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Artificial Intelligence in Sensors, 2nd Volume
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Department of Mechanical Engineering (ME), University of California, Merced, CA 95343, USA
Department of Engineering, University of Campania Luigi Vanvitelli, Via Roma 29, 81031 Aversa, Italy
Department of Electrical & Computer Engineering, Faculty of Engineering, McMaster University, Hamilton, ON L8S 4L8, Canada
Department of Materials Science and Engineering, Gachon University, Seongnam-si 1342, Republic of Korea
School of Engineering, Macquarie University, Sydney, NSW 2109, Australia
REQUIMTE/LAQV, School of Engineering, Polytechnic Institute of Porto, 4249-015 Porto, Portugal
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Artificial Intelligence in Sensors, 2nd Volume

Abstract submission deadline
31 October 2023
Manuscript submission deadline
31 December 2023
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Topic Information

Dear Colleagues,

Following the success of the previous topic “Artificial Intelligence in Sensors”, we are pleased to announce the next in the series, entitled “Artificial Intelligence in Sensors, 2nd Volume”.

This topic comprises several interdisciplinary research areas that cover the main aspects of sensor sciences. There has been an increase in both the capabilities and challenges related within numerous application fields, e.g., robotics, industry 4.0, automotive, smart cties, medicine, diagnosis, food, telecommunication, environmental and civil applications, health and security.

The associated applications constantly require novel sensors to improve their capabilities and challenges. Thus, sensor sciences represents a paradigm characterized by the integration of modern nanotechnologies and nanomaterials into manufacturing and industrial practice to develop tools for several application fields. The primary underlying goal of sensor sciences is to facilitate the closer interconnection and control of complex systems, machines, devices, and people to increase the support provided to humans in several application fields.

Sensor sciences comprises a set of significant research fields, including:

  • Advanced data visualization techniques;
  • Advanced interactive technologies, including augmented/virtual reality;
  • Artificial intelligence;
  • Big data processing and analytics;
  • Biosensors;
  • Chemical sensors;
  • Cognitive computing platforms and applications;
  • Computer vision;
  • Data security;
  • Deep learning;
  • Electronics and mechanicals;
  • Image processing;
  • Instrumentation science and technology;
  • Intelligent sensor;
  • Interdisciplinary sciences.
  • Internet of Things platforms and their applications;
  • Machine learning;
  • Machine vision;
  • Materials and nanomaterials;
  • Measurement science and technology;
  • Mechatronics;
  • MEMS, microwaves and acoustic waves;
  • Microfluidics;
  • Nanotechnology;
  • Optical sensors;
  • Optoelectronics, photonics, and optical fibers;
  • Organic electronics, biophotonics and smart materials;
  • Physical sensors;
  • Physics and biophysics;
  • Remote sensing;
  • Robotics;
  • Sensor networks;
  • Smart sensors and sensing;
  • UAV;
  • UGV.

This topic aims to collect the results of research in these fields and others. Therefore, submitting papers within those areas connected to sensors is strongly encouraged.

Prof. Dr. Yangquan Chen
Dr. Nunzio Cennamo
Prof. Dr. M. Jamal Deen
Dr. Junseop Lee
Prof. Dr. Subhas Mukhopadhyay
Prof. Dr. Simone Morais
Topic Editors

 

Keywords

  • sensors
  • sensing
  • artificial intelligence
  • deep learning
  • machine learning
  • computer vision
  • big data
  • IoT

Participating Journals

Journal Name Impact Factor CiteScore Launched Year First Decision (median) APC
Applied Sciences
applsci 		2.838 3.7 2011 14.9 Days 2300 CHF Submit
Drones
drones 		5.532 7.2 2017 13.6 Days 2000 CHF Submit
Electronics
electronics 		2.690 3.7 2012 14.4 Days 2000 CHF Submit
Remote Sensing
remotesensing 		5.349 7.4 2009 19.7 Days 2500 CHF Submit
Sensors
sensors 		3.847 6.4 2001 15 Days 2400 CHF Submit

Preprints is a platform dedicated to making early versions of research outputs permanently available and citable. MDPI journals allow posting on preprint servers such as Preprints.org prior to publication. For more details about reprints, please visit https://www.preprints.org.

Published Papers (2 papers)

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Article
A Robust Pedestrian Re-Identification and Out-Of-Distribution Detection Framework
by
Abdelhamid Bouzid
,
Daniel Sierra-Sosa
and
Adel Elmaghraby
Drones 2023, 7(6), 352; https://doi.org/10.3390/drones7060352 - 27 May 2023
Viewed by 164
Abstract
Pedestrian re-identification is an important field due to its applications in security and safety. Most current solutions for this problem use CNN-based feature extraction and assume that only the identities that are in the training data can be recognized. On the one hand, [...] Read more.
Pedestrian re-identification is an important field due to its applications in security and safety. Most current solutions for this problem use CNN-based feature extraction and assume that only the identities that are in the training data can be recognized. On the one hand, the pedestrians in the training data are called In-Distribution (ID). On the other hand, in real-world scenarios, new pedestrians and objects can appear in the scene, and the model should detect them as Out-Of-Distribution (OOD). In our previous study, we proposed a pedestrian re-identification based on von Mises–Fisher (vMF) distribution. Each identity is embedded in the unit sphere as a compact vMF distribution far from other identity distributions. Recently, a framework called Virtual Outlier Synthetic (VOS) was proposed, which detects OOD based on synthesizing virtual outliers in the embedding space in an online manner. Their approach assumes that the samples from the same object map to a compact space, which aligns with the vMF-based approach. Therefore, in this paper, we revisited the vMF approach and merged it with VOS to detect OOD data points. Experiment results showed that our framework was able to detect new pedestrians that do not exist in the training data in the inference phase. Furthermore, this framework improved the re-identification performance and holds a significant potential in real-world scenarios. Full article
(This article belongs to the Topic Artificial Intelligence in Sensors, 2nd Volume)
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Article
MFPCDR: A Meta-Learning-Based Model for Federated Personalized Cross-Domain Recommendation
by
Yicheng Di
and
Yuan Liu
Appl. Sci. 2023, 13(7), 4407; https://doi.org/10.3390/app13074407 - 30 Mar 2023
Viewed by 705
Abstract
Cross-domain recommendation systems frequently require the use of rich source domain information to improve recommendations in the target domain, thereby resolving the data sparsity and cold-start problems, whereas the majority of existing approaches frequently require the centralized storage of user data, which poses [...] Read more.
Cross-domain recommendation systems frequently require the use of rich source domain information to improve recommendations in the target domain, thereby resolving the data sparsity and cold-start problems, whereas the majority of existing approaches frequently require the centralized storage of user data, which poses a substantial risk of privacy breaches. Compared to traditional recommendation systems with centralized data, federated recommendation systems with multiple clients trained collaboratively have significant privacy benefits in terms of user data. While users’ interests are often personalized, meta-learning can be used to learn users’ personalized preferences, and personalized preferences can help models make recommendations in cold-start scenarios. We use meta-learning to learn the personalized preferences of cold-start users. Therefore, we offer a unique meta-learning-based federated personalized cross-domain recommendation model that discovers the personalized preferences for cold-start users via a server-side meta-recommendation module. To avoid compromising user privacy, an attention mechanism is used on each client to find transferable features that contribute to knowledge transfer while obtaining embeddings of users and items; each client then uploads the weights to the server. The server accumulates weights and delivers them to clients for update. Compared to traditional recommendation system models, our model can effectively protect user privacy while solving the user cold-start problem, as we use an attention mechanism in the local embedding module to mine the source domain for transferable features that contribute to knowledge transfer. Extensive trials on real-world datasets have demonstrated that our technique effectively guarantees speed while protecting user privacy. Full article
(This article belongs to the Topic Artificial Intelligence in Sensors, 2nd Volume)
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