Search Results (6,634)

Optical coherence tomography (OCT) acquisitions often reduce lateral sampling density to shorten scan time and suppress motion artifacts, but this strategy degrades the signal-to-noise ratio and obscures fine retinal microstructures. To recover these details without hardware modifications, we propose MGFormer, a lightweight Transformer for OCT super-resolution (SR) that integrates a multi-granularity attention mechanism with tensor distillation. A feature-enhancing convolution first sharpens edges; stacked multi-granularity attention blocks then fuse coarse-to-fine context, while a row-wise top-k operator retains the most informative tokens and preserves their positional order. We trained and evaluated MGFormer on B-scans from the Duke SD-OCT dataset at $2\times$, $4\times$, and $8\times$ scaling factors. Relative to seven recent CNN- and Transformer-based SR models, MGFormer achieves the highest quantitative fidelity; at $4\times$ it reaches 34.39 dB PSNR and 0.8399 SSIM, surpassing SwinIR by +0.52 dB and +0.026 SSIM, and reduces LPIPS by 21.4%. Compared with the same backbone without tensor distillation, FLOPs drop from 289G to 233G (−19.4%), and per-B-scan latency at $4\times$ falls from 166.43 ms to 98.17 ms (−41.01%); the model size remains compact (105.68 MB). A blinded reader study shows higher scores for boundary sharpness (4.2 ± 0.3), pathology discernibility (4.1 ± 0.3), and diagnostic confidence (4.3 ± 0.2), exceeding SwinIR by 0.3–0.5 points. These results suggest that MGFormer can provide fast, high-fidelity OCT SR suitable for routine clinical workflows. Full article

Given the complexity of near-surface soil moisture retrieval, a single machine learning algorithm often struggles to capture the intricate relationships among multiple features, resulting in limited generalization and robustness. To address this issue, this study proposes a multi-satellite fusion GNSS-IR soil moisture retrieval method based on heterogeneous ensemble machine learning models. Specifically, two heterogeneous ensemble learning strategies (Bagging and Stacking) are combined with three base learners, Back Propagation Neural Network (BPNN), Random Forest (RF), and Support Vector Machine (SVM), to construct eight ensemble GNSS-IR soil moisture retrieval models. The models are validated using data from GNSS stations P039, P041, and P043 within the Plate Boundary Observatory (PBO) network. Their retrieval performance is compared against that of individual machine learning models and a deep learning model (Multilayer Perceptron, MLP), enabling an optimized selection of algorithms and model architectures. Results show that the Stacking-based models significantly outperform those based on Bagging in terms of retrieval accuracy. Among them, the Stacking (BPNN-RF-SVM) model achieves the highest performance across all three stations, with R of 0.903, 0.904, and 0.917, respectively. These represent improvements of at least 2.2%, 2.8%, and 2.1% over the best-performing base models. Therefore, the Stacking (BPNN-RF-SVM) model is identified as the optimal retrieval model. This work aims to contribute to the development of high-accuracy, real-time monitoring methods for near-surface soil moisture. Full article

Control point detection is a critical initial step in camera calibration. For checkerboard corner points, detection is based on inferences about local gradients in the image. Infrared (IR) imaging, however, poses challenges due to its low resolution and low signal-to-noise ratio, hindering the identification of clear local features. This study proposes a physics-informed neural network (PINN) based on the YOLO target detection model to detect checkerboard corner points in infrared images, aiming to enhance the calibration accuracy of infrared thermal cameras. This method first optimizes the YOLO model used for corner detection based on the idea of enhancing image gradient information extraction and then incorporates camera physical information into the training process so that the model can learn the intrinsic constraints between corner coordinates. Camera physical information is applied to the loss calculation process during training, avoiding the impact of label errors on the model and further improving detection accuracy. Compared with the baselines, the proposed method reduces the root mean square error (RMSE) by at least 30% on average across five test sets, indicating that the PINN-based corner detection method can effectively handle low-quality infrared images and achieve more accurate camera calibration. Full article

As contemporary society and the global economy become increasingly dependent on satellite-based systems, the need for reliable and resilient positioning, navigation, and timing (PNT) services has never been more critical. This study investigates the impact of the geomagnetic storm that occurred in May 2024 on the performance of global navigation satellite system (GNSS) low-cost permanent stations. The research evaluates the influence of ionospheric disturbances on both positioning performance and raw GNSS observations. Two days were analyzed: 8 May 2024 (DOY 129), representing quiet ionospheric conditions, and 11 May 2024 (DOY 132), coinciding with the peak of the geomagnetic storm. Precise Point Positioning (PPP) and static relative positioning techniques were applied to data from a low-cost GNSS station (DYVA), supported by comparative analysis using a nearby geodetic-grade station (TRDS00NOR). The results showed that while RMS positioning errors remained relatively stable over 24 h, the maximum errors increased significantly during the storm, with the 3D positioning error nearly doubling on DOY 132. Short-term analysis revealed even larger disturbances, particularly in the vertical component, which reached up to 3.39 m. Relative positioning analysis confirmed the vulnerability of single-frequency (L1) solutions to ionospheric disturbances, whereas dual-frequency (L1+L2) configurations substantially mitigated errors, highlighting the effectiveness of ionosphere-free combinations during storm events. In the second phase, raw GNSS observation quality was assessed using detrended GPS L1 carrier-phase residuals and signal strength metrics. The analysis revealed increased phase instability and signal degradation on DOY 132, with visible cycle slips occurring between epochs 19 and 21. Furthermore, the average signal-to-noise ratio (SNR) decreased by approximately 13% for satellites in the northwest sky sector, and a 5% rise in total cycle slips was recorded compared with the quiet day. These indicators confirm the elevated measurement noise and signal disruption associated with geomagnetic activity. These findings provide a quantitative assessment of low-cost GNSS receiver performance under geomagnetic storm conditions. This study emphasizes their utility for densifying GNSS infrastructure, particularly in regions lacking access to geodetic-grade equipment, while also outlining the challenges posed by space weather. Full article

The detection of faint, fast-moving objects such as space debris, in optical data is a major challenge due to their low signal-to-background ratio and short visibility time. This work addresses this issue by implementing the Stack-CNN algorithm, originally designed for offline analysis, on an FPGA-based platform to enable real-time triggering capabilities in constrained space hardware environments. The Stack-CNN combines a stacking method to enhance the signal-to-noise ratio of moving objects across multiple frames with a lightweight convolutional neural network optimized for embedded inference. The FPGA implementation was developed using a Xilinx Zynq Ultrascale+ platform and achieves low-latency, power-efficient inference compatible with CubeSat systems. Performance was evaluated using both a physics-based simulation framework and data acquired during outdoor experimental campaigns. The trigger maintains high detection efficiency for 10 cm-class targets up to 30–40 km distance and reliably detects real satellite tracks with signal levels as low as 1% above background. These results validate the feasibility of on-board real-time debris detection using embedded AI, and demonstrate the robustness of the algorithm under realistic operational conditions. The study was conducted in the context of a broader technology demonstration project, called DISCARD, aimed at increasing space situational awareness capabilities on small platforms. Full article

In response to the problem where neural network models fail to fully learn signal sample features due to an insufficient number of signal samples, leading to a decrease in the model’s ability to recognize signal modulation methods, a few-shot signal modulation mode recognition method based on reinforcement metric meta-learning (RMML) is proposed. This approach, grounded in meta-learning techniques, employs transfer learning to building a feature extraction network that effectively extracts the data features under few-shot conditions. Building on this, by integrating the measurement of features of similar samples and the differences between features of different classes of samples, the metric network’s target loss function is optimized, thereby improving the network’s ability to distinguish between features of different modulation methods. The experimental results demonstrate that this method exhibits a good performance in processing new class signals that have not been previously trained. Under the condition of 5-way 5-shot, when the signal-to-noise ratio (SNR) is 0 dB, this method can achieve an average recognition accuracy of 91.8%, which is 2.8% higher than that of the best-performing baseline method, whereas when the SNR is 18 dB, the model’s average recognition accuracy significantly improves to 98.5%. Full article

Persistent luminescence nanoparticles (PLNPs) represent a unique class of optical materials. They possess the ability to absorb and store energy from external excitation sources and emit light persistently once excitation terminates. Because of this distinctive property, PLNPs have attracted considerable attention in various areas. Especially in recent years, PLNPs have revealed marked benefits and extensive application potential in fields such as biological detection, imaging, targeted delivery, as well as integrated diagnosis and treatment. Not only do they potently attenuate autofluorescence interference arising from biological tissues, but they also demonstrate superior signal-to-noise ratio and sensitivity in in vivo imaging scenarios. Therefore, regarding the current research, this paper firstly introduces the classification, synthesis methods, and luminescence mechanism of the materials. Subsequently, the research progress of PLNPs in biological detection and imaging and medical treatment in recent years is reviewed. The challenges faced by materials in biomedical applications and the outlook of future development trends are further discussed, which delivers an innovative thought pattern for developing and designing new PLNPs to cater to more practical requirements. Full article

Next-generation communication systems demand extreme spectral efficiency to handle ever-increasing data traffic. The combination of faster-than-Nyquist (FTN) signaling and reconfigurable intelligent surfaces (RISs) presents a promising solution to meet this demand. However, the aggressive time compression inherent to FTN signaling introduces severe and highly non-linear inter-symbol interference (ISI). This complex distortion is challenging for conventional linear equalizers and even for recurrent neural network (RNN)-based detectors, which can struggle to model long-range dependencies within the signal sequence. To overcome this limitation, this paper proposes a novel signal detection framework based on the transformer model. By leveraging its core multi-head self-attention mechanism, the transformer globally analyzes the entire received signal sequence at once. This enables it to effectively model and reverse complex ISI patterns by identifying the most significant interfering symbols, regardless of their position, leading to superior signal recovery. The simulation results validate the outstanding performance of the proposed approach. To achieve a target bit error rate (BER) of ${10}^{-4}$, the transformer-based detector shows a significant signal-to-noise ratio (SNR) gain of approximately 1.5 dB over a Bi-LSTM detector over 4 dB compared to the conventional FTN-RIS system, while maintaining a high spectral efficiency of nearly 2 bps/s/Hz. Full article

To enhance the accuracy and precision of fault diagnosis and location for the collector lines in wind farms under complex operating conditions, an intelligent combined method based on CNN-LSTM and ICEEMDAN-PE-improved wavelet threshold denoising is proposed. A wind power plant model is established using the PSCADV46/EMTDC software. In response to the issue of indistinct fault current signal characteristics under complex fault conditions, a hybrid fault diagnosis model is constructed using CNN-LSTM. The convolutional neural network is utilized to extract the local time-frequency features of the current signals, while the long short-term memory network is employed to capture the dynamic time series patterns of faults. Combined with the improved phase-mode transformation, various types of faults are intelligently classified, effectively resolving the problem of fault feature extraction and achieving a fault diagnosis accuracy rate of 96.5%. To resolve the problem of small fault current amplitudes, low fault traveling wave amplitudes, and difficulty in accurate location due to noise interference in actual wind farms with high-resistance grounding faults, a combined denoising algorithm based on ICEEMDAN-PE-improved wavelet threshold is proposed. This algorithm, through the collaborative optimization of modal decomposition and entropy threshold, significantly improves the signal-to-noise ratio and reduces the root mean square error under simulated conditions with injected Gaussian white noise, stabilizing the fault location error within 0.5%. Extensive simulation results demonstrate that the fault diagnosis and location method proposed in this paper can effectively meet engineering requirements and provide reliable technical support for the intelligent operation and maintenance system of a wind farm. Full article

To address the challenges of narrow peak characteristics and low signal-to-noise ratio (SNR) detection in Laser-Induced Breakdown Spectroscopy (LIBS), in this paper, we combine the Sparse Bayesian Learning–Baseline Correction (SBL-BC) algorithm with residual skewness monitoring to propose a spectral estimation method tailored for LIBS. In LIBS spectra, discrete peaks are susceptible to baseline fluctuations and noise, while the Gaussian dictionary modeling and fixed convergence criterion of the existing SBL-BC lead to the inaccurate characterization of narrow peaks and high computational complexity. To overcome these limitations, we introduce a residual skewness dynamic tracking mechanism to mitigate residual negative skewness accumulation caused by positivity constraints under high noise levels, preventing traditional convergence criterion failure. Simultaneously, by eliminating the dictionary matrix and directly modeling the spectral peak vector, we transform matrix operations into vector computations, better aligning with LIBS’s narrow peak features and high-channel-count processing requirements. Through simulated and real spectral experiments, the results demonstrate that this method outperforms the SBL-BC algorithm in terms of spectral peak fitting accuracy, computational speed, and convergence performance across various SNRs. It effectively separates spectral peaks, baseline, and noise, providing a reliable approach for both quantitative and qualitative analysis of LIBS spectra. Full article

In low-light optical imaging, the scarcity of incident photons and the inherent limitations of imaging sensors lead to challenges such as low signal-to-noise ratio, limited dynamic range, and degraded contrast, severely compromising image quality and optical information integrity. To address these challenges, we propose a novel low-light image enhancement technique, LightenResDiff, which combines a residual diffusion model with the discrete wavelet transform. The core innovation of LightenResDiff lies in it accurately restoring the low-frequency components of an image through the residual diffusion model, effectively capturing and reconstructing its fundamental structure, contours, and global features. Additionally, the dual cross-coefficients recovery module (DCRM) is designed to process high-frequency components, enhancing fine details and local contrast. Moreover, the perturbation compensation module (PCM) mitigates noise sources specific to low-light optical environments, such as dark current noise and readout noise, significantly improving overall image fidelity. Experimental results across four widely-used benchmark datasets demonstrate that LightenResDiff outperforms existing methods both qualitatively and quantitatively, surpassing the current state-of-the-art techniques. Full article

As a convolutional operation in a space domain, Gaussian filtering involves a large number of computational operations, a number that increases when the sizes of images and the kernel size also increase. Thus, finding methods to accelerate such computations is significant for overall time complexity enhancement, and the current paper proposes the use of partial sums to achieve this acceleration. The MPI (Message Passing Interface) library and the C programming language are used for the parallel program implementation of Gaussian filtering, based on a 1D kernel and 2D kernel working with and without the use of partial sums, and then a theoretical and practical evaluation of the effectiveness of the proposed implementations is made. The experimental results indicate a significant acceleration of the computational process when partial sums are used in both sequential and parallel processing. A PSNR (Peak Signal to Noise Ratio) metric is used to assess the quality of filtering for the proposed algorithms in comparison with the MATLAB implementation of Gaussian filtering, and time performance for the proposed algorithms is also evaluated. Full article

Air moving-target detection is crucial for national defense, civil aviation, and airspace supervision. Spaceborne synthetic aperture radar (SAR) provides high-resolution, continuous observations for this task, but faces challenges including target attitude variation-induced weak signals and Doppler defocusing from targets’ high-speed motion, which hinder target-background separation. To address this, we propose a novel method combining sub-aperture segmentation with GoDec+ low-rank decomposition to enhance signal-to-noise ratio and suppress defocusing. Critically, ADS-B flight data is integrated as ground truth for spatio-temporal validation. Experiments using Sentinel-1 SM mode SLC imagery across farmland, forest, and mountainous regions confirm the method’s effectiveness and robustness in real airspace scenarios. Full article

Extreme ultraviolet (EUV) ptychography is a promising actinic mask metrology technique capable of providing aberration-free images with subwavelength resolution. However, its performance is fundamentally constrained by the strong absorption of EUV light and the limited detection of high-frequency diffraction signals, which are critical for resolving fine structural details. In this study, we demonstrate significant improvements in EUV ptychographic imaging by implementing an upgraded EUV source system with reduced source etendue and applying an illumination aperture to spatially refine the probe. This approach effectively enhances the photon flux and spatial coherence, resulting in an increased signal-to-noise ratio of the high-frequency diffraction components and an extended maximum detected spatial frequency. Simulations and experimental measurements using a Siemens star pattern confirmed that the refined probe enabled more robust phase retrieval and higher-resolution image reconstruction. Consequently, we achieved a half-pitch resolution of 46 nm, corresponding to a critical dimension of 11.5 nm at the wafer plane. These findings demonstrate the enhanced capability of EUV ptychography as a high-fidelity actinic metrology tool for next-generation EUV mask characterization. Full article

Driven by the rising demand for digitalization and intelligent development of infrared payloads, next-generation systems must be developed within compressed timelines. High-precision digital modeling and simulation techniques offer essential data sources but often falter in complex space-based scenarios due to the limited availability of infrared characteristic data, hindering evaluation of the payload effectiveness. To address this, we propose a digital imaging simulation and verification (DISV) model for high-fidelity infrared image generation and closed-loop validation in the context of cloud–sea target detection. Based on on-orbit infrared imagery, we construct a cloud cluster database via morphological operations and generate physically consistent backgrounds through iterative optimization. The DISV model subsequently calculates scene infrared radiation, integrating radiance computations with an electron-count-based imaging model for radiance-to-grayscale conversion. Closed-loop verification via blackbody radiance inversion is performed to confirm the model’s accuracy. The mid-wave infrared (MWIR, 3–5 µm) system achieves mean square errors (RSMEs) < 0.004, peak signal-to-noise ratios (PSNRs) > 49 dB, and a structural similarity index measure (SSIM) > 0.997. The long-wave infrared (LWIR, 8–12 µm) system yields RMSEs < 0.255, PSNRs > 47 dB, and an SSIM > 0.994. Under 20–40% cloud coverage, the target radiance inversion errors remain below 4.81% and 7.30% for the MWIR and LWIR, respectively. The DISV model enables infrared image simulation across multi-domain scenarios, offering vital support for optimizing on-orbit payload performance. Full article