See-through techniques for referential awareness in collaborative virtual reality

Multi-user virtual reality systems enable natural collaboration in shared virtual worlds. Users can talk to each other, gesture and point into the virtual scenery as if it were real. As in reality, referring to objects by pointing results often in a situation whereon objects are occluded from the other users' viewpoints. While in reality this problem can only be solved by adapting the viewing position, specialized individual views of the shared virtual scene enable various other solutions. As one such solution we propose show-through techniques to make sure that the objects one is pointing to can always be seen by others. We first study the impact of such augmented viewing techniques on the spatial understanding of the scene, the rapidity of mutual information exchange as well as the proxemic behavior of users. To this end we conducted a user study in a co-located stereoscopic multi-user setup. Our study revealed advantages for show-through techniques in terms of comfort, user acceptance and compliance to social protocols while spatial understanding and mutual information exchange is retained. Motivated by these results we further analyze whether show-through techniques may also be beneficial in distributed virtual environments. We investigated a distributed setup for two users, each participant having its own display screen and a minimalist avatar representation for each participant. In such a configuration there is a lack of mutual awareness, which hinders the understanding of each other's pointing gestures and decreases the relevance of social protocols in terms of proxemic behavior. Nevertheless, we found that show-through techniques can improve collaborative interaction tasks even in such situations.     

Incremental preprocessing methods for use in BMC

Traditional incremental SAT solvers have achieved great success in the domain of Bounded Model Checking (BMC). Recently, modern solvers have introduced advanced preprocessing procedures that have allowed them to obtain high levels of performance. Unfortunately, many preprocessing techniques such as variable and (blocked) clause elimination cannot be directly used in an incremental manner. This work focuses on extending these techniques and Craig interpolation so that they can be used effectively together in incremental SAT solving (in the context of BMC). The techniques introduced here doubled the performance of our BMC solver on both SAT and UNSAT problems. For UNSAT problems, preprocessing had the added advantage that Craig interpolation was able to find the fixed point sooner, reducing the number of incremental SAT iterations. Furthermore, our ideas seem to perform better as the benchmarks become larger, and/or deeper, which is exactly when they are needed. Lastly, our methods can be integrated into other SAT based BMC tools to achieve similar speedups.     

A new feature extraction method for odour classification

A new method for real time classification of volatile chemical substance traces is presented. The method is based on electrochemical signals of an array of semiconductor gas sensors. In these sensor signals characteristic patterns of different substances are hidden. There are non-linear correlative relationships between the measured sensor signals and the chemical substances which are treated using two methods derived from statistical learning theory (Support Vector Machine - SVM, Maximum Likelihood Estimation - MLE) for the detection of the substance characteristics in the sensor signals. A key criterion for the presented pattern recognition is a newly developed type of features, which is specially adapted to the low frequency signals of semiconductor sensors. The presented features are based on the evaluation of the range of the transient response in the sensor signals in the frequency domain.To derive the new features, both real measurement data and synthetic generated signals were used. In the experiments the focus was set on the creation of reproducible sensor signals to get characteristic signal patterns. Synthetic signals were derived from a Gaussian Plume Model. With the new features, training data sets were calculated using the classification methods SVM and MLE. With these training data sets new sensor measurements may be assigned to the substances which are to be sought. The advantage of the presented method is that no feature reduction is needed and no loss of information occurs in the learning process.The classification results based on the new features have been compared with the classification based on a conventional method for feature extraction. It was proved that the recognition rate of the substances used with the new feature type is higher.The substance classification is primarily limited by the sensitivity of the semiconductor sensors, because sufficiently large sensor signals must have been provided to obtain appropriate substance patterns. At the present stage of development the method presented is suitable for the classification of substance groups, such as nitro aromatics or alcohols, but not for specific substances.     

On Two Continuum Armed Bandit Problems in High Dimensions

We consider the problem of continuum armed bandits where the arms are indexed by a compact subset of \(\mathbb {R}^{d}\). For large d, it is well known that mere smoothness assumptions on the reward functions lead to regret bounds that suffer from the curse of dimensionality. A typical way to tackle this in the literature has been to make further assumptions on the structure of reward functions. In this work we assume the reward functions to be intrinsically of low dimension k ? d and consider two models: (i) The reward functions depend on only an unknown subset of k coordinate variables and, (ii) a generalization of (i) where the reward functions depend on an unknown k dimensional subspace of \(\mathbb {R}^{d}\). By placing suitable assumptions on the smoothness of the rewards we derive randomized algorithms for both problems that achieve nearly optimal regret bounds in terms of the number of rounds n.     

A self-stabilizing algorithm for constructing weakly connected minimal dominating sets

This paper presents a new distributed self-stabilizing algorithm for the weakly connected minimal dominating set problem. It assumes a self-stabilizing algorithm to compute a breadth-first tree. Using an unfair distributed scheduler the algorithm stabilizes in at most O(nmA) moves, where A is the number of moves to construct a breadth-first tree. All previously known algorithms required an exponential number of moves.     

Review In Situ high-temperature optical microscopy

High-temperature optical microscopy is an essential in situ characterisation and monitoring technique with wide applications in different areas of materials science. The devices used include commercial available instruments, known as heating microscopes, and custom-made devices, usually called high-temperature processing microscopes or thermo-optical instruments. The different areas of applications of high-temperature optical microscopy are discussed on the basis of practical examples drawn from the literature. Besides the classical use of the technique to study the melting and softening behaviour of glass, slags, ashes and other silicate and ceramic materials, this review covers alternative applications, in particular the use of heating microscopes as optical dilatometers to investigate the sintering kinetics of powder compacts. In this regard, the advantages of the technique over conventional dilatometry are emphasised. A variety of custom-made devices is described, developed to investigate particular problems, such as delamination and curling of laminate composites during densification, cosintering of multilayer metal-ceramic and ceramic-ceramic systems, and wetting behaviour of liquid phases on rigid substrates. As a particular example of such a custom-made equipment, a novel, multi-purpose high-temperature processing microscope is described, and its application potential, which is well beyond that of commercial devices, is outlined. This instrument is unique in that it combines both vertical and horizontal sample observation capability, as well as the possibility to investigate samples of relatively large sizes (65 mm³), i.e. about 10 times larger than those suitable for commercial heating microscopes.     

Interactive blood damage analysis for ventricular assist devices

Ventricular Assist Devices (VADs) support the heart in its vital task of maintaining circulation in the human body when the heart alone is not able to maintain a sufficient flow rate due to illness or degenerative diseases. However, the engineering of these devices is a highly demanding task. Advanced modeling methods and computer simulations allow the investigation of the fluid flow inside such a device and in particular of potential blood damage. In this paper we present a set of visualization methods which have been designed to specifically support the analysis of a tensor-based blood damage prediction model. This model is based on the tracing of particles through the VAD, for each of which the cumulative blood damage can be computed. The model's tensor output approximates a single blood cell's deformation in the flow field. The tensor and derived scalar data are subsequently visualized using techniques based on icons, particle visualization, and function plotting. All these techniques are accessible through a Virtual Reality-based user interface, which features not only stereoscopic rendering but also natural interaction with the complex three-dimensional data. To illustrate the effectiveness of these visualization methods, we present the results of an analysis session that was performed by domain experts for a specific data set for the MicroMed DeBakey VAD.     

Anisotropic noise samples

We present a practical approach to generate stochastic anisotropic samples with Poisson-disk characteristic over a two-dimensional domain. In contrast to isotropic samples, we understand anisotropic samples as non-overlapping ellipses whose size and density match a given anisotropic metric. Anisotropic noise samples are useful for many visualization and graphics applications. The spot samples can be used as input for texture generation, e.g., line integral convolution (LIC), but can also be used directly for visualization. The definition of the spot samples using a metric tensor makes them especially suitable for the visualization of tensor fields that can be translated into a metric. Our work combines ideas from sampling theory and mesh generation. To generate these samples with the desired properties we construct a first set of non-overlapping ellipses whose distribution closely matches the underlying metric. This set of samples is used as input for a generalized anisotropic Lloyd relaxation to distribute noise samples more evenly. Instead of computing the Voronoi tessellation explicitly, we introduce a discrete approach which combines the Voronoi cell and centroid computation in one step. Our method supports automatic packing of the elliptical samples, resulting in textures similar to those generated by anisotropic reaction-diffusion methods. We use Fourier analysis tools for quality measurement of uniformly distributed samples. The resulting samples have nice sampling properties, for example, they satisfy a blue noise property where low frequencies in the power spectrum are reduced to a minimum.