BioCAS 2016|Oct.17-19|Shanghai,China
Prof. Gianluca Setti

Gianluca Setti

Biography:

Gianluca Setti received a Dr. Eng. degree in Electronic Engineering and a Ph.D. degree in Electronic Engineering and Computer Science from the University of Bologna, Bologna in 1992 and in 1997, respectively. Since 1997 he has been with the School of Engineering at the University of Ferrara, Italy, where he is currently a Professor. Since 2000 he has also been a Faculty member in the Advanced Research Center on Electronic Systems (ARCES) at the University of Bologna.

Dr. Setti has held various visiting positions, most recently at the University of Washington, at IBM T. J. Watson Laboratories, and at EPFL (Lausanne).

He is the recipient of numerous awards, including the 2004 IEEE Circuits and Systems (CAS) Society Darlington Award, the 2013 IEEE CASS Guellemin-Cauer Award and the 2013 IEEE CASS Meritorious Service Award. He is a Distinguished Lecturer for the IEEE CAS. Dr. Setti has also served as Editor-in-Chief for both the IEEE Transactions on Circuits and Systems - Part I: Regular Papers (2008-2009) and the IEEE Transactions on Circuits and Systems - Part II: Express Briefs, as the Technical Program Co-Chair for IEEE ISCAS (2007 and 2008) and as the 2010 President of the CAS Society. In 2013-2014 he served as the Vice President for Publication Services and Products for the IEEE, the first scientist not from North America to serve in this role. He is also a Fellow of the IEEE.

He has authored over 260 publications and has edited 3 books in the areas of nonlinear circuits, recurrent neural networks, implementation and application of chaotic circuits and systems, statistical signal processing, electromagnetic compatibility, biomedical circuits and systems.

Talk Title: Compressive Sensing: Theory, Implementation and Applications

Talk Abstract:

Compressive Sensing [1] is an acquisition technique which relies on the sparsity of the underlying signals, to enable sampling below the classical Nyquist rate. To do so, the signals must be acquired in an incoherent way with respect to the sparsity basis, which is classically obtained in practice by acquiring the signal through projection on a random PAM signal with i.i.d. symbols.

We first show that advantages with respect to the above “classical” compressive sensing approach can be achieved by exploiting the fact that, while sparsity is not under a system designer’s control, incoherence is, and therefore flexibility and creativity in implementing compressed sensing systems rely on the strategic design and control of incoherence. To accomplish this, we then assume that the signals to be acquired are not only sparse, but also localized, e.g. for nearly all practical applications, the signals of interest preferentially occupy a given subspace (for instance they are all low-pass or high-pass in the frequency domain). We show how, for localized signals, the acquisition sequences can be designed to maximize their “rakeness,” that is, to maximize their capability to collect the energy of the samples during the acquisition phase and increase by several dBs the average SNR achieved in signal reconstruction [2].

We will then describe a few implementations of A/D converters based on compressive sensing, mainly based on a Random Modulation Pre-Integration (RMPI) in a 0.18um CMOS technology, highlighting pros and cons of all of them [3]-[6].

Finally, we will show how the use of CS guarantee some level of privacy in information transmission, which makes the CS signal acquisition paradigm even more suitable for applications in the area of Body Area Networks and Internet of Things [7]-[8].

Bibliography:

  1. [1] IEEE Signal Processing Magazine, Special Issue on “Sensing, Sampling and Compression”, Guest Editors: R. G. Baraniuk, E. Candes, R. Novak, M. Vetterli
  2. [2] M. Mangia, R. Rovatti, G. Setti, “Rakeness in the design of Analog-to-Information Conversion of Sparse and Localized Signals,” IEEE Transactions on Circuits and Systems – Part I, vol. 59, n. 5, pp. 1001 – 1014, 2012. DOI: 10.1109/TCSI.2012.2191312 (Recipient of the 2013 IEEE CAS Society Guellemin-Cauer Award)
  3. [3] F. Pareschi, M. Mangia, P. Albertini, G. Frattini, 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, n. 1, pp. 149-162, March. 2016
  4. [4] D. Gangopadhyay, E. Allstot, A. Dixon, K. Natarajan, S. Gupta, and D. Allstot, “Compressed Sensing Analog Front-End for Bio-Sensor Applications,” IEEE J. of Solid-State Circuits, vol. 49, pp. 426–438, Feb. 2014.
  5. [5] M. Shoaran, M. H. Kamal, C. Pollo, P. Vandergheynst and A. Schmid, "Compact Low-Power Cortical Recording Architecture for Compressive Multichannel Data Acquisition," in IEEE Transactions on Biomedical Circuits and Systems, vol. 8, no. 6, pp. 857-870, Dec. 2014.
  6. [6] J. Zhang, Y. Suo, S. Mitra, S. Chin, S. Hsiao, R. F. Yazicioglu, T. D. Tran, and R. Etienne-Cummings, “An efficient and compact compressed sensing microsystem for implantable neural recordings,” IEEE Trans. Biomed. Circuits Syst., vol. 8, no. 4, pp. 485–496, Aug. 201
  7. [7] V. Cambareri, M. Mangia, F. Pareschi, R. Rovatti, G. Setti, “Low-Complexity Multiclass Encryption by Compressed Sensing: Definition and Main Properties”, IEEE Transactions on Signal Processing, vol 63, n. 9, pp. 2183 - 2195, 2015
  8. [8] V. Cambareri, M. Mangia, F. Pareschi, R. Rovatti, G. Setti, “On Known-Plaintext Attacks to a Compressed Sensing-based Encryption: a Quantitative Analysis”, IEEE Transactions on Information Forensics and Security, vol 10, n. 10, pp. 2182-2195, Oct. 2015

Sponsors:

IEEE Circuits And Systems Society Engineering Medical Biology Socierty Solid-State Circuits Society IEEE Brain Community Shanghai Jiao Tong University Hangzhou Dianzi University