Search Results (307)

Ghost imaging (GI) offers a robust framework for remote sensing under degraded visibility conditions. However, atmospheric scattering in phenomena such as fog introduces significant noise and signal attenuation, thereby limiting its efficacy. Inspired by the selective attention mechanisms of biological visual systems, this study introduces a novel deep learning (DL) architecture that embeds a self-attention mechanism to enhance GI reconstruction in foggy environments. The proposed approach mimics neural processes by modeling both local and global dependencies within one-dimensional bucket measurements, enabling superior recovery of image details and structural coherence even at reduced sampling rates. Extensive simulations on the Modified National Institute of Standards and Technology (MNIST) and a custom Human-Horse dataset demonstrate that our bio-inspired model outperforms conventional GI and convolutional neural network-based methods. Specifically, it achieves Peak Signal-to-Noise Ratio (PSNR) values between 24.5–25.5$\mathrm{dB}/\mathrm{m}$ and Structural Similarity Index Measure (SSIM) values of approximately 0.8 under high scattering conditions ($\beta \ge 3.0 \mathrm{dB}/\mathrm{m}$) and moderate sampling ratios ($N \ge 50\%$). A comparative analysis confirms the critical role of the self-attention module, providing high-quality image reconstruction over baseline techniques. The model also maintains computational efficiency, with inference times under $0.12 \mathrm{s}$, supporting real-time applications. This work establishes a new benchmark for bio-inspired computational imaging, with significant potential for environmental monitoring, autonomous navigation and defense systems operating in adverse weather. Full article

We compared scatter correction (SC) in single-photon emission computed tomography (SPECT) images using effective scatter source estimation (ESSE) and the triple-energy window (TEW) method. We acquired ^99mTc and ¹²³I images of brain, myocardial, and performance phantoms containing rods with different diameters. We assessed contrast ratios (CRs) and ROI-based noise metrics (coefficient of variation, signal-to-noise ratio, and contrast-to-noise ratio [CNR] ). Under ^99mTc, ESSE yielded higher CRs than TEW across all phantoms (mean difference 0.04, range 0.01–0.05) and produced the highest CNR in the myocardial phantom, improving the conspicuousness of the simulated defect. Under ¹²³I, CR differences between ESSE and TEW were small and inconsistent (performance phantom: −0.04 to 0.06; brain phantom: −0.01 to 0.00). A Monte Carlo simulation (point source in air) showed substantial photopeak window penetration for cardiac high-resolution collimators (40.0%) but low penetration for medium-energy general-purpose collimators (5.1%), supporting photopeak contamination as a contributor to the ¹²³I findings and potentially attenuating the apparent advantage of model-based SC that does not explicitly account for penetration photons. These findings suggest that SC selection should consider the radionuclide and imaging target and that ESSE might be a reasonable option for ^99mTc myocardial imaging under the settings examined. Full article

Laser-based remote sensing (lidar) is a proven technique for detecting atmospheric features such as clouds and aerosols as well as for determining their vertical distribution with high accuracy. Even simple elastic backscatter lidars can distinguish clouds from aerosols, and accurate knowledge of their vertical location is essential for air quality assessment, hazard avoidance, and operational decision-making. However, daytime lidar measurements suffer from reduced signal-to-noise ratio (SNR) due to solar background contamination. Conventional processing approaches mitigate this by applying horizontal and vertical averaging, which improves SNR at the expense of spatial resolution and feature detectability. This work presents a deep learning-based framework that enhances lidar SNR at native resolution and performs fast layer detection and cloud–aerosol discrimination. We apply this approach to ICESat-2 532 nm photon-counting data, using artificially noised nighttime profiles to generate simulated daytime observations for training and evaluation. Relative to the simulated daytime data, our method improves peak SNR by more than a factor of three while preserving structural similarity with true nighttime profiles. After recalibration, the denoised photon counts yield an order-of-magnitude reduction in mean absolute percentage error in calibrated attenuated backscatter compared with the simulated daytime data, when validated against real nighttime measurements. We further apply the trained model to a full month of real daytime ICESat-2 observations (April 2023) and demonstrate effective layer detection and cloud–aerosol discrimination, maintaining high recall for both clouds and aerosols and showing qualitative improvement relative to the standard ATL09 data products. As an alternative to traditional averaging-based workflows, this deep learning approach offers accurate, near real-time data processing at native resolution. A key implication is the potential to enable smaller, lower-power spaceborne lidar systems that perform as well as larger instruments. Full article

Background: Practical, quantitative ultrasound-based tools for measuring hepatic steatosis are needed in everyday MASLD care. We evaluated a new multiparametric quantitative ultrasound (QUS) platform that integrates ultrasound-guided fat fraction (UGFF), attenuation coefficient (AC), backscatter coefficient (BSC), and signal-to-noise ratio (SNR), using Controlled Attenuation Parameter (CAP) as the reference and examining the effect of breathing. Methods: In a prospective single-center study, adult patients underwent same-day liver QUS and FibroScan. QUS measurements were performed during breath-hold and during normal breathing. Regions of interest were placed in right-lobe parenchyma 2 cm below the capsule, avoiding vessels. Primary outcomes were correlation with CAP and ROC performance at CAP cutoffs for S1 (≥230 dB/m), S2 (≥275 dB/m), and S3 (≥300 dB/m). Results: QUS was feasible in almost all examinations. UGFF, BSC, and SNR were consistent across breathing conditions, while AC was slightly higher during normal breathing. UGFF showed strong correlation with CAP and high accuracy for detecting steatosis. Across grades, AUCs were around 0.89–0.91, with cutoffs (UGFF ≈ 4% for ≥S1 and ≈11% for ≥S3). Conclusions: Multiparametric QUS provides reliable liver fat quantification that aligns closely with CAP and remains robust in practice whether patients hold their breath or breathe normally. These findings support UGFF as a practical, reliable point-of-care alternative for liver fat quantification that can be embedded in routine ultrasound in real time. Validation against MRI-PDFF or histology and multicenter studies will further define cutoffs and generalizability. Full article

Main shaft bearings are among the critical rotating components of subsea drilling rigs, and their health status directly affects the efficiency and reliability of the drilling system. However, in the high-pressure liquid environment of the deep sea, with intense noise, the vibration signals of the bearings attenuate rapidly. As a result, fault-related features have a low signal-to-noise ratio (SNR), which poses a challenge for bearing health monitoring. In recent years, Deep Neural Network (DNN)-based fault diagnosis methods for subsea drilling rig bearings have become a research hotspot in the field due to their strong potential for deep fault feature mining. Nevertheless, their reliance on high-power-consumption computational resources restricts their widespread application in subsea monitoring scenarios. To address the above issues, this paper proposes a fault diagnosis method for the main-spindle bearings of subsea drilling rigs that combines population coding with an adaptive-threshold k-winner-take-all (k-WTA) mechanism. The method exploits the noise robustness of population coding and the sparse activation induced by the adaptive k-WTA mechanism, achieving a noise-robust and energy-efficient fault diagnosis scheme for the main-spindle bearings of subsea drilling rigs. The experimental results confirm the effectiveness of the proposed method. In accuracy and generalization experiments on the CWRU benchmark dataset, the proposed method achieves good diagnostic accuracy that is not inferior to other SOTA methods, indicating relatively strong generalization and robustness. On the Paderborn real-bearing benchmark dataset, the results highlight the importance of selecting features that are adapted to specific operating conditions. Additionally, in the noise robustness and energy efficiency experiments, the proposed method shows advantages in both noise resistance and energy efficiency. Full article

Deep-ultraviolet (DUV, 193 nm) tools for lithography and precision micromachining are often limited by beam-profile nonuniformity, which degrades critical-dimension control, line-edge roughness, and process windows. Conventional phase-dependent homogenizers can lose performance under realistic phase noise and pointing jitter. We investigate two complementary, energy–space-modulation routes to robust homogenization: (i) a metalens-based microlens array (MLA) that forms a flat-top via controlled beamlet overlap and (ii) a chromium-on-sapphire attenuator that equalizes intensity purely by amplitude shaping. Coupled FDTD and optical modeling guide a graded-transmittance Cr design (target transmittance 0.8–0.9) that converts a Gaussian input into a flat-top plateau. Experiments at 193 nm verify that both approaches achieve high static uniformity (Urms <3.5%). Under dynamic conditions, the MLA exhibits sensitivity to transverse-mode hops and phase fluctuations due to its reliance on coherent overlap, leading to reduced uniformity and fill factor. In contrast, the Cr attenuator remains phase-insensitive and maintains stable output under jitter, offering a power-robust, low-maintenance alternative for industrial DUV systems. We discuss design trade-offs and outline hybrid MLA + attenuation schemes that preserve MLA-level flatness while approaching the robustness of amplitude-shaping solutions. Full article

Background: Photon-counting computed tomography (PCCT) represents a major technological advance in clinical CT imaging, offering superior spatial resolution, enhanced material discrimination, and potential radiation dose reduction compared to conventional energy-integrating detector systems. As the first clinically approved PCCT scanner becomes available, establishing a comprehensive characterization of its image quality is essential to understand its performance and clinical impact. Methods: Image quality was evaluated using a commercial quality assurance phantom with acquisition protocols typically used for three anatomical regions—head, abdomen/thorax, and inner ear—representing diverse clinical scenarios. Each region was scanned using both ultra-high-resolution (UHR, 120 × 0.2 mm slices) and conventional (144 × 0.4 mm slices) protocols. Conventional metrics, including signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), slice thickness accuracy, and uniformity, were assessed following international standards. Task-based analysis was also performed through target transfer function (TTF), noise power spectrum (NPS), and detectability index (d′) to evaluate diagnostic relevance. Results: UHR protocols provided markedly improved spatial resolution, particularly in the inner ear imaging, as confirmed by TTF analysis, though with increased noise and reduced low-contrast detectability in certain conditions. CT numbers showed linear correspondence with known attenuation coefficients across all protocols. Conclusions: This study establishes a detailed technical characterization of the first clinical PCCT scanner, demonstrating significant improvements in terms of spatial resolution and accuracy of the quantitative image analysis, while highlighting the need for noise–contrast optimization in high-resolution imaging. Full article

Global Navigation Satellite System (GNSS)/Inertial Navigation System (INS) integrated navigation is one of the key methods for achieving precise positioning in complex urban environments. However, in some scenarios such as urban canyons, overpasses, and foliage occlusion, GNSS signals are frequently attenuated or interrupted, leading to degraded positioning accuracy when relying solely on INSs. To address this limitation, this study developed an improved GNSS/INS-integrated navigation algorithm based on a hybrid framework that combines a Robust Adaptive Kalman Filter (RAKF) with a Radial Basis Function (RBF) neural network. The RAKF allows a multi-criterion optimization strategy to be created to adaptively adjust the measurement noise covariance matrix according to GNSS data quality indicators such as PDOP, the number of satellites, and signal quality factors. This enhances the filter’s robustness and outlier detection capability under degraded GNSS conditions. Meanwhile, the RBF network is trained to predict pseudo-position increments, which substitute missing GNSS measurements during signal outages to maintain continuous navigation. Real-world vehicular experiments were conducted to evaluate the proposed RBF-aided RAKF (RBF-RAKF) against three other methods: the Extended Kalman Filter (EKF), standard RAKF, and RBF-aided Kalman Filter (RBF-KF). The experimental results demonstrate that during GNSS outages the proposed method achieved root mean square (RMS) positioning errors of 0.94, 1.02, and 0.21 m in the north, east, and down directions, respectively, representing improvements of over 90% compared with conventional filters. Moreover, the algorithm maintained meter-level horizontal accuracy and sub-meter vertical precision under severe GNSS signal degradation. These results confirm that the proposed RBF-RAKF algorithm provides stable and high-precision navigation performance in challenging urban environments. Full article

To address the edge blurring issue of drone inspection images of mountainous transmission lines caused by non-uniform haze interference, as well as the low operational efficiency of traditional dehazing algorithms due to increased network complexity, this paper proposes a high-frequency guided dual-branch attention multi-scale hierarchical dehazing network for transmission line scenarios. The network adopts a core architecture of multi-block hierarchical processing combined with a multi-scale integration scheme, with each layer based on an asymmetric encoder–decoder with residual channels as the basic framework. A Mix structure module is embedded in the encoder to construct a dual-branch attention mechanism: the low-frequency global perception branch cascades channel attention and pixel attention to model global features; the high-frequency local enhancement branch adopts a multi-directional edge feature extraction method to capture edge information, which is well-adapted to the structural characteristics of transmission line conductors and towers. Additionally, a fog density estimation branch based on the dark channel mean is added to dynamically adjust the weights of the dual branches according to haze concentration, solving the problem of attention failure caused by attenuation of high-frequency signals in dense haze regions. At the decoder end, depthwise separable convolution is used to construct lightweight residual modules, which reduce running time while maintaining feature expression capability. At the output stage, an inter-block feature fusion module is introduced to eliminate cross-block artifacts caused by multi-block processing through multi-strategy collaborative optimization. Experimental results on the public datasets NH-HAZE20, NH-HAZE21, O-HAZE, and the self-built foggy transmission line dataset show that, compared with classic and cutting-edge algorithms, the proposed algorithm significantly outperforms others in terms of Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM); its running time is 19% shorter than that of DMPHN. Subjectively, the restored images have continuous and complete edges and high color fidelity, which can meet the practical needs of subsequent fault detection in transmission line inspection. Full article

This study investigates the application of phononic crystal plates for automotive vibration and noise attenuation through a combined material–structure design approach. Four materials—aluminum, lead, epoxy resin, and plexiglass—were selected to construct a composite plate with a low-density matrix and high-density metallic inclusions. Finite element modeling in COMSOL Multiphysics identified organic glass–lead as the optimal configuration, balancing wide bandgap performance with low-frequency characteristics and lightweight requirements. Parametric analysis demonstrated that rectangular inclusions provide the widest bandgap under equal area conditions, and increasing their volume fraction shifts the bandgap to lower frequencies while broadening its width. The study verifies the reliability of the finite element method (FEM) and further explains the formation mechanism of the bandgap. This study proposes a phononic crystal plate structure with optimal performance: a rectangular phononic crystal plate with a length of A = 20 mm and a height of B = 10 mm serves as the matrix, and four identical rectangular inclusions each with an area of S = 16 mm² are embedded in it. The matrix material is organic glass, while the material of the inclusions is lead. The resulting optimized structure exhibits a complete Lamb wave bandgap from 6.29 to 22.03 kHz, with strong elastic wave attenuation extending over 6.00–30.00 kHz. Acoustically, it achieves sound transmission loss (STL) exceeding 130 dB within 5.85–27.91 kHz, peaking at 143.99 dB. These results verify the structure’s dual functionality in simultaneous vibration isolation and sound attenuation within the same frequency range, demonstrating the potential of phononic crystal plates for targeted noise and vibration control in automotive engines and rotating machinery. Full article

Underwater laser range-gated imaging (ULRGI) effectively suppresses backscatter from water bodies through a time-gated photon capture mechanism, significantly extending underwater detection ranges compared to conventional imaging techniques. However, as imaging distance increases, rapid laser power attenuation causes localized pixel loss in captured images. To address ULRGI’s limitations in multi-frame stacking—particularly poor real-time performance and artifact generation—this paper proposes the Photon-Level Physically Guided Underwater Laser-Gated Image Restoration Network (PLPGR-Net). To overcome image degradation caused by water scattering and address the challenge of strong coupling between target echo signals and scattering noise, we designed a three-branch architecture driven by photon-level physical priors. This architecture comprises: scattering background suppression module, sparse photon perception module, and enhanced U-Net high-frequency information recovery module. By establishing a multidimensional physical constraint loss system, we guide image reconstruction across three dimensions—pixels, features, and physical laws—ensuring the restored results align with underwater photon distribution characteristics. This approach significantly enhances operational efficiency in critical applications such as underwater infrastructure inspection and cultural relic detection. Comparative experiments using proprietary datasets and state-of-the-art denoising and underwater image restoration algorithms validate the method’s outstanding performance in deeply integrating physical interpretability with deep learning generalization capabilities. Full article

Mine rescue robots operate in extremely adverse subterranean environments, where the acquired video data are frequently affected by severe jitter and motion distortion. Such instability leads to the loss of critical visual information, thereby reducing the reliability of rescue decision-making. To address this issue, a dual-channel visual stabilization framework based on the SuperPoint network is proposed, extending the traditional ORB descriptor framework. Here, dual-channel refers to two configurable and mutually exclusive feature extraction paths—an ORB-based path and a SuperPoint-based path—that can be flexibly switched according to scene conditions and computational requirements, rather than operating simultaneously on the same frame. The subsequent stabilization pipeline remains unified and consistent across both modes. The method employs an optimized detector head that integrates deep feature extraction, non-maximum suppression, and boundary filtering to enable precise estimation of inter-frame motion. When combined with smoothing filters, the approach effectively attenuates vibrations induced by irregular terrain and dynamic operational conditions. Experimental evaluations conducted across diverse scenarios demonstrate that the proposed algorithm achieves an average improvement of 27.91% in Peak Signal-to-Noise Ratio (PSNR), a 55.04% reduction in Mean Squared Error (MSE), and more than a twofold increase in the Structural Similarity Index (SSIM) relative to pre-stabilized sequences. Moreover, runtime analysis indicates that the algorithm can operate in near-real-time, supporting its practical deployment on embedded mine rescue robot platforms.These results verify the algorithm’s robustness and applicability in environments requiring high visual stability and image fidelity, providing a reliable foundation for enhanced visual perception and autonomous decision-making in complex disaster scenarios. Full article

This paper presents a comprehensive review of integrated terahertz (THz) and free-space optical (FSO) communication systems, focusing on their potential to address the escalating demands for high-capacity, long-distance, and ultra-reliable transmission in future six-generation (6G) and space–air–ground integrated networks (SAGIN). The study systematically examines recent advancements in three critical areas: channel modeling, transmission performance, and integrated system architectures. Specifically, it analyzes THz and FSO channel characteristics, including attenuation mechanisms, turbulence effects, pointing errors, and noise sources, and compares their complementary strengths under diverse atmospheric conditions. Key findings reveal that THz communication achieves transmission rates up to several Tbps over distances of several kilometers but is constrained by molecular absorption and weather-induced attenuation, while FSO offers superior bandwidth-distance products yet suffers from turbulence-induced fading, posing significant reliability challenges. The integration of THz and FSO through adaptive switching strategies (e.g., hard and soft switching) demonstrates enhanced reliability and spectral efficiency, with experimental results showing seamless data rates exceeding Tbps in hybrid systems. However, challenges persist in transceiver hardware integration, algorithmic optimization, and dynamic resource allocation. The review concludes by identifying future research directions, including the development of unified channel models, shared architectures, and intelligent switching algorithms to achieve robust integrated communication infrastructures. Full article

High-frequency current detection of partial discharge (PD) at transformers on-site faces complex noise interference, which severely impacts the accuracy of PD detection. To address this issue, an intelligent interference suppression algorithm for PD signals based on adaptive waveform feature recognition is proposed. First, a 10 MHz high-pass filter is applied to eliminate the influence of periodic narrowband interference on the zero-crossing count of the time-series. Non-pulse noise is removed based on the instantaneous zero-crossing density of the signal. Next, the start and end times of each pulse are determined, and the corresponding waveform segments are extracted from the original signal to form a pulse array. Subsequently, waveform features of the pulses are extracted, and discrimination thresholds for the feature parameters are calculated based on univariate analysis. Finally, each pulse is adaptively identified based on its waveform features, and PD signals are screened out. The proposed algorithm was tested using PD signals superimposed with on-site noise as well as field-measured signals. The results demonstrate that the algorithm can intelligently identify PD signals and significantly reduce PD signal attenuation, exhibiting excellent suppression effects on complex noise interference in on-site PD detection at transformers. Full article

To address the challenge of head stability in a biomimetic robotic dolphin during self-propulsion, this study systematically investigates the passive stabilization mechanism of a bio-inspired caudal keel. A combined experimental and computational fluid dynamics (CFD) approach was employed to evaluate four keel geometries across a tail oscillation frequency range of 0.5–2 Hz. The experimental results demonstrate that the optimal keel configuration reduced the standard deviation of the head pitch angle by 20.9% at 2 Hz. CFD analysis revealed a dual stabilization mechanism: an effective keel not only attenuates the intensity of the primary disturbance moment at the driving frequency but, more critically, also enhances the spectral purity of the signal by suppressing high-frequency harmonics and broadband stochastic noise through the systematic reorganization of caudal vortices. A systematic investigation of keel geometry identified non-dimensional height (h/c) as the dominant parameter, with its stabilizing effect exhibiting diminishing returns beyond an optimal range. Furthermore, a quantifiable design trade-off was established, showing an approximate 9.1% increase in the Cost of Transport (CoT) for the most stable configuration. These findings provide quantitative design principles and a deeper physical insight into the passive stabilization of biomimetic underwater vehicles, highlighting the importance of both disturbance intensity and spectral quality. Full article