Search Results (24)

Multi-modal remote sensing images often exhibit complex and nonlinear radiation differences which significantly hinder the performance of traditional feature-based image registration methods such as Scale-Invariant Feature Transform (SIFT). In contrast, structural features—such as edges and contours—remain relatively consistent across modalities. To address this challenge, we propose a novel multi-modal image registration method, Cof-SIFT, which integrates a co-occurrence filter with SIFT. By replacing the traditional Gaussian filter with a co-occurrence filter, Cof-SIFT effectively suppresses texture variations while preserving structural information, thereby enhancing robustness to cross-modal differences. To further improve image registration accuracy, we introduce an extended approach, Cof-SIFT_HOG, which extracts Histogram of Oriented Gradients (HOG) features from the image gradient magnitude map of corresponding points and refines their positions based on HOG similarity. This refinement yields more precise alignment between the reference and image to be registered. We evaluated Cof-SIFT and Cof-SIFT_HOG on a diverse set of multi-modal remote sensing image pairs. The experimental results demonstrate that both methods outperform existing approaches, including SIFT, COFSM, SAR-SIFT, PSO-SIFT, and OS-SIFT, in terms of robustness and registration accuracy. Notably, Cof-SIFT_HOG achieves the highest overall performance, confirming the effectiveness of the proposed structural-preserving and corresponding point location refinement strategies in cross-modal registration tasks. Full article

In this study, we introduce a novel application of the Smooth Overlap of Atomic Positions (SOAP) descriptor for pixel-wise image feature extraction and classification as a benchmark for SOAP point cloud feature extraction, using MNIST handwritten digits as a benchmark. By converting 2D images into 3D point sets, we compute pixel-centered SOAP vectors that are intrinsically invariant to translation, rotation, and mirror symmetry. We demonstrate how the descriptor’s hyperparameters—particularly the cutoff radius—significantly influence classification accuracy, and show that the high-dimensional SOAP vectors can be efficiently compressed using PCA or autoencoders with minimal loss in predictive performance. Our experiments also highlight the method’s robustness to positional noise, exhibiting graceful degradation even under substantial Gaussian perturbations. Overall, this approach offers an effective and flexible pipeline for extracting rotationally and translationally invariant image features, potentially reducing reliance on extensive data augmentation and providing a robust representation for further machine learning tasks. Full article

Background: Life satisfaction serves as a preventive agent against various emotional, cognitive, and behavioral challenges, making it a crucial cognitive indicator of subjective well-being, particularly during adolescence. Accurately assessing life satisfaction is essential for understanding and promoting adolescent mental health, especially in applied settings such as physical education, which plays a key role in fostering psychological well-being and positive youth development. However, additional investigation is needed to confirm the tools used for this purpose. This study aimed to analyze the psychometric properties, metric invariance, and temporal stability of the Satisfaction with Life Scale (SWLS) in adolescents from a region in southeastern Spain. Thus, the present study sought to answer the following research questions: (1) Does the SWLS demonstrate adequate psychometric properties in an adolescent population? (2) Is the SWLS invariant across gender and residential environments? (3) Does the SWLS show adequate stability over time? Methods: A sample of 400 students was assessed using exploratory and confirmatory factor analyses, multigroup comparisons, and test–retest techniques. Results: The results showed significant differences in scale scores in the sex and demographic location variables. Also, a robust unifactorial model with five items demonstrated good performance in terms of goodness of fit and internal consistency. Furthermore, full metric invariance was observed across genders, while configural invariance was supported for residential environment. Concurrent validity analyses revealed significant associations with another unidimensional well-being measure, and temporal stability was confirmed through the intraclass correlation coefficient. Conclusions: The findings support the SWLS as a potentially valid, reliable, and time-effective tool for assessing adolescent life satisfaction. Its strong psychometric properties make it highly suitable for use in mental health research, longitudinal monitoring, and large-scale studies. Moreover, its ease of administration allows its integration into educational, clinical, community-based, and physical education contexts, offering insightful information for the creation of long-lasting mental health regulations and preventive measures meant to improve the well-being of adolescents. Notwithstanding these encouraging results, some restrictions must be noted. The sample was restricted to a single geographic area, and contextual or cultural factors may have an impact on how satisfied people are with their lives. Furthermore, response biases could have been introduced by using self-report measures. Full article

The present study focuses on the development of a diagnostic system for measuring radiated power and core soft X-ray intensity emissions with the goal of detecting a broad spectrum of photon energies emitted from the central plasma region of the DEMO tokamak. The principal objective of the diagnostic apparatus is to deliver a comprehensive characterization of the radiation emitted by the plasma, with a particular focus on estimating the radiated power from the core region. This measurement is essential for determining and monitoring the power crossing the separatrix, which is a critical parameter controlling overall plasma performance. Since diagnostics rely on line-integrated measurements, the application of tomographic reconstruction techniques is necessary to extract spatially resolved information on core plasma radiation. This contribution presents the development of numerical algorithms addressing the problem of radiation tomography reconstruction. A robust and computationally efficient method is proposed for reconstructing the spatial distribution of plasma radiated power, with a view toward enabling real-time applications. The reconstruction methodology is based on a linear model formulated using a set of predefined basis functions, which define the radiation distribution within a specified plasma cross-section. In the initial stages of emissivity reconstruction in tokamak plasmas, it is typically assumed that the radiation distribution is dependent on magnetic flux surfaces. As a baseline approach, the plasma radiative properties are considered invariant along these surfaces and can thus be represented as one-dimensional profiles parameterized by the poloidal magnetic flux. Within this framework, the reconstruction method employs an approximation model utilizing three sets of basis functions: (i) polynomial splines, as well as Gaussian functions with (ii) sigma parameters and (iii) position parameters. The performance of the proposed method was evaluated using two synthetic radiated power emission phantoms, developed for the DEMO plasma scenario. The results indicate that the method is effective under the specified conditions. Full article

In satellite formation flying, maintaining a predefined safety distance between the reference satellite and its accompanying satellites is critical to prevent collisions—a requirement that traditional control methods fail to satisfy. This paper proposes a novel safety control algorithm that enforces the relative position constraint as a formal safety condition. Specifically, we construct a high-order control barrier function (HOCBF) tailored to the dynamics of satellite formations, which defines a safety set that guarantees collision avoidance. By formulating and solving a quadratic programming problem embedded with the HOCBF, we obtain a safe controller that ensures the forward invariance of the safety set. Simulation results demonstrate that our controller effectively maintains the required safety distance and prevents collisions under various disturbance scenarios, outperforming conventional methods in terms of response speed and robustness. These findings confirm the practical advantages of the proposed approach for safe satellite formation flying. Full article

This paper presents both sufficient and necessary conditions for polyhedral sets and symmetric polyhedral sets to be robust positively invariant sets within perturbed linear discrete-time systems. These conditions are derived through the application of optimization and dual optimization theory. By leveraging the definition of a robust positively invariant set and employing the Pontryagin difference, we have obtained robust positively invariant conditions in optimized forms. Through the use of dual optimization theory, various equivalent forms are introduced, offering additional tools for verifying that polyhedral sets are indeed robust positively invariant sets for perturbed linear discrete-time dynamic systems. The efficacy of these conclusions is further evidenced by numerical examples. Full article

This article proposes a novel robust invariance condition for uncertain linear discrete-time systems with state and control constraints, utilizing a method of semidefinite programming duality. The approach involves approximating the robust invariant set for these systems by tackling the dual problem associated with semidefinite programming. Central to this method is the formulation of a dual programming through the application of adjoint mapping. From the standpoint of semidefinite programming dual optimization, the paper presents a novel linear matrix inequality (LMI) conditions pertinent to robust positive invariance. Illustrative examples are incorporated to elucidate the findings. Full article

This research explores the complex dynamics of a Novel Four-Dimensional Fractional Supply Chain System (NFDFSCS) that integrates a quadratic interaction term involving the actual demand of customers and the inventory level of distributors. The introduction of the quadratic term results in significantly larger maximal Lyapunov exponents (MLE) compared to the original model, indicating increased system complexity. The existence, uniqueness, and Ulam–Hyers stability of the proposed system are verified. Additionally, we establish the global Mittag-Leffler attractive set (MLAS) and Mittag-Leffler positive invariant set (MLPIS) for the system. Numerical simulations and MATLAB phase portraits demonstrate the chaotic nature of the proposed system. Furthermore, a dynamical analysis achieves verification via the Lyapunov exponents, a bifurcation diagram, a 0–1 test, and a complexity analysis. A new numerical approximation method is proposed to solve non-linear fractional differential equations, utilizing fractional differentiation with a non-singular and non-local kernel. These numerical simulations illustrate the primary findings, showing that both external and internal factors can accelerate the process. Furthermore, a robust control scheme is designed to stabilize the system in finite time, effectively suppressing chaotic behaviors. The theoretical findings are supported by the numerical results, highlighting the effectiveness of the control strategy and its potential application in real-world supply chain management (SCM). Full article

In scenarios such as nearshore and inland waterways, the ship spots in a marine radar are easily confused with reefs and shorelines, leading to difficulties in ship identification. In such settings, the conventional ARPA method based on fractal detection and filter tracking performs relatively poorly. To accurately identify radar targets in such scenarios, a novel algorithm, namely YOSMR, based on the deep convolutional network, is proposed. The YOSMR uses the MobileNetV3(Large) network to extract ship imaging data of diverse depths and acquire feature data of various ships. Meanwhile, taking into account the issue of feature suppression for small-scale targets in algorithms composed of deep convolutional networks, the feature fusion module known as PANet has been subject to a lightweight reconstruction leveraging depthwise separable convolutions to enhance the extraction of salient features for small-scale ships while reducing model parameters and computational complexity to mitigate overfitting problems. To enhance the scale invariance of convolutional features, the feature extraction backbone is followed by an SPP module, which employs a design of four max-pooling constructs to preserve the prominent ship features within the feature representations. In the prediction head, the Cluster-NMS method and α-DIoU function are used to optimize non-maximum suppression (NMS) and positioning loss of prediction boxes, improving the accuracy and convergence speed of the algorithm. The experiments showed that the recall, accuracy, and precision of YOSMR reached 0.9308, 0.9204, and 0.9215, respectively. The identification efficacy of this algorithm exceeds that of various YOLO algorithms and other lightweight algorithms. In addition, the parameter size and calculational consumption were controlled to only 12.4 M and 8.63 G, respectively, exhibiting an 80.18% and 86.9% decrease compared to the standard YOLO model. As a result, the YOSMR displays a substantial advantage in terms of convolutional computation. Hence, the algorithm achieves an accurate identification of ships with different trail features and various scenes in marine radar images, especially in different interference and extreme scenarios, showing good robustness and applicability. Full article

The measure of sphericity for positive semi-definite matrices plays a crucial role in understanding their geometric properties, especially in high-dimensional settings. This paper introduces a robust measure of sphericity, which remains invariant under orthogonal transformations and scaling. We explore its behavior in finite-dimensional cases. Additionally, we investigate the stochastic case by considering a normal distribution, analyzing the asymptotic normality of random matrices and its implications on the convergence properties of the proposed measure. Full article

The path tracking performance of autonomous vehicles is degraded by common disturbances, especially those that affect the safety of autonomous vehicles (AVs) in obstacle avoidance conditions. To improve autonomous vehicle tracking performances and their computational efficiency when subjected to common disturbances, this paper proposes a tube linear model predictive controller (MPC) framework for autonomous vehicles. A bicycle vehicle dynamics model is developed and employed in the tube MPC control design in the proposed framework. A robust invariant set is calculated with an efficient linear programming (LP) method, and it is used to guarantee that the constraints are satisfied under common disturbance conditions. The results show that the computational cost of robust positively invariant sets that are constructed by the LP method is much less than that obtained by the traditional method. In addition, all the trajectories of the tube linear MPC successfully avoided obstacles when under disturbance conditions, but only about 80% of the trajectories obtained with the traditional MPC successfully avoided obstacles under disturbance conditions. The proposed framework is effective. Full article

Animal activity recognition (AAR) using wearable sensor data has gained significant attention due to its applications in monitoring and understanding animal behavior. However, two major challenges hinder the development of robust AAR models: domain variability and the difficulty of obtaining labeled datasets. To address this issue, this study intensively investigates the impact of unsupervised domain adaptation (UDA) for AAR. We compared three distinct types of UDA techniques: minimizing divergence-based, adversarial-based, and reconstruction-based approaches. By leveraging UDA, AAR classifiers enable the model to learn domain-invariant features, allowing classifiers trained on the source domain to perform well on the target domain without labels. We evaluated the effectiveness of UDA techniques using dog movement sensor data and additional data from horses. The application of UDA across sensor positions (neck and back), sizes (middle-sized and large-sized), and gender (female and male) within the dog data, as well as across species (dog and horses), exhibits significant improvements in the classification performance and reduced the domain discrepancy. The results highlight the potential of UDA to mitigate the domain shift and enhance AAR in various settings and for different animal species, providing valuable insights for practical applications in real-world scenarios where labeled data is scarce. Full article

Although the four-wheel steering system expands the flexibility of vehicle control, it also brings the problem of difficult coordination between driver comfort and vehicle stability. To this end, this paper proposes robust coordinated control for a four-wheel steering (4WS) vehicle considering driving comfort and vehicle stability. First, the vehicle dynamics model is constructed to reflect the lateral motion characteristics of a 4WS vehicle. Then, the driver model is coupled into the 4WS vehicle model to describe the driver’s handling characteristics. To suppress the system perturbation caused by the uncertainties of driver behavior and vehicle states, the Takagi-Sugeno fuzzy robust control method is developed to design the human-machine co-driving system. Moreover, the robust positive invariant set theory is used to guarantee the stability and safety constraints of the vehicle. Finally, the proposed human-machine shared robust control for 4WS vehicle is verified through the driving simulator platform. The results indicate that the fuzzy robust shared control approach comprehensively improves the driving comfort, vehicle stability, and path tracking. Full article

This article discusses the development and preliminary validation of a self-report inventory of the patient’s perception of, and affective reaction to, their therapist during a psychotherapy session. First, we wrote a pool of 131 items, reviewed them based on subject matter experts’ review, and then collected validation data from a clinical sample of adult patients in individual therapy (N = 701). We used exploratory factor analysis and item response theory graded response models to select items, confirmatory factor analysis (CFA) to test the factor structure, and k-fold cross-validation to verify model robustness. Multi-group CFA examined measurement invariance across patients with different diagnoses (unipolar depression, bipolar disorder, and neither of these). Three factors produced short scales retaining the strongest items. The in-Session Patient Affective Reactions Questionnaire (SPARQ) has a two-factor structure, yielding a four-item Negative affect scale and a four-item Positive affect scale. The Relationship In-Session Questionnaire (RISQ) is composed of four items from the third factor with dichotomized responses. Both scales showed excellent psychometric properties and evidence of metric invariance across the three diagnostic groups: unipolar depression, bipolar disorder, and neither of these. The SPARQ and the RISQ scale can be used in clinical or research settings, with particular value for capturing the patient’s perspectives about their therapist and session-level emotional processes. Full article

A robust and effective method for the identification of point-distributed coded targets (IPCT) in a video-simultaneous triangulation and resection system (V-STARS) was reported recently. However, its limitations were the setting of critical parameters, it being non-adaptive, making misidentifications in certain conditions, having low positioning precision, and its identification effect being slightly inferior to that of the V-STARS. Aiming to address these shortcomings of IPCT, an improved IPCT, named I-IPCT, with an adaptive binarization, a more precise ellipse-center localization, and especially an invariance of the point–line distance ratio (PLDR), was proposed. In the process of edge extraction, the adaptive threshold Gaussian function was adopted to realize the acquisition of an adaptive binarization threshold. For the process of center positioning of round targets, the gray cubic weighted centroid algorithm was adopted to realize high-precision center localization. In the template point recognition procedure, the invariant of the PLDR was used to realize the determination of template points adaptively. In the decoding procedure, the invariant of the PLDR was adopted to eliminate confusion. Experiments in indoor, outdoor, and unmanned aerial vehicle (UAV) settings were carried out; meanwhile, sufficient comparisons with IPCT and V-STARS were performed. The results show that the improvements can make the identification approximately parameter-free and more accurate. Meanwhile, it presented a high three-dimensional measurement precision in close-range photogrammetry. The improved IPCT performed equally well as the commercial software V-STARS on the whole and was slightly superior to it in the UAV test, in which it provided a fantastic open solution using these kinds of coded targets and making it convenient for researchers to freely apply the coded targets in many aspects, including UAV photogrammetry for high-precision automatic image matching and three-dimensional real-scene reconstruction. Full article