Real Signals Research Articles

Single-Sensor Engine Multi-Type Fault Detection.

Engine fault detection is conducive to improving equipment reliability and reducing maintenance costs. In practical scenarios, high-quality data is difficult to obtain. Usually, only single-sensor data is available. This paper proposes a fault detection method combining Variational Mode Decomposition (VMD) and Random Forest (RF). At first, the spectral energy distribution is obtained by decomposing and statistic the engine data of multiple working conditions. Based on the spectral energy distribution, the overall optimal mode number was identified, and the quadratic penalty term was optimized using SNR. The improved VMD (IVMD) improves mode aliasing and iterative efficiency and unifies feature dimensions. Decomposition of real signals demonstrates the effectiveness. The paper designs a feature vector composed of seven types of attributes, including unit bandwidth energy, center frequency, maximum singular value and so on. The feature vector is then fed to RF for classification. Features are selected in order of importance to classification to improve the training efficiency. By comparing with various algorithms, the proposed method has higher accuracy and faster training efficiency in single-speed, multi-speed and cross-speed single-sensor data diagnosis. The results show that the method has application prospects with little training data and low hardware requirements.

Image Denoising Method Based on Harmonic Filtering and Non-Subsample Shear Wave Block Matching in Wavelength Domain

<p>Efficient noise filtering is the difficulty of image denoising on the premise of preserving the edge and internal texture information of the image. Therefore, this paper focused on adaptive Gaussian variational model and block matching image denoising method in the wave domain from the non-local point of view. At first, a 3D block match harmonic filtering model was establish in that wavelet domain, 3D transform was use to represent that real signals in the match block group in a sparse form, then the shrinkage threshold was used to achieve the purpose of pre-denoising, and the wavelet transform was then used to extract the high frequency part of the predictor image for filtering. In order to avoid edge blurring, Laplace-Gaussian algorithm was used to construct a new operator into the diffusion model, and the wavelet coefficients were reconstructed to obtain the final approximation of the original image. Secondly, a block matching denoising model based on shear wave was proposed. In order to avoid ill-conditioned problem, The multi-scale geometric analysis method using shearlet transform can improve the edge protection ability, and a block matching denoising model based on shearlet is proposed. The new model predicts the scaling threshold according to the statistical characteristics of the histogram, performs hard threshold filtering on the high-frequency sub-band to obtain the processed sub-band coefficients, and performs block matching 3D filtering on the decomposed low-frequency sub-band coefficients to obtain the processed coefficients. All processed subband coefficients are inversely transformed and reconstructed to obtain a denoised image. The analysis and simulation results showed that the two new models can effectively suppress noise and improve the clarity, and the block matching denoising based on shear wave had more advantages.</p> <p> </p>

An industry perspective on the use of machine learning in drug and vaccine safety

In recent years there has been growing interest in the use of machine learning across the pharmacovigilance lifecycle to enhance safety monitoring of drugs and vaccines. Here we describe the scope of industry-based research into the use of machine learning for safety purposes. We conducted an examination of the findings from a previously published systematic review; 393 papers sourced from a literature search from 2000–2021 were analyzed and attributed to either industry, academia, or regulatory authorities. Overall, 33 papers verified to be industry contributions were then assigned to one of six categories representing the most frequent PV functions (data ingestion, disease-specific studies, literature review, real world data, signal detection, and social media). RWD and social media comprised 63% (21/33) of the papers, signal detection and data ingestion comprised 18% (6/33) of the papers, while disease-specific studies and literature reviews represented 12% (4/33) and 6% (2/33) of the papers, respectively. Herein we describe the trends and opportunities observed in industry application of machine learning in pharmacovigilance, along with discussing the potential barriers. We conclude that although progress to date has been uneven, industry is very interested in applying machine learning to the pharmacovigilance lifecycle, which it is hoped may ultimately enhance patient safety.

Satellite Navigation Spoofing Interference Detection and Direction Finding Based on Array Antenna.

Satellite navigation signals are feeble when they reach the ground, so they are vulnerable to attacks from outside interference signals. By emitting spoofing interference signals similar to real satellite signals, spoofing interference can make receivers give wrong navigation, position, and time information, and it is challenging to detect. This seriously affects the safe use of GNSS; therefore, it is essential to identify spoofing interference signals quickly and accurately. In our study, we proposed a novel six-array spoofing-interference-monitoring array antenna, which achieved the detection and identification of spoofing interference sources by monitoring the relevant peaks and combining an airspace-trapping algorithm. Moreover, we quickly accomplished our search for the whole circumferential ambiguity by using long- and short-baseline algorithms, which can realize the high-precision detection of spoofing interference sources. To verify this method's accuracy, we conducted outdoor real experiments using a special spoofing interference source, and our experimental results show that our proposed array antenna's directional accuracy for spoofing interference signals is kept within 2°, showing high spoofing interference direction-finding capability.

Very low-resolution residential load disaggregation with unsupervised graph signal processing

An unsupervised graph signal processing-based non-intrusive load monitoring algorithm is used to disaggregate very low-resolution 15-min real power signals from advanced metering infrastructure smart meters. This study presents the application of load disaggregation to new datasets that are more representative of single-family houses within the U.S. than those previously presented in the graph signal processing-based load disaggregation literature. Filtering is performed on signals within the signal database used for labeling reconstructed signals resulting from disaggregation. Results demonstrate improved accuracy due to filtering target device signals. Level 2 electric vehicle chargers are demonstrated to be the most promising candidate for very low-resolution load disaggregation using graph signal processing, with most houses demonstrating FM > 0.8 and absolute percent error in energy estimation less than 25%. The implications of these results on demand response assessment are presented, with an estimated 1350 MWh of energy available during peak demand periods from EV chargers for demand response applications in regions with acceptable disaggregation accuracy.

A Novel Specific Emitter Identification Algorithm Based on Amplitude Probability Distribution

We propose a novel specific emitter identification (SEI) algorithm based on amplitude probability distribution (APD). By studying the subtle differences in nonlinear distortion of power amplifiers, the proposed algorithm leverages the real signal amplitude interval probability distribution (AIPD) and analytical signal’s constellation circle interval probability distribution (CIPD) to extract the local distortion features. The simulation results on both the simulated signal and actual signal show that compared to the benchmark methods, the proposed algorithm can achieve significant performance gains with relatively low complexity and has high robustness to training set size and prior information of signal to noise ratio (SNR).

Forward-Looking Super-Resolution Imaging Based on Echo Denoising and Noise Weighting at Low SNR

In forward-looking imaging radar sensors, the targets of our focus are typically sparse in comparison to the whole imaging scene, which provides the feasibility of applying compressed sensing (CS) to forward-looking super-resolution imaging. However, sparsity is not satisfied in radar imaging when there is strong noise. This will result in the emergence of false targets and affect imaging results when traditional CS-based approaches are employed directly. In this article, we propose a noise robust imaging method that combines echo denoising and noise weighting to solve this problem. First, the denoised radar echo is generated using a complex variational mode decomposition (CVMD) method. The CVMD method is an extension of variational mode decomposition (VMD), which has been used to the traditional real signal denoising. According to the signal spectrum characteristics, we successfully applied the proposed CVMD method to radar echo denoising. Second, a noise weighting method based on <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${l}_{1}$ </tex-math></inline-formula> -norm is performed to further overcome strong noise. The weight comes from the CVMD denoising process, and it can better distinguish targets from the support area of noise. Lastly, simulated and measured data are utilized to verify the proposed method’s performance.

Data-driven nonstationary signal decomposition approaches: a comparative analysis

Signal decomposition (SD) approaches aim to decompose non-stationary signals into their constituent amplitude- and frequency-modulated components. This represents an important preprocessing step in many practical signal processing pipelines, providing useful knowledge and insight into the data and relevant underlying system(s) while also facilitating tasks such as noise or artefact removal and feature extraction. The popular SD methods are mostly data-driven, striving to obtain inherent well-behaved signal components without making many prior assumptions on input data. Among those methods include empirical mode decomposition and variants, variational mode decomposition and variants, synchrosqueezed transform and variants and sliding singular spectrum analysis. With the increasing popularity and utility of these methods in wide-ranging applications, it is imperative to gain a better understanding and insight into the operation of these algorithms, evaluate their accuracy with and without noise in input data and gauge their sensitivity against algorithmic parameter changes. In this work, we achieve those tasks through extensive experiments involving carefully designed synthetic and real-life signals. Based on our experimental observations, we comment on the pros and cons of the considered SD algorithms as well as highlighting the best practices, in terms of parameter selection, for the their successful operation. The SD algorithms for both single- and multi-channel (multivariate) data fall within the scope of our work. For multivariate signals, we evaluate the performance of the popular algorithms in terms of fulfilling the mode-alignment property, especially in the presence of noise.

Characterizing Average Seasonal, Synoptic, and Finer Variability in Orbiting Carbon Observatory-2 XCO2 Across North America and Adjacent Ocean Basins.

Variations in atmosphere total column-mean CO2 (XCO2) collected by the National Aeronautics and Space Administration's Orbiting Carbon Observatory-2 satellite can be used to constrain surface carbon fluxes if the influence of atmospheric transport and observation errors on the data is known and accounted for. Due to sparse validation data, the portions of fine-scale variability in XCO2 driven by fluxes, transport, or retrieval errors remain uncertain, particularly over the ocean. To better understand these drivers, we characterize variability in OCO-2 Level 2 version 10 XCO2 from the seasonal scale, synoptic-scale (order of days, thousands of kilometers), and mesoscale (within-day, hundreds of kilometers) for 10 biomes over North America and adjacent ocean basins. Seasonal and synoptic variations in XCO2 reflect real geophysical drivers (transport and fluxes), following large-scale atmospheric circulation and the north-south distribution of biosphere carbon uptake. In contrast, geostatistical analysis of mesoscale and finer variability shows that real signals are obscured by systematic biases across the domain. Spatial correlations in along-track XCO2 are much shorter and spatially coherent variability is much larger in magnitude than can be attributed to fluxes or transport. We characterize random and coherent along-track XCO2 variability in addition to quantifying uncertainty in XCO2 aggregates across typical lengths used in inverse modeling. Even over the ocean, correlated errors decrease the independence and increase uncertainty in XCO2. We discuss the utility of computing geostatistical parameters and demonstrate their importance for XCO2 science applications spanning from data reprocessing and algorithm development to error estimation and carbon flux inference.

CEDR: A Compiler-integrated, Extensible DSSoC Runtime

In this work, we present a C ompiler-integrated, E xtensible D omain Specific System on Chip R untime (CEDR) ecosystem to facilitate research toward addressing the challenges of architecture, system software, and application development with distinct plug-and-play integration points in a unified compile time and runtime workflow. We demonstrate the utility of CEDR on the Xilinx Zynq MPSoC-ZCU102 for evaluating performance of pre-silicon hardware in the trade space of SoC configuration, scheduling policy and workload complexity based on dynamically arriving workload scenarios composed of real-life signal processing applications scaling to thousands of application instances with Fast Fourier Transform and matrix multiply accelerators. We provide insights into the tradeoffs present in this design space through a number of distinct case studies. CEDR is portable and has been deployed and validated on Odroid-XU3, X86, and Nvidia Jetson Xavier-based SoC platforms. Taken together, CEDR is a capable environment for enabling research in exploring the boundaries of productive application development, resource management heuristic development, and hardware configuration analysis for heterogeneous architectures.

The Signaling Mechanism of Fairness Concern in E-CLSC

The E-CLSC (E-closed-loop supply chain) game model dominated by manufacturer is set, and information value about asymmetry fairness concern of E-platform (E-commerce platform) is calculated for manufacturer, recycler, and E-platform. By signaling model under various signal costs, the authors study the condition for E-platform to transmit real information about fairness concern so as to reduce profit loss for all parties in E-CLSC. The authors prove that E-platform has the motivation to disguise or exaggerate fairness concerns in order to obtain more profit, and manufacturers must try to identify the E-platform's real fairness concern to avoid profit loss. Besides, only when different types of E-platforms need significantly different signaling cost, both of them would like to send real fairness-concern signal, and thus manufacturer can effectively identify E-platform's real information about fairness concern so as to improve recycling rate and optimize the whole E-CLSC operation.

Denoising method of weak fault acoustic emission signal under strong background noise of engine based on autoencoder and wavelet packet decomposition

The weak fault acoustic emission (AE) signals collected in the actual operating conditions of the engine are often submerged in the strong background noise. This paper proposes a denoising method of AE signals based on the combination of autoencoder and wavelet packet decomposition (AE-WPD) to address the above problem. Firstly, the wavelet packet is used to decompose engine background noise signals and noise-containing fault AE signals to enhance the local analysis capability of the autoencoder. Then, the dataset of each frequency band after decomposition is created. Among them, background noise signals are regarded as normal datasets. Noise-containing fault signals are treated as outlier datasets. The difference between each frequency band of background noise signals and noise-containing fault signals is analyzed. The autoencoder model is trained, validated and tested for effectiveness. In addition, a comparison is made with other commonly used denoising methods. Four types of evaluation indexes are introduced to quantitatively assess various methods. Finally, the real engine background noise signals with different signal-to-noise ratio (SNR) are added to the fault AE signals to verify the robustness of the proposed AE-WPD method. The experimental results show that the proposed AE-WPD method outperforms other denoising methods at different SNR. This lays the foundation for engine structural condition monitoring and subsequent fault identification and localization.

Weighted singular value decomposition basis of Szegő kernel and its applications to signal reconstruction and denoising

For any circular analytic signal f+(eit)∈H2(T), if the sequence {ak}k=0∞ in the unit disc D satisfies the hyperbolic non-separability condition ∑k=0∞(1−|ak|)=∞, it is known that the first N+1 term approximation function based on the Takenaka–Malmquist system (SN+1f+)(eit)=∑k=0N〈f+(eit),Bk(eit)〉Bk(eit),can approximate the analytic signal f+(eit) in the L2-norm sense, where Bk(eit)=1−|ak|21−ak¯eit∏l=0k−1eit−al1−al¯eit, k∈N is the Takenaka–Malmquist system which can be seen as a generalization of Fourier series. We find that (SN+1f+)(eit) can also be represented by the standard basis, Lagrange basis and weighted SVD basis. Different bases have different merits. The standard basis is simple. The Lagrange basis has a neat and compact form, which is very convenient for theoretical analysis. With the Takenaka–Malmquist system, the first N+1 term approximation function (SN+1f+)(eit) is able to write in an iterative form. The Takenaka–Malmquist system has the advantage of inheritance, and the point ak∈D can be easily selected step by step through the greedy principle. In this paper, we focus our attention on the weighted SVD basis. We give some orthogonal properties of the weighted SVD basis. It demonstrates that any real signal f(eit)∈L2(T) can be precisely reconstructed within the given error range by the weighted SVD basis decomposition. The experimental results reveal that the algorithm based on weighted SVD basis is more stable and accurate. Furthermore, compared with classical Fourier decomposition and adaptive Fourier decomposition algorithms, the experimental results show that the weighted SVD basis decomposition is more insensitive to noise. This will provide a new method for signal denoising.

Classifying Cardiac Arrhythmia from ECG Signal Using 1D CNN Deep Learning Model

Blood circulation depends critically on electrical activation, where any disturbance in the orderly pattern of the heart’s propagating wave of excitation can lead to arrhythmias. Diagnosis of arrhythmias using electrocardiograms (ECG) is widely used because they are a fast, inexpensive, and non-invasive tool. However, the randomness of arrhythmic events and the susceptibility of ECGs to noise leads to misdiagnosis of arrhythmias. In addition, manually diagnosing cardiac arrhythmias using ECG data is time-intensive and error-prone. With better training, deep learning (DL) could be a better alternative for fast and automatic classification. The present study introduces a novel deep learning architecture, specifically a one-dimensional convolutional neural network (1D-CNN), for the classification of cardiac arrhythmias. The model was trained and validated with real and noise-attenuated ECG signals from the MIT-BIH dataset. The main aim is to address the limitations of traditional electrocardiograms (ECG) in the diagnosis of arrhythmias, which can be affected by noise and randomness of events, leading to misdiagnosis and errors. To evaluate the model performance, the confusion matrix is used to calculate the model accuracy, precision, recall, f1 score, average and AUC-ROC. The experiment results demonstrate that the proposed model achieved outstanding performance, with 1.00 and 0.99 accuracies in the training and testing datasets, respectively, and can be a fast and automatic alternative for the diagnosis of arrhythmias.

Fast 3D “wave-consistent” ray tomography based on a polynomial representation of a model

In the paper, we describe an original 3D travel-time tomography approach. It is based on the new realization of the bending method which to some extent takes into account the band-limited nature of real seismic signals propagation. As a result, two-point ray tracing provides more reliable ray trajectories and travel times in complex media. Another original feature of the proposed tomography is that the model is represented using the Chebyshev polynomials. Such parameterization allows analytical calculation of travel times and their derivatives with respect to model parameters and significantly reduces the number of parameters to be recovered during inversion compared to more common grid tomography. In certain situations, the proposed approach provides significant computational advantages. Numerical examples prove its efficiency.

An energy-concentrated wavelet transform for time-frequency analysis of transient signal

Some signals captured in practical applications are usually composed of a series of modes that have multivalued time-dependent instantaneous frequency (IF), i.e., transient signal, which enables some advanced time-frequency analysis (TFA) tools no longer ideal or even ineffective. Fortunately, the group delay (GD) of such signal can be well expressed as a single valued function of frequency. By considering frequency-domain signal model suitable for describing GD, we develop a postprocessing technique called wavelet transform (WT)-based time-reassigned synchrosqueezing transform (WTSST). In WTSST, the TF coefficients diffused in WT are gathered to GD of transient signal aiming to generate a more concentrated time-frequency representation (TFR) and WTSST allows to retrieve the whole or part of signal. Considering analytic WT under Gaussian function, the theoretical analyses of WTSST are provided, which includes the analyses of GD candidate accuracy and signal reconstruction accuracy. Moreover, based on the theoretical analyses of WTSST, the WT-based time-reassigned multisynchrosqueezing transform (WTMSST) is proposed by introducing a fixed-point iteration scheme, which further improves the shortcoming that WTSST is unable to accurately describe strong frequency-varying signal. Simulation and real signal analyses illustrate that the proposed method has capacity to appropriately describe the features of transient signals.

FPGA-Based Decision Support System for ECG Analysis

The high mortality rate associated with cardiac abnormalities highlights the need of accurately detecting heart disorders in the early stage so to avoid severe health consequence for patients. Health trackers have become popular in the form of wearable devices. They are aimed to perform cardiac monitoring outside of medical clinics during peoples’ daily lives. Our paper proposes a new diagnostic algorithm and its implementation adopting a FPGA-based design. The conceived system automatically detects the most common arrhythmias and is also able to evaluate QT-segment lengthening and pulmonary embolism risk often caused by myocarditis. Debug and simulations have been carried out firstly in Matlab environment and then in Quartus IDE by Intel. The hardware implementation of the embedded system and the test for the functional accuracy verification have been performed adopting the DE1_SoC development board by Terasic, which is equipped with the Cyclone V 5CSEMA5F31C6 FPGA by Intel. Properly modified real ECG signals corrupted by a mixture of muscle noise, electrode movement artifacts, and baseline wander are used as a test bench. A value of 99.20% accuracy is achieved by taking into account 0.02 mV for the root mean square value of noise voltage. The implemented low-power circuit is suitable as a wearable decision support device.

An Improved TDOA Method for Land-Based Long-Range HF Skywave Source Geolocation and Experimental Results

The real-time information of the unknown ionospheric environments is difficult to obtain, plaguing the timely and accurate geolocation of high frequency (HF) sources. In this paper, we propose an improved HF skywave source geolocation method based on the time-difference-of-arrival (TDOA) with the semidefinite programming (SDP), and model HF signal propagation paths as paths with significant non-line-of-sight (NLOS) biases. With this method, no priori information about the ionosphere, especially the priori ionospheric virtual heights of reflection, is necessary while timely and accurately geolocating the HF sources. Furthermore, we use the ray tracing technique and build a 3D ionospheric electron density gridded matrix model to simulate realistic HF signal propagation paths. In the simulations, the proposed method is compared with existing methods, and detailed geolocation error distribution maps are given. In the experiments, HF I/Q data captured from different types of HF transmitters are located by six receivers with time synchronization. Simulated and experimental results show that the proposed method improves the positioning accuracy by about 50% compared with existing methods under the same conditions, and the average relative positioning error is less than 2.7%.

Methods of increasing the resolution of signals in optical systems

Optical observation is the most effective way to control the state of various objects. The dynamism of the objects of the observed scene makes it an urgent task to improve the algorithms for obtaining coordinate and non-coordinate information about the observed objects. The issues of increasing the accuracy of measuring the distance and increasing the resolution between the light objects observed by the optical system by the method of double continuous wavelet transformation are considered. It is shown that the use of the second continuous wavelet transform to the curves of the coefficients of the first transform leads to an increase in the maxima of the scalegram and the curves of the coefficients, providing the coordinate sensitivity of the position of the signals by more than two times. The use of different types of wavelets in each continuous wavelet transform of signals gives many options for constructing a processing algorithm and can be used for additional filtering of noise taking into account the characteristics of signals. In this regard, it is proposed to use parallel mathematical models and real signals in a neural network for determining the coordinates of signals and their characteristics, which leads to an increase in accuracy for each type of signal. The indicated approach can be used in systems for multiple signaling from different sources or for combining images in multi-position systems.

An Iterative Warping and Clustering Algorithm to Estimate Multiple Wave-Shape Functions From a Nonstationary Oscillatory Signal

Nonsinusoidal oscillatory signals are everywhere. In practice, the nonsinusoidal oscillatory pattern, modeled as a 1-periodic wave-shape function (WSF), might vary from cycle to cycle. When there are finite different WSFs, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$s_{1},\ldots,s_{K}$</tex-math></inline-formula> , so that the WSF jumps from one to another suddenly, the different WSFs and jumps encode useful information. We present an iterative warping and clustering algorithm to estimate <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$s_{1},\ldots,s_{K}$</tex-math></inline-formula> from a nonstationary oscillatory signal with time-varying amplitude and frequency, and hence the change points of the WSFs. The algorithm is a novel combination of time-frequency analysis, singular value decomposition entropy and vector spectral clustering. We demonstrate the efficiency of the proposed algorithm with simulated and real signals, including the voice signal, arterial blood pressure, electrocardiogram and accelerometer signal. Moreover, we provide a mathematical justification of the algorithm under the assumption that the amplitude and frequency of the signal are slowly time-varying and there are finite change points that model sudden changes from one wave-shape function to another one.