Search Results (105)

Frequency-modulated continuous-wave (FMCW) LiDAR systems frequently experience noise interference during multi-target measurements in real-world applications, resulting in target overlapping and diminished detection accuracy. Conventional denoising approaches—such as Empirical Mode Decomposition (EMD) and wavelet thresholding—are often constrained by challenges like mode mixing and the attenuation of weak target signals, which limits their detection precision. To address these limitations, this study presents a novel denoising framework that integrates an optimized Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) algorithm and singular value decomposition (SVD). The CEEMDAN algorithm’s two critical parameters—the noise standard deviation and the number of noise additions—are optimally determined using particle swarm optimization (PSO), with the envelope entropy of the intrinsic mode functions (IMFs) serving as the fitness criterion. IMFs are subsequently selected based on spectral and amplitude comparisons with the original signal to facilitate initial signal reconstruction. Following CEEMDAN-based decomposition, SVD is employed with a normalized soft thresholding technique to further suppress residual noise. Validation using both synthetic and experimental datasets demonstrates the superior performance of the proposed approach over existing methods in multi-target scenarios. Specifically, it reduces the root mean square error (RMSE) by 45% to 59% and the mean square error (MSE) by 34% to 69%, and improves the signal-to-noise ratio (SNR) by 1.85–4.38 dB and the peak signal-to-noise ratio (PSNR) by 1.18–6.94 dB. These results affirm the method’s effectiveness in enhancing signal quality and target distinction in noisy FMCW LiDAR measurements. Full article

Regarding the issue of constructing Fourier transform infrared (FTIR) spectral characteristics of hot jet of aero-engines, this paper presented a construction algorithm for the FTIR spectral characteristics of an aero-engine hot jet, which integrated staged refined processing and statistical feature fusion. First, a remote-sensing Fourier transform infrared spectrometer was employed to collect data on the hot jets of two distinct types of aero-engines, thereby establishing a measured spectral dataset. Subsequently, a multi-dimensional feature extraction vector construction algorithm was proposed, encompassing a peak feature extraction algorithm based on staged refined processing and a high-order statistical feature extraction algorithm. The peak feature extraction algorithm based on staged refined processing consisted of four steps: “coarse detection—local optimization—dynamic screening—intelligent merging”. It adopted an adaptive threshold for the initial coarse detection of peaks, enhanced the positioning accuracy through local gradient optimization, dynamically screened the local strongest peak according to intensity information, and resolved the problem of overlapping peak resolution via an intelligent merging strategy based on the physical characteristics of spectral lines, achieving high-precision and high-robustness peak feature extraction. The high-order statistical feature extraction algorithm realized the extraction of the intensity distribution information and waveform symmetry information of the spectral signal by fusing the kurtosis and skewness statistics. Compared with the traditional feature construction algorithms, the multi-dimensional feature vector construction algorithm proposed in this paper possessed a higher-dimensional comprehensive representation capability. In the experiment, we selected the GMM classifier of the unsupervised clustering algorithm. The classification accuracy of the features extracted by the algorithm in this paper on this classifier reached 82.42%, thereby validating the effectiveness of the algorithm presented in this paper. Full article

A new deconvolution strategy, reverse curve fitting, was developed to determine peak positions and independent intensities of overlapping Fourier transform (FT) nuclear magnetic resonance (NMR) bands. From the third-order derivative of the overlapping band, the peak position was estimated from its zero-crossing point and the peak intensity was quantitated by partial curve matching with its primary maxima. Every matched peak in the overlapping band was dismembered in turn to weaken the overlap until an independent peak was filtered out. The deconvolution can be refined progressively by manually tuning the peak positions and peak widths. In a simulation study, a closely overlapped ¹³C NMR triplet (overlapping degrees between 0.5 and 1.0) at a signal-to-noise ratio (SNR) of 20:1 was quantitatively deconvoluted by our reverse curve fitting procedure with a routine denoising technique. The noise interference and denoising technique were also studied in the simulation. A real FT-NMR overlapping band of Ethylbenzene (300 MHz) was satisfactorily deconvoluted and compatible with higher resolution literature spectral data. A more complicated overlapping NMR band of Tetraphenyl porphyrin was studied as well. This new approach to the deconvolutions is applicable to other FT spectroscopies. Full article

Signal-of-Opportunity (SOP) positioning based on Low-Earth-Orbit (LEO) constellations has gradually become a research hotspot. Due to their large quantity, wide spectral coverage, and strong signal power, LEO satellite SOP positioning exhibits robust anti-jamming capabilities. However, no in-depth studies have been conducted on their anti-jamming performance, particularly regarding the most common type of interference faced by ground receivers—Periodic Frequency Modulation (PFM) interference. Due to the significant differences in signal characteristics between LEO satellite downlink signals and those of Global Navigation Satellite Systems (GNSSs) based on Medium-Earth-Orbit (MEO) or Geostationary-Earth-Orbit (GEO) satellites, traditional interference suppression techniques cannot be directly applied. This paper proposes a Signal Adaptive Iterative Optimization Resampling (SAIOR) algorithm, which leverages the periodicity of PFM jamming signals and the characteristics of LEO constellation signals. The algorithm enhances the concentration of jamming energy by appropriately resampling the data, thereby reducing the overlap between LEO satellite signals and interference. This approach effectively minimizes the damage to the desired signal during anti-jamming processing. Simulation and experimental results demonstrate that, compared to traditional algorithms, this method can effectively eliminates single/multiple-component PFM interference, improve the interference suppression performance under the conditions of narrow bandwidth and high signal power, and holds a high application value in LEO satellite SOP positioning. Full article

The logarithmic derivative has been shown to be a useful tool for data analysis in applied sciences because of either simplifying mathematical procedures or enabling an improved understanding and visualization of structural relationships and dynamic processes. In particular, spatial and temporal variations in signal amplitudes can be described independently of their sign by one and the same compact quantity, the inverse logarithmic derivative. In the special case of a single exponential decay function, this quantity becomes directly identical to the decay time constant. When generalized, the logarithmic derivative enables local gradients of system parameters to be flexibly described by using exponential behavior as a meaningful reference. It can be applied to complex maps of data containing multiple superimposed and alternating ramping or decay functions. Selected examples of experimental and simulated data from time-resolved plasma spectroscopy, multiphoton excitation, and spectroscopy are analyzed in detail, together with reminiscences of early activities in the field. The results demonstrate the capability of the approach to extract specific information on physical processes. Further emerging applications are addressed. Full article

The optical Vernier effect has been widely studied due to its remarkable effect in improving the sensitivity and resolution of optical sensors. This effect relies on the overlapping envelope of two signals with slightly detuned frequencies. In the application of on-chip optical waveguide resonant cavities with whispering gallery modes, due to the on-chip space limitations, the length of the resonant cavity is restricted, resulting in an increased free spectral range. In the case of a small Vernier effect detuning, the required large Vernier envelope period often exceeds the available wavelength range of the detection system. To address this issue, we propose a novel on-chip waveguide structure to optimize the detection range of the cascaded Vernier effect. The proposed spiral resonant cavity extends the cavity length to 7.50 m within a limited area. The free spectral width (27.46 MHz) is comparable in size to the resonant linewidth (9.41 MHz), shrinking the envelope free spectral width to 371.29 MHz, which greatly facilitates the reading of the Vernier effect. Finally, by connecting two resonant cavities with similar cavity lengths in series and utilizing the Vernier effect, temperature sensing was verified. The results show that compared with a single resonant cavity, the sensitivity was improved by a factor of 14.19. This achievement provides a new direction for the development of wide-range and high-sensitivity Vernier sensing technologies. Full article

In the purification process of zinc hydrometallurgy, the spectra of copper and cobalt seriously overlap in the whole band and are interfered with by the spectra of zinc and nickel, which seriously affects the detection results of copper and cobalt in zinc solutions. Aiming to address the problems of low resolution, serious overlap, and narrow characteristic wavelengths, a novel spectrophotometric method for the robust detection of trace copper and cobalt is proposed. First, the Haar, Db4, Coif3, and Sym3 wavelets are used to carry out the second-order continuous wavelet transform on the spectral signals of copper and cobalt, which improves the resolution of copper and cobalt and eliminates the background interference caused by matrix zinc signals and reagents. Then, the information ratio and separation degree are defined as optimization indexes, a multi-objective optimization model is established with the wavelet decomposition scale as a variable, and the non-inferior solution of multi-objective optimization is solved by the state transition algorithm. Finally, the optimal second-derivative spectra combined with the fine zero-crossing technique are used to establish calibration curves at zero-crossing points for the simultaneous detection of copper and cobalt. The experimental results show that the detection performance of the proposed method is far superior to the partial least squares and Kalman filtering methods. The RMSEPs of copper and cobalt are 0.098 and 0.063, the correlation coefficients are 0.9953 and 0.9971, and the average relative errors of copper and cobalt are 3.77% and 2.85%, making this method suitable for the simultaneous detection of trace copper and cobalt in high-concentration zinc solutions. Full article

In zinc smelting solution, because the concentration of zinc is too high, the spectral signals of trace copper are masked by the spectral signals of zinc, and their spectral signals overlap, which makes it difficult to detect the concentration of trace copper. To solve this problem, a spectrophotometric method based on integrated and partition modeling is proposed. Firstly, the derivative spectra based on continuous wavelet transform are used to preprocess the spectral signal and highlight the spectral peak of copper. Then, the interval partition modeling is used to select the optimal characteristic interval of copper according to the root mean square error of prediction, and the wavelength points of the absorbance matrix are selected by correlation-coefficient threshold to improve the sensitivity and linearity of copper ions. Finally, the partial least squares integrated modeling based on the Adaboost algorithm is established by using the selected wavelength to realize the concentration detection of trace copper in the zinc liquid. Comparing the proposed method with existing regression methods, the results showed that this method can not only reduce the complexity of wavelength screening, but can also ensure the stability of detection performance. The predicted root mean square error of copper was 0.0307, the correlation coefficient was 0.9978, and the average relative error of prediction was 3.14%, which effectively realized the detection of trace copper under the background of high-concentration zinc liquid. Full article

The accuracy of determining arsenic and lead using the optical technique Slurry–Total Reflection X-ray Fluorescence (Slurry-TXRF) was significantly enhanced through the application of a machine learning method, aimed at improving the ecological risk assessment of agricultural soils. The overlapping of the arsenic Kα signal at 10.55 keV with the lead Lα signal at 10.54 keV due to the relatively low resolution of TXRF could compromise the determination of lead. However, by applying a Partial Least Squares (PLS) machine learning algorithm, we mitigated interference variations, resulting in improved selectivity and accuracy. Specifically, the average percentage error was reduced from 15.6% to 9.4% for arsenic (RMSEP improved from 5.6 mg kg⁻¹ to 3.3 mg kg⁻¹) and from 18.9% to 6.8% for lead (RMSEP improved from 12.3 mg kg⁻¹ to 5.03 mg kg⁻¹) compared to the previous univariable model. This enhanced predictive accuracy, within the set of samples concentration range, is attributable to the efficiency of the multivariate calibration first-order advantage in quantifying the presence of interferents. The evaluation of X-ray fluorescence emission signals for 26 different synthetic calibration mixtures confirmed these improvements, overcoming spectral interferences. Additionally, the application of these models enabled the quantification of arsenic and lead in soils from a viticultural subregion of Chile, facilitating the estimation of ecological risk indices in a fast and reliable manner. The results indicate that the contamination level of these soils with arsenic and lead ranges from moderate to considerable. Full article

Adenosine triphosphate (ATP) has a high intracellular millimolar concentration (ca. 2.4 mM) throughout the phylogenetic spectrum of eukaryotes, archaea, and prokaryotes. In addition, the function of ATP as a hydrotrope in the prevention of protein aggregation and maintenance of protein solubilization is essential to cellular, tissue, and organ homeostasis. The ³¹P spectral modulus (PSM) is a measure of the health status of cell, tissue, and organ systems, as well as of ATP, and it is based on in vivo ³¹P nuclear magnetic resonance (³¹P NMR) spectra. The PSM is calculated by dividing the area of the ³¹P NMR integral curve representing the high-energy phosphates by that of the low-energy phosphates. Unlike the difficulties encountered in measuring organophosphates such as ATP or any other phosphorylated metabolites in a conventional ³¹P NMR spectrum or in processed tissue samples, in vivo PSM measurements are possible with NMR surface-coil technology. The PSM does not rely on the resolution of individual metabolite signals but uses the total area derived from each of the NMR integral curves of the above-described spectral regions. Calculation is based on a simple ratio of the high- and low-energy phosphate bands, which are conveniently arranged in the high- and low-field portions of the ³¹P NMR spectrum. In practice, there is essentially no signal overlap between these two regions, with the dividing point being ca. −3 δ. ATP is the principal contributor to the maintenance of an elevated PSM that is typically observed in healthy systems. The purpose of this study is to demonstrate that (1) in general, the higher the metabolic activity, the higher the ³¹P spectral modulus, and (2) the modulus calculation does not require highly resolved ³¹P spectral signals and thus can even be used with reduced signal-to-noise spectra such as those detected as a result of in vivo analyses or those that may be obtained during a clinical MRI examination. With increasing metabolic stress or maturation of metabolic disease in cells, tissues, or organ systems, the PSM index declines; alternatively, with decreasing stress or resolution of disease states, the PSM increases. The PSM can serve to monitor normal homeostasis as a diagnostic tool and may be used to monitor disease processes with and without interventional treatment. Full article

Low Earth orbit (LEO) satellite communication (SATCOM) networks have gradually been recognized as an efficient solution to enhance ground-based wireless networks. As one of the main characteristics of LEO SATCOM, the beam-edge area could be covered by multiple satellite nodes. In this case, user terminals (UTs) located at the beam-edge have the chance to connect one or more LEO satellites. To develop the diversity gain of multiple nodes in the overlapping area, we propose two high spectral efficiency cooperative transmission strategies, i.e., directly combining (DC) and selection combining (SC). In the DC scheme, signals arrived at the UT simultaneously could be combined into one enhanced signal. For downlink time division multiplexing, the SC scheme enables the UT to select the strongest signal path. Further, as there exists a significant channel gain difference of the beam-center and beam-edge areas, UTs in these two areas can be allocated in one resource block. In this case, we derive co-carriers based on DC and SC, respectively. To deeply analyze the novel methods, we study the ergodic sum-rate and outage probability while the outage diversity gain is further provided. Simulation results show that the co-carrier-based DC method has the ability to provide a higher ergodic sum-rate while the SC method performs better in terms of the outage probability. Full article

Various techniques have been employed to detect damage in civil engineering structures. Apart from the model-based approach, which demands the frequent updating of its corresponding finite element method (FEM)-built model, data-driven methods have gained prominence. Environmental and operational effects significantly affect damage detection due to the presence of damage-related trends in their analyses. Time-domain approaches such as autoregression and metrics such as the Mahalanobis squared distance have been utilized to mitigate these effects. In the realm of machine learning (ML) models, their effectiveness relies heavily on the type and quality of the extracted features, making this aspect a focal point of attention. The objective of this work is therefore to deploy and observe potential feature extraction approaches used as input in training fully data-driven damage detection machine learning models. The most damage-sensitive segment (MDSS) feature extraction technique, which potentially treats signals under multiple conditions, is also proposed and deployed. It identifies potential segments for each feature coefficient under a defined criterion. Therefore, 680 signals, each consisting of 8192 data points, are recorded using accelerometer sensors at the Los Alamos National Laboratory in the USA. The data are obtained from a three-story 3D building frame and are utilized in this research for a mainly data-driven damage detection task. Three approaches are implemented to replace four missing signals with the generated ones. In this paper, multiple fast Fourier and wavelet-transformed features are employed to evaluate their performance. Most importantly, a power spectral density (PSD)-based feature extraction approach that considers the maximum variability criterion to identify the most sensitive segments is developed and implemented. The performance of the MDSS selection technique, proposed in this work, surpasses that of all 18 trained neural networks (NN) and recurrent neural network (RNN) models, achieving more than 80% prediction accuracy on an unseen prediction dataset. It also significantly reduces the feature dimension. Furthermore, a sensitivity analysis is conducted on signal segmentation, overlapping, the treatment of a training dataset imbalance, and principal component analysis (PCA) implementation across various combinations of features. Binary and multiclass classification models are employed to primarily detect and additionally locate and identify the severity class of the damage. The collaborative approach of feature extraction and machine learning models effectively addresses the impact of environmental and operational effects (EOFs), suppressing their influences on the damage detection process. Full article

As the application of machine learning technology in predicting and optimizing material performance continues to grow, handling the electromagnetic data of magnetic materials, especially in removing unavoidable data noise and accurately extracting resonance peaks in the imaginary part of electromagnetic information, has become a significant challenge. These steps are crucial for revealing the deep electromagnetic behavior of materials and optimizing their performance. In response to this challenge, this study introduces an innovative approach—Genetic Algorithm-Optimized Variational Mode Decomposition for Signal Enhancement (GAO-VMD-SE). This method, through the Variational Mode Decomposition (VMD) technique optimized by genetic algorithms, not only effectively reduces noise in the data, thereby improving the Signal-to-Noise Ratio (SNR) and reducing the Mean Absolute Error (MAE), but also significantly enhances the hidden resonance peak information in complex permittivity and permeability data to achieve a comprehensive improvement in key performance indicators. Experimental results prove that this method surpasses traditional analysis techniques in key performance metrics such as the peak width ratio, peak overlap ratio, and the number of peaks. Especially in identifying characteristic peaks related to the Snoek limit, GAO-VMD-SE can effectively reveal the peak features hidden in complex data, thus providing important insights for evaluating the performance of materials at specific frequencies. Moreover, the effectiveness of this method in denoising not only enhances the quality and accuracy of material data analysis but also achieves a 1% to 10% enhancement in peak information extraction. This optimized data processing capability and versatility make GAO-VMD-SE not only suitable for evaluating the performance of magnetic materials but also show significant practical application value in processing spectral data and other time series signal data applications. Full article

We develop a 4 × 4-matrix model based on temporal coupled mode theory (TCMT) to elucidate the intricate energy exchange within a non-Hermitian, resonant photonic structure, based on the recently described infinity-loop micro-resonator (ILMR). We consider the structure to consist of four coupled resonant modes, with clockwise and counterclockwise propagating optical fields, the interplay between which gives rise to a rich spectral form with both overlapping and non-overlapping resonances within a single free spectral range (FSR). Our model clarifies the precise conditions for exceptional points (EPs) in this system by examining neighboring resonances over the device free spectral range (FSR). We find that the system is robust to the conditions for observing an EP, despite the presence of non-zero coupling of signals, or crosstalk, between the resonant modes. Full article

Lamb-wave-based structural health monitoring is widely acknowledged as a reliable method for damage identification, classification, localization and quantification. However, due to the complexity of Lamb wave signals, especially after interacting with structural components and defects, interpreting these waves and extracting useful information about the structure’s health is still a major challenge. Deep-learning-based strategy offers a great opportunity to address such challenges as the algorithm can operate directly on raw discrete time-domain signals. Unlike traditional methods, which often require careful feature engineering and preprocessing, deep learning can automatically extract relevant features from the raw data. This paper proposes an autoencoder based on a bidirectional long short-term memory network (Bi-LSTM) with maximal overlap discrete wavelet transform (MODWT). layer to detect the signal anomaly and determine the location of the damage in the composite structure. MODWT decomposes the signal into multiple levels of detail with different frequency resolution, capturing both temporal and spectral features simultaneously. Comparing with vanilla Bi-LSTM, this approach enables the model to greatly enhance its ability to detect and locate structural damage in structures, thereby increasing safety and efficiency. Full article