Christian Nowke, M. Sc.|
Phone: +49 241 80 24914
Fax: +49 241 80 22134
Virtual Reality (VR) has been an active field of research for several decades, with 3D interaction and 3D User Interfaces (UIs) as important sub-disciplines. However, the development of 3D interaction techniques and in particular combining several of them to construct complex and usable 3D UIs remains challenging, especially in a VR context. In addition, there is currently only limited reusable software for implementing such techniques in comparison to traditional 2D UIs. To overcome this issue, we present ViSTA Widgets, a software framework for creating 3D UIs for immersive virtual environments. It extends the ViSTA VR framework by providing functionality to create multi-device, multi-focus-strategy interaction building blocks and means to easily combine them into complex 3D UIs. This is realized by introducing a device abstraction layer along sophisticated focus management and functionality to create novel 3D interaction techniques and 3D widgets. We present the framework and illustrate its effectiveness with code and application examples accompanied by performance evaluations.
Interactive visual data analysis is a well-established class of methods to gather knowledge from raw and complex data. A broad variety of examples can be found in literature presenting its applicability in various ways and different scientific domains. However, fully fledged solutions for visual analysis addressing learning analytics are still rare. Therefore, this paper will discuss visual and interactive data analysis for learning analytics by presenting best practices followed by a discussion of a general architecture combining interactive visualization employing the Information Seeking Mantra in conjunction with the paradigm of coordinated multiple views. Finally, by presenting a use case for ubiquitous learning analytics its applicability will be demonstrated with the focus on temporal and spatial relation of learning data. The data is gathered from a ubiquitous learning scenario offering information for students to identify learning partners and provides information to teachers enabling the adaption of their learning material.
Modeling large-scale spiking neural networks showing realistic biological behavior in their dynamics is a complex and tedious task. Since these networks consist of millions of interconnected neurons, their simulation produces an immense amount of data. In recent years it has become possible to simulate even larger networks. However, solutions to assist researchers in understanding the simulation's complex emergent behavior by means of visualization are still lacking. While developing tools to partially fill this gap, we encountered the challenge to integrate these tools easily into the neuroscientists' daily workflow. To understand what makes this so challenging, we looked into the workflows of our collaborators and analyzed how they use the visualizations to solve their daily problems. We identified two major issues: first, the analysis process can rapidly change focus which requires to switch the visualization tool that assists in the current problem domain. Second, because of the heterogeneous data that results from simulations, researchers want to relate data to investigate these effectively. Since a monolithic application model, processing and visualizing all data modalities and reflecting all combinations of possible workflows in a holistic way, is most likely impossible to develop and to maintain, a software architecture that offers specialized visualization tools that run simultaneously and can be linked together to reflect the current workflow, is a more feasible approach. To this end, we have developed a software architecture that allows neuroscientists to integrate visualization tools more closely into the modeling tasks. In addition, it forms the basis for semantic linking of different visualizations to reflect the current workflow. In this paper, we present this architecture and substantiate the usefulness of our approach by common use cases we encountered in our collaborative work.
The Human Brain Project is one of the largest scientific initiatives dedicated to the research of the human brain worldwide. Over 80 research groups from a broad variety of scientific areas, such as neuroscience, simulation science, high performance computing, robotics, and visualization work together in this European research initiative. This work at hand will identify certain chances and challenges for cognitive systems engineering resulting from the HBP research activities. Beside the main goal of the HBP gathering deeper insights into the structure and function of the human brain, cognitive system research can directly benefit from the creation of cognitive architectures, the simulation of neural networks, and the application of these in context of (neuro-)robotics. Nevertheless, challenges arise regarding the utilization and transformation of these research results for cognitive systems, which will be discussed in this paper. Tools necessary to cope with these challenges are visualization techniques helping to understand and gain insights into complex data. Therefore, this paper presents a set of visualization techniques developed at the Virtual Reality Group at the RWTH Aachen University.
The aim of computational neuroscience is to gain insight into the dynamics and functionality of the nervous system by means of modeling and simulation. Current research leverages the power of High Performance Computing facilities to enable multi-scale simulations capturing both low-level neural activity and large-scale interactions between brain regions. In this paper, we describe an interactive analysis tool that enables neuroscientists to explore data from such simulations. One of the driving challenges behind this work is the integration of macroscopic data at the level of brain regions with microscopic simulation results, such as the activity of individual neurons. While researchers validate their findings mainly by visualizing these data in a non-interactive fashion, state-of-the-art visualizations, tailored to the scientific question yet sufficiently general to accommodate different types of models, enable such analyses to be performed more efficiently. This work describes several visualization designs, conceived in close collaboration with domain experts, for the analysis of network models. We primarily focus on the exploration of neural activity data, inspecting connectivity of brain regions and populations, and visualizing activity flux across regions. We demonstrate the effectiveness of our approach in a case study conducted with domain experts.
In recent years, the simulation of spiking neural networks has advanced in terms of both simulation technology and knowledge about neuroanatomy. Due to these advances, it is now possible to run simulations at the brain scale, which produce an unprecedented amount of data to be analyzed and understood by researchers. As aid, VisNEST, a tool for the combined visualization of simulated spike data and anatomy was developed.
Modeling and simulating a brain’s connectivity produces an immense
amount of data, which has to be analyzed in a timely fashion.
Neuroscientists are currently modeling parts of the brain –
e.g. the visual cortex – of primates like Macaque monkeys in order
to deduce functionality and transfer newly gained insights to
the human brain. Current research leverages the power of today’s
High Performance Computing (HPC) machines in order to simulate
low level neural activity. In this paper, we describe an interactive
analysis tool that enables neuroscientists to visualize the resulting
simulation output. One of the driving challenges behind our development
is the integration of macroscopic data, e.g. brain areas, with
microscopic simulation results, e.g. spiking behavior of individual neurons.