Search Results (3,592)

Continuous in situ marine holographic observation generates data volumes that challenge onboard storage and transmission. Parallel phase-shifting digital holography (PPSDH) is especially sensitive to compression because phase retrieval depends on consistent four-channel demodulation. We present a training-free spatiotemporal compression framework for sparse-particle marine PPSDH sequences based on background–residual decomposition and a shared four-channel processing path. The background is coded once per temporal window by a discrete wavelet transform (DWT) followed by principal component analysis (PCA), and the dynamic residual is decorrelated by temporal principal component analysis before quantization and entropy coding. The framework is evaluated on three primary 64-frame marine PPSDH sequences using a common reconstruction-and-evaluation pipeline with wrapped-phase root-mean-square error (PhaseRMSE) as the primary metric and amplitude peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) as secondary references; expanded supplementary checks are also reported for nine additional selected 64-frame groups spanning sparse to transitional occupancy. On the primary sequence and within the high-fidelity achieved-rate overlap with the JPEG Pleno anchor codec INTERFERE, the proposed framework reduces PhaseRMSE by about 3.3-fold to 3.4-fold while increasing amplitude PSNR by about 11 dB and preserving amplitude SSIM above 0.99997. Lower-bitrate sweeps further quantify the rate–fidelity trade-off rather than claiming universal low-rate superiority. These results support BG–Res spatiotemporal coding as a practical phase-fidelity-oriented option for the tested sparse-to-transitional marine PPSDH conditions; extension to dense scenes, broader marine conditions, and downstream biological tasks requires separate validation. Full article

Today, the rapid progress in non-invasive light–matter interaction analysis is transforming the landscape of biomedical and life sciences driven by low-intensity light detection technologies. As the complexity of photonic applications continues to grow, the importance of single-photon detection techniques becomes pivotal. Among them, Time-Correlated Single-Photon Counting (TCSPC) has become the gold standard for precise, time-resolved reconstruction of rapid and faint optical signals. However, TCSPC has long been constrained by pile-up distortion, which worsens with increasing acquisition speed, typically limiting it to 5% of the excitation frequency. To overcome the operational constraints of conventional implementations, a novel TCSPC acquisition methodology has been introduced, independent of photodetector dead time, excitation intensity, and prior optical signal knowledge, still enabling distortion-free reconstruction of the measured light profiles. In this context, the development of single-photon detectors with short dead time and low timing jitter becomes crucial. This work presents a single-photon detection module based on a Silicon Photomultiplier, which delivers 750 ps FWHM output pulses with a 33.5 ps RMS IRF. Its performance is showcased through fluorescence measurements employing the constraint-free TCSPC methodology, achieving a photon count rate up to 166% of the excitation frequency with a minimal lifetime estimation error of just −1.46%. Full article

The frequency identification technology of differential frequency-hopping (DFH) signals is the key to decoding at the receiver. Aiming to improve frequency identification accuracy under low signal-to-noise ratio (SNR) conditions, a method based on super-resolution image reconstruction technology is proposed for the instantaneous frequency identification of DFH signals. Firstly, the time–frequency image of the DFH signal is obtained using short-time Fourier transform (STFT). Then, a U-Net neural network with an attention mechanism is designed to suppress noise and interference components in the time–frequency image and reconstruct a super-resolution time–frequency image. Furthermore, based on the correlation between adjacent hop signals in accordance with the frequency transfer function, a ResNet neural network is designed to identify frequencies from the super-resolution time–frequency image of DFH signals. Simulation results demonstrate that the designed U-Net neural network can effectively suppress noise and interference components and reconstruct high-quality super-resolution time–frequency images. Comparative experimental results show that the proposed ResNet neural network can significantly improve the identification accuracy of DFH signals under low-SNR conditions. Specifically, the identification accuracy can reach more than 90% when the low SNR is not less than −10 dB, which is a significant improvement compared with other methods. Ablation experiment results indicate that the attention mechanism can improve model performance by 3.74%. Full article

To tackle issues such as high data volatility, temporal dependencies, complex feature extraction, and low parameter tuning efficiency in wind power forecasting, this paper proposes a dual-optimization model for short-term wind power forecasting based on RIME-VMD and MSSA-CNN-BiLSTM. First, the Rime Optimization Algorithm (RIME) is employed to adaptively refine the key parameters of Variational Mode Decomposition (VMD), decomposing wind power into intrinsic modal functions (IMFs) of different frequencies to reduce signal complexity. Second, by integrating the local feature extraction capabilities of Convolutional Neural Network (CNN) with the bidirectional temporal dependency capture capabilities of Bidirectional Long Short-Term Memory Network (BiLSTM), a hybrid deep learning architecture is constructed. Additionally, the Multi-strategy Sparrow Search Algorithm (MSSA) is introduced to perform global hyperparameter optimization, thereby addressing the shortcomings of manual parameter tuning. The final power forecast is obtained through the prediction of each IMF component and the reconstruction of the results. Experiments demonstrate that the presented prediction model attains a root mean square error (RMSE) of 0.0333, a mean absolute error (MAE) of 0.0265, and a coefficient of determination (R²) of 0.9901. Seasonal validation shows that the model’s R² exceeds 0.983 in all four seasons—spring, summer, autumn, and winter—demonstrating good generalization capability. Relative to the BiLSTM model, its RMSE and MAE are reduced by 50.52% and 46.57%, respectively, while R² increases by 3.36%, effectively addressing the issue of insufficient accuracy in short-term wind power forecasting. Full article

Limbal stem cell (LSC) transplantation is an important treatment for limbal stem cell deficiency (LSCD), but low efficacy in maintaining LSC stemness during in vitro expansion greatly affects its wider application. The primary contributing factors include a low proportion of stem cells and the lack of a stable, supportive microenvironment over prolonged culture. Rabbit corneal tissues preserved under deep cryogenic conditions for more than six months retain viable limbal stem cells (LSCs), and primary LSCs isolated from these tissues exhibit robust stem cell characteristics. It is noteworthy that the NLRP3/Caspase-1/IL-1β signaling axis was activated in corneal epithelial cells, and outer limbal layers preserved for one or two years. Based on these findings, a combined strategy integrating deep cryopreservation with IL-1β induction was established for the processing of limbal tissues. The combined cryogenic and IL-1β preconditioning yielded primary LSCs with maintained p63⁺ cell proportions, a reduction in K3⁺ differentiated cells from approximately 80% to 60%, and a 6.25-fold increase in colony-forming efficiency. In addition, an increased proportion of cells in the G2/M phase and enhanced proliferative capacity were observed. The enriched LSC population also exhibited improved stratified epithelial reconstruction potential. These findings identify an effective strategy for preserving and enriching LSCs from limbal tissue, providing a practical and efficient approach for LSC preparation prior to transplantation. Further in vivo studies will be important to validate the functional performance of these cells in ocular surface reconstruction. Full article

Spaceborne interrupted frequency-modulated continuous-wave synthetic aperture radar (iFMCW SAR) employs a single antenna on a single spacecraft operating in a time-division transmit/receive mode, effectively avoiding mutual interference between transmitted and received signals and thereby overturning the design paradigm of spaceborne FMCW SAR systems. However, the periodic switching of the antenna between transmit and receive states results in periodic data gaps along the azimuth direction in the echo signal, leading to spurious artifacts in the reconstructed images and severely degrading image quality. Sparse signal recovery techniques based on compressive sensing models have been shown to effectively suppress such spurious targets. Nevertheless, the generalized orthogonal matching pursuit (GOMP) algorithm requires prior knowledge of the signal sparsity, a condition that is often impractical in real-world scenarios. To address this limitation, this paper investigates the variation pattern of the residual norm with respect to sparsity in the GOMP algorithm and proposes a modified GOMP algorithm based on binary search. This approach enables rapid and accurate determination of the true sparsity level without prior knowledge, thereby achieving sparsity-adaptive reconstruction with GOMP and significantly enhancing the imaging quality of iFMCW SAR. Simulation experiments involving both point and scene targets are provided to demonstrate the effectiveness and potential of the proposed algorithms for practical applications. Full article

Compressed sensing (CS) applied in the ambiguity domain offers an effective approach for recovering time–frequency distributions (TFDs) of non-stationary signals from sparse representations. Existing methods predominantly rely on the Wigner–Ville distribution (WVD) as the initial representation due to its simplicity and high auto-term concentration. However, the WVD performs poorly for signals with higher-order frequency-modulated (FM) components and is highly sensitive to interference and noise, which then propagate into the reconstruction. This paper introduces the systematic use of the L-Wigner distribution (LWD) as the initial representation for CS-based reconstruction, providing front-end adaptability to signal characteristics. By generating a controllable family of TFDs ranging from the spectrogram to cross-term-free polynomial WVDs, the LWD enables effective suppression of interference and noise while simultaneously enhancing auto-term localization for nonlinear FM components. The proposed LWD-based reconstruction framework is evaluated against the conventional WVD-based method using several state-of-the-art reconstruction algorithms, whose parameters are jointly optimized through a multi-objective meta-heuristic framework to ensure a fair comparison. Experiments on noisy synthetic signals and real-world gravitational-wave data demonstrate consistent performance gains. On synthetic signals, the proposed approach reduces the average reconstruction error index by up to 36.6%, improves the ${\ell }_1$-reconstruction error by up to 75.8%, and achieves complete elimination of cross-term energy. In addition, robustness analysis under additive white Gaussian noise shows up to a 75% improvement in ${\ell }_1$ performance. For real gravitational-wave data, the method reduces the mean reconstruction index by up to 5.8% while maintaining auto-term preservation and eliminating cross-term artifacts. These results establish the LWD-based initialization as an effective and general strategy for TFD reconstruction in complex signal environments. Full article

Accurate prediction of the Remaining Useful Life (RUL) of special gas cylinders is critical for industrial safety management. However, the nonlinear, strongly coupled degradation behaviors of these cylinders, combined with non-stationary and high-noise monitoring data, limit the performance of single deep learning models. Traditional hyperparameter tuning and signal processing methods often fail to meet the required prediction accuracy. To address these challenges, this study proposes a hybrid SSA–PSO–ResNet–LSTM–Attention framework for RUL prediction of special gas cylinders. The framework first applies Singular Spectrum Analysis (SSA) to decompose and reconstruct the 12-dimensional multi-source sensor signals, effectively suppressing noise while extracting core degradation trends. Subsequently, a ResNet–LSTM–Attention collaborative model is constructed, where ResNet ensures stable spatial feature propagation, LSTM captures long- and short-term temporal dependencies, and a multi-head attention mechanism emphasizes critical time steps associated with abrupt degradation. Furthermore, a Particle Swarm Optimization (PSO) algorithm is employed to globally optimize key hyperparameters, including the number of convolutional kernels, LSTM hidden units, and learning rate, mitigating the subjectivity of manual tuning. Experimental validation is conducted on 1000 real monitoring samples from 100 composite material gas cylinders, with a cylinder ID-based 7:1:2 train–validation–test split and stratified sampling covering four operating conditions. PSO optimizes hyperparameters using the validation set RMSE as the fitness function, and the test set is exclusively used for final performance evaluation. All results are reported as the mean ± standard deviation from grouped 5-fold cross-validation on the cylinder-wise partition. The proposed model achieves a test RMSE of 71.55, MAE of 50.63, and R² of 0.9584, representing a 34.2% and 30.2% reduction in RMSE and MAE, respectively, compared with the second-best CNN-LSTM model, and significantly outperforming SVR, MLP, and other benchmark models. Ablation studies confirm the positive synergistic effect of each component, with the removal of either the attention mechanism or the ResNet module causing substantial performance degradation. By employing physically calibrated RUL labels and a balanced multi-condition dataset, the proposed framework achieves high predictive accuracy and good potential for industrial application, providing an effective solution for RUL prediction of special gas cylinders and similar high-pressure vessels, with potential applications in intelligent maintenance of complex industrial equipment. Full article

The possibility of securing textual image data sharing exponentially strengthens when it harnesses the potential of cryptography as well as deep learning methods. A review of the existing literature showcases some interesting and productive initiatives; however, they are noted with issues, viz., increased reconstruction error, weak generation of pseudorandom keys, static threshold-based validation, etc. All these issues lead to suboptimal data integrity as well as confidentiality, which is a leading gap in research on neural optimised-based solutions. Therefore, the proposed system introduces an innovative Twister Pseudo Random and Barzilai–Borwein Gradient Autoencoder Neural Network (TPR-BBGAN) for secure textual image data sharing. The model introduces various novel operations, viz., feature extraction using fuzzy batch-normalised preprocessing, key extraction using the Barzilai–Borwein method, an autoencoder, and Mersenne Twister. The TPR-BBGAN determines the optimal threshold dynamically, contributing to a reduction in the reconstruction error while convergence performance is boosted. The experimental outcome shows that the TPR-BBGAN achieves a 12–20% enhancement in data confidentiality, a 6–17% enhancement in data integrity, a 30–46% reduction in bit-error rate, and a 6–20% increase in the Peak Signal-to-Noise Ratio (PSNR) in contrast to existing models. Full article

The accurate reconstruction and real-time prediction of transient three-dimensional flow fields in hydraulic turbines are critical for ensuring operational stability under renewable energy-driven variable-load conditions, yet conventional computational fluid dynamics (CFD) approaches remain too computationally expensive for digital twin applications. This paper proposes a hybrid framework that integrates Proper Orthogonal Decomposition (POD) with Long Short-Term Memory (LSTM) networks to reconstruct and predict the unsteady flow field within the draft tube of a Francis turbine using only four sparse wall-mounted pressure sensors. The methodology begins with high-fidelity Large Eddy Simulation (LES) to establish a comprehensive flow field database under Part Load (PL), Best Efficiency Point (BEP), and High Load (HL) conditions. POD is subsequently applied to extract dominant coherent structures and their temporal coefficients, achieving a low-dimensional representation of the high-dimensional flow field. A comparative analysis between standard POD and weighted POD reveals that under the PL condition characterized by a strong double-helical vortex rope, the weighting effect is significant—standard POD captures 90% of the total energy with the first 2 modes, while weighted POD requires up to 8 modes to reach the same threshold. Under the BEP and HL conditions, the energy distributions of the two methods are nearly identical, yet weighted POD still yields cleaner spatial modes with sharper vortex boundaries and fewer spurious wall-region vortices. An LSTM network is then trained to establish a mapping between time-series signals from the four sensors and the POD temporal coefficients. The results demonstrate that LSTM prediction performance is governed by the spatial correlation between each mode and the sensor locations rather than by temporal regularity. Modes that project strongly onto the sensor locations—PL Modes 1–2 (R² = 0.85 and 0.513), BEP Mode 1 (R² = 0.96), and HL Mode 1 (R² = 0.92)—are reliably predictable, while PL Mode 3 and HL Mode 2, despite their regular temporal oscillations, yield strongly negative R² values (−3.366 and −186.6) because their spatial structures are concentrated away from the wall. With a condition-adaptive strategy predicting only sensor-correlated, energetic modes, the reconstructed pressure fields achieve mean L2 relative errors of 17.01% (PL), 7.17% (BEP), and 12.91% (HL). Because the mean flow dominates total pressure energy (86.66–98.07%), the effective absolute error is substantially lower. The proposed POD-LSTM framework successfully bridges the gap between high-fidelity CFD and real-time monitoring, enabling full-field flow state estimation from sparse sensor measurements without the computational expense of online simulations. This capability is particularly valuable for digital twin applications in hydraulic turbines operating under rapidly varying renewable energy conditions. Full article

This study presents a data-driven analysis of electric powertrain energy flow and traction battery behavior in a modern battery electric vehicle based on real-world on-board diagnostic (OBD) measurements. Time-resolved signals acquired during an urban trip by a Renault 5 E-Tech Electric were processed to reconstruct instantaneous energy exchange between the traction system and the battery, identify distinct operating regimes, and derive physically interpretable empirical models of selected drivetrain relationships. The analysis focused on the traction power, battery current, battery voltage, state of charge, accelerator pedal position, and cell voltage imbalance. The recorded data were decomposed into propulsion, regenerative, and auxiliary-load-dominated operating regimes, which improved the interpretability of the measured signals and the quality of the regression-based models. A second-order model was used to describe the relationship between traction power and accelerator pedal position, while a linear current-voltage model provided a locally accurate approximation of battery electrical behavior. In addition, the dependence of the cell voltage imbalance on the battery current was analyzed as a diagnostic indicator of load-dependent battery response. The results show that auxiliary loads, especially cabin and battery heating under winter conditions, introduce a significant baseline power demand that affects the apparent drivetrain response. Within the analyzed single-trip dataset, the recorded battery signals showed a low cell-voltage imbalance and a consistent local current–voltage trend over the observed operating range. These findings should be interpreted as preliminary and vehicle-specific, since they were obtained from one short winter urban trip and from a restricted set of OBD-accessible signals. Although the study is limited to a single vehicle and a single short trip, it demonstrates that accessible real-world OBD data can support physically interpretable, exploratory analysis of electric powertrain operation and battery response under practical measurement constraints. Full article

This paper addresses the tracking control problem for a class of uncertain strict-feedback nonlinear systems with input delay under communication constraints. The main difficulty is that the input delay degrades tracking performance, while full-state event-triggered transmission provides only intermittent state measurements, which are not directly compatible with the recursive backstepping design. To overcome this difficulty, an adaptive full-state event-triggered backstepping control scheme is developed. First, a Padé approximation is used to transform the delayed-input system into an augmented delay-free model. Then, an improved continuous-state estimator is introduced to reconstruct smooth surrogate state signals from the event-triggered measurements, thereby preserving the implementability of the recursive backstepping design. Based on the reconstructed states, an adaptive controller and an error-dependent event-triggering mechanism are designed to achieve practical tracking with reduced state transmissions. It is shown that all closed-loop signals remain bounded, the tracking error converges to an adjustable compact neighborhood of the origin, and Zeno behavior is excluded. Comparative simulation results further show that the proposed scheme reduces the triggering frequency and estimator-side computational burden compared with the high-order estimator-based scheme considered in the simulations, while maintaining satisfactory practical tracking performance. Full article

The article develops a hybrid multimodal neural network for the automatic prevention of personal digital rights violations, focusing on improving security through anomaly detection and ensuring data confidentiality. The main aim is to integrate several innovative methods, such as federated learning, gating, latent competitive learning, and a variational autoencoder, to improve violation detection accuracy. The key contribution is the development of a training mixture that combines a probabilistic anomaly detector and an autoencoder reconstruction signal, which allows for effective detection of typical incidents and hidden anomalies. The experimental evaluation results showed high-performance indicators, with ROC-AUC at 0.96 and accuracy at 0.94, confirming the system’s effectiveness on anonymized data. The results obtained have a significant practical contribution, as they can be integrated into national information security systems, including SOC and forensic reports, which will ensure a higher level of personal data protection and reduce privacy breach risks. The scope of the proposed system simultaneously covers cybersecurity, personal data protection, national security, SOC systems, and forensic analysis. Full article

While the standard 12-lead ECG is vital for cardiovascular diagnosis, its reliance on clinical settings hinders daily use. Wearable few-lead devices offer a practical alternative, yet this convenience comes at the cost of diagnostic capability due to reduced lead coverage. To bridge this informational gap and enhance single-lead ECG diagnostic performance, we propose a feature-reconstruction-based classification method for single-lead ECGs. It leverages a pre-trained 12-lead ECG model to extract representative features and guide the feature learning process for single-lead signals. A CNN–Transformer-based multi-scale feature extraction module is introduced for robust ECG feature extraction, followed by a transformer encoder-based reconstruction module to align single-lead features with more discriminative 12-lead representations. A cross-attention based feature fusion module subsequently integrates the reconstructed and original single-lead features to enhance classification performance. By focusing on feature reconstruction rather than signal reconstruction, our method effectively avoids the performance degradation typically caused by signal reconstruction errors and inter-lead redundancy, leading to superior classification outcomes. Evaluation on two public datasets demonstrates that our method enhances feature discriminability and improves single-lead ECG classification performance, confirming its robustness and practical potential. Full article

Objectives: Given that the structure-activity relationship between sturgeon chondroitin sulfate (S-CS) and the alleviation of osteoarthritis (OA) remains unclear, we characterized the structure of S-CS and explored the relationship between its structure and its effect in alleviating OA. Methods: Chondroitin sulfate was extracted from sturgeon cartilage by alcohol precipitation. Its structure was thoroughly characterized using infrared spectroscopy, pre-column derivatization, high-performance liquid chromatography with PMP (PMP-HPLC), nuclear magnetic resonance spectroscopy (NMR), and other techniques. A rat OA model was established to explore the mechanism underlying its alleviation of OA. In addition, 16S rRNA sequencing was performed to investigate the role of gut microbiota. Results: S-CS was identified as a sulfated polysaccharide with an average molecular weight of 68.81 kDa and a GlcUA-to-GalN molar ratio of approximately 1:1. NMR analysis confirmed its characteristic 6-/4-sulfation patterns. Oral administration of S-CS at 100 mg/kg/d significantly alleviated joint damage by inhibiting the NF-κB and p38 MAPK signaling pathways. Specifically, S-CS decreased the levels of p65 and p38 by 18.94% and 52.40% (p < 0.05), respectively, and decreased TNF-α concentration. Moreover, 16S rRNA sequencing showed that S-CS enhanced the diversity and richness of gut microbiota and reconstructed the microbial community structure. Conclusions: S-CS may be an effective supplement for OA. Full article