In this paper, the expectation-maximization (EM) algorithm for Gaussian mixture modeling is improved via three statistical tests. The first test is a multivariate normality criterion based on the Mahalanobis distance of a sample measurement vector from a certain Gaussian component center. The first test is used in order to derive a decision whether to split a component into another two or not. The second test is a central tendency criterion based on the observation that multivariate kurtosis becomes large if the component to be split is a mixture of two or more underlying Gaussian sources with common centers. If the common center hypothesis is true, the component is split into two new components and their centers are initialized by the center of the (old) component candidate for splitting. Otherwise, the splitting is accomplished by a discriminant derived by the third test. This test is based on marginal cumulative distribution functions. Experimental results are presented against seven other EM variants both on artificially generated data-sets and real ones. The experimental results demonstrate that the proposed EM variant has an increased capability to find the underlying model, while maintaining a low execution time.

In this paper, we focus on interdisciplinary collaboration using intuitive virtual reality interfaces and building information models in the architecture, engineering, and construction industries. These systems have been a topic of research and development for the past ten years; however, there is still no widely open standard format, related software platform, or guidelines that are sufficiently mature; the complexity of such systems is very high. We review existing state-of-the-art interdisciplinary collaborative virtual reality systems, proposing solutions and standards. Thirteen state-of-the-art systems are reviewed and compared to illustrate emerging trends and insufficiencies. It is found that these systems differ significantly with respect to drawing capabilities, photorealism, construction simulation, and interdisciplinary communication. We discover trends in user interfaces that could be evolved to better standards, and provide future guidelines to developers. Combining the best aspects of existing systems, we provide a blueprint for an ideal system that combines the most advanced features for collaborative design.

Neurally adjusted ventilatory assist (NAVA) delivers airway pressure (P _aw ) in proportion to the electrical activity of the diaphragm (EAdi) using an adjustable proportionality constant (NAVA level, cm·H ₂ O/μV). During systematic increases in the NAVA level, feedback-controlled down-regulation of the EAdi results in a characteristic two-phased response in P _aw and tidal volume (Vt). The transition from the 1st to the 2nd response phase allows identification of adequate unloading of the respiratory muscles with NAVA (NAVA _AL ). We aimed to develop and validate a mathematical algorithm to identify NAVA _AL . P _aw , Vt, and EAdi were recorded while systematically increasing the NAVA level in 19 adult patients. In a multistep approach, inspiratory P _aw peaks were first identified by dividing the EAdi into inspiratory portions using Gaussian mixture modeling. Two polynomials were then fitted onto the curves of both P _aw peaks and Vt. The beginning of the P _aw and Vt plateaus, and thus NAVA _AL , was identified at the minimum of squared polynomial derivative and polynomial fitting errors. A graphical user interface was developed in the Matlab computing environment. Median NAVA _AL visually estimated by 18 independent physicians was 2.7 (range 0.4 to 5.8) cm·H ₂ O/μV and identified by our model was 2.6 (range 0.6 to 5.0) cm·H ₂ O/μV. NAVA _AL identified by our model was below the range of visually estimated NAVA _AL in two instances and was above in one instance. We conclude that our model identifies NAVA _AL in most instances with acceptable accuracy for application in clinical routine and research.