Projected-Gradient-Descent in Rakeness-Based Compressed Sensing with Disturbance Rejection

Compressed Sensing (CS) has recently emerged as an effective tool to simultaneously acquire and compress analog waveforms in low-resource sensing devices. Its mechanisms have been also extended by both adapting the sensing stage to the actual class of input signals, and granting it the ability to reject disturbances. Regrettably, the resulting design flow entails the solution of two optimization problems with a potentially huge number of variables. This work overcomes this impasse by proposing a Project-Gradient-Descend method algorithm that drastically reduces the required CPU time to obtain a solution.

[1]  Riccardo Rovatti,et al.  On Known-Plaintext Attacks to a Compressed Sensing-Based Encryption: A Quantitative Analysis , 2013, IEEE Transactions on Information Forensics and Security.

[2]  Riccardo Rovatti,et al.  Rakeness in the Design of Analog-to-Information Conversion of Sparse and Localized Signals , 2012, IEEE Transactions on Circuits and Systems I: Regular Papers.

[3]  Daibashish Gangopadhyay,et al.  Compressed Sensing Analog Front-End for Bio-Sensor Applications , 2014, IEEE Journal of Solid-State Circuits.

[4]  Riccardo Rovatti,et al.  A Pragmatic Look at Some Compressive Sensing Architectures With Saturation and Quantization , 2012, IEEE Journal on Emerging and Selected Topics in Circuits and Systems.

[5]  David L Donoho,et al.  Compressed sensing , 2006, IEEE Transactions on Information Theory.

[6]  Gianluca Mazzini,et al.  Performance of chaos-based asynchronous DS-CDMA with different pulse shapes , 2004, IEEE Communications Letters.

[7]  A. Goldstein.  Convex programming in Hilbert space , 1964 .

[8]  Riccardo Rovatti,et al.  Disturbance Rejection With Rakeness-based Compressed Sensing: Method and Application to Baseline/Powerline Mitigation in ECGs , 2018, 2018 IEEE International Symposium on Circuits and Systems (ISCAS).

[9]  R. Dykstra.  An Algorithm for Restricted Least Squares Regression , 1983 .

[10]  Luca Benini,et al.  A Low-Power Architecture for Punctured Compressed Sensing and Estimation in Wireless Sensor-Nodes , 2015, IEEE Transactions on Circuits and Systems I: Regular Papers.

[11]  S. Frick,et al.  Compressed Sensing , 2014, Computer Vision, A Reference Guide.

[12]  Stephen P. Boyd,et al.  Graph Implementations for Nonsmooth Convex Programs , 2008, Recent Advances in Learning and Control.

[13]  Michael P. Friedlander,et al.  Probing the Pareto Frontier for Basis Pursuit Solutions , 2008, SIAM J. Sci. Comput..

[14]  Riccardo Rovatti,et al.  A Case Study in Low-Complexity ECG Signal Encoding: How Compressing is Compressed Sensing? , 2015, IEEE Signal Processing Letters.

[15]  Riccardo Rovatti,et al.  Hardware-Algorithms Co-Design and Implementation of an Analog-to-Information Converter for Biosignals Based on Compressed Sensing , 2016, IEEE Transactions on Biomedical Circuits and Systems.

[16]  Kwok-Wo Wong,et al.  On the Security of a Class of Diffusion Mechanisms for Image Encryption , 2015, IEEE Transactions on Cybernetics.

[17]  R. Dykstra,et al.  A Method for Finding Projections onto the Intersection of Convex Sets in Hilbert Spaces , 1986 .

[18]  Luca Benini,et al.  An ultra-low power dual-mode ECG monitor for healthcare and wellness , 2015, 2015 Design, Automation & Test in Europe Conference & Exhibition (DATE).

[19]  Riccardo Rovatti,et al.  Enhanced rake receivers for chaos-based DS-CDMA , 2001 .

[20]  Riccardo Rovatti,et al.  Rakeness-Based Design of Low-Complexity Compressed Sensing , 2017, IEEE Transactions on Circuits and Systems I: Regular Papers.