Compressed Sensing (CS) Circuits, Systems and Algorithms for IoT Nodes Implementation

CS 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. The advantage in in energy saving, which makes the paradigm very suitable for  We showed 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. This can be exploited when, as for nearly all practical cases, sparse signals are also localized, i.e., they preferentially occupy a given subspace (for instance they are all low-pass in the frequency domain). We showed how, for these 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 at least 6dBs the average SNR achieved in signal reconstruction. We used this to optimize the implementation of an ADC based on CS in a 0.18um CMOS technology by following a synergetic design between algorithm-circuit-system. The use of rakeness-based CS acquisition sequences can reduce the complexity of the implemented A/D from 16 to 8 stages for processing ECG signals and from 64 to 24 for EMG ones. Furthermore, rakeness-derived sequences also eliminate the necessity for pre- or post-acquisition filtering stages intended to suppress high frequency artifacts and 60-Hz power-line noise. We also showed how the use of CS guarantee some level of privacy in information transmission.


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ERC Sector:

  • PE7_4 (Micro and nano) systems engineering
  • PE7_7 Signal processing

Keywords:

  • Compressed sensing
  • Internet of Things
  • Biomedical signals

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