Rakeness

Book

M. Mangia, F. Pareschi, V. Cambareri, R. Rovatti, G. Setti, ''Adapted Compressed Sensing for Effective Hardware Implementations'', Springer International Publishing AG, 2018 - doi: 10.1007/978-3-319-61373-4

Journal papers

M. Mangia, F.Pareschi, R. Rovatti, G. Setti, ''Adapted Compressed Sensing: A Game Worth Playing'', IEEE Circuits and Systems Magazine, vol. 20, no. 1, pp. 40-60, 2020 - doi: 10.1109/MCAS.2019.2961727

C.H. Pimentel-Romero, M. Mangia, F. Pareschi, R. Rovatti, G. Setti, ''Geometric Constraints in Sensing Matrix Design for Compressed Sensing'', Signal Processing, vol. 171, no. , pp. -, 2020 - doi: 10.1016/j.sigpro.2020.107498

M. Mangia, F. Pareschi, R. Rovatti, G. Setti, ''Rakeness-based Compressed Sensing and Hub Spreading to Administer Short/Long Range Communication Tradeoff in IoT settings'', IEEE Internet of Things Journal, vol. 5, no. 3, pp. 2220-2233, 2018 - doi: 10.1109/JIOT.2018.2828647

M. Mangia, A. Marchioni, F. Pareschi, R. Rovatti, G. Setti, ''Administering Quality-Energy Trade-Off in IoT Sensing Applications by Means of Adapted Compressed Sensing'', IEEE Journal on Emerging and Selected Topics in Circuits and Systems, vol. , no. , pp. -, 2018 - doi: 10.1109/JETCAS.2018.2846884

M. Mangia, F. Pareschi, R. Varma, R. Rovatti, J. Kovačević, G. Setti, ''Rakeness-Based Compressed Sensing of Multiple-Graph Signals for IoT Applications'', IEEE Transactions on Circuits and Systems II: Express Briefs, vol. 65, no. 5, pp. 682-686, 2018 - doi: 10.1109/TCSII.2018.2821241

A. Marchioni, M. Mangia, F. Pareschi, R. Rovatti, G. Setti, ''Disturbance Rejection with Rakeness-Based Compressed Sensing: Method and Application to Baseline/Powerline Mitigation in ECGs'', Proceedings - IEEE International Symposium on Circuits and Systems, vol. , no. , pp. -, 2018 - doi: 10.1109/ISCAS.2018.8351170

M. Mangia, F. Pareschi, R. Rovatti, G. Setti, ''Low-Cost Security of IoT Sensor Nodes with Rakeness-Based Compressed Sensing: Statistical and Known-Plaintext Attacks'', IEEE Transactions on Information Forensics and Security, vol. 13, no. 2, pp. 327-340, 2018 - doi: 10.1109/TIFS.2017.2749982

M. Mangia, F. Pareschi, R. Rovatti, G. Setti, ''Adaptive Matrix Design for Boosting Compressed Sensing'', IEEE Transactions on Circuits and Systems I: Regular Papers, vol. 65, no. 3, pp. 1016-1027, 2018 - doi: 10.1109/TCSI.2017.2766247

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, V. Cambareri, R. Rovatti and G. Setti, "Rakeness-Based Design of Sparse Projection Matrices for Low-Complexity Compressed Sensing," IEEE Trans. on Circuits and Systems, vol.64, no.5, pp.1201-1213, May 2017 doi: 10.1109/TCSI.2017.2649572

F. Pareschi, P. Albertini, G. Frattini, M. Mangia, R. Rovatti and G. Setti, "Hardware-Algorithms Co-design and Implementation of an Analog-to-Information Converter for Biosignals based on Compressed Sensing," IEEE Trans. Biomedical Circuits and Systems, vol.10, no.1, pp.149-162, Feb. 2016 (it won the IEEE BioCAS Transactions Best Paper Award 2019  ) doi: 10.1109/TBCAS.2015.2444276

abstract - We report the design and implementation of an Analog-to-Information Converter (AIC) based on Compressed Sensing (CS). The system is realized in a CMOS 180 nm technology and targets the acquisition of bio-signals with Nyquist frequency up to 100 kHz. To maximize performance and reduce hardware complexity, we co-design hardware together with acquisition and reconstruction algorithms. The resulting AIC outperforms previously proposed solutions mainly thanks to two key features. First, we adopt a novel method to deal with saturations in the computation of CS measurements. This allows no loss in performance even when 60% of measurements saturate. Second, the system is able to adapt itself to the energy distribution of the input by exploiting the so-called rakeness to maximize the amount of information contained in the measurements. With this approach, the 16 measurement channels integrated into a single device are expected to allow the acquisition and the correct reconstruction of most biomedical signals. As a case study, measurements on real electrocardiograms (ECGs) and electromyograms (EMGs) show signals that these can be reconstructed without any noticeable degradation with a compression rate, respectively, of 8 and 10.

V. Cambareri, M. Mangia, F. Pareschi, R. Rovatti, G. Setti, G., "On Known-Plaintext Attacks to a Compressed Sensing-based Encryption: a Quantitative Analysis," IEEE Trans. on Information Forensics and Security, vol.10, no.10, pp.2182-2195, Oct. 2015 - doi: 10.1109/TIFS.2015.2450676

V. Cambareri, M. Mangia, F. Pareschi, R. Rovatti, G. Setti, G., "Low-Complexity Multiclass Encryption by Compressed Sensing," IEEE Trans. on Signal Processing, vol.63, no.9, pp.2183-2195, May 2015 - doi: 10.1109/TSP.2015.2407315

V. Cambareri, M. Mangia, F. Pareschi, R. Rovatti, G. Setti, G., "A Case Study in Low-Complexity ECG Signal Encoding: How Compressing is Compressed Sensing?," IEEE Signal Processing Letters, IEEE, vol.22, no.10, pp.1743-1747, Oct. 2015 - doi: 10.1109/LSP.2015.2428431

M. Mangia, R. Rovatti and G. Setti, "Rakeness in the Design of Analog-to-Information Conversion of Sparse and Localized Signals," IEEE Trans. on Circuits and Systems I: Regular Papers, vol.59, no.5, pp.1001-1014, May 2012 (it won the IEEE Guillemin-Cauer Best Paper Award 2013  ) - doi: 10.1109/TCSI.2012.2191312

abstract - Design of random modulation preintegration systems based on the restricted-isometry property may be suboptimal when the energy of the signals to be acquired is not evenly distributed, i.e., when they are both sparse and localized. To counter this, we introduce an additional design criterion, that we call rakeness, accounting for the amount of energy that the measurements capture from the signal to be acquired. Hence, for localized signals a proper system tuning increases the rakeness as well as the average SNR of the samples used in its reconstruction. Yet, maximizing average SNR may go against the need of capturing all the components that are potentially nonzero in a sparse signal, i.e., against the restricted isometry requirement ensuring reconstructability. What we propose is to administer the trade-off between rakeness and restricted isometry in a statistical way by laying down an optimization problem. The solution of such an optimization problem is the statistic of the process generating the random waveforms onto which the signal is projected to obtain the measurements. The formal definition of such a problems is given as well as its solution for signals that are either localized in frequency or in more generic domain. Sample applications, to ECG signals and small images of printed letters and numbers, show that rakeness-based design leads to nonnegligible improvements in both cases.

J. Haboba, M. Mangia, F. Pareschi, R. Rovatti, G. Setti, G., "A Pragmatic Look at Some Compressive Sensing Architectures With Saturation and Quantization," Emerging and Selected Topics in Circuits and Systems, IEEE Journal on, vol.2, no.3, pp.443-459, Sept. 2012 - 10.1109/JETCAS.2012.2220392

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," IEEE Trans. on Signal Processing, vol.62, no.6, pp.1603-1612, March, 2014 - doi: 10.1109/TSP.2014.2302737

Some Conferences papers

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, 2019 - doi: 10.1109/ISCAS.2019.8702667

S. Rout, M. Mangia, F. Pareschi, G. Setti, R. Rovatti, W.A. Serdijn, ''Rakeness-based compressed sensing of atrial electrograms for the diagnosis of atrial fibrillation'', Proceedings - IEEE International Symposium on Circuits and Systems, 2019 - doi: 10.1109/ISCAS.2019.8702398

M. Mangia, L. Magenta, A. Marchioni, F. Pareschil, R. Rovatti, G. Setti, ''Projected-gradient-descent in rakeness-based compressed sensing with disturbance rejection'', Prooceedings of New Generation of CAS, NGCAS 2018, - doi: 10.1109/NGCAS.2018.8572115

A. Marchioni, M. Mangia, F. Pareschil, R. Rovatti, G. Setti, ''Rakeness-based Compressed Sensing of Surface ElectroMyoGraphy for Improved Hand Movement Recognition in the Compressed Domain'', Prooceedings of IEEE Biomedical Circuits and Systems Conference 2018 (BioCAS 2018), 2018 - doi: 10.1109/BIOCAS.2018.8584763

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, vol. , no. , 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, vol. , no. , pp. 372-375, 2017 - doi: 10.1109/BIOCAS.2017.8325155

M. Mangia, F. Pareschi, R. Rovatti, G. Setti, ''Countering the false myth of democracy: Boosting compressed sensing performance with maximum-energy approach'', Proceedings - IEEE International Symposium on Circuits and Systems, vol. , no. , pp. -, 2017 - doi: 10.1109/ISCAS.2017.8050532

D. Bortolotti, M. Mangia, A. Bartolini, R. Rovatti, G. Setti, L. Benini, ''An ultra-low power dual-mode ECG monitor for healthcare and wellness'', Proceedings -Design, Automation and Test in Europe, DATE, pp. 1611-1616, 2015 - doi: 10.7873/DATE.2015.0784

Mangia, M.; Pareschi, F.; Rovatti, R.; Setti, G., "Leakage compensation in analog random modulation pre-integration architectures for biosignal acquisition," Biomedical Circuits and Systems Conference (BioCAS), 2014 IEEE, pp.432-435, Oct. 2014

M. Mangia, M. Paleari, P. Ariano, R. Rovatti, G. Setti, "Compressed Sensing based on Rakeness for surface ElectroMyoGraphy," Biomedical Circuits and Systems Conference (BioCAS), 2014 IEEE, pp. 204 - 207, Oct. 2014

D. Bortolotti, M. Mangia, A. Bartolini, R. Rovatti, G. Setti, L. Benini, "Rakeness-based Compressed Sensing on Ultra-Low Power Multi-Core Biomedical Processors," Design & Architectures for Signal & Image Processing (DASIP), 2014 IEEE, pp.1-8, Sep. 2014

M. Mangia, R. Rovatti, G. Setti, P. Vandergheynst, "Combining Spread Spectrum Compressive Sensing with rakeness for low frequency modulation in RMPI architecture," International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2014 IEEE, pp.4146-4150, May 2014

M. Mangia, J. Haboba, R. Rovatti, G. Setti, "Rakeness-based approach to compressed sensing of ECGs," Biomedical Circuits and Systems Conference (BioCAS), 2011 IEEE, pp.424-427, Oct. 2011

M. Mangia, R. Rovatti, G. Setti, "Analog-to-information conversion of sparse and non-white signals: Statistical design of sensing waveforms," International Symposium on Circuits and Systems (ISCAS), 2011 IEEE, pp.2129-2132, Oct. 2011 (it won the IEEE ISCAS 2011 Best Student Paper Award  doi: 10.1109/ISCAS.2011.5938019)