Real world applications of a fuzzy decision model based on relationships between goals for finance services and sequencing for the assembly of cars

Real world applications of a decision model of relationships between goals based on fuzzy relations are presented. In contrast to other approaches the relationships between decision goals or criteria for each decision situation are represented and calculated explicitly. The application fields are decision making for financial services and optimization of production sequences in car manufacturing.     

Brain-computer communication: motivation, aim, and impact of exploring a virtual apartment.

The step away from a synchronized or cue-based brain-computer interface (BCI) and from laboratory conditions towards real world applications is very important and crucial in BCI research. This work shows that ten naive subjects can be trained in a synchronous paradigm within three sessions to navigate freely through a virtual apartment, whereby at every junction the subjects could decide by their own, how they wanted to explore the virtual environment (VE). This virtual apartment was designed similar to a real world application, with a goal-oriented task, a high mental workload, and a variable decision period for the subject. All subjects were able to perform long and stable motor imagery over a minimum time of 2 s. Using only three electroencephalogram (EEG) channels to analyze these imaginations, we were able to convert them into navigation commands. Additionally, it could be demonstrated that motivation is a very crucial factor in BCI research; motivated subjects perform much better than unmotivated ones.     

Morphologic image processing operators: reduction of partial volume effects for improved 3D visualization of CT data

AIM: The quality of segmentation and three-dimensional reconstruction of anatomical structures in tomographic slices is often impaired by disturbances due to partial volume effects (PVE). The potential for artefact reduction by use of the morphological image processing operators (MO) erosion and dilation is investigated. DESIGN: The CT examinations of 31 patients with pathological alterations in lung or brain were segmented using automatic region growing and the MO were applied in a different number of iterations. The processed regions were 3D-reconstructed (shaded surface display, MIP, volume rendering) and the occurrence of PVE-related artefacts using the signal-to-background ratio (SBR) prior to and after MO application was compared. RESULTS: For all patients under review, the artefacts caused by PVE were significantly reduced by erosion (lung: mean SBRpre = 1.67, SBRpost = 4.83; brain: SBRpre = 1.06, SBRpost = 1.29) even with only a small number of iterations. Region dilation was applied to integrate further structures (e.g. at tumor borders) into a configurable neighbourhood for segmentation and quantitative analysis. CONCLUSIONS: The MO represent an efficient approach for the reduction of PVE artefacts in 3D-CT reconstructions and allow optimized visualization of individual objects.     

A neuron membrane mesh representation for visualization of electrophysiological simulations

We present a process to automatically generate three-dimensional mesh representations of the complex, arborized cell membrane surface of cortical neurons (the principal information processing cells of the brain) from nonuniform morphological measurements. Starting from manually sampled morphological points (3D points and diameters) from neurons in a brain slice preparation, we construct a polygonal mesh representation that realistically represents the continuous membrane surface, closely matching the original experimental data. A mapping between the original morphological points and the newly generated mesh enables simulations of electrophysiolgical activity to be visualized on this new membrane representation. We compare the new mesh representation with the state of the art and present a series of use cases and applications of this technique to visualize simulations of single neurons and networks of multiple neurons.     

A heuristic 0–1 integer programming method

Summary In this paper, we present a novel relaxation method for the integer programming problem:Ax=b,A {–1,0,+1}^m×N,x {0,1}^N, N>m. The report includes experimental results of problems with up tom=100 equations andN=400 variables.