Sampling signals with finite rate of innovation: the noisy case
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An attractive formulation of the sampling problem is based on the principle of a consistent signal reconstruction. The requirement is that the reconstructed signal is indistinguishable from the input in the sense that it yields the exact same measurements. ...
Institute of Electrical and Electronics Engineers2007
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Institute of Electrical and Electronics Engineers2008
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The transceivers of a wireless sensor network (WSN) have to fulfill the low-power and low-voltage constraints. The WiseNET project has already proven that it is possible to design a receiver working at 1V and consuming less than 2mW. However this transceiv ...
The field of Compressed Sensing has shown that a relatively small number of random projections provide sufficient information to accurately reconstruct sparse signals. Inspired by applications in sensor networks in which each sensor is likely to observe a ...
Ieee Service Center, 445 Hoes Lane, Po Box 1331, Piscataway, Nj 08855-1331 Usa2007
In many applications, the sampling frequency is limited by the physical characteristics of the components: the pixel pitch, the rate of the A/D converter, etc. A low-pass filter is then often applied before the sampling operation to avoid aliasing. However ...
Institute of Electrical and Electronics Engineers2007
A fast and exact algorithm is developed for the spin +-2 spherical harmonics transforms on equi-angular pixelizations on the sphere. It is based on the Driscoll and Healy fast scalar spherical harmonics transform. The theoretical exactness of the transform ...
In the absence of complexity and delay constraints, the quality of a noisy communication channel can be characterized by a single number, called its capacity and usually measured in bits. Shannon [1] showed that this number is universal in the sense that v ...
Ieee Service Center, 445 Hoes Lane, Po Box 1331, Piscataway, Nj 08855-1331 Usa2007
This paper addresses the problem of sensing or recovering a signal s, captured by distributed low-complexity sensors. Each sensor observes a noisy version of the signal of interest, and independently forms an approximant of its observation. This approximan ...