Within the development of brain-computer interface (BCI) systems, it is crucial to consider the impact of brain network dynamics and neural signal transmission mechanisms on ...electroencephalogram-based motor imagery (MI-EEG) tasks. However, conventional deep learning (DL) methods cannot reflect the topological relationship among electrodes, thereby hindering the effective decoding of brain activity.
Inspired by the concept of brain neuronal forward-forward (F-F) mechanism, a novel DL framework based on Graph Neural Network combined forward-forward mechanism (F-FGCN) is presented. F-FGCN framework aims to enhance EEG signal decoding performance by applying functional topological relationships and signal propagation mechanism. The fusion process involves converting the multi-channel EEG into a sequence of signals and constructing a network grounded on the Pearson correlation coeffcient, effectively representing the associations between channels. Our model initially pre-trains the Graph Convolutional Network (GCN), and fine-tunes the output layer to obtain the feature vector. Moreover, the F-F model is used for advanced feature extraction and classification.
Achievement of F-FGCN is assessed on the PhysioNet dataset for a four-class categorization, compared with various classical and state-of-the-art models. The learned features of the F-FGCN substantially amplify the performance of downstream classifiers, achieving the highest accuracy of 96.11% and 82.37% at the subject and group levels, respectively. Experimental results affirm the potency of FFGCN in enhancing EEG decoding performance, thus paving the way for BCI applications.
Deep convolutional neural networks based remote sensing change detection has recently shown significant performance improvement. However, small region changes and global‐local features in ...high‐resolution remote sensing images are not fully explored. This paper introduces a hybrid U‐shaped and transformer network for change detection in high‐resolution remote sensing images. Specifically, a UNet++‐based backbone to facilitate feature learning across different scales. In addition, we introduce a transformer‐based feature fusion module for extracting long‐range dependencies, which can enhance the representation ability of the network. Furthermore, the introduced efficient channel attention mechanism can efficiently calibrate the feature representation and concentrate on more important feature information. Thanks to the above designs, the proposed method enjoys a strong ability to extract local and global features for remote sensing change detection. Extensive experimental results on different remote sensing images show that our method can achieve superior performance in comparison with state‐of‐the‐art change detection methods.
This paper, introduces a hybrid U‐shaped and transformer network for change detection in high‐resolution remote sensing images. Specifically, we adopt a UNet++‐based backbone to facilitate feature learning across different scales. In addition, a transformer‐based feature fusion module is introduced for extracting long‐range dependencies, which can enhance the representation ability of the network.
In this paper, three small classes of finite fields GF(2 m ) are found for which low complexity bit-parallel multipliers are proposed. The proposed multipliers have lower complexities compared to ...those based on the irreducible pentanomials. It is also shown that there does not always exist an irreducible all-one polynomial, equally-spaced polynomial, or trinomial for the new classes of fields.
In CMOS-based application-specific integrated circuit (ASIC) designs, total power consumption is dominated by dynamic power, where dynamic power consists of two major components, namely, switching ...power and internal power. In this paper, we present a low-power design for a digit-serial finite field multiplier in GF(2 m ). In the proposed design, a factoring technique is used to minimize switching power. To the best of our knowledge, factoring method has not been reported in the literature being used in the design of a finite field multiplier at an architectural level. Logic gate substitution is also utilized to reduce internal power. Our proposed design along with several existing similar works have been realized for GF(2 233 ) on ASIC platform, and a comparison is made between them. The synthesis results show that the proposed multiplier design consumes at least 27.8% lower total power than any previous work in comparison.
Disabled patients using brain computer interface (BCI) applications have a more convenient life. The present study implements an electroencephalogram (EEG)-based signal processing algorithm for ...controlling a wireless mobile vehicle through imagination. The aim is to improve the filtered common spatial pattern (CSP) algorithm for BCI applications. The proposed method is a combination of the CSP projection with a Modified Secondary Projection of the filtered Common Spatial Pattern (MSPCSP). With this algorithm, distinctive differential features are obtained from the combination of the MSPCSP and CSP projection eigenvalues to identify four classes: moving-forward-for-pause, stop-for-pause, moving-forward-continuously, and stopped-continuously. The second contribution is the design of a task to produce clear imaginary movement patterns. The task is a combination of brain stimulation by viewing red and yellow sketches of the right hand that indicate opening the hand and making a fist. Eighteen subjects participated in the experiment for wireless control of a mobile vehicle in offline and real-time modes. The results were then evaluated through an accuracy and paired t-test statistical analysis for offline and real-time signal processing. The results based on the MSPCSP projection showed significant improvements in accuracy in comparison with the CSP projection: 82.16± 9.04% with <inline-formula> <tex-math notation="LaTeX">p < 0.05 </tex-math></inline-formula> and 70.83± 8.27% for offline and real-time processing, respectively. In addition, the MSPCSP projection attained higher accuracies of 14.72% and 13.33% for offline and real-time processing, respectively. It was concluded that the MSPCSP projection generates more discriminant differential features than the filtered CSP projection. Further, the MSPCSP projection with the thresholds extend the limitation of CSP-based methods from two- to four-class identification.
The vacuum vessel (VV) inside and outside inspection of the Demonstration Fusion Power Plant (DEMO) is very difficult due to various constraints, such as non-magnet effect material requirements, ...constrained space, and neutrons on its surfaces. We propose a design method for wall-climbing mobile robots (WMR) based on the vortex principle and investigate key technologies to meet VV inspection requirements. We developed a kinematic model based on the robot’s motion control requirements and a trajectory tracking control algorithm according to the tractrix principle, enabling the robot to follow the path for autonomous inspection. The impeller is designed based on the vortex principle. The aerodynamic characteristics and structural strength of the impeller were also analysed and optimised. A sliding-mode robust pressure control system was designed for the robot’s negative pressure adsorption, and its effectiveness was verified by simulation. Finally, an initial test prototype verified the structural design and vortex adsorption performance. We also addressed the potential applications of the WMR in DEMO and other fusion reactors.
This study introduces a fuzzy based optimal state estimation approach. The new method is based on two principles: Adaptive Unscented Kalman filter, and Fuzzy Adaptive Grasshopper Optimization ...Algorithm. The approach is designed for the optimization of an adaptive Unscented Kalman Filter. To find the optimal parameters for the filter, a fuzzy based evolutionary algorithm, named Fuzzy Adaptive Grasshopper Optimization Algorithm, is developed where its efficiency is verified by application to different benchmark functions. The proposed optimal adaptive unscented Kalman filter is applied to two nonlinear systems: a robotic manipulator, and a servo-hydraulic system. Different simulation tests are conducted to verify the performance of the filter. The results of simulations are presented and compared with a previous version of the unscented Kalman filter. For a realistic test, the proposed filter is applied on the practical servo-hydraulic system. Practical results are discussed, and presented results approve the capability of the presented method for practical applications.
The modeling and simulation of fluid power systems are essential parts of the real-time simulation of virtual prototypes of mobile working machines. In several cases in the dynamic simulation of such ...fluid power systems, a longer simulation time is required. This makes the traditional mathematical models inefficient for real-time simulations, particularly when simulating fluid power systems because of the small volumes in stiff differential equations of pressure. To overcome this issue, a novel hybrid model is proposed for stiff fluid power systems simulation. The main feature of the model is the utilization of a recurrent neural network instead of stiff differential equations of pressure with small volume. At the same time, the dynamics of the rest system are traditionally presented with algebraic and differential equations. The testing results of the introduced hybrid model showed that the novel method can reduce the simulation time, which makes the model suitable for real-time applications. Moreover, the accuracy of the model remains at a fairly high level compared to traditional mathematical models.
This paper proposes an active fault-tolerant control (FTC) scheme for robotic manipulators subject to actuator faults. Its main objective is to mitigate actuator faults and maintain system ...performance and stability, even under faulty conditions. The proposed FTC design combines the robustness and finite time convergence of non-singular terminal synergetic control with the optimization properties of an interval type-2 fuzzy satin bowerbird algorithm. System stability is established via the Lyapunov stability criteria. An adaptive state-augmented extended Kalman filter is implemented as the fault detection and diagnosis (FDD) module, to provide the controller with necessary information about faults in real time. This FDD scheme is based on the simultaneous estimation of the faulty parameters and system states. The effectiveness of the proposed approach is assessed using a simulated two-degree-of-freedom robotic manipulator subject to various faulty scenarios.
This paper introduces a brain control bionic-hand, and several methods have been developed for predicting and quantifying the behavior of a non-linear system such as a brain. Non-invasive ...investigations on the brain were conducted by means of electroencephalograph (EEG) signal oscillations. One of the prominent concepts necessary to understand EEG signals is the chaotic concept named the fractal dimension and the largest Lyapunov exponent (LLE). Specifically, the LLE algorithm called the chaotic quantifier method has been employed to compute the complexity of a system. The LLE helps us to understand how the complexity of the brain changes while making a decision to close and open a fist. The LLE has been used for a long time, but here we optimize the traditional LLE algorithm to attain higher accuracy and precision for controlling a bionic hand. In the current study, the main constant input parameters of the LLE, named the false nearest neighbor and mutual information, are parameterized and then optimized by means of the Water Drop (WD) and Chaotic Tug of War (CTW) optimizers. The optimized LLE is then employed to identify imaginary movement patterns from the EEG signals for control of a bionic hand. The experiment includes 21 subjects for recording imaginary patterns. The results illustrated that the CTW solution achieved a higher average accuracy rate of 72.31% in comparison to the traditional LLE and optimized LLE by using a WD optimizer. The study concluded that the traditional LLE required enhancement using optimization methods. In addition, the CTW approximation method has the potential for more efficient solutions in comparison to the WD method.
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