Tutorial PM1

PM1: Compressed Sensing: Theory, Applications and Implementation of Sensing Nodes for the Internet of Things

Mauro Mangia, University of Bologna
Fabio Pareschi, University of Ferrara

Abstract: Compressed Sensing (CS) is a technique for the reconstruction of a waveform using a number of measurements that is potentially much smaller than the number of samples at the Nyquist rate. CS hinges on a simple early processing of signal samples and is the basis for the so called Analog-to-Information Converters (AIC) where one tries to match resources needed for acquisition with the actual amount of captured information.

This tutorial starts from CS basics and develops recent techniques for the joint design of hardware and algorithms for AICs based on lightweight signal adaptation. Design flows will be exemplified for analog and digital implementations with considerations on power consumption and effect of non-idealities validated by simulation and measurements.

Configurations and conditions in which AIC yields significant improvements over classical acquisition mechanisms will be identified. The discussion will also cover other advantages of the early-processing entailed by CS, e.g., the possibility of embedding partial but zero-cost security into the resulting acquisition processing (thus avoiding the need of dedicated cryptographic stages in non-critical applications) or the possibility to be effectively used in the realization of body-area-network/IoT nodes for biomedical application, a possibility which will be explored in the tutorial.


Fabio Pareschi

He received the Dr. Eng. degree (with honors) in Electronic Engineering from University of Ferrara, Italy, in 2001, and the Ph.D. in Information Technology under the European Doctorate Project (EDITH) from University of Bologna, Italy, in 2007. He is currently an Assistant Professor in the Department of Engineering, University of Ferrara. He is also a faculty member of ARCES – University of Bologna, Italy. He is the author of about 70 technical contribution to international conferences and journals, and one volume. His research activity includes analog and mixed-mode electronic circuit design, statistical signal processing, random number generation and testing, and electromagnetic compatibility.

Mauro Mangia

He received the B.Sc. and M.Sc. in Electronic Engineering and the Ph.D. degree in Information Technology from the University of Bologna (Bologna, Italy), respectively in 2005, 2009 and 2013. He is currently a Postdoctoral Researcher in the statistical signal processing group of ARCES – University of Bologna. In 2009 and 2012, he was a visiting Ph.D. student at the Ecole Polytechnique Federale de Lausanne (EPFL). His research interests are in nonlinear systems, compressed sensing, ultra-wideband systems, and systems biology. He was the recipient of the 2013 IEEE CAS Society Guillemin-Cauer Award and best student paper award at ISCAS2011. He is also the Web and Social Media Chair for ISCAS2018