Digital Systems

Development of innovative products requires people with various skills and know-how to collaborate in multidisciplinary teams and learn from each other. Nowadays, these teams more and more work distributed across locations. This implies that collaborative work and creativity ...

Over the past two decades, great research efforts have been made towards the personalization of e-learning platforms. This feature increases remarkably the quality of the provided learning services, since the users’ special needs and capabilities are respected. The idea of predicting the users’ preferences and adapting the e-learning platform accordingly is the focal point of this paper. In particular, this paper starts with the main requirements of an advanced e-learning system, explains the way a user navigates in such a system, presents the architecture of a corresponding e-learning system and describes its main components. Research is focused on the User Model component, its role in the e-learning system and the parameters that comprise it. In this context, Bayesian Networks are used as a tool for the encoding, learning and reasoning of probabilistic relationships, with the aim to effectively predict user preferences. In support of this vision, four different scenarios are presented, in order to test the way Bayesian Networks apply in the e-learning field.

One of the key features of emerging and future wireless systems is the continuous increase in the number and diversity of available radio access technologies. A great number of research efforts in the past decade have focused on ways to more efficiently exploit the various wireless access technologies. Among the recent trends in this direction are cognitive wireless networks and systems, targeting to tackle the complexity of the diversified radio environment. Eventually, the goal of these evolving systems is to provide personalised services to users at any time, at any place. Clearly in order to really improve the experience of all users, even technology agnostic ones, functionality is required, on both the network and the user-device side, for providing the "always best connection" in a transparent manner. This paper focuses on mechanisms for acquiring and learning information on user preferences, requirements and constraints as part of a cognitive device management system that enables optimal device configuration taking into account in addition to user requirements, environment characteristics and experience established through learning mechanisms. In this context, the paper presents a scheme for learning and estimating user preferences based on Bayesian networks.

The B3G concept can be realized in two complementary ways. The first solution is the integration of the diverse radio access technologies into one composite radio environment. The alternative solution is provided by the concept of reconfigurable (adaptive) networks. Composite radio networks, sometimes also referred to as cooperative networks, jointly handle a difficult condition. Reconfigurable networks on the other hand, support B3G Systems by providing technologies that enable network elements and terminals to dynamically adapt to the environment requirements and conditions, in principle, by means of self-management. This paper provides proof on the business advantages of reconfigurable networks. In this context the paper performs an evaluation of the investment in both composite radio and reconfigurable networks, presenting a methodology that can be used for the financial assessment of such networks by applying investment appraisal techniques. Concrete results for both cases are presented and analyzed. The analysis clearly proves that reconfigurable networks can provide significant business benefits for network operators.

The continuously increasing need for mobility has brought about not only significant facilities in several aspects of human initiative, but also growing traffic congestions, a phenomenon that leads to unpleasant everyday situations at a short time level, but in the long run also to the degradation of the level of quality of living in large cities and the alienation between people. The management of traffic stands, thus, as a fundamental prerequisite for confronting those issues, enhancing transportation and improving the social fabric. This paper considers the concept of car pooling as a structured approach to this problem, by specifying, developing and validating a mobile-community-driven system for collaborative transportation, namely the “transportation management-car pooling system”. This system is capable of proposing optimal, reliable and secure community matches (taking into consideration personality features, talking interests, driving style, etc.), based on user profile and context information. The paper describes the transportation management-car pooling system, presenting its input parameters, decision making process and outcomes. Finally, indicative simulation results showcase its effectiveness.

Learning mechanisms are essential for the attainment of experience and knowledge in cognitive radio (CR) systems, exposed to high dynamics with often unpredictable states [1]. These mechanisms can be associated with user and device profiles, context, and decisions. The focus learning user preferences is the dynamic inference and estimation of current and future user preferences. The acquisition and learning of context information encompasses mechanisms for the system to perceive its current status and conditions in its present environment, as well as estimating (and forecasting) the capabilities of available network configurations. Finally, learning related to decisions addresses the building of knowledge with respect to the efficiency of solutions that can be applied to specific situations encountered. Based on knowledge obtained through learning, decision-making mechanisms can become faster, since the CR system can learn and immediately apply solutions that have been identified as being efficient in the past. Moreover, knowledge obtained through learning mechanisms may be shared among nodes of a system. Thus, more reliable and more optimal decisions can be made by exploiting knowledge obtained through learning mechanisms.

This paper discusses on a system that aims to facilitate the experience of learning a foreign language, and more specifically, enhancing the procedure of learning the respective vocabulary. The presented system is a web-based learning management system, which uses cognition mechanisms in order to take into consideration both users’ past preferences and their current feedback, with respect to the learning style. Bayesian Networks concepts are used for monitoring past preferences, and for acquiring knowledge and determining the likelihood of future preferences, through the use of certain parameters.

The continuous evolution of wireless systems has resulted in a number of new and powerful wireless networking standards. The concept of Beyond the Third Generation Systems (B3G) emerged in an attempt to exploit the variety of the available access standards to the benefit of end-users, operators and manufacturers. In this context, a key topic in the research area of B3G/4G

According to the B3G concept, diverse radio access technologies (RATs) can converge into one composite radio environment, where the user is "always best connected" and can move between the various technologies seamlessly. Cognitive wireless networks [1],[2] have emerged as a complementary concept to B3G networks. In general, cognitive systems establish their behaviour, in a reactive or proactive manner, based on external triggers (environment), as well as their goals, principles (policies, rules), experience and knowledge. This requires, among other things, advanced terminal management functionality for dynamically selecting and adapting to the most appropriate technology, in a seamless, transparent and robust manner. This paper emphasizes on the management of user equipment and discusses on how to further progress the relevant work. The work of this paper complements legacy methods for network selection by presenting cognitive mechanisms and an associated knowledge-based network selection scheme. In particular, a specific learning scheme that relies on the use of Bayesian statistics is proposed. In the direction of assessing the performance of the developed knowledge-based selection scheme a software implementation has been realised. This software implementation also includes a legacy selection scheme thus enabling the comparison with the knowledge-based selection. Corresponding results derived from the software implementation are presented.

The dawn of the Future Internet (FI) era poses new requirements to modern communication networks namely, the demand for new applications/ services, the support for diversified services, the expanded use of wireless access and the need for increased efficiency in resource provisioning and utilization. In this paper we propose a solution to address these requirements. In particular, the proposed solution is based on (i) opportunistic networks, which can be seen as operator-governed, temporary and probably infrastructure-less extensions of the infrastructure-based network, (ii) cognitive systems both for managing the opportunistic networks and for coordinating with the infrastructure, and (iii) control channels for the cooperation of the cognitive management systems. We advocate that the adoption of such a solution will bring about enhanced wireless service provision and extended access capabilities for the Future Internet, through higher resource utilization, lower costs, and management decisions with a larger “green” footprint.

Abstract The current circumstances on a worldwide level urgently impose to exploit technology in order to reduce environmental pollution, CO2. emissions and waste. The current technologies and the provisioned evolution steps take into consideration these ...

Abstract The current circumstances on a worldwide level urgently impose to exploit technology in order to reduce environmental pollution, CO 2 emissions and waste. Moreover, current technologies and the provisioned evolution steps take into ...