Assessing technology-based spin-offs from university support units

This paper takes a regional studies approach to assess spin-offs from a university-based technology transfer network. We first detect the regional objectives, inputs and outputs needed to assess spin-offs from support programmes. We then provide evidence on regional mechanisms for firm creation. We analyse spin-offs created at Catalan universities and find that many efficient spin-offs have formal technology transfer agreements, and emerge from technology-oriented universities. We also find that higher innovation levels and experience from the parent university are associated with higher efficiency, which is positively related to future fundamental profitability. Finally, we propose regional policy making and research directions.

XML-based information management environment for PE technologies

Printed electronics is an emerging concept in electronics manufacturing and it is in very early development stage. The technology is not stable, design kits are not developed, and flows and Computer Aided Design (CAD) tools are not fixed yet. The European project TDK4PE addresses all this issues and this PFC has been realized on this context. The goal is to develop an XML-based information system for the collection and management of information from the technology and cell libraries developed in TDK4PE. This system will ease the treatment of that information for a later generation of specific Design Kits (DK) and the corresponding documentation. This work proposes a web application to generate technology files and design kits in a formatted way; it also proposes a structure for them and a database implementation for storing the needed information. The application will allow its users to redefine the structure of those files, as well as export and import XML files, between other formats.

Spontaneous Speech and Emotional Response modeling based on One-class classifier oriented to Alzheimer Disease diagnosis

The purpose of our project is to contribute to earlier diagnosis of AD and better estimates of its severity by using automatic analysis performed through new biomarkers extracted from non-invasive intelligent methods. The methods selected in this case are speech biomarkers oriented to Sponta-neous Speech and Emotional Response Analysis. Thus the main goal of the present work is feature search in Spontaneous Speech oriented to pre-clinical evaluation for the definition of test for AD diagnosis by One-class classifier. One-class classifi-cation problem differs from multi-class classifier in one essen-tial aspect. In one-class classification it is assumed that only information of one of the classes, the target class, is available. In this work we explore the problem of imbalanced datasets that is particularly crucial in applications where the goal is to maximize recognition of the minority class as in medical diag-nosis. The use of information about outlier and Fractal Dimen-sion features improves the system performance.