Search Results (3,115)

Prevailing rolling bearing fault diagnosis frameworks based on long short-term memory (LSTM) are susceptible to noise interference under industrial strong-noise working conditions, suffering from insufficient feature extraction capability and low diagnostic precision. To address these limitations, this paper proposes a fault diagnosis framework integrating deep learning with signal processing, which consists of probabilistic principal component analysis (PPCA) for noise suppression, the autoregressive (AR) model for discrete interference elimination, spectral kurtosis (SK) for fault feature enhancement, and LSTM-based intelligent classification. To improve the signal-to-noise ratio (SNR) of vibration signals, the proposed method first estimates and suppresses noise via PPCA, and then eliminates periodic discrete frequency interferences represented by gear meshing components using the AR model. Following interference suppression, the SK method is adopted to implement multi-scale resonant frequency band screening and envelope demodulation. Finally, the demodulated features are learned by the LSTM to realize intelligent fault diagnosis of rolling bearings. This novel approach not only improves fault diagnosis accuracy but also enhances the model interpretability with the aid of signal processing techniques. Experimental results on the Case Western Reserve University (CWRU) and industrial field datasets demonstrate that the proposed method achieves superior accuracy compared with state-of-the-art approaches under various SNR conditions. It effectively mitigates the accuracy degradation of deep learning diagnostic models in strong-noise environments, providing a reliable technical solution for the intelligent diagnosis of rolling bearings. Full article

The Paris Agreement aims to limit global warming to well below 2 °C while pursuing efforts to restrict it to 1.5 °C, as exceeding these thresholds may intensify climate-related risks. Understanding climate responses to an additional half-degree of warming remains critical for adaptation planning. Accordingly, the implications of an additional 0.5 °C warming from 1.5 °C to 2 °C over Türkiye were evaluated using 10 km resolution RegCM4.4 simulations driven by the MPI-ESM-MR and HadGEM2-ES models. Temperature- and precipitation-related indicators were analyzed, including mean temperature (T_mean), tropical nights (TN), discomfort index (DI), total precipitation (TP), the simple daily intensity index (SDII), extreme precipitation days (R90P), dry periods lasting at least five consecutive days (CDD5), and consecutive dry days (CDD). The results indicate that the additional 0.5 °C warming does not affect all climate indicators equally, with the strongest responses observed in temperature-related indicators. Temperature-related indicators, particularly TN and DI, show clear and consistent increases, indicating strong sensitivity to additional warming. By contrast, precipitation-related indicators display more heterogeneous and model-dependent responses, with RCM-HG generally projecting increases in precipitation and intensity, while RCM-MPI suggests decreases in certain regions and seasons. The signal-to-noise ratio (SNR) analysis further indicates greater robustness of annual temperature-related changes, whereas inter-model sign agreement varies among indices and seasons, with complete agreement for T_mean. The findings provide a scientific basis for regionally tailored adaptation planning in Türkiye and highlight the potential for reducing climate risks by limiting warming to lower levels. Full article

Exposure to atmospheric particulate matter (PM) is a major public-health concern, in part because PM can perturb cellular redox homeostasis. This study evaluates an in vitro A549/DCFH-DA approach using repeated fluorescence readouts to assess PM2.5-induced oxidative activity. Untreated and assay-specific controls were combined with zymosan and NIST Standard Reference Material^® 2584 suspended in PBS, and fluorescence was monitored at multiple readout times over a 15 min–6 h window. Method performance was characterized using the coefficient of variation (CV) and signal-to-noise ratio (SNR). A dedicated three-concentration SRM 2584 series (0.02, 0.05 and 0.10 mg mL⁻¹) further showed readout-dependent concentration behaviour: at 60 min the untreated-control-corrected mean response increased across the tested concentrations and followed an approximate descriptive linear trend (R² = 0.90), whereas earlier readouts were non-monotonic. Substrate-related effects were examined using paired PTFE and quartz filters. Among the eight matched PTFE–quartz pairs included in the regression analysis, zero-intercept fits showed slopes close to unity for both mass- and air-volume-normalized responses (0.90 and 0.99, respectively; R² ≈ 0.99), demonstrating strong proportional agreement within this comparison set; the limited number of pairs does not support universal substrate interchangeability. Application to chemically characterized field PM2.5 samples from an urban-background site and a high-altitude site showed that DCFH-DA fluorescence did not track PM mass alone and is interpreted in terms of exploratory associations with particle composition, rather than causal effects of individual constituents. Taken together, these findings support the use of the method-performance-characterized workflow for assessing oxidative responses to field-collected PM2.5 across multiple readout times and for investigating their associations with particle chemical characteristics. Full article

Automatic modulation classification (AMC) suffers from performance degradation under low signal-to-noise ratio (SNR) conditions, where modulation characteristics are affected by noise and signals belonging to the same modulation family exhibit similar feature representations. To address these challenges, this paper proposes a Transformer-based spatiotemporal fusion network that jointly exploits local spatial waveform characteristics and temporal dependency information while leveraging the global context modeling capability of the Transformer to integrate complementary multi-dimensional features. The proposed architecture improves feature representation capability under different SNR conditions. Experimental results demonstrate that the proposed method achieves improved classification performance compared with comparative approaches under different SNR conditions. In particular, it achieves an overall classification accuracy of 82.45% over the SNR range from $-10$ to 18 dB, and an average accuracy of 95.55% at SNRs of 2 dB and above, showing improved classification performance in the low-to-medium SNR transition region. Full article

Physiological signal emulators support the calibration, validation, and stress testing of cardiovascular monitoring devices. However, many existing systems generate electrocardiography (ECG) or photoplethysmography (PPG) independently and offer limited control over arrhythmia detection and ECG–PPG coupling. This study presents a programmable physiological signal emulator that integrates a unified event-driven ECG–PPG model with synchronized multichannel hardware. The model represents atrial pacing, atrioventricular conduction, and ventricular activation as separate functional blocks, enabling normal sinus rhythm, first-degree atrioventricular block, second-degree atrioventricular block Mobitz I, complete atrioventricular block, atrial tachycardia, and ventricular tachycardia. A Gaussian-based ECG is generated from the atrial and ventricular event sequences, while a multi-Gaussian PPG waveform is derived from ventricular activation using a beat-class-dependent electromechanical delay. The same processing architecture supports playback of recorded 12-lead clinical ECG data through an inverse lead transformation. The hardware uses an STM32F407VET6 microcontroller and MCP4921 digital-to-analog converters (DACs) to generate 10 synchronized analog outputs, comprising 09 ECG electrodes and 01 PPG channel. Validation covered physiological timing, analog-chain performance, and end-to-end signal reproduction. PR interval errors relative to a commercial electrocardiograph were 1.23 ms for normal sinus rhythm and 1.66 ms for first-degree atrioventricular block. The measured beat-to-beat PR increment during Mobitz I conduction was $40.02\pm 0.04$ ms for a programmed value of 40 ms. At commanded amplitudes of at least 800 mV, both output channels achieved absolute amplitude errors below 0.60%, total harmonic distortion below 1%, and signal-to-noise ratios (SNRs) above 30 dB. Inter-channel R-peak skew remained below the 2 ms sampling interval, and all monitored metrics varied by less than 1.5% during 60 min of continuous operation. Reproduction of a clinical 12-lead recording yielded per-lead $R^2$ values of 0.967–0.986 and a cycle-to-cycle correlation of 0.996. The emulator also reproduced amplitude-dependent bias in automated interval measurements and interpretation labels. These results demonstrate a low-cost, open-source platform for reproducible device calibration, algorithm stress testing, medical training, and physiological signal processing research. Full article

Unresolved or nearly unresolved infrared remote sensing small targets captured by lightweight imaging systems are often severely degraded by field-dependent blur and noise, leading to energy dispersion, reduced local visibility, and degraded interpretability. This work proposes a physics-informed spatially variant image restoration method for this target regime. Unlike conventional restoration methods based on a spatially invariant degradation assumption, the proposed framework explicitly incorporates a spatially variant point spread function (PSF) prior into a learning-based restoration pipeline to model field-dependent imaging degradation. On this basis, a deep Wiener-style restoration mechanism is introduced to jointly recover target energy and suppress noise, while a matched-filter-inspired PSF-correlation-guided soft attention module is designed to emphasize target likelihood regions and mitigate clutter amplification during restoration. In addition, a target-oriented optimization strategy is adopted to promote local target observability for weak small targets under complex infrared backgrounds. By combining physical imaging priors with adaptive deep restoration, the proposed method offers an interpretable solution for spatially variant infrared small-target recovery when accurate local PSFs are available. On a controlled synthetic benchmark with calibrated PSF priors, the proposed method achieves a mean local signal-to-noise ratio (SNR) gain of 12.56 dB. Full article

We present a dual-wavelength analysis of nitrogen-vacancy (NV) center quantum magnetometry enhanced by uniformly coated gold nanoparticles (AuNPs). An ensemble-averaged model is developed to analyze the AuNP-enhanced stimulated absorption at the 532 nm excitation wavelength and the spontaneous emission at 637 nm. The resulting detected optically magnetic resonance (ODMR) signal profiles are simulated for both enhancement mechanisms. We find that the enhanced stimulated absorption at 532 nm significantly amplifies the ODMR signal intensity without broadening the linewidth, thereby improving the signal-to-noise ratio (SNR) without sacrificing the magnetic field sensing resolution. Conversely, enhancing the 637 nm spontaneous emission increases the total photo emission rate but simultaneously broadens the ODMR detection linewidth due to the direct reduction in the effective spin coherence time of excited states by the enhanced spontaneous rate. The dual-wavelength analysis provides valuable design guidance for the development of low-cost spin-coated AuNP-enhanced NV-center quantum magnetometry technologies. Full article

Automatic modulation recognition (AMR) remains challenging under low-signal-to-noise ratio (SNR) conditions, where severe noise can obscure weak modulation-specific waveform patterns. To address this issue, this paper proposes an Adaptive Holistic Fusion Network (AHFN) for robust low-SNR modulation recognition. AHFN first employs a multi-resolution nonlinear fusion module composed of parallel KAN branches with different spline-grid resolutions to extract complementary waveform dynamics from standardized I/Q samples and temporal positional information. An adaptive soft-threshold denoising module then generates node- and channel-specific thresholds to suppress noise-sensitive responses while preserving discriminative modulation cues. Subsequently, a topology-aware multi-scale fusion network performs feature- and structure-adaptive message processing over a fixed temporal graph and combines multi-scale graph representations with Mamba-based long-range sequence modeling. Experiments on RML2016.10A and RML2016.10B show that AHFN consistently improves recognition accuracy over representative baseline methods from $-20$ dB to 0 dB, with gains ranging from 3.03% to 14.34%. These results demonstrate the effectiveness of multi-resolution nonlinear representation, adaptive denoising, and topology-aware local–global fusion for low-SNR AMR. Full article

Objective: This study aims to investigate whether combining automated tube voltage selection (ATVS) with P3T for contrast injection in coronary computed tomography angiography (CCTA) allows for patient-tailored scanning protocols. Specifically, we assessed whether this combination maintains equivalent image quality and adequate diagnostic image quality. Methods: This retrospective study included 378 patients who underwent CCTA using ATVS and P3T technologies. We divided the patients into three groups based on the scanning tube voltage: 70-, 80-, and 90-kV groups. Furthermore, each group was divided into sequence and spiral scanning subgroups based on the acquisition method. The CM injection volume and rate, volume CT dose index (CTDIvol), and dose–length product (DLP) were recorded. The CT value of the enhanced coronary artery, noise, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were calculated by artery segment. Image quality was evaluated using a four-point grading scale in a double-blind manner. Results: The SNR did not differ among the groups in any segments (p > 0.05). And the subjective image quality showed only minor differences among the groups. The body weight, which determines ATVS and P3T protocol algorithm, is positively correlated with the automatic tube voltage, CM injection volume, and effective radiation dose. Furthermore, when using the same tube voltage under the combined ATVS and P3T protocols, sequence scanning yielded a lower radiation dose than spiral scanning (p < 0.05). Conclusions: CCTA combining ATVS and P3T technology enables individualized scanning protocols while maintaining adequate diagnostic image quality. Full article

Millimeter-wave (mm-Wave) massive MIMO systems enable high data rates but face significant challenges due to the limited number of radio-frequency (RF) chains and imperfect channel state information (CSI). This paper proposes a deep learning-based beamforming (DLBF) framework that directly learns analog beamforming vectors under strict hardware constraints. Unlike conventional optimization methods, the proposed approach employs an unsupervised learning strategy to maximize spectral efficiency while satisfying constant modulus constraints. The network model is explicitly designed to be robust against imperfect CSI, hardware phase noise, and varying channel conditions. Simulation results demonstrate that the proposed DLBF framework significantly outperforms traditional hybrid beamforming methods in terms of spectral efficiency, particularly in low-quality channel estimation and low signal-to-noise ratio (SNR) scenarios. Full article

To mitigate the delay spread and inter-symbol interference (ISI) induced by optical scattering in underwater wireless optical communication (UWOC), this paper introduces long short-term memory (LSTM), a convolutional block attention module (CBAM), and residual connections into a convolutional neural network autoencoder (CNN-AE), and proposes a CNN-LSTM-AE-based end-to-end DC-biased optical orthogonal frequency division multiplexing (DCO-OFDM) system. In the proposed system, convolutional layers in the encoder serve to extract local features; CBAM adaptively weights salient features along the channel and spatial dimensions; LSTM layers model the temporal dependencies of signal sequences; and residual connections are incorporated to improve the learning capability for subtle signal features, thereby enhancing the robustness of the system against multipath channels. A symmetric structure is adopted at the receiver, ultimately enabling end-to-end signal recovery. Simulation results show that, under typical clear ocean and coastal ocean channel conditions, the proposed system outperforms end-to-end systems based on a fully connected autoencoder (FC-AE) and a CNN-AE at different modulation orders, i.e., different numbers of bits per symbol. For example, under strong scattering conditions in the coastal ocean channel, when the number of bits per symbol is 2 and the bit error rate (BER) is ${10}^{-3}$, the proposed system achieves signal-to-noise ratio (SNR) gains of approximately 3.33 dB and 1.82 dB over the two baselines. In terms of block error rate (BLER), SNR gains of approximately 4.52 dB and 2.19 dB are achieved over the two comparison systems, which substantiates the superior end-to-end transmission reliability of the proposed system. Full article

This work presents a modular, sensor-aware, end-to-end simulation framework for optical camera communication (OCC). The framework combines modified Monte Carlo ray tracing with pixel-level camera modeling, including optical blur, shutter timing, noise, digitization, and modulation-aware signal processing. It produces physically consistent synthetic images from simulated optical propagation and enables communication performance to be estimated through image-domain signal-to-noise ratio (SNR) and theoretical bit-error-rate (BER) calculations. The simulator is applied to intra-satellite optical links as a representative case study involving confined three-dimensional geometries, line-of-sight (LOS) visibility, partial occlusion, and rolling-shutter image formation. Experimental validation under LOS conditions shows good agreement in the dominant spatial-temporal characteristics of rolling-shutter imagery, with a structural similarity index measure (SSIM) of approximately $0.80$. Simulated SNR values range from approximately $20.5$ to $22.6$ dB, compared with measured values between $20.8$ and $24.4$ dB. No bit errors are observed in the experimental sequences, corresponding to finite-length BER upper confidence bounds, while the BER values derived from the simulated images are theoretical estimates obtained from the image-domain SNR under ideal receiver assumptions. Additional simulations using a detailed 12U CubeSat model demonstrate the capability to assess emitter–receiver placement and partial geometric occlusion, including cases in which the visible portion of the source remains sufficient for bitstream decoding. By jointly modeling optical propagation, camera acquisition, and communication metrics, the proposed framework supports early-stage OCC system analysis and configuration trade-offs without requiring immediate hardware implementation. The approach is applicable to other OCC scenarios in which spatial image formation and sensor dynamics influence communication performance. Full article

(1) Background: Evoked potentials (EPs) are an elegant, non-invasive, and reliable technique for assessing the functional integrity of neural pathways. They are, however, often limited by the difficulty and time needed to consistently distinguish signal from noise. (2) Methods: We have recently proposed a novel technique based on spectral domain evoked-to-background ratio (EBR) that enables fast data acquisition with online feedback about actual signal quality utilizing state of the art analog-to-digital conversion. Furthermore, we have developed a novel model-based signal-to-noise management concept allowing for suppression of biological and technical interference (swallowing, stimulation artifacts, electropolarization, and powerline potentials) and for online assessment of signal-to-noise ratio (SNR) for EPs. In this work, we experimentally confirmed this concept in ten healthy volunteers by investigating cortical EPs and high-frequency oscillations (HFOs) following median nerve stimulation. (3) Results: Both mathematical model and human data demonstrate that spectral target-band EBR governs the progress in SNR with increasing sweep count. For cortical EPs, SNR exceeded 10 dB beyond 90 averages in all participants. An SNR > 20 dB documented excellent signal quality and reproducibility. For HFOs, the SNR shifted to lower values by 12 dB, displaying pronounced individual variation, however, with smaller variation of HFO-band background activity (1.9 vs. 7.6 dB between the 25% and 75% percentile). Thus, individual HFO responses are more important for actual signal extraction compared to background activity. In subjects displaying a high HFO amplitude, reproducibility was confirmed for less than 1000 sweeps. (4) Conclusions: The present investigations confirm that individual EBR is the major factor defining SNR. Background noise can be reduced to a negligible level. Online assessment of background activity will allow for the most accurate moment-to-moment visualization of raw signal quality. This will facilitate termination of the data acquisition and may be based on quantified signal quality rather than predefined sweep count. Full article

Rolling element bearings are critical components in rotating machinery, yet assessing and predicting their reliability under heavy industrial noise remains challenging. Existing methods suffer from three major limitations: (1) single-channel signal processing and single-scale indicators lack robustness against non-stationary noise; (2) classical multi-channel decomposition methods, such as multivariate variational mode decomposition (MVMD), rely on empirical parameter tuning, which frequently leads to over- or under-decomposition; and (3) monolithic deep architectures and homogeneous ensemble models suffer from prediction drift and generalization bottlenecks during long-term temporal extrapolation. To address these issues, this paper introduces an automated framework that combines adaptive multi-channel signal purification with a heterogeneous stacking ensemble (HeteroStack-LR). Unlike conventional MVMD pipelines that fix $[K,\alpha ]$ empirically, the Sequoia Optimization Algorithm (SOA) autonomously determines the globally optimal configuration, achieving a mean SNR of $-2.08\mathrm{dB}$—a $1.88$ to $2.50\mathrm{dB}$ improvement over standard VMD/MVMD baselines—along with up to a $37.1\%$ reduction in computation time. Rather than relying on conventional single-metric intrinsic mode function (IMF) selection, we construct a multi-domain hybrid index integrating the Fault Correlation Factor, energy ratio, and refined composite multiscale dispersion entropy (RCMDE) to robustly identify noise-resistant components, thereby enhancing denoising quality by $22.4\%$ to $32.2\%$ over single-scale criteria. Furthermore, contrasting with linear PCA-based reduction, Diffusive Topology Neighbor Embedding (D-TNE) effectively preserves the nonlinear manifold structure of degradation trajectories in a low-dimensional space. Finally, a heterogeneous stacked ensemble featuring an out-of-fold (OOF) leakage-prevention strategy and a logistic regression meta-learner is designed to suppress prediction drift while avoiding the over-parameterization typical of deep architectures. Experimental results across four bearing datasets demonstrate that HeteroStack-LR achieves a minimal MAE of $0.063$ with a variance of ≤±0.002, outperforming state-of-the-art deep architectures (such as TCN, CNN-LSTM, BiLSTM-Attention, and Transformer) as well as classical baselines (Bi-LSTM, CNN, and LSSVM). Ablation studies confirm that removing SOA and MVMD degrades MAE by $12.7\%$ and $19.0\%$, respectively, validating that the framework’s strength stems not from any isolated module, but from the end-to-end synergistic integration of signal purification and reliability prediction. Full article

Objectives: The objective of this study was to evaluate the impact of virtual monoenergetic image (VMI) reconstructions derived from photon-counting computed tomography (PCCT) on the assessment of carotid arteries, with a focus on optimizing keV selection based on plaque composition. Methods: This retrospective study included 111 patients (mean age 80 ± 7.5 years; 64 men; 47 women) with carotid sclerosis who underwent PCCT between April 2022 and February 2023. One lesion was analyzed per patient, each containing both calcified and non-calcified components. Quantitative measurements were performed in calcified plaque across energy levels from 40 to 120 keV and comprised attenuation, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), corrected image noise (CIN) and artifact index (AIX). Two radiologists independently rated image quality, artifacts, and diagnostic assessability of both components using five-point scales. Results: Attenuation and CNR were highest at 40 to 50 keV, where CIN and AIX were also greatest. SNR followed a U-shaped course, lowest at 90 keV and highest at 110 to 120 keV, where intraluminal attenuation was too low for reliable luminal delineation. Reader ratings were highest at 40 to 50 keV for non-calcified components and at 70 to 80 keV for calcified plaque, with good interobserver agreement throughout (κ 0.74 to 0.92). After correction for multiple testing, adjacent energy levels were frequently indistinguishable. Conclusions: Optimal PCCT VMI energy levels depend on plaque composition. The findings support implementing standardized protocols with automatic dual-range reconstructions (40–50 keV and 70–80 keV) to enable efficient, individualized carotid plaque assessment in clinical practice. Full article