Enterprise networks: The re-engineering of complex software systems

Today technology design can no longer be understood as a design process on a green site. Design and implementation of new technology are always dependent on existing technology and the way it is used by people. In this respect Software-Engineering has also changed to the characteristics of normal technology design taking into account existing computer systems. Experiences show that the conditions and needs of such Software-Reengineering projects are highly complex and differ in their special characteristics ranging from aspects of quality of existing system documentation to organizational structures of the computer departments concerned. The Task-Artifact Cycle presented here gives a suitable reengineering approach emphasizing both analysis and design in Software-Reengineering.     

Mixtures of (N2)1−x : (O2) x at High Pressures and Low Temperatures

We performed Raman measurements at 18 K and pressures up to 25 GPa in order to construct a tentative phase diagram of the (N ₂)_1–x :(O ₂)_x –system at low temperatures. We varied the composition of the mixed system over the whole concentration range. Here we focused on the systems with high nitrogen concentration and pressures above 2 GPa. It is known that at room temperature oxygen is highly solvable in the –phase of N ₂. The experimental results show that oxygen suppresses the disorder–order transition –N ₂.     

Hierarchical vibrations for part-based recognition of complex objects

We propose a technique for the recognition and segmentation of complex shapes in 2D images using a hierarchy of finite element vibration modes in an evolutionary shape search. The different levels of the shape hierarchy can influence each other, which can be exploited in top-down part-based image analysis. Our method overcomes drawbacks of existing structural approaches, which cannot uniformly encode shape variation and co-variation, or rely on training. We present results demonstrating that by utilizing a quality-of-fit function the model explicitly recognizes missing parts of a complex shape, thus allowing for categorization between shape classes.     

iView: a feature clustering framework for suggesting informative views in volume visualization

The unguided visual exploration of volumetric data can be both a challenging and a time-consuming undertaking. Identifying a set of favorable vantage points at which to start exploratory expeditions can greatly reduce this effort and can also ensure that no important structures are being missed. Recent research efforts have focused on entropy-based viewpoint selection criteria that depend on scalar values describing the structures of interest. In contrast, we propose a viewpoint suggestion pipeline that is based on feature-clustering in high-dimensional space. We use gradient/normal variation as a metric to identify interesting local events and then cluster these via k-means to detect important salient composite features. Next, we compute the maximum possible exposure of these composite feature for different viewpoints and calculate a 2D entropy map parameterized in longitude and latitude to point out promising view orientations. Superimposed onto an interactive track-ball interface, users can then directly use this entropy map to quickly navigate to potentially interesting viewpoints where visibility-based transfer functions can be employed to generate volume renderings that minimize occlusions. To give full exploration freedom to the user, the entropy map is updated on the fly whenever a view has been selected, pointing to new and promising but so far unseen view directions. Alternatively, our system can also use a set-cover optimization algorithm to provide a minimal set of views needed to observe all features. The views so generated could then be saved into a list for further inspection or into a gallery for a summary presentation.     

Natural perspective projections for head-mounted displays

The display units integrated in today's head-mounted displays (HMDs) provide only a limited field of view (FOV) to the virtual world. In order to present an undistorted view to the virtual environment (VE), the perspective projection used to render the VE has to be adjusted to the limitations caused by the HMD characteristics. In particular, the geometric field of view (GFOV), which defines the virtual aperture angle used for rendering of the 3D scene, is set up according to the display field of view (DFOV). A discrepancy between these two fields of view distorts the geometry of the VE in a way that either minifies or magnifies the imagery displayed to the user. It has been shown that this distortion has the potential to affect a user's perception of the virtual space, sense of presence, and performance on visual search tasks. In this paper, we analyze the user's perception of a VE displayed in a HMD, which is rendered with different GFOVs. We introduce a psychophysical calibration method to determine the HMD's actual field of view, which may vary from the nominal values specified by the manufacturer. Furthermore, we conducted two experiments to identify perspective projections for HMDs, which are identified as natural by subjects--even if these perspectives deviate from the perspectives that are inherently defined by the DFOV. In the first experiment, subjects had to adjust the GFOV for a rendered virtual laboratory such that their perception of the virtual replica matched the perception of the real laboratory, which they saw before the virtual one. In the second experiment, we displayed the same virtual laboratory, but restricted the viewing condition in the real world to simulate the limited viewing condition in a HMD environment. We found that subjects evaluate a GFOV as natural when it is larger than the actual DFOV of the HMD--in some cases up to 50 percent--even when subjects viewed the real space with a limited field of view.     

Dynamical information fusion of heterogeneous sensors for 3D tracking using particle swarm optimization

This paper presents a new method for three dimensional object tracking by fusing information from stereo vision and stereo audio. From the audio data, directional information about an object is extracted by the Generalized Cross Correlation (GCC) and the object’s position in the video data is detected using the Continuously Adaptive Mean shift (CAMshift) method. The obtained localization estimates combined with confidence measurements are then fused to track an object utilizing Particle Swarm Optimization (PSO). In our approach the particles move in the 3D space and iteratively evaluate their current position with regard to the localization estimates of the audio and video module and their confidences, which facilitates the direct determination of the object’s three dimensional position. This technique has low computational complexity and its tracking performance is independent of any kind of model, statistics, or assumptions, contrary to classical methods. The introduction of confidence measurements further increases the robustness and reliability of the entire tracking system and allows an adaptive and dynamical information fusion of heterogenous sensor information.     

Coping with information overload in email communication: Evaluation of a training intervention

The present paper introduces three facets of information overload in email communication: A large amount of incoming information, inefficient workflow, and deficient communication quality. In order to cope with these facets of information overload, a training intervention was developed and evaluated. Data were collected from 90 employees on several evaluation levels within a longitudinal evaluation design (one pretest double posttest design). The results reveal that the training contributed to an increase in knowledge and media competencies. We also found evidence for a transfer of training contents to the workplace. Finally, strain diminished on several dimensions. In particular, problems with media usage and work impairment decline significantly, an effect that was stronger for those participants who face a large amount of email at their workplaces.     

Service Composition Issues in Pervasive Computing

Providing new services by combining existing ones—or service composition—is an idea pervading pervasive computing. Pervasive computing technologies seek to concurrently exhibit context awareness, manage contingencies, leverage device heterogeneity, and empower users. These four goals prompt service-composition-mechanism design requirements that are unique to pervasive computing. This article catalogs service composition mechanisms and describes their variation points, which indicate how well the resulting compositions meet the four goals.