Wolfgang Mueller-Wittig

EEG Based Stress Monitoring

Everyone experiences stress in life. Moderate stress can be beneficial to human, however, excessive stress is harmful to the health. To monitor stress, different methods can be used. In this work, an algorithm for stress level recognition from Electroencephalogram (EEG) is proposed. To validate the algorithm, an experiment is designed and carried out with 9 subjects. A Stroop colour-word test is used as a stressor to induce 4 levels of stress, and the EEG data are recorded during the experiment. Different feature combinations and classifiers are proposed and analyzed. By combining fractal dimension and statistical features and using Support Vector Machine (SVM) as the classifier, four levels of stress can be recognized with an average accuracy of 67.06%, three levels of stress can be recognized with an accuracy of 75.22%, and two levels of stress can be recognized with an accuracy of 85.71%. The algorithm is integrated into the system CogniMeter for stress state monitoring. Stress level of the user is visualized on the meter in real time. The system can be applied for stress monitoring of air traffic controllers, operators, etc.

Intrinsic computation of centroidal Voronoi tessellation (CVT) on meshes

Centroidal Voronoi tessellation (CVT) is a special type of Voronoi diagram such that the generating point of each Voronoi cell is also its center of mass. The CVT has broad applications in computer graphics, such as meshing, stippling, sampling, etc. The existing methods for computing CVTs on meshes either require a global parameterization or compute it in the restricted sense (that is, intersecting a 3D CVT with the surface). Therefore, these approaches often fail on models with complicated geometry and/or topology. This paper presents two intrinsic algorithms for computing CVT on triangle meshes. The first algorithm adopts the Lloyd framework, which iteratively moves the generator of each geodesic Voronoi diagram to its mass center. Based on the discrete exponential map, our method can efficiently compute the Riemannian center and the center of mass for any geodesic Voronoi diagram. The second algorithm uses the L-BFGS method to accelerate the intrinsic CVT computation. Thanks to the intrinsic feature, our methods are independent of the embedding space, and work well for models with arbitrary topology and complicated geometry, where the existing extrinsic approaches often fail. The promising experimental results show the advantages of our method. We propose two intrinsic methods for computing centroidal Voronoi tessellation (CVT) on triangle meshes.Thanks to their intrinsic nature, our methods compute CVT using metric only.Our results are independent of the embedding space.

Virtual factory - highly interactive visualisation for manufacturing

Funded by the Agency for Science Technology and Research - A*STAR

Mapping of BLASTP Algorithm onto GPU Clusters

Singapore, CAMTech is collaborating with a Singaporean research institute and two industry partners with the objective to improve electronics assembly processes. The goal of this project is to visualise the behaviour of an electronics assembly industry based on discrete event simulation. The traditional scenario - from the customer placing order for a product to delivery - goes through various phases including manufacturing the product. Several major electronics manufacturing stages can be addressed: fabrication, assembly, testing, and packing. Each of these stages accounts for set up, process, failure, and wait time periods. A delay in one process will accumulate over to the future delays. To simulate the discrete events a general-purpose simulation system has been employed. For modelling and visualisation CASUS (Computer Animation of Simulation Traces) system has been used and refined developed by Fraunhofer Institute for Computer Graphics (Fraunhofer-IGD).

CogniMeter: EEG-based Emotion, Mental Workload and Stress Visual Monitoring

Searching protein sequence database is a fundamental and often repeated task in computational biology and bioinformatics. However, the high computational cost and long runtime of many database scanning algorithms on sequential architectures heavily restrict their applications for large-scale protein databases, such as GenBank. The continuing exponential growth of sequence databases and the high rate of newly generated queries further deteriorate the situation and establish a strong requirement for time-efficient scalable database searching algorithms. In this paper, we demonstrate how GPU clusters, powered by the Compute Unified Device Architecture (CUDA), OpenMP, and MPI parallel programming models can be used as an efficient computational platform to accelerate the popular BLASTP algorithm. Compared to GPU-BLAST 1.0-2.2.24, our implementation achieves speedups up to 1.6 on a single GPU and up to 6.6 on the 6 GPUs of a Tesla S1060 quad-GPU computing system. The source code is available at: http://sites.google.com/site/liuweiguohome/mpicuda-blastp

Simple and efficient example-based texture synthesis using tiling and deformation

Real-time EEG (Electroencephalogram)-based users emotion, mental workload and stress monitoring is a new direction in research and development of human-machine interfaces. It has attracted recently more attention from the research community and industry as wireless portable EEG devices became easily available on the market. EEG-based technology has been applied in anesthesiology, psychology, serious games or even in marketing. In this work, we describe available real-time algorithms of emotion recognition, mental workload, and stress recognition from EEG and propose a novel interface Cogni Meter for the users mental state visual monitoring. The system can be used in real time to assess human current emotions, levels of mental workload and stress. Currently, it is applied to monitor the users emotional state, mental workload and stress in simulation scenarios or used as a tool to assess the subjects mental state in human factor study experiments.

Efficient and robust 3D line drawings using difference-of-Gaussian

In computer graphics, textures represent the detail appearance of the surface of objects, such as colors and patterns. Example-based texture synthesis is to construct a larger visual pattern from a small example texture image. In this paper, we present a simple and efficient method which can synthesize a large scale texture in real-time based on a given example texture by simply tiling and deforming the example texture. Different from most of the existing techniques, our method does not perform search operation and it can compute texture values at any given points (random access). In addition, our method requires small storage which is only to store one example texture. Our method is suitable for synthesizing irregular and near-stochastic texture. We also propose methods to efficiently synthesize and map 3D solid textures on 3D meshes.

Using the Chinese Calligraphy brush as a tangible user interface tool in virtual heritage scenarios

Line drawings are widely used for sketches, animations, and technical illustrations because they are effective and easy to draw. The existing computer-generated lines, such as suggestive contours, apparent ridges, and demarcating curves, adopt the two-pass framework: in the first pass, certain geometric features or properties are extracted or computed in the object space; then in the second pass, the line drawings are rendered by iterating each polygonal face or edge. It is known these approaches are very sensitive to the mesh quality, and usually require appropriate preprocessing operations (e.g. smoothing, remeshing, etc.) to the input meshes. This paper presents a simple yet robust approach to generate view-dependent line drawings for 3D models. Inspired by the image edge detector, we compute the difference-of-Gaussian of illumination on the 3D model. With moderate assumption, we show all the expensive computations can be done in the pre-computing stage. Our method naturally integrates object- and image-spaces in that we compute the geometric features in the object space and then adopt a simple fragment shader to render the lines in the image space. As a result, our algorithm is more efficient than the existing object-space approaches, since the lines are generated in a single pass without iterating the mesh edges/faces. Furthermore, our method is more flexible and robust than the existing algorithms in that it does not require the preprocessing on the input 3D models. Finally, the difference-of-Gaussian operator can be extended to the anisotropic setting guided by local geometric features. The promising experimental results on a wide range of real-world models demonstrate the effectiveness and robustness of our method.

Incorporating constraints into a Virtual Reality environment for intuitive and precise solid modelling

This paper first presents a virtual heritage scenario and then discusses the implementation of using a Chinese Calligraphy brush as a tangible user interface tool to fit the context of this scenario. It then closely looks at how the brush has been implemented with basic and extended functionalities to the man-machine interface of the presented virtual environment.

A model representation for solid modelling in a virtual reality environment

The absence of constraints is one of the major limitations in current Virtual Reality (VR) environments. Without constraints, it is difficult to perform precise 3D interactive manipulations in VR environments and precise solid modelling in VR environments cannot be guaranteed. In this paper, constraints are incorporated into the VR environment for intuitive and precise solid modelling. A hierarchically structured constraint-based data model is developed to support solid modelling in the VR environment. Solid modelling in the VR environment is precisely performed in an intuitive manner through constraint-based manipulations. Constraint-based manipulations are accompanied with automatic constraint recognition and precise constraint satisfaction to establish the hierarchically structured constraint-based data model and are realized by allowable motions for precise 3D interactions in the VR environment. The allowable motions are represented as a mathematical matrix for conveniently deriving allowable motions from constraints. A procedure-based degree-of-freedom incorporation approach for 3D constraint solving is presented for deriving the allowable motions. A rule-based constraint recognition engine is developed for both constraint-based manipulations and implicitly incorporating constraints into the VR environment. A prototype system has been implemented for precise solid modelling in an intuitive manner through constraint-based manipulations in the VR environment.