Real Signals Research Articles

Imaging Neutral Particle Analyzer Engineering Design and Installation for the ASDEX Upgrade Tokamak

An imaging neutral particle analyzer (INPA) has been installed in the ASDEX Upgrade (AUG) tokamak to provide simultaneous measurements of the radial profile and the energy of the confined fast-ion (FI) population. The INPA diagnostic leverages the advantages of neutral particle analyzers (NPAs) and FI loss detectors (FILDs) by measuring charge exchange (CX) neutrals ionized by an ultrathin carbon foil is deflected into a scintillator by the local magnetic field. The design of this diagnostic has been developed under a series of constraints, such as the lack of space and hazardous environment (high level of neutron and gamma radiation), the need of high precision in the alignment of the nonsequential optical system (with six optical axes), and the optimization of the scintillator plate (signal emitter). To this end, a modular and adjustable design has been pursued with several degrees of freedom to ensure precise positioning during the installation and optical calibration. The induced electromagnetic and thermal stresses have been assessed and compared against the material limits of the detector, showing that no significant stress is expected for the system. The thermal analysis shows that the diagnostic scintillator can work efficiently during normal operation conditions. The CAD design, a comparison between the synthetic and real optical signals and the mechanical assessment [based on finite element analysis (FEA)], is presented in this contribution.

Pseudo-Passive Time-of-Flight Imaging: Simultaneous Illumination, Communication, and 3D Sensing

The 3D time-of-flight (ToF) cameras have recently received a lot of attention due to their wide range of applications. Despite remarkable advancements in ToF imaging, state-of-the-art ToF cameras are still afflicted by the power hungriness of their illumination sources. To tackle this problem, we exploited existing lighting infrastructure, which ensures the ubiquitous presence of modulated light sources in indoor spaces that serve as opportunity illuminators. We explored the bistatic geometry for passive imaging using the pulse-based ToF approach. Our work is inspired by the recently introduced visible light communication (VLC) or light-fidelity (Li-Fi) infrastructure. VLC allows the infrastructure to provide indoor simultaneous illumination, communication, and sensing (SICS). To this end, we designed a bistatic geometry for the purpose of attaining passive 3D imaging. Such capabilities are achieved by exploiting the pulse shape of the autocorrelation function of real optical signals generated by VLC/Li-Fi modules (e.g., OpenVLC and LiFiMAX). We demonstrated passive imaging by means of matched filtering. In this work, we also studied different sampling strategies in the time-shift domain, including uniform, random, and sparse rulers, which is another step forward toward preserving high depth accuracy with a minimal number of measurements. The proposed methodology achieved successful depth reconstruction with negligible root-mean-square error (RMSE) for the low signal-to-noise ratio (SNR) of the measurements. Parametric models, such as Gaussian and sum of sines, are used to characterize the cross correlation functions and allow for robust parametric depth retrieval from a few measurements. Moreover, we attained a 20-mm worst case error for a target at 25 cm. The experiment proved that the bistatic passive depth reconstruction is feasible.

Estimation for misspecification when theoretical model for signal is smooth but real signal is of cusp-type and driven by a fractional Brownian motion

We study the problem of misspecification when the model proposed by the statistician (theoretical model) through a stochastic differential equation is smooth in the drift but the real model has a cusp-type singularity in the drift function and the driving force is a fractional Brownian motion.

Methodology for the Identification of Dust Accumulation Levels in Photovoltaic Panels Based in Heuristic-Statistical Techniques

The use of renewable energies is increasing around the world in order to deal with the environmental and economic problems related with conventional generation. In this sense, photovoltaic generation is one of the most promising technologies because of the high availability of sunlight, the easiness of maintenance, and the reduction in the costs of installation and production. However, photovoltaic panels are elements that must be located outside in order to receive the sun radiation and transform it into electricity. Therefore, they are exposed to the weather conditions and many environmental factors that can negatively affect the output delivered by the system. One of the most common issues related to the outside location is the dust accumulation in the surface of the panels. The dust particles obstruct the passage of the sunlight, reducing the efficiency of the generation process and making the system prone to experimental long-term faults. Thus, it is necessary to develop techniques that allow us to assess the level of dust accumulation in the panel surface in order to schedule a proper maintenance and avoid losses associated with the reduction of the delivered power and unexpected faults. In this work, we propose a methodology that uses a machine learning approach to estimate different levels of dust accumulation in photovoltaic panels. The developed method takes the voltage, current, temperature, and sun radiance as inputs to perform a statistical feature extraction that describes the behavior of the photovoltaic system under different dust conditions. In order to retain only the relevant information, a genetic algorithm works along with the principal component analysis technique to perform an optimal feature selection. Next, the linear discrimination analysis is carried out using the optimized dataset to reduce the problem dimensionality, and a multi-layer perceptron neural network is implemented as a classifier for discriminating among three different conditions: clean surface, slight dust accumulation, and severe dust accumulation. The proposed methodology is implemented using real signals from a photovoltaic installation, proving to be effective not only to determine if a dust accumulation condition is present but also when maintenance actions must be performed. Moreover, the results demonstrate that the accuracy of the proposed method is always above 94%.

A blended method incorporating a multi-model for pulse wind tunnel aerodynamic identification considering large-scale aircraft

Pulse wind tunnel aerodynamic tests are an important part of hypersonic aircraft design and development. With the development of hypersonic aircraft technology, the aircraft model in pulsed wind tunnels has gradually become large-scale and heavy-loaded. During the force measurement test, due to the influence of the increased size of the aircraft model, the real aerodynamic signal of the force measurement system is submerged by the interference signal. During the effective test time of several hundred milliseconds, it is impossible to obtain high-precision aerodynamic signals of the force measuring system using traditional signal-processing methods. Therefore, for the in-depth study of aerodynamic identification of short-time hypersonic pulsed wind tunnels, there is an urgent need for an identification algorithm that can adapt to the force measurement system of large-scale aircraft. In this paper, a new aerodynamic identification algorithm that combines a traditional signal-processing method and a deep neural network is proposed and applied to pulse combustion wind tunnels. The algorithm is mainly divided into signal preprocessing and deep learning. First, the original signal is decomposed into different sub-signals via variational modal decomposition (VMD), and then the real aerodynamic signal is obtained via convolutional neural network (CNN)-long short-term memory (LSTM) training. For different interference signals in the pulsed wind tunnel test, this algorithm innovatively designs VMD preprocessing and optimizes hyperparameters, to extract signal features for subsequent deep learning and to filter out interference components. To ensure the consistency between the validation and the application, the algorithm was verified using the suspension test bench during training, and satisfactory results were obtained. Finally, the algorithm is applied to the suspension force measurement system of a pulse combustion wind tunnel, and the aerodynamic identification results present satisfactory accuracy.

Parameter analysis of chirplet transform and high-resolution time-frequency representation via chirplets combination

The standard chirplet transform (CT) with the chirp-modulated Gaussian window provides a valuable tool for analyzing linear chirp signals. The parameters present in the window determine the performance of the CT and play a vital role in high-resolution time-frequency (TF) analysis. In this paper, we give the window shape analysis of the CT and compare it with the extension that employs a rotating Gaussian window by the fractional Fourier transform. The given parameters analysis provides theoretical guidance for developing high-resolution CT. We then propose a multi-resolution chirplet transform (MrCT) by combining multiple CTs with different parameter combinations. These are combined geometrically to obtain an improved TF resolution by overcoming the limitations of any single representation of the CT. We further develop a TF post-processing approach to enhance the readability of the MrCT by deriving a combined instantaneous frequency equation, which can characterize the mixture of the chirp-like and impulse-like signals. Numerical experiments on simulated and real signals verify the effectiveness of the proposed approaches.

An electrical-mechanical cohesive zone model combining viscoelasticity and fatigue damage for soft adhesive layer in wearable sensor

The wearable biomedical devices are promising technologies in the field of human healthcare as the sensors are attached to human skin to monitor in real time physiological signal. The signal drift caused by the damage of adhesive layer between the sensor electrode and human skin is an important factor that affects the detection accuracy of the sensor. Considering the viscoelasticity and conductivity of the adhesive layer, a rate-dependent electrical–mechanical cohesive zone model (CZM) was developed to simulate the electrical signal degradation of wearable sensor electrode during cyclic loading condition. In the proposed CZM, the electrical response of the adhesive layer is related to its mechanical behavior and damage state, and the rate-dependent mechanical behavior of the adhesive layer is described by the Maxwell model. In addition, the fatigue damage evolution is also rate-dependent as the threshold of damage initiation and fracture are defined as the function of loading velocity. The proposed CZM was implemented in ABAQUS by the UEL subroutine and applied to simulate the damage and electrical signal degradation of a wearable electrocardiograph (ECG) sensor electrode in working condition. The results demonstrated that the proposed CZM is effective to characterize the rate-dependent fatigue damage behavior and electrical signal degradation behavior of the ECG electrode during cyclic loadings. It is a promising tool for predicting the electrical signal drift of wearable healthcare sensors and other similar components caused by the damage of adhesive layer, which is important for the accurate calibration of the sensors in long-term service.

Detection of Fake Replay Attack Signals on Remote Keyless Controlled Vehicles Using Pre-Trained Deep Neural Network

Keyless systems have replaced the old-fashioned methods of inserting physical keys into keyholes to unlock the door, which are inconvenient and easily exploited by threat actors. Keyless systems use the technology of radio frequency (RF) as an interface to transmit signals from the key fob to the vehicle. However, keyless systems are also susceptible to being compromised by a threat actor who intercepts the transmitted signal and performs a replay attack. In this paper, we propose a transfer learning-based model to identify the replay attacks launched against remote keyless controlled vehicles. Specifically, the system makes use of a pre-trained ResNet50 deep neural network to predict the wireless remote signals used to lock or unlock doors of a remote-controlled vehicle system. The signals are finally classified into three classes: real signal, fake signal high gain, and fake signal low gain. We have trained our model with 100 epochs (3800 iterations) on a KeFRA 2022 dataset, a modern dataset. The model has recorded a final validation accuracy of 99.71% and a final validation loss of 0.29% at a low inferencing time of 50 ms for the model-based SGD solver. The experimental evaluation revealed the supremacy of the proposed model.

Computational ghost imaging with PSF-guiding deep learning through various unknown turbid scattering media

Achieving high signal-to-noise ratio (SNR) imaging through scattering media is challenging. Computational ghost imaging with deep learning (CGIDL) has unique advantages for solving this challenge. However, image reconstruction cannot be guaranteed due to low correlation between real signal and training dataset, when the CGIDL is applied in different unknown scattering media. Point spread function (PSF) determines the quality of CGIDL reconstruction, linking the mathematical features of the scene and the quality of reconstruction. In this study, an innovative CGIDL technology based on the measured PSF method is proposed to improve the correlation between real signal and training dataset. When five new turbid scattering media with unknown scattering strength are introduced, classification of PSF enables high SNR imaging through various turbid scattering media.

4acCPred: Weakly supervised prediction of N4-acetyldeoxycytosine DNA modification from sequences

DNA methylation is one of the earliest epigenetic regulation mechanisms studied extensively, and it is critical for normal development, diseases, and gene expression. As a recently identified chemical modification of DNA, N4-acetyldeoxycytosine (4acC) was shown to be abundant in Arabidopsis and highly associated with gene expression and actively transcribed genes. Precise identification of 4acC is essential for studying its biological function. We proposed the 4acCPred, the first computational framework for predicting 4acC-carrying regions from Arabidopsis genomic DNA sequences. Since the existing 4acC data are not precise for a specific base but only report regions that are hundreds of bases long, we formulated the task as a weakly supervised learning problem and built 4acCPred using a multi-instance-based deep neural network. Both cross-validation and independent testing on the four datasets under different conditions show promising performance, with mean areas under the receiver operating characteristic curve (AUCs) of 0.9877 and 0.9899, respectively. 4acCPred also provides motif mining through model interpretation. The motifs found by 4acCPred are consistent with existing knowledge, indicating that the model successfully captured real biological signals. In addition, a user-friendly web server was built to facilitate 4acC prediction, motif visualization, and data access. Our framework and web server should serve as useful tools for 4acC research.

A Data-Driven OBE Magnetic Interference Compensation Method.

Aeromagnetic compensation is a technology used to reduce aircraft magnetic interference, which plays an important role in aeromagnetic surveys. In addition to maneuvering interferences, the onboard electronic (OBE) interference has been proven to be a significant part of aircraft interference, which must be reduced before further interpretation of aeromagnetic data. In the past, most researchers have focused on establishing linear models to compensate for OBE magnetic interference. However, such methods can only work using accurate reference sensors. In this paper, we propose a data-driven OBE interference compensation method, which can reduce OBE interference without relying on any other reference sensor. This network-based method can integrally detect and repair the OBE magnetic interference. The proposed method builds a prediction model by combining wavelet decomposition with a long short-term memory (LSTM) network to detect and predict OBE interference, and then estimates the local variation of the magnetic field to remove the drift of the interference. In our tests, we construct 10 semi-real datasets to quantitatively evaluate the performance of the proposed method. The F1 score of the proposed method for OBE interference detection is over , and the RMSE of the compensated signal is less than nT. Moreover, we also test our method on real signals, and the results show that our method can detect all interference and significantly reduce the standard deviation of the magnetic field.

Accelerating multimodel Bayesian inference, model selection, and systematic studies for gravitational wave astronomy

Gravitational wave models are used to infer the properties of black holes in merging binaries from the observed gravitational wave signals through Bayesian inference. Although we have access to a large collection of signal models that are sufficiently accurate to infer the properties of black holes, for some signals, small discrepancies in the models lead to systematic differences in the inferred properties. In order to provide a single estimate for the properties of the black holes, it is preferable to marginalize over the model uncertainty. Bayesian model averaging is a commonly used technique to marginalize over multiple models, however, it is computationally expensive. An elegant solution is to simultaneously infer the model and model properties in a joint Bayesian analysis. In this work we demonstrate that a joint Bayesian analysis can not only accelerate but also account for model-dependent systematic differences in the inferred black hole properties. We verify this technique by analysing 100 randomly chosen simulated signals and also the real gravitational wave signal GW200129_065458. We find that not only do we infer statistically identical properties as those obtained using Bayesian model averaging, but we can sample over a set of three models on average $2.5\times$ faster. In other words, a joint Bayesian analysis that marginalizes over three models takes on average only $20\%$ more time than a single model analysis. We then demonstrate that this technique can be used to accurately and efficiently quantify the support for one model over another, thereby assisting in Bayesian model selection.

Hidden Markov model in nonnegative matrix factorization for fetal heart rate estimation using physiological priors

Objective. Fetal heart rate (fHR) analysis remains the most common technique for detecting fetal distress when monitoring the fetal well-being during labor. If cardiotocography (CTG) is nowadays the non-invasive clinical reference technique for fHR measurement, it suffers from several drawbacks, hence an increasing interest towards alternative technologies, especially around abdominal ECG (aECG). Approach. An original solution, using a single abdominal lead, was recently proposed to address both the feasibility in clinical routine and the challenging detection of temporal events when facing interfered signals from real life conditions. Based on a specification of the non-negative matrix factorization (NMF) algorithm, it exploits the semi-periodicity of fetal electrocardiogram (fECG) for fHR estimation. However, this method assumes temporal independence and therefore does not consider the continuity property of fHR values. It is thus proposed to add to the NMF framework a hidden Markov model (HMM) to include physiological information about fHR temporal evolution. Under a statistical setting, constraints have been added by accommodating regularization terms through Bayesian priors. Main results. The proposed method is evaluated on 23 real aECG signals from a new clinical database, according to CTG reference, and compared with the original NMF-only algorithm. The new proposed method improves performance, with an agreement with CTG increasing from 71% to 80%. Significance. This highlights the interest of a better modelization of the fHR characteristics for a more robust estimation.

Exploring entanglement resource in Si quantum dot systems with operational quasiprobability approach

We characterize the quantum entanglement of the realistic two-qubit signals that are sensitive to charge noises. Our working example is the time response generated from a silicon double quantum dot (DQD) platform, where a single-qubit rotation and a two-qubit controlled-NOT operation are conducted sequentially in time to generate arbitrary entangled states. In order to characterize the entanglement of two-qubit states, we employ the marginal operational quasiprobability (OQ) approach that allows negative values of the probability function if a given state is entangled. While the charge noise, which is omnipresent in semiconductor devices, severely affects logic operations implemented in the DQD platform, causing huge degradation in fidelity of unitary operations as well as resulting two-qubit states, the pattern in the OQ-driven entanglement strength turns out to be quite invariant, indicating that the resource of quantum entanglement is not significantly broken though the physical system is exposed to noise-driven fluctuations in exchange interaction between quantum dots.

Quality Analysis of Space and Space-Frequency Adaptive Signal Processing Algorithms in Satellite Navigation Systems

The problem of interference mitigation in satellite navigation signals is considered. Construction algorithms for space- and space-frequency adaptive filters are described. The processing of real satellite signals with broadband interference by means of the constructed filters is carried out in MATLAB. As a result of comparing different processing quality indicators, the most efficient filtering algorithms are identified.

A Novel Lidar Signal-Denoising Algorithm Based on Sparrow Search Algorithm for Optimal Variational Modal Decomposition

Atmospheric lidar is susceptible to the influence of light attenuation, sky background light, and detector dark currents during the detection process. This results in a large amount of noise in the lidar return signal. To reduce noise and extract a useful signal, a novel denoising method combined with variational modal decomposition (VMD), the sparrow search algorithm (SSA) and singular value decomposition (SVD) is proposed. The SSA is used to optimize the number of decomposition layers K and the quadratic penalty factor α values of the VMD algorithm. Some intrinsic mode function (IMF) components obtained from the VMD-SSA decomposition are grouped and reconstructed according to the interrelationship number selection criterion. Then, the reconstructed signal is further denoised by combining the strong noise-reduction ability of SVD to obtain a clean lidar return signal. To verify the effectiveness of the VMD-SSA-SVD method, the method is compared and analysed with wavelet packet decomposition, empirical modal decomposition (EMD), ensemble empirical modal decomposition (EEMD), and adaptive noise-complete ensemble empirical modal decomposition (CEEMD), and its noise-reduction effect is considerably improved over that of the other four methods. The method can eliminate the complex noise in the lidar return signal while retaining all the details of the signal. The signal is not distorted, the waveform is smoother, and far-field noise interference can be suppressed. The denoised signal is closer to the real signal with higher accuracy, which shows the feasibility and the practicality of the proposed method.

Feasibility of a global inversion for spatially resolved glacial isostatic adjustment and ice sheet mass changes proven in simulation experiments

Estimating mass changes of ice sheets or of the global ocean from satellite gravimetry strongly depends on the correction for the glacial isostatic adjustment (GIA) signal. However, geophysical GIA models are different and incompatible with observations, particularly in Antarctica. Regional inversions have resolved GIA over Antarctica without ensuring global consistency, while global inversions have been mostly constrained by a priori GIA patterns. For the first time, we set up a global inversion to simultaneously estimate ice sheet mass changes and GIA, where Antarctic GIA is spatially resolved using a set of global GIA patterns. The patterns are related to deglaciation impulses localized along a grid over Antarctica. GIA associated with four regions outside Antarctica is parametrized by global GIA patterns induced by deglaciation histories. The observations we consider here are satellite gravimetry, satellite altimetry over Antarctica and Greenland, as well as modelled firn thickness changes. Firn thickness changes are also parametrized to account for systematic errors in their modelling. Results from simulation experiments using realistic signals and error covariances support the feasibility of the approach. For example, the spatial RMS error of the estimated Antarctic GIA effect, assuming a 10-year observation period, is 31% and 51%, of the RMS of two alternative global GIA models. The integrated Antarctic GIA error is 8% and 5%, respectively, of the integrated GIA signal of the two models. For these results realistic error covariances incorporated in the parameter estimation process are essential. If error correlations are neglected, the Antarctic GIA RMS error is more than twice as large.Highlightsbullet We present a globally consistent inversion approach to co-estimate glacial isostatic adjustment effects together with changes of the ice mass and firn air content in Greenland and Antarctica. bullet The inversion method utilizes data sets from satellite gravimetry, satellite altimetry, regional climate modelling, and firn modelling together with the full error-covariance information of all input data. bullet The simulation experiments show that the proposed GIA parametrization in Antarctica can resolve GIA effects unpredicted by geophysical modelling, despite realistic input-data limitations.

The Multi-Channel Maximum-Likelihood (MCML) method for infrasound detection and wave parameter estimation: Evaluation, implementation, and first applications

The detection capability of the International Monitoring System (IMS) infrasound network is a key concern since ubiquitous coherent noise interfere with the identification of explosive events. The need to distinguish between incoherent wind noise and real coherent infrasonic wave signals has motivated the development of a new parametric stochastic model. We establish the expressions of both the Generalized Likelihood Ratio Test (GLRT) and the Maximum Likelihood Estimator (MLE). The major results are the expression of the asymptotic distribution of the GLRT under null hypothesis, leading to the p-value computation, and the expression of the asymptotic covariance. The Multi-Channel Maximum-Likelihood (MCML) detection is implemented in the time-frequency domain in order to discriminate between interfering signals. Extensive simulations with synthetic signals show that MCML outperforms the state-of-the-art multi-channel correlation detector algorithms in poor signal-to-noise ratio scenarios. We illustrate the performance of MCML on real IMS data, highlighting its capability to characterize the coherent ocean ambient infrasound noise in high resolution through comparisons with theoretical state-of-the-art numerical wave models as implemented at the LOPS research unit of IFREMER in the DATARMOR HPC center.

DeepNoise: Signal and Noise Disentanglement Based on Classifying Fluorescent Microscopy Images via Deep Learning

The high-content image-based assay is commonly leveraged for identifying the phenotypic impact of genetic perturbations in biology field. However, a persistent issue remains unsolved during experiments: the interferential technical noises caused by systematic errors (e.g., temperature, reagent concentration, and well location) are always mixed up with the real biological signals, leading to misinterpretation of any conclusion drawn. Here, we reported a mean teacher-based deep learning model (DeepNoise) that can disentangle biological signals from the experimental noises. Specifically, we aimed to classify the phenotypic impact of 1108 different genetic perturbations screened from 125,510 fluorescent microscopy images, which were totally unrecognizable by the human eye. We validated our model by participating in the Recursion Cellular Image Classification Challenge, and DeepNoise achieved an extremely high classification score (accuracy: 99.596%), ranking the 2nd place among 866 participating groups. This promising result indicates the successful separation of biological and technical factors, which might help decrease the cost of treatment development and expedite the drug discovery process. The source code of DeepNoise is available at https://github.com/Scu-sen/Recursion-Cellular-Image-Classification-Challenge.

3-D Indoor Localization and Identification Through RSSI-Based Angle of Arrival Estimation With Real Wi-Fi Signals

A highly accurate 3-D indoor passive localization and identification system is presented in this article. The system can monitor various commercial Wi-Fi devices through the proposed received signal strength indicator (RSSI)-based angle of arrival (AoA) estimation technique. RSSI-based localization systems conventionally use additional assistance techniques, such as distance–RSSI calibration, fingerprint analysis, machine learning, and a widespread setup to achieve high accuracy. On the contrary, this proposed system can operate in complex environments full of scattering objects and obstructions without requiring any additional assistance techniques. The proposed technique uses a six-port network to evaluate the phase difference in the carrier waves of Wi-Fi signals without the influence of modulated signals. The network also preserves the modulated signals without the influence of the phase difference of the carrier waves such that Wi-Fi devices can be identified. Regarding practical applications, the system is designed to be capable of detecting devices through walls and thus can be hidden outside the monitored room. The experimental results indicate that in single-source localization, the average error is 0.089 m; in multiple-source localization, it is 0.354 m; and for seeing through the wall, it is 0.24 m. In the worst case scenario, the error is still smaller than 0.63 m. The accuracy in all the experimental results was found to be at the decimeter level. In summary, this study experimentally validated a 3-D indoor localization and identification system for diverse Wi-Fi devices in various environments.