With big data growth in biomedical and healthcare communities, accurate analysis of medical data benefits early disease detection, patient care, and community services. However, the analysis accuracy is reduced when the quality of medical data is incomplete. Moreover, different regions exhibit unique characteristics of certain regional diseases, which may weaken the prediction of disease outbreaks. In this paper, we streamline machine learning algorithms for effective prediction of chronic disease outbreak in disease-frequent communities. We experiment the modified prediction models over real-life hospital data collected from central China in 2013–2015. To overcome the difficulty of incomplete data, we use a latent factor model to reconstruct the missing data. We experiment on a regional chronic disease of cerebral infarction. We propose a new convolutional neural network (CNN)-based multimodal disease risk prediction algorithm using structured and unstructured data from hospital. To the best of our knowledge, none of the existing work focused on both data types in the area of medical big data analytics. Compared with several typical prediction algorithms, the prediction accuracy of our proposed algorithm reaches 94.8% with a convergence speed, which is faster than that of the CNN-based unimodal disease risk prediction algorithm.

Disease Prediction by Machine Learning Over Big Data From Healthcare Communities

The development of light detection and ranging, Radar, camera, and other advanced sensor technologies inaugurated a new era in autonomous driving. However, due to the intrinsic limitations of these sensors, autonomous vehicles are prone to making erroneous decisions and causing serious disasters. At this point, networking and communication technologies can greatly make up for sensor deficiencies, and are more reliable, feasible and efficient to promote the information interaction, thereby improving autonomous vehicle’s perception and planning capabilities as well as realizing better vehicle control. This paper surveys the networking and communication technologies in autonomous driving from two aspects: intra- and inter-vehicle. The intra-vehicle network as the basis of realizing autonomous driving connects the on-board electronic parts. The inter-vehicle network is the medium for interaction between vehicles and outside information. In addition, we present the new trends of communication technologies in autonomous driving, as well as investigate the current mainstream verification methods and emphasize the challenges and open issues of networking and communications in autonomous driving.

Networking and Communications in Autonomous Driving: A Survey

Automated driving is gaining increasing amounts of attention from both industry and academic communities because it is regarded as the most promising technology for improving road safety in the future. The ability to make an automated lane change is one of the most important parts of automated driving. However, there has been little research into automated lane change maneuvers, and current research has not identified a way to avoid potential collisions during lane changes, which result from the state variations of the other vehicles. One important reason is that the lane change vehicle cannot acquire accurate information regarding the other vehicles, especially the vehicles in the adjacent lane. However, vehicle-to-vehicle communication has the advantage of providing more information, and this information is more accurate than that obtained from other sensors, such as radars and lasers. Therefore, we propose a dynamic automated lane change maneuver based on vehicle-to-vehicle communication to accomplish an automated lane change and eliminate potential collisions during the lane change process. The key technologies for this maneuver are trajectory planning and trajectory tracking. Trajectory planning calculates a reference trajectory satisfying the demands of safety, comfort and traffic efficiency and updates it to avoid potential collisions until the lane change is complete. The trajectory planning method converts the planning problem into a constrained optimization problem using the lane change time and distance. This method is capable of planning a reference trajectory for a normal lane change, an emergency lane change and a change back to the original lane. A trajectory-tracking controller based on sliding mode control calculates the control inputs to make the host vehicle travel along the reference trajectory. Finally, simulations and experiments using a driving simulator are conducted. They demonstrate that the proposed dynamic automated lane change maneuver can avoid potential collisions during the lane change process effectively.

A dynamic automated lane change maneuver based on vehicle-to-vehicle communication

Longitudinal safety evaluation of connected vehicles' platooning on expressways.

Autonomous vehicle (AV) technology has advanced rapidly in recent years with some automated features already available in vehicles on the market. AVs are expected to reduce traffic crashes as the majority of crashes are related to driver errors, fatigue, alcohol, or drugs. However, very little research has been conducted to estimate the safety impact of AVs. This paper aims to investigate the safety impacts of AVs using a simulation-based surrogate safety measure approach. To this end, safety impacts are explored through the number of conflicts extracted from the VISSIM traffic microsimulator using the Surrogate Safety Assessment Model (SSAM). Behaviours of human-driven vehicles (HVs) and AVs (level 4 automation) are modelled within the VISSIM’s car-following model. The safety investigation is conducted for two case studies, that is, a signalised intersection and a roundabout, under various AV penetration rates. Results suggest that AVs improve safety significantly with high penetration rates, even when they travel with shorter headways to improve road capacity and reduce delay. For the signalised intersection, AVs reduce the number of conflicts by 20% to 65% with the AV penetration rates of between 50% and 100% (statistically significant at ). For the roundabout, the number of conflicts is reduced by 29% to 64% with the 100% AV penetration rate (statistically significant at ).

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Studying the Safety Impact of Autonomous Vehicles Using Simulation-Based Surrogate Safety Measures

With the increasing demands for vehicle-to-vehicle and vehicle-to-infrastructure communications in intelligent transportation systems, new generation of vehicular telematics inevitably depends on the cooperation of heterogeneous wireless networks. In heterogeneous vehicular telematics, the network selection is an important step to the realization of multimode communications that use multiple access technologies and multiple radios in a collaborative manner. This paper presents an innovative network selection solution for the fundamental technological requirement of multimode communications in heterogeneous vehicular telematics. To guarantee the QoS satisfaction of multiple mobile users and the efficient utilization and fair allocation of heterogeneous network resources in a global sense, a dynamic and self-adaptive method for network selection is proposed. It is biologically inspired by the cellular gene network, which enables terminals to dynamically select an appropriate access network according to the variety of QoS requirements and to the dynamic conditions of various available networks. The experimental results prove the effectiveness of the bioinspired scheme and confirm that the proposed network selection method provides better global performance when compared with the utility function method with greedy optimization.

A Dynamic and Self-Adaptive Network Selection Method for Multimode Communications in Heterogeneous Vehicular Telematics

Dynamic nature of vehicular ad hoc networks VANETs creates great challenges for message dissemination. To satisfy the high reliability and low delivery latency requirements for safety applications in VANETs, a dynamic epidemic broadcasting model is presented in this paper. On the basis of the ordinary differential equations, the proposed optimal control scheme is analyzed to understand the epidemic broadcasting properties, which include the message delivery probability, the expected delivery latency, and the number of copies. The correctness of the theoretical analyses is confirmed by simulations, and the experiment results prove the efficiency of the optimal epidemic broadcasting model. Copyright © 2014 John Wiley & Sons, Ltd.

Optimal epidemic broadcasting for vehicular ad hoc networks

The vehicular ad hoc network has great potential in improving traffic safety One of the most important and interesting issues in the research community is the safety evaluation with limited penetration rates of vehicles equipped with inter-vehicular communications In this paper, a stochastic model is proposed for analyzing the vehicle chain collisions It takes into account the influences of different penetration rates, the stochastic nature of inter-vehicular distance distribution, and the different kinematic parameters related to driver and vehicle The usability and accuracy of this model is tested and proved by comparative experiments with Monte Carlo simulations The collision outcomes of a platoon in different penetration rates and traffic scenarios are also analyzed based on this model These results are useful to provide theoretical insights into the safety control of a heterogeneous platoon

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Modeling chain collisions in vehicular networks with variable penetration rates

Superconductivity becomes more interesting when it encounters dimensional constraint or topology because it is of importance for exploring exotic quantum phenomena or developing superconducting electronics. Here, the coexistence of naturally formed surface superconducting state and 3D topological Dirac state in single crystals of BaMg2Bi2 is reported. The electronic structure obtained from the first‐principles calculations demonstrates that BaMg2Bi2 is an ideal Dirac material, in which the Dirac point is very close to the Fermi level and no other energy band crosses the Fermi level. Superconductivity up to 4.77−−5.17 K can be observed under ambient pressure in the measurements of resistivity. The anisotropic upper critical field and angle dependent magnetoresistance reveals the 2D characteristic of superconductivity, indicating that superconductivity occurs on the surface of the sample and is absent in the bulk state. The study not only provides BaMg2Bi2 as a suitable platform to study the interplay between superconductivity and topological Dirac state but also indicates that MgBi‐based materials may be a promising system for exploring new superconductors.

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Zhenguo Yi

Papers

A Dynamic and Self-Adaptive Network Selection Method for Multimode Communications in Heterogeneous Vehicular Telematics

Optimal epidemic broadcasting for vehicular ad hoc networks

Modeling chain collisions in vehicular networks with variable penetration rates

Observation of Surface Superconductivity in a 3D Dirac Material