Signal Extraction Method Research Articles

Spindle vibration signal extraction method based on improved all phase Fast Fourier Transform

In the modern processing technology, the machine tool plays an irreplaceable role as the main processing tool, and its machining accuracy directly affects the processing quality of the part. Because the spindle vibration caused by the rotor mass imbalance will seriously affect the machining accuracy of the machine tool, it is necessary to dynamically balance the spindle. The key step of dynamic balancing is to accurately extract the characteristics of the vibration signal, the phase of the vibration signal of the spindle was extracted by the all phase fast Fourier transform, the amplitude of vibration signal was extracted by cross correlation analysis, the results are applied to the influence coefficient method to realize the dynamic balance of spindle vibration The results show that the vibration signal feature extraction method combined with the results of the two extraction methods has high precision and stability.

Comparison of data-driven respiratory signal extraction methods from cone-beam CT (CBCT)

In Cone-Beam CT (CBCT) imaging, respiratory motion needs to be considered to mitigate motion artifacts thus increasing the accuracy of reconstructed images. Data driven methods can be used to extract respiratory signal directly from projection data without requiring any additional equipment or surrogate devices. Digital phantoms provide an adequate option to evaluate developing methods prior to clinical implementation. In this study, four data driven methods are used to extract respiratory signal from simulated projections. An in-house 4D MRI-based CBCT digital phantom is used, where actual respiratory signal is available as ground truth. In comparing all four data driven methods, the respiratory signal extracted using the Local Principal Component Analysis (LPCA) method is found to be robust and yielded the highest correlation coefficient of 0.8644 compared to the ground truth.

Cardiac and respiratory signal extraction methods from ballistocardiography signal sensed using fiber bragg grating sensor

This work presents a simple, efficient and an easy-to-build ballistocardiography sys-tem using Fiber Bragg Grating (FBG) sensor and a comprehensive assessment of relevant digital signal processing algorithms to simultaneously extract respiratory and cardiac frequency components from a cluttered mix Ballistocardiography (BCG) signal. The primary purpose of the current study is two-fold: first- to build an analog circuit for BCG signal amplification, and second to evaluate the performance of three state-of-the-art methods, namely: lowpass-highpass filter, weiner filter and ensemble empherical mode decomposition to simultaneously extract respiratory and cardiac frequency components from the BCG signal. BCG measurements from test subjects were used in this study and a commercial digital stethoscope was used to validate the performance of methods used in this study. In addition to the effective amplification of the BCG signal through proposed analog circuit configuration, we demonstrate that a simple low pass high pass filter configuration can be used for accurate measurement of cardiac and respiratory frequencies. Due to its simplicity, the proposed system can be suitably tailored to process BCG signal for simultaneous extraction of respiration and heart rate which can aid as an effective diagnostic tool for identifying critical disorders associated with lungs and heart dysfunction.

Logical Image Reconstruction by Monitoring a Seeded Potential in Variable Detection Planes

Optical image computing, such as spatial differentiation, logical transform, and addition-subtraction operation, is of great importance to signal enhancement, transform, and extraction, but such nonlinear methods are restrained in incoherent or scattering imaging. Here, the logical white-light image reconstruction, as an optical image computing method for incoherent and scattered images, is proposed by monitoring a seeded potential in variable detection planes. The weak white-light signals collectively seed a potential at the expense of scattering noise based on seeded modulation instability in a photorefractive crystal. Then the logical image operation is achieved by sensitively probing the seeded potential and recording the intensity distribution of the probe beam in different detection planes. The proposed method has a strong ability of ballistic signal recording, reproduction, and reversal. The numerical and experimental results show a new physical phenomena in which the indirect interaction between light waves through a seeded potential is presented in different forms. The nonlinear method is proved to effectively operate pure images, the images scattered by a rotating diffuser, and the images scattered by fogs whether under back lighting or under front lighting. Our work suggests an alternative method for all-optical image processing, recovery, and computing.

Diagnostic Signal Informativeness Increase

Modern requirements for the operation of cars lead to the need to improve the efficiency of their maintenance. Diagnosis as an element of the maintenance process allows you to provide information about the technical condition of a particular element, which allows you to respond in a timely manner to the technical condition change of a diagnostic object with minimal resources. In this paper, we consider the way improving the diagnostic signal quality. It is known that a diagnostic signal must meet several requirements. The most important requirement is informativeness, which shows the decrease of uncertainty about the technical condition of an object, represented by a priori entropy after information application from this diagnostic signal, measured during the diagnosis. There are the methods for a diagnostic signal conversion, which allow to get rid of the noise entering it to a different degree or present it in such a way to facilitate the signal analysis process. Three methods are considered in the work: direct spectrum obtaining, signal envelope spectrum obtaining, and adaptive filtering. The analysis of these methods led to the conclusion that adaptive filtering has the greatest efficiency potential. We have proposed the method that is based on adaptive filtering, but with additional operations. In the course of the diagnostic signal studies and the adaptive filtering algorithm, we found that it is possible to set the function to be detected as a variable, as well as several parameters that affect the result quality. Based on this, a new method for a useful signal extraction was proposed. The results of the work were checked on a signal simulating a car gearbox signal. The results show that the method allows you to obtain the necessary knowledge about a defect, which can be used in the diagnosis. The developed method allows to increase the information content of the diagnostic signal by suppressing its other components. The results of the proposed method correlate with the results of other methods for general cases, i.e., when the ratio of the useful signal to noise is such that high sensitivity of the method is not required to identify the useful signal.

Improved Target Signal Source Tracking and Extraction Method Based on Outdoor Visible Light Communication Using an Improved Particle Filter Algorithm Based on Cam-Shift Algorithm

An improved particle filter algorithm based on Cam-Shift algorithm applied to outdoor visible light communication (outdoor-VLC) is presented in this paper. In the outdoor-VLC system, accurately and completely tracking and extracting the target signal source Light Emitting Diode (LED) area is the premise for realizing communication. However, few existing studies pay attention to it. In the dynamic outdoor environment, there will be a lot of different environmental interferences (such as background interference, similar object interference, etc.), which greatly increases the difficulty of tracking and extracting the target signal source LED area. Therefore, in this paper, an improved tracking algorithm is proposed to solve the problem of how to track and extract the target signal source LED area accurately, stably and in real time in the outdoor-VLC system under various environmental interferences, so as to increase the feasibility of practical application of VLC system in outdoor scenes. This improved algorithm combines the particle filter algorithm and Cam-Shift algorithm originally. Experimental results show that the proposed algorithm has good accuracy, robustness and real-time performance under the environment of multiple interference factors. Accordingly, the proposed algorithm can be applied to the outdoor-VLC system with various environmental interferences, and can realize the actual first step of communication in VLC system based on image sensor well, laying a foundation for feature extraction, data transmission and other subsequent steps.

A general-purpose signal processing algorithm for biological profiles using only first-order derivative information

BackgroundAutomatic signal-feature extraction algorithms are crucial for profile processing in bioinformatics. Both baseline drift and noise seriously affect the position and peak area of signals. An efficient algorithm named the derivative passing accumulation (DPA) method for simultaneous baseline correction and signal extraction is presented in this article. It is an efficient method using only the first-order derivatives which are obtained through taking the simple differences.ResultsWe developed a new signal feature extracting procedure. The vector representing the discrete first-order derivative was divided into negative and positive parts and then accumulated to build a signal descriptor. The signals and background fluctuations are easily separated according to this descriptor via thresholding. In addition, the signal peaks are simultaneously located by checking the corresponding intervals in the descriptor. Therefore, the eternal issues of parsing the 1-dimensional output of detectors in biological instruments are solved together. Thereby, the baseline is corrected, and the signal peaks are extracted.ConclusionsWe have introduced a new method for signal peak picking, where baseline computation and peak identification are performed jointly. The testing results of both authentic and artificially synthesized data illustrate that the new method is powerful, and it could be a better choice for practical processing.

Progress on the Fabrication of Smart Textiles Based on Soft Strain Sensors

The focus of this study is on strain sensing research and applications in smart textiles. Strain sensing is the measurement of fabric deformation by embedding a strain-sensitive material in it and subjecting it to stress. This paper presents an extensive classification of knitted textile strain sensors. Salient knitted strain sensor production parameters, such as conductive yarn choice, fabric structure, fabric structure deformation, and its relationship to strain signal extraction are discussed. The study concludes that producing yarn-based soft strain sensors for smart textile applications is viable. However, sensitive yarns with the right conductivity, count, and structural configuration are often unavailable. Work remains in the areas of efficient fabric deformation, signal extraction methods, development of sensor nodes, and robust experimental testing systems.

A lock-in amplifier modeling recovery method to extract the surface nuclear magnetic resonance signal from residual noise.

Surface nuclear magnetic resonance (SNMR) could provide direct insights for hydrological investigations but is often limited because of its low signal-to-noise ratio. Many types of residual noise remain after denoising procedures, including despiking, power harmonic noise cancellation, and random noise attenuation. This residual noise prevents the detection of valid signals, especially in strong noise environments, such as cities and industrial areas. In this work, a lock-in amplifier modeling recovery (LIAMR) method is proposed for extracting SNMR signals from high-level residual noise after denoising. The desired SNMR signals can be extracted directly by establishing a model of the SNMR signal passing through the lock-in amplifier and then transforming mathematically the output of the amplifier. The performance of the proposed method is tested on synthetic SNMR signals under varied average relaxation times, simulation noise at different levels, and field noise. Experiment results show that LIAMR can obtain good estimations of SNMR signal parameters with residual noise. Moreover, the proposed method can provide more precise parameters compared with traditional signal extraction methods. LIAMR provides theoretical support for the application of SNMR technology in strong noise environments.

Elbow-flexion force estimation during arm posture dynamically changing between pronation and supination

Objective. In this study, we proposed a dynamic voluntary contraction force estimation framework and implemented it for elbow-flexion force estimation during arm posture dynamically changing between pronation and supination. Approach. High-density surface electromyography (HD-sEMG) from biceps brachii and brachialis muscles and the elbow-flexion force were measured synchronously. The simplified Hill model was adopted to establish the relation between HD-sEMG and the elbow-flexion force. In the training process of the force estimation model, HD-sEMG data from static isometric elbow flexion tasks in two force modes (staircase and sinusoidal) and two postures (supination or pronation) were used. The nonnegative matrix factorization (NMF) algorithm was adopted to decompose HD-sEMG into activation patterns and the corresponding time-varying coefficient vectors. The major activation pattern was used to select the appropriate sEMG channels for extracting the input signal of the force estimation model. In the testing phase, the elbow-flexion force estimation during arm posture dynamically changing between pronation and supination was conducted. HD-sEMG was also decomposed using NMF algorithm. In this case, the concept of the major activation pattern is no longer applicable because the activation areas of biceps brachii and brachialis vary with the change of arm posture. An improved channel selection scheme based on the ratio of activation intensities was presented to extract the input signal of the well-trained simplified Hill model. Main results. (1) the improved channel selection scheme could locate effectively the primary muscle activation areas related to the change of arm posture; (2) the input signal extraction method based on the ratio of activation intensities obtained the best force estimation performance compared to the method based on all channels of HD-sEMG array and the method based on major activation pattern; (3) the force estimation performance was better when the simplified Hill model was calibrated with the sinusoidal force data rather than staircase data. Significance. This research provides an effective solution to realize muscle force estimation during a dynamic voluntary contraction task. It can be further extended to the research fields of biomechanics, sports, and rehabilitation medicine.

Method for the extraction of shock signal features based on the upper limit of density integral

Shock signal features must be extracted for use in pattern recognition or fault diagnosis. In this work, we proposed a method for the feature extraction of shock signals, which are vibration signals that change faster and have larger amplitude ranges than general signals. First, we proposed the concepts of amplitude density for monotonic functions and piecewise monotonic functions. On the basis of these concepts, we then proposed the concept of the upper limit of density integral (ULDI), which was adopted to obtain signal features. Then, we introduced two types of serious fault cracks to the latch sheet of an automatic gun mechanism that is used on warships. Next, we applied the proposed method to extract the features of shock signals from data acquired when the automatic gun mechanism fired with normal and two fault patterns. Finally, we verified the effectiveness of our proposed method by applying the features that it extracted to a support vector machine (SVM). Our proposed method provided good results and was superior to the traditional statistics-based feature extraction method when applied to a SVM for classification. In addition, the former method demonstrated better generalisation than the latter. Thus, our method is an efficient approach for extracting shock signal features in pattern recognition and fault diagnosis.

Continuous Distant Measurement of the User's Heart Rate in Human-Computer Interaction Applications.

In real world scenarios, the task of estimating heart rate (HR) using video plethysmography (VPG) methods is difficult because many factors could contaminate the pulse signal (i.e., a subjects’ movement, illumination changes). This article presents the evaluation of a VPG system designed for continuous monitoring of the user’s heart rate during typical human-computer interaction scenarios. The impact of human activities while working at the computer (i.e., reading and writing text, playing a game) on the accuracy of HR VPG measurements was examined. Three commonly used signal extraction methods were evaluated: green (G), green-red difference (GRD), blind source separation (ICA). A new method based on an excess green (ExG) image representation was proposed. Three algorithms for estimating pulse rate were used: power spectral density (PSD), autoregressive modeling (AR) and time domain analysis (TIME). In summary, depending on the scenario being studied, different combinations of signal extraction methods and the pulse estimation algorithm ensure optimal heart rate detection results. The best results were obtained for the ICA method: average RMSE = 6.1 bpm (beats per minute). The proposed ExG signal representation outperforms other methods except ICA (RMSE = 11.2 bpm compared to 14.4 bpm for G and 13.0 bmp for GRD). ExG also is the best method in terms of proposed success rate metric (sRate).

Extraction of Partial Discharge Pulses from the Complex Noisy Signals of Power Cables Based on CEEMDAN and Wavelet Packet

While both periodic narrowband noise and white noise are significant sources of interference in the detection and localization of partial discharge (PD) signals in power cables, existing research has focused nearly exclusively on white noise suppression. This paper addresses this issue by proposing a new signal extraction method for effectively detecting random PD signals in power cables subject to complex noise environments involving both white noise and periodic narrowband noise. Firstly, the power cable signal was decomposed using complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), and the periodic narrowband noise and frequency aliasing in the obtained signal components were suppressed using singular value decomposition. Then, signal components contributing significantly to the PD signal were determined according to the cross-correlation coefficient between each component and the original PD signal, and the PD signal was reconstructed solely from the obtained significant components. Finally, the wavelet packet threshold method was used to filter out residual white noise in the reconstructed PD signal. The performance of the proposed algorithm was demonstrated by its application to synthesized PD signals with complex noise environments composed of both Gaussian white noise and periodic narrowband noise. In addition, the time-varying kurtosis method was demonstrated to accurately determine the PD signal arrival time when applied to PD signals extracted by the proposed method from synthesized signals in complex noise environments with signal-to-noise ratio (SNR) values as low as −6 dB. When the SNR was reduced to −23 dB, the arrival time error of the PD signal was only one sampling point.

Target Signal Extraction Method Based on Enhanced ICA with Reference

Target signal extraction has a great potential for applications. To solve the problem of error extraction of target signals in the current constrained independent component analysis (cICA) method, an enhanced independent component analysis with reference (EICA-R) method is proposed. The new algorithm establishes a unified cost function, which combines the negative entropy contrast function and the distance metric function. The EICA-R method transforms the constrained optimization problem into unconstrained optimization problem to overcome the problem of threshold setting of distance metric function in constrained optimization problem. The theoretical analysis and simulation experiment show that the proposed EICA-R algorithm overcomes the problem of the error extraction of the existing algorithm and improves the reliability of the target signal extraction.

Heterogeneity Counts More than Power for HD-sEMG-Based Joint Force Estimation.

The aim of the proposed work is to utilize the heterogeneity information, in input signal extraction, to improve the joint force estimation from high-density surface electromyography (HD-sEMG). For this purpose, joint force and HD-sEMG signals from biceps brachii and brachialis were collected synchronously during isometric elbow flexion. The input signal of the force model was obtained after the following procedures: first, HD-sEMG signals were decomposed by principal component analysis into principal components and weight vectors; second, the first several weight maps were segmented to obtain heterogeneity information by the Otsu and Moore-Neighbor tracing methods, and the principal component covering the most activated areas (maximum heterogeneity) was selected; and last, the selected principal component was low-pass filtered to obtain the input signal. The force model was built using a polynomial fitting technique. The conventional power-based input signal was compared with our obtained input signal. According to the obtained results, the proposed heterogeneity-based input signal can reduce the force estimation error significantly than the power-based input signal. The proposed heterogeneity-based input signal extraction methods had more neuromuscular control information and will be tested in more muscles and force tasks in future works.

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.

An automatic abrupt signal extraction method for fault diagnosis of aero-engines

Fault diagnosis of a mechanical device such as a complicated aero-engine system is an interesting engineering topic. Present paper aims at providing a method to automatically extract abrupt information of signals to diagnose typical faults. This proposed method is based on singular value decomposition (SVD), and it decomposes a signal via reconstruction of singular value matrix. A criterion of difference spectrum is introduced into this method to terminate the analysis procedure. To verify the proposed method, both numerical simulation and experimental work on rotor test rig and an aero-engine generator were carried out. In addition, the kurtosis of rubbing resulting from wavelet, empirical mode decomposition (EMD) and this proposed method was compared. It is shown the proposed method is advanced to wavelet and EMD in rubbing fault diagnosis of aero-engines since it can extract the most significant periodic impact feature of fault signals.

Detecting changes in facial temperature induced by a sudden auditory stimulus based on deep learning-assisted face tracking

Thermal Imaging (Infrared-Imaging-IRI) is a promising new technique for psychophysiological research and application. Unlike traditional physiological measures (like skin conductance and heart rate), it is uniquely contact-free, substantially enhancing its ecological validity. Investigating facial regions and subsequent reliable signal extraction from IRI data is challenging due to head motion artefacts. Exploiting its potential thus depends on advances in analytical methods. Here, we developed a novel semi-automated thermal signal extraction method employing deep learning algorithms for facial landmark identification. We applied this method to physiological responses elicited by a sudden auditory stimulus, to determine if facial temperature changes induced by a stimulus of a loud sound can be detected. We compared thermal responses with psycho-physiological sensor-based tools of galvanic skin response (GSR) and electrocardiography (ECG). We found that the temperatures of selected facial regions, particularly the nose tip, significantly decreased after the auditory stimulus. Additionally, this response was quite rapid at around 4–5 seconds, starting less than 2 seconds following the GSR changes. These results demonstrate that our methodology offers a sensitive and robust tool to capture facial physiological changes with minimal manual intervention and manual pre-processing of signals. Newer methodological developments for reliable temperature extraction promise to boost IRI use as an ecologically-valid technique in social and affective neuroscience.

Creating Color Image Signature Based On Laplacian Equation

Colour image retrieval, identification and recognition play an important role in vital computerized applications, so creating an image signature which can be used as a key to identify the image is an essential and important problem to be solved. In this paper a new method of image signature extraction will be proposed. The proposed method will be implemented and tested and the experimental results will be compared with other method results to prove the efficiency of the proposed method by mean of creating a unique signature which will require a minimum memory space and a smaller time to extract the signature.

Magnetic flux signal extraction method based on wavelet threshold filtering

Magnetic detection based on magnetic flux induction coil is an important means of moving target detection and tracking, which can be applied to various complex environments. Among them, the extraction of the magnetic flux signal of the moving target under complex background conditions is a key technology. This paper first introduces the basic principle of the magnetic flux detection method. According to the different characteristics of the target magnetic flux signal and the noise signal, comprehensively considering the characteristics and applicable range of various signal filtering and noise reduction methods, the wavelet threshold denoising method is selected to carry out the signal. In the laboratory environment, a magnetic flux detection method test system for moving targets was built. The signal extraction method was used to analyse and process the test data and compared with the simulation results. The results show that the filtering method can effectively filter out the Gaussian white noise in the flux signal, separate the signals of different frequencies, and retain the characteristics of the target signal, thus verifying the feasibility and applicability of the method.