19,939 research outputs found
Adaptive Compressed Sensing for Support Recovery of Structured Sparse Sets
This paper investigates the problem of recovering the support of structured
signals via adaptive compressive sensing. We examine several classes of
structured support sets, and characterize the fundamental limits of accurately
recovering such sets through compressive measurements, while simultaneously
providing adaptive support recovery protocols that perform near optimally for
these classes. We show that by adaptively designing the sensing matrix we can
attain significant performance gains over non-adaptive protocols. These gains
arise from the fact that adaptive sensing can: (i) better mitigate the effects
of noise, and (ii) better capitalize on the structure of the support sets.Comment: to appear in IEEE Transactions on Information Theor
Compressed sensing with near-field THz radiation
We demonstrate a form of near-field terahertz (THz) imaging that is compatible with compressed sensing algorithms. By spatially photomodulating THz pulses using a set of shaped binary optical patterns and employing a 6-μm-thick silicon wafer, we are able to reconstruct THz images of an object placed on the exit interface of the wafer. A single-element detector is used to measure the electric field amplitude of transmitted THz radiation for each projected pattern, with the ultra-thin wafer allowing us to access the THz evanescent near fields to achieve a spatial resolution of ∼9 μm∼9 μm
Ensemble Analysis of Adaptive Compressed Genome Sequencing Strategies
Acquiring genomes at single-cell resolution has many applications such as in
the study of microbiota. However, deep sequencing and assembly of all of
millions of cells in a sample is prohibitively costly. A property that can come
to rescue is that deep sequencing of every cell should not be necessary to
capture all distinct genomes, as the majority of cells are biological
replicates. Biologically important samples are often sparse in that sense. In
this paper, we propose an adaptive compressed method, also known as distilled
sensing, to capture all distinct genomes in a sparse microbial community with
reduced sequencing effort. As opposed to group testing in which the number of
distinct events is often constant and sparsity is equivalent to rarity of an
event, sparsity in our case means scarcity of distinct events in comparison to
the data size. Previously, we introduced the problem and proposed a distilled
sensing solution based on the breadth first search strategy. We simulated the
whole process which constrained our ability to study the behavior of the
algorithm for the entire ensemble due to its computational intensity. In this
paper, we modify our previous breadth first search strategy and introduce the
depth first search strategy. Instead of simulating the entire process, which is
intractable for a large number of experiments, we provide a dynamic programming
algorithm to analyze the behavior of the method for the entire ensemble. The
ensemble analysis algorithm recursively calculates the probability of capturing
every distinct genome and also the expected total sequenced nucleotides for a
given population profile. Our results suggest that the expected total sequenced
nucleotides grows proportional to of the number of cells and
proportional linearly with the number of distinct genomes
Info-Greedy sequential adaptive compressed sensing
We present an information-theoretic framework for sequential adaptive
compressed sensing, Info-Greedy Sensing, where measurements are chosen to
maximize the extracted information conditioned on the previous measurements. We
show that the widely used bisection approach is Info-Greedy for a family of
-sparse signals by connecting compressed sensing and blackbox complexity of
sequential query algorithms, and present Info-Greedy algorithms for Gaussian
and Gaussian Mixture Model (GMM) signals, as well as ways to design sparse
Info-Greedy measurements. Numerical examples demonstrate the good performance
of the proposed algorithms using simulated and real data: Info-Greedy Sensing
shows significant improvement over random projection for signals with sparse
and low-rank covariance matrices, and adaptivity brings robustness when there
is a mismatch between the assumed and the true distributions.Comment: Preliminary results presented at Allerton Conference 2014. To appear
in IEEE Journal Selected Topics on Signal Processin
Hybrid MIMO Architectures for Millimeter Wave Communications: Phase Shifters or Switches?
Hybrid analog/digital MIMO architectures were recently proposed as an
alternative for fully-digitalprecoding in millimeter wave (mmWave) wireless
communication systems. This is motivated by the possible reduction in the
number of RF chains and analog-to-digital converters. In these architectures,
the analog processing network is usually based on variable phase shifters. In
this paper, we propose hybrid architectures based on switching networks to
reduce the complexity and the power consumption of the structures based on
phase shifters. We define a power consumption model and use it to evaluate the
energy efficiency of both structures. To estimate the complete MIMO channel, we
propose an open loop compressive channel estimation technique which is
independent of the hardware used in the analog processing stage. We analyze the
performance of the new estimation algorithm for hybrid architectures based on
phase shifters and switches. Using the estimated, we develop two algorithms for
the design of the hybrid combiner based on switches and analyze the achieved
spectral efficiency. Finally, we study the trade-offs between power
consumption, hardware complexity, and spectral efficiency for hybrid
architectures based on phase shifting networks and switching networks.
Numerical results show that architectures based on switches obtain equal or
better channel estimation performance to that obtained using phase shifters,
while reducing hardware complexity and power consumption. For equal power
consumption, all the hybrid architectures provide similar spectral
efficiencies.Comment: Submitted to IEEE Acces
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