The studies of Unmanned Air/Ground Vehicle (UAV/UGV) cooperative detection systems have received much attention due to their wide applications in the disaster rescue, target tracking, intelligent surveillance, and automatical package delivery missions. UAVs provide a broad view and have a fast speed in the air, while UGVs have sufficient load capacity and can serve as repeater stations on the ground. The path planning of a UAV/UGV cooperative system is an important but difficult issue, which aims to plan paths for both the UAVs and the UGVs in the system to cooperatively complete a mission. In this article, we consider the path planning problem of the UAV/UGV cooperative system for illegal urban building detection, by taking the limits of UGV speed, UAV load power, and UAV/UGV communication restriction into consideration. To solve this problem, we first model the path planning problem as a constraint optimization problem which tries to minimize an overall execution time for completing the illegal urban building detection tasks, and then propose a two-level memetic algorithm (called Two-MA) to solve the path planning problems of both the UAV and the UGV. Experiments on both synthetic and real-world data sets show the superiority of the proposed Two-MA over several states-of-the-art algorithms in solving the path planning problems of the UAV and UGV for illegal urban building detection tasks. Note to Practitioners—This article was motivated by the task of detecting illegal buildings in cities by unmanned vehicles. Previous works mainly focus on path planning of either UAVs or UGVs in this task. This article proposes a new approach using an Unmanned Air/Ground Vehicle (UAV/UGV) cooperative system for detecting illegal buildings in parks, by taking the limits of UGV speed, UAV load power, and UAV/UGV communication restriction into consideration. This cooperative system consists of a UAV, UGV, and control center. The UAV equipped with cameras takes aerial photography in the air, and can transmit collected photos to the control center. The UGV executes loading and transportation on the ground, and can serve as takeoff and landing platforms for the UAV. The control center executes computationally intensive tasks such as data transmission and processing, task scheduling, and vehicle coordination. To quickly complete all detection tasks, a memetic algorithm is proposed for path planning of both the UAV and the UGV. The simulated results show that the proposed algorithm enables the UAV/UGV cooperative system to visit all buildings in cities with a minimum task execution time.

Stroke has become a leading cause of death and long-term disability in the world with no effective treatment. Deep learning-based approaches have the potential to outperform existing stroke risk prediction models, but they rely on large well-labeled data. Due to the strict privacy protection policy in health-care systems, stroke data is usually distributed among different hospitals in small pieces. In addition, the positive and negative instances of such data are extremely imbalanced. Transfer learning can solve small data issue by exploiting the knowledge of a correlated domain, especially when multiple source of data are available. In this work, we propose a novel Hybrid Deep Transfer Learning-based Stroke Risk Prediction (HDTL-SRP) scheme to exploit the knowledge structure from multiple correlated sources (i.e., external stroke data, chronic diseases data, such as hypertension and diabetes). The proposed framework has been extensively tested in synthetic and real-world scenarios, and it outperforms the state-of-the-art stroke risk prediction models. It also shows the potential of real-world deployment among multiple hospitals aided with 5 G/B5G infrastructures.

Auscultation is an important tool for diagnosing respiratory-related diseases. Unfortunately, the quality of auscultation is limited by the professional level of the doctor and the environment of the auscultation. Some studies have focused on automated auscultation techniques. However, existing approaches suffer from two challenges: 1) the models cannot learn from data distributed among multiple hospitals and 2) the predictions of the models are difficult to interpret for physicians. To address this issue, this article proposes a novel explainable respiratory sound analysis framework with fuzzy decision tree regularization. This framework develops an ensemble knowledge distillation technique to learn distributed data and achieves good performance in terms of model efficiency and accuracy. Fuzzy decision trees are used to explain the predictions of the model and produce decision rules that can be well accepted by physicians. The effectiveness of this framework is thoroughly validated on the Respiratory Sound database and compared with other existing approaches.

With the development of science and technology, robots have been widely used in smart cities. The traversability mapping of environment perception is the prerequisite for robots to perform tasks. To save the energy consumption of traversability mapping for unmanned ground vehicle (UGV), we fusion a wide range of aerial images and a small amount of ground images to provide vision for UGV. Current map fusion methods are usually constrained by homogeneous model of robotic systems and lack of diverse sensors. As a result, they cannot work well in heterogeneous collaborative robotic systems that consist of aerial and ground robots. In this paper, we use heterogeneous robot systems, including UGV and unmanned aerial vehicles (UAV) to build an occupancy grid map that can be used for navigation. To fuse sensor data of different types, we propose a Collaborative Map Fusion algorithm based on Multi-task Gaussian Process Classification (MTGPC) using heterogeneous robotic systems. Besides, probabilistic model is exploited in traversability mapping, so the active perception can be used to build the map efficiently. Our system is tested in real scenes and can achieve an accuracy of more than 70%. The map fusion using active perception is better than map fusion using random strategy in terms of speed and accuracy. To our knowledge, this is the first work that can build the occupancy grid map using sparse data points sampled from aerial images and ground lidar map.

Homomorphic encryption (HE) is one of the more sophisticated methods of homomorphic cryptography (HC). HC efficiently contacts the interacting parties in open IoT and light-weighted network environments. This approach is capable of analyzing encrypted data without decryption. The operations use private and public keys. Then, during the assessment or evaluation, users may access the original data. Before conducting tests or evaluations, the customer must first encrypt the data and then decrypt it. Since consumers use several main cycles for the whole operation, which creates noise and computation overheads, the growth rate of computation overheads has increased. The growing ratio of noise to computation rate can interrupt the whole system, resulting in machine instability, protection, and privacy concerns. To resolve the security and privacy issues, the proposed schemes used different hardness assumptions, such as over-integer, learning with error, ideal lattices, bootstrapping, etc. In this article, we presents a comprehensive review of HE and its many varieties. The numerous possible applications of HE are covered at a high level in order to highlight the extent to which HE is used in the IoT and other lighted-weighted intelligent industry environments in a variety of various domains.

A delay, interruption, or failure in the wireless connection has a significant impact on the performance of wirelessly connected medical equipment. Researchers presented the fastest technological innovations and industrial changes to address these problems and improve the applications of information and communication technology. The development of the 6G communication infrastructure was greatly aided by the use of Block-chain technology, artificial intelligence (AI), virtual reality (VR), and the Internet of Things (IoT). In this article, we comprehensively discuss 6G technologies enhancement, its fundamental architecture, difficulties, and other issues associated with it. In addition, the outcomes of our research help make 6G technology more applicable to real-world medical environments. The most important thing that this study has contributed is an explanation of the path that future research will take and the current state-of-the-art. This study might serve as a jumping-off point for future researchers in the academic world who are interested in investigating the possibilities of 6G technological developments.