Search Results (1,151)

Background: Advancements in low-cost additive manufacturing and artificial intelligence have enabled new avenues for developing accessible myoelectric prostheses. However, achieving reliable real-time control and ensuring mechanical durability remain significant challenges, particularly for affordable systems designed for resource-constrained settings. Objective: This study aimed to design and validate a low-cost, 3D-printed prosthetic arm that integrates single-channel electromyography (EMG) sensing with machine learning for real-time gesture classification. The device incorporates an anatomically inspired structure with 14 passive mechanical degrees of freedom (DOF) and 5 actively actuated tendon-driven DOF. The objective was to evaluate the system’s ability to recognize open, close, and power-grip gestures and to assess its functional grasping performance. Method: A Fast Fourier Transform (FFT)-based feature extraction pipeline was implemented on single-channel EMG data collected from able-bodied participants. A Support Vector Machine (SVM) classifier was trained on 5000 EMG samples to distinguish three gesture classes and benchmarked against alternative models. Mechanical performance was assessed through power-grip evaluation, while material feasibility was examined using PLA-based 3D-printed components. No amputee trials or long-term durability tests were conducted in this phase. Results: The SVM classifier achieved 92.7% accuracy, outperforming K-Nearest Neighbors and Artificial Neural Networks. The prosthetic hand demonstrated a 96.4% power-grip success rate, confirming stable grasping performance despite its simplified tendon-driven actuation. Limitations include the reliance on single-channel EMG, testing restricted to able-bodied subjects, and the absence of dynamic loading or long-term mechanical reliability assessments, which collectively limit clinical generalizability. Overall, the findings confirm the technical feasibility of integrating low-cost EMG sensing, machine learning, and 3D printing for real-time prosthetic control while emphasizing the need for expanded biomechanical testing and amputee-specific validation prior to clinical application. Full article

Unmanned underwater vehicles (UUVs) capable of agile, high-speed maneuvering in complex environments require propulsion systems that can dynamically modulate three-dimensional forces. The California sea lion (Zalophus californianus) provides an exceptional biological model, using its foreflippers to achieve rapid turns and powerful propulsion. However, the specific kinematic mechanisms that govern instantaneous force generation from its powerful foreflippers remain poorly quantified. This study experimentally characterizes the time-varying thrust and lift produced by a bio-robotic sea lion foreflipper to determine how flipper twist, sweep, and phase overlap modulate propulsive forces. A three-degree-of-freedom bio-robotic flipper with a simplified, low-aspect-ratio planform and single compliant hinge was tested in a circulating flow tank, executing parameterized power and paddle strokes in both isolated and combined-phase trials. The time-resolved force data reveal that the propulsive stroke functions as a tunable hybrid system. The power phase acts as a force-vectoring mechanism, where the flipper’s twist angle reorients the resultant vector: thrust is maximized in a broad, robust range peaking near 45°, while lift increases monotonically to 90°. The paddle phase operates as a flow-insensitive, geometrically driven thruster, where twist angle (0° optimal) regulates thrust by altering the presented surface area. In the full stroke, a temporal-phase overlap governs thrust augmentation, while the power-phase twist provides robust steering control. Within the tested inertial flow regime (Re ≈ 10⁴–10⁵), this control map is highly consistent with propulsion dominated by geometric momentum redirection and impulse timing, rather than circulation-based lift. These findings establish a practical, experimentally derived control map linking kinematic inputs to propulsive force vectors, providing a foundation for the design and control of agile, bio-inspired underwater vehicles. Full article

Teaching-based robotic systems offer an accessible alternative to complex inverse kinematics programming for food processing automation. Traditional model-based approaches require precise system identification and analytical solutions that are challenging for custom-built robots with manufacturing tolerances and mechanical uncertainties. This study developed a custom six-degree-of-freedom robotic arm using modular brushless motors controlled via Controller Area Network communication and Robot Operating System 2, a teaching mode where users manually demonstrate trajectories that are recorded at 100 Hz. Forty-five demonstration trajectories were collected across three geometric patterns (rectangle, triangle, circle) and augmented to 270 samples. A bidirectional Long Short-Term Memory network with attention mechanism was trained to classify patterns, achieving 83.33% test accuracy and outperforming baseline deep learning models (1D-CNN: 77.78%, TCN: 66.67%, GRU: 44.44%), while being marginally exceeded by Random Forest (86.11%). Rectangle patterns showed strongest recognition (78.57% F1-score), while circle patterns achieved highest performance (91.67% F1-score). However, severe overfitting was observed, with validation accuracy peaking at 85.19% at epoch 24 before degradation, indicating insufficient dataset size despite five-fold augmentation. The results demonstrate proof-of-concept feasibility for pattern recognition from limited teaching demonstrations, providing a pathway for robotic food processing without extensive programming expertise, though larger datasets and robust feedback control strategies are required for production deployment. Full article

Ocean waves constitute a vast renewable resource, yet most linear generator-based wave energy converters (WECs) rely on single-degree-of-freedom (SDOF) linear oscillators that exhibit narrow resonance bandwidths and utilise sliding components prone to wear. To address these limitations, this paper presents a nonlinear three-degree-of-freedom (3DOF) magnetic spring power-take-off (PTO) system for broadband wave energy harvesting. The device comprises three axially levitated NdFeB permanent magnets, each coupled to an independent copper coil, forming a compact, friction-free generator column. A coupled electromechanical state-space model was developed and experimentally validated on a laboratory-scale test rig. The 3DOF PTO exhibited three distinct resonance modes at approximately 35, 48, and 69 rad s⁻¹, enabling multi-mode energy capture across a broad frequency range. Under identical excitation (6.5 N amplitude and 3.13 Hz excitation force), the 3DOF configuration achieved a 114.5% increase in RMS voltage compared with the SDOF design and a 44.10% improvement over the 2DOF benchmark, confirming the effectiveness of the coupled resonance mechanism. The levitated magnetic architecture eliminates mechanical contact and lubrication, reducing wear and maintenance while improving long-term reliability in marine environments. A preliminary life-cycle assessment estimated a cradle-to-gate carbon intensity of 40–80 g CO₂-eq kWh⁻¹, significantly lower than that of conventional hydraulic PTOs, owing to reduced steel use and recyclable magnet assemblies. The proposed 3DOF magnetic spring PTO thus offers a sustainable, low-maintenance, and high-efficiency solution for next-generation ocean-energy converters. Full article

Emerging services such as artificial intelligence (AI), 5G, the Internet of Things (IoT), cloud data services and teleworking are growing exponentially, pushing bandwidth needs to the limit. Space Division Multiplexing (SDM) in the spatial domain, along with Ultra-Wide Band (UWB) transmission in the spectrum domain, represent two degrees of freedom that will play a crucial role in the evolution of backbone optical networks. SDM and UWB technologies necessitate the replacement of conventional Wavelength-Selective-Switch (WSS)-based architectures with innovative optical switching elements capable of handling both higher port counts and flexible switching across various granularities. In this work, we introduce a novel Photonic Integrated Circuit (PIC)-based switching element called flex-Waveband Selective Switch (WBSS), designed to provide flexible band switching across the UWB spectrum (~21 THz). The proposed flex-WBSS supports a hierarchical three-layered Multi-Granular Optical Node (MG-ON) architecture incorporating optical switching across various granularities ranging from entire fibers and flexibly defined bands down to individual wavelengths. To evaluate its performance, we develop a custom network simulator, enabling a thorough performance analysis on the critical performance metrics of the node. Simulations are conducted over an existing network topology evaluating three traffic-oriented switching policies: Full Fiber Switching (FFS), Waveband Switching (WBS) and Wavelength Switching (WS). Simulation results reveal high Optical-to-Signal Ratio (OSNR) and low Bit Error Rate (BER) values, particularly under the FFS policy. In contrast, the integration of the WBS policy bridges the gap between existing WSS- and future FFS-based architectures and manages to mitigate capacity bottlenecks, enabling rapid scalable network upgrades in existing infrastructures. Additionally, we propose a probabilistic framework to evaluate the node’s bandwidth utilization and scaling behavior, exploring trade-offs among scalability, component numbers and complexity. The proposed framework can be easily adapted for the design of future transport optical networks. Finally, we perform a SWaP-C (Size, Weight, Power and Cost) analysis. Results show that our novel MG-ON achieves strong performance, reaching a throughput exceeding 10 Pb/s with high OSNR values ≈14–20 dB and BER ≈10⁻⁹ especially under the FFS policy. Moreover, it delivers up to 7.5× cost reduction compared to alternative architectures, significantly reducing deployment/upgrade costs while maintaining low power consumption. Full article

Driven by 5G/6G and the Internet of Things (IoT), wireless sensor networks (WSNs) are confronted with core challenges such as limited energy constraints, unbalanced resource allocation, and security vulnerabilities. To address these, WSNs are integrated with cognitive radio networks (CRNs) to alleviate spectrum scarcity, and reconfigurable intelligent surfaces (RIS) are adopted to enhance performance, but traditional passive RIS suffers from “double fading” (signal path loss from transmitter to RIS and RIS to receiver), which undermines WSNs’ energy efficiency and the physical layer security (PLS) (e.g., secrecy rate, SR) of primary users (PUs) in CRNs. This study leverages symmetry to develop an active RIS framework for WSN-integrated CRNs, constructing a tripartite collaborative model where symmetric beamforming and resource allocation improve WSN connectivity, reduce energy consumption, and strengthen PLS. Specifically, three symmetry types—resource allocation symmetry, beamforming structure symmetry, and RIS reflection matrix symmetry—are formalized mathematically. These symmetries reduce the degrees of freedom in optimization (e.g., cutting precoding complexity by ~50%) and enhance the directionality of green interference, while ensuring balanced resource use for WSN nodes. The core objective is to minimize total transmit power while satisfying constraints of PU SR, secondary user (SU) quality-of-service (QoS), and PU interference temperature, achieved by converting non-convex SR constraints into solvable second-order cone (SOC) forms and using an alternating optimization algorithm to iteratively refine CBS/PBS precoding matrices and active RIS reflection matrices, with active RIS generating directional “green interference” to suppress eavesdroppers without artificial noise, avoiding redundant energy use. Simulations validate its adaptability to WSN scenarios: 50% lower transmit power than RIS-free schemes (with four CBS antennas), 37.5–40% power savings as active RIS elements increase to 60, and a 40% lower power growth slope in multi-user WSN scenarios, providing a symmetry-aided, low-power solution for secure and efficient WSN-integrated CRNs to advance intelligent WSNs. Full article

This study revisits a recently proposed member of the truncated positive family of distributions, referred to as the positively truncated Student’s-t distribution. The distribution retains the structure of the classical Student’s-t distribution while explicitly incorporating a kurtosis parameter, yielding a flexible three-parameter formulation that governs location, scale, and tail behavior. A closed-form quantile function is derived, allowing a novel reparameterization based on the pth quantile and thereby facilitating integration into quantile regression models. The analytical tractability of the quantile function also enables efficient random number generation via the inverse transform method, which supports a comprehensive simulation study demonstrating the strong performance of the proposed estimators, particularly for the degrees-of-freedom parameter. The entire methodology is implemented in the tpn package for the R software. Finally, two real-data applications involving PM2.5 measurements—one without covariates and another with covariates—highlight the model’s robustness and its ability to capture heavy-tailed behavior. Full article

The growing interest in high-power amplifiers for the terahertz (THz) radar system leads to significant performance improvements of THz wave traveling-wave tubes (TWT). This article presents a detailed development of a G-band pulsed wave TWT with 120 W output power. Three approaches have been combined to improve the tube’s output power including proposing the modified folded waveguide (MFWG) slow wave structure (SWS), using large beam current, and adopting phase velocity tapering (PVT). Firstly, the MFWG SWS circuit has an additional degree of freedom that can be used to achieve approximately 36% higher interaction impedance than that in the conventional folded waveguide (CFWG). Subsequently, the electron beam current was increased to approximately 100 mA to boost the DC power of the electron beam. Finally, the PVT technology dramatically enhanced the output power from 98 W to 143 W, concomitant with a notable increase in electronic efficiency from 4.75% to 7.03%. Hot experimental results show that the measured output power can be over 100 W at 20% duty cycle within a bandwidth of 5 GHz when the operation voltage and the current are 22.48 kV and 103.5 mA, respectively. In addition, the maximum power is 121 W with the corresponding electronic efficiency of 5.1%. The proposed G-band 100 W TWT will have broad applications in far-distance high-resolution imaging. Full article

The multi-finger robotic hand exhibits significant potential in grasping tasks owing to its high degrees of freedom (DoFs). Object grasping results in a closed-chain kinematic system between the hand and the object. This increases the dimensionality of trajectory tracking and substantially raises the computational complexity of traditional methods. Therefore, this study proposes the discrete finite-time convergent neurodynamics (DFTCN) algorithm to address the aforementioned issue. Specifically, a time-varying quadratic programming (TVQP) problem is formulated for each finger, incorporating joint angle and angular velocity constraints through log-sum-exp (LSE) functions. The TVQP problem is then transformed into a time-varying equation system (TVES) problem using the Karush–Kuhn–Tucker (KKT) conditions. A novel control law is designed, employing a three-step Taylor-type discretization for efficient implementation. Theoretical analysis verifies the algorithm’s stability and finite-time convergence property, with the maximum steady-state residual error being $O\left({\tau }^3\right)$. Numerical simulations illustrate the favorable convergence and high accuracy of the DFTCN algorithm compared with three existing dominant neurodynamic algorithms. The real-robot experiments further confirm its capability for precise grasping, even in the presence of camera noise and external disturbances. Full article

To mitigate the inadequate performance of traditional hydraulic systems, mechanical feedback-based digital hydraulic technology is applied to a 3-degree-of-freedom (3-DOF) crane. Digital hydraulic cylinders drive the pitch mechanism, and digital hydraulic motors power the rotary and winch mechanisms. By analyzing the working principles of digital hydraulic cylinders and motors, transfer functions of the 3-DOF actuators are derived. AMESim simulation models are established for each actuator, with model validity verified. Based on these models, simulation analysis of the digital hydraulic system is performed to examine key influencing factors: motor speed, motor subdivision, system flow rate, digital valve opening, and throttle groove shape. System characteristics are obtained, and corresponding optimization schemes are proposed. After optimization, the comprehensive performance of the digital hydraulic system is improved by 1.29%. This study provides theoretical support for the engineering application of digital hydraulic systems in cranes, clarifies their operational specifications and optimization pathways, and exhibits substantial engineering application value. Full article

Although parallel mechanisms are used in various fields, their application is often limited by singularities and a restricted workspace. Kinematic redundancy is a promising approach for mitigating these issues while also extending the functionality of the mechanisms. This article contributes to this field by introducing two novel Delta-type kinematically redundant parallel mechanisms with linear actuators. The moving platform in these mechanisms has three translational degrees of freedom and consists of two parts connected by a prismatic joint, providing an extra translation between the parts. First, we present closed-form solutions to the inverse and forward kinematic problems, accompanied by numerical examples that validate the theoretical derivations. Next, we analyze singular configurations of the mechanisms with a symmetrical design, focusing on parallel singularities. Using an iterative approach, we identify points within the workspace corresponding to these configurations, including finite-motion singularities. Based on this analysis, we changed the geometrical parameters of one mechanism and presented the design where the singularity-free region of the workspace occupies 95% of the total workspace. This study forms the basis for future research on the proposed mechanisms and their prototyping. Full article

As a critical component of nuclear and thermal energy conversion systems, the long-term safe operation of a steam generator depends on the structural integrity of its tube bundles. Foreign objects introduced into the secondary side can induce flow-induced vibrations and wear, potentially causing tube wall damage and unplanned outages, thereby affecting overall system reliability. This study systematically investigates the flow-induced vibration behavior of foreign objects within steam generator tube bundles and explores the influence of object geometry through three-dimensional fluid–structure interaction (FSI) simulations. The foreign objects are modeled as single-degree-of-freedom rigid bodies, and their dynamic responses are captured using a coupled flow–motion framework. Results reveal that object geometry significantly influences flow separation, variations in lift and drag forces, and displacement characteristics. Cylindrical and irregular objects exhibit stable, low-amplitude vibrations; plate-shaped objects experience restricted motion due to large drag areas and symmetric contact constraints; whereas helical objects show the largest displacements arising from coupled axial–radial vibrations and complex vortical structures. These findings demonstrate that the interplay between aerodynamic forces and geometric complexity strongly governs the flow-induced vibration of foreign objects, offering insights into their motion behavior and potential impact on steam generator tube bundle integrity. Full article

In micro-production technology (MPT), the demand for ultra-precise machine tools has been steadily increasing. Conventional guideway systems, such as hydrostatic or aerostatic bearings, often face limitations in terms of compactness, media supply, and susceptibility to external disturbances, which restrict their applicability in next-generation precision manufacturing. In order to address these challenges, this paper presents a novel media-free, contactless, and active three-degree-of-freedom (DOF) planar positioning (guiding) system that integrates ultrasonic actuators with electromagnetic actuators. The hybrid concept combines the high load capacity and self-stabilization of double-acting ultrasonic actuators and pronounced controllability of the electromagnetic actuators. A prototype system was developed and experimentally validated. Ultrasonic actuators successfully established a stable levitation state, while electromagnetic actuators provided fine adjustment of the levitation height in the micrometer range. Load tests demonstrated that the system maintained stable levitation under an external load of 30 N. These results confirm the feasibility of the proposed approach for robust and precise positioning. The developed hybrid system therefore represents the potential for next-generation precise manufacturing machines in MPT, offering high accuracy and robustness against external disturbances. Full article

The adoption of active upper-limb prostheses with multiple degrees of freedom is largely lagging due to bulky designs and counterintuitive operation. Accurate gesture prediction with minimal sensors is key to enabling low-profile, user-friendly prosthetic devices. Wearable sensors, such as electromyography (EMG) and accelerometry (ACC) sensors, provide valuable signals for identifying patterns relating muscle activity and arm movement to specific gestures. This study investigates which sensor type (EMG or ACC) has the most valuable information to predict hand grasps and identifies the signal features contributing the most to grasp prediction performance. Using an open-source dataset, we trained two types of subject-specific classifiers (LDA & KNN) to predict 10 grasp types in 13 individuals with and 28 individuals without amputation. Having 4-fold cross-validation, LDA average accuracies using ACC only features (84.7%) were similar to combined ACC & EMG (88.3%) and much greater than with only EMG features (58.1%). Feature importance analysis showed that participants with amputation reached more than 80% accuracy using only three features, two of which were ACC-derived, while able-bodied participants required nine features, with greater reliance on EMG. These findings suggest that ACC is sufficient for robust grasp classification in individuals with amputation and can support simpler, more accessible prosthetic designs. Future work should focus on incorporating object and grip force detection alongside grasp recognition and testing model performance in real-time prosthetic control settings. Full article

The purpose of this paper is to implement a path-following control system based on the kinematics of the Lizard-Inspired Single-Actuated robot (LISA). LISA is a new type of robot that mimics the quadrupedal walking morphology of lizards with a four-bar linkage mechanism and can realize both propulsion and turning with 1 degree-of-freedom. To achieve this purpose, this paper takes 3 approaches: kinematics formulation, control system design, and experimental verification. In the kinematics formulation, we formulate LISA’s turning angle, stride length, posture, propulsive direction, curvature, and position coordinate. In control system design, we design a control system that converges not only the distance error but also the posture error and control input. Conditional equations that can achieve these 3 control targets are formulated using forward kinematics and reference path functions. The experimental verifications were carried out to verify the effectiveness of the designed path-following control system using three types of paths: linear, circular, and combined linear and circular. As a result, it was confirmed that the Root Mean Square values for the control input, the distance error, and the attitude error were sufficiently small in steady state. Therefore, it was confirmed that the 3 control objectives had been achieved. Full article