Blind Signal Extraction Research Articles

Orthogonality constrained analytic CMA for blind signal extraction improvement

The well-known Analytic Constant Modulus Algorithm (ACMA) is a classic algebraic method for signal separation and equalization, which can achieve blind signal recovery and fast convergence. In this paper, we explore new means for incorporating the inherent statistical signal orthogonality in ACMA for improved signal separation and recovery. This statistical orthogonality constraint adds new restrictions to ACMA, leading to a novel approach we denote as Orthogonality Constrained Analytic Constant Modulus Algorithm (OC-ACMA). We further generalize OC-ACMA for applications in blind equalization. Results show that OC-ACMA can successfully separate and recover constant modulus signals at higher accuracy.

Orthogonality Constrained Analytic Cma for Blind Signal Extraction Improvement

Orthogonality Constrained Analytic Cma for Blind Signal Extraction Improvement

Reviews in Dynamic Characteristics of Rolling Interface and Vibration Test/Suppression Methods of the Rolling Mill

Background: Rolling mill vibration mechanism is very complex, and people haven't found a satisfactory vibration control method. Rolling interface is one of the vibration sources of the rolling mill system, and its friction and lubrication state has a great impact on the vibration of the rolling mill system. It is necessary to establish an accurate friction model for unsteady lubrication process of roll gap and a nonlinear vibration dynamic model for rolling process. In addition, it is necessary to obtain more direct and real rolling mill vibration characteristics from the measured vibration signals, and then study the vibration suppression method and design the vibration suppression device. Methods: This paper summarizes the friction lubrication characteristics of rolling interface and its influence on rolling mill vibration, as well as the dynamic friction model of rolling interface, the tribological model of unsteady lubrication process of roll gap, the non-linear vibration dynamic model of rolling process, the random and non-stationary dynamic behavior of rolling mill vibration, etc. At the same time, the research status of rolling mill vibration testing technology and vibration suppression methods were summarized. Time-frequency analysis of non-stationary vibration signals was reviewed, such as wavelet transform, Wigner-Ville distribution, empirical mode decomposition, blind source signal extraction, rolling vibration suppression equipment development. Results: The lubrication interface of the roller gap under vibration state presents unsteady dynamic characteristics. The signals generated by the vibration must be analyzed in time and frequency simultaneously. In the aspect of vibration suppression of rolling mill, the calculation of inherent characteristics should be carried out in the design of rolling mill to avoid dynamic defects such as resonance. When designing or upgrading the mill structure, it is necessary to optimize the structure of the work roll bending and roll shifting system, such as designing and developing the automatic adjustment mechanism of the gap between the roller bearing seat and the mill stand, adding floating support device to the drum shaped toothed joint shaft, etc. In terms of rolling technology, rolling vibration can be restrained by improving roll lubrication, reasonably distributing rolling force of each rolling mill, reducing rolling force of vibration prone rolling mill, increasing entrance temperature, reducing rolling inlet tension, reducing strip outlet temperature and reasonably arranging roll diameter. The coupling vibration can also be suppressed by optimizing the hydraulic servo system and the frequency conversion control of the motor. Conclusion: Under the vibration state, the lubrication interface of roll gap presents unsteady dynamic characteristics. The signal generated by vibration must be analyzed by time-frequency distribution. In the aspect of vibration suppression of rolling mill, the calculation of inherent characteristics should be carried out in the design of rolling mill to avoid dynamic defects such as resonance. It is necessary to optimize the structure of work roll bending and roll shifting system when designing or reforming the mill structure. In rolling process, rolling vibration can be restrained by improving roll lubrication, reasonably distributing rolling force of each rolling mill, increasing billet temperature, reasonably arranging roll diameter and reducing rolling inlet tension. Through the optimization of the hydraulic servo system and the frequency conversion control of the motor, the coupling vibration can be suppressed. The paper has important reference significance for vibration suppression of continuous rolling mill and efficient production of high quality strip products.

Intelligent LED Certification System in Mass Production.

It is impossible to effectively use light-emitting diodes (LEDs) in medicine and telecommunication systems without knowing their main characteristics, the most important of them being efficiency. Reliable measurement of LED efficiency holds particular significance for mass production automation. The method for measuring LED efficiency consists in comparing two cooling curves of the LED crystal obtained after exposure to short current pulses of positive and negative polarities. The measurement results are adversely affected by noise in the electrical measuring circuit. The widely used instrumental noise suppression filters, as well as classical digital infinite impulse response (IIR), finite impulse response (FIR) filters, and adaptive filters fail to yield satisfactory results. Unlike adaptive filters, blind methods do not require a special reference signal, which makes them more promising for removing noise and reconstructing the waveform when measuring the efficiency of LEDs. The article suggests a method for sequential blind signal extraction based on a cascading neural network. Statistical analysis of signal and noise values has revealed that the signal and the noise have different forms of the probability density function (PDF). Therefore, it is preferable to use high-order statistical moments characterizing the shape of the PDF for signal extraction. Generalized statistical moments were used as an objective function for optimization of neural network parameters, namely, generalized skewness and generalized kurtosis. The order of the generalized moments was chosen according to the criterion of the maximum Mahalanobis distance. The proposed method has made it possible to implement a multi-temporal comparison of the crystal cooling curves for measuring LED efficiency.

Low-Element Image Restoration Based on an Out-of-Order Elimination Algorithm

To reduce the consumption of receiving devices, a number of devices at the receiving end undergo low-element treatment (the number of devices at the receiving end is less than that at the transmitting ends). The underdetermined blind-source separation system is a classic low-element model at the receiving end. Blind signal extraction in an underdetermined system remains an ill-posed problem, as it is difficult to extract all the source signals. To realize fewer devices at the receiving end without information loss, this paper proposes an image restoration method for underdetermined blind-source separation based on an out-of-order elimination algorithm. Firstly, a chaotic system is used to perform hidden transmission of source signals, where the source signals can hardly be observed and confidentiality is guaranteed. Secondly, empirical mode decomposition is used to decompose and complement the missing observed signals, and the fast independent component analysis (FastICA) algorithm is used to obtain part of the source signals. Finally, all the source signals are successfully separated using the out-of-order elimination algorithm and the FastICA algorithm. The results show that the performance of the underdetermined blind separation algorithm is related to the configuration of the transceiver antenna. When the signal is 3 × 4 antenna configuration, the algorithm in this paper is superior to the comparison algorithm in signal recovery, and its separation performance is better for a lower degree of missing array elements. The end result is that the algorithms discussed in this paper can effectively and completely extract all the source signals.

Efficient blind nonparametric dependent signal extraction algorithm for determined and underdetermined mixtures

Blind extraction or separation statistically independent source signals from their linear mixtures have been well studied in the last two decades, which is realised by searching for local extrema of certain objective functions. In this paper, by employing nonparametric non-Gaussianity (NG) measure, a blind source separation/extraction (BSS/BSE) algorithm is derived to separate or extract statistically dependent source signals from their underdetermined or determined linear mixtures. Firstly, we show that maximisation NG measure can separate/extract statistically weak dependent source signals. Then, the nonparametric NG measure is defined by statistical distances between different cumulative distribution function (CDF) of separated signals, which can be estimated by quantiles and order statistics (OS) using L2 norm efficiently. Finally, the nonparametric NG measure aided algorithm is optimised by a deflation procedure. Simulation results for synthesis and real world data show that the proposed algorithm can extract the desired dependent source signals and yield expected performance.

Efficient blind nonparametric dependent signal extraction algorithm for determined and underdetermined mixtures

Efficient blind nonparametric dependent signal extraction algorithm for determined and underdetermined mixtures

Efficient blind nonparametric dependent signal extraction algorithm for determined and underdetermined mixtures

Efficient blind nonparametric dependent signal extraction algorithm for determined and underdetermined mixtures

Extraction of independent non-Gaussian components from non-Gaussian background

Independent component analysis (ICA) is a popular method for Blind Signal Separation applied to multichannel linear recordings. Convolutive mixtures of acoustic signals can be separated by applying ICA in the frequency domain to each sub-band separately or jointly as in Independent Vector Analysis (IVA). However, this way, the mixtures are separated into that many components as is the number of sensors. ICA and IVA are therefore not effective in applications where only one (or few) signals are of practical interest. We introduce a new parameterization of the ICA mixing model that is optimized for the extraction of one signal of interest (SOI). The approach is called Independent Component Extraction (ICE) and is closely related to methods for Blind Signal Extraction (BSE) such as One-unit FastICA. However, BSE methods assume that background signals (the other signals than SOI) are all Gaussian, which limits their accuracy. In this work, we show that ICE can be extended also for a non-Gaussian background so that the accuracy of algorithms is improved, although all components are not separated as in ICA. We also introduce an extension for the extraction of a vector component, so-called Independent Vector Extraction.

A BP Neural Network-Based Communication Blind Signal Detection Method With Cyber-Physical-Social Systems

The development of wireless communication technologies will lead to an increase of rapid-time-variants featured occasions and physical occasions of small-data-size communication in network, and the modulation system diversification resulting from social group densification. Though traditional blind signal detection approaches have been improved by reducing their excessive reliance on data size, they still have the defects of various postulated conditions in new settings, complicated computation, unstable detection effect and high error rate. This paper aims to explore a communication blind signal detection method in the setting of cyber-physical-social systems in the field of back-propagation (BP) neural network. First, a BP neural network is used as the equalizer, an error function is redefined, and the back-propagation algorithm is used to train and adjust blind signal data deviation and to update network weight rules for adaptation to network settings with rapid time-variants. Second, a double-sigmoid BP neural network excitation function is constructed to improve poor multiple information processing and network performances resulting from social group densification. Third, self-adaptive variable step size for physical devices’ power is constructed to adjust the conflict between convergence rate and steady-state error caused by different powers with the increase of small-data-size communication. Finally, an output vector is made to be closest to an expected vector; thereby, blind signal detection rate of social groups’ communication using physical devices in the cyber environment can be improved. The experiments show that the communication blind signal detection method proposed in the paper improves signal detection precision, and reduces omission rate (below 2%). Besides, the method is characterized by minor error, removes deficiencies of traditional methods for blind signal extraction, and can effectively accomplish blind signal detection.

Combining blind separation and cyclostationary techniques for monitoring distributed wear in gearbox rolling bearings

This work seeks to study the potential effectiveness of the Blind Signal Extraction (BSE) as a pre-processing tool for the detection of distributed faults in rolling bearings. In the literature, most of the authors focus their attention on the detection of incipient localized defects. In that case, classical techniques (i.e. envelope analysis) are robust in recognizing the presence of the fault and its characteristic frequency. However, when the fault grows, the classical approach fails, due to the change of the fault signature. De facto, in this case the signal does not contain impulses at the fault characteristic frequency, but more complex components with strong non-stationary contents. Moreover, signals acquired from complex machines often contain contributions from several different components as well as noise; thus the fault signature can be hidden in the complex system vibration. Therefore, pre-processing tools are needed in order to extract the bearing signature, from the raw system vibration. In this paper the authors focalize their attention on the application of the BSE in order to extract the bearing signature from the raw vibration of mechanical systems. The effectiveness and sensitivity of BSE is here exploited on the basis of both simulated and real signals. Among different procedures for the BSE computation, the Reduced-Rank Cyclic Regression algorithm (RRCR) is used. Firstly a simulated signal including the effect of gear meshing as well as a localized fault in bearings is introduced in order to tune the parameters of the RRCR. Next, two different real cases are considered, a bearing test-rig as an example of simple machine and a gearbox test-rig as an example of complex machine. In both examples, the bearings were degreased in order to accelerate the wear process. The BSE is compared with the usual pre-processing technique for the analysis of cyclostationary signals, i.e. the extraction of the residual signal. The fault detection is carried out by the computation of the Integrated Cyclic Modulation Spectrum on the extracted signals. The results indicate that the extracted signals via BSE clearly highlight the distributed fault signature, in particular both the appearance of the faults as well as their development are detected, whilst noise still hides fault grow in the residual signals.

Blind chaotic signal extraction based on artificial bee colony algorithm

This paper is to deal with the blind extraction problem of chaotic signals by using a linear mixing model. In this model, a novel method to describe the distance function in a high dimensional space is proposed which relates the kernel function to objective function. When adopting the artificial bee colony algorithm (ABCA) as an alternative method to solve a multi-modal optimization problem, its analysis under a Markov chain model is also presented. The simulation results show that the objective function of this article has low complexity, and the artificial bee colony algorithm converges to a local minimum quickly. To be specific, the target function is constructed by combining the advantages of the proliferation exponent and the distance kernel function. The proliferation exponent can reflect the chaotic properties of a signal to a large extent, and the distance kernel can help to describe the statistical properties in a higher dimension. Due to the fact that only one frame of time-delay embedded signal is adopted, the computational complexity of our target function is low. The artificial bee colony algorithm is shown to be advantageous over other swarm algorithms. Although adopting ABCA for our evaluation function seems easy, we analyze why this algorithm can work, in contrast to the fact that most literature only runs some simulations to confirm its usefulness. Our analysis is only for a special case when the number of employed bees is set to be 2 and the process of onlooker bees and scouts are temporarily omitted. With smaller complexity than the methods based on proliferation exponents and kurtosises, simulations show that our method can have excellent performance when evaluated by correlation coefficients.

FEASIBILITY OF HELIOSPHERIC IMAGING FROM NEAR EARTH

Imaging solar wind structures via Thomson scattered sunlight has proved important to understanding the inner heliosphere. The principal challenge of heliospheric imaging is background subtraction: typical solar wind features are fainter than the zodiacal light and starfield by 2-3 orders of magnitude. Careful post-processing is required to separate the solar wind signal from the static background. Remnant background, and not photon noise, is the dominant noise source in current STEREO data. We demonstrate that 10× shorter exposure times would not strongly affect the noise level in these data. Further, we demonstrate that current processing techniques are sufficient to separate not only the existing background of the STEREO images but also diffuse variable backgrounds such as are expected to be seen from low Earth orbit. We report on a hare-and-hounds style study, demonstrating blind signal extraction from STEREO/HI-2 data that have been degraded by the addition of large-scale, time-dependent artifacts to simulate viewing through airglow or high-altitude aurora. We demonstrate removal of these effects via image processing, with little degradation compared to the original. Even with as few as three highly degraded source images over 48 hr, it is possible to detect and track large coronal mass ejections more than 40° from the Sun. This implies that neither the high altitude aurora discovered by Coriolis/SMEI, nor airglow effects seen from low Earth orbit, are impediments to a hypothetical next-generation heliospheric imager in low Earth orbit; and also that post-processing is as important to heliospheric image qualitiy as are optical contamination effects.

New Negentropy Optimization Schemes for Blind Signal Extraction of Complex Valued Sources

Blind signal extraction, a hot issue in the field of communication signal processing, aims to retrieve the sources through the optimization of contrast functions. Many contrasts based on higher-order statistics such as kurtosis, usually behave sensitive to outliers. Thus, to achieve robust results, nonlinear functions are utilized as contrasts to approximate the negentropy criterion, which is also a classical metric for non-Gaussianity. However, existing methods generally have a high computational cost, hence leading us to address the problem of efficient optimi- zation of contrast function. More precisely, we design a novel reference-based contrast function based on negentropy approximations, and then propose a new family of algorithms (Alg. 1 and Alg. 2) to maximize it. Simula- tions confirm the convergence of our method to a separat- ing solution, which is also analyzed in theory. We also vali- date the theoretic complexity analysis that Alg. 2 has a much lower computational cost than Alg. 1 and existing optimization methods based on negentropy criterion. Finally, experiments for the separation of single sideband signals illustrate that our method has good prospects in real-world applications.

Suppression of noise and late reverberation based on blind signal extraction and Wiener filtering

We introduce a new optimized microphone-array processing method for a spoken-dialogue robot in noisy and reverberant environments. The method is based on frequency-domain blind signal extraction, a signal separation algorithm that exploits the sparseness of a speech signal to separate the target speech and diffuse background noise from the sound mixture captured by a microphone array. This algorithm is combined with multichannel Wiener filtering so that it can effectively suppress both background noise and reverberation, given a priori information of room reverberation time. In this paper, first, we develop an automatic optimization scheme based on the assessment of musical noise via higher-order statistics and acoustic model likelihood. Next, to maintain the optimum performance of the system, we propose the multimodal switching scheme using the distance information provided by robot's image sensor and the estimation of SNR condition. Experimental evaluations have been conducted to confirm the efficacy of this method.

Research on Blind Signal Extraction Based on Normalized Kurtosis

Blind source extraction (BSE) is a promising technique to solve signal mixture problems while only one or a few source signals are desired. In biomedical applications, one often knows certain prior information about a desired source signal in advance. In this paper, we explore specific prior information as a constrained condition so as to develop a flexible BSE algorithm. One can extract a desired source signal while its normalized kurtosis range is known in advance. Computer simulations on biomedical signals confirm the validity of the proposed algorithm.

Analysis of dual-channel ICA-based blocking matrix for improved noise estimation

For speech enhancement or blind signal extraction (BSE), estimating interference and noise characteristics is decisive for its performance. For multichannel approaches using multiple microphone signals, a BSE scheme combining a blocking matrix (BM) and spectral enhancement filters was proposed in numerous publications. For such schemes, the BM provides a noise estimate by suppressing the target signal only. The estimated noise reference is then used to design spectral enhancement filters for the purpose of noise reduction. For designing the BM, ‘Directional Blind Source Separation (BSS)’ was already proposed earlier. This method combines a generic BSS algorithm with a geometric constraint derived from prior information on the target source position to obtain an estimate for all interfering point sources and diffuse background noise. In this paper, we provide a theoretical analysis to show that Directional BSS converges to a relative transfer function (RTF)-based BM. The behavior of this informed signal separation scheme is analyzed and the blocking performance of Directional BSS under various acoustical conditions is evaluated. The robustness of Directional BSS regarding the localization error for the target source position is verified by experiments. Finally, a BSE scheme combining Directional BSS and Wiener-type spectral enhancement filters is described and evaluated.

A steady-state point capture-based blind harmonic signal extraction algorithm under the chaotic background

To further improve the performance of blind extraction of harmonic signals under the chaotic background, an algorithm based on the steady-state point capture was proposed. Since energy distribution of the chaotic signal has an important influence on the extraction accuracy, when the frequency of harmonic signals drop in the energy focused region of the chaotic signals, the data after the chaotic signal meet the steady-state point is capable of significantly improving the extraction precision. The solution to capturing steady-state point of typical chaotic system was obtained by solving the Lyapunov exponent. Furthermore, the influence of the frequency interval between harmonic signals was analysed. An empirical constant value of frequency interval to ensure the effective extraction was also derived by a large number of simulation experiments. Simulation results show the effectiveness of the proposed method, and the method provides an effective means to verify the security of chaotic secure communication system.

Study on Signals Processing in State Detection of Tunnel Excavation Equipment's Cutter Head

In the period of real time mechanical analysis and life style detection to the excavation tunnel equipment's cutter head, it is a necessity for the signals sensed by detection unit to be pretreatment processed. It closely concerns with the accuracy and validity of whole mechanical analysis results. By analyzing the features of all possible interference, this paper gives a study on signals initial processing applying method of digital filter and blind signal extraction. Simulation results show that the proposed scheme is effective enough to the mechanical analysis task.

Cyclic maximization of non-Gaussianity for blind signal extraction of complex-valued sources

This article presents a new algorithm for the blind extraction of communications sources (complex-valued sources) through the maximization of negentropy approximations based on nonlinearities. A criterion based on the square modulus of a nonlinearity of the output is used. We decouple the arguments of the criterion so that the algorithm maximizes it cyclically with respect to each argument by means of the Cauchy–Schwarz inequality. A proof of the ascent of the objective function after each iteration is also provided. Numerical simulations corroborate the good performance of the proposed algorithm in comparison with the existing methods.