Space frame walls : facilitating positive development

This paper reports on progress in developing new design and measurement concepts, and translating these concepts into practical applications. This research addresses gaps in ‘best practice’ green building, and is aimed ultimately at replacing green buildings with sustainable urban environments. Building on the author’s previously articulated concepts of Design for Eco-services and Positive Development, this research will demonstrate how to eco-retrofit cities so that they reverse the negative impacts of past design and generate net positive ecological impacts, at no extra cost. In contrast to ‘restorative’ design,this means increasing ecological carrying capacity and natural and social capital through built environment design. Some exemplars for facilitating Positive development will be presented in this talk,such as Green Scaffolding for retrofits, and Green Space Walls for new construction. These structures have been designed to grow and change over time, be easily deconstructed, and entail little waste. The frames support mini-ecospheres that provide a wide range of ecosystem services and biodiversity habitats, as well as heating, cooling and ventilating. In combination, the modules serve to improve human and environmental health. Current work is focused on developing a range of such space frame walls, optimised through an innovative marriage of eco-logical design and virtual modelling.

Likelihood-maximising frameworks for enhanced in-car speech recognition

Speech recognition in car environments has been identified as a valuable means for reducing driver distraction when operating non-critical in-car systems. Likelihood-maximising (LIMA) frameworks optimise speech enhancement algorithms based on recognised state sequences rather than traditional signal-level criteria such as maximising signal-to-noise ratio. Previously presented LIMA frameworks require calibration utterances to generate optimised enhancement parameters which are used for all subsequent utterances. Sub-optimal recognition performance occurs in noise conditions which are significantly different from that present during the calibration session - a serious problem in rapidly changing noise environments. We propose a dialog-based design which allows regular optimisation iterations in order to track the changing noise conditions. Experiments using Mel-filterbank spectral subtraction are performed to determine the optimisation requirements for vehicular environments and show that minimal optimisation assists real-time operation with improved speech recognition accuracy. It is also shown that the proposed design is able to provide improved recognition performance over frameworks incorporating a calibration session.