SSIGPRO

Topics@SSIGPRO

We are an Italian university research group with many different research interests, all of them with an Information Technology background and sharing a common statistical approach. As an example, topics of our interest include (but are not limited to) the design and the analysis of chaotic circuits, of random or pseudorandom generator circuits, and their applications to improve performance of many Information Technology topics such as digital communications, the Electro-Magnetic Interferences (EMI) reduction or the Compressed Sensing paradigm. Recently we also invesitgate the design of low-complexity anomaly detectors that exploits solutions based on Artificial Intelligence.
Here we report brief descriptions of most relevant topics with lists of recently published papers.

Anomaly Detection

Compressed Sensing

Resonant DC/DC

Pulse Width Modulation

EMI Reduction

Random Number Generation

Anomaly detection

Nowadays, monitoring systems continuously generate large scale multi-dimensional time series that are processed to extract the information needed for making decisions. Suffice it to think of a smart city, a wild protected environment, an infrastructure, or a person’s health where data is collected and processed to monitor the object condition and take actions in case of necessity. In these applications, anomaly detection techniques are fundamental for triggering alarms when abnormal behaviors occur. This is a field, also knows as Outlayer detection, that has been strongly investigated in the last few decades with a large variety of already proposed detectors that cope with a large variety of applications. Our contribution is on the design of systems according to the Intenet of Things (IoT) paradigm, which implies constraints on the resources available in the task of both long-term monitoring and early anomaly detection.

A. Marchioni, M. Mangia, F. Pareschi, R. Rovatti, G. Setti, “Subspace Energy Monitoring for Anomaly Detection @Sensor or @Edge,” Submitted to IEEE Internet of Things Jornal, Sep 2019

A. Burello, A. Marchioni, D. Brunelli, S. Benatti, M. Mangia, L. Benini, “Embedded Streaming Principal Components Analysis for Network Load Reduction in Structural Health Monitoring,” Submitted to IEEE Internet of Things Jornal, Jun 2020

A. Marchioni, M. Mangia, D. Brunelli, R. Rovatti, G. Setti, L. Benini, “Local Processing forEarly Anomaly Detection in Vibration-based Structural Health Monitoring Systems,” Submitted to IEEE Sensors Journal, Jun 2020

L. Prono, A. Marchioni, M. Mangia, F. Pareschi, R. Rovatti, G. Setti, “A High-level Implementation Framework for Non-Recurrent Artificial Neural Networks on FPGA”, PRIME 2019 - 15th Conference on Ph.D. Research in Microelectronics and Electronics, pp. 77-80, 2019 - doi: 10.1109/PRIME.2019.8787830

Compressed Sensing

Compressed Sensing (CS) is a emerging topic in the area of Signal Processing. Our main 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 aprroach (it is based on acquisition sequences generated as instances of independent and identically distributed random variable) the proposed rakeness-based CS produces beneficts that can be used to either reduce the minimum number of CS samples needed to correctly represents a inpu signal instances (to increment the compression ratio) or rather to reduce the reconstruction error for a fixed amount os CS samples. For references and more details go to dedicated web site .
Moreover, our contribution in this area also includes the design of analog to information converters (the adapted signal compression performed in the analog domain such that only the compressed waveforms are then digitalized) as well as innovative solutions for the decoder stage including their deployment on embedded systems. Here a list of some contributions on these topics.

M. Mangia, L. Prono, A. Marchioni, F. Pareschi, R. Rovatti, G. Setti, “Deep Neural Oracles for Short-window Optimized Compressed Sensing of Biosignals,” Submitted to IEEE Tran. on Biomedical Circuits and Systems.

C. Paolino, F. Pareschi, M. Mangia, R. Rovatti, G. Setti, ''A Practical Architecture for SAR-based ADCs with Embedded Compressed Sensing Capabilities'', PRIME 2019 - 15th Conference on Ph.D. Research in Microelectronics and Electronics, Proceedings, pp. 133-136, 2019 - doi: 10.1109/PRIME.2019.8787816

M. Mangia, A. Marchioni, F. Pareschi, R. Rovatti, G. Setti, ''Chained Compressed Sensing for Iot Node Security'', ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, pp. 7580-7584, 2019 - doi: 10.1109/ICASSP.2019.8683303

O.C. Akgun, M. Mangia, F. Pareschi, R. Rovatti, G. Setti, W.A. Serdijn, ''An energy-efficient multi-sensor compressed sensing system employing time-mode signal processing techniques'', Proceedings - IEEE International Symposium on Circuits and Systems, pp. -, 2019 - doi: 10.1109/ISCAS.2019.8702667

A. Marchioni, M. Mangia, F. Pareschi, R. Rovatti, G. Setti, ''Low-Complexity Greedy Algorithm in Compressed Sensing for the Adapted Decoding of ECGs'', Proceedings - 2017 IEEE Biomedical Circuits and Systems Conference, BioCAS 2017, pp. 324-327, 2017 - doi: 10.1109/BIOCAS.2017.8325143

A. Marchioni, M. Mangia, F. Pareschi, R. Rovatti, G. Setti, ''Sparse Sensing Matrix Based Compressed Sensing in Low-Power ECG Sensor Nodes'', Proceedings - 2017 IEEE Biomedical Circuits and Systems Conference, BioCAS 2017, pp. 372-375, 2017 - doi: 10.1109/BIOCAS.2017.8325155

F. Pareschi, M. Mangia, D. Bortolotti, A. Bartolini, L. Benini, R. Rovatti, G. Setti, ''Energy Analysis of Decoders for Rakeness-Based Compressed Sensing of ECG Signals'', IEEE Transactions on Biomedical Circuits and Systems, vol. 11, no. 6, pp. 1278-1289, 2017 - doi: 10.1109/TBCAS.2017.2740059

D. Bortolotti, M. Mangia, A. Bartolini, R. Rovatti, G. Setti, L. Benini, ''Energy-Aware Bio-signal Compressed Sensing Reconstruction on the WBSN-gateway'', IEEE Transactions on Emerging Topics in Computing, vol. , no. , pp. -, 2016 - doi: 10.1109/TETC.2016.2564361

M. Mangia, F. Pareschi, R. Rovatti, G. Setti, ''Security analysis of rakeness-based compressed sensing'', Proceedings - IEEE International Symposium on Circuits and Systems, vol. 2016-July, no. , pp. 241-244, 2016 - doi: 10.1109/ISCAS.2016.7527215

F. Pareschi, P. Albertini, G. Frattini, M. Mangia, R. Rovatti, G. Setti, ''Hardware-Algorithms Co-Design and Implementation of an Analog-to-Information Converter for Biosignals Based on Compressed Sensing'', IEEE Transactions on Biomedical Circuits and Systems, vol. 10, no. 1, pp. 149-162, 2016 - doi: 10.1109/TBCAS.2015.2444276

Resonant DC/DC

In recent years novel class-E converter architectures capable of high-efficiency operation at very-high switching frequency have been investigated with the purpose of increasing the implementation compactness. A completely new, differential equation based, design approach has been developed to both reduce the post-design CAD simulation efforts and increase the accuracy of the mathematically computed design point. Go to dedicated web site for further details.

Nicola Bertoni, Giovanni Frattini, Roberto Massolini, Fabio Pareschi, Riccardo Rovatti, and Gianluca Setti, "An Analytical Approach for the Design of Class-E Resonant DC-DC Converters," in IEEE Transactions on Power Electronics, vol. 31, no. 11, pp. 7701-7713. November 2016 - doi: 10.1109/TPEL.2016.2535387

Fabio Pareschi, Nicola Bertoni, Mauro Mangia, Riccardo Rovatti, and Gianluca Setti, " A Unified Design Theory for Class-E Resonant DC-DC Converter Topologies," in IEEE ACCESS, vol. 7, pp. 83825 - 83838. 2019 - doi: 10.1109/ACCESS.2019.2922743

Fabio Pareschi, Nicola Bertoni, Mauro Mangia, Roberto Massolini, Giovanni Frattini, Riccardo Rovatti, and Gianluca Setti, " Class-E Isolated DC-DC Converter with High-Rate and Cost-Effective Bidirectional Data Channel," in IEEE Transactions on Power Electronics, (to appear), 2020 - doi: 10.1109/TPEL.2019.2940661

Pulse Width Modulation

Exploiting the synergic effect of our expertise in the electronic circuit design and in statistical analysis, we are able to present a novel mixed-mode architecture for the generation of high-quality (i.e., very high signal-to-noise ratio) pulse-width modulated signals.

Salvatore Caporale, Fabio Pareschi, Valerio Cambareri, Riccardo Rovatti, Gianluca Setti, “A Soft-defined Pulse Width Modulation Approach - Part I: Principles”, IEEE Transactions on Circuits and Systems I - Regular Papers, Vol. 62, No. 9., pp. 2280-2289. September 2015 - doi: 10.1109/TCSI.2015.2459555

Salvatore Caporale, Fabio Pareschi, Valerio Cambareri, Riccardo Rovatti, Gianluca Setti, “A Soft-defined Pulse Width Modulation Approach - Part II: System Modeling”, IEEE Transactions on Circuits and Systems I - Regular Papers, Vol. 62, No. 9., pp. 2290-2300. September 2015 - doi: 10.1109/TCSI.2015.2459556

Electro-Magnetic Interferences Reduction

A very common approach among active EMI reduction techniques is the application of spread spectrum clocking. We have been able both to optimize common known approaches, and to propose new innovative techniques capable of outperforming already known ones. Go to the dedicated web site for further details .

Fabio Pareschi, Riccardo Rovatti, and Gianluca Setti, "EMI Reduction via Spread Spectrum in DC/DC Converters: State of the Art, Optimization, and Tradeoffs," IEEE Access, Vol. 3, pp. 2857-2874. 2015. ISSN: 2169-3536. doi: 10.1109/ACCESS.2015.2512383

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, G., "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

Last Master Thesis@SSIGPRO

University of Bologna

Caterina Raghi, low resource algorithms for abnornal instances detection in the Internet of Things framework
Marco Bonazzi, piattaforme edge per l'utilizzo di reti neurali profonde in ambito Internet of Things
Angelo Garofalo, Comparazione di stime spettrali per monitoraggio strutturale basato sull’analisi delle vibrazioni in ottica Big Data
Ettore Cesarini, Stima streaming di sottospazi principali
Elisa Naldi, Implementazione in FPGA di una rete neurale convolutiva profonda per l'elaborazione in tempo reale di immagini

Politecnico di Torino

Masera Riccardo, Studio del Controllo in Potenza di un Convertitore DC-DC in Classe E con Metodo Dual-Frequency
Paolino Carmine, Analysis and Design of a Low Power Analog-to-Information Architecture
Luciano Prono, Metodologie di Progettazione Automatica di Reti Neurali Non Ricorrenti su FPGA
Congia Fabrizio, Progetto di un convertitore analogico-informativo basato su memristori
Celentano Andrea, A Resonant Class-E DC-DC Converter for Wireless Power Transfer in Implantable Stimulators