Signal Extraction Method Research Articles

How different are the results acquired from mathematical and subjective methods in dendrogeomorphology? Insights from landslide movements

Knowledge of past landslide activity is crucial for understanding landslide behaviour and for modelling potential future landslide occurrence. Dendrogeomorphic approaches represent the most precise methods of landslide dating (where trees annually create tree-rings in the timescale of up to several hundred years). Despite the advantages of these methods, many open questions remain. One of the less researched uncertainties, and the focus of this study, is the impact of two common methods of geomorphic signal extraction on the spatial and temporal results of landslide reconstruction. In total, 93 Norway spruce (Picea abies (L.) Karst.) trees were sampled at one landslide location dominated by block-type movements in the forefield of the Orlické hory Mts., Bohemian Massif. Landslide signals were examined by the classical subjective method based on reaction (compression) wood analysis and by a numerical method based on eccentric growth analysis. The chronology of landslide movements obtained by the mathematical method resulted in twice the number of events detected compared to the subjective method. This finding indicates that eccentric growth is a more accurate indicator for landslide movements than the classical analysis of reaction wood. The reconstructed spatial activity of landslide movements shows a similar distribution of recurrence intervals (Ri) for both methods. The differences (maximally 30% of the total Ri ranges) in results obtained by both methods may be caused by differences in the ability of trees to react to tilting of their stems by a specific growth response (reaction wood formation or eccentric growth). Finally, the ability of trees to record tilting events (by both growth responses) in their tree-ring series was analysed for different decades of tree life. The highest sensitivity to external tilting events occurred at tree ages from 70 to 80years for reaction wood formation and from 80 to 90years for eccentric growth response. This means that the ability of P. abies to record geomorphic signals varies with not only eccentric growth responses but also with age.

Fault detection in analog and mixed-signal circuits by using Hilbert–Huang transform and coherence analysis

Using Hilbert–Huang transform (HHT) and coherence analysis, a signature extraction method for testing analog and mixed-signal circuits is proposed in this paper. The instantaneous time–frequency signatures extracted with HHT technique from the measured signal of circuits under test (CUT) are used for faults detection that is implemented through comparing the signatures of faulty circuits with that of the fault-free circuit. The coherence functions of the instantaneous time–frequency signatures and its integral help to test faults in the faulty dictionary according to the minimum distance criterion. The superior capability of HHT-based technique, compared to traditional linear techniques such as the wavelet transform and the fast Fourier transform, is to obtain the subtle time-varying signatures, i.e., the instantaneous time–frequency signatures, and is demonstrated by applying to Leapfrog filter, a benchmark circuit for analog and mixed-signal testing, with 100% of F.D.R (fault detection rate) in the best cases and with the least 24.2% of F.L.R. (fault localization rate) with one signature.

A novel signal extraction technique for online partial discharge (PD) measurement of transformers

One of the major difficulties of online partial discharge (PD) measurement of transformers is the effective extraction of PD signals, which are heavily coupled with noise. Wavelet transform has been widely adopted for the extraction of PD signals. However, its performance is highly dependent on proper selection of mother wavelet, decomposition levels and thresholding method. Such selection involves a series of complicated and extensive computations. Moreover, it is also difficult to find a mother wavelet, which has a high degree of similarity to PD signals generated by different types of PD sources. This paper proposes a novel differential signal extraction method as an alternative to wavelet transform for PD signal extraction. Its implementation is straight-forward as it does not involve any mother wavelet selection and decomposition level determination. It can effectively extract PD signals in the presence of multiple PD sources. The applicability of the proposed method is verified using datasets obtained from experiments in the laboratory and measurements on in-service transformers at substations.

Harmonic signal extraction from noisy chaotic interferencebased on synchrosqueezed wavelet transform**Project supported by the National Natural Science Foundation of China (Grant No. 61171075), the Natural Science Foundation of Hubei Province, China (Grant No. 2015CFB424), the State Key Laboratory Foundation of Satellite Ocean Environment Dynamics, China (Grant No. SOED1405), the Hubei Provincial Key Laboratory Foundation of Metallurgical Industry Process System Science,

For the harmonic signal extraction from chaotic interference, a harmonic signal extraction method is proposed based on synchrosqueezed wavelet transform (SWT). First, the mixed signal of chaotic signal, harmonic signal, and noise is decomposed into a series of intrinsic mode-type functions by synchrosqueezed wavelet transform (SWT) then the instantaneous frequency of intrinsic mode-type functions is analyzed by using of Hilbert transform, and the harmonic extraction is realized. In experiments of harmonic signal extraction, the Duffing and Lorenz chaotic signals are selected as interference signal, and the mixed signal of chaotic signal and harmonic signal is added by Gauss white noises of different intensities. The experimental results show that when the white noise intensity is in a certain range, the extracting harmonic signals measured by the proposed SWT method have higher precision, the harmonic signal extraction effect is obviously superior to the classical empirical mode decomposition method.

Novel ECG Signal Classification Based on KICA Nonlinear Feature Extraction

Electrocardiogram (ECG) signal feature extraction is important in diagnosing cardiovascular diseases. This paper presents a new method for nonlinear feature extraction of ECG signals by combining principal component analysis (PCA) and kernel independent component analysis (KICA). The proposed method first uses PCA to decrease the dimensions of the ECG signal training set and then employs KICA to calculate the feature space for extracting the nonlinear features. Support vector machine (SVM) is utilized to determine the nonlinear features of the ECG signal classification. Genetic algorithm is also used to optimize the SVM parameters. The proposed method is advantageous because it does not require a huge amount of sampling data, and this technique is better than traditional strategies to select optimal features in the multi-domain feature space. Computer simulations reveal that the proposed method yields more satisfactory classification results on the MIT---BIH arrhythmia database, reaching an overall accuracy of 97.78 %.

New building signature extraction method from single very high-resolution synthetic aperture radar images based on symmetric analysis

To monitor urban areas using a synthetic aperture radar (SAR) sensor, we propose a symmetric analysis-based building signature extraction method. Instead of using separated algorithms, a unified framework is proposed to extract both layover and shadow areas. Since these two primitives usually exhibit long strip patterns in very-high-resolution SAR images, symmetry axes are first delineated. After that, local features are extracted from both symmetry and range direction to better distinguish different primitives. Then, these local radiometric features are used to identify different categories (layover, shadow, and background) via an efficient multiclass logistic regression classifier. To discriminate individual primitives, geometric information is adopted via an improved Ramer Douglas Peucker algorithm, which also simplifies the parameters for describing these primitives. To further enhance accuracy, combinatory analysis is implemented to exclude some false detections, and then shadow areas are extended via a local region growing method. The proposed approach is tested on a 0.75-m resolution airborne C band SAR image. The experiments are carried out under both small- and large-scale scenes, and the comparative results show our method has some advantages in low-contrast target detection and false-alarm elimination.

Vibration parameter estimation methods for ultrasonic measurement systems – a review

A review of vibration signal estimation techniques employed in ultrasonic vibration measurement systems is presented. The review focuses on the Doppler signal extraction methods including the theory and analysis pertaining to the background of ultrasonic vibration measurement techniques. The phase modulated received signal in the vibration measurement system involves constant phase shift because of ultrasonic path length, phase shift introduced by Doppler effect because of vibrating object and the parametric phase shift caused by interaction of high frequency ultrasonic wave and low pressure developed by vibrating object. Among the three phase shifts, the Doppler phase shift contains the vibrating signal information and the extraction of Doppler signal helps in knowing the velocity of the vibrating object, amplitude and phase of the vibration. Various techniques of Doppler phase shift retrieval are categorised based on the modulation index estimation procedures from the received ultrasonic signal. An analysis is made based on the information retrieval methods for estimation of vibration signal parameters, range of the vibration amplitude, frequency and the preferable carrier frequency for transmission. Further, the analysis also presents various applications in which ultrasonic vibration measurements system are employed successfully.

Bicoid Signal Extraction with a Selection of Parametric and Nonparametric Signal Processing Techniques

The maternal segmentation coordinate gene bicoid plays a significant role during Drosophila embryogenesis. The gradient of Bicoid, the protein encoded by this gene, determines most aspects of head and thorax development. This paper seeks to explore the applicability of a variety of signal processing techniques at extracting bicoid expression signal, and whether these methods can outperform the current model. We evaluate the use of six different powerful and widely-used models representing both parametric and nonparametric signal processing techniques to determine the most efficient method for signal extraction in bicoid. The results are evaluated using both real and simulated data. Our findings show that the Singular Spectrum Analysis technique proposed in this paper outperforms the synthesis diffusion degradation model for filtering the noisy protein profile of bicoid whilst the exponential smoothing technique was found to be the next best alternative followed by the autoregressive integrated moving average.

An image dimensionality reduction method for rolling bearing fault diagnosis based on singular value decomposition

The fast kurtogram, a faint signal extraction method, has been regarded as an effective approach to detect and characterize faint transient features in vibration signals. However, the fast kurtogram, a band-pass filtering method, which extracts transient signals by optimal frequency band selection and leaves the noise in the selected frequency band unprocessed. Therefore, to overcome the shortcoming of the fast kurtogram method, a method which can wipe off the noise in the whole frequency band is necessary. This paper proposes a novel faint signal extraction method by time–frequency distribution image dimensionality reduction. Since time–frequency distribution image can reveal intrinsic feature of nonstationary signals and can make the weak impulses feature prominent, and besides, the transient impulse feature and the noise component lie in different dimensions, so using the dimensionality reduction method based on singular value decomposition to suppress the background noise in the raw time–frequency distribution image is motivated. A bearing outer race fault signal obtained from a test-to-failure experiment and a bearing inner race fault signal obtained from an experimental motor are employed to demonstrate the enhanced performance of the proposed method in faint signal extraction. The results indicate that the proposed method outperforms the fast kurtogram method and is effective in faint signal extraction.

The L-DVV method for the seismic signal extraction

The delay vector variance (DVV) method enables analyzing the nonlinearity of a time series by the so-called scatter diagram, where the original time series is compared to the linearized version, the so-called surrogate. The surrogate is often constructed by the iterative amplitude adjusted Fourier transformation (iAAFT) method, so a linear signal in the DVV method can be generated by a linear time-invariant system in a white Gaussian noise environment. Owing to the iAAFT method and the linearity criterion, the DVV method is not sensitive to stochastic noise and the results are unstable, which may cause some confusion between the desired signal and the stochastic noise when processing the seismic signal. The now proposed method, the delay vector variance based on the straight line sequence (L-DVV) method, is essentially an extension of the DVV method, so that the straight line sequence is selected from the original signal and defined as the surrogate time series, which is used to construct the L-DVV scatter diagram based on the target variances calculated from both time series. This new scatter diagram is taken as criterion for testing the linearity of the analyzed time series. The analyses as to the stability of the method and its sensitivity to the stochastic noise are based on the quantification of the delay vector variance (QDV) or deviation with respect to bisector in the scatter diagram. These are the two key points – L-DVV scatter diagram and QVD – that we bear in mind when studying some examples of synthetic seismic signals contaminated by different noise levels and also real field data. The L-DVV method has high stability and strong sensitivity to the stochastic noise, and is able to discriminate accurately the seismic signal masked by stochastic noise, thus being a useful tool for the seismic signal processing in the exploratory practice.

Micro-motion frequency estimation of radar targets with complicated translations

Micro-Doppler (m-D) signatures induced by micro-motion dynamics, which are of great importance for target classification, have received increasing attention among the radar community. However, most of the existing m-D signature-extraction methods are based on the assumption that translations of radar targets have been completely compensated by other methods or they merely involve scenarios when radar targets undergo low order polynomial translations. These methods may become invalid for maneuvering targets with complicated translations. In this work, radar targets with micro-motion and complicated translations are considered. Inspired by the differential element method and the shift invariant difference operation, a micro-motion frequency estimation method based on piecewise translation compensation (PTC) and time-frequency squared difference sequences (TFSDS) is proposed. The PTC can compensate the translations of radar targets at the largest extent. After compensation, the TFSDS-based frequency estimator gives an estimation of micro-motion frequency; this estimator can remove the effect of residual translation and avoid the separation of the multicomponent radar echo. The combination of PTC and TFSDS enables us to extract the micro-motion frequency of radar targets with complicated translations without a prior knowledge. Experiments with synthetic and measured data confirm the effectiveness and good performance of the proposed method.

Improvement of speech signal extraction method using detection filter of energy spectrum entropy

In speech recognition system research, recognition system performance has been significantly improved through research and development in the speech recognition area, but environmental noise is still a favorite subject for research due to its numerous environmental changes. And speech extraction techniques, which are widely applied, improve speech signals that are mixed with noise. A least mean square (LMS) adaptation filter is commonly used to help noise estimation and detection algorithms adapt to changing environments. But an LMS filter needs some time to adapt and estimate signals. That weakness can be overcome by using energy spectrum entropy and an average estimate LMS (AELMS) filter to detect robust voice activity in a noisy environment. In this paper, we propose a speech signal extraction method using a detection filter of energy spectrum entropy. The proposed method is polluted speech---signal noise extraction to reduce noise with an AELMS filter to detect robust voice activity. An AELMS filter maintains source features of speech, decreases speech information degradation, and reduces noise in a polluted speech signal. To improve adaptation speed, we calculated an average estimator, and controlled the LMS filter step size with a frame measure. For speech detection of signals synthesized with low-speed and high-speed driving noise, an energy spectrum entropy method was used. Compared to an existing method of using frame energy, the proposed method improved the starting point of the resulting speech by 1.7 % of an error rate and by 3.7 % of an end point error rate.

A Field Cancellation Signal Extraction Method for Magnetic Particle Imaging

In nowadays Magnetic Particle Imaging (MPI) signal detection and excitation happens at the same time. This concept, however, leads to strong coupling of the drive (excitation) field (DF) with the receive chain. As the induced DF signal is several orders of magnitude higher, special measures have to be taken to suppress this signal portion within the receive signal to keep the required dynamic range of the subsequent analog to digital conversion in a technically feasible range. For "frequency space MPI" high-order band-stop-filters have been successfully used to remove the DF signals, which unfortunately as well removes the fundamental harmonic components of the signal of the magnetic nanoparticles (MNP). According to the Langevin theory the fundamental harmonic component has a large signal contribution and is important for direct reconstruction of the particle concentration. In order to separate the fundamental harmonic component of the MNP from the induced DF signal, different concepts have been proposed using signal cancelation based on additional DF signals, also in combination with additional filtering. In this paper, we propose a field-cancelation (FC) concept in which a receive coil (RC) consists of a series connection of a primary coil in combination with an additional cancelation coil. The geometry of the primary coil was chosen to be sensitive for the MNP signal while the cancelation coil was chosen to minimize the overall inductive coupling of the FC-RC with the DF. Sensitivity plots and mutual coupling coefficients were calculated using a thin-wire approximation. A prototype FC-RC was manufactured and effectiveness of the reduction of the mutual inductive coupling (d) was tested in an existing mouse MPI scanner. The difference between simulations (ds =70 dB) and the measurements (dms =55 dB) indicated the feasibility as well as the need for further investigations.

An Integrated SSA-ARIMA Approach to Make Multiple Day Ahead Forecasts for the Daily Maximum Ambient O3 Concentration

An integrated approach that combines a signal extraction method SSA (Singular Spectrum Analysis) with ARIMA (Autoregressive Integrated Moving Average) has been introduced to make multiple day ahead forecasts for daily maximum ambient O3 concentrations. The results are compared with the forecasts obtained by more commonly used signal extraction method FFT (Fast Fourier Transforms) in combination with ARIMA. The data from six different European AIRBASE stations have been analyzed to test their skills in one-day ahead to five-day ahead forecasts. In SSA-ARIMA, the SSA has been used to extract the different structural components of the time series. The first few principal components have been selected based on their dominance in the eigenvalues spectrum. Each component has been modelled separately and their sum is called SSA-component. The residuals of the “SSA-component subtracted from the actual data” exhibited stationarity and have been modeled using ARIMA process. In the second approach called by FFT-ARIMA, FFT has been used to model the periodicities exhibited by the time series. The first few predominant frequencies have been selected based on the power-spectrum characteristics of the time series. The residuals of the “FFT-component subtracted from the actual data” were modeled using ARIMA process. The performances of both the SSA-ARIMA and FFT-ARIMA were evaluated for 30 out of sample days for one to five day ahead forecasts. The statistical indicators RMSE (root mean square error) and MAE (mean absolute error) reveal that the SSA-ARIMA consistently outperforms FFT-ARIMA. Moreover, SSA-ARIMA has also been found to perform better especially for multiple day ahead forecasts. The results clearly indicate that the SSA-ARIMA approach can be a potential alternative to make short term air quality forecasting.

An Extraction Method of Weak Low-Frequency Magnetic Communication Signals Based on Multisensor

It is a technical challenge to effectively remove the influence of magnetic noise from the vicinity of the receiving sensors on low-frequency magnetic communication. The traditional denoising methods are difficult to extract high-quality original signals under the condition of low SNR (the signal-to-noise ratio). In this paper, we analyze the numerical characteristics of the low-frequency magnetic field and propose the algorithms of the fast optimization of blind source separation (FOBSS) and the frequency-domain correlation extraction (FDCE). FOBSS is based on blind source separation (BSS). Signal extraction of low SNR can be implemented through FOBSS and FDCE. This signal extraction method is verified in multiple field experiments which can remove the magnetic noise by about 25 dB or more.

Interpolation Algorithms Based on Rife-Vincent Window for Discrete Fourier Transforms of Damped Signals

The major parameter estimation error source for discrete Fourier transform are spectrum leakage and picket fence effect. A weighted two points vector interpolation algorithm based on Rife-Vincent window for improving the estimation accuracy of parameters for the multi-frequency damped signals is presented, where the signals are weighted by M-order cosine window family before the discrete Fourier transform and the equations with nuknown of frequency deviation and decay factor is established by the ration of two adjacent spectral lines near the real frequency position. The frequencies, amplitudes and phases are calculated by the frequency deviation solved from the above equation. The cosine window family with the characteristic of maximum sidelobe decay can essentially remove the spectral leakage and two point vector interpolation will cope with the problem of the picket fence. To combine the merits of the above two method can effectively increase the accuracy. The simulation and test results show that the proposed algorithm has a higher estimate accuracy and steady estimates. The higher computational efficiency and lower memory demand are suggested especially for poor computing resource situations. It also can be used as an optional method for the features extraction of the multi-frequency damped signals.

Remote sensing change detection methods to track deforestation and growth in threatened rainforests in Madre de Dios, Peru

Two forestry-change detection methods are described, compared, and contrasted for estimating deforestation and growth in threatened forests in southern Peru from 2000 to 2010. The methods used in this study rely on freely available data, including atmospherically corrected Landsat 5 Thematic Mapper and Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation continuous fields (VCF). The two methods include a conventional supervised signature extraction method and a unique self-calibrating method called MODIS VCF guided forest/nonforest (FNF) masking. The process chain for each of these methods includes a threshold classification of MODIS VCF, training data or signature extraction, signature evaluation, k-nearest neighbor classification, analyst-guided reclassification, and postclassification image differencing to generate forest change maps. Comparisons of all methods were based on an accuracy assessment using 500 validation pixels. Results of this accuracy assessment indicate that FNF masking had a 5% higher overall accuracy and was superior to conventional supervised classification when estimating forest change. Both methods succeeded in classifying persistently forested and nonforested areas, and both had limitations when classifying forest change.

Analysis on Signal Extraction under Sudden Changing Noise Interference Based on Time Frequency Analysis

The illegal signal such as the intrusion signal which wanted to be embedded in the normal signal was analyzed for the extraction, and made the network environment be safe and pure, so the signal detection problem was shown as the key and important problem in the assurance of the safety of the network. The signal processing principle for the non stationary signal was analyzed firstly, and the new signal extraction method based on the Wigner Ville distribution and Hough transformation theory of the time frequency analysis. The original signal was filtered and detected, the spectrum analysis was got as the same time for the testing the detection property in the simulation. Simulation and experiment was implemented on the objects of the network intrusion signal with the real collection in the database. Simulation result shows that the performance of decreasing the noise and filtering is very good. The intrusion signal can be detected and extracted perfectly, and also it shows good value in the analysis of the network safety in practice.

Simulation Research on Rolling Element Bearing Fault Signal Extraction Based on Blind Source Separation

In order to extract the fault information from rolling element bearing, combined with Kurtosis criteria and Hessian matrix. An improved rolling element bearing fault signal extraction method is proposed. Kurtosis is the cost function. The method is according to the construction principles of blind source separation (BSS), and it uses an analytically derived Hessian matrix in the maximization process of the cost function used. Then the impact signal is extracted successfully. The effectiveness of the method is demonstrated on simulated signal.

Separation of stimulus-selective and nonselective components in intrinsic optical signals

Optical imaging based on intrinsic optical signals has been widely used in studies on the functional organization of visual cortical areas. The presentation of a visual stimulus usually generates non-stimulus-selective global darkening and stimulus-selective localized spots superposed on it. It is critical to extract the stimulus-selective component for the investigation of the cortical functional organization. In this study, we propose a novel method based on a principal component analysis (PCA) of the spatial optical response patterns, and apply it to the separation of the global darkening in the cat visual cortex. The images submitted to PCA were those responding to the gratings with eight orientations from 0° to 180° in steps of 22.5°. The first principal component represents most of the common signals such as the global darkening and large blood vessels, and occupies more than 80% of the signal energy. By removing the first principal component, the stimulus-selective dark patches in the reconstructed images have higher contrasts and high orientation selectivity consistent with electrophysiological single-cell recording. The results suggest that the proposed method can be used to exclude the stimulus-nonselective component, which provides an alternative method for signal extraction. © 2014 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.