Noura Aknin

Electroinductive waves role in left-handed stacked complementary split rings resonators

In this letter it is presented a Left-Handed Metamaterial design route based upon stacked arrays of screens made of complementary split rings resonators under normal incidence in the microwave regime. Computation of the dispersion diagram highlights the possibility to obtain backward waves provided the longitudinal lattice is small enough. The experimental results are in good agreement with the computed ones. The physics underlying the Left-Handed behavior is found to rely on electroinductive waves, playing the mutual capacitive coupling the major role to explain the phenomenon. Our route to Left-Handed metamaterial introduced in this paper based on stacking CSRRs screens can be scaled to millimeter and terahertz for future applications.

Genetic algorithms to optimize base station sitting in WCDMA networks

In UMTS network, radio planning cannot only be based on signal predictions, but it must also consider the traffic distribution, the power control mechanism as well as the power limits and the signal quality constraints. The present work aims to optimize the number of base stations used in the WCDMA radio network. In this paper, we propose a mathematical programming model for optimizing the base station locations considered for the uplink (mobile to BS) direction, and which considers the typical power control mechanism of WCDMA. The two contrasting objectives of the optimization process are the traffic coverage maximization and the installation costs minimization. A mimetic algorithm (AM) (genetic algorithm+ a local search) is proposed to find good approximate solutions of this NP hard problem. The Numerical results are obtained for reels instances, and generated by using classical propagation models.

Semantic Emergence From Social Tagging Systems

Recently, Social tagging systems folksonomies have become very popular platforms where content is created collaboratively by users. This kind of environments allows users to assign shared resources with freely chosen keywords tags. Folksonomies provide a valuable addition to the knowledge organization methods since they allow users to choose vocabularies that meet their real tastes and cognition. However, the lacking of standardization and the flat structure of tags in folksonomies pose challenges for folksonomy searching and information retrieval. Several researches have been proposed to overcome these drawbacks. In this paper, the authors present, describe and compare the most relevant approaches to capturing hidden semantics in folksonomies and turning it into ontologies. The authors also present and describe many techniques, tools and online resources that can be useful in working on such systems. Finally, the authors propose an approach to extract ontology from social tagging systems.

Process Optimization Time for a Service in 4G Network by SNMP Monitoring and IAAS Cloud Computing

This paper discusses the energy consumption problem of 4G network devices that use a much higher energy compared to the 3G network for example, due to heavy applications that require multiple resources.To overcome this problem, a combination was proposed between network monitoring in 4G, which is based on SNMP protocol for sorting equipment available in the network and use them in an IaaS cloud computing. This combination permits having a minimized time activity of a process and therefore reduces the energy consumed by a client for asked service.

Adaptive handover time scheme for dynamic load balancing in LTE systems

Future mobile communication systems will benefit from a significant degree of self-organization. Self-Organization (SON) methods have become one of the hot topics in wireless networks; these methods are a promising concept to automate the planning, deployment and optimization of radio access networks. In Self-Organizing Networks (SON), load balancing has been recognized as an effective means to increase network performance. In this paper, we present a framework for self-optimizing next generation mobile networks, such as 3G LTE. A load balancing algorithm based on automatic adjustment of handover thresholds in an LTE network is discussed.

Dual-band rectangular dielectric resonator antenna for ISM and 4 GHz bands applications

In this paper, we propose a dual-band rectangular DRA using the strip-fed method for 2.4 GHz ISM and 4 GHz bands applications. The proposed antenna is composed of small ceramic dielectric (21.65 mm × 15 mm × 26.1 mm, alumina). In order to achieve our goals, we made a cylindrical perforation through the DRA. The measured bandwidth and maximum radiation gain are 2.35 – 2.48 GHz (VSWR ≪ 1.05, 8.54 dBi at 2.4 GHz) and 3.5– 4.52 GHz (VSWR ≪ 1.02, 8.29 dBi at 4.25 GHz), respectively. From these results, it is demonstrated that our proposed antenna can be applicable to the ISM and 4 GHz bands applications.

Impact of other-cell interferences on downlink capacity in WCDMA Network

Before the establishment of the UMTS network, operators are obliged to make the planning process to ensure a better quality of service (QOS) for mobile stations belonging to WCDMA cells. This process consists of estimating a set of parameters characterizing the radio cell in the downlink direction. Among them, we are interested in the Node B total required power PTot, and the maximum cell capacity, in the case of voice only and in the case of voice/video. To implement the effect of the other-cell interferences power, modeled as a fraction fDL of the own-cell received power, on various radio parameters described previously, we focused our study on two different scenarios: the first is based on an isolated cell and the second, on multiple cells. In addition, when the WCDMA cell reaches its maximum capacity, the introduction of admission control algorithms is essential to maintain the QOS of the ongoing mobile stations. For this purpose, we have proposed an admission control algorithm, based both on the Node B total required power and the cell loading factor. This algorithm gives rigorous results compared to the existing ones in the literature.

Brainstorming 2.0: Toward collaborative tool based on social networks

Social networks are part of Web 2.0 collaborative tools that have a major impact in enriching the sharing and communication enabling a maximum of collaboration and innovation globally between web users. It is in this context that this article is positioned to be part of a series of scientific research conducted by our research team and that mixes social networks and collaborative decision making on the net. It aims to provide a new tool open source for solving various social problems posed by users in a collaborative 2.0 based on the technique for generating ideas, brainstorming method and social networks together for the maximum possible adequate profiles to the virtual brainstorming session. A tool is run by a user called expert accompanied by a number of users called validators to drive the process of extracting ideas to the loan of various users of the net. It offers then the solution to the problem of sending a satisfaction questionnaire administered by an expert ready for the affected user to measure the level of his satisfaction and also the success of the process launched. For its implementation, we propose a unified modeling using UML language, followed by a realization using the JAVA language.

Towards Semantic Interoperability for IoT: Combining Social Tagging Data and Wikipedia to Generate a Domain-Specific Ontology

Handling large-scale heterogeneous data in the IoT and processing it in real time will be a key factor towards realizing IoT services in users’ daily lives. In this regard, semantic domain ontologies are increasingly seen as a solution for enabling interoperability across heterogeneous data and sensor-based applications. Several ontologies have been proposed with the aim of addressing interoperability issues and various aspects of IoT device observation. However, for most of these ontologies, they are either in the domain of sensor networks or the much broader domain of the IoT. Furthermore, these ontologies have shown slow improvement, as they have been developed by limited groups of domain experts. This paper proposes a model that exploits the collective intelligence which emerges from social tagging systems to generate up-to-date domain-specific ontologies. The evaluation of the proposed model, using a dataset extracted from BibSonomy, demonstrated that the model could effectively learn a domain terminology, and identify more meaningful semantic information for the domain terminology. Furthermore, the proposed model introduces a simple and effective method for the common problems related to tag ambiguity.

Intelligent Adapted e-Learning System based on Deep Reinforcement Learning

Today, E-Learning platforms are getting more popularity and relevance between educational institutions such as open and distance universities and research institutes. But unfortunately, these platforms present yet unsolved problems, due to the absence of face-to-face interaction. Distance formation instructors have real difficulties recognizing who their students are, understanding their students behaviors in the virtual course, whats possibilities they have to achieve the subject, what complications they find. Basically, they require having a reply which assists them to increase the learning/teaching process. So, it is necessary to develop an intelligent and adapted learning strategy based on the recommendation system that helps professors to do their work efficiently.