On-demand transmission model using image-based rendering for remote visualization

Interactive distributed visualization is an emerging technology with numerous applications. However, many of the present approaches to interactive distributed visualization have limited performance since they are based on the traditional polygonal processing graphics pipeline. In contrast, image-based rendering uses multiple images of the scene instead of a 3D geometrical representation, and so has the key advantage that the final output is independent of the scene complexity, and depends on the desired final image resolution. These multiple images are referred to as the light field dataset. In this thesis we propose an on-demand solution for efficiently transmitting visualization data to remote users/clients. This is achieved through sending selected parts of the dataset based on the current client viewpoint, and is done instead of downloading a complete replica of the light field dataset to each client, or remotely sending a single rendered view back from a central server to the user each time the user updates their viewing parameters. The on-demand approach shows stable performance as the number of clients increases because the load on the server and the network traffic are reduced. Furthermore, detailed performance studies show that the proposed on-demand scheme outperforms the current local and remote solutions in terms of interactivity measured in frames per second. In addition, a performance study based on a theoretical cost model is presen ted. The model was able to provide realistic estimations of the results for different ranges of dataset sizes. Also, these results indicate that the model can be used as a predictive tool for estimating timings for the visualization process, enabling the improvement of the process and product quality, as well as the further develop ment of models for larger systems and datasets. In further discussing the strengths and weaknesses of each of the models, we see that to be able to run the system for larger dataset resolution involves a trade-off between generality of hardware (the server and network) and dataset resolution. Larger dataset resolution cannot achieve interactive frame rates on current COTS infrastructure. Finally, we conclude that the design of our 3D visualization system, based on image-based rendering coupled with an on-demand transmission model, has made a contribution to the field, and is a good basis for the future development of collaborative, distributed visualization systems

QoS-aware trust establishment for cloud federation

Cloud federation enables inter-layer resource exchanges among multiple, heterogeneous cloud service providers. This article proposes a Quality of Service (QoS) aware trust model for effective resource allocation in response to the various user requests within the Clouds4Coordination (C4C) federation system. This QoS mainly comprises of nine parameters combined into three categories: (i) node profile, (ii) reliability, and (iii) competence. Numerical values for these parameters are computed every ‘t’ seconds for each cloud provider. All values measured over an interval Δt are further processed by the proposed model to evaluate the utility associated with a provider (referred to as a discipline in the presented case study). The decision about interacting with a discipline in a collaborative project is based on this utility value. The systems architecture, evaluation methodology, proposed model, and experimental evaluation on a practical test bed is outlined. The proposed QoS-aware trust evaluation mechanism allows selection of the most useful (based on a utility value) providers. The proposed approach can be used to support federation of cloud services across a number of different application domains

On-demand transmission model for remote visualization using image-based rendering

Interactive distributed visualization is an emerging technology with numerous applications. However, many of the present approaches to interactive distributed visualization have limited performance because they are based on the traditional polygonal processing graphics pipeline. In contrast, image-based rendering uses multiple images of the scene instead of a three-dimensional geometrical representation, and so has the key advantage that the final output is independent of the scene complexity and depends on the desired final image resolution. Furthermore, the discrete nature of the light field dataset maps well to a hybrid solution, which can overcome the identified drawbacks. In this paper, we propose an on-demand solution for efficiently transmitting visualization data to remote users/clients. This is achieved through sending selected parts of the dataset based on the current client viewpoint, and is done instead of downloading a complete replica of the light field dataset to each client, or remotely sending a single rendered view back from a central server to the user each time the user updates their viewing parameters. The on-demand approach shows stable performance as the number of clients increases because the load on the server and the network traffic are reduced. Furthermore, detailed performance studies show that the proposed scheme outperforms the current solution in terms of interactivity measured in frames per second

The global NAFLD policy review and preparedness index: Are countries ready to address this silent public health challenge?

Background & aims: Non-alcoholic fatty liver disease (NAFLD) is a highly prevalent, yet largely underappreciated liver condition which is closely associated with obesity and metabolic disease. Despite affecting an estimated 1 in 4 adults globally, NAFLD is largely absent on national and global health agendas. Methods: We collected data from 102 countries, accounting for 86% of the world population, on NAFLD policies, guidelines, civil society engagement, clinical management, and epidemiologic data. A preparedness index was developed by coding questions into 6 domains (policies, guidelines, civil awareness, epidemiology and data, NAFLD detection, and NAFLD care management) and categorising the responses as high, medium, and low; a multiple correspondence analysis was then applied. Results: The highest scoring countries were India (42.7) and the United Kingdom (40.0), with 32 countries (31%) scoring zero out of 100. For 5 of the domains a minority of countries were categorised as high-level while the majority were categorised as low-level. No country had a national or sub-national strategy for NAFLD and <2% of the different strategies for related conditions included any mention of NAFLD. National NAFLD clinical guidelines were present in only 32 countries. Conclusions: Although NAFLD is a pressing public health problem, no country was found to be well prepared to address it. There is a pressing need for strategies to address NAFLD at national and global levels. Lay summary: Around a third of the countries scored a zero on the NAFLD policy preparedness index, with no country scoring over 50/100. Although NAFLD is a pressing public health problem, a comprehensive public health response is lacking in all 102 countries. Policies and strategies to address NAFLD at the national and global levels are urgently needed