Independent Component Analysis With Reference Research Articles

VLSI Design of a Fast One-Stage Independent Component Extracting System Based on ICA-R Algorithm

Independent component analysis (ICA) is an efficient blind source separation technique, but the extracted independent components are randomly permuted in classic ICA algorithms, and subsequent identification is required to find the desired component. Such a two-stage method causes inefficiency. By utilizing the prior information, ICA with reference (ICA-R) algorithm can extract the desired source signal in one stage without subsequent processing. There are many theoretical extensions on ICA-R, but few hardware implementations can be found. Therefore, the efficient VLSI design of a fast one-stage independent component extracting system based on ICA-R algorithm is presented in this paper. The proposed system consists of Preprocess module and Iteration module, which are designed highly parallel and pipelined to accelerate the extraction process. The designed system is implemented in Kintex-7 FPGA, and its performance is verified using synthesized signal. Experiment results show that the presented system can extract the desired components in one stage without subsequent identification, and the highly parallel circuit structure of the system speeds up the component extracting process by [Formula: see text] compared to software implementation.

Approach and Application of Semi-Blind Source Separation for Aero-Engine Vibration Signals Using ICA-R

In this paper, the approach and application of semi-blind source separation (SBSS) in aero-engine vibration signal is studied. Firstly, the features of aero-engine vibration signal and difficulties for blind source separation (BSS) are summarized, and the SBSS incorporated the available prior knowledge is match to the goal of signal processing. Then, the ICA with reference (ICA-R) algorithm based on classical FastICA is introduced, with Newton iteration and gradient descent iteration approach to obtain optimal solution. The unique parameters in ICA-R for aero-engine vibration signal are also provided. Finally, the efficacy and the accuracy of the ICA-R algorithm are verified by numerical simulations and real engine vibration signals. The approach of SBSS in this paper perfectly suited to handle aero-engine vibration source separation and it lead to efficient implementation in fault diagnosis.

Evolutionary Based ICA With Reference for EEG <inline-formula> <tex-math notation="LaTeX">$\mu$ </tex-math> </inline-formula> Rhythm Extraction

Independent component analysis with reference (ICA-R), a paradigm of constrained ICA (cICA), incorporates textita priori information about the desired sources as reference signals into the contrast function of ICA. Reference signals direct the search toward the separation of desired sources more efficiently and accurately than the ICA. The penalized contrast function of ICA-R is non-smooth everywhere and the ICA-R algorithm does not always reach the global optimum due to the Newton-like learning used. In this paper, we propose a constrained differential evolutionary algorithm with an improved initialization strategy to solve the constrained optimization problem of ICA-R that can asymptotically converge to the optimum. It completely avoids the formulation of a penalized contrast function and scaling (due to the Lagrangian multipliers) by incorporating the ICA contrast function and the violation of the closeness constraint into the selection process of the evolution. Experiments with synthetic data and isolation of $\mu $ rhythmic activity from EEG showed improved source extraction performance over ICA-R and its recent enhancements.

A Model-Based Transit-Time Ultrasonic Gas Flowrate Measurement Method

Based on the transit-time ultrasonic gas flowrate measurement principle, a new model-based time-of-flight (TOF) measurement method is proposed. The proposed TOF measurement method includes two parts, the signal preprocessing and the model-fitting. Zero-phase filtering and independent component analysis with reference (ICA-R) are used to realize the signal preprocessing. The least-squares algorithm is used to implement the model-fitting and hence to obtain the TOF. A new single-acoustic-path ultrasonic gas flowmeter prototype with the inner diameter of 100 mm is developed and experiments are carried out to test the performance of the prototype. Experimental results show that the proposed signal preprocessing can effectively correct the distorted/noise-polluted measurement signal. In addition, the proposed model-based TOF measurement method is effective and can implement TOF measurement with higher repeatability and low standard deviation. For the gas flowrate ranges from 5 to 50 $\text{m}^{3}$ /h, the repeatability error of gas flowrate measurement is less than 1.99% and the maximum relative error of flowrate measurement is less than 3.27%. For the gas flowrate ranges from 50 to 500 $\text{m}^{3}$ /h, the repeatability error of gas flowrate measurement is less than 0.51% and the maximum relative error of flowrate measurement is less than 1.43%.

Single-trial event-related potential extraction through one-unit ICA-with-reference

Objective. In recent years, ICA has been one of the more popular methods for extracting event-related potential (ERP) at the single-trial level. It is a blind source separation technique that allows the extraction of an ERP without making strong assumptions on the temporal and spatial characteristics of an ERP. However, the problem with traditional ICA is that the extraction is not direct and is time-consuming due to the need for source selection processing. In this paper, the application of an one-unit ICA-with-Reference (ICA-R), a constrained ICA method, is proposed. Approach. In cases where the time-region of the desired ERP is known a priori, this time information is utilized to generate a reference signal, which is then used for guiding the one-unit ICA-R to extract the source signal of the desired ERP directly. Main results. Our results showed that, as compared to traditional ICA, ICA-R is a more effective method for analysing ERP because it avoids manual source selection and it requires less computation thus resulting in faster ERP extraction. Significance. In addition to that, since the method is automated, it reduces the risks of any subjective bias in the ERP analysis. It is also a potential tool for extracting the ERP in online application.

A source contribution quantitative calculation method for mechanical systems based on the simplified independent component analysis with reference

The quantitative calculation of the source contribution is very important and critical for the identification of the main vibration sources and the reduction of vibration and noise in submarine. It is difficult to calculate the source contribution because of the submarine’s complex structure and the large amount of vibration sources. As a typical blind source separation method, independent component analysis (ICA) has recently been proved to be an effective method to solve the source identification problem in which the source signals and mixing models are unknown. However, the outcomes of the ICA algorithm are affected by random sampling and random initialization of variables. In our study, the prior knowledge of the vibration sources can be obtained through the vibration measurement of submarine. Obviously, information in addition to mixed signals from sensors can lead to a more accurate separation. Therefore the contrast function of ICA can be enhanced by the reference signals obtained by the prior knowledge. In this paper, a closeness measurement between the independent components and the reference signals obtained by the prior knowledge is introduced, and the closeness measurement is constructed to have the same optimization direction with the traditional contrast function: negentropy. The closeness measurement is used to enhance the contrast function and then the enhanced contrast function is optimized by means of the Newton iteration and the deflation approach. Thus the simplified independent component analysis with reference (ICA-R) algorithm is obtained. After that a method to quantitatively calculate the source contribution is proposed based on the outcomes of the simplified ICA-R. Finally, the effectiveness of the proposed method is verified by the numerical simulation studies. The performance offered by the proposed method is also investigated by the experiment: it appear as a very appealing tool for the quantitative calculation of the source contribution.

A Fetal Electrocardiogram Signal Extraction Algorithm Based on Fast One-Unit Independent Component Analysis with Reference.

Fetal electrocardiogram (FECG) extraction is very important procedure for fetal health assessment. In this article, we propose a fast one-unit independent component analysis with reference (ICA-R) that is suitable to extract the FECG. Most previous ICA-R algorithms only focused on how to optimize the cost function of the ICA-R and payed little attention to the improvement of cost function. They did not fully take advantage of the prior information about the desired signal to improve the ICA-R. In this paper, we first use the kurtosis information of the desired FECG signal to simplify the non-Gaussian measurement function and then construct a new cost function by directly using a nonquadratic function of the extracted signal to measure its non-Gaussianity. The new cost function does not involve the computation of the difference between the function of the Gaussian random vector and that of the extracted signal, which is time consuming. Centering and whitening are also used to preprocess the observed signal to further reduce the computation complexity. While the proposed method has the same error performance as other improved one-unit ICA-R methods, it actually has lower computation complexity than those other methods. Simulations are performed separately on artificial and real-world electrocardiogram signals.

Saliency detection via PCA of image patches and ICA-R

A variety of computational models based on different views for visual saliency detection have emerged in computer vision. In this paper, we combine RGB space and Lab space to represent original features of each image patch in the vector form, and employ principal component analysis (PCA) to select effective features. The saliency cue is obtained by integrating the contrast and distribution of image patches in the reduced dimensional space and supposed to be the reference for saliency foreground. Then we regard saliency map computation as a source signal separation problem in the framework of independent component analysis with reference (ICA-R). Our approach utilizes the common ICA-R to separate the saliency object from the background of the input image with the saliency cue. The main benefits consist of two aspects. On one hand, the combination of Lab and RGB color spaces in both the saliency reference computation and saliency result computation is able to produce better saliency maps than those using only one color space. On the other hand, the input image is assumed to be a mixture of saliency foreground and background, which enables the saliency detection can be embedded into the source separation problem. This source separation problem is solved by ICA-R with the reference for saliency foreground. Extensive experiments on public saliency benchmarks demonstrate that compared with the state-of-the-art algorithms, the proposed method is more effective.

Surface chest motion decomposition for cardiovascular monitoring.

Surface chest motion can be easily monitored with a wide variety of sensors such as pressure belts, fiber Bragg gratings and inertial sensors, etc. The current applications of these sensors are mainly restricted to respiratory motion monitoring/analysis due to the technical challenges involved in separation of the cardiac motion from the dominant respiratory motion. The contribution of heart to the surface chest motion is relatively very small as compared to the respiratory motion. Further, the heart motion spectrally overlaps with the respiratory harmonics and their separation becomes even more challenging. In this paper, we approach this source separation problem with independent component analysis (ICA) framework. ICA with reference (ICA-R) yields only desired component with improved separation, but the method is highly sensitive to the reference generation. Several reference generation approaches are developed to solve the problem. Experimental validation of these proposed approaches is performed with chest displacement data and ECG obtained from healthy subjects under normal breathing and post-exercise conditions. The extracted component morphologically matches well with the collected ECG. Results show that the proposed methods perform better than conventional band pass filtering.

A novel algorithm for independent component analysis with reference and methods for its applications.

This paper presents a stable and fast algorithm for independent component analysis with reference (ICA-R). This is a technique for incorporating available reference signals into the ICA contrast function so as to form an augmented Lagrangian function under the framework of constrained ICA (cICA). The previous ICA-R algorithm was constructed by solving the optimization problem via a Newton-like learning style. Unfortunately, the slow convergence and potential misconvergence limit the capability of ICA-R. This paper first investigates and probes the flaws of the previous algorithm and then introduces a new stable algorithm with a faster convergence speed. There are two other highlights in this paper: first, new approaches, including the reference deflation technique and a direct way of obtaining references, are introduced to facilitate the application of ICA-R; second, a new method is proposed that the new ICA-R is used to recover the complete underlying sources with new advantages compared with other classical ICA methods. Finally, the experiments on both synthetic and real-world data verify the better performance of the new algorithm over both previous ICA-R and other well-known methods.

An Improved Blind Source Extraction Algorithm

One-unit independent component analysis with reference (ICA-R) is an efficient method capable of extracting a desired source signal by using reference signal. In this paper, a new fast one-unit ICA-R algorithm is derived by using kurtosis contrast function based on new constrained independent component analysis (cICA) theory. The proposed algorithm has lower computational complexity and accurate extraction. Experiments with synthetic signals demonstrate the efficacy and accuracy of the proposed algorithm.

A Novel Solution for the Coupled Faults Isolation in Gear Pairs Using the Conception of Frequency Tracking

In engineering practice, there is always evidence for the existence of multiply faults in the gear meshing pairs. Different failures often occur simultaneously and thus increase the complexity of fault diagnosis. A literature review indicates that very limited work has been done to address the coupled faults isolation for gear pairs. To solve this issue, this work presents a novel approach to detect coupled faults of gear pairs using a conception of Frequency Tracking. The independent component analysis with reference (ICA-R) has been employed as the fault frequency tracking tool to extract critical frequency sources from multi-channel sensors. The frequency tracking scheme uses the prior knowledge of the gear rotational frequencies to construct suitable reference signals to incorporate into the ICA-R. Then the intrinsic frequency sources excited by every failure can be separated one by one. Hence, the coupled faults are simplified into single fault isolation problem. The experimental tests were carried out to evaluate the performance of the proposed approach. The test results show satisfactory isolation of coupled gear faults. DOI: http://dx.doi.org/10.5755/j01.eee.20.3.6680

A comparative study and improvement of two ICA using reference signal methods

In this paper, we present a comparative assessment of two ICA using reference signal methods. Independent Component Analysis (ICA) using reference signal is a useful tool for extracting a desired independent component (IC). Reference signal is served as a priori information to conduct the one-unit ICA to converge to the local extreme point related to a desired IC. There are two methods can perform ICA using reference signal, namely ICA with reference (ICA-R) and fast ICA with reference signal (FICAR). This paper intends to fill in a gap in the previous studies to give comparisons of those methods systematically. Moreover, we also provide useful improvements to the two methods. Firstly, we propose a improved algorithm to fix the flaw in the previous FICAR. Secondly, for ICA-R, we propose a criterion to select appropriate distance measurement.

A Single-Channel ICA-R Method for Speech Signal Denoising combining EMD and Wavelet

According to the problem of speech signal denoising, we propose a novel method in this paper, which combines empirical mode decomposition (EMD), wavelet threshold denoising and independent component analysis with reference (ICA-R). Because there is only one mixed recording, it is a single-channel independent component analysis (SCICA) problem in fact, which is hard to solve by traditional ICA methods. EMD is exploited to expand the single-channel received signal into several intrinsic mode functions (IMFs) in advance, therefore traditional ICA of multi-dimension becomes applicable. First, the received signal is segmented to reduce the processing delay. Secondly, wavelet thresholding is applied to the noise-dominated IMFs. Finally, fast ICA-R is introduced to extract the object speech component from the processed IMFs, whose reference signal is constructed by assembling the high-order IMFs. The simulations are carried out under different noise levels and the performance of the proposed method is compared with EMD, wavelet thresholding, EMD-wavelet and EMD-ICA approaches. Simulation results indicate that the proposed method exhibit superior denoising performance especially when signal-to-noise ratio is low, with a half shorter running time.

Driver fatigue detection from electroencephalogram spectrum after electrooculography artefact removal

A driver fatigue monitoring and detection system with high accuracy could be a valuable countermeasure to decrease fatigue-related traffic accidents. This study proposes methods for drowsiness detection based on electroencephalogram (EEG) power spectrum analysis. First, a new algorithm is proposed for independent component analysis with reference (ICA-R) for electrooculography artefacts removal. Comparison is then carried out between the proposed ICA-R algorithm and an adaptive filter. Secondly, 75 EEG spectrum features are extracted from the cleaned EEG. Among all the EEG spectrum-related features, 40 key features are selected by support vector machine recursive feature elimination to improve the performance of the classifier. The validation results show that 86% of the driver's drowsiness states can be accurately detected among drivers, who participate a driving simulator study.

One-unit second-order blind identification with reference for short transient signals

When prior knowledge is available, it is beneficial to use constrained blind source separation (BSS) algorithms that can utilize more information to distinguish the desired source from artifacts and noise. This paper proposes a one-unit second-order blind identification with reference (SOBI-R) algorithm for short transient signal extraction, which reformulates the conventional second-order blind identification (SOBI) algorithm in an iterative manner to achieve joint diagonalization and the reference information incorporated. The proposed algorithm was applied to single trial extraction of somatosensory evoked potential (SEP). The experimental results demonstrated its effectiveness. Compared with other algorithms including the autoregressive model with exogenous input (ARX), artificial neural networks (ANN) and one-unit the independent component analysis with reference (ICA-R), the proposed SOBI-R algorithm shows high robustness under conditions with low signal-to-noise ratios and less sensitivity to the reference signal.

Human body segmentation based on independent component analysis with reference at two-scale superpixel

In this study, a novel method to segment human body in static image is proposed based on independent component analysis with reference (ICA-R) at two-scale superpixel. In this work, the task is mainly decomposed into torso and lower body recovery. With the detected face, we obtain the reference signal of torso in the coarse torso region estimated by an augmented deformable torso model on the basis of the first-scale superpixel. The hip region is estimated based on the segmented torso for the lower body reference at the second-scale superpixel. Experiments on our dataset show that the proposed approach is robust and can accurately segment human body in images with a variety of poses, backgrounds and clothing.

A reference-based blind source extraction algorithm

By utilizing a priori information available as reference, constrained independent component analysis (cICA) or independent component analysis with reference (ICA-R) achieves some advantages over other methods. However, ICA-R is very time-consuming; moreover, it is very difficult to determine its threshold parameter, once the value is improperly chosen the algorithm will fail to converge. In order to overcome these drawbacks, a very simple blind source extraction method, whose optimization function is simply the closeness measure between the desired output and its corresponding reference in ICA-R, is proposed in this paper. Experiments with synthesized data and real-world electrocardiograph data confirm its validity and superiority.

An improved method for independent component analysis with reference

Through incorporating a priori information available in some applications for independent component analysis (ICA) as the reference into the negentropy contrast function for FastICA, ICA with reference (ICA-R) or constrained ICA (cICA) is obtained as a constrained optimization problem. ICA-R achieves some advantages over earlier methods, whereas its computation load is somewhat high and its performance is strongly dependent on the threshold parameter. By alternately optimizing the negentropy contrast function for FastICA and the closeness measure for ICA-R, an improved method for ICA-R is proposed in this paper which can avoid the inherent drawbacks of ICA-R. The validity of the proposed method is demonstrated by simulation experiments.

A fast algorithm for one-unit ICA-R

Independent component analysis (ICA) aims to recover a set of unknown mutually independent source signals from their observed mixtures without knowledge of the mixing coefficients. In some applications, it is preferable to extract only one desired source signal instead of all source signals, and this can be achieved by a one-unit ICA technique. ICA with reference (ICA-R) is a one-unit ICA algorithm capable of extracting an expected signal by using prior information. However, a drawback of ICA-R is that it is computationally expensive. In this paper, a fast one-unit ICA-R algorithm is derived. The reduction of the computational complexity for the ICA-R algorithm is achieved through (1) pre-whitening the observed signals; and (2) normalizing the weight vector. Computer simulations were performed on synthesized signals, a speech signal, and electrocardiograms (ECG). Results of these analyses demonstrate the efficiency and accuracy of the proposed algorithm.