Visual computing for medical diagnosis and treatment

Diagnostic algorithms and efficient visualization techniques are of major importance for preoperative decisions, intra-operative imaging and image-guided surgery. Complex diagnostic decisions are characterized by a high information flow and fast decisions, requiring efficient and intuitive presentation of complex medical data and precision in the visualization. For intra-operative medical treatment, the pre-operative visualization results of the diagnostic systems have to be transferred to the patient on the operation room table. Via augmented reality, additional information of the hidden regions can be displayed virtually. This state-of-the-art report summarizes visual computing algorithms for medical diagnosis and treatment. After starting with direct volume rendering and tagged volume rendering as general techniques for visualizing anatomical structures, we go into more detail by focusing on the visualization of tissue and vessel structures. Afterwards, algorithms and techniques that are used for medical treatment in the context of image-guided surgery, intra-operative imaging and augmented reality are discussed and reviewed.     

VDES R-Mode: Vulnerability analysis and mitigation concepts

VDES R-Mode aims at providing a contingency maritime positioning and navigation system when the operation of Global Navigation Satellite Systems (GNSS) is disrupted. However, VDES R-Mode, similarly to GNSS, can itself also be subject to different types of attacks, such as jamming or spoofing. In this paper, we evaluate the vulnerabilities of VDES R-Mode and discuss the effectiveness and cost of different types of countermeasures. The outcome of this cost-benefit analysis is a recommendation to introduce authentication for the navigation messages of R-Mode using the Timed Efficient Stream Loss-Tolerant Authentication (TESLA) protocol.     

Making Parameter Dependencies of Time-Series Segmentation Visually Understandable

This work presents an approach to support the visual analysis of parameter dependencies of time-series segmentation. The goal is to help analysts understand which parameters have high influence and which segmentation properties are highly sensitive to parameter changes. Our approach first derives features from the segmentation output and then calculates correlations between the features and the parameters, more precisely, in parameter subranges to capture global and local dependencies. Dedicated overviews visualize the correlations to help users understand parameter impact and recognize distinct regions of influence in the parameter space. A detailed inspection of the segmentations is supported by means of visually emphasizing parameter ranges and segments participating in a dependency. This involves linking and highlighting, and also a special sorting mechanism that adjusts the visualization dynamically as users interactively explore individual dependencies. The approach is applied in the context of segmenting time series for activity recognition. Informal feedback from a domain expert suggests that our approach is a useful addition to the analyst's toolbox for time-series segmentation.     

Influence of viscosity index improver,molecular weight,and base oil on thickening,shear stability,and evaporation losses of multigrade oils

Some SAE 15W‐40 multigrade oils were formulated, using polymethacrylate (PMA) viscosity index improvers of different molecular weights, with a mixture of N 150 and N 500 as the base oil. Kinematic and dynamic viscosities, borderline pumping temperatures, pour points, shear stability, and evaporation losses were measured. The results confirm that viscometric properties as well as shear stability depend strongly on the polymer concentration and molecular weight of the VI improvers. However, evaporation losses are dominated by the volatility properties of the base oil.     

A study of viscometric properties of multigrade lubricants

The rheological behaviour of several multigrade oils formulated with four different polymer-type viscosity index (VI) improvers was experimentally studied at different shear rates ranging from 1000S⁻¹ to 35,000S¹ and at both VI reference temperatures of 40°C and 100°C. The investigation was an attempt to assess the effect of each polymer addition on the viscosity-temperature relationship, as related to its chemical type and to the shear rate applied. It was found that the viscosity loss with increasing shear rate is greater for hydrocarbon polymer types than for methacrylate, especially at low temperatures. It was also found that the viscosity index is affected by the shear rate as well as by the type and concentration of polymer additive; its significance is thus linked with the condition under which it is determined and cannot be used as an absolute criterion for the appraisal of multigrade oil performance.