Perceptual learning, roving and the unsupervised bias

Perceptual learning improves perception through training. Perceptual learning improves with most stimulus types but fails when . certain stimulus types are mixed during training (roving). This result is surprising because classical supervised and unsupervised neural network models can cope easily with roving conditions. What makes humans so inferior compared to these models? As experimental and conceptual work has shown, human perceptual learning is neither supervised nor unsupervised but reward-based learning. Reward-based learning suffers from the so-called unsupervised bias, i.e., to prevent synaptic " drift" , the . average reward has to be exactly estimated. However, this is impossible when two or more stimulus types with different rewards are presented during training (and the reward is estimated by a running average). For this reason, we propose no learning occurs in roving conditions. However, roving hinders perceptual learning only for combinations of similar stimulus types but not for dissimilar ones. In this latter case, we propose that a critic can estimate the reward for each stimulus type separately. One implication of our analysis is that the critic cannot be located in the visual system. © 2011 Elsevier Ltd.

An ecodesign model based on industry experience

With the increase in environmental legislation facing many industrial sectors organisations are now looking for ways to improve their environmental performance. To a large extent organisations have tended to concentrate on their operations inside the "factory gates" with little regard to the environmental performance of the products they produce. However, this is now changing and some organisations are beginning to take a close look at their products and their effects on the environment during its use phase as well as during the manufacture and disposal phases. At Cranfield University we have carried out a 3 year survey of US, Central European and UK companies claiming to practice ecodesign has been undertaken. Thirty electrical and electronic manufacturers were studied, some through in-depth observation of design programmes, most through semi-structured interviews. The survey and action research sought to understand the way in which these companies practised ecodesign and how they .had implemented ecodesign. A common pattern emerged from the data which suggests that companies successfully implementing ecodesign have many similar experiences. The resulting ecodesign model is presented and discussed, and the factors critical to successful implementation at various stages are explored. The factors cover a range of topics including design management, motivation, design tools, design phases, communication and the designers perspective.

A condition monitoring platform using COTS wireless sensor networks - Lessons and experience

Developments in Micro-Electro-Mechanical Systems (MEMS), wireless communication systems and ad-hoc networking have created new dimensions to improve asset management not only during the operational phase but throughout an asset's lifecycle based on using improved quality of information obtained with respect to two key aspects of an asset: its location and condition. In this paper, we present our experience as well as lessons learnt from building a prototype condition monitoring platform to demonstrate and to evaluate the use of COTS wireless sensor networks to develop a prototype condition monitoring platform with the aim of improving asset management by providing accurate and real-time information. © 2010 IEEE.

Inventors and entrepreneurs in academia: What types of skills and experience matter?

This paper aims to improve our understanding of the attributes of academic researchers that influence the capacity to contribute to technical advance, by adding to the pool of technological opportunities available to industry or engaging in the exploitation of entrepreneurial opportunities. We investigate a number of factors associated with the skills developed by academic researchers. We find that contributions to the pool of technological opportunities and exploitation of entrepreneurial opportunities involve different sets of skills and expertise of scientists. Our results show that the former is driven by academic scientists research excellence and discovery of earlier technological opportunities and the latter is driven by previous collaboration with industry partners, scientific breadth and experience of technological discovery. © 2011 Elsevier Ltd. All rights reserved.