Motor Imagery (MI) based on Electroencephalography (EEG), a typical Brain-Computer Interface (BCI) paradigm, can communicate with external devices according to the brain’s intentions. Convolutional Neural Networks (CNN) are gradually used for EEG classification tasks and have achieved satisfactory performance. However, most CNN-based methods employ a single convolution mode and a convolution kernel size, which cannot extract multi-scale advanced temporal and spatial features efficiently. What’s more, they hinder the further improvement of the classification accuracy of MI-EEG signals. This paper proposes a novel Multi-Scale Hybrid Convolutional Neural Network (MSHCNN) for MI-EEG signal decoding to improve classification performance. The two-dimensional convolution is used to extract temporal and spatial features of EEG signals and the one-dimensional convolution is used to extract advanced temporal features of EEG signals. In addition, a channel coding method is proposed to improve the expression capacity of the spatiotemporal characteristics of EEG signals. We evaluate the performance of the proposed method on the dataset collected in the laboratory and BCI competition IV 2b, 2a, and the average accuracy is at 96.87%, 85.25%, and 84.86%, respectively. Compared with other advanced methods, our proposed method achieves higher classification accuracy. Then we use the proposed method for an online experiment and design an intelligent artificial limb control system. The proposed method effectively extracts EEG signals’ advanced temporal and spatial features. Additionally, we design an online recognition system, which contributes to the further development of the BCI system.

Remote sensing object detectors based on convolutional neural networks (CNNs) are hard to model global context dependencies. Transformer-based detectors can overcome this problem via global pairwise interactions, but more comprehensive information interactions are not systemically investigated to boost the detection performance. In view of this issue, we propose TransMIN, a transformer-guided multi-interaction network, which uses ResNet50-FPN (feature pyramid network) as the backbone for remote sensing object detection. Specifically, we implement local-global feature interactions by combining convolution and transformer in the residual blocks of ResNet50 to learn complementary features. We implement cross-view feature interactions via transformers in the pyramid layers of FPN to capture the correlation between reference features (spatial edge priors and channel statistics) and pyramid features. This enhances edge information and suppresses background interference. In the detection head, we adopt a task-interactive sample assigner by considering the interactions of classification and localization losses to obtain high-quality predictions. Experiments on two benchmark datasets demonstrate the superior detection performance of TransMIN over state-of-the-art methods.

The traditional speeded-up robust features (SURF) algorithm has certain stability in scale, rotation, illumination and other changes. However, this algorithm has problems such as large amount of computation, low matching accuracy, and time-consuming in feature extraction and feature matching. An improved adaptive ORB-SURF image matching algorithm is proposed in this paper. Fusing edge features and using improved Oriented FAST and Rotated BRIEF (ORB) algorithms are used by this algorithm to extract image feature points. Moreover, SURF descriptors are used by feature points for feature description. Then an improved fast library for approximate nearest neighbors (FLANN) algorithm was used for adaptive feature matching. The random sample consensus (RANSAC) algorithm is used to eliminate the false matching point pairs after the selected points to be matched. Finally, the excellent matching point pairs reserved by the adaptive FLANN algorithm are combined with the excellent matching point pairs reserved by the RANSAC algorithm to complete the matching. The experimental results show that the average accuracy of the improved algorithm can reach more than 98%, which is about 6% higher than the original SURF algorithm. And the average matching time is 1.2S, which is about 25% lower than the original SURF algorithm. It is worth mentioning that the problem that the original SURF algorithm cannot predict the number of feature points through Hessian threshold is solved by this algorithm. Moreover, compared with the SuperPoint based deep learning image matching algorithm, the image matching time of this algorithm is reduced by 80%.

In this paper, couple-group consensus is investigated for a kind of heterogeneous multi-agent systems (HMASs) with Markov switching. Some novel couple-group consensuses have been proposed, in which cooperative-competitive interaction, Markov switching and time delay are all considered. For Markov switching, the transitive rate of probability can be divided into two cases: known or partly known. Based on stochastic delta operator, probability, graph and stability theories, the leader-following and pinning couple-group consensus of this system be converted into analyzing the stability of related switching delta operator system. In the obtained results, some conservative conditions, such as the balance of in or out degree, strong connectivity and containing a spanning tree, can be longer strictly demanded. Some numerical examples are given to show the validity of the acquired results.

Agile Product Line Engineering (APLE), a relatively new approach combining the two successful methods of Agile Software Development (ASD) and Software Product Lines (SPLs), makes product lines more responsive to ever-changing customer needs or market changes. However, SPLs often fail to keep up with market demand due to high coordination costs, slow development processes, and long release cycles in the case of frequent changes in business requirements; in agile software projects, the lack of a unified specification for describing requirements leads to high coordination costs and inconvenient requirement management. Some studies in the literature have proposed optimized approaches to integrate ASD and SPLs, but they still have not covered all aspects of APLE’s characteristics, and software resource reuse is rarely considered in these approaches during product line development. In view of this, we propose a collaborative framework of agile product line engineering for software resource reuse, namely ScrumOntoSPL. The ScrumOntoSPL approach efficiently merges ASD and SPL based on the agile method Scrum, SPL architecture, and ontology technology. In ScrumOntoSPL, uniform requirement specification is constructed by utilizing ontology, and the Matching Requirement with Component (MRC) process is designed to match product new requirements and software resources stored in a resource pool. In addition, we evaluated the proposed framework and approach with CMMI. In the end, a case study of a software development tool called IMC-Tool based on ScrumOntoSPL for a universal Instrument Microcontroller Chip (IMC) is discussed. The IMC-Tool case illustrates that the ScrumOntoSPL has the advantages of dynamically managing demand changes, enhancing software resource reuse, reducing coordination costs, and reducing time to market.

In this paper, the weighted group consensus for a kind of discrete heterogeneous multi-agent systems (HMASs) with packet loss in cooperative-competitive networks based on self-adaptive controller is studied. Based on self-adaptive controller, packet loss and cooperative-competitive relation, a novel control protocols have been designed for this system without satisfying the in-degree balance of the vertex. Some sufficient conditions have been obtained for the weighted group consensus of this kind of HMASs, by using graph theory, matrix analysis and complex frequency method. Based on weighted parameters, control parameters, packet loss rate and cooperative-competitive relation, the upper bound of the input time delay can be calculated. Finally, some simulation experiments are listed to show the effectiveness of the derived results.