Co-sponsored by: IEEE Circuits and Systems Society Montreal Chapter and ReSMiQ

Compressive sensing (CS) is based on two concept/principles: sparsity, which is related to the signals of interest, and incoherence, which relates to the methods of measurement/acquisition/sampling. The most significant feature of the sampling procedures is that they allow a sensor to capture the information content of a signal without going through the acquisition of its entire time-profile. Most notably all biosignals are in fact sparse, which makes CS particularly appealing for the acquisition of these signals in ultra-low cost wireless body sensor networks for wearable biomedical monitors. Aim of this talk is initially to introduce the above concepts using an intuitive approach. We will then assume that the signals to acquire are not only sparse, but also localized, e.g. as they always happens in practice, they 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 need to be designed to maximize their “rakeness”, that is, to maximize their capability to collect the energy of the samples during the acquisition phase and thus the average SNR used in signal reconstruction. Such a maximization, however, cannot be unconstrained, since the property of incoherence need to be satisfied too and we will show how a simple design procedure can be devised to achieve this result. We will also show how Gaussian acquisition sequences obtained with the above procedure can be used to obtain an improvement of the average SNR of the reconstructed signals of at least 4-5dBs with respect to the classical use of i.i.d. sequences. We will then concentrate on the implementation of a prototype of an A/D converter based on compressive sensing (called Analog-to-Information or A2I converters) and examine pros and cons of the two most interesting architectures proposed so far, namely the Random Modulation Pre Integration (RMPI) and the Random Sampling (RS). We will show that while the RS architecture offers a more straightforward implementation with respect to an RMPI, it can achieve satisfactory performances in terms of probability of correct signal reconstruction when the signal to acquire is sparse with respect to the Fourier basis. On the contrary, the RMPI architecture can be employed for acquiring any kind of sparse signals and offers therefore the most promising solution for implementing an A2I. Next we will deal with the circuit/system design and implementation of a switched capacitor RMPI A2I in a 0.18um CMOS technology. We will show that the direct circuit implementation of the developed theoretical CS algorithms will lead to a highly suboptimal solution, and one need therefore to follow a design relying on an actual algorithm-circuit-system co-design. We will also consider the combination of CS and local signal parameter estimation to achieve a large data rate reduction in applications like wireless sensor networks. We will show how a digital CS architecture can be devised to this aim and that the sampling instant of waveform of interest of known shape can be obtain trough a “puncturing” optimization algorithm to minimize node power consumption while achieving a given estimation performance. We detail a corresponding puncturing algorithm and present the design of an integrated digital compressed estimation unit in 28nm FDSOI CMOS. In a concrete case study, local estimation combined with subsampling is shown to result in a power reduction of up to an order of magnitude with respect to the standard solution of sampling and transmitting samples for off-board processing. Finally we will show how CS may be used to encrypt information from unauthorized receivers, which makes the algorithms even more suitable for the acquisition of biosignals, since it guarantees a certain degree of privacy without requiring any additional hardware for encryption.

Speaker(s): DR. GIANLUCA SETTI,

Agenda:

In order to attend this IEEE CAS DISTINGUISHED LECTURE, you need to register in the event IEEE-CASS & ReSMiQ Innovation Day scheduled for October 19, 2015.

To register for this event, please follow the instructions at (http://www.resmiq.org/?page=innovation-day.php).

The registration deadline is October 14, 2015.

Location:
Room: Amphithéâtre A-1150
Bldg: Pavillon A
École de technologie supérieure (ETS)
1100 Notre-Dame Ouest
Montreal, Quebec
H3C 1K3