The brain-computer interface has become a rapidly developing field, but it has also brought many problems with its development. The main issues are the sparse amount of brain-computer interface data, the inaccurate decoding and classification of data, and the data security of the brain-computer interface. With the development of artificial intelligence, artificial intelligence also provides solutions to many problems. This study mainly uses artificial intelligence algorithms to solve these problems. This paper reviews the integration of artificial intelligence techniques—specifically transfer learning, generative adversarial networks (GANs), Transformer models, and federated learning—to address critical challenges in brain-computer interfaces (BCIs), including data scarcity, classification accuracy, and data security. The hybrid model has many outstanding performances in solving the brain-computer interface problem, and this paper mainly mentions the joint extraction of spatiotemporal features of the CNN-Transformer to make up for the shortcomings of a single model and improve the overall performance. The GAN-TL hybrid model can effectively reduce the influence of individual differences on the model. This paper illustrates the advantages of the hybrid model, which is also the main direction of future research. It highlights how hybrid AI models significantly enhance BCI performance while outlining current limitations and future research directions to ensure robust, efficient, and secure BCI applications.

The identification of modulation mode constitutes a pivotal element within satellite communication systems. Its utilization is pervasive, manifesting in domains such as signal demodulation, resource allocation, and communication quality assessment. However, traditional methods of modulation mode identification are dependent on manual feature extraction, which is both time-consuming and less adaptable in complex environments. Recent advancements in deep learning technology, particularly Convolutional Neural Network (CNN), have introduced novel approaches and methodologies for the identification of radio signal modulation modes. This paper focuses on how deep learning techniques, particularly CNN, can improve the accuracy and efficiency of modulation mode recognition during satellite communications. It summarize key advances in dataset construction, network model design, training methods and optimization techniques. This paper also explores the potential application prospects of CNN technology in 6G communications, emphasizing its critical role in enhancing communication efficiency and service quality. The findings indicate that CNN has significant advantages in satellite communication modulation mode recognition, especially in improving recognition accuracy and robustness.

Robotics has rapidly advanced, revolutionizing manufacturing, healthcare, agriculture, and logistics industries. These advances have enabled robots to perform increasingly complex tasks with greater efficiency and adaptability. However, robotic control remains a major challenge due to robotic systems' complex dynamics, nonlinearities, and uncertainties. Traditional control methods often rely on accurate mathematical models, which are difficult to obtain for complex robots. Data-driven model predictive control (DD-MPC) is a promising solution that overcomes the limitations of traditional methods by leveraging data to learn system dynamics. Unlike model-free methods that lack safety guarantees or model-based methods that struggle with complexity, DD-MPC offers a balance between flexibility and performance. It facilitates real-time optimization, adeptly manages multifaceted constraints, and exhibits adaptability to spatiotemporal dynamic changes. This survey explores the application of DD-MPC in robotic control, highlighting its advantages over other control strategies and its potential to address current challenges in the field.

Machine learning (ML) techniques are increasingly used to enhance economic growth predictions by offering more accurate and robust forecasts. This paper reviews key ML methodologies applied in economic forecasting, including supervised learning models such as regression trees, support vector machines, and ensemble methods like random forests and gradient boosting. It also covers unsupervised learning for pattern recognition and deep learning approaches, including neural networks, for modeling complex data relationships. The paper addresses challenges such as data quality, model interpretability, overfitting, and ethical considerations. It highlights the need for transparency and accountability in ML model development to avoid biases and ensure effective policy-making. By integrating recent research findings, the paper provides insights into best practices for utilizing ML in economic forecasting, aiming to improve both accuracy and reliability in predicting economic growth.

Reversing trucks into parking spaces is challenging, especially in complex and constrained environments due to limited visibility and real-time environmental changes. With the development of automated driving technology, sensory detection technology has been widely used, through multi-sensor fusion technology, such as cameras, LiDAR, radar, etc., to enhance the vehicle’s ability to perceive the surrounding environment and reduce the safety risk. Meanwhile, the introduction of deep learning algorithms further optimizes the detection and decision-making ability of the system. This study explores the sensing technology of truck reverse parking, focusing on the application of sensor devices, data processing and fusion technology, and artificial intelligence in target detection and behavior prediction. In addition, this study analyzes the challenges faced by trucks in the process of reversing, such as blind field, obstacle detection, and misjudgment in complex environments, and proposes targeted solutions. Ultimately, this study offers insights for the continued development of safer, more efficient, and robust truck reverse parking systems.

The traditional multi-head self-attention mechanism models the relationships between elements within a sequence through the interaction between queries, keys, and values, but has certain limitations in capturing global information. To address this issue, this paper proposes an improved self-attention mechanism - global self-attention mechanism, aimed at enhancing the global information capture capability and robustness of Transformer models in sequence modeling tasks. This mechanism introduces a global context vector as the fourth input variable based on the classic QKV structure, endowing the model with the ability to perceive the entire sequence state. Specifically, each query can not only focus on local information in the sequence, but also simultaneously consider the global context, thereby better capturing global dependencies. The innovation of this method lies in the introduction of a global memory token, which enables the model to integrate global information and enhance its sensitivity to long-term trends and global patterns. In summary, this method not only has strong practical value, but also is easy to implement and has a wide range of application prospects, suitable for various sequence modeling tasks.

Tunnel wireless communication refers to the erection of invisible bridges in the winding underground corridor, allowing the radio waves of information to shuttle between the thick rock walls. Nowadays, with the rapid development of transportation, more and more subways, railways and highway tunnels have been built, but there are still some problems in communication in the tunnel that have not been solved. For this reason, many companies and research teams around the world are trying to apply communication technology on the ground to tunnel communication. At present, some of these ideas and attempts have developed into adoptable and mature technologies. So, tunnel wireless communication is not only a demonstration of technology, but also a guardian of safety and efficiency. This paper mainly uses literature analysis to study the basic principles of communication technology and its application in follow-up communication. This article finds that the communication technology currently applied in tunnel communication is Multiple-Input Multiple-Output (MIMO) technology, General Packet Radio Service (GPRS) technology, Synchronous Digital Hierarchy (SDH) technology, collaborative relay technology, Zig Bee technology and WiMAX technology.

The rapid deployment and widespread adoption of 5G networks have rendered the energy consumption and carbon emissions of base stations increasingly prominent, posing a critical challenge for the telecommunications industry in achieving dual-carbon goals. To address the carbon emission prediction challenge in 5G base stations, this study proposes a hybrid forecasting model based on the deep integration of a Backpropagation (BP) neural network and Long Short-Term Memory (LSTM). By collecting multi-dimensional base station data encompassing equipment energy consumption, material usage, transmission coverage range, deployment configurations, and environmental conditions, systematic feature engineering and data preprocessing were conducted to construct a BP-LSTM hybrid model capable of capturing both static characteristics and temporal dynamics. Experimental results demonstrate that the proposed hybrid model achieves superior performance in 5G base station carbon emission prediction, with evaluation metrics reaching R² = 0.98 and MAPE = 3.25%, significantly outperforming individual models. Based on the prediction outcomes, this study further proposes multi-dimensional energy-saving optimization strategies tailored to diverse deployment environments and operational conditions.

Multimodal sensor fusion technology significantly improves the perception and decision-making capabilities of medical and industrial robots by integrating multi-source information such as vision, touch, and mechanics. This article conducts a comparative analysis of the divergent applications and shared characteristics within two predominant domains: medical robotics, which emphasizes surgical safety and employs tactile-visual collaborative technologies to enhance operational precision while addressing data compliance challenges through a privacy protection framework; and industrial robotics, which prioritizes efficiency and safety in dynamic environments by integrating dynamic vision systems and high-precision ranging devices to facilitate real-time obstacle avoidance and fault diagnosis. The study found that although the scene goals are different (medical focuses on biocompatibility, industry focuses on cost efficiency), both face challenges such as sensor redundancy, data heterogeneity, and long-term stability. In the future, it is necessary to promote cross-domain technology interoperability, develop a lightweight adaptive fusion framework, and build an ethical and standardization system to promote the universalization and large-scale application of multimodal fusion technology.

Robots are increasingly being integrated into disease treatment to perform a wide range of functions aimed at optimizing therapeutic outcomes. With the rapid advancement of science and technology, robots are undertaking increasingly complex tasks in medical interventions. For instance, robots achieve exact movements in minimally invasive surgery, minimizing damage to surrounding tissues. In chemotherapy, they enable accurate drug delivery, reducing dosage errors to the lowest possible levels. Driven by factors such as the emergence of novel diseases and the growing complexity of medical demands, the field of disease treatment continues to evolve, and robotic technologies are poised to address these dynamic challenges. This review synthesizes pertinent literature sourced from the Google Scholar database, aiming to achieve two primary objectives: to categorize distinct types of robotic systems utilized in disease management and to clarify their corresponding application contexts. The systematically organized data not only furnishes innovative insights for scientific researchers, but also provides evidence-based support for clinical practitioners in selecting technological solutions, while concurrently supplying empirical foundations for governmental formulation of medical technology development strategies.