As an important factor in fine thunderstorm detections, a multi-time scale thunderstorm monitoring, warning and imaging system is proposed in this paper. The first computing phase involves a decomposition, classification, denoising and reconstruction of the atmospheric electric field signals (AEFSs), collected by a self-made three-dimensional AEF apparatus, based on autocorrelation characteristics and Fuzzy$C$-Means (FCM). Secondly, FCM classifies the equally divided AEFS components. A scale reconstruction rule is put forward and applied to obtain multi-time scale AEF branch data, according to the component temporal continuity in the same class. A corresponding scale correction strategy is then proposed. Thunderstorm point charge coordinate results are calculated by using branch data, and noise points contained in these results are removed. Finally, the curve fitting of denoised coordinate results is performed to image the point charge moving path. Empirical results confirm that the proposed system effectively warns and images thunderstorms, as well as provides a valid reference for multi-scale thunderstorm monitoring.

Abstract The key issue of medical data is patient information sensitivity and dataset finiteness, which need to guarantee high-efficient training. Besides, the current convolutional neural network has a low image classification and poor robustness concerning antagonistic samples. A lack of scalability in healthcare federated learning and incentive mechanism hinders the attraction of ample high-quality datasets. This paper proposes a Federated Learning Incentive Mechanism for Medical Data Classification (FedIn-MC). It realizes a collaborative model training of multi-party medical institutions through the combination of federated learning and blockchain. There is a marked improvement to the model’s robustness through a combination of the distance loss function and the prototype loss regulation. In addition, this incentive mechanism of blockchain in the project is applied to calculate client contribution values and encourage healthcare institutions to active training model participation. Simulation results verify an accomplishment of a multi-party training. With regard to image classifications, this framework also has a higher classification accuracy and stronger robustness concerning invisible class samples.

There is an ever-increasing risk of illegal access-induced Network Intrusion (NI), which calls for prompt detection of illegal network behavior through profound Network Traffic (NT) analyses. However, current intrusion detection methods are limited in accuracy due to insufficient data standardization . This paper puts forward a deoxyribonucleic acid (DNA)-Spatial Information (SI) method to overcome these limitations. A DNA encoding model is formed, which defines a mapping relationship between NT attributes and nucleobases to reconstruct NT samples expressed as DNA sequences. Then, a feature extraction algorithm is constructed that deduces a Spatial Information Feature Matrix (SIFM) to represent sequence statistical features. A Random Forest (RF) algorithm is adopted as a matching process to determine NI behaviors considering the detection efficiency. Following experiments evaluate its method performance on two datasets, NSL-KDD and UNSW-NB15. Results demonstrate that DNA-SI obtains better results than state-of-the-art works, where the accuracy, F1-score, recall, far are 95.75%, 94.41%, 94.12%, 3.26% and 92.30%, 92.78%, 89.82%, 4.66% respectively. The fact that it is insusceptible to minority intrusion samples is another point worth attention. In sum, this quick and accurate network intrusion detection points to a new orientation for safeguarding network security .

Summary Image dehazing technology has attracted much interest in the field of image processing. Most existing dehazing methods based on neural networks are inflexible and do not consider the loss in haze‐related feature space. They sacrificed texture details and perceptual characteristics in images. To overcome these weaknesses, we propose an image‐to‐image dehazing model based on generative adversarial networks (DHGAN) with dark channel prior. The DHGAN takes a hazy image as input and directly outputs a haze‐free image by applying a U‐net‐based generator. In addition to pixelwise loss and perceptual loss, we introduce dark‐channel‐minimizing loss to constrain the generated images to the manifold of natural images, thus leading to better texture details and perceptual properties. Comparative experiments on benchmark images with several state‐of‐the‐art dehazing methods demonstrate the effectiveness of the proposed DHGAN.

The application of BF techniques in power domain non-orthogonal multiple access (NOMA) schemes allows users to share the same single BF vector for operational reliability. The occurrence of inter-beam interference (IBI) is highly probable in a congested cell (i.e., a cell with high user density and active users). IBI cancellation by using 3D polarized BF in order to enhance the practicability of NOMA systems is the focus of this paper. An IBI cancellation scheme is proposed and evaluations of the spectrum efficiency according to the scenario congestion, as well as of the interference reduction by narrowing the generated beam to a desired beam-width, are presented. The security in the physical (PHY) layer in order to achieve confidential and authentic communication is also an important consideration. The proposed scheme checks the PHY layer security constraints on the number of users served per beam. In simulations, the robustness of the proposed scheme allows the average half power beam-width (HPBW) to be brought to about 20° for different steps in HPBW and for different user densities. Furthermore, depending on the user density, spectrum efficiency gains of approximately 4 bits/s/Hz and 9 bits/s/Hz are achieved by the described IBI cancellation scheme.

For more than two decades, the issue of simultaneous localization and mapping (SLAM) has gained more attention from researchers and remains an influential topic in robotics. Currently, various algorithms of the mobile robot SLAM have been investigated. However, the probability-based mobile robot SLAM algorithm is often used in the unknown environment. In this paper, the authors proposed two main algorithms of localization. First is the linear Kalman Filter (KF) SLAM, which consists of five phases, such as (a) motionless robot with absolute measurement, (b) moving vehicle with absolute measurement, (c) motionless robot with relative measurement, (d) moving vehicle with relative measurement, and (e) moving vehicle with relative measurement while the robot location is not detected. The second localization algorithm is the SLAM with the Extended Kalman Filter (EKF). Finally, the proposed SLAM algorithms are tested by simulations to be efficient and viable. The simulation results show that the presented SLAM approaches can accurately locate the landmark and mobile robot.

Mobile robot localization has attracted substantial consideration from the scientists during the last two decades. Mobile robot localization is the basics of successful navigation in a mobile network. Localization plays a key role to attain a high accuracy in mobile robot localization and robustness in vehicular localization. For this purpose, a mobile robot localization technique is evaluated to accomplish a high accuracy. This paper provides the performance evaluation of three localization techniques named Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF), and Particle Filter (PF). In this work, three localization techniques are proposed. The performance of these three localization techniques is evaluated and analyzed while considering various aspects of localization. These aspects include localization coverage, time consumption, and velocity. The abovementioned localization techniques present a good accuracy and sound performance compared to other techniques.

This study focuses on the spectral efficiency of massive multiple‐input–multiple‐output (MIMO) systems over Ricean fading channels. Assuming the channel reciprocity of uplink and downlink transmissions, the channels are estimated through uplink pilot sequence with least minimum mean square error (MMSE) estimator, which facilitates the investigation of equivalent channel model. With the equivalent channel model, adopting joint MMSE receiver, the uplilnk achievable sum‐rate is studied, and finally, the asymptotic expression of spectral efficiency is obtained. Based on the proposed asymptotic spectral efficiency, numerical results give some guidelines about system parameters design: the authors should choose a proper length of the block fading channels to reduce the effect of pilot overhead, and an optimal length of pilot sequence always exists to guarantee a satisfying system performance in terms of spectral efficiency. Furthermore, the simulation results show that when fixing the number of users, shrinking the cell serving area and increasing the number of antennas can improve the system performance.

Privacy-preserving distributed data fusion is a pretreatment process in data mining involving security models. In this paper, we present a method of implementing multiparty data fusion, wherein redundant attributes of a same set of individuals are stored by multiple parties. In particular, the merged data does not suffer from background attacks or other reasoning attacks, and individual attributes are not leaked. To achieve this, we present three algorithms that satisfy K-anonymous and differential privacy. Experimental results on real datasets suggest that the proposed algorithm can effectively preserve information in data mining tasks.

Recommender systems are increasingly playing an important role in our life, enabling users to find “what they need” within large data collections and supporting a variety of applications, from e-commerce to e-tourism. In this paper, we present a Big Data architecture supporting typical cultural heritage applications. On the top of querying, browsing, and analyzing cultural contents coming from distributed and heterogeneous repositories, we propose a novel user-centered recommendation strategy for cultural items suggestion. Despite centralizing the processing operations within the cloud, the vision of edge intelligence has been exploited by having a mobile app (Smart Search Museum) to perform semantic searches and machine-learning-based inference so as to be capable of suggesting museums, together with other items of interest, to users when they are visiting a city, exploiting jointly recommendation techniques and edge artificial intelligence facilities. Experimental results on accuracy and user satisfaction show the goodness of the proposed application.

Summary Secure mechanisms have been adapted to satisfy the needs of mobile subscribers; however, the mobile environment is quite different from a desktop PC or laptop‐based environment. The existing attack patterns in mobile environments are also quite different, and the countermeasures applied should be enhanced. In regards to usability, the mobile environment is based on mobility, and thus, mobile devices are designed and developed to enhance the owner's efficiency. To avoid forgetting passwords, people are willing to adopt simple alphanumeric‐character combinations, which are easy to remember and convenient to enter. As a result, the passwords have a high probability of being cracked or exposed. In this paper, we study the potential security problems caused by simple and weak passwords, discuss drawbacks of some conventional works, and propose 3 creative schemes to increase the complexity and strength of passwords by applying the envisioned features. Note that our proposals are based on the assumption that the textual passwords are not difficult for users to remember or enter and do not cause inconvenience to users. In other words, the proposed methods can increase the complexity of simple passwords without the awareness of users.

Including mobile environment, conventional security mechanisms have been adapted to satisfy the needs of users. However, the device environment-IoT-based number of connected devices is quite different to the previous traditional desktop PC- or mobile-based environment. Based on the IoT, different kinds of smart and mobile devices are fully connected automatically via device controller, such as smartphone. Therefore, controller must be secure compared to conventional security mechanism. According to the existing security threats, these are quite different from the previous ones. Thus, the countermeasures applied should be changed. However, the smart device-based authentication techniques that have been proposed to date are not adequate in terms of usability and security. From the viewpoint of usability, the environment is based on mobility, and thus devices are designed and developed to enhance their owners’ efficiency. Thus, in all applications, there is a need to consider usability, even when the application is a security mechanism. Typically, mobility is emphasized over security. However, considering that the major characteristic of a device controller is deeply related to its owner’s private information, a security technique that is robust to all kinds of attacks is mandatory. In this paper, we focus on security. First, in terms of security achievement, we investigate and categorize conventional attacks and emerging issues and then analyze conventional and existing countermeasures, respectively. Finally, as countermeasure concepts, we propose several representative methods.

Several industries in many different domains are looking at deep learning as a way to take advantage of the insights in their data, to improve their competitiveness, to open up novel business possibilities, or to resolve the problem that thought to be impossible to tackle. The large scale of the systems where deep learning is applied and the need of preserving the privacy of the used data have imposed a shift from the traditional centralized deployment to a more collaborative one. However, this has opened up several vulnerabilities caused by compromised nodes and inputs, with traditional crypto primitives and access control models exploited to offer protection means. Providing security can be costly in terms of higher energy consumption, calling for a wise use of these protection means. This paper exploits game theory to model interactions among collaborative deep learning nodes and to decide when using actions to support security enhancements.

Smart devices have already become a dally necessity of our life since they play a pivot role to record massive amount of Information of our personal life. Current Internet of TWngs era has been seeing more and more people relying on their devices. However, most people are losing patience to set complex passwords to ensure the security for their devices. To avoid forgetting the passwords, people are more willing to choose simple alphanumeric character combinations that are easy for them to remember and convenient to enter. Therefore, their passwords are of high probability to be cracked or exposed. In tWs paper, we study the potential security problems caused by simple and weak passwords, discuss the drawbacks of some conventional works, and propose three schemes to Increase the complexity of simple passwords. Note that our proposals are based on the prediction that the textual passwords are not difficult for users to remember or enter and the proposed schemes can effldently prevent passwords from being cracked or exposed.

In this paper, we propose a new power allocation method on non-orthogonal multiple access (NOMA) schemes in cellular uplink network. It is known that NOMA could be a promising candidate as a wireless access scheme for future 5G radio access technology. To enhance the spectrum efficiency, NOMA adopts a successive interference cancellation (SIC) receiver as the baseline receiver scheme for robust multiple access. With the goal of maximum total throughput for all users and make full use of bandwidth, an effective power allocation method should be utilized, in the conventional algorithm, it is a good way to get a satisfied performance such as average power allocation, water-filling method. We propose a method based on particle swarm optimization with genetic algorithm, which makes the power allocation by taking the advantage of channel state information (CSI) for each sub-channel; and can obtain a better performance comparing with the conventional method.

The use of polarized antenna systems has been receiving considerable attention. However, few studies have addressed to characterize the cross-polarized multiple-input multiple-output (MIMO) channel modeling. In this paper, we consider a scenario where receiver with the dual-polarized antennas moves far from the transmitter. Thus, the incoming signal can be treated to be parallel to the polarized antenna X-Y plane. Different from the conventional polarized MIMO channel models, such as spatial channel model extended (SCM-E) and the polarized channel model in IEEE 802.20 which only produce single output of complex channel gain for each individual time instance or lack of the consideration of variable mobility, the proposed channel model is simple and practical to exploit MIMO spatial multiplexing and/or diversity gains by relying on a limited number of physical parameters. As a result, beamforming technology is easy to be implemented and analyzed under the proposed channel model. The performance of polarized antenna systems with beamforming technology is finally evaluated via a detailed analysis on bit-error-rate (BER) in this paper.

PHY of ship ad-hoc network (SANET) based on 3GPP LTE and IEEE 802.11p (WAVE) specifications are implemented. However, the measured maritime channel has a longer delay spread (DS) than the length of guard interval (GI) of both 3GPP LTE and IEEE 802.11p systems. For the purpose of eliminating severe inter-symbol interference (ISI) due to insufficient GI length, double antenna pattern scheme and advanced time domain decision-feedback equalizer (DFE) are respectively suggested for those two systems.

TransferJet is a new type of close proximity wireless transfer technology, which can achieve high speed transmission by touching two electronic devices. This paper overviews this novel transmission architecture and simulates its performance under white Gaussian noise (AWGN) channel. In addition, we propose a multiple access scheme on TransferJet by modifying the function of data spreader, and we also observe the link performance of the proposed scheme.

Recently, vehicular sensor networks (VSNs) have emerged as a new wireless sensor network paradigm that is envisioned to revolutionize driving experiences and traffic control systems. Many existing routing protocols for VSNs have good performance on data routing in a city environment. In this paper, we propose a traffic control aware routing for VSNs, which is called PUT. It considers two modules of (i) the traffic control aware selection of vertices through which a packet is passed toward its destination and (ii) the greedy forwarding strategy by which a packet is forwarded between two adjacent vertices. The simulation results show that the proposed PUT outperforms conventional protocols in terms of packet delivery ratio, end-to-end delay and routing overhead.