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Background: Reverse nearest neighbors query is an important means to solve many practical applications based on the concept of Influence Sets. It is widely used in various fields such as data mining, decision support, resources allocation, knowledge discovery, data flow, bioinformatics and so on. Objective: This work aims to improve time efficiency of Reverse Nearest Neighbors query of moving objects with large data scale. Methods: A new spatio - temporal index HRNN-tree is developed.Then an algorithm for reverse nearest neighbors query based on HRNN-tree is developed. Results: Our algorithm is superior to the existing method in execution time. The performance of our algorithm is excellent especially for the queries with large data scale and small values of k. Conclusion: This study devises a new spatio - temporal index HRNN-tree. Then an algorithm for reverse nearest neighbor search of moving objects is developed based on this index. This algorithm avoids that the query performance deteriorates rapidly as the data space grows and has a better performance fort the large data space. This work will be helpful to enrich and improve the abilities of intelligent analysis, mobile computing and quantitative query based on distance for spatio - temporal database.

Network function virtualization (NFV) is critical to the scalability and flexibility of various network services in the form of service function chains (SFCs), which refer to a set of Virtual Network Functions (VNFs) chained in a specific order. However, the NFV performance is hard to fulfill the ever-increasing requirements of network services mainly due to the static orchestrations of SFCs. To tackle this issue, a novel Scalable SFC Orchestration (SSCO) scheme is proposed in this paper for NFV-enabled networks via federated reinforcement learning. SSCO has three remarkable characteristics distinguishing from the previous work: (1) A federated-learning-based framework is designed to train a global learning model, with time-variant local model explorations, for scalable SFC orchestration, while avoiding data sharing among stakeholders; (2) SSCO allows for parameter update among local clients and the cloud server just at the first and last epochs of each episode to ensure that distributed clients can make model optimization at a low communication cost; (3) SSCO introduces an efficient deep reinforcement learning (DRL) approach, with the local learning knowledge of available resources and instantiation cost, to map VNFs into networks flexibly. Furthermore, a loss-weight-based mechanism is proposed to generate and exploit reference samples in replay buffers for future training, avoiding the strong relevance of samples. Simulation results obtained from different working scenarios demonstrate that SSCO can significantly reduce placement errors and improve resource utilization ratio to place time-variant VNFs compared with the state-of-the-art mechanisms. Furthermore, the results show that the proposed approach can achieve desirable scalability.

5G networks are expected to provide cost-efficient, reliable, and flexible services for industrial productions and applications potentially, by introducing emerging network technologies like blockchain and network functions virtualization (NFV), which virtualizes network functions and runs them on standard infrastructure rather than customized hardware. However, how to deal with the emerging security challenges and fulfil the requirement of ultra-reliable and low-latency communications (URLLC) has not been fully resolved. In this article, we present an NFV-enabled 5G paradigm for the industry with the guarantee of URLLC through service chain acceleration and dynamic blockchain-based spectrum resource sharing among a variety of industry applications running in NVF-based equipment. First, we elaborate the benefits and shortcomings of NFV for industry, by executing an industry application experiment in virtualized and nonvirtualized data center networks. Then, we illustrate an NFV-enabled 5G paradigm for URLLC in detail, with a special focus on the service chain acceleration and spectrum sharing built on NFV, blockchain, software-defined networking, and mobile edge computing. Finally, we establish a mathematical model to study the worst-cast transmission latency of NFV-enabled 5G with the input of the bursty traffic. The proposed model can be exploited to support the plan, management, and optimization of NFV-enabled 5G URLLC systems for industry.

To investigate the impact of vitamin D levels on early clinical function outcomes and the potential risk factors of moderate-to-severe pain prevalence in postmenopausal women after primary total knee arthroplasty (TKA).From April 2017 to December 2019, 226 women were retrospectively recruited. The women were divided into two groups based on their preoperative serum 25-hydroxyvitamin D levels: (1) vitamin D-sufficient group (≥30 ng/mL); (2) vitamin D-deficient group (30 ng/mL). The visual analog scale, Western Ontario and McMaster Arthritis Index score, and Knee Society Score were used to evaluate clinical outcomes. Risk factors for developing postoperative moderate-to-severe knee pain were studied using multivariate binary logistic regression analyses.There was no significant difference in preoperative clinical function assessment between the two groups. The difference in postoperative Western Ontario and McMaster Arthritis Index score between the two groups was statistically significant (15.3 ± 0.7 vs 15.6 ± 0.7: P = 0.02). However, the differences in postoperative visual analog scale and Knee Society Score scores between the two groups were not significant (P0.05). The incidence of postoperative moderate-to-severe pain was 16.4% (95% CI 11.8%-21.9%). Multivariate logistic regression analysis revealed that vitamin D deficiency, smoking, and high body mass index were potential risk factors for moderate-to-severe knee pain in postmenopausal women early after TKA (P 0.05).Preoperative vitamin D deficiency may adversely affect early functional outcomes in postmenopausal women after TKA. In addition, vitamin D deficiency, smoking, and high body mass index were independent risk factors for moderate-to-severe knee pain after surgery.

Mobile Opportunistic Networks (MONs) are characterized by the lack of continuous end-to-end connectivity between two nodes due to node mobility, sparse deployment, and constrained resources. In order to fulfill ubiquitous communication requirements of them, social-aware routing, which exploits social ties and behaviors among nodes to make forwarding decisions, has been proposed and already become a promising approach to enable data delivery in MONs. In this article, we present an insightful review of social-aware routing in MONs through thorough investigations to provide a comprehensive understanding and inspire more in-depth studies. We first explore the motivations behind social-aware routing and introduce various social metrics commonly used. Then we investigate existing social-aware routing schemes in terms of design principles, evaluation metrics, taxonomy, relay selection and message distribution, respectively. Finally, we outline emerging challenges and open issues with respect to social metrics, social perception, privacy and security, emerging applications and experiments in this area.

The natural gas can improve the flooding effect as its excellent flow property and dissolution ability, but it is also easily to form the serious gas channelling when injecting gas continuously. In...

To solve the testing problem of simultaneous overlapping and distinguish positions of multiple targets in weapon testing system, this paper proposes a method for measuring the spatial position of multiple targets based on a combination of line laser and single plane array camera, sets up the spatial position calculation model of multiple targets; studies a charge threshold crosstalk model for multiple targets with different spatial positions, and calculates the crosstalk probability at different target altitudes in terms of the imaging charge threshold. In the spatial position calculation model, error analysis of the parameter is carried out using a differential method. The factors that influence spatial position measurement errors are analyzed on the basis of the crosstalk probability. By testing the three-dimensional coordinates of flight target, we use the traditional two plane array cameras intersection testing method and a combination of multiple line lasers and single plane array camera method to compare the three-dimensional coordinates testing results when the flight target passes under the same conditions. The results show that the average errors are less than 6.8 mm, the testing system can work steadily and can distinguish effectively positions of multiple targets.

Due to the volatility, randomness and seasonality of the load, there is no effective prediction model for the load development trend after the large-scale "coal to electricity" access to the rural distribution network, which affects the capacity planning of the rural distribution network. Aiming at this problem, this paper deeply analyzes the load characteristics and influencing factors of "coal to electricity" in rural areas, puts forward a screening method of single forecasting model based on grey correlation degree and effectiveness index, establishes the optimal medium and long-term combined forecasting model of "coal to electricity" load, and puts forward the flow chart of forecasting model. Finally, an example based on the historical data of a typical "coal to electricity" area is given to verify the results. The results show that the combined forecasting model can further improve the accuracy of the medium and long-term load forecasting results of "coal to electricity" through dynamic weighted optimization of the combined forecasting results.

Background: There is currently no effective treatment for Japanese encephalitis, which has a high rate of morbidity and mortality. This study assessed the effectiveness of a ganciclovir, methylprednisolone, and immunoglobulin combination (TAGMIC) therapy in decreasing cognitive impairment and mortality among patients with Japanese encephalitis. Methods: We retrospectively assessed the clinical data of 31 patients diagnosed with Japanese encephalitis, who were admitted to an intensive care unit. Patients were divided into the TAGMIC and non-TAGMIC group according to their treatment regime. We compared the 60-day, 6-month, and overall mortality and survival curves between groups. We also compared Barthel Index scores, Montreal Cognitive Assessment (MoCA) scores, and diffusion tensor imaging (DTI) results. Findings: There was no significant difference in the 30-day mortality rate or Kaplan–Meier survival curve between groups. The 60-day, 6-month, and overall mortality rates in the TAGMIC group were significantly reduced (P = 0·043, P = 0·018, and P = 0·018, respectively) compared with the non-TAGMIC group (0%, 0%, 0% vs. 31·25%, 37·5%, 37·5%, respectively). The 60-day, 6-month, and overall Kaplan–Meier survival curves were significantly different between groups (P = 0·020, P = 0·009, P = 0·009, respectively). There was no significant difference in the Barthel Index scores of surviving patients. Among the five patients who underwent MoCA and DTI, four had a score of 0/5 for delayed recall (no cue), while the remaining patient had a score of 2/5. All five patients were able to achieve a score of 5/5 with classification and multiple-choice prompts, and had sparse or broken corpus callosum (or other) fibre bundles. Interpretation: TAGMIC treatment can reduce mortality due to severe Japanese encephalitis. The memory loss of surviving patients is mainly due to a disorder of the memory retrieval process, which may be related to the breakage of related fibre bundles. Funding: Joint construction project of Henan Medical Science and technology research plan (LHGJ20190087) Declaration of Interest: None to declare. Ethical Approval: This study was approved by the Ethics Committee of the First Affiliated Hospital of Zhengzhou University (approval no. 2020-KY-077).

Content Caching at the edge of vehicular networks has been considered as a promising technology to satisfy the increasing demands of computation-intensive and latency-sensitive vehicular applications for intelligent transportation. The existing content caching schemes, when used in vehicular networks, face two distinct challenges: 1) Vehicles connected to an edge server keep moving, making the content popularity varying and hard to predict. 2) Cached content is easily out-of-date since each connected vehicle stays in the area of an edge server for a short duration. To address these challenges, we propose a Mobility-aware Proactive edge Caching scheme based on Federated learning (MPCF). This new scheme enables multiple vehicles to collaboratively learn a global model for predicting content popularity with the private training data distributed on local vehicles. MPCF also employs a Context-aware Adversarial AutoEncoder to predict the highly dynamic content popularity. Besides, MPCF integrates a mobility-aware cache replacement policy, which allows the network edges to add/evict contents in response to the mobility patterns and preferences of vehicles. MPCF can greatly improve cache performance, effectively protect users’ privacy and significantly reduce communication costs. Experimental results demonstrate that MPCF outperforms other baseline caching schemes in terms of the cache hit ratio in vehicular edge networks.

Deep learning has made breakthrough achievements in image classification, object detection, semantic segmentation and other tasks in computer vision. The above results have already promoted the progress of related disciplines. In terms of application, due to the huge differences in the original data, deep learning cannot achieve good results in actual production. The production data is used as the initial sample for network training. With the participation of professionals, a high-precision data set is produced through continuous correction of the sample data and noise removal. Finally, the UpperNet semantic segmentation network was used to classify the area's surface coverage, and the Mean Intersection over Union (mIoU) has reached 85.3%.

Background: Small cell carcinoma (SmCC) of cervix was a rare neoplasm with little recognition. Population-based study describing difference in characteristics and outcomes between SmCC and squamous cell carcinoma (SCC), usual type, was limited. Here, we used the Surveillance, Epidemiology, and End Results (SEER) database to compare SmCC with SCC and investigated the prognostic values of the clinicopathological characteristics and survival outcomes in SmCC of cervix.Methods: Patients diagnosed with cervical SmCC and SCC in SEER database from 2004-2015 were enrolled in analysis. Propensity-score matching analysis (PSM) was used to balance baseline characteristics between patients who were cervical SmCC and those who were cervical SCC. Cox regression models and Kaplan-Meier methods were conducted to analyze survival data before and after PSM. Stratified analyses were performed to investigate the risk of mortality at different stage. Results: In total, 25345 patients including 287 cervical SmCC patients and 25058 cervical SCC patients were enrolled our analysis. Both histological subtypes were more common in unmarried women and in white populations. Compared with cervical SCC patients, cervical SmCC patients showed a higher rate of larger tumor size (tumor size ≥4 cm, 33.8% vs 51.9%, P cohort showed histology and FIGO stage were crucial prognostic factors for survival. Kaplan-Meier survival curves clearly showed that cervical SmCC patients had worse survival than that of patients with SCC in all stages, stage I-II, III, IV before and after PSM analysis.Conclusion: Compared to patients with cervical SCC, those with cervical SmCC showed a worse survival before and after adjustment baseline characteristic in all stages. SmCC was an independent poor prognostic factor in cervical cancer patients.

Cellular communications have been regarded as promising approaches to deliver high-broadband communication links for Unmanned Aerial Vehicles (UAVs), which have been widely deployed to conduct various missions, e.g. precision agriculture, forest monitoring and border patrol. However, the unique features of aerial UAVs including high-altitude manipulation, three-dimension (3D) mobility, and rapid velocity changes, pose challenging issues to realize reliable cellular-enabled UAV communications, especially with the severe inter-cell interference generated by UAVs. To deal with this issue, we propose a novel position-based robust beamforming algorithm through complementarily integrating the navigation information and wireless channel information to improve the performance of cellular-enabled UAV communications. Specifically, in order to achieve the optimal beam weight vector, the navigation information of the UAV system is innovatively exploited to predict the changes of Direction-of-arrival (DoA) angle. To fight against the high mobility of UAV operations, an optimization problem is formed by considering the tapered surface of DoA angle and solved to correct the inherent position error. Comprehensive simulation experiments are conducted and the results show that the proposed robust beamforming algorithm could achieve over 90% DoA estimation error reduction and up to 14dB SINR gain compared with five benchmark beamforming algorithms, including Linearly Constrained Minimum Variance (LCMV), Position-based beamforming, Diagonal Loading (DL), Robust Capon Beamforming (RCB) and Robust LCMV algorithm.

The aim of present study was to design an oil separator for the venting systems of gas turbine engines at consumption of gaseous medium 200 m3/h. In order to accomplish the objective, we applied separation gradient aerosol technologies, which consider all the forces and effects that influence deposition of the highly dispersed particles. A scientific base is substantiated for the intensification of gradient processes of the transfer of aerosol media in the boundary layers of multifunctional surfaces in the purification of dispersed polyphase flows for developing the technical devices that ensure an increase in energy saving and ecological improvement of power plants. We designed a section-by-section structural scheme and a three-dimensional model of the oil separator in finite elements for the calculation of hydrodynamics and separation. The calculations were conducted of the hydrodynamic situation and particle trajectory in the flow area of an oil separator. Using the calculated distribution of speed in the oil separator at G=100…200 m3/h, it was determined that velocity in the coagulation profile does not exceed 10 m/s. It was established according to the results of static pressure distribution for G=100, 200 m3/h that the pressure differential in the separation coagulators reaches 2.5…3.9 kPa, respectively. Results of the calculation at G=100…200 m3/h demonstrated that the summary pulsation effect from the deposition of highly dispersed particles amounts to 25.1 %. Based on the calculations, we designed the prototype of an oil separator and tested it experimentally on the test bench in the form of an open type wind tunnel. Coefficient of the total effectiveness of purification was determined, which reaches 99.9 %. The modernization of purifiers for capturing the aerosols in different systems of power plants is possible based on the separation gradient aerosol technologies. The studies conducted make it possible to develop in the future a range of separators for gas consumption from 20 to 2000 m3/h.

Network function virtualization (NFV) has been considered as a promising technology for future Internet to increase the network flexibility, accelerate the service innovation, and reduce the Capital Expenditures and Operational Expenditures costs through migrating network functions from dedicated network devices to commodity hardware. Recent studies reveal that although this migration of network function brings the network operation unprecedented flexibility and controllability, NFV-based architecture suffers from serious performance degradation compared with traditional service provisioning on dedicated devices. In order to achieve a comprehensive understanding of the service provisioning capability of NFV, this paper proposes a novel analytical model based on Stochastic Network Calculus (SNC) to quantitatively investigate the end-to-end performance bound of the NFV networks. To capture the dynamic and on-demand NFV features, both the non-bursty traffic, e.g., the Poisson process, and the bursty traffic, e.g., the Markov Modulated Poisson Process, are jointly considered in the developed model to characterize the arriving traffic. To address the challenges of resource competition and end-to-end NFV chaining, the property of convolution associativity and leftover service technologies of SNC are exploited to calculate the available resources of the Virtual Network Function nodes in the presence of multiple competing traffic and transfer the complex NFV chain into an equivalent system for performance derivation and analysis. Both the numerical analysis and extensive simulation experiments are conducted to validate the accuracy of the proposed analytical model. Results demonstrate that the analytical performance metrics match well with those obtained from the simulation experiments and numerical analysis. In addition, the developed model is used as a practical and cost-effective tool to investigate the strategies of the service chain design and resource allocations in the NFV networks.

Background The infarct volume is associated with the clinical outcome after ischemic stroke. We investigated the factors associated with infarct growth and its effect on functional outcome in patients with acute ischemic stroke. Methods The present retrospective study included 158 patients with acute ischemic stroke receiving endovascular treatment at the University of California, Los Angeles, Medical Center from 2011 to 2016. The patients' clinical parameters were collected. Magnetic resonance imaging was performed before treatment and 24 hours after endovascular treatment to calculate the infarct volume. The infarct sizes were measured using apparent diffusion coefficient maps. The modified Rankin scale (mRS) scores at discharge and 90 days after discharge were used to evaluate the functional outcomes. An mRS score of 0–2 was defined as a good outcome. The predictors of infarct growth and mRS score at 90 days were analyzed using regression analysis. Results Patients with smaller infarct growth had significantly better clinical outcomes as measured by the mRS score at discharge and at 90 days and mortality at 90 days (P Conclusions Infarct growth was associated with the functional outcome. Smaller infarct growth correlated with better outcomes. A greater Alberta Stroke Program Early CT score and better perfusion predicted for smaller infarct growth.

Glioma is the most common malignant tumor of the central nervous system with poor survival. Temozolomide (TMZ) is the first-line chemotherapy drug for initial and recurrent glioma treatment with a relatively good efficacy, which exerts its antitumor effects mainly through cell death induced by DNA double-strand breaks in the G1 and S phases. However, endogenous or acquired resistance to TMZ limits glioma patients' clinical outcome and is also an important cause of glioma replase. MicroRNA-195 (miR-195) plays an important role in the regulation of G1-phase/S-phase transition, DNA damage repair, and apoptosis of tumor cells. We found that miR-195 expression was significantly decreased in TMZ-resistant glioma cells induced with TMZ and correlated to the resistance index negatively. Also, the exogenous expression of miR-195 reversed TMZ resistance and induced the apoptosis of TMZ-resistant glioblastoma cells. Further bioinformatics analysis showed cyclin E1 (CCNE1) was a potential target gene of miR-195. Knockdown of CCNE1 partially reversed the effect of decreased miR-195 on TMZ resistance. The data from The Cancer Genome Atlas - Cancer Genome further suggested that hsa-miR-195 could negatively regulate the expression of CCNE1 in glioma. In conclusion, miR-195 reverses the resistance to TMZ by targeting CCNE1 in glioma cells and it could act as a potential target for treatment in glioma with TMZ resistance.

Mobile Cloud Computing (MCC) is regarded as a promising method to increase the data storage and enhance the processing power of mobile devices. Technologies such as Software Defined Networking (SDN) and Network Function Virtualisation (NFV) will be deployed in MCC to simplify the network management and accelerate mobile service deployment. In order to achieve a deeper understanding of future MCC, we developed a comprehensive analytical model to investigate the performance of MCC in the presence of both NFV service chains and SDN networks. The model is capable of capturing the interactions between SDN and NFV when they share the same underlying physical infrastructure. The end-to-end latency is derived for different scales of service deployments and network configurations. Comprehensive simulation experiments are conducted and the results demonstrate that the proposed analytical model corresponds well with the simulation experiments. In addition, we show how the analytical model can be a useful tool to investigate the impact of centralised SDN control on the performance of NFV traffic transmission.

A blockchain is a distributed database or ledger that maintains an ever-growing list of data records in opposition to tampering and revision. It provides immutable data storage over a distributed network and supports a large number of encrypted and coded interactions, which improves the reliability of the entire network interaction system and reduces the need for trust. Even if some nodes in the blockchain are hacked and fail, the system can run as usual. (In such a scenario, users are enabled to form a distributed peer -to peer (P2P) network in which they could interact with each other in an efficient manner without a trusted intermediary). In addition to being famous for decentralization, blockchain has shown other significant characteristics during its development, such as reliability, anonymity, transparency, auditability and programming. According to the different degree of openness and coverage, the current blockchain can be classified into three categories: public blockchain, consortium blockchain and private blockchain. In this process, the evolution of blockchain has gone through three processes: blockchain 1.0, 2.0 and 3.0. Blockchain 1.0, known as digital currency stage, is strongly related with the decentralization and payment of cryptocurrencies. Blockchain 2.0, known as digital finance stage, introduces economic, financial and market applications by programming far from simple currency transactions. Among them, the most significant features of blockchain 2.0 are the introduction and application of smart contracts. Blockchain 3.0, known as digital society stage, provides decentralized solutions for a variety of industries beyond just financial scene.

In this chapter, we first present potential blockchain applications in the world and then summarize the ongoing blockchain projects and its implementations, along with comprehensive compares among them.

The connected vehicle paradigm empowers vehicles with the capability to communicate with neighboring vehicles and infrastructure, shifting the role of vehicles from a transportation tool to an intelligent service platform. Meanwhile, the transportation electrification pushes forward the electric vehicle (EV) commercialization to reduce the greenhouse gas emission by petroleum combustion. The unstoppable trends of connected vehicle and EVs transform the traditional vehicular system to an electric vehicular network (EVN), a clean, mobile, and safe system. However, due to the mobility and heterogeneity of the EVN, improper management of the network could result in charging overload and data congestion. Thus, energy and information management of the EVN should be carefully studied. In this paper, we provide a comprehensive survey on the deployment and management of EVN considering all three aspects of energy flow, data communication, and computation. We first introduce the management framework of EVN. Then, research works on the EV aggregator (AG) deployment are reviewed to provide energy and information infrastructure for the EVN. Based on the deployed AGs, we present the research work review on EV scheduling that includes both charging and vehicle-to-grid (V2G) scheduling. Moreover, related works on information communication and computing are surveyed under each scenario. Finally, we discuss open research issues in the EVN.

© 2018 [2018 Optical Society of America.]. One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modifications of the content of this paper are prohibited. Virtual Data Centre (VDC) solutions provide an environment that is able to quickly scale-up and where virtual machines and network resources can be quickly added on-demand through self-service procedures. VDC providers must support multiple simultaneous tenants with isolated networks on the same physical substrate. The provider must make efficient use of their available physical resources whilst providing high bandwidth and low-latency connections to tenants with a variety of VDC configurations. This paper utilises state of the art optical network elements to provide high bandwidth optical interconnections and develop an VDC architecture to slice the network and the compute resources dynamically, to efficiently divide the physical network between tenants. We present a Data Centre Virtualisation architecture with an SDN-controlled all-optical data plane combining Optical Circuit Switching (OCS) and Time Shared Optical Network (TSON). Developed network orchestration dynamically translates and provisions VDCs requests onto the optical physical layer. The experimental results show the provisioned bandwidth can be varied by adjusting the number of time slots allocated in the TDM network. These results lead to recommendations for provisioning TDM connections with different performance characteristics. Moreover, application level optical switch reconfiguration time is also evaluated to fully understand the impact on application performance in VDC provision. The experimental demonstration confirmed the developed VDC approach introduces negligible delay and complexity on the network side.

Aiming at solving the scaling issues of bandwidth and latency in current hierarchical data center network (DCN) architectures, we propose and investigate a novel optical flat DCN architecture in which the number of interconnected ToRs scales as the square of the optical packet switches' (OPS) port count (OPSquare). The proposed flat DCN architecture consists of two parallel interand intra-cluster networks that are built on a single-hop OPS with nanosecond time and wavelength switching for efficient statistical multiplexing operations. Fast optical flow control is implemented for solving packet contentions that may occur at the buffer-less optical switches. The performance of OPSquare DCN in terms of scalability, packet loss, latency, and throughput is assessed by a numerical simulation employing OMNeT++ under realistic data center (DC) traffic. The results report a server-to-server latency of less than 2 ?s (including packet retransmission), a packet loss

The development of high-capacity 1310-nm optical interconnects and the limitation of the electrical switches have necessitated the investigation of 1310-nm optical switches for flat data center networks. We have performed an experimental assessment of the scalable and fast 1310-nm optical switching system with three types of traffic, namely, the waveband $4\times 25$ Gb/s on–off keying (OOK), 28 Gb/s 4-level pulse-amplitude modulation (PAM4), and 40 Gb/s discrete multi-tone (DMT) by using the prototyped 1310-nm optical switch. 10-dB input power dynamic range has been achieved at 16 ports for waveband $4\times 25$ Gb/s, 28-Gb/s PAM4, and 40-Gb/s DMT with power penalty of 1, 3, and 3.3 dB, respectively. Waveband $4\times 25$ Gb/s OOK traffic shows the most promising results with port-count scaling up to 64 ports with limited penalty and allows for the potential scaling to larger port-count and more wavelength channels.

In the testing process of the dynamic multitarget parameter of a weapon range, multiple targets often occur in the same plane simultaneously, so that the existing optical testing methods cannot recognize and match the spatial positions of all the targets. To address this problem, we propose a new optical testing method that combines a line laser with a plane array camera, establishing a multiparameter calculation model using the intersection of the imaging optical axis of a plane array camera and the two line-laser detection screens. Based on the established multiparameter model, the recognition model based on time-space information constraint relationship between the line laser and plane array camera is developed, and it provides a constraint condition discriminant function. We also introduce the recognition and matching method based on fuzzy information fusion, analyze the matching mechanism of correlation parameters of the data fusion algorithm, and provide the solution method of key parameters. On the basis of the inherent parameters of the proposed testing method, the multitarget images at different velocities are collected, and the test data under the contrast experimental conditions is obtained. The results show that the proposed testing method can effectively solve the matching problem when multitargets appear at the same time causing occlusion and overlapping. The test error meets the measurement requirements.

This chapter presents a basic overview of network function virtualization (NFV)-based Internet of Things (IoT) in fifth generation (5G) networks, with the focus on NFV-based IoT benefits, its key technologies, potential applications, and emerging challenges. The NFV-based IoT will serve as the access and local communication networks and be responsible for data gathering and transmission in 5G networks. The NFV-based IoT will be partially or fully virtualized into the software to work with 5G networks. Smart home is a typical example of the IoT integrating with 5G networks, which have been partially or fully virtualized through full-blown virtualization technologies. IoT data is becoming related to almost all aspects of human network activities from just recording events to research, design, production, and digital services over networks. 5G mobile crowdsensing applications will collect detailed information from sensors and their owners during task management procedures.

This is the author accepted manuscript. The final version is available from CRC Press via the link in this record

Unmanned Aerial Vehicles (UAVs) have demonstrated exceptional capabilities in many real-world applications such as remote sensing, emergency medicine delivery and precision agriculture. LTE networks are regarded as key candidates to offer high performance broadband wireless services to support UAV applications and safe deployment. However, the unique features of aerial UAVs including high-altitude manipulation, three-dimension (3D) mobility and rapid velocity changes, pose challenging issues for optimising LTE wireless communications to support UAVs, especially under the severe inter-cell interference generated by UAVs in the sky. To deal with this issue, we propose a novel position-based robust beamforming algorithm to improve the performance of LTE networks to serve UAVs. For obtaining optimal weight vectors that could tolerate Direction-of-Arrival (DoA) estimation errors, we propose a hybrid method to integrate the channel information and UAV flight information to accurately estimate the DoA angle range. In order to validate the performance of the proposed robust beamforming algorithm, we conduct comprehensive simulation experiments under practical configurations. The results show that the proposed robust beamforming algorithm outperforms benchmark Linearly Constrained Minimum Variance (LCMV) beamforming and GPS-based beamforming algorithms.

We present an estimation method and an investigation on the phase-noise stabilization for a near-infrared fiber Mach–Zehnder interferometer (MZI) using a laser diode at 1530 nm as the light source. Based on the theoretical analysis, the phase noise is mainly produced by the tiny fluctuations of external temperature, whose amplitude is proportional to the optical path difference (OPD) between two arms of optical fibers in the interferometer. By minimizing the length difference between two fibers in the MZI and specifying the compensation signal applied to the piezoelectric transducer (PZT) modulator actively, the interferometer worked stably at different phase differences. When the interferometer was working at the 90° phase difference, which is usually necessary for practical application, the RMS noise was minimized to 0.47°. By quantitative analysis, the magnification ratio of noise amplitude at room temperature per unit OPD of the MZI was 132.29°/m ( = 0.73 r a d / m ). In this experiment, a good linear correlation between the initial OPD and the phase signal intensity was successfully observed. Using this estimation method, an optical fiber interferometer of different wavelengths can be built more conveniently in practical engineering applications.

With the development of photovoltaic projects for poverty alleviation, large number of distributed photovoltaic power plant is incorporated rural grid. On the one hand, photovoltaic poverty project can increase the income of farmers, on the other hand, it can improve the low voltage problem of rural distribution network. While photovoltaic power plant incorporating makes the distribution network from traditional radial network become active network, and causes a change of voltage distribution in feeder. Rural grid distribution network is the weakest link in the entire grid. So that it is necessary to analyse the influence factors of voltage distribution and the interconnection principles of photovoltaic power station. In this paper, through the modeling and simulation of distributed photovoltaic access to rural distribution network, analyses the law of the capacity and access location of photovoltaic power integration in the distribution network, and summarizes the interconnection requirements of the photovoltaic power station. The results show that only reasonable and proper use the distributed photovoltaic power plant, can play the role of voltage support and solve the problem of low voltage in rural distribution network.

Software-Defined Networking (SDN) is an emerging architecture for the next-generation Internet, providing unprecedented network programmability to handle the explosive growth of big data driven by the popularisation of smart mobile devices and the pervasiveness of content-rich multimedia applications. In order to quantitatively investigate the performance characteristics of SDN networks, several research efforts from both simulation experiments and analytical modelling have been reported in the current literature. Among those studies, analytical modelling has demonstrated its superiority in terms of cost-effectiveness in the evaluation of large-scale networks. However, for analytical tractability and simplification, existing analytical models are derived based on the unrealistic assumptions that the network traffic follows the Poisson process, which is suitable to model nonbursty text data, and the data plane of SDN is modelled by one simplified Single-Server Single-Queue (SSSQ) system. Recent measurement studies have shown that, due to the features of heavy volume and high velocity, the multimedia big data generated by real-world multimedia applications reveals the bursty and correlated nature in the network transmission. With the aim of capturing such features of realistic traffic patterns and obtaining a comprehensive and deeper understanding of the performance behaviour of SDN networks, this article presents a new analytical model to investigate the performance of SDN in the presence of the bursty and correlated arrivals modelled by the Markov Modulated Poisson Process (MMPP). The Quality-of-Service performance metrics in terms of the average latency and average network throughput of the SDN networks are derived based on the developed analytical model. To consider a realistic multiqueue system of forwarding elements, a Priority-Queue (PQ) system is adopted to model the SDN data plane. To address the challenging problem of obtaining the key performance metrics, for example, queue-length distribution of a PQ system with a given service capacity, a versatile methodology extending the Empty Buffer Approximation (EBA) method is proposed to facilitate the decomposition of such a PQ system to two SSSQ systems. The validity of the proposed model is demonstrated through extensive simulation experiments. To illustrate its application, the developed model is then utilised to study the strategy of the network configuration and resource allocation in SDN networks.

The mα-tangent method is a simple way to estimate limit load for mechanical components. The method is based on a linear elastic finite element analysis to estimate the limit loads. Present work reports limit loads for branch pipe junctions under internal pressure and in-plane bending which is determined by ma-tangent method. All results are compared with published closed-form solutions and also FE results. The FE results can be found by small-strain three-dimensional finite element (FE) limit load analyses using elastic–perfectly plastic materials. Various branch pipe geometries are considered to verify the accuracy of the ma-tangent method.

We demonstrate a novel fast optical add-drop multiplexer (OADM) for high-capacity and low-latency next-generation metro network by exploiting optical label switching technology. The parallel optical label processing technique allows nanoseconds OADM reconfiguration time for efficient statistical multiplexing of the data packets, and facilitates per channel power monitoring for implementing nanoseconds semiconductor optical amplifier-based per channel power equalization. Experimental results of a ring network with four OADM prototypes confirm nanoseconds dynamic add/drop operation, including multicasting and power equalization for 50-Gb/s data payload. The average delivery time is -6 for a load of 0.4 and 16-packet buffer.

Clean energy technologies, especially photovoltaic, have recently become more and more popular and important due to their substantial benefits for environment, economy, and energy security. How to improve the management and usage efficiency of photovoltaic power stations is a challenging problem that needs to be investigated deeply. In this paper, Wireless sensor networks (WSNs) are utilized to efficiently deliver the monitoring data of the photovoltaic (PV) modules from power stations to the monitoring center located in Cloud datacenter. With the aim of detecting the problems of PV modules from the monitoring big data, a two-class data fusion method is firstly developed to integrate the monitoring data at sensor nodes of WSNs; then an innovative semi-supervised Support vector machine (SVM) classifier is designed and trained by existing solar irradiance big data at the monitor center. With the prediction model provided by the trained classifier, an outlier detection algorithm is devised to classify and locate the problems of PV modules through calculating the average value of the questionable data. In order to evaluate the performance of the proposed methods, a comprehensive experimental platform is set up. The experimental results show that the predicted values match well with the theoretical value of power generation.

Optical Packet Switches (OPS) could provide the needed low latency transmissions in today large data centers. OPS can deliver lower latency and higher bandwidth than traditional electrical switches. These features are needed for parallel High Performance Computing (HPC) applications. For this purpose, it has been recently designed full optical network architectures for HPC system such as the Architecture-On-Demand (AoD) network infrastructure. Although light-based transmission has its advantage over electrical-based transmissions, optical devices such as OPS cannot store light. Therefore, in case of an optical packet collision occurs for accessing the same output port in OPS only one packet can proceed and the others must be dropped, triggering afterwards a retransmission procedure. Obviously, packet retransmissions are delaying the actual transmission and also increase the buffer utilization at the network interfaces cards (NICs) that deals with retransmissions. In this paper, it is proposed a technique based on mapping application processes to servers reducing the number of simultaneous packets in the network (Concurrency) and therefore, it is able to significantly reduce optical collisions at the OPS while substantially reduces the resource needed at the NICs for retransmissions. Our proposed concurrency-aware mapping technique can reduce the extra buffer size utilization up to 4.2 times and the execution time degradation up to 2.6 times.

It has become a hot topic in teaching reform to combine classroom teaching with information technology. Blended-learning has made the new definition of teaching, and build a new "teachers leader-students body" teaching relations, and put forward new requirements for the education relation of subjects including students, teachers, managers. Paper discusses the multiple-body perspective of blended-learning system construction, containing the class distribution of internal and external learning, pre-class teaching materials, design of autonomous learning task, design of problem feedback mechanism, design of class learning activity, after-class homework and auxiliary learning resources construction, and the way of evaluating multiple links. Paper claims that the blended-learning reform of colleges and universities is an inevitable trend. The multiple subject perspective of blended-learning can promote students' autonomous learning, improve teaching effect, enrich and develop the existing single type of online and offline teaching mode.

Information-centric networking (ICN) is an emerg- ing paradigm that decouples content from the host to achieve fast and cost-efficient communication and content distribution in the future Internet. A key feature of ICN is the deployment of ubiquitous in-network caching to speed up service delivery and improve network resource utilisation. ICN caching has been widely studied in terms of caching strategies and caching performance. However, the economic aspect of ICN has received marginal consideration so far, although it is vital to understand the potential cost- efficiency of ICN before its wide deployment in service provider network. To address this issue, we propose a cost- aware caching scheme to study the Quality-of-Service (QoS) and cost of ICN and investigate the inner association between them. Two new models are designed to characterise the cost and QoS of ICN with arbitrary topology under heterogeneous bursty content requests. A multi- objective evolution algorithm is adopted to find the optimal cache resource allocation. Numerical results show the effectiveness of the proposed scheme in achieving cost- efficiency and QoS guarantee in ICN caching.

We present a Data Centre Virtualisation architecture with an SDN-controlled all-optical data plane combining OCS and TSON. Orchestration dynamically translates and provisions Virtual Data Centres requests onto the optical layer. We describe an implementation and characterisation of the data plane.

Scaling the capacity while maintaining low latency and power consumption is a challenge for hierarchical data center networks (DCNs) based on electrical switches. In this work we present a novel optical DCN architecture called OPSquare that potentially addresses the scaling issues by employing parallel intra- and inter-cluster switching networks based on distributed fast WDM optical cross-connect (OXC) switches and a novel top-of-rack (ToR) switch architecture. The WDM OXC switches with nanoseconds reconfiguration time allow flexible switching capability in both wavelength and time domains and statistical multiplexing. Packet loss, latency, throughput, multi-path dynamic switching with flow control operation, and scalability will be discussed and experimentally evaluation by employing a 4×4 OXC prototypes. The potential of switching higher-order modulation and waveband signals further proves the suitability of OPSquare architecture for Petabit/s and low-latency DCN by using optical switches with moderate radix.

We present two hybrid fabrics integrating optical and electronic switches with SDN, and a novel approach improving the scaling of fast optical switches. C-Share reroute flows through optical switches increasing network performance. E-WDM exploits the optical switch transparency by emulating wavelength switching in electronic switches.