IEEE Computational Intelligence Magazine - November 2023 - 10

Publication
Spotlight
Yongduan Song
Chongqing University, CHINA
Dongrui Wu
Huazhong University ofScience and Technology, CHINA
Carlos A. Coello Coello
CINVESTAV-IPN, MEXICO
Georgios N. Yannakakis
University ofMalta, MALTA
Huajin Tang
Zhejiang University, CHINA
Yiu-Ming Cheung
Hong Kong Baptist University, HONG KONG
Hussein Abbass
University ofNew South Wales, AUSTRALIA
CIS Publication Spotlight
IEEE Transactions on Neural
Networks andLearning Systems
Improving the Accuracy of Spiking
Neural Networks for Radar Gesture
Recognition Through Preprocessing, by
A.Safa,F.Corradi,L.Keuninckx,
I. Ocket, A. Bourdoux, F. Catthoor,
andG.G.E.Gielen, IEEE
Transactions on Neural Networks and
Learning Systems, Vol. 34, No. 6,
Jun. 2023, pp. 2869-2881.
Digital Object Identifier: 10.1109/
TNNLS.2021.3109958
" Event-based neural networks are
currently being explored as efficient solutions
for performing AI tasks at the
extreme edge. To fully exploit their
potential, event-based neural networks
coupled to adequate preprocessing must
be investigated. Within this context, we
demonstrate a 4-b-weight spiking neural
network (SNN) for radar gesture
recognition, achieving a state-of-the-art
93% accuracy within only four processing
time steps while using only one
convolutional layer and two fully
Digital Object Identifier 10.1109/MCI.2023.3306163
Date ofcurrent version: 17 October 2023
comparing the gesture recognition
accuracy achieved with our SNN to a
DNN with the same architecture and
similar training. Unlike previously proposed
neural networks for radar processing,
this work enables ultralowpower
radar-based gesture recognition
for extreme-edge devices. "
IMAGE LICENSED BY INGRAM PUBLISHING
IEEE Transactions on Fuzzy Systems
connected layers. This solution consumes
very little energy and area if
implemented in event-based hardware,
which makes it suited for embedded
extreme-edge applications. In addition,
we demonstrate the importance of signal
preprocessing for achieving this high
recognition accuracy in SNNs compared
to deep neural networks (DNNs)
with the same network topology and
training strategy. We show that efficient
preprocessing prior to the neural network
is drastically more important for
SNNs compared to DNNs. We also
demonstrate, for the first time, that the
preprocessing parameters can affect
SNNs and DNNs in antagonistic ways,
prohibiting the generalization of conclusions
drawn from DNN design to
SNNs. We demonstrate our findings by
10 IEEE COMPUTATIONAL INTELLIGENCE MAGAZINE | NOVEMBER 2023
More Than Accuracy: A Composite
Learning Frameworkfor Interval Type-2
Fuzzy Logic Systems, by A. Beke and
T. Kumbasar, IEEE Transactions on
Fuzzy Systems, Vol. 31, No. 3, Mar.
2023, pp. 734-744.
Digital Object Identifier: 10.1109/
TFUZZ.2022.3188920
" In this article, we propose a novel
composite learning framework for interval
type-2 (IT2) fuzzy logic systems
(FLSs) to train regression models with a
high accuracy performance and capable
of representing uncertainty. In this context,
we identify three challenges, first,
the uncertainty handling capability, second,
the construction of the composite
loss, and third, a learning algorithm that
overcomes the training complexity while
1556-603X ß 2023 IEEE

https://orcid.org/0000-0002-2167-1861 https://orcid.org/0000-0002-7153-9703 https://orcid.org/0000-0002-8435-680X https://orcid.org/0000-0001-7793-1450 https://orcid.org/0000-0002-9857-1543 https://orcid.org/0000-0001-7629-4648 https://orcid.org/0000-0002-8837-0748

IEEE Computational Intelligence Magazine - November 2023

Table of Contents for the Digital Edition of IEEE Computational Intelligence Magazine - November 2023