Instrumentation & Measurement Magazine 23-6 - 17

[22]	P. Pouladzadeh and S. Shirmohammadi, "Mobile multi-food
recognition using deep learning," ACM Trans. Multimedia
Computing, Commun. Applications, vol. 13, no. 3s, article 36, Aug.

[39]	W. Lu et al., "Early fault detection approach with deep architectures,"
IEEE Trans. Instrum. Meas., vol. 67, no. 7, pp. 1679-1689, Jul. 2018.
[40]	M. Ma et al., "Discriminative deep belief networks with ant colony
optimization for health status assessment of machine," IEEE

2017.
[23]	M. Kopaczka et al., "A thermal infrared face database with facial
landmarks and emotion labels," IEEE Trans. Instrum. Meas., vol.

Trans. Instrum. Meas., vol. 66, no. 12, pp. 3115-3125, Dec. 2017.
[41]	B. Taji et al., "False alarm reduction in atrial fibrillation detection
using deep belief networks," IEEE Trans. Instrum. Meas., vol. 67,

68, no. 5, pp. 1389-1401, May 2019.
[24]	C. Nuzzi et al., "Deep learning-based hand gesture recognition for
collaborative robots," IEEE Instrum. Meas. Mag., vol. 22, no. 2, pp.

no. 5, pp. 1124-1131May 2018.
[42]	B. Andò et al., "Low-order nonlinear finite-impulse response soft
sensors for ionic electroactive actuators based on deep learning,"

44-51, Apr. 2019.
[25]	Z. Qiu et al., "RGB-DI images and full convolution neural
network-based outdoor scene understanding for mobile robots,"
IEEE Trans. Instrum. Meas., vol. 68, no. 1, pp. 27-37, Jan. 2019.
[26]	F. Wang et al., "Automatic generation of synthetic LiDAR point
clouds for 3-D data analysis," IEEE Trans. Instrum. Meas., vol. 68,

IEEE Trans. Instrum. Meas., vol. 68, no. 5, pp. 1637-1646, May 2019.
[43]	J. Sun et al., "Intelligent bearing fault diagnosis method
combining compressed data acquisition and deep learning," IEEE
Trans. Instrum. Meas., vol. 67, no. 1, pp. 185-195, Jan. 2018.
[44]	Z. Chen and W. Li, "Multisensor feature fusion for bearing fault
diagnosis using sparse autoencoder and deep belief network,"

no. 7, pp. 2671-2673, Jul. 2019.
[27]	C. Ma et al., "Binary volumetric convolutional neural networks
for 3-D object recognition," IEEE Trans. Instrum. Meas., vol. 68, no.

IEEE Trans. Instrum. Meas., vol. 66, no. 7, pp. 1693-1702, Jul. 2017.
[45]	G. Jiang et al., "Stacked multilevel-denoising autoencoders: a new
representation learning approach for wind turbine gearbox fault

1, Jan. 2019.
[28]	I-H. Kao et al., "Analysis of permanent magnet synchronous
motor fault diagnosis based on learning," IEEE Trans. Instrum.
Meas., vol. 68, no. 2, pp. 310-324, Feb. 2019.
[29]	X. Ding and Q. He, "Energy-fluctuated multiscale feature learning
with deep ConvNet for intelligent spindle bearing fault diagnosis,"
IEEE Trans. Instrum. Meas., vol. 66, no. 8, pp. 1926-1935, Aug. 2017.
[30]	J. Feng et al., "Injurious or noninjurious defect identification from
MFL images in pipeline inspection using convolutional neural
network," IEEE Trans. Instrum. Meas., vol. 66, no. 7, pp. 1883-

diagnosis," IEEE Trans. Instrum. Meas., vol. 66, no. 9, pp. 23912402, Sep. 2017.
[46]	F. Gu et al., "Accurate step length estimation for pedestrian dead
reckoning localization using stacked autoencoders," IEEE Trans.
Instrum. Meas., vol. 68, no. 8, pp. 2705-2713, Aug. 2019.
[47]	M. Singha Roy et al., "Improving photoplethysmographic
measurements under motion artifacts using artificial neural
network for personal healthcare," IEEE Trans. Instrum. Meas., vol.
67, no. 12, pp. 2820-2829, Dec. 2018.
[48]	J. Li et al., "CGAN-MBL for reliability assessment with

1892Jul. 2017.
[31]	I. Kamwa et al., "Recurrent neural networks for phasor detection
and adaptive identification in power system control and

imbalanced transmission gear data," IEEE Trans. Instrum. Meas.,
vol. 68, no. 9, pp. 3173-3183, Sep. 2019.

protection," IEEE Trans. Instrum. Meas., vol. 45, no. 2, pp. 657-663,
Jul. 1996.
[32]	D. Liu et al., "An integrated probabilistic approach to lithium-ion
battery remaining useful life estimation," IEEE Trans. Instrum.
Meas., vol. 64, no. 3, pp. 660-670, Mar. 2015.
[33]	I. Nancovska et al., "Case study of the predictive models used for
stability improvement of the dc voltage reference source," IEEE
Trans. Instrum. Meas., vol. 47, no. 6, pp. 1487-1491, Dec. 1998.
[34]	C. Alippi and V. Piuri, "Experimental neural networks for
prediction and identification," IEEE Trans. Instrum. Meas., vol. 45,
no. 2, pp. 670-676, Apr. 1996.
[35]	C. Alippi and V. Piuri, "Neural modeling of dynamic systems
with nonmeasurable state variables," IEEE Trans. Instrum. Meas.,
vol. 48, no. 6, pp. 1073-1080, Dec. 1999.

Mounib Khanafer is an Associate Professor of Electrical and
Computer Engineering at the Department of Engineering at
the American University of Kuwait, Kuwait. He conducts research in the general areas of Wireless Sensor Networks and
Internet of Things and their applications in vehicular networks.
He has three years of industrial experience at Nortel Networks,
Canada and worked as a Postdoctoral Fellow at the School of
Electrical Engineering and Computer Science at the University
of Ottawa, Canada from 2012-2013. He received his B.Sc. degree
in electrical engineering from Kuwait University, Kuwait in
2002 and his M.Sc. and Ph.D. degrees in electrical and computer
engineering from the University of Ottawa, Canada in 2007 and
2012, respectively. He is a Senior Member of IEEE.

[36]	A. H. Tan and K. Godfrey, "Modeling of direction-dependent
processes using wiener models and neural networks with
nonlinear output error structure," IEEE Trans. Instrum. Meas., vol.
53, no. 3, pp. 744-753, Jun. 2004.
[37]	A. I. Moustapha and R. R. Selmic, "Wireless sensor network
modeling using modified recurrent neural networks: application
to fault detection," IEEE Trans. Instrum. Meas., vol. 57, no. 5, pp.
981-988, May 2008.
[38]	A. Malhi et al., "Prognosis of defect propagation based on
recurrent neural networks," IEEE Trans. Instrum. Meas., vol. 60,
no. 3, pp. 703-711, Mar. 2011.
September 2020	

Shervin Shirmohammadi (M '04, SM '04, F '17) is currently a Professor with the School of Electrical Engineering and Computer
Science, University of Ottawa, Canada, where he is Director
of the Distributed and Collaborative Virtual Environment Research Laboratory and his research focuses on multimedia
systems and networks, including measurement techniques and
applied AI for networking, video streaming, and health systems. He received his Ph.D. degree in electrical engineering from
University of Ottawa, Canada and currently serves as the Editorin-Chief of IEEE Transactions on Instrumentation and Measurement.

IEEE Instrumentation & Measurement Magazine	17
Instrumentation & Measurement Magazine 23-6

Table of Contents for the Digital Edition of Instrumentation & Measurement Magazine 23-6
