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<bibtex> @inbook{232, author={Yang Yu; Vasken Genc; Sean Murphy; Liam Murphy}, title={Network Planning for IEEE 802.16j Relay Networks}, publisher={WiMAX Network Planning and Optimization }, year={2009}, abstract={In this chapter, a problem formulation for determining optimal node location for Base Stations (BSs) and Relay Stations (RSs) in relay-based 802.16 networks is developed. A number of techniques are proposed to solve the resulting Integer Programming (IP) problem - these are compared in terms of the time taken to find a solution and the quality of the solution obtained. Finally, there is some analysis of the impact of the ratio of Base Station (BS)/Relay Station (RS) costs on the solutions obtained. Three techniques are studied to solve the IP problem: (1) a standard branch and bound mechanism, (2) an approach in which state space reduction techniques are applied in advance of the branch and bound algorithm and (3) a clustering approach in which the problem is divided into a number of sub-problems which are solved separately, followed by a final overall optimisation step. These different approaches were used to solve the problem. The results show that the more basic approach can be used to solve problems for small metropolitan areas; the state space reduction technique reduces the time taken to ¯nd a solution by about 50%. Finally, the clustering approach can be used to find solutions of approximately equivalent quality in about 30% of the time required in the first case. After the scalability tests were performed, some rudimentary experiments were performed in which the ratio of BS/RS cost was varied. The initial results show that for the scenarios studied, reducing the RS costs results in more RSs in the solution, while also decreasing the power required to communicate from the mobile device to its closest infrastructure node (BS or RS).}, keywords={relay planning}, } </bibtex> <bibtex> @inbook{159, author={Moebs, S. and Weibelzahl, S.}, title={Blended Learning: Towards a Mix for SMEs - Stakeholders and their Priorities}, publisher={Blended Learning}, year={2007}, pages={162-173},


While blended learning seems to be quite suitable for small and medium sized enterprises (SMEs), current uptake of this learning method is low.
In this paper we propose a research design to examine the requirements for blended learning in SMEs. It is based on a three-round ranking-type Delphi
study. Participants for the panels were carefully selected. Our method takes into account that the area and the term of blended learning are discussed in very
different, partially contradicting connotations. For this purpose, we first provide the background of the initial research question and describe our research design.
Next, we present preliminary results of the Delphi study and the steps in preparation of round 2. Participants were selected for the online-Delphi and grouped into panels of SME learners, trainers and providers of e-learning as well as learners from large companies as a control group for the SME learners.


keywords={blended learning, SME, learning technology, Delphi study, Requirements}, pdf={wbl2007_BL4SMEs.pdf}, } </bibtex> <bibtex> @inbook{457, author={O.Ormond;Gabriel-Miro Muntean}, title={Dynamic Handover in Wireless LAN/MAN Heterogenous Networks}, publisher={Mobile WiMAX:Towards Broadband Wireless Metropolitan Area Networks}, year={2007}, abstract={In future generations of wireless networks, it is expected that different users with various multi-homed personal wireless devices will have the option of accessing their desired services via different available radio access networks. Given the variability of the radio environment properties and user mobility, the availability and characteristics of an access network will change in time and are highly dependent on location. As a result, dynamic reselection of the access network will be a necessary part of the mobility management mechanism (which maintains the session connectivity as the user moves and/or the available access characteristics change). In their selection of a radio access network customers will consider cost and perceptive quality preferences for the current application and will rely on intelligent network selection decision strategies to aid or automate their choice.}, keywords={Wireless Network,Radio Network}, pdf={dynamic.pdf}, } </bibtex> <bibtex> @inbook{458, author={C.H.Muntean;Gabriel-Miro Muntean}, title={End User Quality of Experience Aware Personalised in E-Learning}, publisher={Architecture Solutions for E-Learning Systems}, year={2007}, abstract={In the context of new devices and with a variety of network technologies that allow access to the Internet, the providers of e-learning materials have to ensure that the users have a positive experience using their e-learning systems and they are happy to re-use them. Adaptive Hypermedia research aims to provide personalised educational material that ensures a positive learning experience for the end-users. However, user experience is dependent not only on the content served to them, but also on the user perceived performance of the e-learning system. This leads to a new dimension of individual differences between Web users: the end-user Quality of Experience (QoE). We have proposed a solution for Adaptive Hypermedia Systems (AHS) that provides satisfactory end-user QoE through the use of a new QoE layer. This layer attempts to take into account multiple factors affecting QoE in relation to the delivery of a wide range of Web components such as text, images, video, audio. The effectiveness of our QoE layer has been tested in comparison to a standard educational AHS and the results of these tests are presented in this paper. Different educational-based evaluation techniques such as learner achievement analysis, learning performance assessment, usability survey and correlation analysis between individual student performance and judgment on system usability were applied in order to fully assess the performance of the proposed QoE layer. Results of the tests showed that the use of the QoE layer brought significant improvements in terms of user learning performance, system usability and user satisfaction with the personalised e-learning system while not affecting the user learning achievement }, keywords={end-user QoE, adaptive hypermedia, e-learning, end-user perceived performance, learning performance }, pdf={end-user.doc}, } </bibtex>