Search Results (1,194)

Human population growth and increasing transportation demands have led to rising global tire consumption and associated waste. In response, various material and energy recovery strategies, such as pyrolysis, have been developed to produce high-value-added products such as pyrolysis oils, which can be reused as materials or fuels. However, these oils often contain heteroatom-containing compounds (e.g., nitrogen, oxygen, sulfur) that can hinder their valorization and must therefore be identified and removed. To characterize heteroatomic compounds present in distillation fractions of pyrolysis oils, GC×GC/TOFMS and GC/HRMS were employed. For non-target analysis, data processing parameters were optimized using a Central Composite Design (CCD). The most influential parameters for GC×GC/TOFMS were the minimum number of mass-to-charge ratio (m/z) signals kept in the deconvoluted spectra (minimum stick count) and peak signal-to-noise ratio (S/N), while for GC/HRMS, optimization focused on the m/z S/N threshold, peak S/N, and total ion current (TIC). Under optimal conditions, 129 and 92 heteroatomic compounds were identified via GC×GC/TOFMS and GC/HRMS, respectively, within a single distillation fraction, with 57 compounds identified using both techniques. Notably, GC×GC/TOFMS exclusively identified 72 compounds, while there were only 5 unique to GC/HRMS. These results highlight the effectiveness of GC×GC/TOFMS in characterizing heteroatomic compounds in complex mixtures, while also underlining the complementary value of GC/HRMS. Full article

Optical frequency domain reflectometry (OFDR) is one of the key diagnostic tools for fiber optic components and circuits built on them. A low signal-to-noise ratio, resulting from the low intensity of backscattered signals, prevents the correct quantitative description of the medium parameters. Known methods of signal denoising, such as empirical mode decomposition, frequency filtering, and activation function dynamic averaging, make the signal smoother but introduce errors into its dynamic characteristics, changing the intensity of reflection peaks and distorting the backscattering level. We propose a method to reduce OFDR trace noise using elliptical arc fitting (EAF). The obtained results indicate that this algorithm efficiently processes both areas with and without contrasting back reflections, with zero distortion of Fresnel reflection peaks, and with zero attenuation error in regions without Fresnel reflections. At the same time, other methods distort reflection peaks by 14.2–42.6% and shift the correct level of Rayleigh scattering by 27.2–67.3%. Further work will be aimed at increasing the accuracy of the method and testing it with other types of data. Full article

Maritime search and rescue (SAR) demands reliable sensing and communication under sea clutter. Emerging integrated sensing and communication (ISAC) technology provides new opportunities for the development and modernization of maritime radio communication, particularly in relation to search and rescue. This study investigated the dual-function capability of a phase-coded frequency modulated continuous wave (FMCW) system for search and rescue at sea, in particular for life signs detection in the presence of sea clutter. The detection capability of the FMCW system was enhanced by applying phase-modulated codes on chirps, and radar-centric communication function is supported simultaneously. Various phase-coding schemes including Barker, Frank, Zadoff-Chu (ZC), and Costas were assessed by adopting the peak sidelobe level and integrated sidelobe level of the ambiguity function of the established signals. The interplay of sea waves was represented by a compound K-distribution model. A multiple-input multiple-output (MIMO) architecture with the ZC code was adopted to detect multiple objects with a high resolution for micro-Doppler determination by taking advantage of spatial coherence with beamforming. The effectiveness of the proposed method was validated on the 4-transmit, 4-receive (4 × 4) MIMO system with ZC coded FMCW signals. Monte Carlo simulations were carried out incorporating different combinations of targets and user configurations with a wide range of signal-to-noise ratio (SNR) settings. Extensive simulations demonstrated that the mean squared error (MSE) of range estimation remained low across the evaluated SNR setting, while communication performance was comparable to that of a baseline orthogonal frequency-division multiplexing (OFDM)-based system. The high performance demonstrated by the proposed method makes it a suitable maritime search and rescue solution, in particular for vision-restricted situations. Full article

Staining variability in histopathological images compromises automated diagnostic systems by affecting the reliability of computational pathology algorithms. Existing normalization methods prioritize color consistency but often sacrifice critical morphological details essential for accurate diagnosis. This work proposes a novel deep learning framework, integrating enhanced residual learning with multi-scale attention mechanisms for structure-preserving stain normalization. The approach decomposes the transformation process into base reconstruction and residual refinement components, incorporating attention-guided skip connections and progressive curriculum learning. The method was evaluated on the MITOS-ATYPIA-14 dataset containing 1420 paired H&E-stained breast cancer images from two scanners. The framework achieved exceptional performance with a structural similarity index (SSIM) of 0.9663 ± 0.0076, representing 4.6% improvement over the best baseline (StainGAN). Peak signal-to-noise ratio (PSNR) reached 24.50 ± 1.57 dB, surpassing all comparison methods. An edge preservation loss of 0.0465 ± 0.0088 demonstrated a 35.6% error reduction compared to the next best method. Color transfer fidelity reached 0.8680 ± 0.0542 while maintaining superior perceptual quality (FID: 32.12, IS: 2.72 ± 0.18). The attention-guided residual learning framework successfully maintains structural integrity during stain normalization, with superior performance across diverse tissue types, making it suitable for clinical deployment in multi-institutional digital pathology workflows. Full article

Wearable systems are increasingly adopted for health monitoring and wellness promotion. Among the most relevant biosignals, the electrocardiogram (ECG) plays a key role; however, in wearable settings (e.g., during physical activity), it is often corrupted by electromyogram (EMG) interference. This study presents a novel adaptive algorithm, template masking (TM), which integrates the stationary wavelet transform (SWT) with template matching for denoising the ECG from EMG. The method identifies the optimal wavelet and decomposition level to maximise detail sparsity. To mitigate EMG interference, after alignment in the SWT domain with a template, the detail coefficients are multiplied by a binary mask and smoothed. TM was compared with soft and hard thresholding on (1) simulations combining clinical ECGs (MIT-BIH database) and synthetic EMGs with different signal-to-noise ratios (SNRs), and (2) experimental signals including ECGs acquired with dry electrodes corrupted by EMGs (SimEMG database, also varying SNRs), as a potential wearable scenario. In both cases, TM yielded significantly lower reconstruction errors at SNRs below 5 dB ($p<0.01$) and significantly outperformed thresholding in the sensitivity of R-peaks estimation ($p<0.001$). These results demonstrate the potential of TM, highlighting the value of adaptive denoising algorithms. Full article

In this study, we propose a novel pixel-level non-local similarity (PNS)-based reconstruction method for millimeter-wave interferometric synthetic aperture radiometer (InSAR) imaging. Unlike traditional compressed sensing (CS) methods, which rely on predefined sparse transforms and often introduce artifacts, our approach leverages structural redundancies in InSAR images through an enhanced sparse representation model with dynamically filtered coefficients. This design simultaneously preserves fine details and suppresses noise interference. Furthermore, an iterative refinement mechanism incorporates raw sampled data fidelity constraints, enhancing reconstruction accuracy. Simulation and physical experiments demonstrate that the proposed InSAR-PNS method significantly outperforms conventional techniques: it achieves a 1.93 dB average peak signal-to-noise ratio (PSNR) improvement over CS-based reconstruction while operating at reduced sampling ratios compared to Nyquist-rate fast fourier transform (FFT) methods. The framework provides a practical and efficient solution for high-fidelity millimeter-wave InSAR imaging under sub-Nyquist sampling conditions. Full article

Cloud-aerosol interactions represent a critical uncertainty in climate systems. Using 2006–2021 CALIPSO products, we investigated upper tropospheric clouds and aerosol layers across four Russian regions: Western Plains, West Siberian Plain, Central Siberian Plateau, and Eastern Mountains. Top Cloud Optical Depth (TCOD), Top Depolarization Ratio of clouds (TDRc), and Layer Level (LLc) exhibit pronounced seasonal and diurnal variations, peaking during summer and nighttime when convection intensifies. Upper aerosol layers show low Total Aerosol Optical Depth (TAOD) and Color Ratio (CRa), often displaying multi-layered structures influenced by spring–summer dust transport and biomass burning. We constructed a correlation matrix of 49 parameter pairs (7 cloud × 7 aerosol parameters), revealing moderate positive correlations between cloud and aerosol layer heights under coexistence conditions. TDRc showed weak linear but strong nonlinear relationships with aerosol parameters, indicating complex coupling mechanisms beyond simple linear models. Nighttime observations demonstrated superior signal-to-noise ratios and correlation coefficients compared to daytime measurements. These findings enhance understanding of cloud-aerosol coupling at middle-high latitudes, providing parameterization constraints for improving global climate model representations of these processes. Full article

Light-field (LF) imaging transforms occlusion removal by using multiview data to reconstruct hidden regions, overcoming the limitations of single-view methods. However, this advanced capability often comes at the cost of increased computational complexity. To overcome this, we propose the U²-LFOR network, an end-to-end neural network designed to remove occlusions in LF images without compromising performance, addressing the inherent complexity of LF imaging while ensuring practical applicability. The architecture employs Residual Atrous Spatial Pyramid Pooling (ResASPP) at the feature extractor to expand the receptive field, capture localized multiscale features, and enable deep feature learning with efficient aggregation. A two-stage U²-Net structure enhances hierarchical feature learning while maintaining a compact design, ensuring accurate context recovery. A dedicated refinement module, using two cascaded residual blocks (ResBlock), restores fine details to the occluded regions. Experimental results demonstrate its competitive performance, achieving an average Peak Signal-to-Noise Ratio (PSNR) of 29.27 dB and Structural Similarity Index Measure (SSIM) of 0.875, which are two widely used metrics for evaluating reconstruction fidelity and perceptual quality, on both synthetic and real-world LF datasets, confirming its effectiveness in accurate occlusion removal. Full article

To overcome the limitations of low texture accuracy in traditional sculpture color restoration methods, this study proposes an improved Deep Convolutional Generative Adversarial Network (DCGAN) model incorporating a dual attention mechanism (spatial and channel attention) and a channel converter to enhance restoration quality. First, the theoretical foundations of the DCGAN algorithm and its key components (generator, discriminator, etc.) are systematically introduced. Subsequently, a DCGAN-based application model for sculpture color restoration is developed. The generator employs a U-Net architecture integrated with a dual attention module and a channel converter, enhancing both local feature representation and global information capture. Meanwhile, the discriminator utilizes an image region segmentation approach to optimize the assessment of consistency between restored and original regions. The loss function follows a joint optimization strategy, combining perceptual loss, adversarial loss, and structural similarity index (SSIM) loss, ensuring superior restoration performance. In the experiments, mean square error (MSE), peak signal-to-noise ratio (PSNR), and SSIM were used as evaluation metrics, and sculpture color restoration tests were conducted on an Intel Xeon workstation. The performance of the proposed model was compared against the traditional DCGAN and other restoration models. The experimental results demonstrate that the improved DCGAN outperforms traditional methods across all evaluation metrics, and compared to traditional DCGAN, the proposed model achieves significantly higher SSIM and PSNR, while reducing MSE. Compared to other restoration models, PSNR and SSIM are further enhanced, MSE is reduced, and the visual consistency between the restored and undamaged areas is significantly improved, with richer texture details. 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

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

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

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

Photoacoustic tomography (PAT), which combines optical absorption and ultrasonic detection, enables the monitoring of dehydration-driven structural changes in extracted teeth over time. In this proof-of-concept study, 2D photoacoustic images of a wisdom tooth were generated on the same scanning plane at days 0, 1, 3, 6, 10, 15, 21, and 28 post-extraction, using day 0 as the reference. Measurements were performed in forward-detection mode with a single ultrasound transducer and a 532 nm pulsed laser. For the comparative analysis of variations between images, four metrics were used: Pearson correlation coefficient, Structural Similarity Index (SSIM), Mean Squared Error (MSE), and Peak Signal-to-Noise Ratio (PSNR). Structural changes were also examined through radial intensity profiles extracted from each image. The results revealed marked differences in the central region, evidencing progressive structural and acoustic modifications within the tooth. The most significant change occurred on day 1, followed by small but consistent variations on subsequent days. These differences are associated with dehydration-induced changes in tissue density, which affect sound propagation. This study highlights the value of PAT for noninvasive monitoring of post-extraction dental changes, with implications for diagnosis, treatment guidance, and biomaterials research in dentistry. Full article

This paper explores the application of transform-domain sparsification and compressed sensing (CS) techniques to improve the efficiency and quality of magnetic resonance imaging (MRI). We implement and evaluate three sparsifying methods—discrete wavelet transform (DWT), fast Fourier transform (FFT), and discrete cosine transform (DCT)—which are used to simulate subsampled reconstruction via inverse transforms. Additionally, one accurate CS reconstruction algorithm, basis pursuit (BP), using the L₁-MAGIC toolbox, is implemented as a benchmark based on convex optimization with L₁-norm minimization. Emphasis is placed on basis pursuit (BP), which satisfies the formal requirements of CS theory, including incoherent sampling and sparse recovery via nonlinear reconstruction. Each method is assessed in MATLAB R2024b using standardized DICOM images and varying sampling rates. The evaluation metrics include peak signal-to-noise ratio (PSNR), root mean square error (RMSE), structural similarity index measure (SSIM), execution time, memory usage, and compression efficiency. The results show that although discrete cosine transform (DCT) outperforms the others under simulation in terms of PSNR and SSIM, it is inconsistent with the physics of MRI acquisition. Conversely, basis pursuit (BP) offers a theoretically grounded reconstruction approach with acceptable accuracy and clinical relevance. Despite the limitations of a controlled experimental setup, this study establishes a reproducible benchmarking framework and highlights the trade-offs between the quality of transform-based reconstruction and computational complexity. Future work will extend this study by incorporating clinically validated CS algorithms with L₀ and nonconvex Lp (0 < p < 1) regularization to align with state-of-the-art MRI reconstruction practices. Full article