Search Results (8,055)

Traditional stochastic models that rely solely on elevation angle and signal-to-noise ratio (SNR) struggle to adapt to the precision differences in BDS-3 multi-frequency observations, making it difficult to support high-precision positioning in complex scenarios. To address the issues that existing models fail to adapt to the differentiated error characteristics of BDS-3 five-frequency observations, lack adaptive modeling capabilities, and cannot support high-precision five-frequency PPP-AR in complex environments, this study proposes a Transformer-based adaptive stochastic model for five-frequency precise point positioning ambiguity resolution (PPP-AR). Satellite elevation angle, the SNR, and position dilution of precision (PDOP) are used as inputs, while observation noise labels derived from pseudorange post-fit residuals support supervised training. The predicted noise standard deviations are introduced into the observation covariance matrix for adaptive weighting. To distinguish generalization from memorization, the model was evaluated using observations from different days. On day of year (DOY) 244, the Transformer model achieved a mean post-convergence three-dimensional root-mean-square (3D RMS) error of 0.029 m, outperforming the comparison models, which yielded errors of 0.0037–0.0039 m. It also reduced the mean convergence time to 20.33 min, compared with 21.22–22.17 min for the comparison models. At the HARB station, the Transformer and elevation angle models both converged in 7 min, only one 30 s epoch faster than the multilayer perceptron (MLP) and SNR models. At the GAMG station, the ambiguity fix rate reached 31.25%, exceeding those of the elevation angle and SNR models by 18.28 and 15.40 percentage points, respectively. The results for DOY 245 and DOY 246 further support short-term transferability, but not long-term temporal generalization. Overall, the proposed model improves aggregate positioning accuracy and convergence efficiency while maintaining competitive ambiguity fixing performance. Full article

In this study, an electrochemical sensor based on a graphitized carboxylated multi-walled carbon nanotube/polyaniline (G-COOH-MWCNTs/PANI) composite was developed for the highly sensitive detection of Pb²⁺ in seawater. A G-COOH-MWCNTs/PANI composite dispersion was prepared via a solution blending method and subsequently drop-cast onto a glassy carbon electrode (GCE) to fabricate the modified electrode. Differential pulse anodic stripping voltammetry (DPASV) was employed for the quantitative determination of Pb²⁺. The morphology of the composite was characterized by scanning electron microscopy (SEM), while the electrochemical behavior of the modified electrode was investigated using cyclic voltammetry (CV) and differential pulse voltammetry (DPV). Critical experimental parameters, including the type and pH of the supporting electrolyte, deposition potential, deposition time, and loading amount of the composite film, were systematically optimized. In addition, the optimal concentration ratio of G-COOH-MWCNTs to PANI was determined using an orthogonal experimental design. Under the optimized experimental conditions, the proposed sensor exhibited a linear response toward Pb²⁺ over the concentration range of 25–220 μg/L, with the regression equation Ip = 2.757C + 4.316 (R² = 0.997). The limit of detection (LOD), calculated at a signal-to-noise ratio (S/N) of 3, was 0.0337 μg/L. The sensor also demonstrated excellent reproducibility (relative standard deviation, RSD = 1.7%), satisfactory anti-interference capability, and good long-term stability, retaining 94.1% of its initial response after 35 days of storage. Spike recovery experiments using real seawater samples yielded recoveries ranging from 96.73% to 99.73%, with RSD values below 3%, indicating excellent accuracy and precision in complex seawater matrices. These results demonstrate that the proposed sensor enables accurate determination of Pb²⁺ in seawater without complicated sample pretreatment and exhibits considerable potential for applications in marine environmental monitoring of heavy metal contamination. Full article

This paper proposes a retrodirective cross-eye jamming (RCJ) recognition and angle estimation algorithm based on directional modulation and multiple signal classification (DM-MUSIC). Because RCJ intercepts the radar transmit waveform to produce a monopulse angle measurement result that deviates from the true target, the traditional phased-array (TPA) radar that radiates identical transmit waveforms across the spatial domain fails to recognize RCJ by calculating the normalized cross-correlation function (NCCF). In DM-MUSIC, the monopulse angle measurement result induced by RCJ is derived, and the transmit phase matrix synthesis criterion in digital array radar is then formulated to minimize the NCCFs between the transmit waveform for the detection direction and RCJ-induced monopulse angle deception directions by utilizing the flexibility of DM in the waveform domain. Sequential quadratic programming combined with the limited-memory Broyden–Fletcher–Goldfarb–Shanno algorithm is employed to calculate the transmit phase matrix. The RCJ system is then recognized by comparing the NCCFs of the received jamming signal associated with the DM transmit waveform in the detection direction and RCJ-induced monopulse angle deception directions. Finally, the synthesized DM transmit waveform and forward–backward spatial smoothing are used to decorrelate the jamming signals, and the RCJ angle is estimated by MUSIC. The simulation results demonstrate that DM-MUSIC achieves high recognition probability and accurate RCJ angle estimation. The recognition probability reaches $98.1\%$ at a jamming-to-noise ratio (JNR) of $5\mathrm{dB}$, with an amplitude gain of 1 and a phase shift of $179°$. At a JNR of $20\mathrm{dB}$, with an amplitude gain of 0.97 and a phase shift of $179°$, the Root Mean Square Error of angle estimation result is reduced from $0.13°$ for FBSS-MUSIC to $0.063°$ for DM-MUSIC. Full article

Electromagnetic scattering from plunging breaking waves generates strong sea-surface radar returns that degrade radar-based maritime surveillance and target detection performance. This study develops a deep learning framework for automatic classification of simulated plunging-breaker scattering conditions using high-range-resolution profile (HRRP) data. The electromagnetic scattering data are generated using a physics-based Capillary Wave Modification Facet Scattering Model (CWMFSM) combined with ray-tracing techniques. Eight simulated plunging-breaker scattering conditions are constructed by combining two wind speeds, 7 m/s and 10 m/s, with four temporal conditions, Δt1, Δt10, Δt14, and Δt16. A total of 8000 HRRP samples are generated, with 100 normalized range-cell features extracted from each sample. Two deep learning classifiers, an artificial neural network (ANN) and a one-dimensional residual convolutional neural network (1D ResNet CNN), are comparatively evaluated. The ANN achieves an overall classification accuracy of 96%, compared with 91% for the 1D ResNet CNN under the simulated dataset and adopted model configurations. Robustness analysis under controlled additive white Gaussian noise (AWGN) conditions further shows that classification performance decreases as the signal-to-noise ratio is reduced, while noise-augmented training improves the robustness of both classifiers. Overall, the results demonstrate the feasibility of HRRP-based deep learning for distinguishing simulated plunging-breaker scattering conditions from sea-surface radar returns, providing a basis for further investigation of sea-clutter characterization and maritime radar applications. Full article

The vehicle scanning method (VSM) enables indirect structural damage detection using onboard sensors, but its application to shield tunnels remains challenging due to the high stiffness-to-mass ratio of tunnels and the presence of segmental joints. This study investigates whether segmental joints produce false-positive damage indications and evaluates a modified signal extraction strategy. A refined vehicle–tunnel coupled finite element model incorporating shear and rotational springs at segment interfaces is established. Instead of applying Variational Mode Decomposition (VMD) before filtering, the proposed strategy applies low-pass filtering prior to VMD to extract a purer driving component (DC). The DC is quantitatively compared with the previously identified optimal component (D2) in terms of energy amplitude, background suppression, and noise robustness. Results show that (1) segmental joints produce no spurious wavelet energy peaks above the detection threshold in undamaged tunnels; (2) the DC substantially outperforms D2, achieving a signal-to-background ratio of 83.1 (vs. 18.8 for D2) and a 12% higher detection rate for 5% damage under SNR = 10 dB noise; and (3) liner and support damage produce single sharp wavelet energy peaks, while joint damage produces a broadened peak spanning two adjacent elements, suggesting a potential signature for distinguishing joint-related damage from element-related damage in the cases examined. These findings suggest that the method may be applicable to jointed shield tunnels within the range of conditions considered in this numerical study, without requiring joint-specific calibration. The modified DC extraction offers a simpler and more sensitive damage indicator for practical inspection. Full article

This article presents the results of the classification of measurement errors of electromyography signals caused by interference and noise. It is shown that interference and measurement noise mainly occur during the registration of electromyography signals and significantly reduce the measurement accuracy. To classify measurement errors, the article analyzes and identifies the features of interference and measurement noise. This analysis is based on the mechanism of occurrence and the physical nature of interference and noise, as well as their spectral, frequency, and statistical characteristics. Initially, to analyze and identify the features of interference and noise, the article examines the characteristics of electromyography signals recorded during the movement of a human limb. It is shown that the informative components of the motor units of electromyography signals during limb movement are variable. The analysis results revealed that measurement noise and interference are slowly variable and have a Gaussian distribution. The frequency composition of low- and high-frequency interference and measurement noise is distinct from that of the measured electromyography signals. To systematize the results of the analysis, a classification scheme for measuring errors in electromyography signals is presented. It is recommended to use the measurement error classification scheme when solving the problem of signal filtering, where it is necessary to identify informative components for measuring and evaluating the movement of human limbs using electromyography signals. Furthermore, a comparative evaluation of band-pass filters of different orders was conducted to eliminate interference. A lower-order band-pass filter, even when implemented unidirectionally, improves the signal-to-noise ratio and highlights informative components. Full article

Gas-insulated switchgear (GIS) is an important component of power transmission systems. Accurate partial discharge (PD) pattern recognition is a key requirement for identifying internal insulation defects within the equipment. However, ultra-high-frequency (UHF) PD pulse sequences produced by different insulation defects usually contain complex nonlinear temporal structures and multi-scale periodic variations. These coupled characteristics are difficult to describe adequately via a single feature-mapping strategy. Thus, this paper proposes a temporal-frequency dual-branch stochastic configuration network (TF-SCN), which consists of two heterogeneous hidden-layer branches, for GIS PD pattern recognition. Specifically, in the temporal branch, the model uses a non-periodic, nonlinear activation function similar to that used in a conventional SCN to capture the nonlinear temporal characteristics. The frequency-sensitive branch introduces paired sine–cosine harmonic nodes with shared random projection parameters to capture frequency-sensitive features. The hidden outputs of the two branches are concatenated into a joint temporal-harmonic feature space, and the output weights are solved under the residual inequality constraints for GIS PD classification. To verify the superiority of the proposed model, comparative experiments are conducted on a dataset containing four PD patterns collected from the GIS PD experimental platform. Several baseline models, including 1DCNN, BPNN, SVM, KELM, RVFL, and SCN, are selected for performance comparison. The results show that, compared to 1DCNN, BPNN, SVM, KELM, RVFL, and SCN, TF-SCN effectively extracts distinguishable features in both the time and frequency domains, thereby achieving the best overall performance. Furthermore, its recognition performance remains consistently superior even on noisy data with signal-to-noise ratios ranging from 50 dB to 20 dB. By integrating highly sensitive UHF sensors with the proposed TF-SCN, this study presents a robust, AI-enhanced intelligent sensing and fault diagnosis system for continuous condition monitoring of power equipment. Full article

In recent years, the use of UAVs has become increasingly widespread, and the public safety risks posed by unauthorized UAV flights have become increasingly prominent, creating an urgent need for effective detection and identification of UAV targets. However, such targets are small in size, have low contrast, and exhibit an extremely low signal-to-noise ratio; conventional detection methods generally suffer from insufficient feature discrimination, missed detections, and false alarms in complex backgrounds. To address these challenges, this paper proposes a Frequency-Guided Cross-scale Refinement Network (FGCR-Net). Based on an encoder-decoder architecture, this network achieves end-to-end collaborative optimization through cross-layer feature fusion, side-channel prediction refinement, and frequency-domain background suppression. First, a multi-path selective cross-layer fusion module (SCFM) is designed. This module employs coordinated modeling via both channel and spatial paths, supplemented by adaptive weighting with learnable coefficients, to perform differentiated selective fusion of the encoder’s fine-grained features and the decoder’s semantic features, thereby bridging the semantic gap at jump connections; Second, we designed a Cross-Scale Adaptive Fusion Enhancement Attention Module (CAFEM), which cascades multi-receptive-field hollow convolutions, strip pooling, and a bidirectional semantic guidance mechanism to perform cross-scale refinement on the side outputs of each decoder layer, thereby alleviating the issues of blurred boundaries and false alarms caused by inconsistent quality of multi-scale prediction maps and insufficient cross-layer consistency; finally, we design a Frequency-Guided Semantic Enhancement Module (FGSEM), which uses the Fast Fourier Transform (FFT) to decouple encoder features into the frequency domain. By leveraging low-frequency energy to predict the background confidence map and applying spatially selective suppression to high-frequency components, this module distinguishes, from a frequency-domain perspective, the high-frequency responses of complex backgrounds and targets that are highly similar in the spatial domain. Experiments on MSDS-UAV, a self-built multi-scenario UAV dataset for small targets, demonstrate that our method consistently outperforms existing state-of-the-art methods across multiple performance metrics, with Pixel Accuracy, Mean Intersection over Union, and Probability of Detection reaching 92.76%, 70.91%, and 92.69%, respectively; Compared to the baseline model, these three metrics improved by 1.90, 3.20, and 3.76 percentage points, respectively, fully validating the effectiveness and superiority of the proposed method. Full article

In the present circumstances, it is exceedingly hard for people to monitor their blood sugar levels on a regular basis. Checking the blood glucose levels of diabetic individuals is often an essential part of managing diabetes. Now, this means repeatedly pricking your finger and bleeding. Non-invasive (NI) detection methods are anticipated to have several benefits, including the elimination of discomfort, avoidance of sharp items and biohazardous chemicals, the possibility of more frequent testing, and, therefore, better regulation of glucose levels. Infrared technology has become one of the most important technologies for the development of the NI self-monitoring of blood glucose (NI-SMBG). One good thing about this approach is that it does not need any chemicals and can employ fiber optic parts. So, only insulators come into direct contact with the skin. Also, the spectrometer may be made without any moving parts, which makes it strong. For this method to be effective, the spectral signature of glucose must be uniquely identifiable from all other chemical constituents in the human body, and this glucose-specific data must be obtained with a sufficiently high signal-to-noise ratio to facilitate reliable differentiation between glucose-dependent signals and those generated by other matrix components. In this paper, we have addressed the issue of infrared signature analysis for blood glucose, which has been done in the infrared region of the electromagnetic spectrum. Firstly, the analysis is carried out for the glucose molecule only. Later, looking at the presence of numerous other analyses in whole blood, tissues, skin, etc., for in vivo measurement of blood glucose, a set of wavelengths is identified on which in vivo measurements can be done with minimal interference from other body fluid analyses. Absorption of spectroscopic information collected on these wavelengths, along with a suitable calibration model, can be a step ahead for in vivo NI glucose measurement. The main innovative features of the present study are non-invasive glucose sensing, Patient-friendly and continuous monitoring opportunity, Progress towards wearable and real-time diagnostics, Clinical and Social Relevance and Contribution to Research. The paper investigates a non-invasive infrared-based methodology for glucose estimation, focusing on the spectral response characteristics of glucose in biological tissue. While the complexity of tissue spectroscopy involves potential interference from other biomolecules, the present work emphasizes the feasibility of glucose detection without invasive blood extraction, rather than conducting a dedicated interference-analysis study. Full article

Partial discharge (PD) detection is a crucial means for the early warning of incipient insulation defects in arresters. However, under strong electromagnetic interference and background noise, PD signals are prone to distortion, making it difficult to accurately determine the pulse onset front and thus severely degrading the accuracy of time-delay estimation. To address the difficulty of time-delay estimation under low signal-to-noise ratio (SNR) and multi-channel aliasing conditions, this paper proposes a method for arrester PD detection and high-precision time-delay estimation based on joint denoising and improved cross-correlation. First, a joint denoising strategy that integrates singular value decomposition (SVD), variational mode decomposition adaptively optimized by the sparrow search algorithm (SSA-VMD), and the Teager energy operator (TEO) is constructed. This strategy suppresses white noise and periodic narrowband interference while effectively extracting the oscillatory onset characteristics of PD pulses. Second, an enhanced time-delay estimation method based on HB-weighted generalized quadratic cross-correlation is introduced. By employing the dual mechanisms of HB frequency-domain weighting and amplitude weighting to sharpen the correlation peak, the estimation robustness under low SNR is improved. Simulation results show that the proposed method attains an accuracy of 99.9911%, significantly outperforming conventional cross-correlation, PHAT-SCOT, and NLMS methods. Finally, experiments are conducted on a needle-plate discharge platform. In multiple comparative experiments with different spatial distance differences (ranging from <30 cm to >50 cm), the maximum relative error is kept within 0.6%, verifying the reliability and accuracy of the proposed algorithm under controlled laboratory conditions. This method can provide a new approach for online monitoring and accurate fault location of arresters in power systems. Full article

Edge-intelligent sensing systems demand ultra-low-power wireless connectivity to sustainably support massive sensor deployments. Ambient backscatter communication (AmBC) meets this demand by harvesting and modulating existing radio-frequency (RF) signals, eliminating dedicated carriers. However, conventional on–off keying (OOK) demodulation in AmBC is highly susceptible to noise, while existing frequency-shift keying (FSK) alternatives relying on first-order derivatives perform poorly at low signal-to-noise ratios (SNRs), compromising the reliability of edge sensing data. In this paper, we propose a signal detection method that exploits high-order derivatives to enhance the demodulation of FSK-modulated ambient backscatter signals. By analytically evaluating the power of interference and noise after high-order differentiation, we reveal that the interference power is minimized at the second order while the noise power increases monotonically with the derivative order, leading to a favorable trade-off in typical AmBC regimes where the modulation frequency is much smaller than the sampling rate and comparable to the ambient signal bandwidth. We then design a phase-preserving frequency amplitude comparison detection (FACD) rule to recover the embedded information. Simulation results show that the proposed second-order derivative-based FACD achieves the lowest bit error rate among all compared schemes, particularly at low SNR. Full article

Remote electroencephalography (EEG) systems require compact representations that remain useful when communication noise corrupts the transmitted message. We evaluated whether task-oriented residual refinement of filter bank common spatial pattern (FBCSP) features compressed by principal component analysis (PCA) improves four-class motor imagery (MI) decoding without increasing the transmitted dimension. The BNCI2014-001 dataset was assessed in nine subjects using bidirectional subject-specific cross-session evaluation. All methods transmitted $K\in \{16,32,64\}$ power-normalized real values through additive white Gaussian noise (AWGN) at seven signal-to-noise ratios (SNRs) and a noise-free reference. Balanced accuracy was averaged over 20 paired noise realizations per noisy condition, and paired subject-level differences were evaluated with exact joint sign-flip max-$\left|t\right|$ inference. At $K=32$, the proposed method achieved 41.10%, 49.46%, and 56.15% balanced accuracy at $-10$, $-5$, and 0 dB, compared with 38.63%, 46.28%, and 53.26% for conventional FBCSP–PCA transmission. Ten of the 24 semantic-versus-conventional comparisons were significant after family-wise max-$\left|t\right|$ correction, including all nine comparisons at $-10$, $-5$, and 0 dB. Receiver-only controls closely reproduced conventional performance at all three message dimensions, whereas alternative loss weights, uniform-SNR training and selection, and removal of the 0 dB/noise-free reference-condition guard retained positive low-SNR gains. Overall, baseline-preserving task-oriented refinement improved MI decision robustness under severe AWGN without increasing the number of transmitted values. Full article

Over the past several decades, numerous methods have been developed to improve the signal-to-noise ratio, perceptual quality, and intelligibility of speech. In practice, no single method is universally optimal, as each category exhibits distinct strengths and limitations under specific acoustic conditions. The proposed taxonomy classifies speech enhancement methods according to their dominant signal modeling and enhancement mechanism, ranging from the classical signal processing approaches to modern machine learning techniques and advanced hybrid frameworks. The mathematical formulation illustrates the theoretical foundations of the different approaches, providing a clear understanding of their underlying principles and the evolution of performance across successive generations of speech enhancement methods. The comparative analysis demonstrates that statistical and subspace-based approaches offer low operational complexity but show limitations under highly nonstationary acoustic conditions. Adaptive filtering, transform-domain, and speech model-based methods exploit temporal, spectral, and speech production characteristics to achieve improved noise suppression, while perceptual methods improve subjective listening quality by incorporating psychoacoustic principles, thereby providing a more natural and intelligible listening experience. Machine learning-based techniques achieve excellent performance; however, they incur higher computational complexity and substantial training requirements. More recently, hybrid approaches have integrated complementary techniques from multiple paradigms, achieving robust performance under adverse acoustic environments. Overall, this review provides a unified perspective on speech enhancement techniques, identifies their strengths, and highlights emerging research opportunities for the development of robust, efficient, and intelligent speech enhancement systems. Full article

Emotional prosody is a key cue for affective communication, yet everyday speech often occurs in background noise. It remains unclear whether masking only reduces Mandarin emotional prosody recognition accuracy or also alters emotion-confusion structure. We examined this issue with speech-shaped noise (SSN) and multi-talker babble (MTB). Thirty-six native Mandarin-speaking young adults identified semantically neutral sentences conveying happiness, sadness, anger, and surprise in silence and under SSN and MTB at 0 dB and −4 dB signal-to-noise ratios. Recognition accuracy was analyzed using trial-level generalized linear mixed-effects models, whereas confusion structure was assessed using confusion matrices, sadness–high-arousal boundary errors, and Net Flow, an index of directed target–response asymmetry. Accuracy declined under masking relative to silence, with the poorest performance in the MTB −4 dB condition. Across emotions, estimated accuracy was lowest for surprise and comparatively high for sadness. The MTB −4 dB condition increased sadness–high-arousal boundary errors; Net Flow characterized a source-like pattern for surprise and a more sink-like pattern for sadness, particularly in this condition. These findings suggest that background masking affects Mandarin emotional prosody perception not only by reducing recognition accuracy but also by altering directed response-error structure. Full article

Distributed Acoustic Sensing (DAS) has revolutionized ambient noise interferometry, yet the reliability of retrieved empirical Green’s functions (EGFs) remains highly sensitive to non-diffuse noise fields and anthropogenic transients. In complex or noisy environments, global metrics frequently misclassify signal convergence due to out-of-band environmental noise, scattered coda, and non-stationary directional transients. Using both 30 min and 4 h passive recordings, this study presents a physics-guided quality control framework for evaluating interferometric diagnostics. Specifically, this study employs the signal-to-trailing noise ratio (STRN), signal-to-precursory noise ratio (SPNR), and spectral signal-to-noise ratio (SSNR) within the surface-wave arrival window $t=x/v$. Global phase metrics remain heavily suppressed $(\overline{x}\approx 0.05)$ regardless of stacking duration, whereas the surface-windowed SPNR exhibits an extraordinary statistical shift (p < 0.001), reaching 0.870 ± 0.106 at 30 min and 0.967 ± 0.034 at 4 h. We implement one-to-one correspondence between surface-windowed indicator values and fundamental-mode Rayleigh wave dispersion sharpness. In severely noise-contaminated segments, unwindowed global metrics yield distorted dispersion ridges with severe energy leakage, but the surface-wave window results in an increase in the SPNR above 0.70, fully reconstructing continuous dispersion trajectories (250–500 m/s). Grounded in these results, we formalize a standardized four-step quality control workflow (from velocity windowing to metric calculation, automation, and data output) and outline tailored adaptation guidelines for urban, mountainous, and industrial DAS deployments. This framework provides an automated, physically sound protocol that eliminates manual selection, optimizes computational efficiency, and ensures reliable dispersion extraction for passive DAS imaging. Full article