and overcome the problem of sensitivity to initialization.

Fast Independent Component Analysis Algorithm-Based Diagnosis of L5 Nerve Root Compression and Changes of Brain Functional Areas Using 3D Functional Magnetic Resonance Imaging.

In this paper, the application of 3-dimensional (3D) functional magnetic resonance imaging (FMRI) in the diagnosis of the 5th lumbar (L5) nerve root compression and brain functional areas in patients with lumbar disc herniation (LDH) was analyzed. The traditional fast independent component analysis (Fast ICA) algorithm was optimized based on the modified whitening matrix to establish a new type of Modified-Fast ICA (M-Fast ICA) algorithm that was compared with the introduced traditional Fast ICA and ICA. M-Fast ICA was applied to the 3D FMRI diffusion tensor imaging (DTI) evaluation of 65 patients with L5 nerve root pain due to LDH (group A) and 50 healthy volunteers (group B). The values of fractional anisotropy (FA) and apparent diffusion coefficient (ADC) in the lumbar nerve roots (L3, L4, L5, and the 1st sacral vertebra (S1)) were recorded among subjects from the two groups. Besides, the score of edema degree in the lumbar nerve roots (L5 and S1) and activity of brain functional areas were also recorded among all subjects of the two groups. The results showed that the mean square error of M-Fast ICA was smaller than that of traditional Fast ICA and ICA, while its signal-to-noise ratio (SNR) was greater than that of Fast ICA and ICA (P < 0.05). The FA of L5 and S1 nerve roots in patients of group A was sharply lower than the values of group B, while the ADC of patients in group A was greater than that of the control group (P < 0.05). Besides, the score of edema in L5 and S1 nerve roots of patients in group A increased in contrast to group B (P < 0.05). The brain areas were activated after surgery including bilateral temporal lobe, left thalamus, splenium of corpus callosum, and right internal capsule. In conclusion, the 3D image denoising performance of M-Fast ICA optimized and constructed in this study was superior to that of the traditional Fast ICA and ICA. The FA of patients with L5 nerve root pain due to LDH decreased steeply, while the ADC increased dramatically. L5 nerve root pain caused by LDH resulted in changes in brain functional areas of the patients to inhibit the resting state default network activity, and the corresponding brain functional areas could be activated through treatment.