Identification of image global processing operator chain based on feature decoupling

Discrimination between normal sinus rhythm (NSR), atrial fibrillation (AF) and atrial flutter (AFL) using the surface electrocardiogram (ECG) through direct Fourier analysis is not effective because ventricular activity (VA) overlaps in frequency with atrial activity (AA) and, moreover, AA frequency components present considerably lower amplitude than VA. This contribution exploits the assumption that AA and VA can be considered as generated by independent sources of cardioelectric activity. Hence, AA free from spurious VA and other disturbances can be extracted through the application of blind source separation (BSS) techniques. After the blind separation and AA identification process, it was applied a logistic regression over a group of spectral parameters extracted from the power spectral density of the AA signal that was able to discriminate between AF, AFL and NSR with a 100% accuracy considering only two spectral parameters.

Recently, many forensic techniques have been developed to detect the use of a certain processing operation. When utilizing several manipulations to alter an image, artifacts left by manipulations that have been applied later can potentially disguise traces left by manipulations that were applied earlier. Therefore, the detection of manipulations become difficult. In this paper, we focus on identifying the manipulations in an image operation chain composed of multiple manipulations in a certain order. To address this issue, we analyze the relationship between manipulations identification and blind signal separation. Then, we propose a features decoupling method based on blind signal separation, which decouples the coupled features due to the superimposed processing artifacts and exploits the decoupled features to identify multiple operations. The experiments carried out on two image operation chains confirm the effectiveness of the proposed method.

Combining the advantages of local mean decomposition and variational Bayesian hidden Markov model, a blind source separation method for mechanical faults based on local mean decomposition and variational Bayesian hidden Markov model is proposed. In the proposed method, the non-stationary signals are decomposed into a series of production functions by the local mean decomposition, then the obtained production functions and the original observed signals are used to construct new observation signals, therefore the underdetermined blind source separation problem is transformed into the overdetermined blind source separation problem. Finally, the source signals are estimated by the variational Bayesian hidden Markov model method. The proposed method has been successfully applied to the blind separation of bearing faults, and the experiment results verify the effectiveness of the proposed method. The obtained outcome in this paper provides an effective separation method for mechanical fault diagnosis.

The indirect method of using a passing vehicle to identify modal properties of a girder bridge has become attractive recently. Compared to the direct method, which requires a lot of sensors installed directly on the bridge itself, the indirect method only requires a single sensor installed on the vehicle to indirectly measure the response of the bridge. However, it is difficult to eliminate the adverse effect of road surface roughness. An indirect approach based on blind source separation is proposed for the first time in this study to identify the bridge element stiffness where two movable vehicles are used. Two identical vehicles stay at rest at the designated measurement points and their vertical accelerations are collected. After one measurement, the two vehicles move to other designated measurement points and the accelerations are collected again. The same procedure is repeated until the two vehicles have moved over all the designated measurement points. Then the blind source separation technique is employed to extract the fundamental mode shape of the bridge and the improved direct stiffness method is adopted to estimate the bridge element stiffness based on the collected data, which are used to monitor the health of the bridge structure and to maintain structure safety and natural symmetry. The proposed method only requires the output response of the vehicle due to the involvement of the blind separation technique. In addition, the proposed method can overcome the adverse effect of road surface roughness because the vehicles only move between two measurements and they stay at rest during one measurement. Numerical simulation was conducted to validate the proposed method, and the effect of various factors such as bridge damping ratio and measurement noise was investigated. Field measurement on Min-Xie bridge in Chongqing city was also carried out to further investigate the feasibility of the proposed method and showed that it can perform well in extracting the fundamental mode shape and evaluating bridge element stiffness.

Blind Signal Separation II. Linear, Convolutive Combinations: II. Linear, Convolutive Combinations

Neural models for the blind separation of signals (BSS) from their linear mixtures are traditionally based on higher order moments. For example, models based on PCA extensions, such as nonlinear PCA, perform analysis of signals into independent components. However, second order models have been also used for BSS under the assumptions of non-correlation (as opposed to independence) and of the different spectral coloring of the sources. Yet these models were either based on second order optimization or on linear extensions of PCA. We show that standard PCA neural models can perform BSS through the equation (temporal filtering+PCA=BSS), which states that BSS is PCA preceded by temporal filtering. This result is both shown theoretically and demonstrated by simulation. Although almost any temporal filter will work, the question on the optimal filter is still open. Some discussion on this issue for filters of length 2 is given.

In order to overcome the problem of low accuracy and poor stability caused by the one-sided information blind separation analysis of the current communication channel interference suppression research methods, this paper proposes the optical communication network channel interference suppression method based on blind signal separation algorithm. The receiver unit, field programmable gate array (FPGA) unit, memory chip group and other modules are used to construct the channel signal acquisition system, and the collected signals are preprocessed by centralisation and whitening. Based on the signal preprocessing results, the blind separation of instantaneous linear mixed signals, blind separation of convolutional mixed signals and blind separation of sparse sources were comprehensively analysed to obtain the source signals and realise channel interference suppression of optical communication network. Experimental results show that this method has high interference precision, strong stability and good application prospect.

ERP is commonly obtained by averaging over segmented EEC epochs. In case artifacts are present in the raw EEC measurement, pre-processing is required to prevent the averaged ERP waveform being interfered by artifacts. The simplest pre-processing approach is by rejecting trials in which presence of artifact is detected. Alternatively artifact correction instead of rejection can be performed by blind source separation, so that waste of ERP trials is avoided. In this paper, we propose a second order statistics based blind source separation approach to ERP artifact correction. Comparing with blind separation using independent component analysis, second order statistics based method does not rely on higher order statistics or signal entropy, and therefore leads to more robust separation even if only short epochs are available.

In this paper, we propose a novel algorithm for underdetermined blind identification problem in blind source separation. The proposed algorithm is based on the charrelation matrix of observed signals. The charrelation matrix not only can be considered as a generalized second-order covariance matrix, but also incorporates higher-order information. It is significant for blind separation problem based on statistic characteristics to extract statistical information. The problem of underdetermined blind identification is converted as a tensor decomposition model. The mixing matrix can be estimated from the rank-1 terms of the tensor decomposition. Theoretical analysis and simulation results illustrate the proposed algorithm performs competitive estimation performance compared to the underdetermined blind identification algorithm based on second-order covariance and fourth-order cumulant.

The technique of blind source separation (BSS) has been well studied. In this paper, we apply the BSS technique, particularly based on independent component analysis (ICA), to a meeting situation. The goal is to enhance the spoken utterances and to estimate the location of each speaker by means of multiple microphones. The technique may help us to take the minutes of a meeting.

Upgrading Sparse NMF algorithm for blind source separation through Adaptive Parameterized Hybrid Kernel based approach

Indoor vehicle pass-by noise applications deal with measuring the exterior noise from a vehicle fixed on a chassis dynamometer in a large hemi-anechoic room. During a standardised acceleration test, the noise is measured with an array of microphones placed in the far-field, and the overall noise level versus vehicle position can be simulated. The indoor facility allows controlled and repeatable measurements independent of weather. For engineering purposes, pass-by contribution analysis can be included in the test leading to information about the pass-by noise contribution from major noise sources. This work presents a novel application of blind source separation to vehicle measurements from an indoor pass-by measurement campaign. In contrast to the classical transfer path approach using point sources for modelling vehicle noise sources and combining an operational measurement with transfer functions, the blind approach does not consider a specific noise source model. It only assumes that the noise is produces by a set of independent noise sources using only a single operational measurement for a given vehicle condition as input. Near-field microphone measurements are blindly separated into independent components and further correlated with the signals measured at the far-field indoor pass-by microphones to get the time-domain contributions. Finally, we apply the indoor simulated pass-by algorithm to produce noise contribution levels as a function of vehicle position. We discuss the specified application of blind source separation to vehicle measurements for different operating conditions from a real indoor pass-by test. Separation of tyre and engine related noise at tyre near-field microphones is verified. Furthermore, the tyre pass-by noise contribution is extracted from the overall vehicle measurement.

Blind signal separation is currently an active research topic, and many techniques for accomplishing it have been discussed in the literature. In his excellent survey article [4], Cardoso describes blind signal separation (BSS) as the recovery of “unobserved signals of ‘sources’ from several observed mixtures”. This paper describes a particular implementation, a recently constructed two-channel processor capable of operating on convolutive mixtures of QAM signals running at symbol rates as fast as 40 MHz. This processor has been applied to several practical problems, including the cancellation of cross-pole interference from a digital radio signal of interest and the diversity combination of signals received from two apertures or antenna feeds.

Recently, blind source separation (BSS) methods have gained significant attention in the area of signal processing. Independent component analysis (ICA) and second-order blind identification (SOBI) are two popular BSS methods that have been applied to modal identification of mechanical and structural systems. Published results by several researchers have shown that ICA performs satisfactorily for systems with very low levels of structural damping, for example, for damping ratios of the order of 1% critical. For practical structural applications with higher levels of damping, methods based on SOBI have shown significant improvement over ICA methods. However, traditional SOBI methods suffer when nonstationary sources are present, such as those that occur during earthquakes and other transient excitations. In this paper, a new technique based on SOBI, called the modified cross-correlation method, is proposed to address these shortcomings. The conditions in which the problem of structural system identification can be posed as a BSS problem is also discussed. The results of simulation described in terms of identified natural frequencies, mode shapes, and damping ratios are presented for the cases of synthetic wind and recorded earthquake excitations. The results of identification show that the proposed method achieves better performance over traditional ICA and SOBI methods. Both experimental and large-scale structural simulation results are included to demonstrate the applicability of the newly proposed method to structural identification problems.

In order to overcome the problem of low accuracy and poor stability caused by the one-sided information blind separation analysis of the current communication channel interference suppression research methods, this paper proposes the optical communication network channel interference suppression method based on blind signal separation algorithm. The receiver unit, field programmable gate array (FPGA) unit, memory chip group and other modules are used to construct the channel signal acquisition system, and the collected signals are preprocessed by centralisation and whitening. Based on the signal preprocessing results, the blind separation of instantaneous linear mixed signals, blind separation of convolutional mixed signals and blind separation of sparse sources were comprehensively analysed to obtain the source signals and realise channel interference suppression of optical communication network. Experimental results show that this method has high interference precision, strong stability and good application prospect.