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This paper presents a new haptic-based virtual environment system for diagnosis and rehabilitation of Traumatic Brain Injury (TBI) patients. By using the latest technologies, including Virtual Reality (VR), haptic force feedback and telecommunications, the system can work as an alternative to traditional labor intensive and expensive diagnosis and rehabilitation procedures for TBI patients. This paper also introduces a general approach to the design and prototyping of a haptic-based VR system for motor skill assessment and rehabilitation. A numerical model is presented to describe and record the motor skill assessment results and parameterize the rehabilitation training process. The prototype system demonstrates the potential for using advanced information and haptic-based VR technologies to build more effective and intelligent tools for healthcare. The specific techniques developed in this research can be used for motor-skill evaluation in clinical practice.

The general trend in character modeling is toward the personalization of models with higher levels of visual realism. This becomes possible with the development of commodity computation resources that are capable of processing massive data in parallel across multiple processors. On the other hand, there is always a trade-off between the quantity of the model features that are simulated and the plausibility of the visual realism because of the limited computation resources. Also, to keep the resources' to be used efficiently within the other modeling approaches such as skin reflectance, aging, animation, etc., one must consider the efficiency of the method being used in the simulation. In this paper, we present an efficient method to customize the size of a human body model to personalize it with industry standard parameters. One of the major contributions of this method is that it is possible to generate a range of different size body models by using anthropometry surveys. This process is not limited by data-driven mesh deformation but also adapts the skeleton and motion to keep the consistency between different body layers.

In modern biology, major efforts are undertaken to understand diseases, aging, evolution, and many other aspects of life. Biological networks play a key role in this research. Examples of such networks are protein-protein interaction (PPI) networks, gene regulatory networks, and metabolic pathways. Intuitive and comprehensible visualizations can significantly support the understanding and the analysis of such biological networks. Therefore, much research is conducted in the areas of bio-informatics and information visualization dealing with the problem of visualizing networks.In the course of evolution biological networks changed gradually. Therefore, the conserved core parts of the networks of different species can be aligned by matching the corresponding instances in the species, such as orthologous proteins or genes. Analyzing these alignments is of high relevance, as they convey significant information on protein function and organismal phenotype. For analysis purposes, the alignments have to be made transparent. Visualization methods are needed to display alignments in conjunction with the individual network structures. This is no standard network visualization problem, as we have to deal with more than one network and inter-network relationships.Several approaches to visualize aligned networks exist. In this survey we present and discuss these different approaches and report on their advantages and drawbacks. We draw conclusions on the applicability of the various approaches.