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Information fusion in automated vehicle for various datatypes emanating from many resources is the foundation for making choices in intelligent transportation autonomous cars. To facilitate data sharing, a variety of communication methods have been integrated to build a diverse V2X infrastructure. However, information fusion security frameworks are currently intended for specific application instances, that are insufficient to fulfill the overall requirements of Mutual Intelligent Transportation Systems (MITS). In this work, a data fusion security infrastructure has been developed with varying degrees of trust. Furthermore, in the V2X heterogeneous networks, this paper offers an efficient and effective information fusion security mechanism for multiple sources and multiple type data sharing. An area-based PKI architecture with speed provided by a Graphic Processing Unit (GPU) is given in especially for artificial neural synchronization-based quick group key exchange. A parametric test is performed to ensure that the proposed data fusion trust solution meets the stringent delay requirements of V2X systems. The efficiency of the suggested method is tested, and the results show that it surpasses similar strategies already in use.This paper studies the problem of distributed spectrum/channel access for cognitive radio-enabled unmanned aerial vehicles (CUAVs) that overlay upon primary channels. Under the framework of cooperative spectrum sensing and opportunistic transmission, a one-shot optimization problem for channel allocation, aiming to maximize the expected cumulative weighted reward of multiple CUAVs, is formulated. To handle the uncertainty due to the lack of prior knowledge about the primary user activities as well as the lack of the channel-access coordinator, the original problem is cast into a competition and cooperation hybrid multi-agent reinforcement learning (CCH-MARL) problem in the framework of Markov game (MG). Then, a value-iteration-based RL algorithm, which features upper confidence bound-Hoeffding (UCB-H) strategy searching, is proposed by treating each CUAV as an independent learner (IL). To address the curse of dimensionality, the UCB-H strategy is further extended with a double deep Q-network (DDQN). Numerical simulations show that the proposed algorithms are able to efficiently converge to stable strategies, and significantly improve the network performance when compared with the benchmark algorithms such as the vanilla Q-learning and DDQN algorithms.This article presents the design and experimental evaluation of a non-invasive wearable sensor system that can be used to acquire crucial information about athletes' performance during inline figure skating training. By combining distance and time-of-flight sensors and gyroscopes, the system is able to detect when jumps are performed and provides a live view of the data (e.g., the number and height of jumps) through a graphical user interface. The main novelty of our approach lies in the way in which the optical sensors are orientated. Typically, the sensors are orientated horizontally and positioned in pairs on the ground, where they measure the time interval between the moment the athlete leaves the ground and the moment they land. In our system, an optical sensor is placed under each foot and is vertically orientated so as to constantly measure the distance from the ground. In addition, a gyroscope sensor is placed on the athlete's back, which provides information on the direction and angular momentum of the movement. By combining this data, the system provides the accurate detection of various jumps and technical elements without any constraints on the training ground. In this paper, the system is also compared to similar platforms in the literature, although there are no other specific systems that are available for inline figure skating. The results of the experimental evaluation, which was performed by high profile athletes, confirm its effectiveness in correctly detecting jumps, especially considering its compromise between precision and the overall cost of the equipment.The automatic emotion recognition domain brings new methods and technologies that might be used to enhance therapy of children with autism. The paper aims at the exploration of methods and tools used to recognize emotions in children. It presents a literature review study that was performed using a systematic approach and PRISMA methodology for reporting quantitative and qualitative results. Diverse observation channels and modalities are used in the analyzed studies, including facial expressions, prosody of speech, and physiological signals. Regarding representation models, the basic emotions are the most frequently recognized, especially happiness, fear, and sadness. Both single-channel and multichannel approaches are applied, with a preference for the first one. For multimodal recognition, early fusion was the most frequently applied. SVM and neural networks were the most popular for building classifiers. Qualitative analysis revealed important clues on participant group construction and the most common combinations of modalities and methods. All channels are reported to be prone to some disturbance, and as a result, information on a specific symptoms of emotions might be temporarily or permanently unavailable. The challenges of proper stimuli, labelling methods, and the creation of open datasets were also identified.A fast inversion algorithm combined with the transient electromagnetic (TEM) detection system has important significance for improving the detection efficiency of unexploded ordnance. The traditional algorithms, such as differential evolution or Gauss-Newton algorithms, usually require tens to thousands of iterations to locate the underground target. A new algorithm with a magnetic gradient tensor and singular value decomposition (SVD) to estimate the target position and characterization quickly and accurately is proposed in this paper. Two modes of magnetic gradient tensor are constructed to accurately locate shallow and deep targets, respectively. learn more The SVD algorithm is applied to the responses to estimate the electromagnetic characteristics of the target quickly and accurately. To verify the performance of the proposed algorithm, a towed TEM sensor is designed, which is constructed with three transmitting coils and nine three-component receiving coils arranged in a 3 × 3 array. Field experiments in survey and cued modes were taken to verify the performance of the proposed algorithm and the towed system. Results show that the magnetic gradient tensor algorithm proposed in this paper can accurately locate a single target within 2.0 m depth, and the error of depth is no more than 8 cm. Even for overlapping response of multi targets, the error of depth is no more than 12 cm. The underground target can be accurately characterized by the SVD algorithm. For targets with depths over 2.0 m, the signal-to-noise ratio of characteristic response estimated by SVD is higher than that of the traditional method. The proposed method needs approximately 40 ms, only 1% of the traditional one, considerably improving detection efficiency and laying a theoretical and experimental foundation for real-time data processing.Civil infrastructure O&M requires intelligent monitoring techniques and control methods to ensure safety. Unfortunately, tedious modeling efforts and the rigorous computing requirements of large-scale civil infrastructure have hindered the development of structural research. This study proposes a method for impact response prediction of prestressed steel structures driven by digital twins (DTs) and machine learning (ML). The high-fidelity DTs of a prestressed steel structure were constructed from the perspective of both a physical entity and virtual entity. A prediction of the impact response of prestressed steel structure's key parts was established based on ML, and a structure response prediction of the parts driven by data was realized. To validate the effectiveness of the proposed prediction method, the authors carried out a case study in an experiment of a prestressed steel structure. This study provides a reference for fusion applications with DTs and ML in impact response prediction and analysis of prestressed steel structures.The Internet of Things paradigm in healthcare has boosted the design of new solutions for the promotion of healthy lifestyles and the remote care. Thanks to the effort of academia and industry, there is a wide variety of platforms, systems and commercial products enabling the real-time information exchange of environmental data and people's health status. However, one of the problems of these type of prototypes and solutions is the lack of interoperability and the compromised scalability in large scenarios, which limits its potential to be deployed in real cases of application. In this paper, we propose a health monitoring system based on the integration of rapid prototyping hardware and interoperable software to build system capable of transmitting biomedical data to healthcare professionals. The proposed system involves Internet of Things technologies and interoperablility standards for health information exchange such as the Fast Healthcare Interoperability Resources and a reference framework architecture for Ambient Assisted Living UniversAAL.Energy harvesting wireless sensor network (EH-WSN) is considered to be one of the key enabling technologies for the internet of things (IoT) construction. Although the introduced EH technology can alleviate the energy limitation problem that occurs in the traditional wireless sensor network (WSN), most of the current studies on EH-WSN fail to adequately consider the relationship between energy state and data buffer constraint, and thereby they do not address well the issues of energy efficiency and long end-to-end delay. In view of the above problems, a brand new greedy strategy-based energy-efficient routing protocol is proposed in this paper. Firstly, in the system modeling process, we construct an energy evaluation model, which comprehensively considers the energy harvesting, energy consumption and energy classification factors, to identify the energy state of node. Then, we establish a channel feature-based communication range judgment model to determine the transmission area of nodes. Combining these two models, a reception state adjustment mechanism is designed. It takes the buffer occupancy and the MAC layer protocol into account to adjust the data reception state of nodes. On this basis, we propose a greedy strategy-based routing algorithm. In addition, we also analyze the correctness and computational complexity of the proposed algorithm. Finally, we conduct extensive simulation experiments to show that our algorithm achieves optimum performance in energy consumption, packet delivery ratio, average hop count and end-to-end delay and acceptable performance in energy variance.Automatic systems are increasingly being applied in the automotive industry to improve driving safety and passenger comfort, reduce traffic and increase energy efficiency. The objective of this work is focused on improving the automatic brake assistance systems of motor vehicles trying to imitate human behaviour but correcting possible human errors such as distractions, lack of visibility or time reaction. The proposed system can optimise the intensity of the braking according to the available distance to carry out the manoeuvre and the vehicle speed to be as less aggressive as possible, thus giving priority to the comfort of the driver. A series of tests are carried out in this work with a vehicle instrumented with sensors that provide real-time information about the braking system. The data obtained experimentally during the dynamic tests are used to design an estimator using the Artificial Neural Network (ANN) technique. This information makes it possible to characterise all braking situations based on the pressure of the brake circuit, the type of manoeuvre and the test speed.