Estimation of the surface area and volume of the retinal capillary basement membrane using the stereologic method of vertical sections

The modern stereologic method of vertical sections was applied to the retina as a means of generating unbiased estimates of three-dimensional structure. The method is illustrated with real data on the volume and surface area of the capillary basement membrane from the central retina of the rat. Novel methods of estimating the volume of retina sampled and of creating accurate vertical sections are described. The advantages of using stereologic methods to generate quantitative information on the three-dimensional structure of the retina are discussed and compared to those of previous quantitative methods that provide data on two-dimensional structure only.

Context-Aware Body Area Networks (CABAN) for interactive smart environments:Interference characterization

Body Area Networks are unique in that the large-scale mobility of users allows the network itself to travel across a diverse range of operating domains or even to enter new and unknown environments. This network mobility is unlike node mobility in that sensed changes in inter-network interference level may be used to identify opportunities for intelligent inter-networking, for example, by merging or splitting from other networks, thus providing an extra degree of freedom. This paper introduces the concept of context-aware bodynets for interactive environments using inter-network interference sensing. New ideas are explored at both the physical and link layers with an investigation based on a 'smart' office environment. A series of carefully controlled measurements of the mesh interconnectivity both within and between an ambulatory body area network and a stationary desk-based network were performed using 2.45 GHz nodes. Received signal strength and carrier to interference ratio time series for selected node to node links are presented. The results provide an insight into the potential interference between the mobile and static networks and highlight the possibility for automatic identification of network merging and splitting opportunities. © 2010 ACM.