Search Results (111)

Future lunar exploration will depend on a clearer understanding of regolith behavior, as underscored by adhesion issues observed during Apollo. The Lunaris Payload, a compact instrument developed in Poland, targets in situ assessment of lunar regolith adhesion to engineering materials using a resource-constrained optical approach. Here we introduce and validate six lightweight 2D-to-3D geometric models for estimating particle volume from planar images, benchmarked against the high-resolution micro-computed tomography (micro-CT) ground truth. The tested methods include spherical, cylindrical, fixed-aspect-ratio ellipsoid, adaptive ellipsoid, and Feret-based models and an empirically scaled voxel proxy. Using micro-CT scans of adhered simulant particles, we evaluate accuracy across >8000 particles segmented from 2D projections. Ellipsoid-based models consistently outperform the alternatives, with absolute percentage errors of 30–35%, while fixed-aspect-ratio variants offer strong accuracy–complexity trade-offs suitable for mass- and power-limited payloads. To our knowledge, this is the first comprehensive benchmarking of six 2D-to-3D volume models against micro-CT for bulk-adhered lunar regolith analogs. The results provide a validated, efficient framework for in situ dust characterization and reliable particle mass estimation, advancing Lunaris’ capability to quantify regolith adhesion and supporting broader goals in dust mitigation, ISRU, or habitat construction. Full article

The Loran-C system employs the spherical hyperbola positioning (SHP) method. However, SHP has three drawbacks in inland regions: first, approximating the Earth’s ellipsoid as a sphere introduces positioning errors; second, hyperbola positioning inherently suffers from a high geometric dilution of precision (GDOP) value; third, it is not easy to simultaneously receive long-wave signals from an entire chain of stations under complex propagation paths, which, to some extent, limits the application and development of the Loran-C system in inland areas. This paper addresses the limitations of the SHP algorithm and introduces the ellipsoidal pseudorange positioning (EPP) method, which eliminates the need to approximate the Earth’s ellipsoid as a sphere. This pseudorange positioning algorithm reduces the GDOP value, enabling navigation and positioning with signals from just three stations, thereby breaking through the restriction of requiring signals from a single chain. Simulation analyses were conducted for various Loran-C chains in China. Due to differences in the geometric layout of the chains, the EPP algorithm improved the positioning coverage area by 129.1% to 284.6% compared to the SHP algorithm. In field test data from the Maoming region of Guangdong Province, China (a typical inland mountainous environment), the EPP algorithm significantly reduced the root mean square error (RMSE), from 417.2 m with the SHP algorithm to 43.1 m, representing an improvement of 89.7%. Both the simulation and experimental results demonstrate that the EPP method effectively addresses errors in Earth ellipsoid modeling, significantly reduces the GDOP, and substantially improves the positioning accuracy and usable area of the Loran-C system in complex inland terrain. This provides more reliable technical support for Loran-C applications in inland navigation, timing, and timing backup. Full article

Accurate localization plays a critical role in enabling underwater vehicle autonomy. In this work, we develop a robust infrastructure-based localization framework that estimates the position and orientation of underwater vehicles using only range measurements from long baseline (LBL) acoustic beacons to multiple on-board receivers. The proposed framework integrates three key components, each formulated as a convex optimization problem. First, we introduce a robust calibration function that unifies multiple sources of measurement error—such as range-dependent degradation, variable sound speed, and latency—by modeling them through a monotonic function. This function bounds the true distance and defines a convex feasible set for each receiver location. Next, we estimate the receiver positions as the center of this feasible region, using two notions of centrality: the Chebyshev center and the maximum volume inscribed ellipsoid (MVE), both formulated as convex programs. Finally, we recover the vehicle’s full 6-DOF pose by enforcing rigid-body constraints on the estimated receiver positions. To do this, we leverage the known geometric configuration of the receivers in the vehicle and solve the Orthogonal Procrustes Problem to compute the rotation matrix that best aligns the estimated and known configurations, thereby correcting the position estimates and determining the vehicle orientation. We evaluate the proposed method through both numerical simulations and field experiments. To further enhance robustness under real-world conditions, we model beacon-location uncertainty—due to mooring slack and water currents—as bounded spherical regions around nominal beacon positions. We then mitigate the uncertainty by integrating the modified range constraints into the MVE position estimation formulation, ensuring reliable localization even under infrastructure drift. Full article

To address the decline in measurement accuracy of magnetometers due to process errors and environmental interference, as well as the insufficient robustness of traditional calibration algorithms under strong interference conditions, this paper proposes an ellipsoid fitting algorithm based on Dynamic Adaptive Elite Particle Swarm Optimization (DAEPSO). The proposed algorithm integrates three enhancement mechanisms: dynamic stratified elite guidance, adaptive inertia weight adjustment, and inferior particle relearning via Lévy flight, aiming to improve convergence speed, solution accuracy, and noise resistance. First, a magnetometer calibration model is established. Second, the DAEPSO algorithm is employed to fit the ellipsoid parameters. Finally, error calibration is performed based on the optimized ellipsoid parameters. Our simulation experiments demonstrate that compared with the traditional Least Squares Method (LSM) the proposed method reduces the standard deviation of the total magnetic field intensity by 54.73%, effectively improving calibration precision in the presence of outliers. Furthermore, when compared to PSO, TSLPSO, MPSO, and AWPSO, the sum of the absolute distances from the simulation data to the fitted ellipsoidal surface decreases by 53.60%, 41.96%, 53.01%, and 27.40%, respectively. The results from 60 independent experiments show that DAEPSO achieves lower median errors and smaller interquartile ranges than comparative algorithms. In summary, the DAEPSO-based ellipsoid fitting algorithm exhibits high fitting accuracy and strong robustness in environments with intense interference noise, providing reliable theoretical support for practical engineering applications. Full article

As a globally important cash crop, the optimization of tomato yield and quality is strategically significant for food security and sustainable agricultural development. In order to address the problem of missing point cloud data on fruits in a facility agriculture environment due to complex canopy structure, leaf shading and limited collection viewpoints, the traditional geometric fitting method makes it difficult to restore the real morphology of fruits due to the dependence on data integrity. This study proposes an adaptive symmetry self-matching (ASSM) algorithm. It dynamically adjusts symmetry planes by detecting defect region characteristics in real time, implements point cloud completion under multi-symmetry constraints and constructs a triple-orthogonal symmetry plane system to adapt to multi-directional heterogeneous structures under complex occlusion. Experiments conducted on 150 tomato fruits with 5–70% occlusion rates demonstrate that ASSM achieved coefficient of determination (R²) values of 0.9914 (length), 0.9880 (width) and 0.9349 (height) under high occlusion, reducing the root mean square error (RMSE) by 23.51–56.10% compared with traditional ellipsoid fitting. Further validation on eggplant fruits confirmed the cross-crop adaptability of the method. The proposed ASSM method overcomes conventional techniques’ data integrity dependency, providing high-precision three-dimensional (3D) data for monitoring plant growth and enabling accurate phenotyping in smart agricultural systems. Full article

Fruit orchard canopy recognition and characteristic extraction are the key problems faced in orchard precision production. To this end, we built a fruit tree canopy detection platform based on millimeter-wave radar, verified the feasibility of millimeter-wave radar from the two perspectives of fruit orchard canopy recognition and canopy characteristic extraction, and explored the detection accuracy of millimeter-wave radar under spray conditions. For fruit orchard canopy recognition, based on the DBSCAN algorithm, an ellipsoid model adaptive clustering algorithm based on a variable-axis (E-DBSCAN) was proposed. The feasibility of the proposed algorithm was verified in the real operation scene of the orchard. The results show that the F₁ score of the proposed algorithm was 96.7%, the precision rate was 93.5%, and the recall rate was 95.1%, which effectively improves the recognition accuracy of the classical DBSCAN algorithm in multi-density point cloud clustering. Regarding the extraction of the canopy characteristics of fruit trees, the RANSAC algorithm and coordinate method were used to extract crown width and plant height, respectively, and a point cloud density adaptive Alpha_shape algorithm was proposed to extract volume. The number of point clouds, crown width, plant height, and volume value under spray conditions and normal conditions were compared and analyzed. The average relative errors of crown width, plant height, and volume were 2.1%, 2.3%, and 4.2%, respectively, indicating that the spray had little effect on the extraction of canopy characteristics by millimeter-wave radar, which could inform spray-related decisions for precise applications. Full article

We present a method for 3D gravity inversion using ellipsoidal parametrization and Particle Swarm Optimization (PSO), aimed at estimating the geometry, density contrast, and orientation of subsurface bodies from gravity anomaly data. The subsurface is modeled as a set of prolate ellipsoids whose parameters are optimized to minimize the misfit between observed and predicted anomalies. This approach enables efficient forward modeling with closed-form solutions and allows the incorporation of geometric and physical constraints. The algorithm is first validated on synthetic models with Gaussian noise, successfully recovering complex multi-body configurations with acceptable uncertainty. A statistical analysis based on multiple PSO runs provides interquartile ranges (IQRs) to quantify inversion stability. The method is then applied to a real microgravity dataset from the Nirano Salse mud volcanoes (northern Italy) using a field acquisition strategy previously described in the literature. Unlike earlier studies based on commercial software, our inversion uses the ellipsoidal–PSO framework. The best-fitting model includes four ellipsoids (two low- and two high-density), reproducing the main features of the observed Bouguer anomaly with a prediction error of 20–25%. The inferred geometry suggests that fluid migration is controlled by fault-related damage zones rather than shallow reservoirs. This method is robust, interpretable, and applicable to both synthetic and real cases, with potential uses in geotechnical, volcanic, and hydrogeological studies. Full article

To address the measurement accuracy degradation of triaxial magnetometers caused by manufacturing errors and environmental interference, and the limited robustness of traditional calibration methods, this study proposes a Dynamic Hierarchical Elite-guided Particle Swarm Optimization (DHEPSO)-based ellipsoid fitting algorithm. First, an error model for the triaxial magnetometers is established. Next, the DHEPSO algorithm is utilized to fit the ellipsoid parameters by integrating a dynamic hierarchical mechanism, elite guidance strategy, and adaptive inertia weight adjustment, thereby balancing global exploration and local exploitation to efficiently optimize the parameters. Finally, error compensation and precise calibration are achieved using the optimized parameters. The simulation results show that, compared to the Least Squares Method (LSM), it reduces the absolute distance between the simulated data and the ellipsoid by 63.10% and the post-calibration total magnetic field intensity standard deviation by 60% under outlier interference. Against the traditional PSO, TSLPSO, MPSO, and AWPSO, DHEPSO achieves total distance reductions of 48.52%, 47.74%, 56.71%, and 33.09%, respectively, with faster convergence. The statistical analysis of 60 trials confirms DHEPSO’s stability, exhibiting lower median error and interquartile range. The results validate DHEPSO’s high precision and robustness in high-noise environments, offering theoretical support for engineering applications. Full article

Background: Rhegmatogenous retinal detachment (RRD) is an ophthalmological emergency that can lead to vision loss if left untreated. Pars plana vitrectomy (PPV) is the preferred procedure for most complex RRD cases with a high success rate. However, certain parameters related to the patient, disease history, or ocular presentation may influence surgical outcomes. Methods: A systematic review of studies from 2010 to 2023 was conducted using PubMed/Medline (National Library of Medicine, Bethesda, MD, USA) and Scopus (Elsevier, Netherlands). The main objective of this review is to present the most significant data published in the scientific literature over the last 10 years, focusing on the latest implications of prognostic factors affecting the success of PPV in RRD. The search included terms such as “prognostic factors”, “visual outcome”, “functional outcome”, and “rhegmatogenous retinal detachment”. The database search returned 3489 studies. The included studies had to involve participants with RRD treated mainly by PPV, a minimum of 10 participants, and at least a 6-month follow-up period. Studies were excluded if they involved patients with previous PPV treatment or trauma. After reviewing their abstracts, titles, and applying the exclusion criteria, 19 articles were selected. Because it is an ample and interesting topic, many authors explored the connection between prognostic factors involved in the management of RRD and the final visual and functional outcomes. Methodological quality was assessed using PRISMA guidelines. Results: various factors have been studied, ranging from classic ophthalmological parameters, such as refractive error, axial length, lens status, visual acuity, duration of symptoms, description of the RRD, and retinal tears, to more complex findings on optical coherence tomography. Conclusions: The factors that significantly influenced postoperative prognosis in RRD included preoperative best-corrected visual acuity (BCVA), duration of symptoms, macular status (on/off), extent of retinal detachment, presence of macular hole, and proliferative vitreoretinopathy (PVR). Disruption of the ellipsoid zone (EZ), presence of epiretinal membrane (ERM), and lack of external limiting membrane (ELM) integrity were associated with poorer outcomes following RRD surgery. Full article

With the advent of cloud computing and the era of big data, there is an increasing focus on privacy computing. Consequently, homomorphic encryption, being a primary technique for achieving privacy computing, is held in high regard. Nevertheless, the efficiency of homomorphic encryption schemes is significantly impacted by bootstrapping. Enhancing the efficiency of bootstrapping necessitates a dual focus: reducing the computational burden of outer product operations integral to the process while rigorously constraining the noise generated by bootstrapping within predefined threshold limits. The FINAL scheme is a fully homomorphic encryption scheme based on the number theory research unit (NTRU) and learning with errors (LWE) assumptions. The performance of the FINAL scheme is better than that of the TFHE scheme, with faster bootstrapping and smaller bootstrapping and key-switching keys. In this paper, we introduce ellipsoidal Gaussian sampling to generate keys $\mathit{f}$ and $\mathit{g}$ in the bootstrapping of the FINAL scheme, so that the standard deviations of keys $\mathit{f}$ and $\mathit{g}$ are different and reduce the bootstrapping noise by 76%. However, when q is fixed, the boundary for bootstrapping noise remains constant. As a result, larger decomposition bases are used in bootstrapping to reduce the total number of polynomial multiplications by 47%, thus improving the efficiency of the FINAL scheme. The optimization scheme outperforms the original FINAL scheme with 33.3% faster bootstrapping, and the memory overhead of blind rotation keys is optimized by 47%. Full article

Robust and highly accurate localization using a camera is a challenging task when appearance varies significantly. In indoor environments, changes in illumination and object occlusion can have a significant impact on visual localization. In this paper, we propose a visual localization method based on an ellipse-ellipsoid model, combined with object-level instance topology and alignment. First, we develop a CNN-based (Convolutional Neural Network) ellipse prediction network, DEllipse-Net, which integrates depth information with RGB data to estimate the projection of ellipsoids onto images. Second, we model environments using 3D (Three-dimensional) ellipsoids, instance topology, and ellipsoid descriptors. Finally, the detected ellipses are aligned with the ellipsoids in the environment through semantic object association, and 6-DoF (Degree of Freedom) pose estimation is performed using the ellipse-ellipsoid model. In the bounding box noise experiment, DEllipse-Net demonstrates higher robustness compared to other methods, achieving the highest prediction accuracy for 11 out of 23 objects in ellipse prediction. In the localization test with 15 pixels of noise, we achieve $ATE$ (Absolute Translation Error) and $ARE$ (Absolute Rotation Error) of 0.077 m and $2.{70}^{\circ }$ in the $fr2\_desk$ sequence. Additionally, DEllipse-Net is lightweight and highly portable, with a model size of only 18.6 MB, and a single model can handle all objects. In the object-level instance topology and alignment experiment, our topology and alignment methods significantly enhance the global localization accuracy of the ellipse-ellipsoid model. In experiments involving lighting changes and occlusions, our method achieves more robust global localization compared to the classical bag-of-words based localization method and other ellipse-ellipsoid localization methods. Full article

Breast volume is crucial for ensuring proper bra fit and comfort, significantly influencing women’s physiological and psychological well-being. This study aims to develop a novel method for breast-volume self-measurement, allowing women to accurately assess their breast volume without specialized equipment. We employed a geometric approximation of the breast as a combination of a partial elliptical cone and an irregular partial ellipsoid, leading to the formulation of a new volume equation. The method was validated against established standards, including the specimen drainage method and 3D scanning techniques. The findings revealed that our self-measurement approach achieved a relative error of only 3.8%, outperforming the 4.8% of 3D scanning and the 86.3% associated with traditional breast-volume equations. This innovative self-measurement technique enhances accuracy and serves as a practical solution for health and nutritional assessments, alongside body image evaluations. Its user-friendly nature positions it as a valuable tool for women’s health, particularly in personal fitness and ergonomic design. Full article

In recent years, interest in airships for cargo transport and stratospheric platforms has increased, necessitating accurate dynamic modeling for stability analysis, autopilot design, and mission planning, specifically through the calculation of stability derivatives, like added mass and inertia. Despite the several CFD methods and analytical solutions available to calculate added masses, experimental validation remains essential. This study introduces a novel methodology to measure these in a wind tunnel, comparing the results with prior studies that utilized towing tanks. The approach involves designing the test model and a crank-slider mechanism to generate motion within the wind tunnel, considering load cell sensitivity, precision, frequency range, and Reynolds numbers. A revolution ellipsoid model, made from extruded polystyrene, was used to validate analytical solutions. The test model, measuring 1 m in length with an aspect ratio of 6, weighing 482 g, was moved along rails by the crank-slider system. By increasing the motion frequency, structural vibrations affecting load cell measurements were minimized. Proper signal processing, including high-pass filtering and second-order Fourier series fitting, enabled successful virtual mass calculation, showing only a 2.1% deviation from theoretical values, significantly improving on previous studies with higher relative errors. Full article

A feasibility study on the compensation of 3D mispositioning errors of the probe occurring in the characterization of a long antenna, via a non-redundant (NR) near to far-field (NTFF) transformation with helicoidal scan, is conducted in this article. Such types of errors can result from imperfections in the rail driving the linear motion of the probe and from an imprecise synchronization of the linear and rotational movements of the probe and the antenna when drawing the scan helix. To correct them, an approach, which proceeds through two steps, is proposed. The former step uses a technique called cylindo rical wave (CW) correction for compensating the phase of the near-field (NF) samples, which, owing to the rail imperfections, result in not being acquired over the measurement cylinder surface. The latter exploits an iterative scheme to restore the samples at the sampling points required by the adopted NR representation along the scan helix from those obtained by applying the CW correction technique and impaired by 2D mispositioning errors. The so compensated NF samples are then effectively recovered via a 2D optimal sampling interpolation (OSI) scheme to accurately obtain the input data required to carry out the standard cylindrical NTFF transformation. The OSI representation is determined here by assuming a long antenna under test as enclosed in a prolate ellipsoid or cylinder ending into two hemispheres (cigar) in order to make, depending on the particular geometry of the considered antenna, the representation effectively non-redundant. The reported numerical simulation results show the capability of the proposed approach to compensate even severe 3D mispositioning errors, thus enabling its usage in a real measurement scenario. Full article

The NVH (Noise, Vibration, and Harshness) characteristics of micro-motors used in vehicles directly affect the comfort of drivers and passengers. However, various factors influence the motor’s structural parameters, leading to uncertainties in its NVH performance. To improve the motor’s NVH characteristics, we propose a method for optimizing the structural parameters of automotive micro-motors under uncertain conditions. This method uses the motor’s maximum magnetic flux as a constraint and aims to reduce vibration at the commutation frequency. Firstly, we introduce the Pareto ellipsoid parameter method, which converts the uncertainty problem into a deterministic one, enabling the use of traditional optimization methods. To increase efficiency and reduce computational cost, we employed a data-driven method that uses the one-dimensional Inception module as the foundational model, replacing both numerical models and physical experiments. Simultaneously, the module’s underlying architecture was improved, increasing the surrogate model’s accuracy. Additionally, we propose an improved NSGA-III (Non-dominated Sorting Genetic Algorithm III) method that utilizes adaptive reference point updating, dividing the optimization process into exploration and refinement phases based on population matching error. Comparative experiments with traditional models demonstrate that this method enhances the overall quality of the solution set, effectively addresses parameter uncertainties in practical engineering scenarios, and significantly improves the vibration characteristics of the motor. Full article