Figure - available from: Computers, Materials & Continua
This content is subject to copyright. Terms and conditions apply.
Illustration on signal deflections during voluntary blinking when the subject's attention level is within the elevated range (i.e., ν>40)
Source publication
![Two types of visual stimuli employed in this study [22]; Stimulus 1...](publication/360680726/figure/fig3/AS:11431281262613471@1721649243605/Two-types-of-visual-stimuli-employed-in-this-study-22-Stimulus-1-left-involves_Q320.jpg)
A single-channel electroencephalography (EEG) device, despite being widely accepted due to convenience, ease of deployment and suitability for use in complex environments, typically poses a great challenge for reactive brain-computer interface (BCI) applications particularly when a continuous command from users is desired to run a motorized actuato...
Similar publications
A model involving Gaussian processes (GPs) is introduced to simultaneously handle multi-task learning, clustering, and prediction for multiple functional data. This procedure acts as a model-based clustering method for functional data as well as a learning step for subsequent predictions for new tasks. The
model is instantiated as a mixture of mult...
Citations
... [7], [8]. However, in practice, DWMRs operate under speed and actuator constraints, which introduce nonlinearities into their models [9], [10]. Consequently, a variety of controllers have been developed, with adaptive control and robust control emerging as prominent approaches for managing complex nonlinear control systems. ...
... Table 2 presents the upper and lower limits imposed during the control parameter optimization. As discussed in the previous section, a hierarchical optimization method is employed where the first phase involves optimizing the BSC control parameters, i.e. k 1 , k 2 , and k 3 as described in (10). To ensure the reliability of the results, each algorithm was executed 20 times. ...
Improving trajectory tracking in Differential Wheeled Mobile Robots (DWMRs) is vital for enhancing their effectiveness in various applications, such as autonomous cleaning, mowing, and leader-following scenarios. These scenarios often involve navigating complex, nonlinear paths, requiring advanced control strategies for enhanced performance. This work presents the novel integration of a Backstepping Controller (BSC) and a Fractional-Order Proportional-Integral-Derivative (FOPID) controller within a cascade closed-loop structure for Differential Wheeled Mobile Robots (DWMRs). The proposed BSC-FOPID controller addresses velocity saturations and nonlinearities, ensuring system stability and precise trajectory tracking. A key contribution is the enhanced Grey Wolf Optimization strategy, termed GWO-SMA, which integrates Grey Wolf Optimization (GWO) with Slime Mould Algorithm (SMA). By leveraging opposition space and optimum cache concepts, GWO-SMA improves fitness optimization in each iteration, enhancing both exploration and exploitation efficiency. This hybrid approach optimizes controller parameters using a multi-metric cost function that incorporates Integral Absolute Error (IAE) and Integral Squared Error (ISE) to minimize long-term steady-state error and enhance responsiveness to larger deviations. Simulations demonstrate the superior performance of the proposed GWO-SMA algorithm compared to existing optimization techniques, such as Particle Swarm Optimization (PSO), Gazelle Optimization Algorithm (GOA), and its individual components, GWO and SMA, which have shown strong performance in recent literature for optimizing PID-type controllers. In addition, simulation results using three distinct reference paths, i.e. lemniscate, square, and cloverleaf; demonstrate that the GWO-SMA-optimized BSC-FOPID controller outperforms both adaptive dynamic compensation control (ADCC) and BSC-PID controller in position and posture tracking accuracy. Specifically, the BSC-FOPID controller achieves significant improvements, including average reductions of 55.65% in ISE and 38.25% in IAE for position control, as well as 62.12% and 38.95% improvements in ISE and IAE for posture control, respectively. These improvements highlight the controller's enhanced responsiveness and smoother error convergence, particularly during maneuvers involving sharp curves.
... They have found increasing deployment in diverse and unconventional settings, including hospitals, mining sites, battlefields, and disaster relief operations (Cuebong Wong et al., 2018;Chen, 2023;Sun et al., 2020;Liu et al., 2022;Teo et al., 2020). Notably, the emergence of the COVID-19 pandemic in 2020 has heightened our recognition of the practical utility of mobile robots (Banjanovic-Mehmedovic et al., 2021;Abdel-Basset et al., 2022;Ahmad et al., 2022). These robots have proven invaluable as they can deliver essential supplies with-out direct human contact, thereby reducing the risk of disease transmission and enhancing operational efficiency. ...
This study introduces an enhanced algorithm for global path planning of Differential Wheeled Mobile Robots (DWMRs) that merges the Whale Optimization Algorithm (WOA) and Particle Swarm Optimization (PSO). This hybrid strategy, termed HWPSO, is designed to leverage WOA's exploration strength with PSO's efficient exploitation, specifically targeting the challenges of non-holonomic constraints in complex terrains. To validate the effectiveness of the proposed algorithm, its performance is evaluated across five diverse environments and compared against PSO, WOA, and Grey Wolf Optimization which is widely used for mobile robot path planning. Moreover, the comparison broadens to encompass four established environments from the literature where algorithms based on firefly, ant colony, A*, and other PSO variants have previously exhibited optimal performance. Additionally, a new environment is introduced to analyze the efficacy of the proposed approach for path planning for two DWMRs. Simulation results consistently demonstrate the superiority of the proposed HWPSO, manifesting performance improvements of up to 19.3% for path length reduction and up to 12.7% for DWMR travel duration reduction when compared to other methods. This underscores the efficacy of the proposed hybrid approach in achieving enhanced path planning outcomes for DWMRs in diverse scenarios.
... Neural networks (NN), also known as artificial neural networks, are machine learning algorithms utilized for tasks such as classification, pattern recognition, and function approximation [60]. These networks emulate the functioning of biological brains, comprising interconnected processing units termed nodes or neurons. ...
Research on autonomous vehicles (AV) - self-navigating machines that transport both man and cargo has proliferated lately. While once limited to industrial or military uses, more attention is now given to their potential applications in broader society, especially in taking over the mundane, risky and taxing jobs from humans. From self-driving cars, to autonomous mobile robots, to unmanned air, surface and underwater vehicles, one challenge common to all of them is the need to navigate autonomously without human intervention as they operate in their intended environment. The ability to accurately detect and safely avoid obstacles is thus imperative to achieving greater autonomy in vehicles. In recent years, light detection and ranging (LiDAR) sensors - known for their accuracy and reliability in measuring distances-have been widely used for obstacle avoidance. However, as AVs are expected to function under a multitude of conditions, the usage of a single sensor is insufficient. Sensor fusion becomes the next logical step to allow the vehicle to detect and respond to a wider variety of situations. In this paper, we investigate the ways sensor fusion can be applied to improve the obstacle avoidance capability of various indoor and outdoor LiDAR-based AVs by reviewing recent publications over the past decade. The core contribution includes examining the types of secondary sensor used, the motivation behind their selection, as well as the obstacle avoidance algorithm used. Finally, the obstacle avoidance research trends driving indoor and outdoor AVs are discussed and future research directions are presented.
... The study in [40] combines IR and vision and employs an SVM classifier to classify 10 types of materials where the achieved is 73.4%. Unlike artificial neural networks (ANNs) which model complex relationships between inputs and outputs through layers of interconnected nodes [41]- [44], SVM finds the optimal separating hyperplane for classification [45], [46]. A recent survey in [47] has found that both SVM and ANN outperform other machine learning methods in material classification tasks [47]. ...
Material classification is pivotal across materials science, engineering, and various industrial sectors. Despite the high accuracy of traditional material classification methods, they often entail large, intricate, and costly setups that demand skilled operators. In this study, we introduce the MCT-array, a newly developed compact RF antenna array system measuring 100×100×2mm, which functions as a transceiver. This device, equipped with 32 receiving antennas and 2 transmitters, leverages dynamic power adjustments to refine material detection accuracy. The study evaluates three machine learning classifiers, namely Support Vector Machine (SVM), Multi-Layer Perceptron (MLP), and Random Forest (RandF) on twelve different materials. MATLAB simulations are initially conducted to identify optimal transceiver configurations. Following the identification of optimal parameters from these simulations, real-world experiments are conducted with the materials positioned 30 cm away from the antenna. Results demonstrate that RandF achieves a material classification accuracy of 94.84%, followed by SVM at 94.5%, and MLP at 94.1%. Detailed analysis further reveals that RandF is the preferred option for tasks demanding the highest levels of accuracy, SVM strikes an optimal balance between processing speed and accuracy, while MLP stands out for its rapid prediction times, making it especially suitable for real-time applications. Integrating an innovative portable RF transceiver with these machine learning models, achieving an impressive average accuracy of over 94%, represents a scalable and effective solution. This innovation holds significant promise for sectors engaged in material classification, particularly in the realms of robotics and automation.
... More recently, deep learning approaches, including Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) networks, and hybrid CNN-LSTM models, have been introduced. While these methods are effective, their reliance on extensive iterations can hinder classification accuracy [13]. ...
Ultra-wideband (UWB) positioning systems are widely applied in localization. However, in complex environments with numerous obstacles, Non-Line-of-Sight (NLOS) propagation of UWB signals can occur, significantly affecting accuracy. Effective identification of Line-of-Sight (LOS) and NLOS signals is essential for precise localization. In this study, we propose a novel NLOS/LOS recognition method based on Extreme Gradient Boosting (XGBoost). The method begins with feature selection using the Pearson Correlation Coefficient to filter less correlated features of the UWB Channel Impulse Response (CIR) data, followed by outlier handling. Preliminary NLOS identification and classification are performed using Fuzzy Comprehensive Evaluation (FCE), with subsequent optimization of FCE weights via the Pattern Search algorithm (PSa). Final classification and recognition are then achieved through XGBoost. This approach, initially trained in one scenario, demonstrates seamless transition and strong generalization across six additional scenarios. Compared to traditional machine learning and neural networks, it offers lower system requirements. Experimental results on an open-source dataset covering seven scenarios show average accuracy, precision, recall, and F1 score of 93.2%, 94.8%, 90.3%, and 92.5%, respectively, highlighting the method’s superior performance and robustness in diverse environments.
... In 2019, German Martin [35] and others elaborated on the research and development process of indoor positioning based on Bluetooth. Overall, BLE technology still has several limitations, such as a relatively small coverage range, slow data transfer speed, and lower data transmission security [36]. However, due to its lower hardware cost and efficiency, BLE has become a good choice for PS in GDE. ...
Although Global Navigation Satellite Systems (GNSS) typically provide sufficient accuracy for outdoor positioning, positioning systems face significant challenges in complex application scenarios, particularly in GPS-denied environments (GDE). As application demands increase, single-sensor positioning methods are increasingly inadequate to meet the requirements for high accuracy and robustness, making multi-sensor fusion techniques a growing research focus. In recent years, while many studies have aimed to achieve indoor positioning stability and reliability through Simultaneous Localization and Mapping (SLAM), Ultra-Wideband (UWB), odometry, and Wi-Fi fusion technologies, these efforts have often been conducted in isolation, lacking a systematic synthesis of these fusion methods. Notably, there is a particular gap in the comprehensive analysis of Dynamic Target Localization (DTL). DTL involves real-time tracking of moving targets such as pedestrians, robots, vehicles, and Unmanned Aerial Vehicle (UAV), which not only requires maintaining high accuracy in complex and dynamic environments but also demands the capability to handle the challenges posed by rapidly moving targets. This paper addresses this research gap by systematically reviewing the existing sensor fusion techniques, providing a detailed analysis of the application of SLAM fusion, UWB fusion, odometry fusion, and Wi-Fi fusion in DTL, and thoroughly evaluating their effectiveness and performance. Additionally, this paper summarizes the application of these technologies across different types of moving targets, discusses future development trends and potential challenges, and offers valuable insights for advancing the widespread application of multi-sensor fusion techniques in dynamic target localization. Finally, the challenges facing future work in this field are discussed.
... One notable device emerging from BLE technology is the beacon, which is an inexpensive device equipped with a central processing unit (CPU), a radio, and batteries. These beacons are pivotal in improving IL in complex environments without disrupting other wireless infrastructures [36], [37]. ...
Indoor localization (IL) systems are crucial for enhancing operational efficiency, safety, and user experience by allowing precise tracking of objects, robots, or individuals within various environments such as healthcare, retail, and industrial sectors. Despite their increasing importance, there remains a notable deficiency in the literature, particularly concerning systematic reviews that consolidate findings from experimental research. This work fills this crucial gap by rigorously assessing the advancements and challenges faced by Wireless Sensor Network (WSN)-based IL systems, with a focused examination of experimental studies conducted over the past five years. It delves into both radio frequency (RF) and non-RF technologies, critically evaluating a spectrum of localization methods including fingerprinting, geometric mapping, proximity, and dead-reckoning. It systematically evaluates the advantages, limitations, and current solutions of each method, based on their citation metrics and prevalence in scholarly literature. Furthermore, the paper explores innovative performance enhancement techniques, including the integration of machine learning and the hybridization of multiple technologies, to demonstrate significant improvements in IL functionalities. It also identifies and analyzes key trends, such as the choice of technologies for specific methods, typical network density configurations, and accuracy enhancements achieved through different approaches. Research gaps are highlighted, including the need for advancements in machine learning for offline and edge computing, standardization of sensor components, and improvements in interoperability and energy efficiency. The paper concludes by proposing strategic future research directions, outlining a roadmap for advancing IL research and development in this rapidly evolving field.
... The extensive body of literature in this domain emphasizes the complexity of path planning for mobile robots and underscores several crucial challenges. One of these challenges is the imperative to generate routes that are not only collision-free but also wellstructured, ensuring feasible navigation for mobile robots particularly those that are subject to kinematic and dynamic constraints [7]- [10]. However, they come with certain limitations, including computational complexity in dense environments, rigidity in accommodating additional constraints or objectives, suboptimal performance in dynamic settings, and lack of robustness. ...
This study presents a hybrid HHO-AVOA which is a novel optimization method that combines the strengths of Harris Hawks Optimization (HHO) and African Vulture Optimization Algorithm (AVOA) to address the path planning challenges encountered by differential wheeled mobile robots (DWMRs) navigating both static and dynamic environments, while accommodating kinematic constraints. By synergizing the strengths of both algorithms, the proposed hybrid method effectively mitigates the limitations of individual approaches, resulting in efficient and obstacle-avoiding navigation towards the target within reduced timeframes. To evaluate its efficiency, the proposed approach is compared against HHO and AVOA as well as other established methods which include whale optimization, grey wolf optimization and sine-cosine algorithms. Simulation results along with Monte Carlo analysis consistently demonstrate the superior performance of the hybrid method in both environments. In static scenarios, the hybrid algorithm achieves an average reduction of approximately 14% in path length and a 17% decrease in DWMR travel duration. In dynamic cases, it outperforms the rest with an average reduction of 27.6% in path length and a 27.2% decrease in travel duration. The algorithm’s low computational complexity is also exhibited via its fast convergence during path optimization which is a crucial attribute for real-time implementation, particularly in dynamically changing environments that demand quick decision-making. The superiority of the proposed hybrid method to balance the exploration and exploitation is also affirmed through a Wilcoxon rank-sum test with a 95% confidence interval.
... Examples of the latter include sliding mode controls [15], fuzzy logic control [16], [17], artificial neural network [18], and deep learning [19]. Aside from that, the Gaussian process (GP) has also been employed to its capability to create flexible nonlinear nonparametric models [20]. Chen et al. [21] explained for instance, a control design based on a learned GP regression model is proposed to alleviate the effects from modeling errors. ...
p>A two-wheeled self-balancing robot (TWSBR) is an underactuated system that is inherently nonlinear and unstable. While many control methods have been introduced to enhance the performance, there is no unique solution when it comes to hardware implementation as the robot’s stability is highly dependent on accuracy of sensors and robustness of the electronic control systems. In this study, a TWSBR that is controlled by an embedded NI myRIO-1900 board with LabVIEW-based control scheme is developed. We compare the performance between proportional-integral-derivative (PID) and linear quadratic regulator (LQR) schemes which are designed based on the TWSBR’s model that is constructed from Newtonian principles. A hybrid PID-LQR scheme is then proposed to compensate for the individual components’ limitations. Experimental results demonstrate the PID is more effective at regulating the tilt angle of the robot in the presence of external disturbances, but it necessitates a higher velocity to sustain its equilibrium. The LQR on the other hand outperforms PID in terms of maximum initial tilt angle. By combining both schemes, significant improvements can be observed, such as an increase in maximum initial tilt angle and a reduction in settling time.</p
... Plus, the equilibrium position is greatly influenced by uncertainties of the proximity sensors and magnetic field strength [11], [12]- [14]. In some cases where the uncertainties are large but statistical such as those used in high-speed trains, Gaussian process is required to extract the dynamic properties of the systems [15]- [17]. ...
This work demonstrates the design and development of a magnetic levitation (MagLev) system that is able to control both the position and orientation of the levitated object. For the position control, a pole placement method was exploited to estimate parameters of the proportional integral derivative (PID) controller. In addition, the MagLev was constructed using a pair of electromagnets, two infrared (IR) receiver-emitter pairs and a servo motor to allow the orientation of the object to be controlled. The proposed controller was programmed in a LabVIEW environment, which was then compiled and deployed into an embedded NI myRIO board. Experimental results demonstrated that the proposed method was able to achieve a zero steady-state orientation error when the object was rotated from 0 ◦ to ±90◦ , a steady-state position error of 0.3 cm without rotation, and steady-state position errors of no greater than 1.2 cm with rotation.
