Recovered Signals Research Articles

MSAACNN for intense noise suppression in DAS-VSP records

Distributed optical fiber sensor (DAS) is an emerging acquisition technique and has begun to be widely applied in seismic exploration owing to its advantages in acquisition and deployment. Nonetheless, DAS record has a low signal-to-noise ratio (SNR) due to the intense background noise. How to suppress the DAS background noise and increase the SNR of the DAS records has gradually become one of the hot issues in the field of seismic data processing. To solve the challenging tasks in intense DAS noise suppression, a multiscale sparse asymmetric attention convolutional neural network (MSAACNN) is proposed. The network uses dilated convolutions to expand the receptive field and map more feature information. Moreover, asymmetric convolutions are introduced to form an asymmetric unit, aiming to strengthen the feature extraction ability and realize the interaction between feature information of different scales. Finally, a pyramid attention module is used to enhance the primary features and improve the network denoising performance. The experimental results show that MSAACNN can effectively suppress the complex background noise in DAS records, compared with the traditional denoising methods and typical convolutional neural network (CNN) architecture. Additionally, the recovered signal components in the processing results are clear and complete, with significantly improved SNR.

Evaluation of the influence of temperature and time exposure on Peruvian Maca (Lepidium meyenii) flour samples through NIR spectroscopy and chemometrics

In this study, the three most known varieties of the Peruvian Maca (Lepidium meyenii), yellow, red, and black, were fractionated in multiple sub-samples and submitted to heating (from 25 to 200 °C) during one hour at 10-minute intervals to be measured using the near-infrared (NIR) spectroscopy after 24 h resting in a desiccator. The study focused on changes related to heating for considering new applications on Maca-enriched food products such as bread, cakes, and cookies, which are prepared over high temperatures during different time exposures. To perform the experiment, 9 portions of 1 g of each sample remained in an oven at each temperature (25, 50, 100, 150, 160, 170, 180, 190, and 200 °C) for different times (10, 20, 30, 40, 50, and 60 min). The principal component analysis (PCA) and the multivariate curve resolution with alternating least squares (MCR-ALS) were applied to interpret the results. In general, it was observed that spectral variations start to occur from heating samples from above 100 °C. The recovered signals indicated that most spectral variations occurred in regions assigned to NH vibrations, suggesting that heating influences the protein and/or macamides structures, which are responsible for most of the properties attributed to the Peruvian Maca. The yellow Maca phenotype was the most susceptible to spectral variations after heating, while the black Maca displayed the slowest spectral transformations. This study may encourage deeper investigations into the use of Maca-derived foods regarding their health-beneficial effects after heating to indicate the limitations of Maca applications.

Output Formation Containment for Multiagent Systems Under Multipoint Multipattern FDI Attacks: A Resilient Impulsive Compensation Control Approach.

The increasing number of devices and frequent interactions of agents from networked multiagent systems (MASs) exacerbate the risks of potential cyber attacks, especially the different point attacks and multiple pattern attacks. This article considers the output formation-containment problem for MASs under multipoint multipattern false data injection (FDI) attacks. The multipoint describes the attacks simultaneously occurring on the sensors, actuators, and communication channels; the multipattern captures that sensor and actuator attack signals are both continuous deterministic variables, and the communication channel attack signals are intermittent random variables, obeying the Bernoulli distribution. For such compromised MASs, a novel hybrid protocol is proposed, which integrates a state observer, an attack estimator, an impulsive interactor and a compensation controller. Thereinto, the state observer and the attack estimator are constructed to recover the unmeasured system states and the unknown FDI attack signals, respectively; the impulsive interactor is designed to guarantee that the neighbor's signals are transmitted only at impulsive instants, and meanwhile the channel attacks are randomly launched; using the recovered signals, the compensation controller is devised to alleviate the effect of attacks. A sufficient condition is identified, under which the output formation containment is achieved with cooperative uniform ultimate boundedness (UUB). Finally, simulation results are carried out to validate the effectiveness and advantages of the proposed approach.

Compressive sensing of complex-valued data using Gaussian entropy

In this paper, we propose an effective compressive sensing algorithm based on Gaussian entropy for complex-data. Compared with the traditional mean squared error (MSE) method, we consider the full second order statistics information of Gaussian noise in the new algorithm, including relevant information and conjugate information, which makes the recovered signal closer to the original input signal. Simulation results of the synthesized 1D signal and 2D signal show that the proposed algorithm has better performance than the MSE method.

Adaptive DBSCAN Clustering and GASA Optimization for Underdetermined Mixing Matrix Estimation in Fault Diagnosis of Reciprocating Compressors.

Underdetermined blind source separation (UBSS) has garnered significant attention in recent years due to its ability to separate source signals without prior knowledge, even when sensors are limited. To accurately estimate the mixed matrix, various clustering algorithms are typically employed to enhance the sparsity of the mixed matrix. Traditional clustering methods require prior knowledge of the number of direct signal sources, while modern artificial intelligence optimization algorithms are sensitive to outliers, which can affect accuracy. To address these challenges, we propose a novel approach called the Genetic Simulated Annealing Optimization (GASA) method with Adaptive Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering as initialization, named the CYYM method. This approach incorporates two key components: an Adaptive DBSCAN to discard noise points and identify the number of source signals and GASA optimization for automatic cluster center determination. GASA combines the global spatial search capabilities of a genetic algorithm (GA) with the local search abilities of a simulated annealing algorithm (SA). Signal simulations and experimental analysis of compressor fault signals demonstrate that the CYYM method can accurately calculate the mixing matrix, facilitating successful source signal recovery. Subsequently, we analyze the recovered signals using the Refined Composite Multiscale Fuzzy Entropy (RCMFE), which, in turn, enables effective compressor connecting rod fault diagnosis. This research provides a promising approach for underdetermined source separation and offers practical applications in fault diagnosis and other fields.

Multiplex Assays of MicroRNAs by Using Single Particle Electrochemical Collision in a Single Run.

It is important to quantify multiple biomarkers in a single run due to the advantages of precious samples and diagnostic accuracy. Based on the distinguishability of two types of current signals from single particle electrochemical collision (SPEC), step-type current transients produced by Pt nanoparticles (PtNPs) catalyzed hydrazine oxidation and peak-type current transients produced by Ag nanoparticles (AgNPs) oxidation, a kind of multiplex immunoassay of target microRNAs (miRNA-21 and Let-7a) have been established during SPEC in a single run. When the single-stranded DNA (ssDNA1) that was perfectly complementary to miRNA-21 was coupled to the surface of PtNPs, the SPEC of PtNPs electrocatalysis was inhibited and the step-type current transients disappeared, while the single-stranded DNA (ssDNA2) that was perfectly complementary to Let-7a was coupled to the surface of AgNPs, the SPEC of AgNPs oxidation was inhibited, and the peak-type current transients disappeared, thus the signals were in the "off" state at this time. After that, miRNA-21 and Let-7a were added into solution, complementary base pairing disrupted the weak DNA-NP interaction and restored the electrocatalysis of PtNPs and the electrooxidation of AgNPs, and the step-type current signals and peak-type current signals were in the "on" state. Moreover, the frequencies from two different recovered signals (PtNPs catalysis and AgNPs oxidation) corresponded to the amount of added miRNA-21 and Let-7a, thus a multiplex immunoassay method for dual quantification of miRNA-21 and Let-7a in a single run was established.

A sensitive fluorescence nanoplatform for monitoring benzoyl peroxide in food using carbon dots coupled with glutathione capped gold nanoparticles as FRET probe.

Herein, a sensitive fluorescence nanoplatform for benzoyl peroxide (BPO) detection is constructed from carbon dots (CDs) and glutathione capped gold nanoparticles (GSH-AuNPs). The fluorescence of CDs is first quenched due to the fluorescence resonance energy transfer (FRET) effect in the presence of GSH-AuNPs, and then effectively recovered when adding BPO. The detection mechanism lies in the aggregation of AuNPs in a high salt background due to oxidation of GSH caused by BPO, thus the amount of BPO is reflected by the variations of the recovered signals. The linear range and detection limit in this detection system is found to be 0.05-200μM (R2=0.994) and 0.1µgg-1 (3σ/K), respectively. Several possible interferents with high concentration show little influence on BPO detection. The proposed assay exhibits good performance for BPO determination in wheat flour and noodles, demonstrating its applicability for facile monitoring BPO additive amount in real foods.

Sky-averaged 21-cm signal extraction using multiple antennas with an SVD framework: the REACH case

ABSTRACT In a sky-averaged 21-cm signal experiment, the uncertainty of the extracted signal depends mainly on the covariance between the foreground and 21-cm signal models. In this paper, we construct these models using the modes of variation obtained from the singular value decomposition of a set of simulated foreground and 21-cm signals. We present a strategy to reduce this overlap between the 21-cm and foreground modes by simultaneously fitting the spectra from multiple different antennas, which can be used in combination with the method of utilizing the time dependence of foregrounds while fitting multiple drift scan spectra. To demonstrate this idea, we consider two different foreground models (i) a simple foreground model, where we assume a constant spectral index over the sky, and (ii) a more realistic foreground model, with a spatial variation of the spectral index. For the simple foreground model, with just a single antenna design, we are able to extract the signal with good accuracy if we simultaneously fit the data from multiple time slices. The 21-cm signal extraction is further improved when we simultaneously fit the data from different antennas as well. This improvement becomes more pronounced while using the more realistic mock observations generated from the detailed foreground model. We find that even if we fit multiple time slices, the recovered signal is biased and inaccurate for a single antenna. However, simultaneously fitting the data from different antennas reduces the bias and the uncertainty by a factor of 2–3 on the extracted 21-cm signal.

Compressive Spectral Video Sensing using the Convolutional Sparse Coding framework CSC4D

Spectral Videos (SV) contain a scene’s spatial–spectral-time information. Just as with Spectral Images (SI), SVs require expensive sensing hardware, storage plus high frame ratios. Although Super Resolution techniques improve the quality of low-resolution SVs, Compressive Spectral Video Sensing (CSVS) senses high-quality SVs by extending the Compressive Sensing Image (CSI) techniques. CSI uses the universal Sparse Signal Representation (SSR) model for SVs and SIs despite the limited quality of the recovered signals. On the other hand, dictionaries synthesis models are used successfully for representing SIs, SVs, and in CSI. This work proposes the 4D convolutional sparse representation (CSC4D) for recovering full-resolution SV from CSVS measurements. It is based on a multidimensional formulation of the CSC model, profiting from its robustness without additional optical flow information. Extensive numerical simulations (two CSI architectures and noise models) show that the proposed CSC4D+CSVS improves the state-of-the-art in both quality and border sharpness by up to 1.5 dB.

Signal distortion awakened from optical memory estimated using a calculation method with spatiotemporal separation.

The fidelity of photonic storage and retrieval is an essential criterion in long-distance all-optical network nodes. However, the recovered signals from optical memories based on the photon echo (PE) protocol are accompanied by undesired waveform variation and temporal drift. In this study, we use a numerical calculation method with spatiotemporal separation to investigate the essence of signal distortion. The results show that the asynchronous evolution of the macroscopic population difference and the macroscopic dipole moment with time is responsible for echo signal real distortion caused by phase shifts at the in-phase point of the recorded information. The constructive interference of the dipoles at the moment of reaching the in-phase point induces the photon emission, and this point with a nonspecific phase will be naturally accompanied by waveform changes, a small amount of time advance and delay of the PE signal, which is actually a false signal distortion. Such radiation mechanism of the inhomogeneous broadening media provides a perspective to accurately and correctly recognize the temporal drift and waveform variation of the recovered optical signal.

Evaluation of the Fast Synchrophasors Estimation Algorithm Based on Physical Signals

The goal of this study is to evaluate the performance of the fast algorithm for synchrophasor estimation proposed on the basis of a physical system. The test system is represented by a physical model of a power system with four synchronous generators (15 and 5 kVA). Three synchronous machines represent steam turbine generators, while the fourth machine represents a hydro generator. The proposed method of accuracy assessment is based on comparison of the original and the recovered signals, using values of amplitude and phase angle. The experiments conducted in the study include three-phase faults, two-phase faults and single-phase faults at various buses of the test model. Functional dependencies of initial signal standard deviation from the recovered signal are obtained, as well as those for sampling rate and window width. Based on the results, the following requirements for measurement system and window width are formulated: sampling rate of analog-to-digital converter should be 10 kHz; and window width should start from 5 ms. In addition, the fast algorithm of synchrophasor estimation was tested on event recorder signals. The sampling rate of these signals was 2 kHz. Acceptable window width for event recorder signals is 8 ms. The algorithm was implemented using programming language Python 3 for the testing purposes. The proposed fast algorithm of synchrophasor estimation can be applied in methods for emergency control and equipment state monitoring with short time response.

Concurrent estimation of noise and compact-binary signal parameters in gravitational-wave data

Gravitational-wave parameter estimation for compact binary signals typically relies on sequential estimation of the properties of the detector Gaussian noise and of the binary parameters. This procedure assumes that the noise variance, expressed through its power spectral density, is perfectly known in advance. We assess the impact of this approximation on the estimated parameters by means of an analysis that simultaneously estimates the noise and compact binary parameters, thus allowing us to marginalize over uncertainty in the noise properties. We compare the traditional sequential estimation method and the new full marginalization method using events from the GWTC-3 catalog. We find that the recovered signals and inferred parameters agree to within their statistical measurement uncertainty. At current detector sensitivities, uncertainty about the noise power spectral density is a subdominant effect compared to other sources of uncertainty.

Improved particle swarm optimization-based adaptive multiresolution dynamic mode decomposition with application to fault diagnosis of rolling bearing

It is very important to detect fault and extract fault features of mechanical systems at an early stage, because the above two steps promise normal operation of mechanical systems. However, they are also very challenging. In this context, this article has put forward improved particle swarm optimization-based adaptive multiresolution dynamic mode decomposition of rolling bearing (IPSO-AMDMD). Multiresolution dynamic mode decomposition (MRDMD) is used to decompose signals of rolling bearing at the early stage, multiscale fuzzy entropy (MFE) is employed to divide low-rank components and sparse components. In order to make up for the shortcomings of the above two methods, namely truncated rank of MRDMD and inaccurate selection in threshold of MFE, this paper has proposed a new fitness function, which is called synthetic envelope kurtosis characteristic energy difference ratio, and adopted the improved particle swarm optimization algorithm (IPSO) to select the optimal parameters adaptively. With these two steps, signals can be decomposed perfectly. Finally, reconstructed signals, which are obtained through the combination of signals from each layer according to a certain weight, go through DMD again, thus getting the final recovered signal. Through simulation experiment and in-field experiment, it has proved that IPSO-AMDMD is viable and sound in accurately extracting features from fault signals.

The Development of Spatial Attention U‐Net for the Recovery of Ionospheric Measurements and Extraction of Ionospheric Parameters

AbstractWe train a deep learning artificial neural network model, Spatial Attention U‐Net, to recover useful ionospheric signals from noisy ionogram data measured by Hualien's Vertical Incidence Pulsed Ionospheric Radar. Our results show that the model can well identify F2 layer ordinary and extraordinary modes (F2o and F2x) and the combined signals of the E layer (ordinary and extraordinary modes and sporadic Es). The model is also capable of identifying some signals that were not labeled. The performance of the model can be significantly degraded by insufficient number of samples in the data set. From the recovered signals, we determine the critical frequencies of F2o and F2x and the intersection frequency between the two signals. The difference between the two critical frequencies is peaking at 0.63 MHz, with the uncertainty being 0.18 MHz.

Cutting Compensation in the Time-Frequency Domain for Smeared Spectrum Jamming Suppression

Smeared spectrum (SMSP) jamming is a new type of distance false-target jamming. It consists of multiple sub-pulses, which results in dense false targets at the radar receiver and affects the detection of target signal. Aiming at the suppression of SMSP jamming, in this paper we propose a fast jamming suppression method based on the time-frequency domain according to the time-frequency distribution characteristic of SMSP jamming. This method completely suppresses SMSP jamming in the time-frequency domain, retains the time-frequency points of the remaining target signal, uses the compensation method to obtain the lost target signal, and then restores the time-frequency distribution characteristic of the target signal. It will not produce jamming sidelobe after the recovered signal matched filtering in the time domain. Moreover, we can obtain the Doppler frequency in the time-frequency domain, which can be adopted in practical engineering applications. The simulation results illustrate the effectiveness of the proposed method.

Radio frequency receiver of long-term evolution system design by MATLAB Simulink

For wireless broadband communication long-term evolution (LTE) is a standard for also mobile devices and data terminals, by using different radio interface together with core network improvements LTE increases the capacity and speed of mobile network. In this paper radio frequency receiver of radio-frequency long-term evolution (RF-LTE) is design and simulated using MATLAB Simulink, where the design based on illustrating parameters of each stage in LTE RF receiver from generating LTE waveform to error vector magnitude (EVM) measurements, where simulation results with 8 MHz bandwidth the transmitted signal power -3200 dBm, the received signal power (-140 to -160) dBm, while the demodulated signal reaches to -60 dBm difference between main loop and side loop witch lead to high confident recovered signal, also complementary cumulative distribution function (CCDF) measurements applied on output signals so that computes the power of complementary for cumulative distribution CCDF function from signal in time domain. Where CCDF curve shows value of time that a signal stand still above the level of average power for the measured signal or the probability of signal power will be above the level of average power.

Compressed Sensing Framework for Heart Sound Acquisition in Internet of Medical Things

For continuous monitoring of cardiovascular diseases, this article presents a novel framework for heart sound acquisition. The proposed approach uses compressed sensing for signal sampling, and a two-stage reconstruction is developed for reconstruction. The first stage aims to give a tentative recovered signal, on which a peak detection technique is developed to identify whether there is a peak in current segment and, if so, its location. With such information, an adaptive dictionary is selected for the second round reconstruction. Because the selected dictionary is adaptive to the morphology of current frame, the signal reconstruction performance is consequently promoted. Experiment results indicate that a satisfactory performance can be obtained when the frame length is 256 and the signal morphology is divided into 16 categories. Furthermore, the proposed algorithm is compared with a series of counterparts, and the results well demonstrate the advantages of our proposal, especially at high compression ratios.

Adjacent channel interference suppression based on spectral extrapolation to enhance electromagnetic compatibility of co‐site radio devices

AbstractInterference suppression is an effective technique to enhance the electromagnetic compatibility of co‐site radio devices. In adjacent channel interference (ACI) suppression, a wideband analog‐to‐digital converter (ADC) is always required to sample the nonlinear characteristics of the interference signal to reconstruct the ACI, resulting in a significant increase in the cost of the receiver. To solve the problem, this article adopts spectral extrapolation in the ACI suppression. First, narrowband bandpass filter and ADC are employed to sample the in‐band component of the interference signal. Second the original wideband signal is recovered from the filtered signal by an inverse filter. Finally, the recovered signal is used for ACI reconstruction and cancellation. Simulation and experimental results show that when the adjacent channel leakage ratio is 45 dBc, the proposed method can achieve ACI suppression performance equivalent to that of the wideband ADC method. However, the required ADC sampling rate is only 1/3 of that of the wideband ADC.

Signal Processing-Based Deep Learning for Blind Symbol Decoding and Modulation Classification

Blindly decoding a signal requires estimating its unknown transmit parameters, compensating for the wireless channel impairments, and identifying the modulation type. While deep learning can solve complex problems, digital signal processing (DSP) is interpretable and can be more computationally efficient. To combine both, we propose the dual path network (DPN). It consists of a signal path of DSP operations that recover the signal, and a feature path of neural networks that estimate the unknown transmit parameters. By interconnecting the paths over several recovery stages, later stages benefit from the recovered signals and reuse all the previously extracted features. The proposed design is demonstrated to provide 5% improvement in modulation classification compared to alternative designs lacking either feature sharing or access to recovered signals. The estimation results of DPN along with its blind decoding performance are shown to outperform a blind signal processing algorithm for BPSK and QPSK on a simulated dataset. An over-the-air software-defined-radio capture was used to verify DPN results at high SNRs. DPN design can process variable length inputs and is shown to outperform relying on fixed length inputs with prediction averaging on longer signals by up to 15% in modulation classification.

Mapping the Thermal Structure of the Amazon Craton to Constrain the Tectonic Domains

AbstractRheology of solids is primarily a function of temperature. Mapping depth to the bottom of the magnetic layer (DBML), which is assumed as a proxy for the depth to Curie temperature, can be used to constrain the thermal structure of the lithosphere. Heat flow data are commonly used to address the thermal structure of the lithosphere. However, these data are scarce for the Amazon Craton. Therefore, as an alternative, DBML was calculated using fractal magnetic source distribution. The Amazon craton is usually described in terms of geochronological provinces models. Nevertheless, geophysical data are poorly discussed in these models. We provide a DBML model that was integrated with the potential field, seismic tomography, and crustal thickness data to address this issue. Our model varies from 10 to 80 km, and almost 90% of the estimates are in the 20‐50 km range. The recurrence of long‐wavelength features on independent datasets supports the assessment that the model is robust and recovered real geophysical signals. Our model suggests that the limits between Carajás and Bacajás domains should be revisited as well as the limits between Tapajós and Iriri Xingú domains. Moreover, we also correlate the model with main mineral deposits. Further, the identified DBML anomaly suggests the mantle is serpentinized in the eastern Carajás domain. This feature is highly correlated with high p‐wave velocity indicating fertile mantle conditions, thus making this region favorable for world‐class mineral deposits.