Search Results (45)

Background/Objectives: The cerebellum contributes to both motor and cognitive functions. As basketball requires the integration of these abilities, basketball athletes provide an ideal model for exploring cerebellar adaptations. This study aimed to examine multidimensional cerebellar adaptations in basketball athletes and their associations with physical performance. Methods: In this study, 55 high-level basketball athletes and 55 non-athletes matched for age and gender were recruited for multimodal magnetic resonance imaging data collection and physical fitness tests. We compared the structural and functional differences in the brain between the two groups and analyzed the correlations between regional brain indices and physical fitness test outcomes. Results: Basketball athletes exhibited increased gray matter volume in Crus I, alongside heightened ALFF signal in Crus I and improved regional homogeneity in Crus II and VII b compared to non-athletes. Diffusion kurtosis imaging analysis demonstrated that athletes perform elevated kurtosis fractional anisotropy and decreased radial kurtosis within the cerebellar cortex and peduncles, with cortical modifications mainly localized around Crus I and lobule VI. Notably, both kurtosis fractional anisotropy and the amplitude of low-frequency fluctuations displayed positive correlations with vertical jump performance, an indicator specific to basketball ability. Conclusions: Basketball athletes exhibit structural, microstructural, and functional cerebellar adaptations, especially in Crus I. These modifications involve regions associated with motor and cognitive representations within the cerebellum, and part of the indexes are linked to the athletes’ physical performance. This study enhances our understanding of cerebellar adaptive changes in athletes, providing new insights for future research aimed at fully elucidating the role of the cerebellum in these individuals. Full article

Accurate modeling of Brushless DC (BLDC) motors is crucial for the multi-domain simulation of complex electromechanical systems like electric torque tools, especially when high fidelity is required for Model-Based Design (MBD) and controller validation. Standard BLDC models often employ simplifications that may not capture critical operational details. This paper presents a comparative analysis of four distinct BLDC motor simulation models: two based on ready-to-use MATLAB/Simulink/Simscape Electrical library blocks (Specialized Power Systems/Electrical Machines/Permanent Magnet Synchronous Machine and Electromechanical/Permanent Magnet/BLDC) and two custom models developed by the authors at AGH University. The models are evaluated based on their structure, underlying equations, and performance in simulating typical operational scenarios of an electric torque tool. Key assessment criteria include the ability to implement realistic (e.g., tabulated, non-ideal) back-EMF (electromotive force) profiles, incorporate cogging torque, model commutation effects, and flexibility for modification. Simulation results indicate that while all models can be suitable for basic control design, the custom-developed models offer greater flexibility and fidelity in representing detailed motor phenomena such as irregular back-EMF waveforms and cogging torque, making them better suited for advanced, high-precision applications. Conversely, standard library models, particularly the one underlying the PMSM block, exhibit limitations in custom back-EMF implementation. This study concludes by recommending models based on specific application requirements and outlines directions for future enhancements, including thermal modeling and iron loss representation. Full article

In today’s fast-paced world, secure and efficient access control is crucial for places like schools, gated communities, and corporate campuses. The system must overcome the issues of manual checking and record maintenance of traditional methods like RFID cards or license plate recognition. Our work introduces a budget-friendly, automated solution. A prototype was developed for a vehicle sticker recognition system to control and monitor gate access at NFC IET University as a case study. The automated system design will replace manual checking by detecting the car stickers issued to each vehicle by the university administration. An optimized lightweight YOLOv8 model is trained to identify three categories: IET stickers (authorized for access), non-IET stickers (unauthorized), and no sticker (denied access). A webcam connected to the Raspberry Pi 4 scans approaching vehicles. Authorized vehicles are allowed when the relevant class is detected, which signals a servo motor to open the gate. Otherwise, access to the gate is denied, and infrared (IR) sensors close the gates. A second set of IR sensors and a servo motor was also added to manage the exit side, preventing unauthorized tailgating. The system’s modular design makes it adaptable for different environments, and its use of affordable hardware and open-source tools keeps costs low, which is ideal for smaller institutions or communities. The prototype model is tested and trained on self-collected datasets comprising 506 images. Full article

Spinal cord injury (SCI) is a neurological condition often resulting in permanent motor and sensory deficits, for which effective treatments remain limited. RNA interference (RNAi) is a post-transcriptional mechanism of the downregulation of gene expression mediated by small interfering RNAs. RNAi has demonstrated therapeutic efficacy in various neurological disorders, positioning it as a promising yet underexplored therapeutic strategy for SCI. Here, we provide a focused overview of the key pathological processes in SCI, including primary mechanical injury and secondary cascades such as inflammation, mitochondrial dysfunction, excitotoxicity, oxidative stress, multiple forms of cell death, and others. The potential of RNAi to selectively silence genes implicated in these pathological processes, thereby enhancing neuroprotection and functional recovery, is highlighted. We point out that not only protein-coding genes, but non-coding RNAs (ncRNAs) are suitable targets for RNAi. Novel RNAi tools such as CRISPR-Cas13 might revolutionize the field and offer new opportunities for SCI therapy. However, despite all these promising findings, relevant translational studies of RNAi remain scarce. Challenges related to delivery methods, long-term efficacy, and cell-specific targeting must be addressed. Importantly, combining RNAi with other strategies such as cell- or biomaterial-based therapies may enhance therapeutic outcomes. Future investigations should prioritize systematic comparisons of RNAi targets and delivery systems, ideally at single-cell resolution and in different SCI models, to identify the most relevant molecular pathways for clinical translation. Overall, RNAi represents a compelling but still underdeveloped approach for SCI therapy, requiring continued refinement to reach clinical application. Full article

Magnetically levitated artificial hearts impose stringent requirements on the blood-pump motor: zero friction, minimal heat generation and full biocompatibility. Traditional mechanical-bearing motors and permanent-magnet bearingless motors fail to satisfy all of these demands simultaneously. A bearingless switched reluctance motor (BSRM), whose rotor contains no permanent magnets, offers a simple structure, high thermal tolerance, and inherent fault-tolerance, making it an ideal drive for implantable circulatory support. This paper proposes an 18/15/6-pole dual-stator BSRM (DSBSRM) that spatially separates the torque and levitation flux paths, enabling independent, high-precision control of both functions. To suppress torque ripple induced by pulsatile blood flow, a variable-overlap TSF-PWM-DITC strategy is developed that optimizes commutation angles online. In addition, a grey-wolf-optimized fast non-singular terminal sliding-mode controller (NRLTSMC) is introduced to shorten rotor displacement–error convergence time and to enhance suspension robustness against hydraulic disturbances. Co-simulation results under typical artificial heart operating conditions show noticeable reductions in torque ripple and speed fluctuation, as well as smaller rotor radial positioning error, validating the proposed motor and control scheme as a high-performance, biocompatible, and reliable drive solution for next-generation magnetically levitated artificial hearts. Full article

This paper proposes an iterative auto-tuning algorithm for PI controllers in permanent magnet synchronous motor (PMSM) drive systems. The controller gains are initially set using motor-parameter-based formulas derived from pole–zero cancelation, providing a theoretical first-order approximation. To address discrepancies caused by practical non-idealities such as delays, nonlinearities, and unmodeled dynamics, the proposed method iteratively refines the gains based on real-time measurements of time-domain performance indices. In each iteration, rise time, peak time, and percent overshoot are evaluated against predefined target values, and gain compensation terms are calculated accordingly. These compensations are applied to update the controller gains until all performance indices fall within the desired range, at which point the tuning process terminates automatically. The effectiveness of the proposed algorithm is validated through both MATLAB/Simulink simulations and real-time hardware experiments, demonstrating significant improvements in transient response, overshoot suppression, and closed-loop stability compared to conventional tuning approaches. Full article

The dynamics of the vibrational mode of motion of the driving member of a nonideal system, a mixing–whipping device based on a simple slide-crank mechanism, was studied. The highly nonlinear differential equations of motion were solved numerically by the Runge–Kutta method. The interaction of the mixing–whipping device with the nonideal excitation source causes the rotational speed of the engine shaft and the rotation angle of the driving member to fluctuate, accomplishing a damped process. The parameters of the device and the nonideal energy source have an effect on the kinematic, vibrational and energy characteristics of the system. An increase in the engine’s torque, crank length, number and radius of piston holes, and piston mass, as well as a decrease in the fluid’s density, leads to a reduction in the oscillation range of the crank angle, amplitude and period of angular velocity oscillations of the engine shaft and the mixing–whipping force power. The effects of a nonideal energy source may be used in designing a mixing–whipping device based on a slider-crank mechanism to select effective system parameters and an energy-saving motor in accordance with the requirements of technological processes and products. Full article

The turbocharging of hydrogen fuel cell systems (FCSs) has recently become a prominent research area, aiming to improve FCS efficiency to help decarbonise the energy and transport sectors. This work compares the performance of an electrically assisted variable-geometry turbocharger (VGT) with a fixed-geometry turbocharger (FGT) by optimising both the sizing of the components and their operating points, ensuring both designs are compared at their respective peak performance. A MATLAB-Simulink reduced-order model is used first to identify the most efficient components that match the fuel cell air path requirements. Maps representing the compressor and turbines are then evaluated in a 1D flow model to optimise cathode pressure and stoichiometry operating targets for net system efficiency, using an accelerated genetic algorithm (A-GA). Good agreement was observed between the two models’ trends with a less than 10.5% difference between their normalised e-motor power across all operating points. Under optimised conditions, the VGT showed a less than 0.25% increase in fuel cell system efficiency compared to the use of an FGT. However, a sensitivity study demonstrates significantly lower sensitivity when operating at non-ideal flows and pressures for the VGT when compared to the FGT, suggesting that VGTs may provide a higher level of tolerance under variable environmental conditions such as ambient temperature, humidity, and transient loading. Overall, it is concluded that the efficiency benefits of VGT are marginal, and therefore not necessarily significant enough to justify the additional cost and complexity that they introduce. Full article

Surface electromyography (sEMG) non-invasively captures the electrical activity generated by muscle contractions, offering valuable insights into motion intentions. While sEMG has been widely applied to general gesture recognition in rehabilitation, there has been limited exploration of specific, intricate daily tasks, such as the pouring action. Pouring is a common yet complex movement requiring precise muscle coordination and control, making it an ideal focus for rehabilitation studies. This research proposes a granular computing-based deep learning approach utilizing ConvMixer architecture enhanced with feature fusion and granular computing to improve gesture recognition accuracy. Our findings indicate that the addition of hand-crafted features significantly improves model performance; specifically, the ConvMixer model’s accuracy improved from 0.9512 to 0.9929. These results highlight the potential of our approach in rehabilitation technologies and assistive systems for restoring motor functions in daily activities. Full article

Permanent magnet synchronous motors (PMSMs) are widely used in a variety of fields such as aviation, aerospace, marine, and industry due to their high angular position accuracy, energy conversion efficiency, and fast response. However, driving errors caused by the non-ideal characteristics of the driver negatively affect motor control accuracy. Compensating for the errors arising from the non-ideal characteristics of the driver demonstrates substantial practical value in enhancing control accuracy, improving dynamic performance, minimizing vibration and noise, optimizing energy efficiency, and bolstering system robustness. To address this, the mechanism behind these non-ideal characteristics is analyzed based on the principles of space vector pulse width modulation (SVPWM) and its circuit structure. Tests are then conducted to examine the actual driver characteristics and verify the analysis. Building on this, a real-time compensation method is proposed, physically matched to the driver. Using the volt–second equivalence principle, an input–output voltage model of the driver is derived, with model parameters estimated from test data. The driving error is then compensated with a voltage method based on the model. The results of simulations and experiments show that the proposed method effectively mitigates the influence of the driver’s non-ideal characteristics, improving the driving and speed control accuracies by 88.07% (reducing the voltage error from 0.7345 V to 0.0879 V for a drastic command voltage with a sinusoidal amplitude of 10 V and a frequency of 50 Hz) and 53.08% (reducing the speed error from 0.0130°/s to 0.0061°/s for a lower command speed with a sinusoidal amplitude of 20° and a frequency of 0.1 Hz), respectively, in terms of the root mean square errors. This method is cost-effective, practical, and significantly enhances the control performance of PMSMs. Full article

Solar water pumps are crucial for farmers, significantly reducing energy costs and providing independence from conventional fuels. Their adoption is further incentivized by government subsidies, making them a practical choice that aligns with sustainable agricultural practices. However, the cost of the required solar panels for the chosen power makes it essential to optimize solar water pumping systems (SWPS) for economic viability. This study enhances the efficiency and reliability of permanent magnet synchronous motor (PMSM)-driven SWPS in rural areas using hybrid maximum power point tracking (MPPT) algorithms and voltage-to-frequency (V/f) control strategy. It investigates the sensorless scalar control method for PMSM-based water pumps and evaluates various MPPT algorithms, including grey wolf optimization (GWO), particle swarm optimization (PSO), perturb and observe (PO), and incremental conductance (INC), along with hybrid combinations. The study, conducted using MATLAB Simulink, assesses these algorithms on convergence time, MPPT accuracy, torque ripple, and system efficiency under different partial shading conditions. Findings reveal that INC-GWO excels, providing higher accuracy, faster convergence, and reduced steady-state oscillations, thus boosting system efficiency. The V/f control strategy simplifies control mechanisms and enhances performance. Considering system non-idealities and maximum duty cycle limitations, PMSM-based SWPS achieve superior efficiency and stability, making them viable for off-grid water pumping applications. Full article

Acute limb ischemia (ALI) is a sudden lack of blood flow to a limb, primarily caused by arterial embolism and thrombosis. Various experimental animal models, including non-invasive and invasive methods, have been developed and successfully used to induce limb ischemia-reperfusion injuries (L-IRI). However, there is no consensus on the methodologies used in animal models for L-IRI, particularly regarding the assessment of functional recovery. The present study aims to compare different approaches that induce L-IRI and determine the optimal animal model to study functional limb recovery. In this study, we applied a pneumatic cuff as a non-invasive method and ligated the aorta, iliac, or femoral artery as invasive methods to induce L-IRI. We have measured grip strength, motor function, creatine kinase level, inflammatory markers such as nuclear factor NF-κB, interleukin-6 (IL-6), hypoxia markers such as hypoxia-induced factor-1$\mathsf{\alpha }$ (HIF-1$\mathsf{\alpha }$), and evaluated the muscle injury with hematoxylin and eosin (H&E) staining in Sprague Dawley rats after inducing L-IRI. The pneumatic pressure cuff method significantly decreased the muscle strength of the rats, causing the loss of ability to hold the grid and inducing significant limb function impairment, while artery ligations did not. We conclude from this study that the tourniquet cuff method could be ideal for studying functional recovery after L-IRI in the rat model. Full article

Self-supervised learning (SSL) has gained significant attention in the past decade for its capacity to utilize non-annotated datasets to learn meaningful data representations. In the medical domain, the challenge of constructing large annotated datasets presents a significant limitation, rendering SSL an ideal approach to address this constraint. In this study, we introduce a novel pretext task tailored to stimulus-driven eye movement data, along with a denoising task to improve the robustness against simulated eye tracking failures. Our proposed task aims to capture both the characteristics of the pilot (brain) and the motor (eye) by learning to reconstruct the eye movement position signal using up to 12.5% of the unmasked eye movement signal patches, along with the entire REMOBI target signal. Thus, the encoder learns a high-dimensional representation using a multivariate time series of length 8192 points, corresponding to approximately 40 s. We evaluate the learned representation on screening eight distinct groups of pathologies, including dyslexia, reading disorder, and attention deficit disorder, across four datasets of varying complexity and size. Furthermore, we explore various head architecture designs along with different transfer learning methods, demonstrating promising results with improvements of up to approximately 15%, leading to an overall macro F1 score of 61% and 61.5% on the Saccade and the Vergence datasets, respectively. Notably, our method achieves macro F1 scores of 64.7%, 66.1%, and 61.1% for screening dyslexia, reading disorder, and attention deficit disorder, respectively, on clinical data. These findings underscore the potential of self-learning algorithms in pathology screening, particularly in domains involving complex data such as stimulus-driven eye movement analysis. Full article

A calibration system was designed to evaluate the accuracy of linear optical encoders at the micron level in a fast and economical manner. The system uses a commercial interferometer and motor stage as the calibrator and moving platform. Error analysis is necessary to prove the effectiveness and identify areas for optimization. A fixture was designed for the scale and interferometer target to meet the Abbe principle. A five-degree-of-freedom manual stage was utilized to adjust the reading head in optimal or suboptimal working conditions, such as working distance, offset, and angular misalignment. The results indicate that the calibration system has an accuracy of ±2.2 μm. The geometric errors of the calibration system, including mounting errors and non-ideal motions, are analyzed in detail. The system could be an inexpensive solution for encoder manufacturers and customers to calibrate a linear optical encoder or test its performance. Full article

Torque control accuracy is a significant index of permanent magnet synchronous motors (PMSMs) and affects the safety of many applications greatly. Due to the strong nonlinearity of the motor as well as the disturbance of non-ideal factors such as temperature fluctuation and the parameter error in field-oriented control (FOC), it is undoubtedly difficult to accurately control the actual output torque. Meanwhile, the parameter differences between motors and sensors during mass production and the assembly process affect the consistency of output torque and even increase the factory failure rate of the motor. No torque sensor is implemented due to the cost and limited space. Accurate estimation of the motor torque becomes essential to realize the closed-loop feedback for torque and improve the accuracy at a lower cost. In this paper, a look-up table (LUT) model that can reflect the nonlinear mapping relationship between power and torque is established based on numerous offline experiments, which avoids the calculation of complex losses. A multi-closed-loop control strategy is proposed to dynamically adjust the amplitude and angle of the preset current command, respectively, to improve the torque accuracy. The effectiveness of the strategy has been validated by experimental results. Full article