Rakeness and beyond in zero-complexity digital compressed sensing: A down-to-bits case study

Mauro Mangia, Fabio Pareschi, Riccardo Rovatti, Gianluca Setti

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Compressed sensing can be seen as a lossy data compression stage processing vectors of digital words that correspond to time windows of the signal to acquire. We here show that if the second-order statistical features of such a signal are known, they may be exploited to obtain extremely high compression ratios by means of an almost zero-complexity hardware that is limited to signed adders and very few other elementary algebraic blocks. Optimization is obtained and demonstrated against non-optimized compressed sensing both by specializing classical rakeness-based design and by employing and even simpler and novel principal-component-based method that in some cases may outperform the former. Simulations are performed taking into account bit-wise operations and yield the true compression ratios that would be produced by the real system entailing only very low-depth fixed-point arithmetic. In the case of real-workd ECGs, good reconstruction with bitwise compression ratios up to 9 is demonstrated.
Original languageEnglish (US)
Title of host publicationProceedings - 2016 IEEE Biomedical Circuits and Systems Conference, BioCAS 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages4
ISBN (Print)9781509029594
StatePublished - Jan 1 2016
Externally publishedYes


Dive into the research topics of 'Rakeness and beyond in zero-complexity digital compressed sensing: A down-to-bits case study'. Together they form a unique fingerprint.

Cite this