Gestures are vital in human-computer interaction, which have demonstrated substantial application potential in domains such as bionic prosthetic control and medical rehabilitation. Despite significant advancements in Surface Electromyography (sEMG) pattern recognition driven by deep learning, contemporary methodologies and models often exhibit limitations in capturing cross-channel dependencies which are crucial for recognizing the strong correlation between gesture actions and channel intensity distributions. To address these challenges, an innovative and transferable architecture named Inverted-Xception was proposed to integrate the Xception parallel framework with Inverted Residual Network (Inverted ResNet) by Multi-stream Fusion (MSF). The MSE-TDC module, specifically designed for sEMG, significantly enhances global feature representation and reduces computational cost while optimizing cross-channel attention allocation and spatial principal pattern perception. On the public NinaPro DB5 dataset, the proposed Inverted-Xception network achieved an average accuracy of 95.10 % across multiple subjects, outperforming previous models. Furthermore, with the improved transfer framework, Inverted-Xception overcomes individual variability and achieves cross-subject prediction performance ranging from 81.25 % to 92.19 %. Its outstanding performance establishes Inverted-Xception as a state-of-the-art solution for sEMG-based gesture recognition, offering promising prospects for future applications.

The atmospheric density error affects the accuracy of the Strap-down inertial navigation system (SINS)/Refraction celestial navigation system (RCNS) integrated navigation seriously by degrading the accuracy of the atmospheric refraction model. At present, the mechanism and extent of the impacts of atmospheric density error on measurements measurement models of SINS/RCNS integrated navigation are unclear. To solve this problem, this study first derives the relationship among atmospheric density error, measurements and measurement models (including refraction apparent height, refraction angle, and star pixel coordinates) of SINS/RCNS integrated navigation. Then, the quantitative relationship is provided based on it. The results show that 3.0% atmospheric density error can cause errors of 190.22 m in refraction apparent height measurement, 4.40" in refraction angle measurement model, and 0.13 pixels in star pixel coordinates measurement model. And these errors increase approximately linearly with the increase of atmospheric density error. Furthermore, simulations are carried out to verified the theoretical derivation and the linear relationship between the impacts of the atmospheric density error, which has a significant impact on position error and a relatively small impact on velocity error and posture error. When the atmospheric density error are 3.0%, 6.0%, and 9.0%, the position errors of the SINS/RCNS integrated navigation are about 1.4 times, 2.1 times, and 2.9 times of those without atmospheric density error. The research provides a theoretical basis for the elimination of the atmospheric density error.

Deep space internet of things (DS-IoT) aims to establish an intelligent space-based interconnected network covering the Mars and beyond. The deployment of DS-IoT relies critically on high-precision navigation technologies. The pulsar/Doppler integrated navigation can provide DS-IoT satellites with high-accuracy position and velocity information. However, the measured spectral bands are narrow, and Doppler shift can introduce truncation effects that compromise accurate Doppler velocity estimation, thereby degrading navigation accuracy. We analyze spectral distortion from truncation-induced nonlinear phase differences and propose a Fourier domain phase estimation method using ConvMixerCNN-Vision Transformer optimized sparse representation (FPE-CViT-SP). Our approach: (1) constructs a phase shift dictionary via DCT-based spectrum decomposition and shifted reconstruction; (2) formulates a sparse representation objective for optimal spectrum-dictionary matching; (3) employs a ConvMixerCNN-Vision Transformer to efficiently minimize the objective, replacing computationally intensive cross-correlation searches; and (4) applies sparse code-based super-resolution for precise Doppler estimation, effectively addressing nonlinear phase shifts. This method effectively addresses nonlinear phase shift issues through sparse representation characteristics. Furthermore, we develop a pulsar/Doppler integrated navigation system for DS-IoT satellites that significantly enhances collective positioning accuracy through inter-satellite link-based navigation data sharing. Comparative experiments demonstrate that our method achieves 32.17% and 35.29% improvement in position and velocity estimation accuracy respectively over X-ray pulsar navigation.

Automated analytical techniques for magnetocardiography (MCG) are essential for diagnosing and predicting cardiovascular diseases. Clinically acquired MCG signals are often contaminated by various types of noise, which negatively impact subsequent signal analysis. However, traditional methods have limitations in denoising long-term MCG signals with complex spatial structures. We propose a high-precision, robust representation learning method based on skip connection multi-scale feature fusion (SkipDAEformer) for effectively removing random mixed noise in MCG signals. SkipDAEformer integrates attention fusion mechanisms into a basic denoising autoencoder to extract and fuse critical temporal and spatial information from each feature map, thus enhancing the model's ability to capture long-range dependencies and spatial features in MCG signals. Meanwhile, we further supplement and refine the semantic information for the feature maps through a global feature fusion method. By fusing multi-scale features from different skip connections, SkipDAEformer can learn more comprehensive representations of MCG signals, enabling the effective separation of clean signals from noise. Experimental results demonstrate that SkipDAEformer outperforms existing methods in denoising performance, channel consistency, feature consistency, and generalization ability and can be extended to a self-supervised learning framework. In actual noise reduction and diagnostic classification tasks, SkipDAEformer shows superior clinical acceptability and diagnostic value, potentially advancing MCG data analysis.

Source estimation in magnetoencephalography (MEG) involves solving a highly ill-posed problem without a unique solution. Accurate estimation of the time course and spatial extent of the source is important for studying the mechanisms of brain activity and preoperative functional localization. Traditional methods tend to yield small-amplitude diffuse or large-amplitude focused source estimates. Recently, the structured sparsity-based source imaging algorithm has emerged as one of the most promising algorithms for improving source extent estimation. However, it suffers from a notable amplitude bias. To improve the spatiotemporal resolution of reconstructed sources, we propose a novel method called the source imaging method based on spatial smoothing and edge sparsity (SISSES). In this method, the temporal dynamics of sources are modeled using a set of temporal basis functions, and the spatial characteristics of the source are represented by a first-order Markov random field (MRF) model. In particular, sparse constraints are imposed on the MRF model residuals in the original and variation domains. Numerical simulations were conducted to validate the SISSES. The results demonstrate that SISSES outperforms benchmark methods for estimating the time course, location, and extent of patch sources. Additionally, auditory and median nerve stimulation experiments were performed using a 31-channel optically pumped magnetometer MEG system, and the SISSES was applied to the source imaging of these data. The results demonstrate that SISSES correctly identified the source regions in which brain responses occurred at different times, demonstrating its feasibility for various practical applications.

The accurate estimation of source extent using magnetoencephalography (MEG) is important for the study of preoperative functional localization in epilepsy. Conventional source imaging techniques tend to produce diffuse or focused source estimates that fail to capture the source extent accurately. To address this issue, we propose a novel method called the two-stage Champagne approach (TS-Champagne). TS-Champagne divides source extent estimation into two stages. In the first stage, the Champagne algorithm with noise learning (Champagne-NL) is employed to obtain an initial source estimate. In the second stage, spatial basis functions are constructed from the initial source estimate. These spatial basis functions consist of potential activation source centers and their neighbors, and serve as spatial priors, which are incorporated into Champagne-NL to obtain a final source estimate. We evaluated the performance of TS-Champagne through numerical simulations. TS-Champagne achieved more robust performance under various conditions (i.e., varying source extent, number of sources, signal-to-noise level, and correlation coefficients between sources) than Champagne-NL and several benchmark methods. Furthermore, auditory and median nerve stimulation experiments were conducted using a 31-channel optically pumped magnetometer (OPM)-MEG system. The validation results indicated that the reconstructed source activity was spatially and temporally consistent with the neurophysiological results of previous OPM-MEG studies, further demonstrating the feasibility of TS-Champagne for practical applications.

The advent of optically pumped magnetometers (OPMs) facilitates the development of on-scalp magnetoencephalography (MEG). In particular, the triaxial OPM emerged recently, making simultaneous measurements of all three orthogonal components of vector fields possible. The detection of triaxial magnetic fields improves the interference suppression capability and achieves higher source localization accuracy using fewer sensors. The source localization accuracy of MEG is based on the accurate co-registration of MEG and MRI. In this study, we proposed a triaxial co-registration method according to combined principal component analysis and iterative closest point algorithms for use of a flexible cap. A reference phantom with known sensor positions and orientations was designed and constructed to evaluate the accuracy of the proposed method. Experiments showed that the average co-registered position errors of all sensors were approximately 1 mm and average orientation errors were less than 2.5° in the X -and Y orientations and less than 1.6° in the Z orientation. Furthermore, we assessed the influence of co-registration errors on the source localization using simulations. The average source localization error of approximately 1 mm reflects the effectiveness of the co-registration method. The proposed co-registration method facilitates future applications of triaxial sensors on flexible caps.

In the future manned lunar exploration mission, astronauts would work with the lunar robots, which has a high requirement for human–robot interaction (HRI). As the accuracy of gesture recognition interaction does not fulfill the requirement for human–robot joint exploration missions, we propose the DMS-SK/BLSTM-CTC hybrid network to improve the performance of HRI. For gesture recognition, considering VGG-SK has low accuracy and complex architecture, we delete the fourth convolution module, optimize the last global pooling layer, introduce dilated convolution block and multiscale convolution block in VGG-SK, and get the DMS-SK-based gesture recognition sub-network. Compared with the traditional recognition methods, the accuracy and performance of DMS-SK improve. For speech recognition, considering that Bidirectional long–short-term memory unit (BLSTM) has the advantages of processing temporal information, and the Connectionist Temporal Classification (CTC) algorithm can simplify speech data preprocessing, we use BLSTM based on CTC as the speech recognition sub-network. Finally, we combine DMS-SK with BLSTM-CTC, and propose the DMS-SK/BLSTM-CTC hybrid network as the gesture/speech hybrid network. In addition, we use 10 gestures in the American Sign Language (ASL) dataset and 10 speech commands to construct the gesture/speech hybrid dataset. Experimental results show that compared with the pure gesture or pure speech networks, the recognition accuracy of the gesture-speech hybrid network improves by 2% and 12%, respectively, its accuracy reaches 97.38%, which fulfills the requirement of astronauts for HRI.