Infinite-dimensional compressed sensing deals with the recovery of analog signals (functions) from linear measurements, often in form integral transforms such as Fourier transform. This framework is well-suited to many real-world inverse problems, which are typically modelled infinite-dimensional spaces, and where application finite-dimensional approaches can lead noticeable artefacts. Another ...

Spectrum-sparse signals are vital for wideband radars and wireless communication applications. A high-speed analog-to-digital converter (ADC) with the capacities of tens gigahertz sampling rates is often required to acquire these signals. In this work, an enhanced photonic-assisted approach combination time-interleaved ADC compressed sensing techniques presented, which enables measured signal b...

In this paper we consider the problem of low power SpO2 sensors for acquiring Photoplethysmograph (PPG) signals. Most of the power in SpO2 sensors goes to lighting red and infra-red LEDs. We use compressive sensing to lower the amount of time LEDs are lit, thereby reducing the signal acquisition power. We observe power savings by a factor that is comparable to the sampling rate. At the receiver...

The sampling rate of the sensors in wireless sensor networks (WSNs) determines the rate of its energy consumption since most of the energy is used in sampling and transmission. To save the energy in WSNs and thus prolong the network lifetime, we present a novel approach based on the compressive sensing (CS) framework to monitor 1-D environmental information in WSNs. The proposed technique is ba...

This paper discusses sample allocation problem (SAP) in frequency-domain Compressive Sampling (CS) of timedomain signals. An analysis that is relied on two fundamental CS principles; the Uniform Random Sampling (URS) and the Uncertainty Principle (UP), is presented. We show that CS on a singleand multi-band signals performs better if the URS is done only within the band and suppress the out-ban...

Introduction Compressed sensing (CS) has been shown to provide accurate reconstructions from highly undersampled data for certain types of MR acquisitions [1, 2]. This offers the promise of faster MR acquisitions, and further speed gains are possible when CS is used in conjunction with parallel acquisition schemes such as SENSE [3]. Several approaches have been recently proposed to reconstruct ...

The potential of large tactile arrays to improve robot perception for safe operation in human-dominated environments and of high-resolution tactile arrays to enable humanlevel dexterous manipulation is well accepted. However, the increase in the number of tactile sensing elements introduces challenges including wiring complexity, data acquisition, and data processing. To help address these chal...