Signal Separation Algorithm Research Articles

Research on compound fault diagnosis of rolling bearing based on intrinsic time scale decomposition and information fusion

In view of the difficulty to effectively extract compound faults of rolling bearing from aero-engine and precisely identify their types, the paper has proposed a method integrating signal separation algorithm and information fusion. Firstly, the method decomposes the vibration acceleration signals collected by sensors from different positions at the same moment based on intrinsic time scale decomposition algorithm. Secondly, cross correlation analysis is given to the proper rotation component (PRC) of the same layer, which are obtained after decomposition and correspond to the sensors from different positions and cross-correlation function is introduced to embody information fusion. Thirdly, signals are reconstructed according to cross-correlation function of each PRC. Finally, based on the frequency spectrum of reconstructed signal, extract the characteristics of rolling bearing and identify the type of faults under different sensor combinations and multiple compound fault types. The result shows, the proposed method can effectively extract the characteristics of compound faults of bearing and precisely identify the type of faults under different sensor combinations and multiple compound fault types of rolling bearing.

Intelligent Algorithm for Variable Scale Adaptive Feature Separation of Mechanical Composite Fault Signals

With the development of modern industry and scientific technology, production equipment plays an increasingly important role in military and industrial production, and the fault detection signal of gears and bearings state in transmission equipment becomes very important. Therefore, this paper proposes a gear-bearing composite fault signal decomposition and reconstruction method, which combines the marine predator algorithm (MPA) and variational mode decomposition (VMD) technologies. For the parameters’ selection of VMD, the optimization algorithm allows us to quickly and accurately obtain the results with the best kurtosis correlation index after signal decomposition and reconstruction. The experiments demonstrate the excellent performance of our method in the field of separation and denoising mixed gear-bearing fault signals.

Phase-coupled EEG sources predict motor cortex excitability probed with TMS

Abstract TMS over primary motor cortex (M1) elicits motor evoked potentials (MEPs) in the target muscle, which may be used as a readout of instantaneous excitability of stimulated neuronal populations. Previous studies showed that the phase of the cortical oscillations at the TMS onset modulates cortical excitability. We studied whether states of phase-coupling across cortical regions prior to TMS pulse contribute to spontaneous changes in corticospinal excitability, as seen in variability of MEP amplitudes. We applied the analytic Common Spatial Patterns (aCSP) method – a supervised signal separation algorithm – to EEG signals to isolate oscillatory features distinctly different between high- and low-excitability states. The algorithm allows individual estimation of spatial filters without prior assumptions on relevant sources of activity. Amplitudes and phase shifts of the signals across sensors were used as the basis for extraction of relevant features. Analysis was performed on a dataset consisting of concurrent TMS–EEG on healthy human subjects with single-pulse TMS over left M1. Trials were divided into “high” vs “low” excitability classes based on high and low MEP amplitudes, respectively. EEG signals within 7–30-Hz band within 0.5 sec prior to TMS pulse onset were transformed to construct new aggregated signals predictive of the excitability class. Features derived from the transformed signals were used for classification of trials into either class. Out of the 12 analyzed subjects, 9 exhibited above-chance prediction accuracy (average accuracy ± SD: 60±0.03%, N=9). While predictive oscillatory patterns were variable across subjects, there was a recurrent pattern of phase shift between the stimulated region (i.e. left M1) and regions anterior to it during high excitability states. This pattern may signify Supplementary Motor Area (SMA) – M1 phase-coupling as contributing to excitability modulations of M1. Low-excitability state was typically predicted by parieto-occipital phase-couplings. This approach could potentially be beneficial for individualized brain-state-guided TMS therapy. Keywords: excitability, phase-coupling, TMS, EEG

Design and Implementation of Blind Source Separation Based on BP Neural Network in Space-Based AIS

In space-based AIS (Automatic Identification System), due to the high orbit and wide coverage of the satellite, there are many self-organizing communities within the observation range of the satellite, and the signals will inevitably conflict, which reduces the probability of ship detection. In this paper, to improve system processing power and security, according to the characteristics of neural network that can efficiently find the optimal solution of a problem, proposes a method that combines the problem of blind source separation with BP neural network, using the generated suitable data set to train the neural network, thereby automatically generating a traditional blind signal separation algorithm with a more stable separation effect. At last, through the simulation results of combining the blind source separation problem with BP neural network, the performance and stability of the space-based AIS can be effectively improved.

Interoperable Nanoparticle Sensor Capable of Strain and Vibration Measurement for Rotor Blade Monitoring.

Recent advances in nanomaterials technology create the new possibility to fabricate high performance sensors. However, there has been limitations in terms of multivariate measurable and interoperable sensors. In this study, we fabricated an interoperable silver nanoparticle sensor fabricated by an aerodynamically focused nanomaterial (AFN) printing system which is a direct printing technique for inorganic nanomaterials onto a flexible substrate. The printed sensor exhibited the maximum measurable frequency of 850 Hz, and a gauge factor of 290.62. Using a fabricated sensor, we evaluated the sensing performance and demonstrated the measurement independency of strain and vibration sensing. Furthermore, using the proposed signal separation algorithm based on the Kalman filter, strain and vibration were each measured in real time. Finally, we applied the printed sensor to quadrotor condition monitoring to predict the motion of a quadrotor.

Accurate AM-FM signal demodulation and separation using nonparametric regularization method

In this paper we propose a novel adaptive nonparametric regularization (NPR) method for solving optimization problems with linear constraint. The regular parameter in NPR algorithm is adaptively updated. We prove that the NPR is convergent and provide an early stop method (ESM) with a termination criterion to handle the perturbation problem which is unsolved in other methods such as the augmented Lagrange method (ALM). We then introduce a new differential operator which can absolutely annihilate the amplitude-modulated and frequency-modulated (AM-FM) signals (AM-FM operator, AFO). The proposed operator is a precise operator and thus can obtain a more accurate solution in operator based signal demodulation and separation problems. We apply the NPR algorithm in signal demodulation and separation based on the AFO and propose signal demodulation (NPR-AFOSD) and separation (NPR-AFOSS) algorithms. The experimental results on both synthetic AM-FM signals and the real-life data demonstrate that the proposed demodulation and separation methods are more effective and robust than the state-of-the-art methods.

2D DOA Estimation of Coherently Distributed Sources for Planar Arrays Based on Blind Signal Separation

A novel method is presented to estimate the direction-of-arrival (DOA) of coherently distributed (CD) sources for planar arrays. In the proposed method, the two-dimensional (2D) angular parameters are obtained by making use of the estimated generalized array manifold matrix, which is separated from the received observation data based on blind signal separation algorithm. The proposed method offers a number of advantages. First, the 2D central DOAs and angular spread variances can be estimated simultaneously. Second, the deterministic angular distributed functions (DADFs) of the CD sources do not need to know. Third, the special array geometries are not required, as the method is applicable to planar arrays. Computer simulations are shown to verify the efficacy of the proposed method.

Dynamic welding process monitoring based on microphone array technology

Abstract In this paper, microphone array technology was used to monitor the dynamic pulsed GMAW process. At first, the splash sound signal is successfully separated out based on FastICA blind signal separation algorithm, and its frequency domain energy distribution is mainly concentrated in the high frequency band of 6000−8000 Hz. Through time and frequency domain analysis, it is found that the short-time energy of 500−1000 Hz band of observed signal and the short-time energy of splash signal can identify the burn-through defects well, furthermore the ratio of them can be used as a robust feature because of its high sensitivity and anti-interference performance. Since the splash sound signal is a characteristic signal separated by microphone array, it is shown that the abundant dynamic information provided by microphone array can better assist in the identification and monitoring of welding defects compared to a single microphone. In order to solve the serious imbalance problem between positive and negative samples, the logistic regression and BP neural network model are improved based on the machine learning method. The experimental results show that the recognition accuracy of the optimization models have been greatly improved, even could reach 99.6 %.

Sparse pursuit and dictionary learning for blind source separation in polyphonic music recordings

We propose an algorithm for the blind separation of single-channel audio signals. It is based on a parametric model that describes the spectral properties of the sounds of musical instruments independently of pitch. We develop a novel sparse pursuit algorithm that can match the discrete frequency spectra from the recorded signal with the continuous spectra delivered by the model. We first use this algorithm to convert an STFT spectrogram from the recording into a novel form of log-frequency spectrogram whose resolution exceeds that of the mel spectrogram. We then make use of the pitch-invariant properties of that representation in order to identify the sounds of the instruments via the same sparse pursuit method. As the model parameters which characterize the musical instruments are not known beforehand, we train a dictionary that contains them, using a modified version of Adam. Applying the algorithm on various audio samples, we find that it is capable of producing high-quality separation results when the model assumptions are satisfied and the instruments are clearly distinguishable, but combinations of instruments with similar spectral characteristics pose a conceptual difficulty. While a key feature of the model is that it explicitly models inharmonicity, its presence can also still impede performance of the sparse pursuit algorithm. In general, due to its pitch-invariance, our method is especially suitable for dealing with spectra from acoustic instruments, requiring only a minimal number of hyperparameters to be preset. Additionally, we demonstrate that the dictionary that is constructed for one recording can be applied to a different recording with similar instruments without additional training.

Blind signal separation with Noise Reduction for efficient speaker identification

Generally, most blind signal separation algorithms deal with the separation problem in the absence of noise. The presence of noise degrades the performance of separated signals. This paper deals with the problem of blind separation of audio signals from noisy mixtures. Blind signal separation algorithm is applied on the discrete cosine transform, the discrete sine transform or the discrete wavelet transform of the mixed signals, instead of performing the separation on the mixtures in the time domain. All of these transforms have an energy compaction property, which concentrates most of the signal energy in a few coefficients in the transform domain, leaving most of the transform-domain coefficients close to zero. As a result, the separation is performed on a few coefficients in the transform domain. Another advantage of signal separation in transform domains is that the effect of noise on the signals in the transform domains is smaller than that in the time domain. The paper presents also an investigation of the rule of the speech enhancement techniques as pre- and post-processing steps for the blind signal separation process, instead of performing the separation on the mixtures in the time domain. The considered speech enhancement techniques are the spectral subtraction, the Wiener filtering, the adaptive Wiener filtering, and the wavelet denoising techniques. Both blind signal separation and noise reduction are applied within a real speaker identification system to reduce the effect of interference and noise on the system performance. The simulation results confirm the superiority of transform domain separation to time domain separation and the importance of the wavelet denoising technique, when used as a pre-processing step for noise reduction. Moreover, the speaker identification system performance is enhanced with blind signal separation and noise reduction.

ОБЗОР ИССЛЕДОВАНИЙ АДАПТИВНОГО ФОРМИРОВАНИЯ ДИАГРАММЫ НАПРАВЛЕННОСТИ И ЦИФРОВОЙ ОБРАБОТКИ СИГНАЛОВ

Introduction: Recently, there has been an active development of algorithms for adaptive beamforming. Such work is required in order to improve the efficiency of receiving a useful signal, especially in mobile communication systems. This article provides an overview of research on this topic. Papers devoted to the improvement of the classical least mean square algorithm, blind signal separation methods, antenna selection algorithms in MIMO systems and other topics of adaptive signal processing are presented. Practical relevance: Adaptive beamforming is a key development for modern wireless communication systems, in particular 5G. Increasing requirements that impose a limitation on the spectral bandwidth of the signal and its center frequency with limited signal power lead to the strategy of forming a narrow signal transmission beam, thereby increasing the energy efficiency of signal transmission and reducing the overhead associated with the reception of various interference. This article provides an overview of studies on this topic. The results of the analysis of works devoted to the improvement of the classical algorithm for minimizing the mean square error, blind signal separation, antenna selection algorithms in systems of several antennas, and other topics of adaptive signal processing are presented. Discussion: It is shown that the main directions of research are focused on the improvement of classical diagramming algorithms, as well as on the improvement of methods for receiving digital signals in MIMO systems. Algorithms for recognizing the type of signal modulation, an algorithm for blind separation of second-order signals, an improved algorithm for automatic gain control, an algorithm for suppressing noise-like interference, and an algorithm for estimating radiation pattern parameters were also considered.

A Reliable Separation Algorithm of ADS-B Signal Based on Time Domain

With the development of Aerospace Science and technology, automatic dependent surveillance-broadcast has become a core technique in the field of air surveillance. Installing ADS-B receiver on LEO satellite can solve the problem of small coverage of ground receiver, to realize global coverage and monitoring. However, the satellite ADS-B system is faced with serious collision and overlap problems, which has a serious impact on the signal decoding, leading to the wrong decoding or even loss of important information. In this paper, a time-domain ADS-B blind signal separation algorithm is proposed. When there is a certain power difference between the two source signals, the overlap signals are offset by the high-power signal and low-power signal to get the corresponding cancellation signals. According to the superposition mode of different pulses, different bit decision results are obtained according to the amplitude, to recover the source signal. Simulations demonstrate that the proposed algorithm is feasible and has a lower bit error rate.

Identification of neutrons and gamma rays using a combination of three algorithms for separating signals of the scintillation detector

Scintillation detectors with organic scintillators are widely used for fast neutrons detection in high gamma ray background. The peculiarity of this type of detector is that the pulse shape depends on the type of the detected particle. Traditionally, the Pulse Shape Discrimination (PSD) histogram is used to determine the number of detected neutrons. The PSD parameter is calculated from the shape of the detector pulse and assigned to each pulse. A typical PSD histogram contains two peaks corresponding to neutrons and gamma rays that overlap in the region between the peaks. With this approach, it is impossible to identify each individual signal in the area between the peaks. Therefore, it is not possible to calculate the overall signal identification coefficient. We have proposed a new method for the identification of neutrons and gamma quanta, which includes a combination of three signal separation algorithms: the traditional histogram PSD, the dependence of the area of signals on their amplitude, Tau histogram (tau means the fall constant of the detector pulses). This combination of three algorithms makes it possible to calculate the value of the signal identification coefficient. To test a new method for identifying neutrons and gamma quanta, we used a Pu-Be neutron source, a scintillation detector with a p-terphenyl crystal and a CAEN DT5730 Digitizer (14 bit, 500 MHz). When a scintillation detector registered neutron from a Pu-Be source, the signal identification coefficient was 91.6%. A new method for identifying signals from a scintillation detector is used to register neutrons at the light ion accelerator.

Signal Separation and Tracking Algorithm for Multi-Person Vital Signs by Using Doppler Radar.

Noninvasive monitoring is an important Internet-of-Things application, which is made possible with the advances in radio-frequency based detection technologies. Existing techniques however rely on the use of antenna array and/or frequency modulated continuous wave radar to detect vital signs of multiple adjacent objects. Antenna size and limited bandwidth greatly limit the applicability. In this paper, we propose our system termed 'DeepMining' which is a single-antenna, narrowband Doppler radar system that can simultaneously track the respiration and heartbeat rates of multiple persons with high accuracy. DeepMining uses a number of signal observations over a period of time as input and returns the trajectory of the respiration and heartbeat rates of each person. The extraction is based on frequency separation algorithms using successive signal cancellation. The proposed system is implemented using the self-injection locking radar architecture and tested in a series of experiments, showing accuracies of 90% and 85% for two and three objects, respectively, even for closely located persons.

A Two-Stage Separation Algorithm for Weak Correlation Source Signals

In this paper, we propose a separation algorithm for weakly correlated source signals. At present, there are many blind source separation algorithms based on independent component analysis, which can only deal with the separation of uncorrelated source signals. However, the assumption that the source signals are independent is too strict in practical application, so the application of the separation algorithm is limited. The proposed weak correlation source signal separation algorithm is divided into two stages. In the first stage, the correlation matrices of mixed signals are first decomposed into two parts, and then their image matrices are introduced. Finally, the objective function is constructed. Using the iterative algorithm to compute this function, we prove that when the function tends to zero, similar matrices of independent parts of the aforementioned correlation matrices are obtained. In the second stage, the joint diagonal matrix model is established by using these similarity matrices, and finally the separable separation matrix is obtained by optimizing the model so as to achieve the purpose of separating the source signals. Simulation experiments also verify the effectiveness of the algorithm.

A beamformer for multiple packet reception based on blank intervals. Part I: The flat-fading channel case

Multiple packet reception (MPR) has become a promising technique to considerably improve the throughput in a wireless network due to its provided collision resolution capability. In this paper we present a blind algorithm to separate several data packets, up to an ambiguity in the extracted signal phase, exploiting its asynchronous nature, which in general has been considered an undesirable effect in wireless networks. The separation algorithm consists on finding the minor subspace of each data packets in the received mixed signal. This leads to an equalizer which minimizes multiple access interference (MAI), on the desired data packet. It can be adapted in a simple form to perform in synchronous scenarios without the necessity of further processing compared to other techniques such as matrix decomposition for signal projection or the use of CMA or KMA or some of their derived algorithms for signal separation.

Evaluating internal condition of hardwood logs based on AR-minimum entropy deconvolution combined with wavelet based spectral kurtosis approach

Abstract The aim of this research was to explore the potential of acoustic impact test to evaluate the condition of hardwood logs in regard to internal decay, void, crack and defect ratio using an acoustic signal separation and enhancement algorithm. Longitudinal acoustic signals were obtained from 15 logs of four hardwood species through acoustic impact testing. The defect components were separated from the acoustic response signals and enhanced based on the autoregressive minimum entropy deconvolution (AR-MED) method, and from which the kurtosis was derived and used as the global feature parameter for evaluating the internal condition of logs. Compared with the acoustic velocity obtained directly from the original signal, the kurtosis was deemed to be a more powerful predictor of log defect ratio with higher coefficient of determination (R 2 = 0.89) and was not affected by log species. To identify the type of defects, a complex Morlet wavelet-based spectral kurtosis (SK) method was proposed. The research results indicated that the SK can not only determine the type and primary and secondary major defects, but also be able to identify those that were not detectable by global acoustic parameters.

Low-Cost Implementation of Independent Component Analysis for Biomedical Signal Separation Using Very-Large-Scale Integration

This brief proposes a low-cost implementation of the extended InfoMax independent component analysis (ICA) algorithm for biomedical signal separation by using very-large-scale integration (VLSI). On the basis of this algorithm, the implemented VLSI ICA core can be used to separate a mixed signal from a super-Gaussian signal source. By using mathematical operations of merging and simplification, the ICA core uses the systolic array and lookup table to implement the extended InfoMax ICA algorithm, thus achieving low cost and high speed. The measured results indicated that the ICA core consumed 9.5 mW when operated at 100 MHz using TSMC 90-nm complementary metal–oxide–semiconductor technology, with a gate count of only 36.8K. This represents a 59% reduction in area and a 35% saving in power compared with the previous design. Furthermore, the proposed ICA core was applied to mixed electrocardiogram signals, and the separated signals exhibited excellent quality.

Cyclostationary-based BSS method for time-frequency overlapped BPSK signals in electromagnetic surveillance

A cyclostationary-based blind signal separation (BSS) algorithm is proposed in this paper. The proposed separation algorithm is used for simultaneously received multiple spectrum overlapped BPSK signals in single channel electromagnetic surveillance system. The mixed signal is separated by means of blind adaptive frequency shift (BA-FRESH) filter based on the independent signals’ cyclostationary. This BA-FRESH filtering technique does not require training signals. It can separate the independent signals only by knowing some of their cycle frequencies, which is gained by the second order cycle cumulants in this work. Numerical simulation results proved the effectiveness of this separation method.

Source Diagnosis Method for Radiated EMI Noise Based on Wavelet and ICA Strategy

In this paper, the independent component analysis (ICA) algorithm, combined with wavelet algorithm, are introduced to diagnose the radiation noise source, also to segregate various radiation noise components. The nonlinear time domain signals of supeposed rradiation noises are measured using multi-channel high-speed digital oscilloscope. Double frequency domain analysis of radiation noise are conducted with the proposed method. Furtherly, time-frequency joint graph of excessive radiation noise are accessed, according to overclock points. The radiation noise component is determined by employing signal separation algorithm. The effective diagnosis of common frequency radiation noise is thus realized, which contributes to reducing the radiated noise electronic device emitted.