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We elaborate on the possibility of exploiting the (pseudo)random projection operator, which is at the heart of the most common architecture for compressed sensing, to prevent access to the acquired information by unauthorized receivers. In low-resource applications, this approach may make dedicated cryptographic layers unnecessary when the security requirement is not particularly high. Beyond proving...
While classical compressive sensing aims to reduce the number of measurements with respect to Nyquist-based sampling methods, it is usually important to consider the total number of bits needed to represent this small amount of measurements in order to maintain the same signal quality. In this paper we study an architecture for signal acquisition that produces a stream of 1-bit measurements, and we...
The paper aims to highlight relative strengths and weaknesses of some of the recently proposed architectures for hardware implementation of analog-to-information converters based on Compressive Sensing. To do so, the most common architectures are analyzed when saturation of some building blocks is taken into account, and when measurements are subject to quantization to produce a digital stream. Furthermore,...
Compressed sensing is an analog signal acquisition technique that aims at converting the intrinsic information contained in the signal when some a priori assumptions can be made on its structure. The most common of these assumptions is that the signal is sparse. We here present a methods that works when the signal to acquire is not only sparse but also localized. Benefits of this method will be shown...
Compressed sensing exploits special signal features to extract its information content with a smaller amount of samples with respect to acquisition based on Nyquist theorem. While many theoretical results have proved the capabilities of this new paradigm, hardware implementations are still far from being practical. Here, we present a new architecture of analog to information converter that produces...
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