Search Results (80)

Precise and real-time acquisition of aircraft model attitude is fundamental for aerodynamic analysis in wind tunnel experiments, yet achieving high-precision non-contact measurement remains a significant challenge. To address this, this paper proposes a pose measurement framework based on a monocular Time-of-Flight (ToF) camera that fuses keyframe global registration with non-keyframe local registration. First, a novel hand-crafted local feature based on three-plane encoded height and density is introduced. When combined with the Two-stage Consensus Filtering RANSAC (TCF-RANSAC) algorithm, this feature achieves robust global registration of keyframes, providing reliable initial pose estimates for the system. Subsequently, leveraging the continuity constraint of model motion, fast incremental local registration of non-keyframes is performed using the Generalized Iterative Closest Point (GICP) algorithm, which avoids falling into local optima while significantly improving computational efficiency. Evaluation results on simulated datasets with synthetic noise and a real experimental platform demonstrate that the method achieves a single-axis rotation angle error of less than ${0.03}^{\circ }$ while processing at over 40 FPS, satisfying real-time measurement requirements. Comparative evaluations against multiple existing registration methods indicate that the proposed framework achieves superior accuracy and robustness, reducing rotation angle errors by 9% to 39% compared to mainstream global registration methods under single-view ToF sensing conditions. Furthermore, this study quantifies the error distribution characteristics of monocular ToF-based pose estimation, revealing an “axis-sensitivity” phenomenon where the rotation error around the optical axis is significantly lower (e.g., ${0.02}^{\circ }$, ${0.03}^{\circ }$) than that around the orthogonal axes (e.g., ${0.38}^{\circ }$, ${0.26}^{\circ }$). These findings provide practical guidance for camera placement and system design in high-precision aerodynamic measurement scenarios. Full article

This study investigates the application of NDTs for the detection of sub-surface defects in an ancient book, with the aim of improving conservation methods in the field of cultural heritage. A sequence of thermographic images in a solar loading thermography (SLT) scenario was acquired during a diagnostic campaign in Harbin, China, to identify four distinct fabricated dowels made of Wool, Rubber, Teflon^®, and Synthetic material. The images were processed in two ways: the first combined advanced image-processing methods: pre-processing via MdFIF, post-processing, PCT and RPCT, applied both to the original sequence and to the MdFIF-filtered thermograms. The second approach employed numerical simulations in COMSOL Multiphysics^® to develop a predictive thermal model. The comparison of localized thermal anomalies obtained from the two approaches demonstrated the capability of NDTs to reliably reveal artificial defects, confirming their suitability for diagnostic conservation. Overall, the integration of advanced image processing with numerical simulation enhances diagnostic accuracy, particularly for subtle or low-contrast anomalies, thereby enabling more informed condition assessment and supporting rapid, targeted, and preventive conservation strategies. Full article

Accurate long-term temperature prediction is critical for the reliable operation of mass-produced electrical machines. However, due to the randomness inherent in the manufacturing process, machines with identical design parameters often exhibit distinct thermal properties. The aging of the insulation system can also lead to variation in thermal performance. Conventional lumped-parameter thermal network (LPTN) models with fixed parameters fail to account for these factors, thus leading to biased prediction results for long-term temperature forecasting of mass-produced machines. To enhance the robustness of LPTN models, this paper proposes a methodology for adaptive online parameter updating. Based on the mathematical formulation of LPTN, a fast Jacobian matrix calculation method for model prediction errors is developed, which avoids the time-consuming numerical computation process. To further alleviate the computational burden, key parameters with significant impacts on prediction errors are screened prior to each optimization iteration. These improvements collectively reduce computational resource requirements and enable real-time online implementation. Finally, experimental verification is conducted on a 10 kW permanent magnet machine. Comparative analyses against the numerical method and extended Kalman filter (EKF) demonstrate that the proposed method can be efficiently realized and is more effective in estimating the model parameters online. Full article

This work proposes a novel tree detection methodology, named DTCD (Distance Transform Circle Detection), based on a fast circle detection method via Distance Transform and Akaike Information Criterion (AIC) optimization. More specifically, a visible-band vegetation index (RGBVI) is calculated to enhance canopy regions, followed by morphological filtering to delineate individual tree crowns. The Euclidean Distance Transform is then applied, and the local maxima of the smoothed distance map are extracted as candidate tree locations. The final detections are iteratively refined using the AIC to optimize the number of trees with respect to canopy coverage efficiency. Additionally, this work introduces DTCD-PC, a modified algorithm tailored for point clouds, which significantly enhances detection accuracy in complex environments. This work makes a significant contribution to tree detection in the following ways: (1) by creating a tree detection framework entirely based on an unsupervised technique, which outperforms state-of-the-art unsupervised and supervised tree detection methods; (2) by introducing a new urban dataset, named AgiosNikolaos-3, that consists of orthomosaics and photogrammetrically reconstructed 3D point clouds, allowing the assessment of the proposed method in complex urban environments. The proposed DTCD approach was evaluated on the Acacia-6 dataset, consisting of UAV images of six-month-old Acacia trees in Southeast Asia, demonstrating superior detection performance compared to existing state-of-the-art techniques, both unsupervised and supervised. Additional experiments were conducted in the custom-developed Urban Dataset, confirming the robustness and generalizability of the DTCD-PC method in heterogeneous environments. Full article

High-precision and consensus tracking of a long-range maneuvering target presents a significant challenge for unmanned aerial vehicles (UAVs) in complex denied environments. Earlier studies rarely considered the fast convergence and fusion accuracy of distributed consensus tracking in bearing-only UAV networks. This article proposes a distributed consensus-based estimation (DCE) method with inverse covariance intersection (ICI) fusion rule in the framework of local estimation, consensus iteration, and fusion estimation. Combined with the contribution of measurements from neighboring UAVs, the local estimation of target tracking can be achieved by a square-root cubature information filter (SRCIF) in bearing-only UAVs. Based on local estimation and centralities in a multi-UAV network, each UAV platform can obtain consensus results in a finite number of iterations. Then, the fusion estimations are the consensus with the global ICI fusion rule. Furthermore, the fusion estimations are analyzed in consensus, finiteness, and boundedness. Numerical simulations are performed to validate the effectiveness and superiority of the proposed DCE–ICI method. Full article

Medical image segmentation acts as a foundational element of medical image analysis. Yet its accuracy is frequently limited by the scale fluctuations of anatomical targets and the intricate contextual traits inherent in medical images—including vaguely defined structural boundaries and irregular shape distributions. To tackle these constraints, we design a multi-scale feature attention network (MFA-Net), customized specifically for thyroid nodule, skin lesion, and breast lesion segmentation tasks. This network framework integrates three core components: a Bidirectional Feature Pyramid Network (Bi-FPN), a Slim-neck structure, and the Convolutional Block Attention Module (CBAM). CBAM steers the model to prioritize boundary regions while filtering out irrelevant information, which in turn enhances segmentation precision. Bi-FPN facilitates more robust fusion of multi-scale features via iterative integration of top-down and bottom-up feature maps, supported by lateral and vertical connection pathways. The Slim-neck design is constructed to simplify the network’s architecture while effectively merging multi-scale representations of both target and background areas, thus enhancing the model’s overall performance. Validation across four public datasets covering thyroid ultrasound (TNUI-2021, TN-SCUI 2020), dermoscopy (ISIC 2016), and breast ultrasound (BUSI) shows that our method outperforms state-of-the-art segmentation approaches, achieving Dice similarity coefficients of 0.955, 0.971, 0.976, and 0.846, respectively. Additionally, the model maintains a compact parameter count of just 3.05 million and delivers an extremely fast inference latency of 1.9 milliseconds—metrics that significantly outperform those of current leading segmentation techniques. In summary, the proposed framework demonstrates strong performance in thyroid, skin, and breast lesion segmentation, delivering an optimal trade-off between high accuracy and computational efficiency. Full article

The article presents a comprehensive determination and analysis of the dynamic accuracy of the AC traction network–pantograph interface using an equivalent lumped-parameter RLC model derived from a distributed-parameter representation of the traction line. The study investigates the system’s response to representative excitation signals: step, sinusoidal, and multi-harmonic, where the root mean square value of the voltage error at the network–pantograph interface is adopted as the main performance indicator. A novel contribution of this work lies in determining the upper bound on the dynamic error (UBDE) for input signals constrained by realistic physical limitations: initially by magnitude and duration, and subsequently extended with an additional rate of change constraint. In the first case, an iterative optimization procedure is applied to determine the constrained excitation and its corresponding error, while in the extended case, the problem of maximizing the dynamic error energy is solved numerically using a genetic algorithm. In both formulations, the objective is to identify extreme, physically admissible excitation waveforms that represent the most unfavorable dynamic scenarios for voltage reproduction within the traction network–pantograph RLC interface. The results obtained in this study are of both theoretical and practical significance. They allow the identification of frequency ranges and resonance conditions that intensify dynamic errors, support the design of compensation and filtering strategies, and enable the assessment of the system robustness to fast disturbances and supply voltage distortions. From a theoretical point of view, the article introduces a unified methodology for the determination and evaluation of dynamic errors and their worst-case upper estimates under realistic signal constraints, providing a foundation for future research on control design, optimization, and voltage quality requirements in AC traction power systems. Full article

Feature selection is a critical preprocessing step for mitigating the curse of dimensionality in machine learning. Existing methods present a difficult trade-off: filter methods are fast but often suboptimal as they evaluate features in isolation, while wrapper methods are powerful but computationally prohibitive due to their iterative nature. In this paper, we propose LAFS (Learnable Attention for Feature Selection), a novel, end-to-end differentiable framework that achieves the performance of wrapper methods at the speed of simpler models. LAFS employs a neural attention mechanism to learn a context-aware importance score for all features simultaneously in a single forward pass. To encourage the selection of a sparse and non-redundant feature subset, we introduce a novel hybrid loss function that combines the standard classification objective with an information-theoretic entropic regularizer on the attention weights. We validate our approach on real-world high-dimensional benchmark datasets. Our experiments demonstrate that LAFS successfully identifies complex feature interactions and handles multicollinearity. In general comparison, LAFS achieves very close and accurate results to state-of-the-art RFE-LGBM and embedded FSA methods. Our work establishes a new point on the accuracy-efficiency frontier, demonstrating that attention-based architectures provide a compatible solution to the feature selection problem. 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

Accurate registration of multi-platform light detection and ranging (LiDAR) point clouds is essential for detailed forest structure analysis and ecological monitoring. In this study, we developed a novel two-stage method for aligning terrestrial and unmanned aerial vehicle LiDAR point clouds in forest environments. The method first performs coarse alignment using canopy-level digital surface models and Fast Point Feature Histograms, followed by fine registration with Iterative Closest Point. Experiments conducted in six forest plots achieved an average registration accuracy of 0.24 m within 5.14 s, comparable to manual registration but with substantially reduced processing time and human intervention. In contrast to existing tree-based methods, the proposed approach eliminates the need for individual tree segmentation and ground filtering, streamlining preprocessing and improving scalability for large-scale forest monitoring. The proposed method facilitates a range of forest applications, including structure modeling, ecological parameter retrieval, and long-term change detection across diverse forest types and platforms. Full article

The recursive least-squares (RLS) algorithm stands out as an appealing choice in adaptive filtering applications related to system identification problems. This algorithm is able to provide a fast convergence rate for various types of input signals, which represents its main asset. In the current paper, we focus on the regularized version of the RLS algorithm, which also owns improved robustness in noisy conditions. Since convergence and robustness are usually conflicting criteria, the data-reuse technique is used to achieve a proper compromise between these performance features. In this context, we develop a computationally efficient approach for the data-reuse process in conjunction with the regularized RLS algorithm, using an equivalent single step instead of multiple iterations (for data-reuse). In addition, different regularization techniques are involved, which lead to variable-regularized algorithms, with time-dependent regularization parameters. This allows a better control in different challenging conditions, including noisy environments and other external disturbances. The resulting data-reuse regularized RLS algorithms are tested in the framework of echo cancellation, where the obtained results support the theoretical findings and indicate the reliable performance of these algorithms. Full article

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

Accuracy and nonlinear processing capabilities are critical to the positioning and navigation of autonomous vehicles in visual–inertial odometry (VIO). Existing filtering-based VIO methods struggle to deal with strongly nonlinear systems and often exhibit low precision. To this end, this paper proposes a VIO method based on the Schur complement and Iterated Extended Kalman Filtering (IEKF). The algorithm first enhances ORB (Oriented FAST and Rotated BRIEF) features using Multi-Layer Perceptron (MLP) and Transformer architectures to improve feature robustness. It then integrates visual information and Inertial Measurement Unit (IMU) data through IEKF, constructing a complete residual model. The Schur complement is applied during covariance updates to compress the state dimension, improving computational efficiency and significantly enhancing the system’s ability to handle nonlinearities while maintaining real-time performance. Compared to traditional Extended Kalman Filtering (EKF), the proposed method demonstrates stronger stability and accuracy in high-dynamic scenarios. The experimental results show that the algorithm achieves superior state estimation performance on several typical visual–inertial datasets, demonstrating excellent accuracy and robustness. Full article

Massive multiple input–multiple output (M-MIMO) is a promising and pivotal technology in contemporary wireless communication systems that can effectively enhance link reliability and data throughput, especially in uplink scenarios. Even so, the receiving end requires more computational complexity to reconstitute the signal. This problem has emerged in fourth-generation (4G) MIMO system; with the dramatic increase in demand for devices and data in beyond-5G (B5G) systems, this issue will become yet more obvious. To take into account both complexity and signal-revested capability at the receiver, this study uses the matrix iteration method to avoid the staggering amount of operations produced by the inverse matrix. Then, we propose a highly efficient multi-user detector (MUD) named hybrid SOR-based Chebyshev acceleration (CHSOR) for the uplink of M-MIMO orthogonal frequency-division multiplexing (OFDM) and universal filtered multi-carrier (UFMC) waveforms, which can be promoted to B5G developments. The proposed CHSOR scheme includes two stages: the first consists of successive over-relaxation (SOR) and modified successive over-relaxation (MSOR), combining the advantages of low complexity of both and generating a better initial transmission symbol, iteration matrix, and parameters for the next stage; sequentially, the second stage adopts the low-cost iterative Chebyshev acceleration method for performance refinement to obtain a lower bit error rate (BER). Under constrained evaluation settings, Section (Simulation Results and Discussion) presents the results of simulations performed in MATLAB version R2022a. Results show that the proposed detector can achieve a 91.624% improvement in BER performance compared with Chebyshev successive over-relaxation (CSOR). This is very near to the performance of the minimum mean square error (MMSE) detector and is achieved in only a few iterations. In summary, our proposed CHSOR scheme demonstrates fast convergence compared to previous works and as such possesses excellent BER and complexity performance, making it a competitive solution for uplink M-MIMO B5G systems. Full article

Existing array calibration methods rely on the geometric characteristics of the array or signal matrix, which limits their flexibility and robustness. This study addresses this challenge by proposing a novel joint sparse estimation method for array gain and phase calibration. By leveraging the sparsity of calibration signals and the dictionary mismatch model, the proposed method, based on the fast iterative shrinkage-thresholding algorithm (FISTA), jointly estimates the discrete on-grid azimuths and continuous off-grid offsets of the direction of arrival (DOA) of calibration signals. The method employs a spatial domain filtering technique based on the maximum a posteriori probability to mitigate the bias induced by phase errors in the calibration signal estimation, enhancing calibration accuracy. Furthermore, the iterative estimation framework was optimized to extend the applicability of the method from linear to uniform planar arrays. The results demonstrated that the root mean squared error (RMSE) of the beam pattern for various array types decreased by one to two orders of magnitude after calibration. Compared with existing state-of-the-art methods, the proposed approach exhibited stable performance and superior estimation accuracy under conventional signal-to-noise ratio conditions. Moreover, the proposed method maintained high stability and lower RMSE as the gain and phase error values increased. Full article