Signal Processing Procedure Research Articles

Sound Sensing: Generative and Discriminant Model-Based Approaches to Bolt Loosening Detection.

The detection of bolt looseness is crucial to ensure the integrity and safety of bolted connection structures. Percussion-based bolt looseness detection provides a simple and cost-effective approach. However, this method has some inherent shortcomings that limit its application. For example, it highly depends on the inspector's hearing and experience and is more easily affected by ambient noise. In this article, a whole set of signal processing procedures are proposed and a new kind of damage index vector is constructed to strengthen the reliability and robustness of this method. Firstly, a series of audio signal preprocessing algorithms including denoising, segmenting, and smooth filtering are performed in the raw audio signal. Then, the cumulative energy entropy (CEE) and mel frequency cepstrum coefficients (MFCCs) are utilized to extract damage index vectors, which are used as input vectors for generative and discriminative classifier models (Gaussian discriminant analysis and support vector machine), respectively. Finally, multiple repeated experiments are conducted to verify the effectiveness of the proposed method and its ability to detect the bolt looseness in terms of audio signal. The testing accuracy of the trained model approaches 90% and 96.7% under different combinations of torque levels, respectively.

Can AI Predict the Magnitude and Direction of Ortho-K Contact Lens Decentration to Limit Induced HOAs and Astigmatism?

Background: The aim is to investigate induced higher-order aberrations (HOA)s and astigmatism as a result of non-toric ortho-k lens decentration and utilise artificial intelligence (AI) to predict its magnitude and direction. Methods: Medmont E300 Video topographer was used to scan 249 corneas before and after ortho-k wear. Custom-built MATLAB codes extracted topography data and determined lens decentration from the boundary and midpoint of the central flattened treatment zone (TZ). An evaluation was carried out by conducting Zernike polynomial fittings via a computer-coded digital signal processing procedure. Finally, an AI-based machine learning neural network algorithm was developed to predict the direction and magnitude of TZ decentration. Results: Analysis of the first 21 Zernike polynomial coefficients indicate that the four low-order and four higher-order aberration terms were changed significantly by ortho-k wear. While baseline astigmatism was not correlated with lens decentration (R = 0.09), post-ortho-k astigmatism was moderately correlated with decentration (R = 0.38) and the difference in astigmatism (R = 0.3). Decentration was classified into three groups: ≤0.50 mm, reduced astigmatism by -0.9 ± 1 D; 0.5~1 mm, increased astigmatism by 0.8 ± 0.1 D; >1 mm, increased astigmatism by 2.7 ± 1.6 D and over 50% of lenses were decentred >0.5 mm. For lenses decentred >1 mm, 29.8% of right and 42.7% of left lenses decentred temporal-inferiorly and 13.7% of right and 9.4% of left lenses decentred temporal-superiorly. AI-based prediction successfully identified the decentration direction with accuracies of 70.2% for right and 71.8% for left lenses and predicted the magnitude of decentration with root-mean-square (RMS) of 0.31 mm and 0.25 mm for right and left eyes, respectively. Conclusions: Ortho-k lens decentration is common when fitting non-toric ortho-k lenses, resulting in induced HOAs and astigmatism, with the magnitude being related to the amount of decentration. AI-based algorithms can effectively predict decentration, potentially allowing for better control over ortho-k fitting and, thus, preferred clinical outcomes.

Identification of cutter flat wear for rock TBMs using vibration signal of shield

Identification of cutter wear status is one of the critical issues for the successful operation of tunnel boring machines (TBM). This study proposed a novel model for predicting the locations of cutters experiencing flat wear, leveraging measured vibration signals from the TBM shield. Field tests were conducted to collect vibration acceleration data using accelerometers. This paper outlines the equipment and setup for monitoring vibration acceleration and develops procedures for signal processing. Analysis of vibration acceleration patterns reveals distinct characteristics corresponding to varying numbers and locations of cutters with flat wear. A linear relationship can be identified between the frequency of different vibration acceleration peak intervals and the extent of flat wear on the cutters (number and location of wear cutters). The proposed model is then applied to a TBM tunnelling section in Guangzhou, China, and validated using on-site wear data. This method offers real-time insight into the potential need for cutter replacement during tunnelling operations in challenging hard rock conditions.

Synchronous averaging with sliding narrowband filtering for low-speed bearing fault diagnosis

The health condition of low-speed rolling bearing, such as the main bearing in wind turbines which bears the heavy dead weight and operates under variable speeds, has a big impact on the safe operation of the machinery. Therefore, damage detection of low-speed bearings plays a key role in the health management of large-scale rotating machinery. However, in popular vibration based bearing damage detection algorithms, due to the fact that the additional vibration features incurred by the low-speed operated bearing damage are typically weak in amplitude and low in frequency contents, the additional responses caused by damage are difficult to be isolated by conventional algorithms. Especially, in the cases of variable speed operations, the smearing effect caused by Fourier transform makes it more difficult to extract the damage features by spectrum analysis based methods. To deal with these issues, we developed a damage detection procedure specially designed for bearings operated at low and variable speeds. According to the dynamic properties of the vibration signals incurred by a low-speed bearing with damage, an envelope analysis method based on synchronous averaging with sliding narrow band-pass filters is designed and developed for extracting damage features in the low frequency range. The fundamental theory used in the method is derived first. Then, a damage detection signal processing procedure is constructed based on the elaborated theory. The feasibility and advantages of the proposed methodology are validated by numerical simulations as well as the measured data from a wind turbine field example.

Zero group velocity feature in CFRP-Nomex honeycomb structure and its use for debonding detection

Carbon fiber reinforced plastic (CFRP) − Nomex adhesive honeycomb structures are widely used in aerospace due to their excellent properties. However, debonding defects pose a significant challenge to structural safety due to their hidden nature and high risk. In this work, to address the debonding detection in the CFRP-Nomex honeycomb structure, a method based on the zero group velocity (ZGV) feature is proposed. By calculating the dispersion curve of the structure, it is found that there is the ZGV on the S1 branch. Further analysis revealed that this ZGV feature is generated by the repulsion between the S2 and S1 branches. The simulation and experimental analysis of the acoustic propagation characteristics in the honeycomb structure confirmed the presence of the ZGV feature with its energy localized near the exciting point. When debonding defects occur in the honeycomb structure, this feature disappears. Therefore, the ZGV feature can be used for the debonding detection of CFRP-Nomex honeycomb structures. Compared with ultrasonic C-scan, the results demonstrate that the proposed method can accurately detect and locate debonding defects with a diameter of 20 mm. Due to its simplicity, the method does not require sophisticated equipment or complex signal processing procedures, making it suitable for in-service inspection.

A generic time-frequency analysis-based signal processing and imaging approach for air-coupled ultrasonic testing

The utilization of air-coupled ultrasonic technology is crucial for examining objects that are challenging to establish contact with or cannot endure coupling. Nevertheless, air-coupled ultrasonic is susceptible to various factors such as impedance mismatch, sound wave attenuation, and environmental noise, consequently leading to a diminished signal-to-noise ratio of the received signal. This reduction in signal quality amplifies the complexity of analysis and imaging processes. In this study, a generic air-coupled ultrasonic signal processing and imaging procedure for ultrasonic inspection is proposed, which capitalizes on time-frequency analysis. This methodology attains a heightened level of stability and superior signal-to-noise ratio imaging through a series of procedural steps, encompassing feature extraction, denoising, and the reconstruction of the A-scan signals. The efficacy of the proposed approach is quantitatively assessed in terms of signal-to-noise ratio, probability of detection, robustness, and generality. This innovative method presents a newfangled resolution for automated air-coupled ultrasonic testing.

Gradient-descent noise whitening techniques for short-reach IM-DD optical interconnects with severe bandwidth limitation.

Bandwidth limitation in optoelectrical components and the chromatic dispersion-induced power fading phenomenon cause severe inter-symbol interference (ISI) in high-speed intensity modulation and direct detection (IM-DD) optical interconnects. While the equalizer implemented in the receiver's digital signal processing procedure can mitigate ISI, it also inevitably enhances the noise located in the decayed frequency region, known as equalization-enhanced colored noise (EECN). Additionally, the nonlinear impairments of the modulator and photodetector also deteriorate the performance of the IM-DD system, especially for high-order modulation formats. In this work, we propose a gradient-descent noise whitening (GD-NW) algorithm to address EECN and extend it by introducing nonlinear kernels to simultaneously mitigate EECN and nonlinear impairments. The proposed algorithms are compared with conventional counterparts in terms of the achievable baud rate and the receiver optical power sensitivity. As a proof-of-concept experiment, we validate the principles of the proposed algorithms by successfully transmitting 360-GBd on-off-keying (OOK) and 180-GBd 4-level pulse-amplitude-modulation (PAM-4) signals in the back-to-back case under a 62-GHz brick-wall bandwidth limitation. 280-GBd OOK and 150-GBd PAM-4 transmissions are also demonstrated over 1-km standard single-mode fiber with a bit error rate below 7% hard-decision forward error correction aided by the proposed approach.

2CFastICA: A Novel Method for High Density Surface EMG Decomposition Based on Kernel Constrained FastICA and Correlation Constrained FastICA.

This study presents a novel high density surface electromyography (EMG) decomposition method, named as 2CFastICA, because it incorporates two key algorithms: kernel constrained FastICA and correlation constrained FastICA. The former focuses on overcoming the local convergence of FastICA without requiring the peel-off strategy used in the progressive FastICA peel-off (PFP) framework. The latter further refines the output of kernel constrained FastICA by correcting possible erroneous or missed spikes. The two constrained FastICA algorithms supplement each other to warrant the decomposition performance. The 2CFastICA method was validated using simulated surface EMG signals with different motor unit numbers and signal to noise ratios (SNRs). Two source validation was also performed by simultaneous high density surface EMG and intramuscular EMG recordings, showing a matching rate (MR) of (97.2 ± 3.5)% for 170 common motor units. In addition, a different form of two source validation was also conducted taking advantages of the high density surface EMG characteristics of patients with amyotrophic lateral sclerosis, showing a MR of (99.4 ± 0.9)% for 34 common motor units from interference and sparse datasets. Both simulation and experimental results indicate that 2CFastICA can achieve similar decomposition performance to PFP. However, the efficiency of decomposition can be greatly improved by 2CFastICA since the complex signal processing procedures associated with the peel-off strategy are not required any more. Along with this paper, we also provide the MATLAB open source code of 2CFastICA for high density surface EMG decomposition.

Multichannel ballistocardiography: A comparative analysis of heartbeat detection across different body locations.

The paper presents a validation of novel multichannel ballistocardiography (BCG) measuring system, enabling heartbeat detection from information about movements during myocardial contraction and dilatation of arteries due to blood expulsion. The proposed methology includes novel sensory system and signal processing procedure based on Wavelet transform and Hilbert transform. Because there are no existing recommendations for BCG sensor placement, the study focuses on investigation of BCG signal quality measured from eight different locations within the subject's body. The analysis of BCG signals is primarily based on heart rate (HR) calculation, for which a J-wave detection based on decision-making processes was used. Evaluation of the proposed system was made by comparing with electrocardiography (ECG) as a gold standard, when the averaged signal from all sensors reached HR detection sensitivity higher than 95% and two sensors showed a significant difference from ECG measurement.

Hold-up formation in bubble channel reactors: A bubble-scale investigation

Experiments were carried out to measure the air volume fraction and the hold-up in a bubble channel reactor fed by porous pipes.A new processing method was proposed for the signal acquired by means of a resistivity probe for the air volume fraction. This method avoids the loss of the bubble residence time on the probe tip, which affects the most used signal processing procedures. The probe measurements were compared with other studies regarding bubble curtains.The measured air hold-up was in agreement with correlations available in the literature and formulated for bubble columns fed by a porous sparger. A bubble-scale model was proposed to explain the functional form of the dependence of the hold-up on the air superficial velocity. Moreover, a link between the hold-up and the Koide equation for bubble dimensions was established.The experiments were reproduced by means of CFD simulations. The air volume fraction measurement method and the hold-up model found a confirmation in the numerical results. Moreover, the latter showed how the bubble plumes interact inside the reactor and the consequences on the air hold-up.

Development of SDR-based network positioning technology. LTE reference signals reception and processing validation

Introduction. The positioning of devices according to the base stations reference signals of LTE cellular radio networks deployed everywhere is an important area of research and development that is in demand due to dense urban development with unreliable signal reception from global navigation satellite systems. The instrument for experimental approbation of technical solutions is the software and hardware of the SDR software defined radio technology. This work continues the study on the use of SDR technology for network positioning problems in terms of the experimental testing of the device for receiving reference signals of the LTE standard.Materials and methods. The correctness of the software implementation of the LTE reference signal processing procedures was verified in the course of laboratory tests that showed the accuracy of primary measurements in units of meters allowing us to speak about the satisfactory performance of the implemented SDR layout of the user device that receives the LTE reference signals.Results. Insufficient synchronization accuracy of SDR models of base stations for navigation tasks confirmed the need for hardware-software implementation of the subsystem for generating LTE reference signals in HDL for FPGAs.Discussion and conclusions. The next stage of research in the field of SDR network positioning technology is field testing of the implemented sub-system for generating LTE reference signals with sufficient synchronization accuracy of eNB models for navigation tasks.

Identification of the error excitation in gear systems: A mediator algorithm between simulation and experiment

There are three primary sources of internal excitation in gear systems: time-varying mesh stiffness, meshing damping and tooth error. Compared with the first two excitations, it is more difficult to evaluate or measure the error excitation. The error excitation is usually assumed empirically or even neglected in the majority of dynamic models. To address this issue, we desired to propose a mediator algorithm between the simulation and the experiment to identify the error excitation of gear systems. Based on the second-order cyclostationarity of gear signals, a signal processing procedure is proposed to acquire the mediator signal with a high signal-to-noise ratio. The mediator signal is served as the “media” between the simulation and experiment. The error parameter is updated repeatedly in the iterative optimization until the optimal matching is found between the simulation and the experiment. Simultaneously, the pre-decomposition technique is proposed to accelerate the solution of the simulation dynamic model. The performance of the proposed identification algorithm is verified both numerically and experimentally. Moreover, noise immunity analysis shows that the proposed identification algorithm is robust to noise, damping and phase differences between the experiment and simulation. The proposed identification algorithm provides an indirect approach to estimating the error excitation when the direct measurement is unavailable.

Separation of pressure signals caused by waves traveling in opposite directions

Abstract Hydraulic transient analysis allows the condition assessment of pipeline systems by the measurement of a system's transient pressure response subject to input pressure excitations. The detection of a pressure wave's arrival time and amplitude at one or more sections can be used to detect unexpected anomalies, such as leaks, blockages, or corroded sections. Wave separation approaches, based on signal processing techniques involving two sensors, enable a directional attribution to any measured pressure perturbations. Being able to determine the direction of origin of a perturbation through a signal-splitting approach greatly facilitates anomaly detection through the resolution of this ambiguity. The signal-splitting procedure can be sensitive to the analysis conditions (i.e. the signal processing procedure used, the presence of noise within the signal, and the spacing of the sensors) and, as a result, produce spurious results. This paper explores this issue and proposes, and analyses, a range of strategies to improve the signal-splitting results. The strategies explored involve the consideration of alternative time- and frequency-domain formulations; the use of filters and wavelet to condition the signal; and processing the time-shifted differenced signal as opposed to the original raw signal. Results are presented for a range of numerical and laboratory systems.

Frequency Spectrum Modification Process-Based Anti-Collusion Mechanism for Audio Signals.

The collusion attack combines multiple multimedia files into one new file to erase the user identity information. The traditional anti-collusion methods (which aim to trace the traitors) can defend the collusion attack, but they cannot well defend some hybrid collusion attacks (e.g., a collusion attack combined with desynchronization attacks). To address this issue, we propose a frequency spectrum modification process (FSMP) to defend the collusion attack by significantly downgrading the perceptual quality of the colluded file. The severe perceptual quality degradation can demotivate the attackers from launching the collusion attack. Because FSMP is orthogonal to the existing traitor-trace-based methods, it can be combined with the existing methods to provide a double-layer protection against different attacks. In FSMP, after several signal processing procedures (e.g., uneven framing and smoothing), multiple signals (called FSMP signals) can be generated from the host signal. Launching collusion attack using the generated FSMP signals would lead to the energy disturbance and attenuation effect (EDAE) over the colluded signals. Due to the EDAE, FSMP can significantly degrade the perceptual quality of the colluded audio file, thereby thwarting the collusion attack. In addition, FSMP can well defend different hybrid collusion attacks. Theoretical analysis and experimental results confirm the validity of the proposed method.

Increasing the resolution of phased antenna arrays of the optical wave band by forming a virtual antenna array

Formulation of the problem. When solving the direction-finding problem of determining the direction to radiation sources located at an angular distance less than the width of the main lobe of the direction-finding antenna, the problem of insufficiency of resolution arises. This leads to the fact that two closely located sources will be perceived by the direction finding complex as one. Purpose. The purpose of this work is to study the possibility of solving the problem of resolution of two sources using an optical antenna array with a width of the main lobe smaller than the angular distance between the radiation sources. Results. As a solution to the problem of insufficient resolution, it is proposed to use the method of virtual antenna arrays, which consists in the formation of additional ("virtual") antenna elements using the mathematical apparatus of the Kirchhoff integral and the method of point sources. The article presents the results obtained with the help of mathematical modeling, which confirm the high efficiency of the proposed method. Practical significance. The obtained results can be used to improve the resolution of the direction finders of the optical range only due to an additional signal processing procedure, without making changes to the design of the direction-finding antenna.

Statistical Validation of An Automated Method for Calculating Time Domain Heart Rate Variability on The QT Dataset.

Heart rate variability measures calculated from electrocardiography recordings reflect social competence. Clinical assessments of social skills have found that reduced heart rate variability is related to differences in the development of social skills in children and increase the risk of mental disorders. Limited by widespread manual signal processing and R-peak detection in current clinical assessments, most literature reports only short-term baseline studies, with fewer studies reporting social interaction settings with prolonged recording. There is an urgent need for an automated physiological signal processing toolbox to detect R-peaks and perform heart rate variability measurements in social settings. This paper proposes a modified automated Neurokit2 toolbox with signal processing procedures similar to the MindWare software that requires manual inspection of R-peak locations. We calculate time domain heart rate variability metrics from the publicly available QT database by PhysioNet collected at resting states and under stress tests, mimicking social interaction stress scenarios. Statistical analysis conveys that heart rate variability metrics calculation applying both signal processing approaches using the Neurokit2 toolbox are statistically equivalent in comparison to the hand-labelled R-peaks from the QT database (n= 10 in the normal sinus rhythm group, and n= 6 in the ST Change group). Such validation results are crucial for the adoption of automated toolboxes for heart rate variability measures in social interaction assessments, where more movement and mood changes of participants are expected.Clinical Relevance- This contributes to the body of evidence of the reliability of the Neurokit2 toolbox for automatic cleaning of prolonged cardiac electrophysiological signals and calculation of heart rate variability in time-domain characterization in social interaction stress assessment.

A Fast Impedance Measurement Method for Lithium-Ion Battery Using Power Spectrum Property

Electrochemical Impedance Spectroscopy (EIS) can provide fruitful information for Lithium-ion (Li-ion) battery modelling and diagnosis, yet EIS measurement is time-consuming with low-frequency signal injection. By stacking a group of broadband signals, Pseudo Random Sequence (PRS) makes it possible to obtain the battery EIS in a few seconds at the expense of measurement accuracy and Signal-to-Noise Ratio (SNR). Thus, this paper focuses on developing a highly effective signal processing procedure to extract useful information from the PRS for accurate EIS measurement. To enhance the ability of the data cleaning procedure, a three-dimensional cloud is firstly reconstructed for each impedance by integrating its Power Spectrum (PS). The impedance with lower PS can be easily removed through a statistical based multiple selection mechanism, which enables the extraction of the EIS without altering the original measurement. Experimental results on a 3000mAh Li-ion battery prove the effectiveness of the proposed method.

Advanced Signal Processing in Distributed Acoustic Sensors Based on Submarine Cables for Seismology Applications

Photonic integrated sensing and communication technology based on optical fibers has recently attracted great research attention. In particular, distributed acoustic sensing (DAS) with telecommunication fibers is an emerging technology in the research areas of geology and seismology. With the massive deployment of telecommunication submarine cables, it provides an effective and low-cost approach for studying various oceanic geological and seismic activities. Though there have been some demonstrations of using DAS with submarine cables for the oceanic and geological analysis, no systematic analysis of the method has been presented, especially the signal processing techniques employed for the study. In this work, we discuss the signal processing procedure adopted to carry out seismic analysis using data obtained with the underwater DAS in detail, including the preprocessing of raw DAS data, the seismic recognition after feature extraction and event classification, and the seismic analysis of localization and magnitude estimations. Based on these signal processing methods, we successfully detect and analyze different seismic activities with our underwater DAS testbed, which utilizes the telecommunication submarine cable between two islands in the Pearl River estuary area of South China Sea.

МЕТОД ПОДАВЛЕНИЯ ИМПУЛЬСНЫХ ПОМЕХ В АДАПТИВНОЙ АНТЕННОЙ РЕШЕТКЕ С ЛОГИЧЕСКОЙ ОБРАБОТКОЙ СИГНАЛОВ

A method for suppressing pulse interference in adaptive antenna arrays is proposed, characterized by the use of a procedure for logical signal processing in the main and compensation channels to form a covariance interference matrix. To form the directional pattern of the main channel, weight coefficients are used to ensure a low level of side lobes. The radiation pattern of the compensation channel is obtained by subtracting the radiation patterns of orthogonal linear antenna arrays highlighted in the opening of the antenna array. Logical processing is used to determine the time domain, which does not contain a signal component, but contains external interference. It is shown that the use of logical processing of the main and compensation channels ensures the exclusion of the signal component from the covariance interference matrix and increases the noise immunity of the antenna.

Nonlinearity-Compensated Short-Range FMCW Radar for Weak Target Imaging

An easily implementable, low-cost, and portable broadband frequency-modulated continuous wave (FMCW) synthetic aperture radar (SAR) system is presented for short-range imaging applications. Design considerations and radar performance metrics are discussed in detail while investigating systematic and nonsystematic performance-limiting factors. This article introduces a signal processing procedure based on a closed-form comprehensive mathematical model to characterize the impact of the nonlinear frequency sweep on radar performance. Based on the proposed model, the nonlinearity is compensated using the time-resampling technique and without needing a reference response. A single-zone calibration does not provide enough accuracy when multiple targets are widely distributed in the cross-range direction. A range-and angle-dependent calibration scheme is proposed to mitigate the second-order effects more precisely, which are otherwise difficult to model mathematically. Detecting less reflective targets in the presence of a strong scatterer is challenging. A range-gating method based on a tunable active bandpass filter (BPF) is proposed to improve the radar system’s sensitivity by suppressing the dominant reflection and enhancing the weaker scatterer. The results are verified by developing SAR images of targets in free space and through a residential wall.