26,468 research outputs found
Rapid Sequence Identification of Potential Pathogens Using Techniques from Sparse Linear Algebra
The decreasing costs and increasing speed and accuracy of DNA sample
collection, preparation, and sequencing has rapidly produced an enormous volume
of genetic data. However, fast and accurate analysis of the samples remains a
bottleneck. Here we present DRAGenS, a genetic sequence identification
algorithm that exhibits the Big Data handling and computational power of the
Dynamic Distributed Dimensional Data Model (D4M). The method leverages linear
algebra and statistical properties to increase computational performance while
retaining accuracy by subsampling the data. Two run modes, Fast and Wise, yield
speed and precision tradeoffs, with applications in biodefense and medical
diagnostics. The DRAGenS analysis algorithm is tested over several
datasets, including three utilized for the Defense Threat Reduction Agency
(DTRA) metagenomic algorithm contest
Multidimensional Index Modulation in Wireless Communications
In index modulation schemes, information bits are conveyed through indexing
of transmission entities such as antennas, subcarriers, times slots, precoders,
subarrays, and radio frequency (RF) mirrors. Index modulation schemes are
attractive for their advantages such as good performance, high rates, and
hardware simplicity. This paper focuses on index modulation schemes in which
multiple transmission entities, namely, {\em antennas}, {\em time slots}, and
{\em RF mirrors}, are indexed {\em simultaneously}. Recognizing that such
multidimensional index modulation schemes encourage sparsity in their transmit
signal vectors, we propose efficient signal detection schemes that use
compressive sensing based reconstruction algorithms. Results show that, for a
given rate, improved performance is achieved when the number of indexed
transmission entities is increased. We also explore indexing opportunities in
{\em load modulation}, which is a modulation scheme that offers power
efficiency and reduced RF hardware complexity advantages in multiantenna
systems. Results show that indexing space and time in load modulated
multiantenna systems can achieve improved performance
Grid-free compressive beamforming
The direction-of-arrival (DOA) estimation problem involves the localization
of a few sources from a limited number of observations on an array of sensors,
thus it can be formulated as a sparse signal reconstruction problem and solved
efficiently with compressive sensing (CS) to achieve high-resolution imaging.
On a discrete angular grid, the CS reconstruction degrades due to basis
mismatch when the DOAs do not coincide with the angular directions on the grid.
To overcome this limitation, a continuous formulation of the DOA problem is
employed and an optimization procedure is introduced, which promotes sparsity
on a continuous optimization variable. The DOA estimation problem with
infinitely many unknowns, i.e., source locations and amplitudes, is solved over
a few optimization variables with semidefinite programming. The grid-free CS
reconstruction provides high-resolution imaging even with non-uniform arrays,
single-snapshot data and under noisy conditions as demonstrated on experimental
towed array data.Comment: 14 pages, 8 figures, journal pape
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