491 research outputs found
Efficient Compressive Sampling of Spatially Sparse Fields in Wireless Sensor Networks
Wireless sensor networks (WSN), i.e. networks of autonomous, wireless sensing
nodes spatially deployed over a geographical area, are often faced with
acquisition of spatially sparse fields. In this paper, we present a novel
bandwidth/energy efficient CS scheme for acquisition of spatially sparse fields
in a WSN. The paper contribution is twofold. Firstly, we introduce a sparse,
structured CS matrix and we analytically show that it allows accurate
reconstruction of bidimensional spatially sparse signals, such as those
occurring in several surveillance application. Secondly, we analytically
evaluate the energy and bandwidth consumption of our CS scheme when it is
applied to data acquisition in a WSN. Numerical results demonstrate that our CS
scheme achieves significant energy and bandwidth savings wrt state-of-the-art
approaches when employed for sensing a spatially sparse field by means of a
WSN.Comment: Submitted to EURASIP Journal on Advances in Signal Processin
Stable image reconstruction using total variation minimization
This article presents near-optimal guarantees for accurate and robust image
recovery from under-sampled noisy measurements using total variation
minimization. In particular, we show that from O(slog(N)) nonadaptive linear
measurements, an image can be reconstructed to within the best s-term
approximation of its gradient up to a logarithmic factor, and this factor can
be removed by taking slightly more measurements. Along the way, we prove a
strengthened Sobolev inequality for functions lying in the null space of
suitably incoherent matrices.Comment: 25 page
Sparse Recovery from Combined Fusion Frame Measurements
Sparse representations have emerged as a powerful tool in signal and
information processing, culminated by the success of new acquisition and
processing techniques such as Compressed Sensing (CS). Fusion frames are very
rich new signal representation methods that use collections of subspaces
instead of vectors to represent signals. This work combines these exciting
fields to introduce a new sparsity model for fusion frames. Signals that are
sparse under the new model can be compressively sampled and uniquely
reconstructed in ways similar to sparse signals using standard CS. The
combination provides a promising new set of mathematical tools and signal
models useful in a variety of applications. With the new model, a sparse signal
has energy in very few of the subspaces of the fusion frame, although it does
not need to be sparse within each of the subspaces it occupies. This sparsity
model is captured using a mixed l1/l2 norm for fusion frames.
A signal sparse in a fusion frame can be sampled using very few random
projections and exactly reconstructed using a convex optimization that
minimizes this mixed l1/l2 norm. The provided sampling conditions generalize
coherence and RIP conditions used in standard CS theory. It is demonstrated
that they are sufficient to guarantee sparse recovery of any signal sparse in
our model. Moreover, a probabilistic analysis is provided using a stochastic
model on the sparse signal that shows that under very mild conditions the
probability of recovery failure decays exponentially with increasing dimension
of the subspaces
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