The neo-society : realities of new socio-virtual paradigms

Chapter 14Thinking of space as a construct is by no means an easy feat. Transpose that concept from a real environment to a virtual space and blocks are not readily discernible. Th is is a world that has been immersed in digital otherness as far back as the early 1990s since the birth of the world wide web (WWW) proposal. Th ere exist two dichotomies: those pertaining to the younger generation and those to the older ones, where the former are aware of the digital fantastic worlds and the latter know the real haptic worlds, one where they can still remember that there was a time when a map was something one sought from a bookshop as against one that prompts one with the name of the street, the direction to turn, an occasional warning of a speed camera… In such a scenario, the older generation would be expected to know the physical world to a high degree and less that related to immersive technology; on the other hand the younger generation with their instant maps and online access would be expected to have a greater knowledge of their surroundings through the same access.peer-reviewe

Hydrodynamic object recognition using pressure sensing

Hydrodynamic sensing is instrumental to fish and some amphibians. It also represents, for underwater vehicles, an alternative way of sensing the fluid environment when visual and acoustic sensing are limited. To assess the effectiveness of hydrodynamic sensing and gain insight into its capabilities and limitations, we investigated the forward and inverse problem of detection and identification, using the hydrodynamic pressure in the neighbourhood, of a stationary obstacle described using a general shape representation. Based on conformal mapping and a general normalization procedure, our obstacle representation accounts for all specific features of progressive perceptual hydrodynamic imaging reported experimentally. Size, location and shape are encoded separately. The shape representation rests upon an asymptotic series which embodies the progressive character of hydrodynamic imaging through pressure sensing. A dynamic filtering method is used to invert noisy nonlinear pressure signals for the shape parameters. The results highlight the dependence of the sensitivity of hydrodynamic sensing not only on the relative distance to the disturbance but also its bearing

Fast LIDAR-based Road Detection Using Fully Convolutional Neural Networks

In this work, a deep learning approach has been developed to carry out road detection using only LIDAR data. Starting from an unstructured point cloud, top-view images encoding several basic statistics such as mean elevation and density are generated. By considering a top-view representation, road detection is reduced to a single-scale problem that can be addressed with a simple and fast fully convolutional neural network (FCN). The FCN is specifically designed for the task of pixel-wise semantic segmentation by combining a large receptive field with high-resolution feature maps. The proposed system achieved excellent performance and it is among the top-performing algorithms on the KITTI road benchmark. Its fast inference makes it particularly suitable for real-time applications