From Big Data to Big Displays: High-Performance Visualization at Blue Brain

Blue Brain has pushed high-performance visualization (HPV) to complement its HPC strategy since its inception in 2007. In 2011, this strategy has been accelerated to develop innovative visualization solutions through increased funding and strategic partnerships with other research institutions. We present the key elements of this HPV ecosystem, which integrates C++ visualization applications with novel collaborative display systems. We motivate how our strategy of transforming visualization engines into services enables a variety of use cases, not only for the integration with high-fidelity displays, but also to build service oriented architectures, to link into web applications and to provide remote services to Python applications.Comment: ISC 2017 Visualization at Scale worksho

Interpreting Multitouch Gestures

Interpreting multitouch interactions can be as simple as understanding code that handles these multitouches and this code\u27s associated actions. As more touch events are added to an input, inputs become more complex. There are multiple approaches to interpreting these inputs between users and touchscreens. Researchers in this field find answers to common problems and provide developers with tools that make interactions with multitouch devices easier to describe and incorporate into their systems. These tools are then used to create gestures through different approaches, specifically through demonstration and by declaration. In this paper, these researchers\u27 tools are described and compared

<em>SurfaceSlide</em>: A Multitouch Digital Pathology Platform

Background: Digital pathology provides a digital environment for the management and interpretation of pathological images and associated data. It is becoming increasing popular to use modern computer based tools and applications in pathological education, tissue based research and clinical diagnosis. Uptake of this new technology is stymied by its single user orientation and its prerequisite and cumbersome combination of mouse and keyboard for navigation and annotation.Methodology: In this study we developed SurfaceSlide, a dedicated viewing platform which enables the navigation and annotation of gigapixel digitised pathological images using fingertip touch. SurfaceSlide was developed using the Microsoft Surface, a 30 inch multitouch tabletop computing platform. SurfaceSlide users can perform direct panning and zooming operations on digitised slide images. These images are downloaded onto the Microsoft Surface platform from a remote server on-demand. Users can also draw annotations and key in texts using an on-screen virtual keyboard. We also developed a smart caching protocol which caches the surrounding regions of a field of view in multi-resolutions thus providing a smooth and vivid user experience and reducing the delay for image downloading from the internet. We compared the usability of SurfaceSlide against Aperio ImageScope and PathXL online viewer.Conclusion: SurfaceSlide is intuitive, fast and easy to use. SurfaceSlide represents the most direct, effective and intimate human–digital slide interaction experience. It is expected that SurfaceSlide will significantly enhance digital pathology tools and applications in education and clinical practice

Supporting collaborative work using interactive tabletop

PhD ThesisCollaborative working is a key of success for organisations. People work together around tables at work, home, school, and coffee shops. With the explosion of the internet and computer systems, there are a variety of tools to support collaboration in groups, such as groupware, and tools that support online meetings. However, in the case of co-located meetings and face-to-face situations, facial expressions, body language, and the verbal communications have significant influence on the group decision making process. Often people have a natural preference for traditional pen-and-paper-based decision support solutions in such situations. Thus, it is a challenge to implement tools that rely advanced technological interfaces, such as interactive multi-touch tabletops, to support collaborative work. This thesis proposes a novel tabletop application to support group work and investigates the effectiveness and usability of the proposed system. The requirements for the developed system are based on a review of previous literature and also on requirements elicited from potential users. The innovative aspect of our system is that it allows the use of personal devices that allow some level of privacy for the participants in the group work. We expect that the personal devices may contribute to the effectiveness of the use of tabletops to support collaborative work. We chose for the purpose of evaluation experiment the collaborative development of mind maps by groups, which has been investigated earlier as a representative form of collaborative work. Two controlled laboratory experiments were designed to examine the usability features and associated emotional attitudes for the tabletop mind map application in comparison with the conventional pen-and-paper approach in the context of collaborative work. The evaluation clearly indicates that the combination of the tabletop and personal devices support and encourage multiple people working collaboratively. The comparison of the associated emotional attitudes indicates that the interactive tabletop facilitates the active involvement of participants in the group decision making significantly more than the use of the pen-and-paper conditions. The work reported here contributes significantly to our understanding of the usability and effectiveness of interactive tabletop applications in the context of supporting of collaborative work.The Royal Thai governmen

Stay-At-Home Motor Rehabilitation: Optimizing Spatiotemporal Learning on Low-Cost Capacitive Sensor Arrays

Repeated, consistent, and precise gesture performance is a key part of recovery for stroke and other motor-impaired patients. Close professional supervision to these exercises is also essential to ensure proper neuromotor repair, which consumes a large amount of medical resources. Gesture recognition systems are emerging as stay-at-home solutions to this problem, but the best solutions are expensive, and the inexpensive solutions are not universal enough to tackle patient-to-patient variability. While many methods have been studied and implemented, the gesture recognition system designer does not have a strategy to effectively predict the right method to fit the needs of a patient. This thesis establishes such a strategy by outlining the strengths and weaknesses of several spatiotemporal learning architectures combined with deep learning, specifically when low-cost, low-resolution capacitive sensor arrays are used. This is done by testing the immunity and robustness of those architectures to the type of variability that is common among stroke patients, investigating select hyperparameters and their impact on the architectures’ training progressions, and comparing test performance in different applications and scenarios. The models analyzed here are trained on a mixture of high-quality, healthy gestures and personalized, imperfectly performed gestures using a low-cost recognition system

Development platform for elderly-oriented tabletop games

Tese de mestrado integrado. Engenharia Informática e Computação. Universidade do Porto. Faculdade de Engenharia. 201