Search Results (2,975)

This work aims to investigate the buckling and post-buckling behaviour of wood-based sandwich structures with and without a manufacturing defect, under compressive loading. The specimens were made by gluing birch veneers to a balsa wood core. The defect consisted of a central zone where glue was lacking between the skin and the core. A compression load was applied to the plate using the VERTEX test rig, with the plate placed on the upper surface of a rectangular box and bolted at its borders. The upper surface of the plate was monitored using optical and infrared cameras. The stereo digital image correlation method was used to capture the in-plane and out-of-plane deformations of the specimen, and to calculate the strains and stresses. The infrared camera enabled the failure scenario to be identified. The buckling behaviour of pristine specimens showed small local debonding in the post-buckling range, which was not detrimental to overall performance. In the presence of a manufacturing defect, the decrease in buckling load was only about 15%, but final failure occurred at lower compressive loads. Full article

Single-pixel imaging enables two-dimensional image capture through a single-pixel detector, yet extending this to three-dimensional or higher-dimensional information capture in single-pixel optical imaging systems has remained a challenging problem. In this study, we present a single-pixel camera system for three-dimensional (3D) imaging based on compressed sensing (CS) with continuous wave (CW) pseudo-random volume structured illumination. An estimated image, which incorporates both spatial and depth information of a 3D scene, is reconstructed using an L1-norm minimization reconstruction algorithm. This algorithm employs prior knowledge of non-overlapping objects as a constraint in the target space, resulting in improved noise performance in both numerical simulations and physical experiments. Our simulations and experiments demonstrate the feasibility of the proposed 3D CS framework. This approach achieves compressive sensing in a 3D information capture system with a measurement ratio of 19.53%. Additionally, we show that our CS 3D capturing system can accurately reconstruct the color of a target using color filter modulation. 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

Digital dentistry is undergoing rapid evolution, with three-dimensional imaging technologies increasingly integrated into routine clinical workflows. Originally developed for accurate dental arch reconstruction, modern intraoral scanners have demonstrated expanding versatility in capturing intraoral mucosal as well as perioral cutaneous structures. Concurrently, photogrammetry has emerged as a powerful method for full-face digital reconstruction, particularly valuable in orthodontic and prosthodontic treatment planning. These advances offer promising applications in forensic sciences, where high-resolution, three-dimensional documentation of anatomical details such as palatal rugae, lip prints, and bite marks can provide objective and enduring records for legal and investigative purposes. This study explores the forensic potential of two digital acquisition techniques by presenting two cadaveric cases of animal bite injuries. In the first case, an intraoral scanner (Dexis 3600) was used in an unconventional extraoral application to directly scan skin lesions. In the second case, photogrammetry was employed using a digital single-lens reflex (DSLR) camera and Agisoft Metashape, with standardized lighting and metric scale references to generate accurate 3D models. Both methods produced analyzable digital reconstructions suitable for forensic archiving. The intraoral scanner yielded dimensionally accurate models, with strong agreement with manual measurements, though limited by difficulties in capturing complex surface morphology. Photogrammetry, meanwhile, allowed for broader contextual reconstruction with high texture fidelity, albeit requiring more extensive processing and scale calibration. A notable advantage common to both techniques is the avoidance of physical contact and impression materials, which can compress and distort soft tissues, an especially relevant concern when documenting transient evidence like bite marks. These results suggest that both technologies, despite their different origins and operational workflows, can contribute meaningfully to forensic documentation of bite-related injuries. While constrained by the exploratory nature and small sample size of this study, the findings support the viability of digitized, non-destructive evidence preservation. Future perspectives may include the integration of artificial intelligence to assist with morphological matching and the establishment of digital forensic databases for pattern comparison and expert review. Full article

Artificial intelligence (AI) is revolutionarily shaping the entire landscape of medicine and particularly the privileged field of radiology, since it produces a significant amount of data, namely, images. Currently, AI implementation in radiology is continuously increasing, from automating image analysis to enhancing workflow management, and specifically, pediatric neuroradiology is emerging as an expanding frontier. Pediatric neuroradiology presents unique opportunities and challenges since neonates’ and small children’s brains are continuously developing, with age-specific changes in terms of anatomy, physiology, and disease presentation. By enhancing diagnostic accuracy, reducing reporting times, and enabling earlier intervention, AI has the potential to significantly impact clinical practice and patients’ quality of life and outcomes. For instance, AI reduces MRI and CT scanner time by employing advanced deep learning (DL) algorithms to accelerate image acquisition through compressed sensing and undersampling, and to enhance image reconstruction by denoising and super-resolving low-quality datasets, thereby producing diagnostic-quality images with significantly fewer data points and in a shorter timeframe. Furthermore, as healthcare systems become increasingly burdened by rising demands and limited radiology workforce capacity, AI offers a practical solution to support clinical decision-making, particularly in institutions where pediatric neuroradiology is limited. For example, the MELD (Multicenter Epilepsy Lesion Detection) algorithm is specifically designed to help radiologists find focal cortical dysplasias (FCDs), which are a common cause of drug-resistant epilepsy. It works by analyzing a patient’s MRI scan and comparing a wide range of features—such as cortical thickness and folding patterns—to a large database of scans from both healthy individuals and epilepsy patients. By identifying subtle deviations from normal brain anatomy, the MELD graph algorithm can highlight potential lesions that are often missed by the human eye, which is a critical step in identifying patients who could benefit from life-changing epilepsy surgery. On the other hand, the integration of AI into pediatric neuroradiology faces technical and ethical challenges, such as data scarcity and ethical and legal restrictions on pediatric data sharing, that complicate the development of robust and generalizable AI models. Moreover, many radiologists remain sceptical of AI’s interpretability and reliability, and there are also important medico-legal questions around responsibility and liability when AI systems are involved in clinical decision-making. Future promising perspectives to overcome these concerns are represented by federated learning and collaborative research and AI development, which require technological innovation and multidisciplinary collaboration between neuroradiologists, data scientists, ethicists, and pediatricians. The paper aims to address: (1) current applications of AI in pediatric neuroradiology; (2) current challenges and ethical considerations related to AI implementation in pediatric neuroradiology; and (3) future opportunities in the clinical and educational pediatric neuroradiology field. AI in pediatric neuroradiology is not meant to replace neuroradiologists, but to amplify human intellect and extend our capacity to diagnose, prognosticate, and treat with unprecedented precision and speed. Full article

Background/Objectives: The precise localization of cervical vertebrae in X-ray imaging was essential for effective diagnosis and treatment planning, particularly as the prevalence of cervical degenerative conditions increased with an aging population. Vertebrae from C2 to C7 were commonly affected by disorders such as ossification of the posterior longitudinal ligament (OPLL) and nerve compression caused by posterior osteophytes, necessitating thorough evaluation. However, manual annotation remained a major aspect of traditional clinical procedures, making it challenging to manage increasing patient volumes and large-scale medical imaging data. Methods: To address this issue, this study presented an automated approach for localizing cervical vertebrae and measuring neural foraminal distance. The proposed technique analyzed the neural foramen distance and intervertebral space using image enhancement to determine the degree of nerve compression. YOLOv8 was employed to detect and segment the cervical vertebrae. Moreover, by integrating automated cervical spine analysis with advanced imaging technologies, the system enabled rapid detection of abnormal intervertebral disc gaps, facilitating early identification of degenerative changes. Results: According to the results, the system achieved a spine localization accuracy of 99.5%, representing an 11.7% improvement over existing approaches. Notably, it outperformed previous methods by 66.67% in recognizing the C7 vertebra, achieving a perfect 100% accuracy. Conclusions: Furthermore, the system significantly streamlined the diagnostic workflow by processing each X-ray image in just 17.9 milliseconds. This approach markedly improved overall diagnostic efficiency. Full article

Post-surgical complications are common complications following saphenectomy surgery, and strategies to facilitate its resolution are essential for postoperative recovery. This study evaluated the effects of adhesive elastic taping on edema control in patients undergoing saphenectomy. A randomized controlled clinical trial was conducted with 40 patients of both sexes, divided into two groups: intervention (IG), which received taping immediately after surgery combined with standard compression, and a control group, which received standard treatment with compression stockings (CG). Assessments were performed preoperatively and seven days after surgery, including limb volume (indirect calculation), edema (dielectric constant analysis), Skin Elasticity Assessment (durometer), pain (Visual Analog Scale—VAS), limb functionality (Lower Extremity Functional Scale—LEFS), and ecchymosis area (Image J, version 1.51). Both groups showed a significant increase in edema postoperatively (IG: p = 0.003; CG: p = 0.001). The intervention group exhibited a trend toward volume reduction (p = 0.069), better functionality (p = 0.006)—skin elasticity was assessed using a durometer—and fewer ecchymoses (p = 0.002). Only the control group showed a significant increase in tissue firmness (p = 0.012). No significant difference in pain was observed between groups (p = 0.203). The application of taping demonstrated beneficial effects on postoperative functional recovery and ecchymosis control following saphenectomy. Full article

Secret image sharing (SIS) is an image protection technique based on cryptography. However, traditional SIS schemes have limited noise resistance, making it difficult to ensure reconstructed image quality. To address this issue, this paper proposes a robust SIS scheme based on QR codes, which enables the efficient and lossless reconstruction of the secret image without pixel expansion. Moreover, the proposed scheme maintains high reconstruction quality under noisy conditions. In the sharing phase, the scheme compresses the length of shares by optimizing polynomial computation and improving the pixel allocation strategy. Reed–Solomon coding is then incorporated to enhance the anti-noise capability during the sharing process, while achieving meaningful secret sharing using QR codes as carriers. In the reconstruction phase, the scheme further improves the quality of the reconstructed secret image by combining image inpainting algorithms with the error-correction capability of Reed–Solomon codes. The experimental results show that the scheme can achieve lossless reconstruction when the salt-and-pepper noise density is less than $d\le 0.02$, and still maintains high-quality reconstruction when $d\le 0.13$. Compared with the existing schemes, the proposed method significantly improves noise robustness without pixel expansion, while preserving the visual meaning of the QR code carrier, and achieves a secret sharing strategy that combines robustness and practicality. Full article

In the context of secure and efficient data transmission over the future Internet, particularly for remote sensing and geospatial applications, reversible data hiding (RDH) in encrypted hyperspectral images (HSIs) has emerged as a critical technology. This paper proposes a novel RDH scheme specifically designed for encrypted HSIs, offering enhanced embedding capacity without compromising data security or reversibility. The approach introduces a multi-layer block labeling mechanism that leverages the similarity of most significant bits (MSBs) to accurately locate embeddable regions. To minimize auxiliary information overhead, we incorporate an Extended Run-Length Encoding (ERLE) algorithm for effective label map compression. The proposed method achieves embedding rates of up to 3.79 bits per pixel per band (bpppb), while ensuring high-fidelity reconstruction, as validated by strong PSNR metrics. Comprehensive security evaluations using NPCR, UACI, and entropy confirm the robustness of the encryption. Extensive experiments across six standard hyperspectral datasets demonstrate the superiority of our method over existing RDH techniques in terms of capacity, embedding rate, and reconstruction quality. These results underline the method’s potential for secure data embedding in next-generation Internet-based geospatial and remote sensing systems. Full article

Agricultural image processing technology plays a critical role in enabling precise disease detection, accurate yield prediction, and various smart agriculture applications. However, its practical implementation faces key challenges, including environmental interference, data scarcity and imbalance datasets, and the difficulty of deploying models on resource-constrained edge devices. This paper presents a systematic review of recent advances in addressing these challenges, with a focus on three core aspects: environmental robustness, data efficiency, and model deployment. The study identifies that attention mechanisms, Transformers, multi-scale feature fusion, and domain adaptation can enhance model robustness under complex conditions. Self-supervised learning, transfer learning, GAN-based data augmentation, SMOTE improvements, and Focal loss optimization effectively alleviate data limitations. Furthermore, model compression techniques such as pruning, quantization, and knowledge distillation facilitate efficient deployment. Future research should emphasize multi-modal fusion, causal reasoning, edge–cloud collaboration, and dedicated hardware acceleration. Integrating agricultural expertise with AI is essential for promoting large-scale adoption, as well as achieving intelligent, sustainable agricultural systems. 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

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

Background and Clinical Significance: Petrous bone cholesteatoma is a rare and complex condition that poses significant challenges in terms of its diagnosis and treatment. This benign yet locally aggressive lesion can cause extensive destruction of the surrounding structures, potentially leading to serious complications. Case Presentation: We present a case of extensive petrous bone cholesteatoma involving nearly the entire skull base. High-resolution CT and MRI were used to assess the extent of the lesion and its relationship with critical neurovascular structures. The cholesteatoma extended from the petrous apex to the clivus, involving the internal auditory canal and Meckel’s cave, encasing the internal carotid artery, and compressing the brainstem. The surgical strategy included combined endoscopic transsphenoidal and transclival techniques with a retrolabyrinthine approach. The endoscopic component provided access to the anterior and central skull base regions, whereas the retrolabyrinthine approach allowed us to gain access to the posterior petrous area. Careful dissection was performed to separate the cholesteatoma from the internal carotid artery and the brainstem. Neuromonitoring was performed throughout the procedure to ensure cranial nerve integrity. This combined approach enabled gross total resection, and postoperative imaging confirmed successful tumor removal. The patient’s recovery was uneventful, and no new neurological deficits were observed. Conclusions: The successful management of this complex case demonstrates the efficacy and safety of combining endoscopic surgical approaches for extensive skull base cholesteatomas. This multi-corridor approach allows for maximal tumor resection while also minimizing the risks to critical neurovascular structures. Full article

A friction composite material which contains cellulose fiber, carbon fiber, wollastonite, graphite, and resin for use in oil-cooled friction units, hydromechanical boxes, and couplings was developed. The fabrication technique includes the formation of a paper layer based on the mixture of stated fibers via a wet-laid process, impregnation of the layer with phenolic resin, and hot pressing onto a steel carrier. The infrared spectra of the polymeric base (phenolic resin) were studied by solvent extraction. The structural-phase analysis of the obtained material was carried out by the SEM method, and the particle size distribution parameters of the composite components were estimated based on the images of the sample surface. The surface roughness parameters of the samples are as follows: Ra = 5.7 μm Rz = 31.4 μm. The tribotechnical characteristics of the material were tested in an oil medium at a load of 5.0 MPa and a rotation mode of 2000 rpm for 180 min in a pair with a steel 45 counterbody. The coefficient of friction of the developed material was 0.11–0.12; the degree of wear was 6.17 × 10⁻⁶ μm/mm. The degree of compression deformation of the composite is 0.36%, and the compressive strength is 7.8 MPa. The calculated kinetic energy absorbed and power level are 205 J/cm² and 110 W/cm², respectively. The main tribotechnical characteristics of the developed friction material correspond to industrial analogues. Full article

Background and Clinical Significance: Congenital malformations of the right atrium are rare. Their clinical presentation varies widely, from the absence of symptoms to sudden death, often being diagnosed incidentally by cardiac imaging. First described in 1955, the right atrial diverticulum is usually characterized as a pouch-like structure originating from the free atrial wall, or right atrial appendage. The prevalence of congenital malformations of the right atrium is unknown because few clinical cases have been reported. Associated complications include arrhythmias, pulmonary thromboembolism, progressive dilatation marked by a high risk of compression and rupture. In these cases, the optimal therapeutic approach is surgical resection. Case Presentation: We present the case of a 58-year-old, hypertensive female with a history of COVID-19 (Coronavirus Disease 2019), who was admitted for persistent dyspnea and chest pain. An electrocardiogram on arrival showed no arrhythmias or ischemic changes, and echocardiography revealed severe systolic dysfunction—a left ventricular ejection fraction (LVEF) of 20%, moderate mitral and tricuspid regurgitations, and a pericardial collection, adjacent to the right atrium, considered to be a localized pericardial effusion. Coronary angiography excluded ischemic etiology and a viral myocarditis was further suspected. Cardiac magnetic resonance imaging (IRM) showed a non-ischemic scar pattern in the interventricular septum, but also detected a well-defined large mass, which communicated with the right atrium through a 20 mm opening, suggestive of a right atrial diverticulum. Contrast echocardiography confirmed the communication between the cavity and the right atrium. A surgical resection of the large diverticulum was performed. Conclusions: The particularity of this case consists in the incidental identification of a rare cardiac malformation in an adult patient. Full article