and tracked. In the second stage the facial features are extracted for further processing. In last stage the most crucial parameter is monitored which is eye’s status. In the last stage it is determined that whether the eyes are closed or open. On the basis of this result the warning is issued to the driver

number of membrane-bound, ultrastructurally well-preserved fragments. In the second stage these apoptotic bodies are shed from epithelial-lined surfaces or are taken up by other cells, where they undergo a series of changes resembling in vitro autolysis within phagosomes, and are rapidly degraded

Abstract. Unlike fixed combining rules, the trainable combiner is appli-cable to ensembles of diverse base classifier architectures with incompa-rable outputs. The trainable combiner, however, requires the additional step of deriving a second-stage training dataset from the base classifier outputs

string breaking to produce massive states requiring further decay. This sequence terminates at the second stage of fragmentation where only relatively “light ” heavy meson systems are formed. Thus we naturally account for the phenomenologically required factor of (1 −z). We also predict that the ratio

-term, aesthetic and functional prosthetic restora-tions. This refers particularly to the attached gingival around the implants –periimplantitis develops quickly due to its absence. Different techniques can be used for implants ’ second stage surgery time – uncovering with puncher, with ceramic bur

. In the first stage the histogram gridis reduced to a onedimensional polar histogramthat is constructed around the robot's momentary location. Each sector in the polar histogramcontains a value representing the polar obstacle densityin that direction. In the second stage, the algorithm selects the most

In this secondary analysis of videotape data, we describe birth talk demonstrated by caregivers to women during the second stage of labor. Birth talk is a distinctive verbal register or a set of linguistic features that are used with particular behaviors during specific situations, has a particular

-output behavior. Many different programs and algorithms have been integrated into SIS, allowing the user to choose among a variety of techniques at each stage of the process. It is built on top of MISII [5] and includes all (combinational) optimization techniques therein as well as many enhancements. SIS serves

in inferior temporal cortex that are used for object recognition in primate vision. Features are efficiently detected through a staged filtering approach that identifies stable points in scale space. Image keys are created that allow for local geometric deformations by representing blurred image gradients

This paper describes our joint recommendations for how to run second stage ecological regressions, and provides detailed analyses to back up our claims