Instrumentation & Measurement Magazine 24-8 - 16

University of Toronto, Ontario, Canada: the collaboration is
active in the form of several research visits. Within the collaboration,
the research activities focused on the study and
realization of innovative pattern recognition methods and algorithms
based on image processing and artificial intelligence
techniques, with a specific focus on biometric and biomedical
applications [7], [10], [11].
Dissemination
The results of the research activities performed by the TC have
been published in several journal articles [4]-[6] and conference
papers [7]-[9], [11]. In the following, we give a short
description of the main result achievements.
A Comprehensive Survey of Databases and Deep Learning Methods
for Cybersecurity and Intrusion Detection Systems: the article
has been published in the IEEE Systems Journal in June 2021
and describes a comprehensive overview of methods based on
machine learning for intrusion detection, in particular regarding
methods based on Deep Learning [4].
A Decision Support System for Wind Power Production: the article
has been published in the IEEE Transactions on Systems,
Man, and Cybernetics: Systems in January 2020 and proposes
a novel method based on artificial intelligence for the longterm
prediction of electrical power, generated by renewable
sources [5].
3-D Granulometry Using Image Processing: the article has
been published in the IEEE Transactions on Industrial Informatics
in March 2019 and proposes an innovative three- dimensional
vision system to compute the granulometry of thin falling particles
in real time [6].
Histopathological transfer learning for Acute Lymphoblastic
Leukemia detection: the article has been published in the 2021
IEEE Int. Conf. on Computational Intelligence and Virtual Environments
for Measurement Systems and Applications (CIVEMSA
2021) and proposes a method based on deep learning and
transfer learning, to increase the detection accuracy of tumoral
cells [11] (https://iebil.di.unimi.it/cnnALL/index.htm).
Acute Lymphoblastic Leukemia Detection Based on Adaptive
Unsharpening and Deep Learning: the article has been published
in the 2021 IEEE Int. Conf. on Acoustics, Speech, and Signal Processing
(ICASSP 2021) and describes an approach based on
machine learning and deep learning for adaptively unsharpening
blood sample images, with the purpose of increasing
the classification accuracy of tumoral cells [7] (https://iebil.
di.unimi.it/cnnALL/index.htm).
Driver Attention Assistance by Pedestrian/Cyclist Distance
Estimation from a Single RGB Image: A CNN-based Semantic Segmentation
Approach: the article has been published in the 22nd
IEEE Int. Conf. on Industrial Technology (ICIT 2021) and introduces
the first method in the literature for estimating the
distances of pedestrians from the vehicle in automotive applications,
using only an RGB image and deep learning [8]
(https://iebil.di.unimi.it/DistPedCNN/index.htm).
IoT-based Architectures for Sensing and Local Data Processing
in Ambient Intelligence: Research and Industrial Trends: the article
has been published in the 2019 IEEE Int. Instrumentation and
16
Measurement Technology Conf. (I2MTC 2019) and reviews recent
methods based on Internet of Things for Ambient Intelligence
applications, with a specific focus on approaches that prefer local
data processing over cloud-based computing [9].
Teaching
The members of the TC are also active in teaching degree
courses, related to the scope of the committee, at the Università
degli Studi di Milano (University of Milan, Italy) such
as the Ph.D. course " Deep Learning for signal and image
processing " and the M.Sc. course " Intelligent systems for
industry, supply chain, and environment. "
Deep Learning for Signal and Image Processing: the course
presents recent artificial intelligence and machine learning
techniques for multi-dimensional signal processing and
pattern recognition, with a specific focus on Deep Learning
(DL) approaches, which-with respect to traditional
pattern recognition algorithms-have the advantage of automatically
extracting distinctive data representations from
multidimensional signals, thus reducing the need of domain
expertise in a specific field. In particular, the course
describes the main DL approaches for signal and image
processing, such as Convolutional Neural Networks, Autoencoders,
and Generative Adversarial Networks. Then,
the course presents application examples for heterogenous
scenarios, including scenarios related to intelligent
measurements systems, such as industrial monitoring and
ambient intelligence (http://dottorato.di.unimi.it/index.
php/teaching/2019-2021/101-deep-learning-for-signal-andimage-processing-november-december-2021).
Intelligent
Systems for Industry, Supply Chain, and Environment:
the course introduces the methodologies and techniques
for intelligent systems for monitoring and control of industrial,
environmental and supply chain applications, typically
based on artificial intelligence techniques (https://www.
unimi.it/en/education/degree-programme-courses/2021/
intelligent-systems-industry-supply-chain-and-environment).
Future Activities
The TC will organize, together with other IEEE members,
the next edition of the International Conference on
Computational Intelligence and Virtual Environments for
Measurement Systems and Applications (CIVEMSA 2022)
(https://civemsa2022.ieee-ims.org). As the previous years,
the conference will be an interesting venue for researchers and
interested scholars to publish works and exchange ideas related,
among others, to the key activities of the TC. Topics in
the scope of the CIVEMSA conference include:
◗ Intelligent measurement systems;
◗ Multi-sensor data fusion and intelligent sensor fusion;
◗ Intelligent monitoring and control systems;
◗ Neural and fuzzy signal/image processing for industrial,
environmental and domotics applications;
◗ Machine and deep learning for intelligent systems;
◗ Computational intelligence technologies for robotics and
vision;
IEEE Instrumentation & Measurement Magazine
November 2021

http://dottorato.di.unimi.it/index.php/teaching/2019-2021/101-deep-learning-for-signal-and-image-processing-november-december-2021 http://dottorato.di.unimi.it/index.php/teaching/2019-2021/101-deep-learning-for-signal-and-image-processing-november-december-2021 http://dottorato.di.unimi.it/index.php/teaching/2019-2021/101-deep-learning-for-signal-and-image-processing-november-december-2021 https://www.unimi.it/en/education/degree-programme-courses/2021/intelligent-systems-industry-supply-chain-and-environment https://www.unimi.it/en/education/degree-programme-courses/2021/intelligent-systems-industry-supply-chain-and-environment https://iebil.di.unimi.it/cnnALL/index.htm https://www.unimi.it/en/education/degree-programme-courses/2021/intelligent-systems-industry-supply-chain-and-environment https://iebil.di.unimi.it/cnnALL/index.htm https://civemsa2022.ieee-ims.org https://iebil.di.unimi.it/cnnALL/index.htm https://iebil.di.unimi.it/DistPedCNN/index.htm

Instrumentation & Measurement Magazine 24-8

Table of Contents for the Digital Edition of Instrumentation & Measurement Magazine 24-8