Big Data Based Self-Optimization Networking in Next Generation Mobile Networks

Using self-optimization techniques is the only viable solution for increasing the efficiency in next generation mobile networks. The goal of proposing a self-optimization model is to maximize the network efficiency and increase the quality of services provided to microcell and femto-cell users, considering the limited resources in radio access networks. To increase the model efficiency, we applied the big data technique for analyzing data and increasing the accuracy of the decision-making process. Based on the meaningful extracted information, the SON decision maker will be able to adjust network parameters and resource allocation factors in a more intelligent manner. The experimental results show that despite the tremendous volume of the analyzed data—which is hundreds of times bigger than usual methods—it is possible to improve the KPIs, such as throughput, up to 30% by optimal resource allocation and reducing the signaling load. Also, the presence of feature extraction and parameter selection modules will reduce the response time of the self-optimization model up to 25% when the number of parameters is too high. Moreover, numerical results indicate the superiority of using support vector machine learning algorithm. It improves the accuracy level of decision making based on the rule-based expert system. Finally, uplink quality improvement and 15% increment of the coverage area under satisfied SINR conditions can be considered as outcome of the proposed scheme.     

Psychiatric manifestations of systemic lupus erythematosus

Low-intensive laser radiation used by topical, intravenous, and transendoscopic routes was employed in the complex antituberculosis therapy of 120 patients with pulmonary tuberculosis concurrent with chronic nonspecific respiratory diseases (n = 48), diabetes mellitus (n = 20 patients), gastrointestinal diseases (n = 45). The results of treatment were compared with the parameters of 62 patients with the similar clinical and X-ray characteristics of a tuberculosis process and concomitant diseases. It was ascertained that laser therapy might enhance the efficiency of treatment of patients with concurrent abnormality statistically significantly, by reducing the time of bacterial isolation, closure of decay cavities in the lung tissue, hospital treatment and by accelerating the onset of stable remission of concomitant diseases.     

Porcelain veneers: concept, preparation, temporization, laboratory, and placement

The success of porcelain veneers depends on knowledge of chairside and laboratory techniques. Improvements in porcelain materials and techniques have dramatically increased the ease and success of these procedures. The learning objective of this article is a review of the new porcelain veneer techniques. The same case is followed from conception to completion, describing and illustrating the techniques.     

A General Model of Unit Testing Efficacy

Much of software engineering is targeted towards identifying and removing existing defects while preventing the injection of new ones. Defect management is therefore one important software development process whose principal aim is to ensure that the software produced reaches the required quality standard before it is shipped into the market place. In this paper, we report on the results of research conducted to develop a predictive model of the efficacy of one important defect management technique, that of unit testing. We have taken an empirical approach. We commence with a number of assumptions that led to a theoretical model which describes the relationship between effort expended and the number of defects remaining in a software code module tested (the latter measure being termed correctness). This model is general enough to capture the possibility that debugging of a software defect is not perfect and could lead to new defects being injected. The Model is examined empirically against actual data and validated as a good predictive model under specific conditions. The work has been done in such a way that models are derived not only for the case of overall correctness but also for specific types of correctness such as correctness arising from the removal of defects contributing to shortcoming in reliability (R-type), functionality (F-type), usability (U-type) and maintainability (M-type) aspects of the program subject to defect management.     

A flexible energy management approach for smart healthcare on the internet of patients (IoP)

Considering the importance of biosensors on the Internet of the patient body that collect vital signs and transmit them to the coordinator, energy consumption and network lifetime are essential challenges in these networks. This paper, it has been tried to present a method based on adapting sampling rate through patient’s risk and discovered pattern by employing an intelligence method based on adaptive neuro-fuzzy inference system, interpolation function, and a biosensor patron. It causes restricting sensed and transmitted data to the coordinator. In the proposed schema, three methods containing Grid partitioning, Subtractive Clustering and fuzzy c-means have been used in two modes, including hybrid and error backpropagation, to predict the individual’s behavioral pattern and determine the patient's risk, attentively. The simulation results in MATLAB R2018b show that the proposed method reduces the network communications. It has improved energy consumption by up to three times and also reduced traffic by more than 80% compared to similar methods.

     

Accuracy and availability modeling of social networks for Internet of Things event detection applications

Wireless Networks - As Social networks are widely used by the people around the world, if this infrastructure can be used for event detection systems like fire forest detection, the overall cost of...     

People tracking using a network-based PTZ camera

In this paper, we propose a method for online upper body tracking using an IP PTZ camera. This type of camera uses a built-in Web server resulting in variable response times when sending control commands. Furthermore, communicating with a Web server involves network delays. Thus, because the camera is inside a control loop, the effective frame rate that can be processed by a computer vision method is irregular and in general low (2–6 fps). Our tracking method has been specifically designed to perform in such conditions. It detects, at every frame, candidate blobs using motion detection, region sampling, and region color appearance. The target is detected among candidate blobs using a fuzzy classifier. Then, a movement command is sent to the camera using the target position and speed. The proposed method can cope with low frame rate, and thus with large motion of the target, even in the case of a fast walk. Results show that our system has a good target detection precision (>88%) and low track fragmentation, and the target is almost always localized within 1/6th of the image diagonal from the image center.     

Development of novel method for prediction of gas density in operational conditions

The performance of gas industries is extensively function of gas properties such as gas density. Due to this importance in the present work, a novel grid partitioning based fuzzy inference system method applied to predict gas density base on pressure, temperature and molecular weight of gas. To this end, the required experimental data are collected from reliable sources. Different comparison scenarios are used to evaluate the ability of model. The coefficients of determination (R²) for training and testing phases are calculated as 0.9985 and 0.9980 respectively. The determined indexes and graphical evaluations show that predicting model can estimate gas density in high degree of accuracy. According to the obtained results, the predicting model can be used as a simple and powerful software in gas industries to predict different processes.     

激光作用下金属平板热应力的数值模拟

本文首先对ANASYS有限元软件进行了简单介绍,在此基础上,重点介绍了运用该软件对激光作用下金属平板温度场及热应力场进行数值模拟的过程,并给出了模拟结果.文章最后对运用有限元软件对金属平板热应力进行数值模拟的意义和用途进行了说明.     

Enhancement of cushioning performance with paperboard crumple inserts

This paper introduces a novel approach to using multi‐layered corrugated paperboard to provide improved protection against severe mechanical shocks and drops. Conventionally, cushion design requires the determination of the maximum expected shock levels or drop heights as well as their probability of occurrence. These are usually determined from statistical analysis of original field measurements or published drop height distribution data. With this approach, it is acknowledged that the cushioning element will provide adequate protection for statistically likely events but not for extreme, statistically unusual, events. A multi‐layer cushioning system made entirely of corrugated paperboard, designed to extend the cushioning protection range to include these extreme events, has been investigated. The main feature of the cushion is the inclusion of a corrugated paperboard crumple element designed to provide the necessary energy absorption for high compression stress levels. The effect of the complex deceleration produced by the crumple element on the product is analysed by means of the shock response spectrum. Experiments have shown that the paperboard crumple insert dramatically extends the protection range of the cushioning system by generally lowering the shock response spectrum, thus extending the cushion curve static load range. This results in a significant increase in the allowable drop height for a limited number of extreme events. Although this approach may be extended to a combination of conventional cushioning materials, the benefits of providing product protection with recyclable paperboard material are significant. Copyright © 2005 John Wiley & Sons, Ltd.