Statistical Signal Processing Group

The statistical signal processing group of the Engineering Department of the University of Ferrara has many different research interests, all of them with an Information Technology background and sharing a common statistical approach. Furthermore, almost all of these interests are focused to the design of many different analog integrated circuits (ranging from A/D converters, signal acquisition circuits, to random number generators, switching power converters) where our target is to achieve some advantage with respect to the state-of-the-art solutions by exploiting the statistical approach.

Some recent achievements:

-       Electro-Magnetic Interferences (EMI) reduction

We are active since many years in spread spectrum optimization for the EMI reduction purposes. We have been able both to optimize common known approaches, and to propose new innovative techniques capable of outperforming already known ones. We have designed several integrated circuits for EMI reduction, ranging from Serial-ATA protocol to switching DC/DC converters.

Fabio Pareschi, Gianluca Setti, Riccardo Rovatti, and Giovanni Frattini, “Short-term Optimized Spread Spectrum Clock Generator for EMI Reduction in Switching DC/DC Converters”, in IEEE Transactions on Circuits and Systems I - Regular Papers, Vol. 61, No. 10, pp. 3044-3053. October 2014 - doi: 10.1109/TCSI.2014.2327273

Fabio Pareschi, Gianluca Setti, Riccardo Rovatti, and Giovanni Frattini, “Practical Optimization of EMI Reduction in Spread Spectrum Clock Generators with Application to Switching DC/DC Converters”, in IEEE Transactions on Power Electronics, Vol. 29, No. 9, pp. 4646-4657. September 2014 - doi: 10.1109/TPEL.2013.2286258

Fabio Pareschi, Gianluca Setti, and Riccardo Rovatti, “A 3 GHz Serial ATA Spread Spectrum Clock Generator Employing a Chaotic PAM Modulation”, in IEEE Transactions on Circuits and Systems I - Regular Papers, Vol. 57, No. 10, pp. 2577-2587. October 2010 - doi: 10.1109/TCSI.2010.2048771

-       Compressed Sensing

Compressed Sensing (CS) is a emerging topic in the area of Signal Processing. Our contribution focuses on a new methodology for CS, which is based on the design of acquisition sequences that are able to maximize what we call “rakeness” (corresponding to the average energy of the CS sample). With respect to the standard CS approach (it is based on acquisition sequences generated as instances of independent and identically distributed random variable) the proposed rakeness-based CS produces benefits that can be used to either reduce the minimum number of CS samples needed to correctly represents an input signal instances (to increment the compression ratio) or rather to reduce the reconstruction error for a fixed amount of CS samples. Following this, we have designed an Analog-to-Information converter that can acquire a Biomedical signal according to the CS paradigm. This chip features the “rakeness” approach and a very interesting and effective “smart saturation checking”.

Fabio Pareschi, Pierluigi Albertini, Giovanni Frattini, Mauro Mangia, Riccardo Rovatti, Gianluca Setti, “Hardware-Algorithms Co-design and Implementation of an  Analog-to-Information Converter for Biosignals based on Compressed Sensing”, accepted for publication in IEEE Transaction on Biomedical Circuits and Systems, 2015. DOI: 10.1109/TBCAS.2015.2444276

Mangia, M.; Rovatti, R.; Setti, G., "Rakeness in the Design of Analog-to-Information Conversion of Sparse and Localized Signals," Circuits and Systems I: Regular Papers, IEEE Transactions on, vol.59, no.5, pp.1001-1014, 2012 - doi: 10.1109/TCSI.2012.2191312

Valerio Cambareri, Mauro Mangia, Fabio Pareschi, Riccardo Rovatti, and Gianluca Setti, “Low-Complexity Multiclass Encryption by Compressed Sensing”, in IEEE Transactions on Signal Processing, Vol. 63, No. 9, pp. 2183-2195. May 2015. DOI: 10.1109/TSP.2015.2407315

-       Random Number generation

We are active since many years in the field of Random Numbers. We have obtained significant results in the generation of high quality true-random bits by means of chaotic circuits, as well as in the statistical testing of random bit sequences. We are also able to propose generators capable of generating random bit sequences with prescribed higher-order expectations

Fabio Pareschi, Gianluca Setti, and Riccardo Rovatti, “Implementation and Testing of High-speed CMOS True Random Number Generators based on Chaotic Systems”, in IEEE Transactions on Circuits and Systems I - Regular Papers, Vol. 57, No 12, pp. 3124-3137. December 2010 - DOI: 10.1109/TCSI.2010.2052515

Fabio Pareschi, Riccardo Rovatti, and Gianluca Setti, “On Statistical Tests for Randomness included in the NIST SP800-22 test suite and based on the Binomial Distribution”, in IEEE Transactions on Information Forensics and Security, Vol. 7, No. 2, pp. 491-505. April 2012 - DOI: 10.1109/TIFS.2012.2185227

A. Caprara, F. Furini, A. Lodi, M. Mangia, R. Rovatti, G. Setti, "Generation of Antipodal Random Vectors With Prescribed Non-Stationary 2-nd Order Statistics," Signal Processing, IEEE Transactions on, vol.62, no.6, pp.1603-1612, March, 2014 - DOI: 10.1109/TSP.2014.2302737

For further details, see our webpage at www.signalprocessing.it