Weak Signal Extraction Research Articles

Weak signal detection algorithm for colored noise in laser guidance

Correlation detection can be used to extract weak signals such as those weakened by noise and attenuation. However, complex background noise contains both white and colored noise. The white noise is independent in the time domain, whereas the colored noise has a certain dependency, and the correlation detection algorithm cannot extract useful signals from the background mixed with colored noise. An improved correlation detection algorithm for whitening colored noise is discussed. Simulation experiments show that for laser signal amplitude similar to the noise amplitude, the correlation detection algorithm can extract the pulse signal, and the white noise background has a significantly improved signal-to-noise ratio of 2.3 times higher than the colored noise background. The experiment also proves that the whitening algorithm discussed can extract weak laser signals very well.

Ensemble tensor decomposition for infrared thermography cracks detection system

Eddy Current Pulsed Thermography (ECPT) has received much attention for its high sensitive of detectability on cracks with infrared cameras. However, when it comes to the detection in a movement way, it remains as challenges. This paper proposed an ensemble tensor decomposition to extract weak target signal of infrared thermography videos for cracks detection. The proposed algorithm jointly models the background and foreground tensor patterns as well as removing the ghosting. In order to verify the effectiveness and robustness of the proposed method, experimental studies have been carried out by applying electromagnetic thermal imaging system for cracks detection on samples with different geometry. The results of the experiments have indicated that the proposed method has significantly enhanced the contrast ratio between the defective regions and the non-defective regions.

Extraction Algorithm of English Text Information From Color Images Based on Radial Wavelet Transform

The Chinese and English text information of color image reflects some important contents of color image to a certain extent, and these texts are extracted automatically, which has important application value in the fields of information retrieval, digital library, web page retrieval and intelligent transportation. In this paper, various signal extraction techniques based on radial wavelet transform modulus Maxima are analyzed, and it is found that these techniques have poor ability to extract weak signals, and the higher requirements for directionality lead to pseudo-boundary phenomena in two-dimensional image extraction results. Based on the correlation denoising method of radial wavelet coefficients, the radial wavelet entropy is introduced into the field of signal extraction, and the complex Morlet radial wavelet is selected as the basis function. A complex Morlet radial wavelet entropy extraction algorithm suitable for extracting the English text characteristics of weak signal color images is proposed. In addition, a method of scene text recognition is proposed. Based on D-SIFT local features and spatio-temporal histogram, this method vectorizes text samples in the framework of Bo Ws model. Because the spatio-temporal histogram can flexibly model the structural information of the text, this method can effectively describe the scene text. On this basis, the selective ensemble learning method is used to improve the performance of the text extractor. In order to expand the application range of the algorithm and improve the running efficiency of the integrated extractor, a model compression method based on color image English text samples and integrated extractor is proposed. The integrated extractor which takes up more space and runs slowly is compressed into an equivalent and more efficient one. In order to reduce the number of pseudo-samples needed in the process of model compression, a method of training local extractor based on color image English text samples is proposed, which greatly reduces the number of pseudo-samples needed and improves the efficiency of the model compression method.

On bearing fault diagnosis by nonlinear system resonance

The response of a nonlinear system may present strong resonance under a periodic excitation. Based on the nonlinear system resonance, a method of bearing fault diagnosis is proposed in this paper. The system resonance is generated by finding the proper system parameters after the characteristic signal inputting the Duffing system. Through the resonance, the fault characteristic is amplified and easy to recognize. However, the resonance effect is different in several typical systems. Thus, three intuitional studies on the undamped, underdamped, and overdamped Duffing system are performed to select the optimal nonlinear system. The results show that the undamped Duffing system is superior to the other kind of systems in the weak fault feature extraction. It can not only amplify the fault characteristic but also suppress other frequency components in the spectrum. Meanwhile, the improved quality factor can be used to determine the resonance induced by the external excitation. The accuracy of bearing fault diagnosis is improved greatly. Both numerical simulations and experimental verifications certify the correctness and validity of the method. System resonance method has a certain reference value in enhancing a weak signal. It provides a new idea in weak signal extraction. It can be used in weak characteristic information extraction and other related fields.

Application of Lock-in Amplifier Technique in AC FSM based on AD630

Field Signature Method (FSM) is one of non-intrusive technique for pipeline internal corrosion monitoring, but the commercial FSM systems nowadays are all using DC input which have many shortcomings. In this paper, AC FSM based on AC input has been developed, DC FSM can be replaced by AC FSM with low-frequency. To extract weak signals, the lock-in amplifier based on AD630 has been designed and manufactured, the functions and designed circuits of each part have been proposed. Experimental results show that, the application of lock-in amplifier technique in AC FSM based on AD630 can measure the weak signal from the noise interference effectively, ensuring the accuracy and security at the same time.

Development of Weak Signal Recognition and an Extraction Algorithm for Raman Imaging.

Improving time resolution of Raman imaging is essential for the observation of dynamic processes involved in interfacial catalysis and biological systems. The crucial step is how to recognize and extract weak Raman signals overwhelmed in the strong noise under the low signal-to-noise ratio (SNR) condition. Here, by exploring the relationship between the SNR of a single Raman spectrum and the structural similarity (SSIM; the key parameter evaluating the image quality) of the whole image, we determined a semiempirical threshold with SNR = 0 dB for clear imaging for the first time. Therefore, we proposed one signal processing algorithm for fast Raman imaging by reconstructing the Raman spectrum with the aid of weak signal processing: extracting the reliable Raman signal of the target under the low SNR and then determining the suitable scanning time to obtain the Raman image with a trustworthy image quality. In the first step, fast Fourier transform (FFT), least squares, and 2-D median filter are sequentially applied to improve the SNR of each raw Raman spectrum. In the second step, a local SNR evaluation strategy is developed to predict image quality as well as the determination of clear imaging. The proposed method was successfully applied to the fast imaging of the cell under the low SNR condition.

Detection and Extraction of Weak Pulse Signals in Chaotic Noise with PTAR and DLTAR Models

With the development in communications, the weak pulse signal is submerged in chaotic noise, which is very common in seismic monitoring and detection of ocean clutter targets, and is very difficult to detect and extract. Based on the threshold autoregressive model, pulse linear form, Markov chain Monte Carlo (MCMC), and profile least squares (PrLS) algorithm, phase threshold autoregressive (PTAR) model and double layer threshold autoregressive (DLTAR) model are proposed for detection and extraction of weak pulse signals in chaotic noise, respectively. Firstly, based on noisy chaotic observation, phase space is reconstructed according to Takens’s delay embedding theorem, and the phase threshold autoregressive (PTAR) model is presented to detect weak pulse signals, and then the MCMC algorithm is applied to estimate parameters in the PTAR model; lastly, we obtain one-step prediction error, which is used to realize adaptively detection of weak signals with the hypothesis test. Secondly, a linear form for the pulse signal and PTAR model is fused to build a DLTAR model to extract weak pulse signals. The DLTAR model owns two kinds of parameters, which are affected mutually. Here, the PrLS algorithm is applied to estimate parameters of the DLTAR model and ultimately extract weak pulse signals. Finally, accurate rate (Acc), receiver operating characteristic (ROC) curve, and area under ROC curve (AUC) are used as the detector performance index; mean square error (MSE), mean absolute percent error (MAPE), and relative error (Re) are used as the extraction accuracy index. The presented scheme does not need prior knowledge of chaotic noise and weak pulse signals, and simulation results show that the proposed PTAR-DLTAR model is significantly effective for detection and extraction of weak pulse signals under chaotic interference. Specifically, in very low signal-to-interference ratio (SIR), weak pulse signals can be detected and extracted compared with support vector machine (SVM) class and neural network model.

A New Second-Order Tristable Stochastic Resonance Method for Fault Diagnosis

Vibration signals are used to diagnosis faults of the rolling bearing which is symmetric structure. Stochastic resonance (SR) has been widely applied in weak signal feature extraction in recent years. It can utilize noise and enhance weak signals. However, the traditional SR method has poor performance, and it is difficult to determine parameters of SR. Therefore, a new second-order tristable SR method (STSR) based on a new potential combining the classical bistable potential with Woods-Saxon potential is proposed in this paper. Firstly, the envelope signal of rolling bearings is the input signal of STSR. Then, the output of signal-to-noise ratio (SNR) is used as the fitness function of the Seeker Optimization Algorithm (SOA) in order to optimize the parameters of SR. Finally, the optimal parameters are used to set the STSR system in order to enhance and extract weak signals of rolling bearings. Simulated and experimental signals are used to demonstrate the effectiveness of STSR. The diagnosis results show that the proposed STSR method can obtain higher output SNR and better filtering performance than the traditional SR methods. It provides a new idea for fault diagnosis of rotating machinery.

A multi-parameter constrained potential underdamped stochastic resonance method and its application for weak fault diagnosis

Vibration information is widely used in the fault diagnosis of rotating machinery, but the weak components reflecting early faults are often difficult to extract because of strong background noise. Among the variety of weak information extraction methods, the stochastic resonance (SR) method has the unique advantage of transferring noise energy to weak signals and has great application prospect in weak signal extraction. The current underdamped SR method with two adjustment parameters cannot achieve an optimal potential model structure, so it is easy to fall prey to the local optimization problem without having a better feature extraction method. Therefore, in this paper, we present a multi-parameter constrained potential underdamped stochastic resonance method by investigating the potential model and signal-to-noise ratio of the system. Moreover, the weighted ant colony algorithm is introduced to optimize potential parameters and a damping factor is used to improve the ability of weak fault information extraction. Furthermore, several types of bearing failure experiments were implemented to verify the effectiveness of the proposed method. The results show that the proposed method has excellent weak signal extraction ability and is suitable for early fault diagnosis.

Analysis and Simulation of Extracting Radar Modulation Signal Based on CIC Filter

Aimed at the difficulty of extracting weak information such as projectile movement and precession during flight., this paper analyzes the characteristics of extracting the periodic motion information of the projectile from the original data, and designs the resampling and filtering methods based on the characteristics and measured data of a certain type of continuous wave radar system. This method achieves the purpose of non-destructive extraction of low frequency weak signals.

Vibration analysis for large-scale wind turbine blade bearing fault detection with an empirical wavelet thresholding method

Blade bearings, also termed pitch bearings, are joint components of wind turbines, which can slowly pitch blades at desired angles to optimize electrical energy output. The failure of blade bearings can heavily reduce energy production, so blade bearing fault diagnosis is vitally important to prevent costly repair and unexpected failure. However, the main difficulties in diagnosing low-speed blade bearings are that the weak fault vibration signals are masked by many noise disturbances and the effective vibration data is very limited. To address these problems, this paper firstly deals with a naturally damaged large-scale and low-speed blade bearing which was in operation on a wind farm for over 15 years. Two case studies are conducted to collect the vibration data under the manual rotation condition and the motor driving condition. Then, a method called the empirical wavelet thresholding is applied to remove heavy noise and extract weak fault signals. The diagnostic results show that the proposed method can be an effective tool to diagnose naturally damaged large-scale wind turbine blade bearings.

Weak Signal Extraction from Lunar Penetrating Radar Channel 1 Data Based on Local Correlation

Knowledge of the subsurface structure not only provides useful information on lunar geology, but it also can quantify the potential lunar resources for human beings. The dual-frequency lunar penetrating radar (LPR) aboard the Yutu rover offers a Special opportunity to understand the subsurface structure to a depth of several hundreds of meters using a low-frequency channel (channel 1), as well as layer near-surface stratigraphic structure of the regolith using high-frequency observations (channel 2). The channel 1 data of the LPR has a very low signal-to-noise ratio. However, the extraction of weak signals from the data represents a problem worth exploring. In this article, we propose a weak signal extraction method in view of local correlation to analyze the LPR CH-1 data, to facilitate a study of the lunar regolith structure. First, we build a pre-processing workflow to increase the signal-to-noise ratio (SNR). Second, we apply the K-L transform to separate the horizontal signal and then use the seislet transform (ST) to reserve the continuous signal. Then, the local correlation map is calculated using the two denoising results and a time–space dependent weighting operator is constructed to suppress the noise residuals. The weak signal after noise suppression may provide a new reference for subsequent data interpretation. Finally, in combination with the regional geology and previous research, we provide some speculative interpretations of the LPR CH-1 data.

Multidimensional Feature Explorer for Unbalanced Spatiotemporal Data

AbstractFeature analysis of weak nonlinear signals from geographic spatiotemporal data has received increasing attention. Most existing signal processing methods cannot effectively perform comprehensive feature analysis because of the multiple dimensions and unbalance of spatiotemporal data. We developed a divide–aggregate–explore method for the feature analysis of spatiotemporal data. In our method, strategies for dividing different dimensions are defined for multidimensional analysis, and the tensor–block structure is adopted to reorganize the original data and distinguish differences in dimensions. Then, information‐based data aggregation is used to weaken the impact of dimensional unbalance. Case studies based on climatic reanalysis field data released by the National Oceanic and Atmospheric Administration showed that the proposed method can effectively extract weak propagation signals such as the El Niño–Southern Oscillation and El Niño–Southern Oscillation Modoki. Our method can also reveal more detailed evolutionary characteristics of complex coupling systems in different dimensions compared with classical feature detection methods such as principal component analysis and tensor decomposition.

SERS in Plain Sight: A Polarization Modulation Method for Signal Extraction.

Surface-enhanced Raman spectroscopy (SERS) is a powerful analytical spectroscopy offering advantages ranging from "vibrational fingerprints" to multiplexed detection. However, the use of this technique in real-world applications has been limited due to difficulties in detecting inherently weak Raman signals often embedded in strong interfering background signals. A variety of plasmonics-active platforms have been developed to increase Raman signals but are not sufficient to extract weak SERS signals from intense interfering background signals. Herein, we describe a practical method, referred to as polarization modulation-SERS (PM-SERS), which utilizes the polarization dependence of anisotropic SERS-active nanostructures to modulate the plasmonic effect to extract SERS signals and remove background. The modulation is obtained by switching the polarization of the excitation source at a specific frequency involving addition of only few optical components such as liquid crystal polarizers to a typical Raman setup. In this work, we characterized the polarization-dependent response of the SERS substrates fabricated using the oblique angle evaporation (OAV) technique and their response under laser excitation using a polarization modulated source. We demonstrated that the PM-SERS method can extract the analyte weak SERS signals from the strong interfering background signal in different situations, involving a fluorescent sample and a strong background light, and we show the possibility of using PM-SERS at a quasi-real time rate (0.5 Hz). We believe that the PM-SERS method will help expand the translation of applications that utilize SERS-substrates to real-world settings.

Signal Detection Based on Second-Order Underdamped Tristable Stochastic Resonance and Its Application to Weak Fault Diagnosis

The advantages of stochastic resonance (SR) have received extensive attention and research in the field of weak signal extraction. It can be used to extract fault signals from rotating machinery. To deeply improve the output signal-to-noise ratio (SNR), a second-order underdamped tristable stochastic resonance (SUTSR) is proposed in the present study. The potential function, the steady-state probability density of particles and the SNR are used to evaluate the model. Firstly, the relationship between the noise intensity and the SNR of SUTSR is studied. Then, using the steady-state solution curve, the output response of the SUTSR system is discussed from the perspective of steady-state input, and the output response of the system is further studied when inputting low-frequency harmonic signals. Finally, SUTSR model is used to process bearing signal data with inner ring fault and rolling element fault, and the processing result is compared with tristable system and second-order underdamped bistable system. The results show that, in the background of strong noise, the SUTSR system can accurately identify the characteristic frequency of the fault signal and then greatly improve the energy of the weak fault signal under appropriate system parameters.

Towards a Fourier domain waveform for non-spinning binaries with arbitrary eccentricity

Although the gravitational waves observed by advanced LIGO and Virgo are consistent with compact binaries in a quasi-circular inspiral prior to coalescence, eccentric inspirals are also expected to occur in nature. Due to their complexity, we currently lack ready-to-use, analytic waveforms in the Fourier domain valid for sufficiently high eccentricity, and such models are crucial to coherently extract weak signals from the noise. We here take the first steps to derive and properly validate an analytic waveform model in the Fourier domain that is valid for inspirals of arbitrary orbital eccentricity. As a proof-of-concept, we build this model to leading post-Newtonian order by combining the stationary phase approximation, a truncated sum of harmonics, and an analytic representation of hypergeometric functions. Through comparisons with numerical post-Newtonian waveforms, we determine how many harmonics are required for a faithful (matches above 99%) representation of the signal up to orbital eccentricities as large as 0.9. As a first byproduct of this analysis, we present a novel technique to maximize the match of eccentric signals over time of coalescence and phase at coalescence. As a second byproduct, we determine which of the different approximations we employ leads to the largest loss in match, which could be used to systematically improve the model because of our analytic control. The future extension of this model to higher post-Newtonian order will allow for an accurate and fast phenomenological hybrid that can account for arbitrary eccentricity inspirals and mergers.

Frequency-shift multiscale noise tuning stochastic resonance method for fault diagnosis of generator bearing in wind turbine

The wind energy industry has developed very fast, while the condition monitoring and fault diagnosis for wind turbine is increasingly becoming the focus of manufacturers and wind-farm operators. However, due to the influence of harsh operation environment and complex internal structure of wind turbine, collected vibration signals are corrupted by strong noise. Effective extraction of useful feature information submerged in strong noise that is indicative of structural defects has remained a major challenge. In this paper, a novel frequency-shift multiscale noise tuning stochastic resonance (SR) method is proposed with the advantage of SR using noise to enhance weak signal features. Firstly, the frequency shift modulation algorithm is adopted to move the target signal to the designated low frequency domain. Then the obtained modulated signal is processed by the multiscale noise tuning algorithm based on discrete wavelet transform (DWT). The resulting output is used as the input of SR system. Finally, the tuning parameter of multiscale noise is determined by maximizing the modified signal-to-noise ratio (SNR) of system output. The proposed method can realize the feature enhancement and extraction of weak signal with arbitrary frequency. Experiments and fault diagnosis case of generator bearing in wind turbine validate the effectiveness of the proposed method.

Word-Fi: Accurate Handwrite System Empowered by Wireless Backscattering and Machine Learning

Word-Fi is a handwriting input system, driven by wireless backscattering technology and machine learning methods. It could effectively mitigate the surrounding noise and extract the weak signals incurred by tiny writing gestures accurately. Leveraging our customized wireless backscattering system, Word-Fi could be noise tolerant across relatively complex environments, especially when multiple persons are presented around, which significantly differs from status quo wireless sensing systems that suffer from multi-user presentation. For weak signal extraction, Word- Fi incorporates an efficient feature selection scheme for classification and improves the classifier by fully exploiting the physical layer information. After using the word suggestion module, it could recognize writing words with fairly high accuracy (above 90 percent) across different volunteers (7-10).

An improved adaptive stochastic resonance with general scale transformation to extract high-frequency characteristics in strong noise

The idea of general scale transformation is introduced in detail. Based on this idea, an improved adaptive stochastic resonance (SR) method is proposed to extract weak signal features. Different periodic signals are considered to verify the proposed method. Compared with the normalized scale transformation, the output signal-to-noise ratio (SNR) of the proposed method is increased to a greater extent. Further, the influences of some key parameters on the responses of the two methods are discussed minutely. Results show that the improved adaptive SR method with general scale transformation is obviously superior to the traditional normalized scale transformation that is used in the former literatures. For different noise intensities and time scales, the proposed approach can always obtain the optimal response of SR to enhance the weak signal characteristics.

Implication of two-coupled tri-stable stochastic resonance in weak signal detection**Project supported by the National Natural Science Foundation of China (Grant Nos. 61071025 and 61502538).

Stochastic resonance (SR) has been proved to be an effective approach to extract weak signals overwhelmed in noise. However, the detection effect of current SR models is still unsatisfactory. Here, a coupled tri-stable stochastic resonance (CTSSR) model is proposed to further increase the output signal-to-noise ratio (SNR) and improve the detection effect of SR. The effects of parameters a, b, c, and r in the proposed resonance system on the SNR are studied, by which we determine a set of parameters that is relatively optimal to implement a comparison with other classical SR models. Numerical experiment results indicate that this proposed model performs better in weak signal detection applications than the classical ones with merits of higher output SNR and better anti-noise capability.