Search Results (3,391)

This paper proposes a novel motion-aware framework to address key challenges in 4D millimeter-wave radar detection for autonomous driving. While existing methods struggle with sparse point clouds and dynamic object characterization, our approach introduces three key innovations: (1) A Bird’s Eye View (BEV) fusion network incorporating velocity vector decomposition and dynamic gating mechanisms, effectively encoding motion patterns through independent XY-component convolutions; (2) a gradient-aware multi-task balancing scheme using learnable uncertainty parameters and dynamic weight normalization, resolving optimization conflicts between classification and regression tasks; and (3) a two-phase progressive training strategy combining multi-frame pre-training with sparse single-frame refinement. Evaluated on the TJ4D benchmark, our method achieves 33.25% mean Average Precision (mAP)_3D with minimal parameter overhead (1.73 M), showing particular superiority in pedestrian detection (+4.16% AP) while maintaining real-time performance (24.4 FPS on embedded platforms). Comprehensive ablation studies validate each component’s contribution, with thermal map visualization demonstrating effective motion pattern learning. This work advances robust perception under challenging conditions through principled motion modeling and efficient architecture design. Full article

This is a review article that covers the history of the development of the equation of motion for solid particles in fluids, starting with the early work, before the Navier–Stokes equations were developed. Particular emphasis is placed on the development of the transient equation of motion, which features the history (or memory) term and the added mass (virtual mass) term. The salient features of the equation and the methods of their derivation are pointed out. Creeping, non-inertia flows as well as advective flows are surveyed, with particular emphasis on their effects on the functional form of the history term. Modifications to the hydrodynamic force due to possible interface slip are also examined. The review also deals with the inclusion of the weaker lateral (lift) forces and the inclusion of the effects of Brownian movement, which gives rise to thermophoresis—an important source of nanoparticle movement and surface deposition. The drag on irregularly shaped particles—another important feature of nanoparticles—is also examined. The review concludes with a short section on significant unknown issues and work that may be carried out in the near future for the theoretical and computational development of the subject. Full article

This paper presents the design and implementation of a novel autonomous underwater robot with fully vectored propulsion based on model predictive control (MPC) to rapidly respond to the position and attitude required for autonomous operation. Specifically, the mechatronic design of the eight vector-distributed thruster layout for the robot’s fully vectored propulsion is detailed, and the software architecture based on the robot operating system (ROS) is constructed. Then, the corresponding dynamics model is established by adopting the Fossen approach for the prediction and optimization of the control process. To achieve autonomous control, an MPC-based controller is designed and implemented to calculate the control input for the specified control objective. Finally, way-point tracking and trajectory-tracking experiments are carried out in an indoor tank equipped with a motion-capture system to validate the feasibility and effectiveness of the robot’s design and control framework. In addition, the robustness of the robot system is verified by artificially perturbing the robot in the hovering state. Full article

During the magmatic stage, base and rarer metals segregate from silicate melts to form ore deposits. The usual case is the porphyry (PD) type (Cu, Mo, and W) above subduction zones. The metal grade increases from some ppb or ppm up to percent levels. A new type of trans-porphyry (TPD) deposits (Sn, Ta, Nb, and gems) results from large-scale shear between cratons within continental plates, internal decoupling, and vertical motion. The bulk ore generation process develops along three stages: from magma generation, emplacement, and the formation of an immiscible magmatic phase (MIP), fluids, and melt. However, in TPD, metals segregate from the crust during melting below 800 °C, biotites break down, and the melt remains below the critical point (731 °C). Fluid advection competes with chemical diffusion, yielding the required enrichment. The subcritical MIP splits into a silicate-rich and an aqueous-rich phase, which are both incompatible with each other. Granite, pegmatites, and greisen coexist in the magma chamber. Their respective extraction from a composite mush involves electron exchanges between charges, or orbitals, yielding metal oxides through chemical diffusion. In contrast, in metals (Nb and Ta) observed in pegmatites, and also in gems, electrons rearrange their electronic cloud through their polarizability. Lastly, gems independently grow under the influence of the extremely hard fluids (Li, Be, and B). Magma generation, involving the lower crust (garnet and pyroxene), results in melts that form the two observed pegmatite groups (NYF and LCT), with each being associated with alkaline (A-type) or continental (S-type) granitic melts. Full article

Human activity recognition in privacy-sensitive indoor environments requires sensing modalities that remain robust under illumination variation and background clutter while preserving user anonymity. To this end, this study proposes a lightweight radar–camera fusion deep learning model that integrates motion signatures from FMCW radar with coarse spatial cues from ultra-low-resolution camera frames. The radar stream is processed as a Range–Doppler–Time cube, where each frame is flattened and sequentially encoded using a Transformer-based temporal model to capture fine-grained micro-Doppler patterns. The visual stream employs a privacy-preserving $4\times 5$-pixel camera input, from which a temporal sequence of difference frames is extracted and modeled with a dedicated camera Transformer encoder. The two modality-specific feature vectors—each representing the temporal dynamics of motion—are concatenated and passed through a lightweight fully connected classifier to predict human activity categories. A multimodal dataset of synchronized radar cubes and ultra-low-resolution camera sequences across 15 activity classes was constructed for evaluation. Experimental results show that the proposed fusion model achieves 98.74% classification accuracy, significantly outperforming single-modality baselines (single-radar and single-camera). Despite its performance, the entire model requires only 11 million floating-point operations (11 MFLOPs), making it highly efficient for deployment on embedded or edge devices. Full article

We present a virtual reality (VR) framework for controlling dual-arm robotic manipulators through immersive interfaces, integrating both simulated and real-world platforms. The system combines the Webots robotics simulator with Unreal Engine 5.6.1 to provide real-time visualization and interaction, enabling users to manipulate each arm’s tool point via VR controllers with natural depth perception and motion tracking. The same control interface is seamlessly extended to a physical dual-arm robot, enabling teleoperation within the same VR environment. Our architecture supports real-time bidirectional communication between the VR layer and both the simulator and hardware, enabling responsive control and feedback. We describe the system design and performance evaluation in both domains, demonstrating the viability of immersive VR as a unified interface for simulation and physical robot control. Full article

Event-based vision sensors offer microsecond temporal resolution and low power consumption, making them attractive for edge robotics and simultaneous localization and mapping (SLAM). Contrast maximization (CMAX) is a widely used direct geometric framework for rotational ego-motion estimation that aligns events by warping them and maximizing the spatial contrast of the resulting image of warped events (IWE). However, conventional CMAX is computationally inefficient because it repeatedly processes the full event set and a full-resolution IWE at every optimization iteration, including late-stage refinement, incurring both event-domain and image-domain costs. We propose coarse-to-fine contrast maximization (CCMAX), a computation-aware CMAX variant that aligns computational fidelity with the optimizer’s coarse-to-fine convergence behavior. CCMAX progressively increases IWE resolution across stages and applies coarse-grid event subsampling to remove spatially redundant events in early stages, while retaining a final full-resolution refinement. On standard event-camera benchmarks with IMU ground truth, CCMAX achieves accuracy comparable to a full-resolution baseline while reducing floating-point operations (FLOPs) by up to 42%. Energy measurements on a custom RISC-V–based edge SoC further show up to 87% lower energy consumption for the iterative CMAX pipeline. These results demonstrate an energy-efficient motion-estimation front-end suitable for real-time edge SLAM on resource- and power-constrained platforms. Full article

Monocular dynamic scene reconstruction is a challenging task due to the inherent limitation of observing the scene from a single viewpoint at each timestamp, particularly in the presence of object motion and illumination changes. Recent methods combine Gaussian Splatting with deformation modeling to enable fast training and rendering; however, their performance in real-world scenarios strongly depends on accurate point cloud initialization. When such initialization is unavailable and random point clouds are used instead, reconstruction quality degrades significantly. To address this limitation, we propose an optimization strategy that relaxes the requirement for accurate initialization in Gaussian-Splatting-based monocular dynamic scene reconstruction. The scene is first reconstructed under a static assumption using all monocular frames, allowing stable convergence of background regions. Based on reconstruction errors, a subset of Gaussians is then activated as dynamic to model motion and deformation. In addition, an annealing jitter regularization term is introduced to improve robustness to camera pose inaccuracies commonly observed in real-world datasets. Extensive experiments on established benchmarks demonstrate that the proposed method enables stable training from randomly initialized point clouds and achieves reconstruction performance comparable to approaches relying on accurate point cloud initialization. Full article

The generalized trigonometric functions called Ateb-functions are considered. On this basis, a generalization of the Fourier transform is constructed and called the Ateb-transform. From the operator theory point of view, the Ateb-transform is considered as a formalism of the convolution algebra in which multiplication is defined by means of hypergroups of the generalized shift operator. In this formal approach, the algebraic structure is presented, and its properties are developed. The eigenvalue problem for the differential equation of nonlinear oscillation is investigated. Some properties are illustrated numerically. The application of this approach for modeling vibration motion is considered. Full article

In contemporary semiconductor manufacturing, wafer-handling robots are essential for achieving high-speed and high-precision wafer transportation. However, the demand for rapid motion and lightweight design introduces flexible transmission components that are prone to residual vibrations, which degrade positioning accuracy and system stability. To address this challenge, this paper proposes a vibration-suppression trajectory planning method based on the Gray Goose Optimization (GGO) algorithm. The proposed algorithm integrates grouped global search with local optimization capabilities, making it well suited for solving multi-objective optimization problems. Comparative tests conducted on eight randomly selected multimodal benchmark functions from the CEC2013 test suite verify the effectiveness and robustness of the GGO algorithm. Establishing a multi-objective function that considers both motion time and vibration energy enables the GGO algorithm to determine the switching time points of an S-shaped velocity profile, thereby generating smooth trajectories with continuous velocity and acceleration. By varying different initial conditions, the trade-off between motion time and vibration energy is systematically analyzed with respect to angular displacement, initial acceleration, and time-weighting factors. Simulation results indicate that the planned trajectories exhibit negligible displacement variation under zero-mean disturbances. The velocity error remains within 0.1 deg·s⁻¹, and the acceleration error is confined within 0.2 deg·s⁻². Consequently, Pareto-optimal solutions are successfully obtained with respect to both motion time and residual vibration energy. Full article

Satellite pointing mechanisms for earth observation require ultra-low speed scanning (approximately ${70}^{″}/\mathrm{s}$) and precise variable-speed compensation. However, traditional Field-Oriented Control (FOC) suffers from significant velocity bias and instability under these conditions. To address these issues, this paper proposes a position-loop-based speed control scheme integrated with a variable structure control strategy. By substituting the speed command with a position loop, the proposed method effectively suppresses steady-state velocity bias, while the variable structure strategy mitigates fluctuations during variable-speed motion. Experimental results indicate that, compared to traditional FOC, the proposed method reduces velocity bias error by over $30\%$ during uniform tracking and decreases the amplitude of velocity fluctuations by more than $40\%$ in variable-speed scenarios. This strategy significantly enhances the control precision of satellite pointing mechanisms and improves on-orbit imaging compensation accuracy. Full article

Cable-driven robotic systems are widely used in applications requiring lightweight structures, large workspaces, and accurate force regulation. In such systems, the mechanical behavior of cable-driven actuators is strongly influenced by the elastic properties of the cable, transmission elements, and supporting structure, leading to an effective stiffness that varies with pretension, applied load, cable length, and operating conditions. These stiffness variations have a direct impact on force control performance but are often implicitly treated or assumed constant in control-oriented studies. This paper investigates the effects of operating-point-dependent (incremental) cable stiffness on actuator-level force control performance in cable-driven robotic systems. The analysis is conducted at the level of an individual cable-driven actuator to isolate local mechanical effects from global robot dynamics. Mechanical stiffness is characterized within a limited elastic domain through local linearization around stable operating points, avoiding the assumption of global linear behavior over the entire force range. Variations in effective stiffness induced by changes in pretension, load, and motion regime are analyzed through numerical simulations and experimental tests performed on a dedicated test bench. The results demonstrate that stiffness variations significantly affect force tracking accuracy, dynamic response, and disturbance sensitivity, even when controller structure and tuning parameters remain unchanged. Full article

This study aims to assess hand function recovery in stroke patients during the mid-to-late Brunnstrom stages and to encourage active participation in rehabilitation exercises. To this end, a deep residual network (ResNet) integrated with Focal Loss is employed for gesture recognition, achieving a Macro F1 score of 91.0% and a validation accuracy of 90.9%. Leveraging the millimetre-level precision of Leap Motion 2 hand tracking, a mapping relationship for hand skeletal joint points was established, and a static assessment gesture data set containing 502,401 frames was collected through analysis of the FMA scale. The system implements an immersive augmented reality interaction through the Unity development platform; C# algorithms were designed for real-time motion range quantification. Finally, the paper designs a rehabilitation system framework tailored for home and community environments, including system module workflows, assessment modules, and game logic. Experimental results demonstrate the technical feasibility and high accuracy of the automated system for assessment and rehabilitation training. The system is designed to support stroke patients in home and community settings, with the potential to enhance rehabilitation motivation, interactivity, and self-efficacy. This work presents an integrated research framework encompassing hand modelling and deep learning-based recognition. It offers the possibility of feasible and economical solutions for stroke survivors, laying the foundation for future clinical applications. Full article

Finding Noether symmetries for time-dependent damped dynamical systems remains a significant challenge. This paper introduces a complete geometric algorithm for determining all Noether point symmetries and first integrals for the general class of Lagrangians $L=A\left(t\right)L_0$, which model motion with general linear damping in a Riemannian space. We derive and prove a central Theorem that systematically links these symmetries to the homothetic algebra of the kinetic metric defined by $L_0$. The power of this method is demonstrated through a comprehensive analysis of the damped Kepler problem. Beyond recovering known results for constant damping, we discover new quadratic first integrals for time-dependent damping $\varphi \left(t\right)=\gamma /t$ with $\gamma =-1$ and $\gamma =-1/3$. We also include preliminary results on the Noether symmetries of the damped harmonic oscillator. Finally, we clarify why a time reparameterization that removes damping yields a physically inequivalent system with different Noether symmetries. This work provides a unified geometric framework for analyzing dissipative systems and reveals new integrable cases. Full article

Motion capture experiments can be conducted more easily if marker-based motion (marker-based MoCap) can be captured using an asynchronous multicamera system (Async MCS). However, camera calibration is essential for marker-based MoCap, and a wand calibration method that utilizes timestamp functions has been proposed for Async MCS. However, in practice, many cameras do not provide accurate timestamp functions, limiting the applicability of existing methods in such environments. A wand calibration method for Async MCS that does not rely on timestamp functions is proposed to evaluate the accuracy of estimated camera parameters. In conventional methods, the time offset in image acquisition is obtained from timestamp information, and synchronous coordinates are estimated by interpolating time-series digitized coordinates of wand markers. In this study, the time offset is treated as an optimization variable, which enables camera parameter estimation without using timestamp functions. Consequently, the three-dimensional reconstruction errors of fixed points obtained using the proposed method are significantly smaller (1.445 ± 0.833 and 1.746 ± 0.908 mm) compared to estimations that ignore time offsets. These findings demonstrate that the proposed method enables more accurate camera calibration. Full article