Search Results (291)

Three-dimensional holographic imaging technology is increasingly applied in biomedical detection, materials science, and industrial non-destructive testing. Achieving high-resolution, large-field-of-view, and high-speed three-dimensional imaging has become a significant challenge. This paper proposes and implements a three-dimensional holographic imaging method based on trillion-frame-frequency all-optical multiplexing. This approach combines spatial and temporal multiplexing to achieve multi-channel partitioned acquisition of the light field via a two-dimensional diffraction grating, significantly enhancing the system’s imaging efficiency and dynamic range. The paper systematically derives the theoretical foundation of holographic imaging, establishes a numerical reconstruction model based on angular spectrum propagation, and introduces iterative phase recovery and image post-processing strategies to optimize reproduction quality. Experiments using standard resolution plates and static particle fields validate the proposed method’s imaging performance under static conditions. Results demonstrate high-fidelity reconstruction approaching diffraction limits, with post-processing further enhancing image sharpness and signal-to-noise ratio. This research establishes theoretical and experimental foundations for subsequent dynamic holographic imaging and observation of large-scale complex targets. Full article

Background/Objectives: Occlusal analysis is an important component of oral rehabilitation with a determining role in the prognosis of restorations. Over time, several qualitative and quantitative occlusal analysis methods have been proposed, starting with occlusion wax up to the most advanced digital systems. The objective of the present study was to evaluate and compare the data obtained through dental occlusion analysis using the Medit i700 and Exocad Elefsina v3.2 in a group of subjects, in order to establish the reliability or compatibility between the two occlusal analysis systems. Methods: The study was conducted on 20 subjects, aged between 24 and 53 years, who presented in the Dental Prosthetics Clinic of the University of Medicine and Pharmacy of Craiova. Digital impressions were acquired using the Medit Link v.3.3.6 intraoral scanner, and the digital files were subsequently uploaded from the Medit i700 into the Medit Occlusion Analyzer application and the Dental CAD Exocad software. For the analysis of occlusion in dynamics, mandibular movements and data acquisition, positions of edge-to-edge in protrusion, edge-to-edge in right laterotrusion and edge-to-edge in left laterotrusion were recorded, using the corresponding print screens. The 2D occlusal contact images generated by the two software programs were converted into .jpeg format and subsequently imported into Adobe Photoshop CS6 (2021) for comparative analysis. The data were statistically processed for each software used and the obtained data were subsequently compared. Results: The occlusal surfaces recorded with the Medit Occlusion Analyzer application represent 94% of the occlusal surfaces recorded with the Exocad software for the maxilla and 90% of the occlusal surfaces recorded for the mandible. In maximum intercuspation, the highest values were recorded by the Medit i700 software, whereas in edge-to-edge protrusion and both right and left edge-to-edge laterotrusion positions, the highest values were reported by the Exocad software. The discrepancy between maxillary and mandibular values arises from the conversion of the data from a three-dimensional to a two-dimensional format during image processing. Conclusions: The occlusal areas recorded by the DentalCAD Exocad software show higher values than those provided by the Medit Link software with the Medit Occlusion Analyzer application. The differences in recorded values, in the case of the digital flow of prosthetic restorations, require the intervention of the dentist to perform clinical adjustments to optimize occlusal relationships after the fabrication and cementation of restorations. Full article

High-resolution three-dimensional (3D) Inverse Synthetic Aperture Radar (ISAR) imaging is essential for the characterization of target scattering in various environments. The practical application of this technique is frequently impeded by the lengthy measurement time necessary for comprehensive data acquisition with turntable-based systems. Sub-sampling the aperture can decrease acquisition time; however, traditional reconstruction algorithms that utilize matched filtering exhibit significantly impaired imaging performance, often characterized by a high peak side-lobe ratio. A methodology is proposed that integrates compressed sensing(CS) theory with sparse-aperture optimization to achieve high-fidelity 3D imaging from sparsely sampled data. An optimized sparse sampling aperture is introduced to systematically balance the engineering requirement for efficient, continuous turntable motion with the low mutual coherence desired for the CS matrix. A deep Bayesian optimization framework was developed to automatically identify physically realizable optimal sampling trajectories, ensuring that the sensing matrix retains the necessary properties for accurate signal recovery. This method effectively addresses the high-sidelobe problem associated with traditional sparse techniques, significantly decreasing measurement duration while maintaining image quality. Quantitative experimental results indicate the method’s efficacy: the optimized sparse aperture decreases the number of angular sampling points by roughly 84% compared to a full acquisition, while reconstructing images with a high correlation coefficient of 0.98 to the fully sampled reference. The methodology provides an effective solution for rapid, high-performance 3D ISAR imaging, achieving an optimal balance between data acquisition efficiency and reconstruction fidelity. Full article

Background/Objectives: The recent development of four-dimensional X-ray velocimetry (4DXV) technology (three-dimensional space and time) provides a unique opportunity to obtain preclinical quantitative functional lung images. Only single-scan measurements in non-survival studies have been obtained to date; thus, methodologies enabling animal survival for repeated imaging to be accomplished over weeks or months from the same animal would establish new opportunities for the assessment of pathophysiology drivers and treatment response in advanced preclinical drug-screening efforts. Methods: An anesthesia protocol developed for animal recovery to allow for repetitive, longitudinal scanning of individual animals over time. Test–retest imaging scans from the lungs of healthy mice were performed over 8 weeks to assess the repeatability of scanner-derived quantitative imaging metrics and variability. Results: Using a murine model of fibroproliferative lung disease, this longitudinal scanning approach captured heterogeneous progressive changes in pulmonary function, enabling the visualization and quantitative measurement of averaged whole lung metrics and spatial/regional change. Radiation dosimetry studies evaluated the effects of imaging acquisition protocols on X-ray dosage to further adapt protocols for the minimization of radiation exposure during repeat imaging sessions using these newly developed image acquisition protocols. Conclusions: Overall, we have demonstrated that the 4DXV advanced imaging scanner allows for repeat measurements from the same animal over time to enable the high-resolution, noninvasive mapping of quantitative lung airflow dysfunction in mouse models with heterogeneous pulmonary disease. The animal anesthesia and image acquisition protocols described will serve as the foundation on which further applications of the 4DXV technology can be used to study a diverse array of murine pulmonary disease models. Together, 4DXV provides a novel and significant advancement for the longitudinal, noninvasive interrogation of pulmonary disease to assess spatial/regional disease initiation, progression, and response to therapeutic interventions. Full article

To address the technical challenges in dynamic monitoring of grout diffusion patterns under complex geological conditions, in this study, a distributed parallel grouting monitoring system based on electrical resistivity tomography was developed. The system achieves three-dimensional visualization of grout propagation through hardware architecture innovation and the integration of inversion algorithms. At the hardware level, a cascadable distributed data acquisition terminal was designed, employing a dynamic optimization strategy for electrode combinations. This breakthrough overcomes traditional serial acquisition limitations. Algorithmically, a Bayesian estimation-based geological unit merging inversion model was proposed; it dynamically calculates merging thresholds through the noise posterior probability, achieving an improvement of more than 30% in the inversion boundary resolution compared with traditional least squares methods. Numerical simulations and physical experiments demonstrated that dipole arrays with 0.5 m electrode spacing exhibit optimal sensitivity to variations in grout resistivity, accurately capturing electrical response characteristics during diffusion. In practical roadbed grouting applications, the system yielded a grout diffusion radius showing only a 0.3 m deviation from the core sampling verification results, with three-dimensional imaging clearly depicting the diffusion morphology. This system provides reliable technical support for the precise control and quality assessment of underground engineering grouting processes. Full article

Currently, the integration of deep learning technology with metasurface holographic imaging technology has propelled the development of optical imaging. Owing to the precise control of metasurfaces over the characteristics of light waves, holographic imaging technology can produce corresponding three-dimensional images after processing. Therefore, their integration enables the acquisition of high-quality images. The number of articles on metasurface design using neural network-based deep learning methods is increasing day by day; however, reviews on this topic remain scarce. This review introduces the development of neural networks and the relevant content on metasurface design using the four types of networks and the applications of deep learning-designed metasurface holographic imaging technology, thereby enhancing readers’ systematic understanding of such technologies. 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

The structural health monitoring (SHM) of existing infrastructure and heritage buildings is essential for their preservation and safety. This is a review paper which focuses on modern three-dimensional (3D) measurement techniques, particularly those that enable the assessment of the structural response to environmental actions and operational conditions. The emphasis is on the detection of fractures and the identification of the crack geometry. While traditional monitoring systems—such as pendula, callipers, and strain gauges—have been widely used in massive, quasi-brittle structures like dams and masonry buildings, advancements in non-contact and computer-vision-based methods are increasingly offering flexible and efficient alternatives. The integration of drone-mounted systems facilitates access to challenging inspection zones, enabling the acquisition of quantitative data from full-field surface measurements. Among the reviewed techniques, digital image correlation (DIC) stands out for its superior displacement accuracy, while photogrammetry and time-of-flight (ToF) technologies offer greater operational flexibility but require additional processing to extract displacement data. The collected information contributes to the calibration of digital twins, supporting predictive simulations and real-time anomaly detection. Emerging tools based on machine learning and digital technologies further enhance damage detection capabilities and inform retrofitting strategies. Overall, vision-based methods show strong potential for outdoor SHM applications, though practical constraints such as drone payload and calibration requirements must be carefully managed. Full article

This study proposes a novel vehicle speed prediction model for plateau transportation—CLS-DW Stacking (Constrained Least Squares Dynamic Weighting Model Stacking)—which holds significant implications for the sustainable development of transportation systems in high-altitude regions. Research on sharp-curved roads on mountainous plateaus remains scarce. Compared with plain areas, data acquisition in such regions is constrained by government confidentiality policies, while complex environmental and topographical conditions lead to substantial variations in road alignment and elevation. To address these challenges, this study presents a sustainable data acquisition and construction method: unmanned aerial vehicle (UAV) video data are processed through road image segmentation, trajectory tracking, and three-dimensional modeling to generate multi-source heterogeneous datasets for both single-curve and continuous-curve scenarios. Building upon these datasets, the proposed framework integrates constrained least squares with multiple deep learning methods to achieve accurate traffic flow prediction. Bi-LSTM (Bidirectional Long Short-Term Memory), Informer, and GRU (Gated Recurrent Unit) are employed as base learners, and the loss function is redefined with non-negativity and normalization constraints on the weights. This ensures optimal weight coefficients for each base learner, with the final prediction obtained via weighted summation. The experimental results show that, compared with single deep learning models such as Informer, the proposed model reduces the mean squared error (MSE) by 1.9% on the single curve dataset and by 7.7% on the continuous curve dataset. Furthermore, by combining vehicle speed predictions across different altitude gradients with decision tree-based interpretable analysis, this research provides scientific support for developing altitude-specific and precision-oriented speed limit policies. The outcomes contribute to accident risk reduction, traffic congestion mitigation, and carbon emission reduction, thereby improving road resource utilization efficiency. This work not only fills the research gap in traffic prediction for sharp-curved plateau roads but also supports the construction of green transportation systems and the broader objectives of sustainable development in high-altitude regions. Full article

With the advancement of precision agriculture, Unmanned Aerial Vehicle (UAV)-based remote sensing has been increasingly employed for monitoring crop water and nutrient status due to its high flexibility, fine spatial resolution, and rapid data acquisition capabilities. This review systematically examines recent research progress and key technological pathways in UAV-based remote sensing for crop water and nutrient monitoring. It provides an in-depth analysis of UAV platforms, sensor configurations, and their suitability across diverse agricultural applications. The review also highlights critical data processing steps—including radiometric correction, image stitching, segmentation, and data fusion—and compares three major modeling approaches for parameter inversion: vegetation index-based, data-driven, and physically based methods. Representative application cases across various crops and spatiotemporal scales are summarized. Furthermore, the review explores factors affecting monitoring performance, such as crop growth stages, spatial resolution, illumination and meteorological conditions, and model generalization. Despite significant advancements, current limitations include insufficient sensor versatility, labor-intensive data processing chains, and limited model scalability. Finally, the review outlines future directions, including the integration of edge intelligence, hybrid physical–data modeling, and multi-source, three-dimensional collaborative sensing. This work aims to provide theoretical insights and technical support for advancing UAV-based remote sensing in precision agriculture. Full article

Objectives: This study aimed to evaluate the usefulness of a short magnetic resonance (MR) protocol for knee evaluation, using 3D PDW SPAIR sequences compared with traditional 2D ones. Methods: A prospective analysis included 76 patients with knee pain. MR was performed using a 1.5 T scanner. The standard protocol consisted of multiplanar 2D proton density-weighted (PDW) SPectral Attenuated Inversion Recovery (SPAIR) and additional T1-weighted (T1W) and T2-weighted (T2W) sequences, with a total acquisition time of 17 min. The simulated short protocol included a 3D PDW SPAIR sequence with isotropic voxels and a slice thickness of 0.6 mm, coronal T1W, and gradient echo (GRE) axial sequences, with a total acquisition time of 9 min. Two radiologists independently reviewed images and collected pathological processes. Results: The 3D PDW SPAIR sequence demonstrated a significantly higher subjective image quality compared to standard 2D sequences [κ = 0.712 (p < 0.001) vs. κ = 0.144 (p = 0.63); p < 0.001]. Artifacts were not significantly different between the two protocols (p = 0.201). Qualitative assessments showed superior ratings for 3D images (excellent quality: 72.4% vs. 26.3% for 3D and 2D, respectively; p < 0.001). Diagnostic performance was comparable between the two protocols for ACL injuries, medial and lateral collateral ligament injuries, and chondropathies. Three-dimensional sequences were more effective in detecting medial meniscal injuries (p < 0.001), particularly radial and complex tears, likely due to higher spatial resolution and multiplanar reconstruction capability. Conclusions: The 3D PDW SPAIR sequence offers advantages in knee MRI study, including improved image quality, reduced acquisition time, and superior detection of meniscal injuries. Full article

Hyperspectral imaging for seed vigor detection faces the challenges of handling high-dimensional spectral data, information loss after dimensionality reduction, and low feature differentiation between vigor levels. To address the above issues, this study proposes an improved dynamic optimize MDS (DO-MDS) dimensionality reduction algorithm based on multidimensional scaling transformation. DO-MDS better preserves key features between samples during dimensionality reduction. Secondly, a dual-stream spectral collaborative attention (DSCA) module is proposed. The DSCA module adopts a dual-modal fusion approach combining global feature capture and local feature enhancement, deepening the characterization capability of spectral features. This study selected commonly used rice seed varieties in Zhejiang Province and constructed three individual spectral datasets and a mixed dataset through aging, spectral acquisition, and germination experiments. The experiments involved using the DO-MDS processed datasets with a convolutional neural network embedded with the DSCA attention module, and the results demonstrate vigor discrimination accuracy rates of 93.85%, 93.4%, and 96.23% for the Chunyou 83, Zhongzao 39, and Zhongzu 53 datasets, respectively, achieving 94.8% for the mixed dataset. This study provides effective strategies for spectral dimensionality reduction in hyperspectral seed vigor detection and enhances the differentiation of spectral information for seeds with similar vigor levels. Full article

This research addresses the long-standing dependence on printed maps for navigation and highlights the limitations of existing digital services like Google Street View and Google Street View Player in providing comprehensive solutions for route analysis and understanding. The absence of a systematic approach to route analysis, issues related to insufficient street view images, and the lack of proper image mapping for desired roads remain unaddressed by current applications, which are predominantly client-based. In response, we propose an innovative automatic system designed to generate videos depicting road routes between two geographic locations. The system calculates and presents the route conventionally, emphasizing the path on a two-dimensional representation, and in a multimedia format. A prototype is developed based on a cloud-based client–server architecture, featuring three core modules: frames acquisition, frames analysis and elaboration, and the persistence of metadata information and computed videos. The tests, encompassing both real-world and synthetic scenarios, have produced promising results, showcasing the efficiency of our system. By providing users with a real and immersive understanding of requested routes, our approach fills a crucial gap in existing navigation solutions. This research contributes to the advancement of route planning technologies, offering a comprehensive and user-friendly system that leverages cloud computing and multimedia visualization for an enhanced navigation experience. Full article

In order to meet the needs for the rapid detection of crop growth and support variable management in farmland, an intelligent chlorophyll content in cotton leaves (CCC) detection system based on hyperspectral imaging (HSI) technology was designed and developed. The system includes a near-infrared (NIR) hyperspectral image acquisition module, a spectral extraction module, a main control processor module, a model acceleration module, a display module, and a power module, which are used to achieve rapid and non-destructive detection of chlorophyll content. Firstly, spectral images of cotton canopy leaves during the seedling, budding, and flowering-boll stages were collected, and the dataset was optimized using the first-order differential algorithm (1D) and Savitzky–Golay five-term quadratic smoothing (SG) algorithm. The results showed that SG had better processing performance. Secondly, the sparrow search algorithm optimized backpropagation neural network (SSA-BPNN) and one-dimensional convolutional neural network (1DCNN) algorithms were selected to establish a chlorophyll content detection model. The results showed that the determination coefficients $R_p^2$ of the chlorophyll SG-1DCNN detection model during the seedling, budding, and flowering-boll stages were 0.92, 0.97, and 0.95, respectively, and the model performance was superior to SG-SSA-BPNN. Therefore, the SG-1DCNN model was embedded into the detection system. Finally, a CCC intelligent detection system was developed using Python 3.12.3, MATLAB 2020b, and ENVI, and the system was subjected to application testing. The results showed that the average detection accuracy of the CCC intelligent detection system in the three stages was 98.522%, 99.132%, and 97.449%, respectively. Meanwhile, the average detection time for the samples is only 20.12 s. The research results can effectively solve the problem of detecting the nutritional status of cotton in the field environment, meet the real-time detection needs of the field environment, and provide solutions and technical support for the intelligent perception of crop production. Full article

Technological development has strongly impacted all processes related to the design, construction, and management of real estate assets. In fact, the introduction of the BIM approach has required the application of three-dimensional survey technologies, and in particular the use of LiDAR instruments, both in their static (TLS—terrestrial laser scanner) and dynamic (iMMS—indoor mobile mapping system) implementations. Operators and developers of LiDAR technologies, for the implementation of scan-to-BIM procedures, initially placed particular care on the 3D surveying accuracy obtainable from such tools. The incorporation of RGB sensors into these instruments has progressively expanded LiDAR-based applications from essential topographic surveying to geospatial applications, where the emphasis is no longer on the accurate three-dimensional reconstruction of buildings but on the capability to create three-dimensional image-based visualizations, such as virtual tours, which allow the recognition of assets located in every area of the buildings. Although much has been written about obtaining the best possible accuracy for extensive asset surveying of large-scale building complexes using iMMS systems, it is now essential to develop and define suitable procedures for controlling such kinds of surveying, targeted at specific geospatial applications. We especially address the design, field acquisition, quality control, and mass data management techniques that might be used in such complex environments. This work aims to contribute by defining the technical specifications for the implementation of geospatial mapping of vast asset survey activities involving significant building sites utilizing iMMS instrumentation. Three-dimensional models can also facilitate virtual tours, enable local measurements inside rooms, and particularly support the subsequent integration of self-locating image-based technologies that can efficiently perform field updates of surveyed databases. Full article