A Virtual Environment for Remote Testing of Complex Systems

Complex systems, realized by integration of several components or subsystems, pose specific problems to simulation environments. It is, in fact, desirable to simulate the complex system altogether, and not component by component, since the operation of the single part depends on the surrounding system and an early verification can prevent damages and save time for modifications. The availability of detailed and validated models of the single parts is therefore critical. This task may be difficult to achieve. In fact, in industrial applications, where a system can be a mix of different devices produced by different manufacturers, the physical device may not be accessible to the modeler for proprietary or safety concerns. Starting from this point, the idea of creating a virtual environment able to test the real single component remotely, employing simulators with remote signal processing capability, has been considered. In this paper a methodology for remote model validation is presented. The effectiveness of the approach is experimentally verified locally and remotely. For the remote testing, in particular, the physical device under test is located at the Politecnico di Milano, Italy, and the Virtual Test Bed model is located at the University of South Carolina

Web and knowledge-based decision support system for measurement uncertainty evaluation

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel UniversityIn metrology, measurement uncertainty is understood as a range in which the true value of the measurement is likely to fall in. The recent years have seen a rapid development in evaluation of measurement uncertainty. ISO Guide to the Expression of Uncertainty in Measurement (GUM 1995) is the primary guiding document for measurement uncertainty. More recently, the Supplement 1 to the "Guide to the expression of uncertainty in measurement" – Propagation of distributions using a Monte Carlo method (GUM SP1) was published in November 2008. A number of software tools for measurement uncertainty have been developed and made available based on these two documents. The current software tools are mainly desktop applications utilising numeric computation with limited mathematical model handling capacity. A novel and generic web-based application, web-based Knowledge-Based Decision Support System (KB-DSS), has been proposed and developed in this research for measurement uncertainty evaluation. A Model-View-Controller architecture pattern is used for the proposed system. Under this general architecture, a web-based KB-DSS is developed based on an integration of the Expert System and Decision Support System approach. In the proposed uncertainty evaluation system, three knowledge bases as sub-systems are developed to implement the evaluation for measurement uncertainty. The first sub-system, the Measurement Modelling Knowledge Base (MMKB), assists the user in establishing the appropriate mathematical model for the measurand, a critical process for uncertainty evaluation. The second sub-system, GUM Framework Knowledge Base, carries out the uncertainty evaluation process based on the GUM Uncertainty Framework using symbolic computation, whilst the third sub-system, GUM SP1 MCM Framework Knowledge Base, conducts the uncertainty calculation according to the GUM SP1 Framework numerically based on Monte Carlo Method. The design and implementation of the proposed system and sub-systems are discussed in the thesis, supported by elaboration of the implementation steps and examples. Discussions and justifications on the technologies and approaches used for the sub-systems and their components are also presented. These include Drools, Oracle database, Java, JSP, Java Transfer Object, AJAX and Matlab. The proposed web-based KB-DSS has been evaluated through case studies and the performance of the system has been validated by the example results. As an established methodology and practical tool, the research will make valuable contributions to the field of measurement uncertainty evaluation.Brunel Universit

Architectural Improvements Towards an Efficient 16-18 Bit 100-200 MSPS ADC

As Data conversion systems continue to improve in speed and resolution, increasing demands are placed on the performance of high-speed Analog to Digital Conversion systems. This work makes a survey about all these and proposes a suitable architecture in order to achieve the desired specifications of 100-200MS/s with 16-18 bit of resolution. The main architecture is based on paralleled structures in order to achieve high sampling rate and at the same time high resolution. In order to solve problems related to Time-interleaved architectures, an advanced randomization method was introduced. It combines randomization and spectral shaping of mismatches. With a simple low-pass filter the method can, compared to conventional randomization algorithms, improve the SFDR as well as the SINAD. The main advantage of this technique over previous ones is that, because the algorithm only need that ADCs are ordered basing on their time mismatches, the absolute accuracy of the mismatch identification method does not matter and, therefore, the requirements on the timing mismatch identification are very low. In addition to that, this correction system uses very simple algorithms able to correct not only for time but also for gain and offset mismatches