Humans perceive the world with all our five senses: visuals, audio, smell, feel and taste. Crossmodal effects, i.e. the interaction of the senses, can have a major influence on how environments are perceived, even to the extent that large amounts of detail of one sense may be ignored when in the presence of other more dominant sensory inputs. If virtual environments are ever to be used as an authentic representation of reality then they need to achieve the same perceptual level of realism as the real scene they are attempting to represent. Real Virtuality environments (also known as there-reality) are true high-fidelity multi-sensory virtual environments which provide the same perceptual response from viewers as if they were actually present, or there in the real scene being portrayed. Unlike traditional virtual reality environments, Real Virtuality allows all five senses to be stimulated concurrently in a natural way. This talk gives an overview of Real Virtuality, describes how such a system may be achieved, and shows why Real Virtuality is a step-change from current virtual reality systems.

Fault tolerance is achieved by introducing redundancy. Redundancy can appear in different forms. It can be space redundancy (additional hardware), information redundancy (additional information helping to verify some data), and time redundancy (multiple sequential executions of the same code, and/or execution of additional verification code). Combinations of these redundancy types are also possible. Fault-tolerant system design based on space redundancy has a quite long tradition, and many generic architectures and concepts have been developed that have proven well in traditional safety-critical application fields like aerospace or (nuclear) power plants. However, the ongoing introduction of microelectronic systems for safety-relevant functions in cars is bringing up new problems that cannot be solved by simply applying the existing approaches. Two main reasons for this are (i) enormous cost pressure in the automotive industry and (ii) the huge amount of variants and configuration options for these systems. In my talk I will report on our experiences in this context. More specifically I will present a dual-core architecture that we have developed and optimized together with the automotive industry. I will use this example to touch upon the topics of error detection in hardware, memory protection, comparison of (very simple) fault-tolerant computer architectures, common cause faults, and fault-tolerance evaluation by means of fault injection.

Hopcroft's famous DFA minimization algorithm runs in O( n alpha log n ) time, where n is the number of states and alpha is the number of different labels. In 2008, an improvement to Hopcroft's algorithm was published that runs in O( m log n ) time, where m is the number of transitions. This is an improvement, because m is at most n alpha and is often much smaller. The improvement was later applied to the so-called Paige--Tarjan algorithm, yielding an O( m log n ) time algorithm for bisimulation minimization in the presence of transition labels. Another improvement, published in 2010, significantly simplified an existing O( m log n ) time algorithm for optimizing Markov chains. All these algorithms are block splitting algorithms. This talk presents these results and some other, small improvements that apply to block splitting algorithms.

A central task in biomedical image analysis is the segmentation and quantification of 3D image structures. A large variety of segmentation approaches is based on deformable models. Deformable models allow, for example, to incorporate a priori information about the image structures. This talk gives an overview of different types of deformable models such as active contour models, active shape models, active appearance models, and analytic parametric models. Moreover, this talk presents in more detail 3D parametric intensity models, which are utilized in different approaches for high-precision localization and quantification of 3D image structures. In these approaches, the intensity models are used in conjunction with an accurate, efficient, and robust model fitting scheme. These segmentation approaches have been successfully applied to different biomedical applications such as the localization of 3D anatomical point landmarks in 3D MR and 3D CT images, the quantification of vessels in 3D MRA and 3D CTA images, as well as the segmentation and quantification of cells and subcellular structures in 3D microscopy images.

•