230 research outputs found
Computational Methods for Sparse Solution of Linear Inverse Problems
The goal of the sparse approximation problem is to approximate a target signal using a linear combination of a few elementary signals drawn from a fixed collection. This paper surveys the major practical algorithms for sparse approximation. Specific attention is paid to computational issues, to the circumstances in which individual methods tend to perform well, and to the theoretical guarantees available. Many fundamental questions in electrical engineering, statistics, and applied mathematics can be posed as sparse approximation problems, making these algorithms versatile and relevant to a plethora of applications
How to find real-world applications for compressive sensing
The potential of compressive sensing (CS) has spurred great interest in the
research community and is a fast growing area of research. However, research
translating CS theory into practical hardware and demonstrating clear and
significant benefits with this hardware over current, conventional imaging
techniques has been limited. This article helps researchers to find those niche
applications where the CS approach provides substantial gain over conventional
approaches by articulating lessons learned in finding one such application; sea
skimming missile detection. As a proof of concept, it is demonstrated that a
simplified CS missile detection architecture and algorithm provides comparable
results to the conventional imaging approach but using a smaller FPA. The
primary message is that all of the excitement surrounding CS is necessary and
appropriate for encouraging our creativity but we all must also take off our
"rose colored glasses" and critically judge our ideas, methods and results
relative to conventional imaging approaches.Comment: 10 page
Matching pursuit-based compressive sensing in a wearable biomedical accelerometer fall diagnosis device
There is a significant high fall risk population, where individuals are susceptible to frequent falls and obtaining significant injury, where quick medical response and fall information are critical to providing efficient aid. This article presents an evaluation of compressive sensing techniques in an accelerometer-based intelligent fall detection system modelled on a wearable Shimmer biomedical embedded computing device with Matlab. The presented fall detection system utilises a database of fall and activities of daily living signals evaluated with discrete wavelet transforms and principal component analysis to obtain binary tree classifiers for fall evaluation. 14 test subjects undertook various fall and activities of daily living experiments with a Shimmer device to generate data for principal component analysis-based fall classifiers and evaluate the proposed fall analysis system. The presented system obtains highly accurate fall detection results, demonstrating significant advantages in comparison with the thresholding method presented. Additionally, the presented approach offers advantageous fall diagnostic information. Furthermore, transmitted data accounts for over 80% battery current usage of the Shimmer device, hence it is critical the acceleration data is reduced to increase transmission efficiency and in-turn improve battery usage performance. Various Matching pursuit-based compressive sensing techniques have been utilised to significantly reduce acceleration information required for transmission.Scopu
Sparse Vector Distributions and Recovery from Compressed Sensing
It is well known that the performance of sparse vector recovery algorithms
from compressive measurements can depend on the distribution underlying the
non-zero elements of a sparse vector. However, the extent of these effects has
yet to be explored, and formally presented. In this paper, I empirically
investigate this dependence for seven distributions and fifteen recovery
algorithms. The two morals of this work are: 1) any judgement of the recovery
performance of one algorithm over that of another must be prefaced by the
conditions for which this is observed to be true, including sparse vector
distributions, and the criterion for exact recovery; and 2) a recovery
algorithm must be selected carefully based on what distribution one expects to
underlie the sensed sparse signal.Comment: Originally submitted to IEEE Signal Processing Letters in March 2011,
but rejected June 2011. Revised, expanded, and submitted July 2011 to EURASIP
Journal special issue on sparse signal processin
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