A Framework for Quality-Driven Delivery in Distributed Multimedia Systems

In this paper, we propose a framework for Quality-Driven Delivery (QDD) in distributed multimedia environments. Quality-driven delivery refers to the capacity of a system to deliver documents, or more generally objects, while considering the users expectations in terms of non-functional requirements. For this QDD framework, we propose a model-driven approach where we focus on QoS information modeling and transformation. QoS information models and meta-models are used during different QoS activities for mapping requirements to system constraints, for exchanging QoS information, for checking compatibility between QoS information and more generally for making QoS decisions. We also investigate which model transformation operators have to be implemented in order to support some QoS activities such as QoS mapping

E-government evaluation: Reflections on three organisational case studies

The deployment of e-Government continues at a significant cost and pace in the worldwide public sector. An important area of research is that of the evaluation of e-Government. In this paper the authors report the findings from three interpretive in-depth organisational case studies that explore e-Government evaluation within UK public sector settings. The paper elicits insights to organisational and managerial aspects with the aim of improving knowledge and understanding of e- Government evaluation. The findings that are extrapolated from the analysis of the three case studies are classified and mapped onto a tentative e-Government evaluation framework and presented in terms lessons learnt. These aim to inform theory and improve e- Government evaluation practice. The paper concludes that e-Government evaluation is an under developed area and calls for senior executives to engage more with the e- Government agenda and commission e-Government evaluation exercises to improve evaluation practice

Economic Sizing of Distributed Energy Resources for Reliable Community Microgrids

Community microgrids offer many advantages for power distribution systems. When there is an extreme event happening, distribution systems can be seamlessly partitioned into several community microgrids for uninterrupted supply to the end-users. In order to guarantee the system reliability, distributed energy resources (DERs) should be sized for ensuring generation adequacy to cover unexpected events. This paper presents a comprehensive methodology for DERs selection in community microgrids, and an economic approach to meet the system reliability requirements. Algorithms of discrete time Fourier transform (DTFT) and particle swarm optimization (PSO) are employed to find the optimal solution. Uncertainties of load demand and renewable generation are taken into consideration. As part of the case study, a sensitivity analysis is carried out to show the renewable generation impact on DERs' capacity planning.Comment: 5 pages, 6 figures, 1 table, 2017 IEEE Power & Energy Society General Meeting. arXiv admin note: substantial text overlap with arXiv:1708.0102

Multi-scale Discriminant Saliency with Wavelet-based Hidden Markov Tree Modelling

The bottom-up saliency, an early stage of humans' visual attention, can be considered as a binary classification problem between centre and surround classes. Discriminant power of features for the classification is measured as mutual information between distributions of image features and corresponding classes . As the estimated discrepancy very much depends on considered scale level, multi-scale structure and discriminant power are integrated by employing discrete wavelet features and Hidden Markov Tree (HMT). With wavelet coefficients and Hidden Markov Tree parameters, quad-tree like label structures are constructed and utilized in maximum a posterior probability (MAP) of hidden class variables at corresponding dyadic sub-squares. Then, a saliency value for each square block at each scale level is computed with discriminant power principle. Finally, across multiple scales is integrated the final saliency map by an information maximization rule. Both standard quantitative tools such as NSS, LCC, AUC and qualitative assessments are used for evaluating the proposed multi-scale discriminant saliency (MDIS) method against the well-know information based approach AIM on its released image collection with eye-tracking data. Simulation results are presented and analysed to verify the validity of MDIS as well as point out its limitation for further research direction.Comment: arXiv admin note: substantial text overlap with arXiv:1301.396

Quality Function Deployment and Fuzzy TOPSIS Methods in Decision Support System for Internet Service Provider Selection

Internet Service Provider (ISP) is a company or business organization that provides access to intenet and services related for individual consumer or companies. There are many ISP in Indonesia recently, and they have almost the same product to offered. This problem makes internet service provider selection become a major issue. Decision support system can be used to recommend the best ISP company based on need. The aim of this research is to used Quality Function Deployment with Fuzzy TOPSIS sequentially to select the best ISP company as needed, and implemented in decision support system for internet service provider selection. Quality Function Deployment and Fuzzy TOPSIS methods used to evaluate, and then recommend the ISP company by ranked. Quality Function Deployment method used to find out customers requirements about internet network, the weighting of the criteria and the assessment of each ISP company. Fuzzy TOPSIS used to rank ISP company. These two methods produce consistent ratings when sensitivity analysis is performed for fuzzy and crisp value. These two methods make decision support system result can be trusted