In recent years, more and more frameworks have been applied to brain-computer interface technology, and electroencephalogram-based motor imagery (MI-EEG) is developing rapidly. However, it is still a challenge to improve the accuracy of MI-EEG classification. A deep learning framework termed IS-CBAM-convolutional neural network (CNN) is proposed to address the non-stationary nature, the temporal localization of excitation occurrence, and the frequency band distribution characteristics of the MI-EEG signal in this paper. First, according to the logically symmetrical relationship between the C3 and C4 channels, the result of the time-frequency image subtraction (IS) for the MI-EEG signal is used as the input of the classifier. It both reduces the redundancy and increases the feature differences of the input data. Second, the attention module is added to the classifier. A convolutional neural network is built as the base classifier, and information on the temporal location and frequency distribution of MI-EEG signal occurrences are adaptively extracted by introducing the Convolutional Block Attention Module (CBAM). This approach reduces irrelevant noise interference while increasing the robustness of the pattern. The performance of the framework was evaluated on BCI competition IV dataset 2b, where the mean accuracy reached 79.6%, and the average kappa value reached 0.592. The experimental results validate the feasibility of the framework and show the performance improvement of MI-EEG signal classification.

In a D2D (device-to-device) communication system, this paper proposes a relay selection strategy based on social perception. Firstly, the social threshold is introduced into the D2D relay network to screen and filter the potential relay users, thus effectively reducing the detection cost. Then, an auction algorithm is used to motivate the relay users to increase their transmission power. The simulation results show that the algorithm not only improves the throughput but also reduces the probability of a system outage.

Time synchronization is one of the key technologies of wireless sensor networks. At present, most studies assume that the transmission delay is a fixed value, and there is no effective processing of historical clock data. Therefore, this paper proposes a clock frequency drift and phase offset estimation scheme which is considering variable transmission delay and historical clock data. This paper has clarified the effectiveness of the Gaussian delay model in the calculation of time synchronization error. On this basis, this paper calculated the maximum likelihood estimations of frequency drift and phase offset and the corresponding Cramer-Rao lower bound. Meanwhile, the transmission delay is optimized by comparing the chi-square distance between measured and standard data. Also, this paper conducts autonomous training on historical data to provide services for improving the accuracy of fitting estimation. Simulation results validate the performance of the proposed scheme.

In response to the significant challenge of occlusion in optical navigation, this paper proposes an innovative surgical navigation instrument positioning technique based on the fusion of Ultra-Wideband (UWB) and Inertial Measurement Unit (IMU) data. By installing UWB positioning modules and IMU sensors on the surgical instrument arm, this study introduces a machine learning-based data fusion method to fully utilize UWB ranging and IMU high-precision roll and pitch angles, addressing the issues of attitude angles and Z-axis accuracy in UWB pose estimation. The method employs machine learning techniques to model the complex nonlinear relationships among three sets of UWB ranging values, two IMU attitude angles, and the position and yaw angle of the surgical instrument. Experimental results demonstrate that the proposed method achieves an average absolute error of less than 2cm in three-axis position and less than 1° in three-axis attitude angles. This surgical navigation instrument positioning technique exhibits significant advantages in realizing an efficient and accurate surgical navigation system. By optimizing the positioning accuracy of surgical instruments, this technology offers a novel approach to enhancing the performance of surgical navigation systems, providing innovative solutions for positioning requirements in medical practice.