Real Signals Research Articles

Multichannel high noise level ECG denoising based on adversarial deep learning

This paper proposes a denoising method based on an adversarial deep learning approach for the post-processing of multi-channel fetal electrocardiogram (ECG) signals. As it’s well known, noise leads to misinterpretations of fetal ECG signals and thus limits the use of fetal electrocardiography for healthcare applications. Therefore, denoising algorithms are essential for the exploitation of non-invasive fetal ECG. The proposed method is based on the combination of three end-to-end trained sub-networks to convert noisy fetal ECG signals into clean signals. The first two sub-networks are linked by skip connections and form a deep convolutional network that downsamples the noisy signals into a latent representation and subsequently upsamples this latent representation to recover clean signals. The third sub-network aims to boost the decoder sub-network to generate realistic clean signals. Experiments carried out on synthetic and real data showed that the proposed method improved by the signal-to-noise (SNR) of fetal ECG signals with input SNR ranging from -,30 to 0 dB by an average of 20 dB, and improve fetal signal quality by significantly increasing the number of true detected QRS complexes and halving QRS complex detection errors.

Numerical and experimental research on vibration signal characteristics of water pipeline leakage

In the leak detection of water pipelines, interference and environmental noise directly affect the effectiveness of leak detection and the accuracy of leak point location, so it is necessary to study the real signal. Previous studies mostly rely on a single test to improve the positioning accuracy by collecting vibration signals for denoising, but denoising will also affect the real signal, and the actual detection situation is complex, so it is difficult to deal with all working conditions only through the test. With the rapid development of computer technology, the combination of numerical simulation and experiment is expected to solve this problem. Therefore, this paper takes the acceleration signal as the research object, collects and simulates the leakage signals under different pressure strengths by combining experiments with numerical simulation, and verifies the feasibility and accuracy of a numerical simulation model. The generation of leakage vibration acceleration under different pressure and different leakage hole radius is simulated. Research findings: a. the leakage signal energy has a power function relationship with the pressure and an exponential function relationship with the leakage radius; b. the characteristic frequency is the variable of the characteristics of pipeline and leakage hole, which is independent of the pressure; c. the power of each peak frequency has a step-by-step change trend with the energy of the signal; d. the power of acceleration signal concentrates to a higher frequency with the increase of pressure and the decrease of leakage radius. This study provides a basis for identifying, detecting, and extracting leakage signals in a real noise environment, helps to improve the accuracy of acoustic emission signal detection, and can provide a theoretical basis for pipeline leakage detection research.

Design and Experiments of a Convex Curved Surface Type Grain Yield Monitoring System

Precision agriculture relies heavily on measuring grain production per unit plot, and a grain flow monitoring system performs this using a combine harvester. In response to the high cost, complex structure, and low stability of the yield monitoring system for grain combine harvesters, the objective of this research was to design a convex curved grain mass flow sensor to improve the accuracy and practicality of grain yield monitoring. In addition, it involves the development of a grain yield monitoring system based on a cut-and-flow combine harvester prototype. This research examined the real output signal of the convex curved grain mass flow sensor. Errors caused by variations in terrain were reduced by establishing the zero point of the sensor’s output. Measurement errors under different material characteristics, flow rates, and grain types were compared in indoor experiments, and the results were subsequently confirmed through field experiments. The results showed that a sensor with a cantilever beam-type elastic element and a well-constructed carrier plate may achieve a measurement error of less than 5%. After calibrating the sensor’s zero and factors, it demonstrated a measurement error of less than 5% during the operation of the combine harvester. These experimental results align with the expected results and can provide valuable technical support for the widespread adoption of impulse grain flow detection technology. In future work, the impact of factors such as vehicle vibration will be addressed, and system accuracy will be improved through structural design or adaptive filtering processing to promote the commercialization of the system.

Attention-enhanced conditional-diffusion-based data synthesis for data augmentation in machine fault diagnosis

Data scarcity and class imbalance are pervasive challenges in machine fault diagnosis, impeding the development and broad adaptation of accurate and reliable deep-learning-based fault detection systems. To address these issues, we propose a novel attention-enhanced conditioning-guided diffusion-based approach for synthesizing additional training data. By generating noisy motor-current signals and employing a diffusion process to reduce noise levels, we obtain synthetic data that closely resembles the statistical properties and patterns of real-world data. The quality of the synthesized samples is assessed using a four-layer, pre-trained convolutional neural network, achieving an impressive classification rate of 96.39%. This demonstrates the effectiveness of our attention-enhanced conditioned diffusion-based model in synthesizing critical features for fault detection, making it well-suited for the introduced fault detection task. To further investigate the quality of the synthesized data samples, we implement a specific training strategy that uses the generated samples as training data. The trained diffusion model generates samples per class, which are then used to train a simple four-layer convolutional neural network for fault classification. Remarkably, the model achieves an accuracy of 98.96% when evaluated with real inverter signals, indicating that the generated samples effectively capture the distribution of real data. For further evaluation, multiple scenarios are created confirming the value of the additionally synthesized samples for real-world bearing fault detection. Our approach presents a well-suited solution to the data scarcity and class imbalance problem in machine fault diagnosis. The proposed method enhances the accuracy and reliability of fault diagnosis systems, offering valuable insights for various real-world condition monitoring applications.

Tracking Differentiators for Both the Real Time Signals and the Time Delayed Signals

Tracking Differentiators for Both the Real Time Signals and the Time Delayed Signals

Predicting radiofrequency thermocoagulation surgical outcomes in refractory focal epilepsy patients using functional coupled neural mass model.

The success rate of achieving seizure freedom after radiofrequency thermocoagulation surgery for patients with refractory focal epilepsy is about 20-40%. This study aims to enhance the prediction of surgical outcomes based on preoperative decisions through network model simulation, providing a reference for clinicians to validate and optimize surgical plans. Twelve patients with epilepsy who underwent radiofrequency thermocoagulation were retrospectively reviewed in this study. A coupled model based on model subsets of the neural mass model was constructed by calculating partial directed coherence as the coupling matrix from stereoelectroencephalography (SEEG) signals. Multi-channel time-varying model parameters of excitation and inhibitions were identified by fitting the real SEEG signals with the coupled model. Further incorporating these model parameters, the coupled model virtually removed contacts destroyed in radiofrequency thermocoagulation or selected randomly. Subsequently, the coupled model after virtual surgery was simulated. The identified excitatory and inhibitory parameters showed significant difference before and after seizure onset (p < 0.05), and the trends of parameter changes aligned with the seizure process. Additionally, excitatory parameters of epileptogenic contacts were higher than that of non-epileptogenic contacts, and opposite findings were noticed for inhibitory parameters. The simulated signals of postoperative models to predict surgical outcomes yielded an area under the curve (AUC) of 83.33% and an accuracy of 91.67%. The multi-channel coupled model proposed in this study with physiological characteristics showed a desirable performance for preoperatively predicting patients' prognoses.

Subject-Specific Modeling of EEG-fNIRS Neurovascular Coupling by Task-Related Tensor Decomposition.

Neurovascular coupling (NVC) connects neural activity with hemodynamics and plays a vital role in sustaining brain function. Combining electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) is a promising way to explore the NVC. However, the high-order property of EEG data and variability of hemodynamic response function (HRF) across subjects have not been well considered in existing NVC studies. In this study, we proposed a novel framework to enhance the subject-specific parametric modeling of NVC from simultaneous EEG-fNIRS measurement. Specifically, task-related tensor decomposition of high-order EEG data was performed to extract the underlying connections in the temporal-spectral-spatial structures of EEG activities and identify the most relevant temporal signature within multiple trials. Subject-specific HRFs were estimated by parameters optimization of a double gamma function model. A canonical motor task experiment was designed to induce neural activity and validate the effectiveness of the proposed framework. The results indicated that the proposed framework significantly improves the reproducibility of EEG components and the correlation between the predicted hemodynamic activities and the real fNIRS signal. Moreover, the estimated parameters characterized the NVC differences in the task with two speeds. Therefore, the proposed framework provides a feasible solution for the quantitative assessment of the NVC function.

A Multistage Algorithm for Estimating Electromechanical Modes of Power Systems

Low-frequency oscillation modes of electromechanical origin, when they have low damping rates, can affect the stability of power systems. The Identification of these electromechanical modes is essential to preserve the continuous and safe operation of power systems. This paper proposes a multistage algorithm to determine the electromechanical modes of signals collected by Phasor Measurement Units (PMUs) in power systems. The proposed algorithm is composed of four processes and at each stage an electromechanical mode is estimated and evaluated. Case studies of a real PMU signal were conducted and discussed.

Neurophysiologically Meaningful Motor Imagery EEG Simulation With Applications to Data Augmentation.

Motor imagery-based Brain-Computer Interfaces (MI-BCIs) have gained a lot of attention due to their potential usability in neurorehabilitation and neuroprosthetics. However, the accurate recognition of MI patterns in electroencephalography signals (EEG) is hindered by several data-related limitations, which restrict the practical utilization of these systems. Moreover, leveraging deep learning (DL) models for MI decoding is challenged by the difficulty of accessing user-specific MI-EEG data on large scales. Simulated MI-EEG signals can be useful to address these issues, providing well-defined data for the validation of decoding models and serving as a data augmentation approach to improve the training of DL models. While substantial efforts have been dedicated to implementing effective data augmentation strategies and model-based EEG signal generation, the simulation of neurophysiologically plausible EEG-like signals has not yet been exploited in the context of data augmentation. Furthermore, none of the existing approaches have integrated user-specific neurophysiological information during the data generation process. Here, we present PySimMIBCI, a framework for generating realistic MI-EEG signals by integrating neurophysiologically meaningful activity into biophysical forward models. By means of PySimMIBCI, different user capabilities to control an MI-BCI can be simulated and fatigue effects can be included in the generated EEG. Results show that our simulated data closely resemble real data. Moreover, a proposed data augmentation strategy based on our simulated user-specific data significantly outperforms other state-of-the-art augmentation approaches, enhancing DL models performance by up to 15%.

Digital Model of a Wind Turbine Oriented to Broken Tooth Analysis

Digital Twins are a valuable tool for managing the health of industrial components, thanks to their ability to detect and diagnose failures. This paper presents a novel Digital Model as the foundational step towards developing a Digital Twin. It enables the detection of broken tooth failure in wind turbine drive-trains by using wind data and vibration analysis. The model is projected into the future as a monitoring tool by allowing visual indicators of failure patterns to be identified and subsequently compared with real signals of this critical component.

Investigation of the quality of functioning of a quadrature signals receiver of a continuous automatic cab signaling on records of real signals and interference

Ensuring a safe transportation process is impossible without the use of complex systems for interval regulation of train traffic. In particular, such a system is a continuous automatic cab signaling (CACS) where signals are affected by various kinds of interference, leading to system failures. To reduce the number of system failures, the authors previously proposed a quadrature signal receiver of CACS (QSR of CACS). The article presents the methodology and results of an experimental study of the quality of the functioning of the QSR of CACS on records of real signals and interference. A simulation model of the test facility has been developed in the Simulink simulation environment. The study showed that the QSR of CACS provides reliable reception of CACS code signals with a low level of interference. However, it needs to apply additional measures in it to reduce the influence of impulse noise.

Conservative method for vertical electrooculogram attenuation based on local suppression of ongoing EEG artifact templates.

Eye movement during blinking can be a significant artifact in Event-Related Potentials (ERP) analysis. Blinks produce a positive potential in the vertical electrooculogram (VEOG), spreading towards the posterior direction. Two methods are frequently used to suppress VEOGs: linear regression to subtract the VEOG signal from the electroencephalogram (EEG) and Independent Component Analysis (ICA). However, some information is lost in both. The present algorithm (1) statistically identifies the position of VEOGs in the frontopolar channels; (2) performs EEG averaging for each channel, which results in 'blink templates'; (3) subtracts each template from the respective EEG at each VEOG position, only when the linear correlation index between the template and the segment is greater than a chosen threshold L. The signals from twenty subjects were acquired using a behavioral test and were treated using FilterBlink for subsequent ERP analysis. A model was designed to test the method for each subject using twenty copies of the EEG signal from the subject's mid-central channel (with nearly no VEOG) representing the EEG channels and their respective blink templates. At the same 200 equidistant time points (marks), a signal (2.5 sinusoidal cycles at 1050 ms emulating an ERP) was mixed with each model channel and the respective blink template of that channel, between 500 to 1200 ms after each mark. According to the model, VEOGs interfered with both ERPs and the ongoing EEG, mainly on the anterior medial leads, and no significant effect was observed on the mid-central channel (Cz). FilterBlink recovered approximately 90% (Fp1) to 98% (Fz) of the original ERP and EEG signals for L = 0.1. The method reduced the VEOG effect on the EEG after ERP and blink-artifact averaging in analyzing real signals. The method is straightforward and effective for VEOG attenuation without significant distortion in the EEG signal and embedded ERPs.

How to Measure "Spillover Spread".

Since the development of the first instrument in the late 1960s, flow cytometry (FC) has become a powerful tool in both the clinical and research space. As one of the earliest single-cell analytical techniques, flow cytometry can measure thousands of cells in minutes, allowing researchers an unprecedented understanding of the biology of their system of interest. There are commercial systems available that can measure over 40 different parameters at the same time. The most common assay, immunophenotyping, involves labeling cells with fluorescently conjugated antibodies. The process of fluorescence occurs when a fluorescent molecule first absorbs a photon of light, which promotes an electron to a higher energy state. This energy is released by the emission of a photon of lower energy (thus a higher wavelength). The emitted photon will be within a range of visible wavelengths. When measured on a flow cytometer, this results in the fluorescent signal being measured not just in the primary detector but also in one or more secondary detectors. Termed "spillover," this is when the fluorescent signal measured in a detector other than the intended one creates a problem in identifying the real signal. The process of compensation is used to address this spectral spillover. However, in correcting for the spillover by compensation, the spread of the data is revealed. This spread can be quantified, and, here, we discuss two methods that can be used to identify and measure this spectral spread for any combination of fluorochromes. The output of these methods is useful in experimental design and monitoring instrument quality control. Armed with this information, the researcher can better design polychromatic panels to minimize the impact of spread on their data.

Periodic functions with variable period – basic concepts and certain investigation results

Investigation of real signals is one of the most important applied areas of mathematics. According to their properties, signals are quite diverse, and methods of their research are different as well. Among this diversity, periodic signals with variable period make up a significant proportion. Till present, no attention was paid to the theory of such signals. In this paper, we define periodic functions with variable period, which are the model of these signals. Some properties of the variable period are considered. Examples of the analytical formulation of functions with variable period in the form of trigonometric functions with variable period are given and their variable periods are recorded. It is pointed out that these functions can be used as basic ones for constructing orthogonal system of trigonometric functions with variable period and its use for constructing Fourier series of functions with variable period.

Underwater Object Identification by Means of Reflected Sonar Signals

The paper presents the research results of the underwater object identification method on the basis of periodogram analysis of hydroacoustic signals reflected from them with the application of voting algorithm. The identification of an object in the article means making a decision to distinguish it from other objects. Hydroacoustic signals are the main means of obtaining information about the underwater environment, they are also widely used to detect and identify underwater objects, including ships, submarines and underwater structures. The identification of underwater objects is an important task to ensure the safety of underwater exploration and the efficient utilization of marine resources in a noisy and complex underwater environment, including the presence of several natural objects and the use of various underwater exploitation techniques. The study proposes an approach that uses periodogram analysis followed by a voting algorithm to solve the problem of underwater object identification. Periodogram analysis is a method that identifies characteristic time and frequency features in hydroacoustic signals reflected from different types of objects. The proposed approach takes into account different characteristics of the signal periodogram to identify the unique features of each underwater object. The main purpose of this work is to develop and investigate a set of features using periodograms that take into account the specificity of hydroacoustic signals and the unique properties of different types of underwater objects under study. This approach allows to achieve a sufficiently high accuracy of identification. The paper presents the results of experiments conducted on real hydroacoustic signals. These results allow us to conclude about the effectiveness of the underwater object identification method by reflected hydroacoustic signals on the basis of periodogram analysis. The results of the study have practical significance and can be applied in applications related to underwater research.

Computational Complexity Comparison between DC-biased Optical OFDM and Asymmetrically Clipped Optical OFDM in Visible Light Communication

Optical wireless systems are constrained to send real and positive values to the optical transmitter as only intensity of a signal is used to carry information. Therefore, conventional OFDM cannot be directly applied in optical systems. To combat multipath distortion, several modified OFDM systems have been studied, such as DC-biased optical OFDM (DCO-OFDM) and asymmetrically clipped optical OFDM (ACO-OFDM). In order to transmit real signal in optical environments, there is a Hermitian symmetric constraint with Discrete Fourier transform (DFT). This paper compares transceiver complexity of ACO-OFDM with that of DCO-OFDM which is used in Visible Light Communication (VLC), and it is found that for the same number of subcarriers, computational complexity is higher in ACO-OFDM.

Modelling and Simulation Tool for the Analysis of Electric Power Quality Issues.

In this paper a software tool developed for analysing the power quality of electric systems is described. The program provides accurate modelling of non-linear elements that can be included into typical electric network. Users may choose to build the network or select between typical distribution networks provided within the program. The software allows the user to simulate an entire electric network or input real time signals in order to characterize, classify, and analyse power quality events. It provides a fast and low cost solution for power quality analysis. An example is included in the paper to illustrate the usage and capabilities of the program.

An efficient wavelet thresholding strategy and robust shrinkage approach for de‐noising ECG signal

AbstractThis paper introduces an efficient wavelet thresholding strategy and robust shrinkage approach (WTS) for de‐noising the ECG signals. The optimal mother wavelet for de‐noising the ECG signal is automatically selected based on cross‐correlation and energy‐to‐entropy indices. A dynamic threshold is applied to various levels of decomposition to eliminate different types of noise. A shrinkage approach is suggested to attain seamless on–off transitions. The achievement of the WTS is compared with the state of the art reported in the literature using 180 real ECG signals collected from 60 volunteers. The ECG signals were recorded using three standard ECG machines without applying the filtering option to provide a diverse range of noisy ECG signals. The de‐noising outcomes show that the presented WTS can automatically select the optimal mother wavelet for the particular ECG signal, and it is superior to other techniques in terms of the average predicted signal‐to‐noise ratio (), the average predicted root‐mean‐square error (), and the average execution time (AET).

The fast computation of multi-angle discrete fractional Fourier transform

The discrete fractional Fourier transform (DFRFT) plays an important role in processing time-varying signals. Nevertheless, directly computing the DFRFT involves high complexity, particularly when addressing multi-angle DFRFT scenarios. This article presents a method to compute multi-angle DFRFTs through the execution of a single complex DFRFT, exploiting several properties of the DFRFT. We first calculate the DFRFTs with rotation angles 0 and α. Subsequently, when dealing with the DFRFTs of two real signals with rotation angles α and β, which is so-called the multi-angle DFRFTs, our method only need one complex DFRFT and some additional manipulations with complexity O(M), which reduce the computational complexity efficiently. Furthermore, the proposed method is also applicable to the processing of two dimensional (2D) signals. Additionally, as a generalized form of the DFRFT, the fast computation of the multi-angle discrete affine Fourier transform (DAFT) is also considered. Finally, the simulation results confirm that the proposed methods can effectively reduce the computational complexity without compromising precision.

Removal of electrical stimulus artifact in local field potential recorded from subthalamic nucleus by using manifold denoising

Background:Deep brain stimulation (DBS) is an effective treatment for movement disorders such as Parkinson’s disease (PD). However, local field potentials (LFPs) recorded through lead externalization during high-frequency stimulation (HFS) are contaminated by stimulus artifacts, which require to be removed before further analysis. New Method:In this study, a novel stimulus artifact removal algorithm based on manifold denoising, termed Shrinkage and Manifold-based Artifact Removal using Template Adaptation (SMARTA), was proposed to remove artifacts by deriving a template for each stimulus artifact and subtracting it from the signal. Under a low-dimensional manifold assumption, a matrix denoising technique called optimal shrinkage was applied to design a similarity metric such that the template for stimulus artifacts could be accurately recovered. Result:SMARTA was evaluated using semirealistic signals, which were the combination of semirealistic stimulus artifacts recorded in an agar brain model and LFPs of PD patients with no stimulation, and realistic LFP signals recorded in patients with PD during HFS. The results indicated that SMARTA removes stimulus artifacts with a modest distortion in LFP estimates. Comparison with Existing Methods:SMARTA was compared with moving-average subtraction, sample-and-interpolate technique, and Hampel filtering. Conclusion:The proposed SMARTA algorithm helps the exploration of the neurophysiological mechanisms of DBS effects.