Remote sensing traffic scene retrieval based on learning control algorithm for robot multimodal sensing information fusion and human-machine interaction and collaboration

In light of advancing socio-economic development and urban infrastructure, urban traffic congestion and accidents have become pressing issues. High-resolution remote sensing images are crucial for supporting urban geographic information systems (GIS), road planning, and vehicle navigation. Additionally, the emergence of robotics presents new possibilities for traffic management and road safety. This study introduces an innovative approach that combines attention mechanisms and robotic multimodal information fusion for retrieving traffic scenes from remote sensing images. Attention mechanisms focus on specific road and traffic features, reducing computation and enhancing detail capture. Graph neural algorithms improve scene retrieval accuracy. To achieve efficient traffic scene retrieval, a robot equipped with advanced sensing technology autonomously navigates urban environments, capturing high-accuracy, wide-coverage images. This facilitates comprehensive traffic databases and real-time traffic information retrieval for precise traffic management. Extensive experiments on large-scale remote sensing datasets demonstrate the feasibility and effectiveness of this approach. The integration of attention mechanisms, graph neural algorithms, and robotic multimodal information fusion enhances traffic scene retrieval, promising improved information extraction accuracy for more effective traffic management, road safety, and intelligent transportation systems. In conclusion, this interdisciplinary approach, combining attention mechanisms, graph neural algorithms, and robotic technology, represents significant progress in traffic scene retrieval from remote sensing images, with potential applications in traffic management, road safety, and urban planning

Arrhythmia classification detection based on multiple electrocardiograms databases.

According to the World Health Organization, cardiovascular diseases are the leading cause of deaths globally. Electrocardiogram (ECG) is a non-invasive approach for detecting heart diseases and reducing the risk of heart disease-related death. However, there are limited numbers of ECG samples and imbalance distribution for existing ECG databases. It is difficult to train practical and efficient neural networks. Based on the analysis and research of many existing ECG databases, this paper conduct an in-depth study on three fine-labeled ECG databases, to extract heartbeats, unify the sampling frequency, and propose a self-processing method of heartbeats, and finally form a unified ECG arrhythmia classification database, noted as Hercules-3. It is separated into training sets (80%) and testing sets (the remaining 20%). In order to verify its capabilities, we have trained a 16-classification fully connected neural network based on Hercules-3 and it achieves an accuracy rate of up to 98.67%. Compared with other data processing, our proposed method improves classification recall by at least 6%, classification accuracy by at least 4%, and F1-score by at least 7%

Resource Allocation for Tethered UAVs Aided NOMA Networks: A Location-Aware Air-Ground Collaborative Perspective

The research of the mutli-UAV assisted NOMA networks has received a lot of attention because of its superior ability to improve spectrum efficiency and high maneuverability simultaneously. In this research, we take into account downlink NOMA networks with multiple tethered UAV assistance, and investigate the joint UAVs location, user scheduling, user pairing and power distribution problem. The design aims to maximize the sum-rate that can be achieved with a minimum rate restriction, as a complex problem involving mixed-integer programming. First, we determine the best user pairing and power distribution methods for established UAVs positions, which gives the closed-form solution for parameters. Afterwards, we formulate the UAV position optimization problem as a local altruistic game from the viewpoint of air-ground cooperation on the bias of interference graphs. It has been shown to be an exact potential game that permits more than one pure approach Nash equilibrium (PNE). A centralized-distributed iterative learning method is proposed to reach the ideal PNE as rapidly as possible, maximizing the specified network utility measure. The proposed algorithm performs better than the current techniques, according to simulation findings, and greatly boosts network utility. Simulation results show that, nearly 5% and 26% networks utility can be enhanced by the proposed method compared with “head-pairs-tail” and random schemes respectively

Compare with other papers.

According to the World Health Organization, cardiovascular diseases are the leading cause of deaths globally. Electrocardiogram (ECG) is a non-invasive approach for detecting heart diseases and reducing the risk of heart disease-related death. However, there are limited numbers of ECG samples and imbalance distribution for existing ECG databases. It is difficult to train practical and efficient neural networks. Based on the analysis and research of many existing ECG databases, this paper conduct an in-depth study on three fine-labeled ECG databases, to extract heartbeats, unify the sampling frequency, and propose a self-processing method of heartbeats, and finally form a unified ECG arrhythmia classification database, noted as Hercules-3. It is separated into training sets (80%) and testing sets (the remaining 20%). In order to verify its capabilities, we have trained a 16-classification fully connected neural network based on Hercules-3 and it achieves an accuracy rate of up to 98.67%. Compared with other data processing, our proposed method improves classification recall by at least 6%, classification accuracy by at least 4%, and F1-score by at least 7%.</div

Data information of ECG databases used in this work.

Data information of ECG databases used in this work.</p

Confusion matrix of our test.

According to the World Health Organization, cardiovascular diseases are the leading cause of deaths globally. Electrocardiogram (ECG) is a non-invasive approach for detecting heart diseases and reducing the risk of heart disease-related death. However, there are limited numbers of ECG samples and imbalance distribution for existing ECG databases. It is difficult to train practical and efficient neural networks. Based on the analysis and research of many existing ECG databases, this paper conduct an in-depth study on three fine-labeled ECG databases, to extract heartbeats, unify the sampling frequency, and propose a self-processing method of heartbeats, and finally form a unified ECG arrhythmia classification database, noted as Hercules-3. It is separated into training sets (80%) and testing sets (the remaining 20%). In order to verify its capabilities, we have trained a 16-classification fully connected neural network based on Hercules-3 and it achieves an accuracy rate of up to 98.67%. Compared with other data processing, our proposed method improves classification recall by at least 6%, classification accuracy by at least 4%, and F1-score by at least 7%.</div

Data statistics of different databases used in this work.

Data statistics of different databases used in this work.</p

The 5-layers fully connected neural network.

According to the World Health Organization, cardiovascular diseases are the leading cause of deaths globally. Electrocardiogram (ECG) is a non-invasive approach for detecting heart diseases and reducing the risk of heart disease-related death. However, there are limited numbers of ECG samples and imbalance distribution for existing ECG databases. It is difficult to train practical and efficient neural networks. Based on the analysis and research of many existing ECG databases, this paper conduct an in-depth study on three fine-labeled ECG databases, to extract heartbeats, unify the sampling frequency, and propose a self-processing method of heartbeats, and finally form a unified ECG arrhythmia classification database, noted as Hercules-3. It is separated into training sets (80%) and testing sets (the remaining 20%). In order to verify its capabilities, we have trained a 16-classification fully connected neural network based on Hercules-3 and it achieves an accuracy rate of up to 98.67%. Compared with other data processing, our proposed method improves classification recall by at least 6%, classification accuracy by at least 4%, and F1-score by at least 7%.</div

Heartbeats classes of MIT-BIH arrhythmia databases.

Heartbeats classes of MIT-BIH arrhythmia databases.</p