Search Results (5,668)

This work presents the design of a compact, wideband circular microstrip patch antenna for microwave imaging-based brain tumor detection. The main contribution is the development of a compact antenna structure incorporating enhanced ground-plane slot modifications, which significantly improves impedance bandwidth while maintaining a small electrical size, making it highly suitable for medical imaging systems. In addition, the study integrates antenna design, safety evaluation, and microwave imaging analysis within a unified framework to assess tumor localization feasibility using a realistic head model in CST Microwave Studio. The proposed antenna is fabricated on an FR-4 substrate with dimensions of 37 × 54.5 × 1.6 mm³, corresponding to an electrical size of 0.176λ × 0.260λ × 0.0076λ at the lowest operating frequency of 1.43 GHz. Ground-plane slot enhancements are introduced to achieve wideband performance, resulting in an impedance bandwidth from 1.43 to 4 GHz and a fractional bandwidth of 94.7%. The antenna exhibits a maximum realized gain of 3.7 dB. To evaluate its suitability for medical applications, specific absorption rate (SAR) analysis is performed using a realistic human head model at multiple antenna positions and at 1.5, 2.1, 2.5, 3.3, and 3.9 GHz frequencies. The computed SAR values range from 0.109 to 1.56 W/kg averaged over 10 g of tissue, satisfying the IEEE C95.1 safety guideline limit of 2 W/kg. For tumor detection assessment, time-domain simulations are conducted in CST Microwave Studio using a monostatic radar configuration, where the antenna operates as both transmitter and receiver at twelve angular positions around the head with 30° increments. The collected scattered signals are processed using the Delay-and-Sum (DAS) beamforming algorithm to reconstruct dielectric contrast maps and localize the tumor. It should be noted that the tumor-imaging demonstrations presented in this work are based on numerical simulations, while experimental validation is limited to the characterization of the fabricated antenna. Nevertheless, the findings indicate that the proposed antenna is a promising candidate for noninvasive, low-cost microwave brain tumor imaging applications. Full article

Numerous studies have demonstrated the potential of Synthetic Aperture Radar (SAR) in monitoring glacier velocity. However, owing to the complex dynamics of glaciers and the variability of their surface features, velocity fields derived from even short-interval SAR image pairs often exhibit missing parts. This study proposes a missing glacier velocity recovery method based on a spatiotemporal hybrid neural network to solve the above problem. Considering the spatiotemporal characteristics of glacier velocity fields, a hybrid network combining an Artificial Neural Network (ANN) and a Denoising Autoencoder (DAE) is developed. The ANN is first employed to capture spatial correlations associated with missing values, after which it is integrated with the DAE to model temporal variations using a time-aware loss function. An iterative weighting strategy adaptively balances spatial and temporal features during training. The method is applied to SAR–derived velocity fields of Petermann Glacier. Experimental results show that the method significantly improves the performance of glacier velocity recovery compared to traditional methods. Additionally, the study compares and analyzes the velocity of Petermann Glacier in different seasons, and the findings indicate that the glacier exhibits more pronounced seasonal differences in the accumulation zone. Full article

Total variation (TV)-based image denoising effectively suppresses noise while preserving edges, but it often introduces staircase artifacts in flat regions. To address this limitation, we propose a novel denoising model that combines adaptive fractional-order total variation with a minimax-concave (MC) penalty in the regularization term. The adaptive fractional-order TV alleviates staircase effects in homogeneous areas while preserving fine details in textured regions. The MC penalty provides a more accurate estimation of image sparsity, improving restoration fidelity compared to traditional L1-based regularization. The resulting model, termed AFTVMC, is efficiently solved using an alternating direction method of multipliers (ADMM). Extensive numerical experiments on synthetic and natural images demonstrate that AFTVMC outperforms classical TV, higher-order LLT, adaptive ATV, and state-of-the-art MCFOTV models in both objective metrics—peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM)—and subjective visual quality, particularly in suppressing staircase artifacts and preserving complex texture details. Full article

Background/Objectives: Accurate and reproducible lymph node (LN) measurement is essential for oncologic staging and therapy monitoring but is subject to inter-reader variability. This study evaluated the accuracy and reproducibility of a fully automated artificial intelligence (AI)-based LN measurement workflow in contrast-enhanced chest CT, using multi-reader manual measurements as reference. Methods: Sixty thoracic LNs from seven patients were independently measured by 13 radiologists in two reading rounds. The median of all measurements served as the ground truth (GT). Automated short- and long-axis diameters were derived from fully automated 3D CNN-based segmentations. Agreement between AI and manual measurements was assessed using Friedman testing, intraclass correlation coefficients (ICCs), and concordance correlation coefficients (CCCs). Measurement stability was evaluated across repeated runs on different hardware systems. Results: A total of 2280 manual measurements were analyzed. Manual assessment showed significant inter-reader variability (p < 0.01), while intra-reader agreement was high. No significant differences were observed between AI-based measurements and the GT (all p > 0.01). Agreement was good, with CCC values of 0.86 (SAD) and 0.79 (LAD). AI-based measurements were numerically stable across hardware configurations. Conclusions: Fully automated AI-based LN measurements in chest CT scans provide strong agreement with multi-reader consensus and high numerical stability. Automated measurement may support more standardized and reproducible oncologic imaging assessment. Full article

In industrial imaging scenarios, semiconductor wafer defect classification is crucial for chip manufacturing yield and reliability. However, numerous challenges persist, including weak imaging responses and detail loss during downsampling, complex backgrounds that interfere with feature extraction, and the trade-off between performance and efficiency on edge devices. Traditional CNNs and ViTs exhibit limitations in modeling long-range dependencies and managing edge deployment costs. To address these issues, we leverage the VMamba architecture, a Visual State Space Model (SSM) that achieves global contextual modeling with linear computational complexity. Based on the VMamba architecture, we propose FCS-VMamba, a domain-adapted model that integrates three core modules, namely Frequency Attention (FA), Cross-Layer Cross-Attention (CLCA), and Saliency Feature Suppression (SFS). The experimental results show that FCS-VMamba achieved $86.06\%$ macro-precision and $87.91\%$ Top-1 accuracy with only $1.2$ M parameters. These results demonstrate that FCS-VMamba provides a practical and parameter-efficient baseline for industrial wafer defect recognition. Full article

Calving front position is a key indicator of glacier and ice-sheet dynamics and an important variable for assessing mass loss and sea-level rise. Rapid growth in satellite data availability and image analysis techniques has driven the development of numerous automated calving front detection algorithms; however, the methodological landscape remains fragmented. This scoping review aims to map the existing literature on automated calving front detection, characterize the types of algorithms and data sources used, and identify trends, gaps, and challenges in current approaches. A systematic search of major bibliographic databases and complementary sources was conducted to identify studies describing automated or semi-automated calving front detection from satellite imagery or derived datasets. Eligible studies included peer-reviewed articles and relevant grey literature using optical, synthetic aperture radar (SAR), or multi-sensor data. Data were charted using a predefined framework that captures the algorithmic approach, input data characteristics, spatial and temporal coverage, validation strategies, and reported performance metrics. The review identifies a wide range of methods, from early threshold- and edge-based techniques to recent machine learning and deep learning approaches, with a strong shift toward convolutional neural networks over the past few years. Despite methodological progress, validation practices and evaluation metrics remain heterogeneous, and standardized benchmark datasets are scarce. This scoping review provides a structured overview of the field and highlights priorities for future methodological development and benchmarking. Full article

Background/Objectives: Accurate preoperative assessment of renal tumor complexity is essential for surgical planning and for predicting perioperative outcomes after partial nephrectomy (PN). RENAL and PADUA nephrometry scores, traditionally derived from two-dimensional (2D) computed tomography (CT) imaging, are widely used to quantify renal tumor complexity and surgical risk. However, the introduction of hyperaccuracy three-dimensional (HA3D) models has enabled enhanced anatomical visualization, potentially improving the assessment of surgical difficulty and the prediction of postoperative complications. The aim of this study was to compare conventional CT-based RENAL and PADUA scores with HA3D-derived nephrometry scores in predicting perioperative complications in patients undergoing robot-assisted or laparoscopic PN. Methods: A total of 17 consecutive patients with intermediate- or high-complexity category renal tumors (RENAL ≥ 7) and moderate- or high-risk category tumors (PADUA ≥ 8) were prospectively enrolled. Preoperative demographic and clinical parameters, as well as intraoperative and postoperative data, were prospectively collected. Tumor characteristics were evaluated using both CT-based RENAL and PADUA scoring systems and HA3D nephrometry reconstruction. Associations between nephrometry scores and perioperative outcomes were assessed using Spearman’s correlation. Predictive performance for postoperative complications and early chronic kidney disease (CKD) was evaluated using receiver operating characteristic (ROC) analysis. Results: Overall, 41% and 35% of cases were downgraded according to three-dimensional (3D) RENAL and PADUA complexity–risk category assessment, respectively. Operative time demonstrated a moderate correlation with 3D RENAL (ρ = 0.57) and 3D PADUA (ρ = 0.49) scores. ROC curve analysis demonstrated numerical differences in area under the curve (AUC) values between 3D- and 2D-based nephrometry scores in predicting overall complications (RENAL: 0.61 vs. 0.54; PADUA: 0.69 vs. 0.46). 3D RENAL score demonstrated numerically higher AUC values for early postoperative CKD compared with 2D RENAL score (AUC: 0.72 vs. 0.67). Conclusions: HA3D-based nephrometry scores were associated with enhanced anatomical visualization, frequent downgrading of tumor complexity–risk categories, and numerical differences in predictive performance for postoperative complications and early renal functional decline compared with conventional CT-based scores. These findings suggest a potential role for HA3D modeling in preoperative planning for PN. However, given the limited sample size, these observations should be interpreted as exploratory and hypothesis-generating, and warrant validation in larger multicenter cohorts. Full article

Background/Objectives: Pulp stones are calcified masses within the dental pulp that may complicate endodontic procedures. Although cone beam computed tomography (CBCT) has been reported to detect pulp stones more frequently than two-dimensional radiography, direct comparisons between digital periapical radiography (DPR) and CBCT, particularly at different voxel sizes, remain limited. This study aimed to compare pulp stone detection rates between DPR and CBCT acquired at two voxel sizes and to evaluate inter-modality agreement using a location-based analysis for pulp stone identification in extracted teeth. Methods: Fifty-two extracted human teeth were examined using DPR and CBCT at voxel sizes of 0.2 mm and 0.1 mm under standardized ex vivo conditions. Pulp stones were evaluated in the coronal and radicular regions using a binary scoring system (presence/absence). Detection rates were compared across imaging modalities, and inter-modality agreement was evaluated using McNemar’s test in the analysis stratified by pulp stone location. Associations between pulp stone detection and age, gender, tooth status, and stone location were explored using chi-square tests. Results: CBCT at 0.1 mm demonstrated the highest detection rate for pulp stones (63.5%), followed by CBCT at 0.2 mm (57.7%) and DPR (50%), with no statistically significant differences among modalities (p > 0.05). Agreement analysis showed that CBCT at 0.2 mm had higher agreement with CBCT at 0.1 mm than DPR, particularly in the coronal region, whereas DPR showed lower agreement in the radicular region. No significant associations were observed between pulp stone detection and age, gender, or tooth status. Conclusions: Under standardized ex vivo conditions, CBCT showed numerically higher pulp stone detection rates than DPR. Location-based agreement analysis indicated greater consistency between CBCT voxel sizes than between CBCT and DPR, particularly in the radicular region. These findings highlight differences in pulp stone detectability across imaging modalities and voxel resolutions and may help explain variability in radiographic detection under controlled conditions. Full article

In this work, we provide a convolution type operator ${\mathsf{\Lambda }}_{\nu ,b}$ that is produced by the generalized Marcum Q-function and examine how it maps to various Janowski-type subclasses of harmonic univalent functions. Since the Marcum Q-function has an integral form via the lower incomplete gamma function, the convolution operator ${\mathsf{\Lambda }}_{\nu ,b}$ can be understood as a fractional type integral operator operating on the coefficients of harmonic mappings. Applying ${\mathsf{\Lambda }}_{\nu ,b}$ to harmonic mappings $f=h+\overline{g}$ in the unit disk $\mathbb{D}$, we derive coefficient inequalities, and inclusion relations for various subclasses of harmonic and analytic univalent functions. In particular, we give sufficient conditions for ${\mathsf{\Lambda }}_{\nu ,b}\left(f\right)$ to belong to Janowski-starlike families such as ${\mathcal{S}}_H^{\ast }(F,G)$, ${\mathcal{K}}_H^0$, and ${\mathcal{R}}_H(F,G)$. Closure properties of the Janowski class under the proposed operator are also established. Numerical tables and examples confirm the inclusion results, and graphical plots illustrate how the operator reshapes the image domains for different parameter pairs $(\nu ,b)$. Numerical illustrations are provided to visualize the geometric steering effect induced by the Marcum Q-function and its fractional-order damping behavior. Full article

This research delves into the impact of varying printing angles in the range (0°, 15°, 30°, 45°) on the thermal and mechanical characteristics of carbon fibre–PLA/PHA composites fabricated via fused filament fabrication (FFF). The microstructural arrangement within the 3D-printed PLA/PHA is unveiled through the application of SEM, X-ray microtomography and optical imaging. Tensile loading conditions are employed to extract meaningful mechanical parameters such as Young’s modulus, tensile strength, elongation at break, and mechanical energy, all of which are associated with the printing angle settings. The results indicate that the filaments exhibit a porosity of approximately 3%, while the porosity of the printed structure ranges from 27% to 38%, depending on the printing angle. Tensile modulus in the range 840 to 890 MPa is found not to be highly sensitive to the printing angle. However, tensile strength reaches 37 MPa for a printing angle of 30°. The variations across conditions are limited to approximately 6% in tensile stiffness and 16% in tensile strength. Finite element simulations based on 3D imaging indicate that an effective modulus of the solid phase between 1.6 and 1.8 GPa provides the closest agreement between experimental measurements and numerical predictions. This study presents novel findings concerning the deformation mechanisms associated with different length scales, from filament composite to filament arrangement, in the carbon fibre–PLA/PHA composite. This study highlights that while printing angle has a moderate influence on mechanical response, the overall structural integrity and interlayer cohesion of carbon fibre–PLA/PHA composites remain robust across a wide range of processing parameters, demonstrating their potential for reliable structural applications in additive manufacturing. Full article

Metal-enhanced fluorescence (MEF) has found widespread application in biomedical sensing and in vivo tissue imaging systems. To enhance MEF efficiency, it is necessary to optimize the interaction between the metal nanoparticle plasmon and the fluorophore molecule. The size and shape of the nanoparticle, the nanoscale gap between the fluorescent molecule and the nanoparticle, and the excitation wavelength are critical parameters. In this study, we propose a model for a more complete and accurate description of the processes of molecular excitation and generation of the fluorescence spectral response, introducing a new concept of effective properties for the field enhancement factor, quantum yield, and fluorescence enhancement factor. The influence of the spectral properties of both the nanostructure plasmon and the fluorophore molecule on the optimal tuning of fluorescent complexes is studied. Particular attention is paid to the analysis of the spectral properties of plasmon resonance and calculations of the near-field intensity enhancement of the plasmonic nanostructure’s excitation field. Numerical results for optimizing the MEF of fluorescent complexes based on TagRFP and gold (silver) nanorod composites are presented. The advantages of the proposed model for the optimal design of new nanomaterials with unique fluorescent properties are discussed. Full article

We propose Distribution-Preserving Latent Steganography via Conditional Optimal Transport (DPL-COT), a coverless image steganography framework for latent diffusion models. Unlike classical cover-modifying schemes, DPL-COT embeds a bitstream directly into the initialization noise latent ${\mathbf{z}}_T\sim \mathcal{N}(\mathbf{0},\mathbf{I})$ without model retraining. Our primary objective is high recoverability and a low bit error rate (BER) under deterministic inversion, which is inherently imperfect due to numerical discretization and VAE nonlinearity. To maximize decoding stability, we restrict embedding to the natural tails of the latent prior by selecting the largest-magnitude coordinates, thereby increasing the sign decision margin against inversion drift. To preserve distributional stealth, per-bit target values are analytically derived from truncated Gaussians matching the marginal distribution of the selected coordinates. Conditional 1D optimal transport is applied independently for each bit class, mapping every coordinate to its target value while preserving rank order. We generate 5000 stego images using a pretrained diffusion model and demonstrate a favorable capacity–reliability trade-off (e.g., 4916 bits/image with 0.473% mean BER) and strong robustness to JPEG compression (sub-1% mean BER at $Q=60$). Compared with LDStega, a recent LDM-based scheme reporting 99.28% clean-channel accuracy, DPL-COT achieves 99.53% at a comparable operating point and sustains above-99% accuracy under all tested JPEG quality factors. Latent-space tests further confirm negligible cover–stego distribution shift (mean ${\mathrm{KS}}_2<0.003$, mean $W_1<0.003$), a property not formally addressed by prior methods. Full article

Olive pomace biochar, obtained through the pyrolysis of lignocellulosic biomass, has emerged as a sustainable and multifunctional additive for polymer composites. Its physicochemical properties, including porosity, surface area, and electrical conductivity, can be tailored by controlling feedstock type and pyrolysis conditions. Although mechanical reinforcement and thermal stability improvements are well documented, the influence of biochar on surface-related properties such as wettability and contact angle remains insufficiently explored for environmentally relevant composite systems. In this study, epoxy-based composites containing biochar synthesized at 750 °C were evaluated in terms of their water interaction behavior by monitoring the evaporation dynamics of ultra-pure water droplets (10 μL, 0.055 mS/cm conductivity) at eight time intervals between 20 and 580 s using high-resolution digital microscopy. Image enhancement and segmentation were performed prior to Discrete Cosine Transform (DCT) analysis to describe droplet geometry in the frequency domain. Time-dependent variations in the standard deviations of DCT coefficients were optimized using the Bat Algorithm, resulting in mathematical models capable of accurately representing droplet evolution and surface–fluid interactions. The primary novelty of this study lies in the development of a hybrid experimental–computational framework that integrates droplet-based wettability measurements with signal-domain analysis and metaheuristic optimization. Unlike conventional studies focusing solely on material characterization, this approach establishes quantitative relationships between surface behavior and numerical descriptors derived from DCT and the Bat Algorithm. The proposed methodology provides a data-driven tool for predicting wettability trends in biochar-reinforced composites and supports the development of moisture-resistant materials for coatings, packaging, and thermal insulation applications within the context of sustainable composite design. Full article

In this paper, we propose an efficient numerical algorithm for solving large-scale ill-posed linear inverse problems encountered in image restoration. To boost computational efficiency, we extend the structured fast iterative shrinkage-thresholding algorithm (sFISTA) for addressing the corresponding $l_1$-regularized minimization problem, and further introduce the over-relaxation technique to accelerate the algorithm. The proposed algorithm is termed structured over-relaxed monotone FISTA (sOMFISTA). The convergence analysis of sOMFISTA is also conducted. The algorithmic framework of sOMFISTA is universally applicable to any non-smooth convex regularization term, exhibiting remarkable flexibility. Extensive numerical experiments are carried out to systematically validate the superiority in efficiency and performance of the proposed sOMFISTA. Full article

Coherent noise in digital holographic microscopy (DHM) seriously degrades the accuracy of quantitative phase imaging, limiting its applications in fields such as nondestructive testing. However, traditional numerical denoising methods struggle to achieve an ideal balance between noise suppression, detail preservation, and computational efficiency. To address this challenge, we propose a multi-scale attention efficient network (MAENet). This network employs a dual-encoder architecture to achieve complementary extraction of multi-scale features. To efficiently integrate the features from these two branches, a dual-branch dense attention fusion (DDAF) module is designed. It performs a weighted fusion of features from the dual branches via an adaptive attention mechanism and enhances feature representation via dense residual connections, significantly boosting the model’s denoising performance. Furthermore, a hierarchical fusion strategy is adopted to preserve high-frequency details in the shallow layers of the network while performing feature fusion in the deeper layers, thereby maximizing protection of image textures while effectively suppressing noise. To address the lack of paired training data in real-world scenarios, a DHM simulation system capable of simulating the key physical characteristics of coherent noise was constructed. Extensive experiments on the simulated dataset show that MAENet achieves a PSNR of 33.25 dB and an SSIM of 0.93042, outperforming various mainstream denoising algorithms and demonstrating its excellent performance in suppressing coherent noise, providing an effective solution for denoising in coherent imaging systems. Full article