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Success criteria:<br />
• Opinion of beneficiaries on the need for the generated applications, based on their<br />
knowledge about where the major unknowns are<br />
• Opinion of beneficiaries about the functionalities provided<br />
• Opinion of beneficiaries about optimal values of paramete<strong>rs</strong> involved.<br />
Objective 2: To develop the system architecture, to determine the functionalities of all<br />
building blocks, and to develop hardware (HW), communications, and software (SW)<br />
requirements.<br />
All details related to hardware, software, and communications have to be developed. Software<br />
details include issues like data mining, semantic web, and concept modelling. Details imply<br />
algorithm, procedures, and the operating system. The suggested DiaMus HW module will<br />
acquire various physical quantities using remote senso<strong>rs</strong>. The interpretation of acquired data<br />
is a demanding task, which requires a skilled expert.<br />
The main objective is to develop a system (software platform) for automatic signal<br />
processing, pattern recognition and final interpretation of processed data. SW platform is<br />
determined by the data acquisition process, mode of communication with HW module and<br />
methods used for signal processing and data interpretation. On that basis a suitable data<br />
model, GUI, report contents and views will be specified.<br />
All measurements will be performed in controlled laboratory conditions that will enable the<br />
construction of the knowledge database.<br />
Success criteria:<br />
• Potential speed of processing and communications,<br />
• Software and hardware architecture which permits easy expandability<br />
• Compatibility with existing platforms<br />
Objective 3: Adding intelligence to the system (for all six types of AW, for a relatively<br />
large number of application scenario, to define CSP and EAP paramete<strong>rs</strong>, and to<br />
develop related KEP procedures, appropriate CEA algorithms, and business-oriented<br />
RRG generato<strong>rs</strong>)<br />
For all use cases mentioned, specific algorithms, procedures, and operating system routines<br />
will be defined, to incorporate elements of artificial intelligence (AI), using the concept<br />
modelling (CM) approach (coauthored by the membe<strong>rs</strong> of this consortium, A Survey of<br />
Concept Modelling, submitted to IEEE Computer, 2009, and given in Appendix 4). For all of<br />
them, optimal values of related paramete<strong>rs</strong> will be determined and built into the algorithms,