Measurement Applications of Compressed Sensing

Dear Colleagues,

Compressed sensing (CS) has been with us for over a decade, attracting widespread research interest in a variety of fields. In instrumentation, CS offered promises of major breakthroughs in resolution and accuracy, and exceptional efficiency in data acquisition, often referred to as “sub-Nyquist” sampling. Both aspects are in some way consequences of “sparsity”, the basic information modelling assumption underlying compressive sensing.

Given this general setting, a practitioner is still left with considerable design freedom, for instance, in the choice of a suitable sensing matrix and/or in the determination of a sparse model that appropriately represents measurement information of interest. These aspects can determine success or failure in a CS-based instrumentation application.

As researchers shifted their attention from the hype of pioneering works to the thorough performance analyses required by real-life applications, more solid assessments of the strengths and weaknesses of CS-based approaches have become possible. In turn, these improve our understanding of CS and of its usefulness as a tool in instrument design. Time now seems ripe for an overview of what has been achieved by CS in this field.

This Special Issue aims primarily at presenting a selection of successful applications. Contributions are invited to show advances in instrumentation made possible by CS in any area of engineering and science. The emphasis is expected to be on how CS has been applied, or adapted, for some specific purpose, but more theoretically oriented papers discussing instrumentation-oriented issues in CS are also welcome.

Prospective authors are invited to submit their papers. Subjects may include, but are not limited to:

• monitoring in telecommunications
• medical diagnostic imaging
• monitoring and instrumentation for electrical systems
• remote sensing
• sensor networks
• compressed measurement
• signal analysis and feature extraction
• accuracy analysis of CS-based measurement methods

Prof. Claudio Narduzzi
Guest Editor

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