Stakeholder engagement in early stage product-service system development for healthcare informatics

This paper presents the findings from four case studies on stakeholder engagement in new health information and communication technology (ICT) product-service system (PSS) development. The degree of connectivity between the new health ICT PSS and its intended operating environment has emerged to be an important contextual factor that may impact the decision of stakeholder engagement in the early stage development process. Along with the proposition of a four-level framework to guide stakeholder identification for new PSS development, three stakeholder engagement propositions that are based on the degree of connectivity are developed. Analysis has shown that there can be two types of connectivity: data and process. Moreover, each connectivity type can be characterized by how much the new PSS is connected with its environment: independent if there is no linkage, linked if it interfaces with, or incorporated if it is embedded into. Furthermore, depending upon whether and to what extent the PSS has data and process connectivity with its intended operating environment, the stakeholder engagement needs in early stage development vary. The propositions presented in this paper provide important directions for future work exploring PSS characterization and stakeholder engagement decision in early stage new PSS development in the healthcare industry. © 2013 PICMET.

An automatic speaker recognition system for intelligence applications

This paper presents an automatic speaker recognition system for intelligence applications. The system has to provide functionalities for a speaker skimming application in which databases of recorded conversations belonging to an ongoing investigation can be annotated and quickly browsed by an operator. The paper discusses the criticalities introduced by the characteristics of the audio signals under consideration - in particular background noise and channel/coding distortions - as well as the requirements and functionalities of the system under development. It is shown that the performance of state-of-the-art approaches degrades significantly in presence of moderately high background noise. Finally, a novel speaker recognizer based on phonetic features and an ensemble classifier is presented. Results show that the proposed approach improves performance on clean audio, and suggest that it can be employed towards improved real-world robustness. © EURASIP, 2009.

Introduction to the development of an information management system for soil mix technology using artificial neural networks

This paper introduces current work in collating data from different projects using soil mix technology and establishing trends using artificial neural networks (ANNs). Variation in unconfined compressive strength as a function of selected soil mix variables (e.g., initial soil water content and binder dosage) is observed through the data compiled from completed and on-going soil mixing projects around the world. The potential and feasibility of ANNs in developing predictive models, which take into account a large number of variables, is discussed. The main objective of the work is the management and effective utilization of salient variables and the development of predictive models useful for soil mix technology design. Based on the observed success in the predictions made, this paper suggests that neural network analysis for the prediction of properties of soil mix systems is feasible. © ASCE 2011.