With the increasing penetration of the Internet of things (IoT) into people's lives, the limitations of traditional medical systems are emerging. First, the typical way of handling sensitive information can easily lead to privacy disclosure. Second, the medical system is relatively isolated. It is difficult for one medical system to share data with another, and the scope of users' activities is limited within the system boundary. To solve these two problems, we propose a new privacy-preserving medical data-sharing scheme by introducing the authorization mechanism and attribute-based encryption (ABE) based on blockchain, which breaks system boundaries and realizes data sharing among several medical institutions. ABE is used to realize scalable access control. In addition, doctors can share their knowledge to diagnose users by introducing many-to-many matching, which means that patients' health data can be represented by multiple keywords and doctors' expertise can be represented by multiple interests. We provide the correctness and security analysis of our scheme and implement a prototype tool on Ethereum. The experimental results show that our scheme solves the contradiction between the privacy preservation of medical data and the necessity of data sharing.

A novel single-wire power transmission system (SWPT) with two high turns ratio coupling coils is proposed in this work. The high-voltage coils of both the transmitting side and the receiving side of the system are shorted, while the bottom ends of the two high-voltage coils are directly connected by a single-wire. After testing this system over a range of conditions, two optimal operating frequencies are identified and a transmission efficiency of 70% is achieved when the transmission distance is 5m and the load power is 150W. Based on the experimental results, the energy flow process is analyzed and the preliminary equivalent circuit models of the SWPT system are established. The key advantages of this system include: 1) high voltage like Tesla's coil is eliminated, and thus it is of no risk posed by the radiation and environmental benign; 2) reliable and resilient energy transmission is achieved as is not easily interrupted by the surrounding environment. If an alternative technology can be leveraged to replace the single-wire, it is expected to achieve wireless transmission of electrical energy.

In this study, the authors consider the utilisation of multicast technology in cellular networks given different arrival patterns for the content requests of mobile users. Traditionally, the performance evaluation of multicast in the literature usually depends on the adoption of temporal Poisson processes for content requests, which is not accurate any more according to many real data measurements. Therefore, to make use of the bursty nature of content requests, they propose a hybrid unicast/multicast strategy where the base station (BS) can perform the unicast or multicast procedure according to its serving status. By modelling the complete process into a circular Markov chain, they derive the average latency of content requests and the average power consumption of BSs under different arrival patterns and serving configurations in theoretical and/or simulative ways. Moreover, the multicast threshold introduced in their strategy can be dynamically adjusted to achieve a joint optimisation between average latency and power consumption when confronted with varied demands. Numerous results show that the proposed strategy can not only reduce the average latency of content requests but also decrease the average power consumption of BSs, especially under the bursty request arrival patterns.

To understand the spatial deployment of base stations (BSs) is the first step to analyze the performance of cellular networks and further design efficient networking protocols. Poisson point process (PPP), which has been widely adopted to characterize the deployment of BSs and established the reputation to give tractable results in the stochastic geometry analyses, usually assumes a static BS deployment density in homogeneous PPP (HPPP) models or delicately designed location-dependent density functions in in-homogeneous PPP models. However, the simultaneous existence of attractiveness and repulsiveness among BSs practically deployed in a large-scale area defies such an assumption, and the α-stable distribution, one kind of heavy-tailed distributions, has recently demonstrated superior accuracy to statistically model the varying BS density in different areas. In this paper, we start with these new findings and investigate the intrinsic feature (i.e., the spatial self-similarity) embedded in the BSs. Afterwards, we refer to a generalized PPP setup with α-stable distributed density and theoretically derive the related coverage probability. In particular, we give an upper bound of the derived coverage probability for high signal-to-interference-plus-noise ratio thresholds and show the monotonically decreasing property of this bound with respect to the variance of BS density. Besides, we prove that our model could reduce to the single-tier HPPP for some special cases and demonstrate the superior accuracy of the α-stable model to approach the real environment.

In this paper the robust droop controller based virtual synchronous machines are implemented in a complete system level simulation to validate their active participation in the power system regulation and the benefits they can bring to the power grid. The IEEE 12-bus system with four conventional synchronous generators are adopted as the test system. The system structure and the robust droop controllers are briefly introduced. The simulations are carried out where the entire system operates in the normal condition and after the disturbances. The simulation results are discussed and the advantages of the virtual synchronous machines are illustrated through comparisons.

When we talk about the queueing theory in cellular networks, M/M/1 always comes first, which supposes the inter-arrival time and service time are both exponentially distributed. However, with the prosperity of Mobile Instant Messaging (MIM), many researchers found that the inter-arrival times of users' MIM follow lognormal distribution and there are also many keep-alive(KA) messages produced by the keep-alive mechanism accompanied with the users' messages. Based on this discovery, we rethink the queueing model that people might apply to analyze the MIM traffic. Firstly, we focus on a queueing system with lognormally distributed inter-arrival time and exponentially distributed service time, and find that the time a customer spends for waiting in queue and the length of the queue are shorter and smaller than the situation that the inter-arrival time is exponential one as proposed by 3GPP. Secondly, we study the influence of the keep-alive mechanism on queueing process. After that, we carry out simulations for this model, and the numerical results show that there exists an optimal KA duration, which determines the minimal average waiting time.

Understanding traffic characteristics in cellular networks is of great significance for better network design and performance optimization. The rapid development of various social networking applications for smart devices makes it an imperative to carry out cellular data traffic analysis further into the application level. In this paper, based on a plenty of practical mobile data traffic records, we focus on three typical application types and draw conclusions in terms of statistical characteristics and appropriate distribution model for social mobile data traffic. Firstly, the universal existence of burstiness and self-similarity is demonstrated by testing traffic series at different time scales. Afterwards, α-stable distributions are used to model traffic series benefiting from their internal burstiness and self- similarity. The minor fitting errors verify the validity of α-stable model and a preliminary traffic prediction shows the usefulness of α-stable model for further traffic analysis.

A new digital control technique is proposed to improve the speed of both load current and source voltage transient responses for dc-dc converters operating in continuous conduction mode (CCM). The key structure of the proposed control technique is a combination of a limiting integral loop and related arithmetic operations to produce the desired switching sequence during transient condition. The limiting integral loop is built as a novel structure for capacitor charge balance operation. In contrast to previous approaches, the proposed control technique is valid for three basic converters: 1) buck; 2) boost; and 3) buck-boost. Moreover, since the transient period is forced to be an integer multiple of the switching cycle, the advantage of fixed switching frequency is inherently provided. Simulation and experimental results from a noninverting synchronous buck-boost converter prototype are given to validate the effectiveness of the proposed solution.

Multi-sensor image fusion can overcome the limitation of single data source, enhance auto-interpretation capability and improve image quality and usability. In this paper a fusion method based on transferable parameters is presented. The parameters transferability is based on spatial dependence and structural similarity between higher and lower resolution images. The key techniques include fusion model establishment and optimal parameter searching. In order to validate the effectiveness of the proposed method, experiments have been conducted on Landsat TM and SPOT PAN images. The experimental results show that the presented method performs better than some conventional fusion methods both in spatial resolution enhancement and in spectral information preservation.