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IEEE SIGNAL PROCESSING MAGAZINE [8] MARCH 2015
TITLE, AUTHOR, PUBLICATION YEAR
IEEE SPS PUBLICATIONS ABSTRACT
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IMAGE SUPER-RESOLUTION VIA SPARSE
REPRESENTATION
Yang, J.; Wright, J; Huang, T.S.; Ma, Y.
IEEE Transactions on Image Processing
vol. 19, no. 11, 2010, pp. 2861–2873
This paper presents an approach to
single-image superresolution, based upon
sparse signal representation of low and
high resolution patches.
84 81 55 92 27 12
K-SVD: AN ALGORITHM FOR DESIGNING
OVERCOMPLETE DICTIONARIES FOR
SPARSE REPRESENTATION
Aharon, M.; Elad, M.; Bruckstein, A.
IEEE Transactions on Signal Processing
vol. 54. no. 11, 2006, pp. 4311–4322
K-SVD is an iterative method that
alternates between sparse coding of the
examples based on the current dictionary
and a process of updating the dictionary
atoms to better fit the data in a
computationally efficient manner.
85 1
AN OVERVIEW OF MASSIVE MIMO:
BENEFITS AND CHALLENGES
Lu, L.; Li, G.Y.; Swindlehurst, A.L.;
Ashikhmin, A.; Zhang, R.
IEEE Journal on Selected Topics in Signal
Processing
vol. 8, no. 5, 2014, pp. 742–758
Equipping cellular base stations with a
very large number of antennas, potentially
allows for orders of magnitude
improvement in spectral and energy
efficiency. This paper presents an extensive
overview and analysis of massive MIMO
systems.
89 1
TENSORS: A BRIEF INTRODUCTION
Comon, P.
IEEE Signal Processing Magazine
vol. 31, no. 3, 2014, pp. 44–53
This article explains the different
properties of tensors and matrices. In
particular the canonical polyadic tensor
decomposition and singular-value matrix
decomposition.
97 23 2
THE PAST, PRESENT, AND THE FUTURE
OF UNDERWATER ACOUSTIC SIGNAL
PROCESSING
Vaccaro, R.J.
IEEE Signal Processing Magazine
vol. 15, no. 4, 1998, pp. 21–51
A collection of articles by members of the
Underwater Acoustic Signal Processing
Technical Committee ranging from 1960s
history to future applications including
synthetic aperture sonar.
89 1
SPARSE REPRESENTATION FOR BRAIN
SIGNAL PROCESSING: A TUTORIAL ON
METHODS AND APPLICATIONS
Li, Y.; Yu, Z.L.; Bi, N.; Xu, Y.; Gu, Z.;
Amari, S.I.
IEEE Signal Processing Magazine
vol. 31, no. 3, 2014, pp. 96–106
Formulates the task of blind source
separation of brain signals and other brain
signal processing problems as an
underdetermined linear model and solves
via sparse representations. Includes
applications such as BSS and EEG inverse
imaging, feature selection, and
classification.
72 1
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