The Critical Challenges from International High-Tech and Computer-Related Crime at the Millennium

The automotive industry stands in front of a great challenge, to decrease its impact on the environment. One important part in succeeding with this is to decrease the structural weight of the body structure and by that the fuel consumption or the required battery power. Carbon fibre composites are by many seen as the only real option when traditional engineering materials are running out of potential for further weight reduction. However, the automotive industry lacks experience working with structural composites and the methods for high volume composite manufacturing are immature. The development of a composite automotive body structure, therefore, needs methods to support and guide the conceptual work to improve the financial and technical results. In this thesis a framework is presented which will provide guidelines for the conceptual phase of the development of an automotive body structure. The framework follows two main paths, one to strive for the ideal material diversity, which also defines an initial partition of the body structure based on the process and material selection. Secondly, a further analysis of the structures are made to evaluate if a more cost and weight efficient solution can be found by a more differential design and by that define the ideal part size. In the case and parameter studies performed, different carbon fibre composite material systems and processes are compared and evaluated. The results show that high performance material system with continuous fibres becomes both more cost and performance effective compared to industrialised discontinuous fibre composites. But also that cycle times, sometimes, are less important than a competitive feedstock cost for a manufacturing process. When further analysing the manufacturing design of the structures it is seen that further partition(s) can become cost effective if the size and complexity is large enough.      QC 20140527</p

A survey of machine learning techniques applied to self organizing cellular networks

In this paper, a survey of the literature of the past fifteen years involving Machine Learning (ML) algorithms applied to self organizing cellular networks is performed. In order for future networks to overcome the current limitations and address the issues of current cellular systems, it is clear that more intelligence needs to be deployed, so that a fully autonomous and flexible network can be enabled. This paper focuses on the learning perspective of Self Organizing Networks (SON) solutions and provides, not only an overview of the most common ML techniques encountered in cellular networks, but also manages to classify each paper in terms of its learning solution, while also giving some examples. The authors also classify each paper in terms of its self-organizing use-case and discuss how each proposed solution performed. In addition, a comparison between the most commonly found ML algorithms in terms of certain SON metrics is performed and general guidelines on when to choose each ML algorithm for each SON function are proposed. Lastly, this work also provides future research directions and new paradigms that the use of more robust and intelligent algorithms, together with data gathered by operators, can bring to the cellular networks domain and fully enable the concept of SON in the near future