Signal Separation Approach Research Articles

Identification of Partial Discharge Sources by Feature Extraction from a Signal Conditioning System.

This paper addresses the critical challenge of detecting, separating, and classifying partial discharges in substations. It proposes two solutions: the first involves developing a signal conditioning system to reduce the sampling requirements for PD detection and increase the signal-to-noise ratio. The second approach uses machine learning techniques to separate and classify PD based on features extracted from the conditioned signal. Three clustering algorithms (K-means, Gaussian Mixture Model (GMM), and Mean-shift) and the Support Vector Machine (SVM) method were used for signal separation and classification. The proposed system effectively reduced high-frequency components up to 50 MHz, improved the signal-to-noise ratio, and effectively separated different sources of partial discharges without losing relevant information. An accuracy of up to 93% was achieved in classifying the partial discharge sources. The successful implementation of the signal conditioning system and the machine learning-based signal separation approach opens avenues for more economical, scalable, and reliable PD monitoring systems.

Voltage-based separation of respiration and cardiac activity by harmonic analysis in electrical impedance tomography

This study aims to improve the accuracy of Electrical Impedance Tomography (EIT) measurements for monitoring ventilation and cardiac signal in medical imaging by proposing a new signal separation approach that does not require contrast agents. Conventionally, contrast agents like high-conductive saline solutions are used for signal separation in EIT measurements. This study uses a harmonic analysis on EIT raw voltage data to separate the ventilation- and cardiac-related signals (early separation). It evaluates its efficacy with a simulation model at low (1%) and high (10%) superimposed additive noise levels against the already published harmonic analysis at pixel level after EIT image reconstruction (late separation). The findings indicate that the voltage-based harmonic analysis approach, i.e., early separation, provides reliable signal separation, especially under high noise conditions, compared to the late separation. This method enables the possibility of incorporating independent cardiac-specific or ventilation-specific prior knowledge into the image reconstruction process, potentially improving the resulting images.

Groundwater storage dynamics in the Lake Chad Basin revealed by GRACE and a multi-sensor signal separation approach

Groundwater storage dynamics in the Lake Chad Basin revealed by GRACE and a multi-sensor signal separation approach

Fractal component analysis: an integrated approach for autocorrelated signal separation and health monitoring of feedback control system

ABSTRACT In a feedback control system like the manufacturing equipment or physiological system, its observed data usually manifests a specific autocorrelation structure and mixes with two external signals: disturbance and noise. The external disturbance and noise need to be estimated and compensated by the feedback control mechanism. However, the autocorrelation structure could mask problem signals and affect the monitoring of the system’s inner health. Without separating the mixed signals, neither the evaluation of inner health nor the compensation of external disturbance can be achieved. A new signal separation approach, fractal component analysis (FCA), is developed to identify the autocorrelation structure for improving the health of the feedback control system. As a result, our proposed method outperformed FastICA and principal component analysis (PCA).

Research on Anti-Jamming Technology of Chaotic Composite Short Range Detection System Based on Underdetermined Signal Separation and Spectral Analysis

The chaotic phase-modulated linear frequency modulation short-range detection system (CPLF-SrDS) is a new type of composite detection system. Compared with traditional methods, it benefits from the short-term randomness and long-term certainty of chaos and is more powerful in determining the distance. To design such a system, the key problem lies in robustly recovering the echo signal against externally suppressed interferences. Combining 4th-order cumulant and joint diagonalization, this paper first develops a new signal separation approach to realize an underdetermined separation of the echo signal and an interference signal from their mixture. Then, both bispectrum analysis and singular spectrum analysis are adapted to analyze the separated signals and extract their characteristic parameters. Furthermore, the GA-BP network is utilized to classify the echo and interference signals. Finally, the anti-interference analysis of this CPLF-SrDS is realized. The experimental results show the effectiveness of the proposed method in the presence of externally suppressed amplitude modulation (AM) interference and frequency modulation (FM) interference.

Complex-valued differential operator-based method for multi-component signal separation

The null space pursuit (NSP) algorithm is an operator-based signal separation approach which separates a signal into a set of additive subcomponents using adaptively estimated operators and parameters. In this paper, a new operator termed complex-valued differential (CD) operator is proposed. Combining with the CD operator, this paper proposes NSP-CD algorithm to solve the CD operator-based signal separation problem. The NSP-CD algorithm can separate the multi-component signal into sum of amplitude-modulated and frequency-modulated (AM–FM) signals in the form of A(t)exp(j)(ϕ(t)). The proposed NSP-CD algorithm has many advantages. Firstly, the proposed CD operator can ensure that the AM–FM signal totally lies in the null space of the operator rather than close to the null space that the original used operator may reach. Secondly, compared with the original NSP algorithm, our algorithm provides a more reasonable strategy to update the regularization parameter λ and the leakage factor γ. Finally, we have proved that the proposed algorithm has quadric convergence theoretically. Experiments on both synthetic and real-life signals demonstrate that the NSP-CD algorithm is more robust and effective than other state-of-the-art methods.

Adaptive Integral Operators for Signal Separation

The operator-based signal separation approach uses an adaptive operator to separate a signal into a set of additive subcomponents. In this paper, we show that differential operators and their initial and boundary values can be exploited to derive corresponding integral operators. Although the differential operators and the integral operators have the same null space, the latter are more robust to noisy signals. Moreover, after expanding the kernels of Frequency Modulated (FM) signals via eigen-decomposition, the operator-based approach with the integral operator can be regarded as the matched filter approach that uses eigen-functions as the matched filters. We then incorporate the integral operator into the Null Space Pursuit (NSP) algorithm to estimate the kernel and extract the subcomponent of a signal. To demonstrate the robustness and efficacy of the proposed algorithm, we compare it with several state-of-the-art approaches in separating multiple-component synthesized signals and real-life signals.

A denoising method based on null space pursuit for infrared spectrum

The reduction of noise in infrared spectrum is of vital importance, as the infrared signal is often buried in excessive noise. In this paper, a novel denoising method based on null space pursuit (NSP) is proposed to eliminate noise in the infrared signal. The NSP is an adaptive operator-based signal separation approach, which can decompose signal into subband components and residue according to their characteristics. The residue is processed by mean filter because it contains very little useful information. Then, the subband components and the filtered residue are used to reconstruct the noise-free signal. Experimental results show that the proposed denoising method is effective in suppressing noise while retaining signal characteristics.

A detection method for bearing faults using null space pursuit and S transform

In this paper, a novel measurement method based on null space pursuit and S transform is presented to detect the faults of the bearing vibration signal. Since the features of bearing faults in the vibration signal may be submerged by machine-borne vibration and the high intensity of noise, it is very difficult to detect the bearing faults directly. The proposed method is a hybrid of null space pursuit and S transform. As a signal separation approach, the null space pursuit based on operator can extract the fault components from the vibration signal efficiently. Then, S transform is introduced to represent a “Magnitude-Frequency” and a “Time-Frequency” contour of the extracted fault components. Finally, several real vibration signals containing bearing fault signature are investigated to verify the effectiveness of the proposed method.

Multicomponent AM-FM signal separation and demodulation with null space pursuit

The operator-based signal separation approach, which formulates signal separation as an optimization problem, uses an adaptive operator to separate a signal into additive subcomponents. Furthermore, it is possible to design different operators to fit different signal models. In this paper, we propose a new kind of differential operator to separate multicomponent AM-FM signals. We then use the estimated operators to calculate each sub-component’s envelope and instantaneous frequency. To demonstrate the efficacy of the proposed method, we compare the decomposition and AM-FM demodulation results of several signals, including real-life signals.

A user voice reduction algorithm based on binaural signal separation for portable digital imaging devices

In this paper, a user voice reduction algorithm for portable digital imaging devices is proposed based on a binaural signal separation approach in order to improve the naturalness of user-generated video contents. The proposed algorithm first estimates the interaural time differences (ITDs) from binaural signals recorded by the microphones equipped on a device. Then, the estimated ITDs are used to obtain the time-frequency domain masking patterns of a user voice against an actual subject sound of video content. Finally, the user voice recorded in video content can be reduced by applying the mask patterns to the binaural signals. In order to demonstrate the effectiveness of the proposed algorithm, the proposed algorithm is implemented on a portable digital imaging device having a clock speed of 600 MHz. It is shown from the performance evaluation by measuring a sound pressure level that the proposed algorithm reduces user voice by around 10 dB.

Null Space Pursuit: An Operator-based Approach to Adaptive Signal Separation

The operator-based signal separation approach uses an adaptive operator to separate a signal into additive subcomponents. The approach can be formulated as an optimization problem whose optimal solution can be derived analytically. However, the following issues must still be resolved: estimating the robustness of the operator's parameters and the Lagrangian multipliers, and determining how much of the information in the null space of the operator should be retained in the residual signal. To address these problems, we propose a novel optimization formula for operator-based signal separation and show that the parameters of the problem can be estimated adaptively. We demonstrate the effectiveness of the proposed method by processing several signals, including real-life signals.

ESTIMATION OF INSTANTANEOUS FREQUENCY PARAMETERS OF THE OPERATOR-BASED SIGNAL SEPARATION METHOD

The main function of the operator-based signal separation approach is to construct an operator from a signal and use it to decompose the signal into two subcomponents. The procedure involves two steps: estimating the operator, and decomposing the signal into two subcomponents. Existing approaches estimate the operator's parameters from the local extrema of a signal. In contrast, we show that the parameters can be estimated by adopting a variational approach. Because the proposed approach imposes a global constraint on the operator's parameter, the estimated parameters are more robust than those derived from the local extrema of the signal. We also compare the signal separation results with those obtained by using the empirical mode decomposition (EMD) method.

Determinant Maximization of a Nonsymmetric Matrix with Quadratic Constraints

This paper presents the problem of maximizing the determinant of a real $K\times K$-matrix $B$, subject to the constraint that each row $b_k$ of $B$ satisfies $b_k^t \Gamma_kb_k \leq 1$, where $\Gamma_1, \ldots,\Gamma_K$ are $K$ given real symmetric positive definite matrices. This problem comes from a specific blind signal separation approach, but the criterion differs from approximate diagonalization criteria usually encountered in this area. Furthermore our criterion corresponds to the following nice geometrical problem: given $K$ ellipsoids in ${\rm\bf R}^K, \varepsilon_k =\{x: x^t\Gamma_kx \leq1\}, k=1, \ldots,K$, find $K$ vectors, $b_1\in \varepsilon_1, \ldots, b_K\in \varepsilon_K$, such that the volume of the parallelepiped defined by these vectors is maximum. Existence and uniqueness of the solution are discussed. An iterative algorithm, based on a relaxation technique, is proposed in order to solve this problem, and its convergence is proved under a simple sufficient condition. Some numerical experiments are performed showing the behavior of the algorithm and its comparison with Newton’s methods for nonlinear optimization.

A two-stage algorithm for MIMO blind deconvolution of nonstationary colored signals

A new two-stage algorithm is proposed for the deconvolution of multi-input multi-output (MIMO) systems with colored input signals. While many blind deconvolution algorithms in the literature utilize high order statistics of the output signal for white input signals, the additional information contained in colored input signals allows the design of second-order statistical algorithms. In fact, practical signal sources such as speech signals do have distinct, nonstationary, colored power spectral densities. We present a two-stage signal separation approach in which the first step utilizes a matrix pencil between output auto-correlation matrices at different delays, whereas the second stage adopts a subspace method to identify and deconvolve MIMO systems.