Search Results (86)

To solve the low efficiency of traditional sheet metal measurement, this paper proposes a digital inspection method for sheet metal parts based on 3D point clouds. The 3D point cloud data of sheet metal parts are collected using a 3D laser scanner, and the topological relationship is established by using a K-dimensional tree (KD tree). The pass-through filtering method is adopted to denoise the point cloud data. To preserve the fine features of the parts, an improved voxel grid method is proposed for the downsampling of the point cloud data. Feature points are extracted via the intrinsic shape signatures (ISS) algorithm and described using the fast point feature histograms (FPFH) algorithm. After rough registration with the sample consensus initial alignment (SAC-IA) algorithm, an initial position is provided for fine registration. The improved iterative closest point (ICP) algorithm, used for fine registration, can enhance the registration accuracy and efficiency. The greedy projection triangulation algorithm optimized by moving least squares (MLS) smoothing ensures surface smoothness and geometric accuracy. The reconstructed 3D model is projected onto a 2D plane, and the actual dimensions of the parts are calculated based on the pixel values of the sheet metal parts and the conversion scale. Experimental results show that the measurement error of this inspection system for three sheet metal workpieces ranges from 0.1416 mm to 0.2684 mm, meeting the accuracy requirement of ±0.3 mm. This method provides a reliable digital inspection solution for sheet metal parts. Full article

Corrosion in maritime ballast tanks is a major driver of maintenance costs and operational risks for maritime assets. Inspections are hampered by complex geometries, hazardous conditions, and the limitations of conventional methods, particularly visual assessment, which struggles with subjectivity, accessibility, and early detection, especially under coatings. This paper critically examines these challenges and explores the potential of Light Detection and Ranging (LiDAR) and Hyperspectral Imaging (HSI) to form the basis of improved inspection approaches. We discuss LiDAR’s utility for accurate 3D mapping and providing a spatial framework and HSI’s potential for objective material identification and surface characterization based on spectral signatures along a wavelength range of 400-1000nm (visible and near infrared). Preliminary findings from laboratory tests are presented, demonstrating the basic feasibility of HSI for differentiating surface conditions (corrosion, coatings, bare metal) and relative coating thickness, alongside LiDAR’s capability for detailed geometric capture. Although these results do not represent a deployable system, they highlight how LiDAR and HSI could address key limitations of current practices and suggest promising directions for future research into integrated sensor-based corrosion assessment strategies. Full article

This paper presents a cloud-based architecture for the acquisition, transmission, and processing of acoustic data from hydrophone arrays, designed to enable the detection and monitoring of both surface and underwater vehicles. The proposed system offers a modular and scalable cloud infrastructure that supports real-time and distributed processing of hydrophone data collected in diverse aquatic environments. Acoustic signals captured by heterogeneous hydrophones—featuring varying sensitivity and bandwidth—are streamed to the cloud, where several machine learning algorithms can be deployed to extract distinguishing acoustic signatures from vessel engines and propellers in interaction with water. The architecture leverages cloud-based services for data ingestion, processing, and storage, facilitating robust vehicle detection and localization through propagation modeling and multi-array geometric configurations. Experimental validation demonstrates the system’s effectiveness in handling high-volume acoustic data streams while maintaining low-latency processing. The proposed approach highlights the potential of cloud technologies to deliver scalable, resilient, and adaptive acoustic sensing platforms for applications in maritime traffic monitoring, harbor security, and environmental surveillance. Full article

Atmospheric turbulence severely degrades high-resolution satellite videos through spatiotemporally coupled distortions, including temporal jitter, spatial-variant blur, deformation, and scintillation, thereby constraining downstream analytical capabilities. Restoring turbulence-corrupted videos poses a challenging ill-posed inverse problem due to the inherent randomness of turbulent fluctuations. While existing turbulence mitigation methods for long-range imaging demonstrate partial success, they exhibit limited generalizability and interpretability in large-scale satellite scenarios. Inspired by refractive-index structure constant ($C_n^2$) estimation from degraded sequences, we propose a physics-informed turbulence signature (TS) prior that explicitly captures spatiotemporal distortion patterns to enhance model transparency. Integrating this prior into a lucky imaging framework, we develop a Physics-Based Turbulence Mitigation Network guided by Turbulence Signature (TMTS) to disentangle atmospheric disturbances from satellite videos. The framework employs deformable attention modules guided by turbulence signatures to correct geometric distortions, iterative gated mechanisms for temporal alignment stability, and adaptive multi-frame aggregation to address spatially varying blur. Comprehensive experiments on synthetic and real-world turbulence-degraded satellite videos demonstrate TMTS’s superiority, achieving 0.27 dB PSNR and 0.0015 SSIM improvements over the DATUM baseline while maintaining practical computational efficiency. By bridging turbulence physics with deep learning, our approach provides both performance enhancements and interpretable restoration mechanisms, offering a viable solution for operational satellite video processing under atmospheric disturbances. Full article

Detecting small targets in infrared imagery remains highly challenging due to sub-pixel target sizes, low signal-to-noise ratios, and complex background clutter. This paper proposes PSHNet, a hybrid deep-learning framework that combines dense spatial heatmap supervision with geometry-aware regression for accurate infrared small-target detection. The network generates position–scale heatmaps to guide coarse localization, which are further refined through sub-pixel offset and size regression. A Complete IoU (CIoU) loss is introduced as a geometric regularization term to improve alignment between predicted and ground-truth bounding boxes. To better preserve fine spatial details essential for identifying small thermal signatures, an Enhanced Low-level Feature Module (ELFM) is incorporated using multi-scale dilated convolutions and channel attention. Experiments on the NUDT-SIRST and IRSTD-1k datasets demonstrate that PSHNet outperforms existing methods in IoU, detection probability, and false alarm rate, achieving IoU improvement and robust performance under low-SNR conditions. Full article

In this study, we investigate the presence of the Griffiths-like anomaly in the geometrically frustrated antiferromagnet ${\mathrm{HoBaCo}}_4$${\mathrm{O}}_{7+\delta }$ and globally its absence in ${\mathrm{ErBaCo}}_4$${\mathrm{O}}_{7+\delta }$, despite only small differences in the ionic radii, f-electron occupancy, and the corresponding crystal structures of the ${\mathrm{Ho}}^{3+}$ and ${\mathrm{Er}}^{3+}$-members. Previous studies have identified the Griffiths phase in the Dy-analog, ${\mathrm{DyBaCo}}_4$${\mathrm{O}}_{7+\delta }$, suggesting certain inherent features of this class of materials that regularly give rise to such anomalies. To explore the curious disappearance of such an anomalous feature in ${\mathrm{ErBaCo}}_4$${\mathrm{O}}_{7+\delta }$, we prepared a series of compounds with varying compositions ${\mathrm{Ho}}_{1-x}$${\mathrm{Er}}_x$${\mathrm{BaCo}}_4$${\mathrm{O}}_{7+\delta }$ ($0\le x\le 1$) and systematically studied the evolution of various physical properties as a function of Er-doping. Our experimental studies, including X-ray diffraction (XRD), magnetic, X-ray absorption spectroscopy (XAS), X-ray photoelectron spectroscopy (XPS), heat capacity, and muon spin relaxation spectroscopy ($\mu$SR spectroscopy), revealed that while the Griffiths-like anomaly indeed disappears with doping at the macroscopic level, signatures of inhomogeneity are retained in ${\mathrm{ErBaCo}}_4$${\mathrm{O}}_{7+\delta }$ too, at least at the local level. Overall, our results highlight the significant role of ionic radius and local structural distortions in stabilizing the Griffiths phase in this class of systems. Full article

The present study evaluates the viability of fabricating unmanned aerial vehicle (UAV) propellers using fused filament fabrication (FFF), with an emphasis on low-cost, desktop-scale production. The study’s backdrop is the recent adoption of UAVs and advancements in additive manufacturing. While the scope targets accessibility for individual and small-scale users, the results have broader implications for scalable UAV propulsion systems. The research was conducted within an experimental UAV development framework aimed at optimizing propeller performance through strategic material selection, geometrical design optimization, and additive manufacturing processes. Six propeller variants were manufactured using widely available thermoplastic polymers, including polyethylene terephthalate glycol-modified (PETG) and thermoplastic polyurethane (TPU), as well as photopolymer-based propellers fabricated using vat photopolymerization, also known as digital light processing (DLP). Mechanical and aerodynamic characterizations were performed to assess the structural integrity, flexibility, and performance of each material under dynamic conditions. Two blade configurations, a toroidal propeller with anticipated aerodynamic advantages and a conventional tri-blade propeller (Gemfan 51466-3)—were comparatively analyzed. The primary contribution of this work is the systematic evaluation of performance metrics such as thrust generation, acoustic signature, mechanical strength, and thermal stress imposed on the electrical motor, thereby establishing a benchmark for polymer-based propeller fabrication via additive manufacturing. The findings underscore the potential of polymeric materials and layer-based manufacturing techniques in advancing the design and production of UAV propulsion components. Full article

A well-known idea is to identify spheres, points, and hyperplanes in Euclidean space ${\mathbb{R}}^n$ with points in real projective space. To address geometric constraints such as intersections, tangencies, and angle requirements, it is important to also encode the orientations of hyperplanes and spheres. The natural space for encoding such geometric objects is the real projective quadric with signature $(n+1,2)$. In this article, we first provide a general formula for calculating the angles formed by the geometric objects encoded by the points of the quadric. The main result is the determination of a very simple parametrization of a Laguerre-type subgroup that acts transitively on the quadric while preserving the geometric nature of its points. That is, points of the quadric representing oriented spheres, points, and oriented hyperplanes in ${\mathbb{R}}^n$ are mapped by the action to points of the same geometric type. We also provide simple parametrizations of the isotropies of the action. The action described in this article provides the foundation for an effective solution to geometric constraint problems. Full article

The mid-wave infrared (MWIR) spectral range can provide a larger bandwidth for optical sensing and communication when the near-infrared band becomes congested. This range of thermal signatures can provide more information for digital imaging and object recognition, which can be unraveled from polarization-sensitive detection by integrating the metasurface of the subwavelength-scale structured interface to control light–matter interactions. To enforce the metasurface-enabled simultaneous detection and parallel analysis of polarization states in a compact footprint for 4-micron wavelength, we designed a high-contrast germanium metasurface with an axially asymmetric triangular nanoantenna with a height 0.525 times the working wavelength. First, we optimized linear polarization separation of a 52-degree angle with about 50% transmission efficiency, holding the meta-element aspect ratio within the 3.5–1.67 range. The transmission modulation in terms of periodicity and lattice resonance for the phase-gradient high-contrast dielectric metasurface in correlation with the scattering cross-section for both 1D and 2D cases has been discussed for reducing the aspect ratio to overcome the nanofabrication challenge. Furthermore, by employing the geometric phase, we achieved 40% and 60% transmission contrasts for the linear and circular polarization states, respectively, and reconstructed the Stokes vectors and output polarization states. Without any spatial multiplexing, this single metasurface unit cell can perform well in the division of focal plane Stokes thermal imaging, with an almost 10-degree field of view, and it has an excellent refractive index and height tolerance for nanofabrication. Full article

This study explores the acoustic properties of European Churches, influenced by architectural design, historical context, and spatial configurations. A comparative analysis of 83 Churches from different regions and periods combines literature reviews and empirical data to understand the interplay between architecture and acoustics. Key geometric parameters—volume, surface area, length, height, and aisle count—were compared with acoustic metrics to provide a comprehensive view of these sacred spaces. The study identified the key factors influencing acoustic characteristics, uncovering significant variability within the same architectural style. Linear Discriminant Analysis (LDA) further highlighted distinct patterns and outliers, showing that Gothic, Neoclassical, and modern architectural styles possess unique acoustic signatures. These findings challenge the assumption of uniform acoustics within similar styles, revealing that even minor architectural differences can substantially impact sound behavior. Outliers were particularly informative, representing Churches with unique acoustic properties, which shed light on how specific design elements affect sound propagation. The study underscores the complexity of the relationship between architecture and acoustics in Churches and suggests that further research should consider both quantitative measures and subjective experiences to fully capture the acoustic environment of these historic spaces. Full article

The high leaf morphological variability of European white oaks is largely documented in the botanical literature, and several papers have been published in the last two decades focusing on inter- and intraspecific leaf phenotypic plasticity. Studies involving landmark-based geometric morphometrics proved to be useful in highlighting relationships between leaf size and shape variation and environmental factors, phylogenetic patterns, or hybridization events. In this paper, the leaf size and shape variations of 18 populations of Quercus petraea distributed throughout a wide geographical area were analyzed by means of geometric morphometric methods (GMMs). This study involved 10 European countries and investigated the intraspecific leaf variability of Q. petraea within a wide latitudinal and longitudinal gradient. Analyses of variance for shape and centroid size were performed through Procrustes ANOVA. Multivariate analysis procedures, partial least squares method, and regression analyses were used to highlight possible patterns of covariation between leaf shape and size and geographical/environmental variables. The results revealed that the Q. petraea populations analyzed mainly differed in their leaf size, where a decrease was observed according to a north to south geographical gradient. Both leaf size and shape were found to be significantly related to latitude, and, to a lesser extent, to mean annual temperature and the leaf isotopic signature of ¹⁵N. All the other variables considered did not provide significant results. Unexpected differences observed comparing the leaf traits of geographically strictly adjacent populations suggest the involvement of local hybridization/introgression events. However, with a few exceptions, Q. petraea turned out to be quite conservative in its leaf shape and size at both the local and continental scale. Full article

The aim of the present work is the improvement of a free-wake model for the analysis of a small-scale two-bladed propeller in hover. The simulations are carried out using a BEM approach implemented in the medium-fidelity solver RAMSYS. An acoustic validation is also performed using the developed tool ACO-FWH. The work proves that even mild discrepancies in the propeller geometry must be accounted for as their influence is not negligible, especially on the aeroacoustics of the propeller. In particular, the proper modeling of the blades enables the correct identification of the sub-harmonics of the SPL spectra. An optimization procedure based on the application of the evolutionary Genetic Algorithm is followed to identify the values of the parameters describing the dissipative and diffusive properties in the Bhagwat–Leishman vortex core model, an upgraded version of the classical Lamb–Oseen one. On average, this approach enabled the further improvement of the accuracy of the numerical model in terms of acoustic signature evaluation with respect to the one obtained by only modeling blade dissimilarities. The results obtained demonstrate the promising capabilities of a fine-tuned free-wake medium-fidelity approach to simulate the aerodynamic and acoustic details of a small-scale propeller in hover, provided the accurate geometrical modeling of the propeller and the selection of suitable parameters to be used in the wake modeling. Full article

In this review work, we outline a conceptual path that, starting from the numerical investigation of the transition between weak chaos and strong chaos in Hamiltonian systems with many degrees of freedom, comes to highlight how, at the basis of equilibrium phase transitions, there must be major changes in the topology of submanifolds of the phase space of Hamiltonian systems that describe systems that exhibit phase transitions. In fact, the numerical investigation of Hamiltonian flows of a large number of degrees of freedom that undergo a thermodynamic phase transition has revealed peculiar dynamical signatures detected through the energy dependence of the largest Lyapunov exponent, that is, of the degree of chaoticity of the dynamics at the phase transition point. The geometrization of Hamiltonian flows in terms of geodesic flows on suitably defined Riemannian manifolds, used to explain the origin of deterministic chaos, combined with the investigation of the dynamical counterpart of phase transitions unveils peculiar geometrical changes of the mechanical manifolds in correspondence to the peculiar dynamical changes at the phase transition point. Then, it turns out that these peculiar geometrical changes are the effect of deeper topological changes of the configuration space hypersurfaces ${\sum }_v=V_N^{-1}\left(v\right)$ as well as of the manifolds ${\{M_v=V_N^{-1}((-\infty ,v]\left)\right\}}_v\in \mathbb{R}$ bounded by the ∑_v. In other words, denoting by vc the critical value of the average potential energy density at which the phase transition takes place, the members of the family ${\left\{{\sum }_v\right\}}_{v<v_c}$ are not diffeomorphic to those of the family ${\left\{{\sum }_v\right\}}_{v>v_c}$; additionally, the members of the family ${\left\{M_v\right\}}_{v>v_c}$ are not diffeomorphic to those of ${\left\{M_v\right\}}_{v>v_c}$. The topological theory of the deep origin of phase transitions allows a unifying framework to tackle phase transitions that may or may not be due to a symmetry-breaking phenomenon (that is, with or without an order parameter) and to finite/small N systems. Full article

The ATLAZUL project is an Interreg effort among 18 partners from Spain and Portugal along the Atlantic Iberian coasts. One of its objectives is the development of new methods and data processing for oceanic information to produce useful products for private and public stakeholders. This study proposes a new insight on the sea surface dynamic of the ATLAZUL area based on almost two years of multiscale high resolution sea surface temperature imagery. The use of techniques such as the Karhunen–Loève transform (Empirical Orthogonal Function) and the Maximum Entropy Spectral Analysis were applied to study long- and short-term features in the sea surface temperature imagery. Mathematical Morphology and the Geometrical Theory of Measure are utilized to compute the Medial Axis Transform and the Hausdorff dimension. The results can be summarized as follows: (i) the tow upwelling areas are identified along the Galician–Portugal coast as indicated in the second and third modes of KLT/EOF analysis, and they are partially affected by wind; (ii) the tow warm water outflows from the Bay of Cádiz to the Gulf of Cádiz are identified as the second and third modes of KLT/EOF analysis, which are also influenced by wind; (iii) the skeletons of the surface signature of the upwelling and of the warmer water outflow, along with their fractal dimensions, indicate a chaotic pattern of spatial distribution and (iv) the harmonic prediction model should be combined with the wind prediction. Full article

In the process of using structured light technology to obtain indoor point clouds, due to the limited field of view of the device, it is necessary to obtain multiple point clouds of different wall surfaces. Therefore, merging the point cloud is necessary to acquire a complete point cloud. However, due to issues such as the sparse geometric features of the wall point clouds and the high similarity of multiple point clouds, the merging effect of point clouds is poor. In this paper, we leverage artificially enhanced features to improve the accuracy of registration in this scenario. Firstly, we design feature markers and present their layout criteria. Then, the feature information of the marker is extracted based on the Color Signature of Histograms of OrienTations (Color-SHOT) descriptor, and coarse registration is realized through the second-order similarity measure matrix. After that, precise registration is achieved using the Iterative Closest Point (ICP) method based on markers and overlapping areas. Finally, the global error of the point cloud registration is optimized by loop error averaging. Our method enables the high-precision reconstruction of integrated home design scenes lacking significant features at a low cost. The accuracy and validity of the method were verified through comparative experiments. Full article