The co-design methodologies on click router application system

Mémoire numérisé par la Direction des bibliothèques de l'Université de Montréal

S6: a Smart, Social and SDN-based Surveillance System for Smart-cities

Abstract In the last few years, Software Defined Networks (SDN) and Network Functions Virtualization (NFV) have been introduced in the Internet as a new way to design, deploy and manage networking services. Working together, they are able to consolidate and deliver the networking components using standard IT virtualization technologies not only on high-volume servers, but also in end user premises, Telco operator edge and access nodes thus allowing the emergence of new services. In this context, this paper presents a smart video surveillance platform designed to exploit the facilities offered by full SDN-NFV networks. This platform is based on free and open source software running on Provider Equipment (PE), so allowing function deployment simplification and management cost reduction

Techniques and Emerging Trends for State of the Art Equipment Maintenance Systems - A Bibliometric Analysis

The increasing interconnection of machines in industrial production on one hand, and the improved capabilities to store, retrieve, and analyze large amounts of data on the other, offer promising perspectives for maintaining production machines. Recently, predictive maintenance has gained increasing attention in the context of equipment maintenance systems. As opposed to other approaches, predictive maintenance relies on machine behavior models, which offer several advantages. In this highly interdisciplinary field, there is a lack of a literature review of relevant research fields and realization techniques. To obtain a comprehensive overview on the state of the art, large data sets of relevant literature need to be considered and, best case, be automatically partitioned into relevant research fields. A proper methodology to obtain such an overview is the bibliometric analysis method. In the presented work, we apply a bibliometric analysis to the field of equipment maintenance systems. To be more precise, we analyzed clusters of identified literature with the goal to obtain deeper insight into the related research fields. Moreover, cluster metrics reveal the importance of a single paper and an investigation of the temporal cluster development indicates the evolution of research topics. In this context, we introduce a new measure to compare results from different time periods in an appropriate way. In turn, among others, this simplifies the analysis of topics, with a vast amount of subtopics. Altogether, the obtained results particularly provide a comprehensive overview of established techniques and emerging trends for equipment maintenance systems

Application of mainstream object relational database to real time database applications in industrial automation

This thesis examines the proposition that because of recent huge increases in processing power, disk and memory capacities the commercial mainstream object relational databases may now be a viable option to replace dedicated real-time databases in industrial automation. The benefits are lower product cost, greater availability of trained manpower for development and maintenance and lower risks due to larger installed base and larger number of platforms supported. The issues considered in testing this proposition were performance, ability to mimic critical real-time database features, replication of the real-time database application development and administration tools and finally the low overhead high speed, real-time data compression facility available in real-time databases. An efficient yet simple real-time compression algorithm was developed for use with relational databases and benchmarked. Extensive comparative benchmarking has been done to convincingly prove the proposition. The results overwhelmingly show, that for a majority of industrial real-time database applications, the performance offered by a commercial object relational database on a current platform are more than adequate

University Information Technology Services' Advanced IT Facilities: The least every researcher needs to know

This is an archived document containing instructions for using IU's advanced IT facilities ca. 2003. A version of this document updated in 2011 is available from http://hdl.handle.net/2022/13620. Further versions are forthcoming.This document is designed to be read as a printed document, and designed to permit anyone at all familiar with computers and the Internet to start at the beginning, get a general overview of UITS' advanced IT facilities and what they offer, and then read the detailed portions of the document that are of interest. In many cases, examples are provided, as well as directions on how to download sample files. And in some cases there is information that one is best off really not learning – for example the process of logging into IU's IBM supercomputer the first time involves setup steps that should be followed, keystroke by keystroke, from the directions presented herein, and then promptly forgotten. This document is intended to be a starting point, not a comprehensive guide. As such it should get any reader off to a good start, but then point the reader in the direction of consulting staff and online resources that will permit the reader to get additional help and information as needed. Most of all, this document is provided for the convenience of researchers, who may peruse this information at their leisure. Our hope and expectation is that consultants in UITS will provide extensive help and programming assistance to IU researchers who wish to make use of these excellent IT facilities.The facilities described in this document were made possible in part through funding from Indiana University, the Indiana University Office of the Vice President for Information Technology, the State of Indiana, Shared University Research Grants from IBM, Inc., the National Science Foundation under Grant No. 0116050 and Grant CDA- 9601632, and from the Lilly Endowment through their support of the Indiana Genomics Initiative. The Indiana Genomics Initiative (INGEN) of Indiana University is supported in part by Lilly Endowment Inc

Management Methods for Complex Projects

This freely available project management eBook is the start of your journey in the field of complex project management methodologies, introducing you to some of the core methods, processes and tools as recognised by the project management discipline. This eBook lays out methodologies such as XP, Agile, Scrum, Kanban, Six Sigma, PRINCE2, Waterfall, PRiSM, Soft Systems Methodology as well as introducing Project Design as a method so you can leverage the right project management approach. This eBook will be of value to students, practitioners, and businesses in Australia and overseas seeking professional development in the field of project management methodologies

Traffic pattern prediction in cellular networks.

PhDIncreasing numbers of users together with a more use of high bit-rate services complicate radio resource management in 3G systems. In order to improve the system capacity and guarantee the QoS, a large amount of research had been carried out on radio resource management. One viable approach reported is to use semi-smart antennas to dynamically change the radiation pattern of target cells to reduce congestion. One key factor of the semi-smart antenna techniques is the algorithm to adjust the beam pattern to cooperatively control the size and shape of each radio cell. Methods described in the literature determine the optimum radiation patterns according to the current observed congestion. By using machine learning methods, it is possible to detect the upcoming change of the traffic patterns at an early stage and then carry out beamforming optimization to alleviate the reduction in network performance. Inspired from the research carried out in the vehicle mobility prediction field, this work learns the movement patterns of mobile users with three different learning models by analysing the movement patterns captured locally. Three different mobility models are introduced to mimic the real-life movement of mobile users and provide analysable data for learning. The simulation results shows that the error rates of predictions on the geographic distribution of mobile users are low and it is feasible to use the proposed learning models to predict future traffic patterns. Being able to predict these patterns mean that the optimized beam patterns could be calculated according to the predicted traffic patterns and loaded to the relevant base stations in advance