Signal Processing - March 2017 - 122

social makes automating vehicles
an ultrahard problem to tackle. We
observe cues from vehicles while we
drive that suggest we should drive
more cautiously (e.g., a pedestrian
looking at his smartphone while walking toward an intersection) or even
avoid certain driving scenarios (e.g.,
an overzealous driver swerving through
lanes). While it's unreasonable to
expect that self-driving cars could make
the same social observations that we
make as humans, what we can expect is
that technology will assist us in being
as aware and informed as possible.
Already there have been advancements
made by the signal processing community to estimate driver distraction using
in-vehicle sensors and cue the driver to
focus on the road. However, there are
many other opportunities for signal processing engineers to analyze human
behavior data associated with driving,

ReADeR's ChoiCe

Author note
Some parts of this article originally
appeared on Robotic Tips website; http://
www.robotictips.com.

Author
Wade Trappe (trappe@winlab.rutgers
.edu) received his B.A. degree in mathematics from the University of Texas at
Austin and his Ph.D. degree in applied
mathematics and scientific computing
from the University of Maryland. He is
an IEEE Fellow, and a professor of
electrical and computer engineering
at Rutgers University, New Brunswick,
New Jersey.

References

[1] D. Ngo. (14, Sept. 2016). Tragic Tesla crashes in
China. [Online]. Available: www.cnet.com/news/
dash-cam-showed-fatal-tesla-crash-in-china
[2] D Shepardson. (26 July 2016). Tragic Tesla crashes in the U.S. [Online]. Available: www.reuters.com/
article/us-tesla-autopilot-idUSKCN1062CT
[3] A Webb. (19 July 2016). Cybersecurity is biggest
risk of autonomous cars: Survey finds. [Online]. Available: www.bloomberg.com/news/articles/2016-07-19/
cybersecurity-is-biggest-risk-of-autonomous-carssurvey-finds

(continued from page 13)

Tensor Decompositions for Signal
Processing Applications: From
Two-Way to Multiway Component
Analysis
Cichocki, A.; Mandic, D.; Lathauwer,
L. De; Zhou, G.; Zhao, Q.; Caiafa,
C.; Phan, H.A.
The authors have shown that tensor
decompositions are a good match for
exploratory analysis of multifaceted
data sets and have illustrated their
applications in multisensor and
multimodal signal processing. Their
empha sis has shown that tensor decompositions and multilinear algebra
open up completely new possibilities
for component analysis, as compared
with the flat view of standard twoway methods.
March 2015

122

which will be essential for improving
driver and pedestrian safety.
The future of vehicular systems is
data and sensor driven. Vehicles will
become increasingly networked and outfitted with sensors and share their data
with a variety of in-vehicle and cloudbased computing services. The societal
benefits associated with improved vehicular systems range from energy efficiency resulting from swarm driving to the
potential for saving many lives should
the technology mature. While this future
is exciting, engineers, researchers, and
technologists must quickly act to develop the new signal and information processing innovations required to make
future vehicular systems safe.

Euclidean Distance Matrices: Essential
Theory, Algorithms, and Applications
Dokmanic, I.; Parhizkar, R.; Ranieri,
J.; Vetterli, M.
This article reviews the fundamental
properties of Euclidean distance matrices (EDMs) and shows how the various
EDM properties can be used to design
algorithms for completing and denoising distance data. Some directions are
given for further research.
November 2015

Bayesian Machine Learning:
EEG/MEG Signal Processing
Measurements
Wu, W.; Nagarajan, S.; Chen, Z.
To review recent advances and to foster
new research ideas, this article provides a
tutorial on several important emerging

IEEE Signal Processing Magazine

March 2017

Bayesian machine learning research topics in electroencephalography (EEG)/
magnetoencephalography (MEG) signal
processing and presents representative
examples in EEG/MEG applications.
January 2016

Compressive Covariance Sensing:
Structure-Based Compressive
Sensing Beyond Sparsity
Romero, D.; Ariananda, D.D.;
Tian, Z.; Leus, G.
This article is concerned with the
reconstruction of second-order statistics, such as covariance and power
spectrum, instead of the reconstruction
of signals in compressed sensing, even
in the absence of sparsity priors.
January 2016
sp

http://www.cnet.com/news/ http://www.reuters.com/ http://www.bloomberg.com/news/articles/2016-07-19/ http://http:// http://www.robotictips.com

Table of Contents for the Digital Edition of Signal Processing - March 2017