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Sound quality targets for new vehicles are currently specified by jury evaluation techniques based upon listening studies in a sound laboratory. However, jury testing is costly, time consuming and at present there are no methods to include customer expectations or brand requirements. This paper describes a neural computing approach that is being developed to generate knowledge and tools to enable objective measures of a product's sound to be converted into a prediction of the subjective impression of potential customers without carrying out the traditional jury evaluation tests.

As systems necessarily become more integrated and increasingly complex through market demands for more features, technical risks and therefore business risks increase. It becomes correspondingly harder to show that the properties desired of these Systems of Systems (SoS) actually hold under normal or abnormal operation. In particular, it is hard to detect emergent properties of a SoS because properties of individual systems are not necessarily compositional, especially during failure. This paper describes the objectives of a project addressing the problem of Dependable System of Systems and other related research in the field of Automotive Electronics. The capability being developed is based upon the scalable ‘Assumption-Commitment’[1] paradigm so that it can be applied to large and complex systems of systems.