Alexis Vander Biest

Problem-Based Learning in Instrumentation: Synergism of Real and Virtual Modular Acquisition Chains

As part of an instrumentation course, a problem-based learning framework was selected for laboratory instruction. Two acquisition chains were designed to help students carry out realistic instrumentation problems. The first tool is a virtual (simulated) modular acquisition chain that allows rapid overall understanding of the main problems in instrumentation. The second tool is an actual modular chain allowing students to test these modules in practice and fine-tune the results. The didactic impact of the laboratory-and especially of the two acquisition chains-was evaluated using three approaches: an evaluation questionnaire, the problem outcomes, and a written examination. These results show that the students valued the laboratory-based coursework and found both tools useful. Furthermore, they were able to address problems at a high cognitive level.

A framework introducing model reversibility in SoC design space exploration

In this paper we present a general framework for the support of flexible models representation and execution in the context of SoC design space exploration. Coming as a C++ library, it allows the user to gather models from its own and existing models into larger and more complete models. Compared to existing modeling systems we introduce the notion of model reversibility that allows the user to turn any parameter appearing in a model into the output : it increases the model flexibility and enables its reuse in very different problems. Aside from providing specification and execution support, the framework also permits dynamic model sensitivity analysis and efficient parameter sensitivity analysis for closed-formed models. Through this paper we explain our original 3-level hierarchical representation of model and explain meanwhile how it offers flexibility and model robustness using a XML schema grammar.

A Multi-objective and Hierarchical Exploration Tool for SoC Performance Estimation

In this paper we present a flexible performance estimation tool called Nessie developed to provide system-on-chip designers with automated multi-objective design space exploration and its related tool called Yeti building and executing reusable closed-formed models. After reviewing the existing closed-formed expressions based and application/platform mapping performance estimation tools, we propose an hybrid tool to cope with their limitations. We present a brief summary of the functionalities of Yeti and describe Nessie, our hierarchical application/platform performance estimation mapping tool which banalizes all the degrees of freedom for in-depth design space exploration and introduces multi-objective modeling. Through this paper, we explain how the combination of these tools provides the designer with innovative and powerful functionalities for performance prediction at the earlier stages of the design flow.

Building a virtual data acquisition chain to teach and learn instrumentation

A modular virtual data acquisition chain implemented on LabVIEW is presented in this article, which is intended to illustrate and teach instrumentation. The signal can be viewed at any point of the chain and under the influence of any parameter change. The acquisition chain accuracy and student applications are detailed in the case of an electroencephalograph (EEG) setup. 1 Freely available for download at http://beams.ulb.ac.be/beams/research/all/virtualchain.html

A taxonomy for technology extrapolation

Having analyzed recent papers, we present some new concepts helping to classify and understand the different kinds of models used in technology extrapolation, the specific discipline aimed at early technological bottlenecks detection and a priori performance estimation. We introduce several criteria allowing to organize explicitly the various relations and models proposed in literature and highlight some key properties that help to understand the challenges and limits of technology extrapolation.