Special Issue "Deep Learning Techniques for Manned and Unmanned Ground, Aerial and Marine Vehicles"
Deadline for manuscript submissions: 31 October 2021.
2. Faculty of Computers and Artificial Intelligence, Benha University, Benha 13518, Egypt
Interests: control theory & applications; robotics; process control; artificial intelligence; machine learning
Special Issues and Collections in MDPI journals
2. CISTER and ISTER, INESC-TEC, ISEP, Polytechnic Institute of Porto, 4249-015 Porto, Portugal
Interests: aerial image processing; deep learning; precision agriculture; remote sensing; computer vision
Interests: smart mobility; autonomous and connected vehicles; cognitive IoT; machine learning; combinatorial opti-mization
Interests: artificial intelligence; field robotics; autonomous navigation; path planning; automation and control
Interests: Computational intelligence; knowledge discovery from data; intelligent data analysis; matrix factorizations
Special Issues and Collections in MDPI journals
Special Issue in Electronics: Electronics Application in Medicine & Health Care
Manned and unmanned ground, aerial, and marine vehicles enable many promising and revolutionary civilian and military applications that will change our lives in the near future. These applications include, but are not limited to, surveillance, search and rescue, environment monitoring, infrastructure monitoring, self-driving cars, contactless last-mile delivery vehicles, autonomous ships, precision agriculture, and transmission line inspection, to name just a few. These vehicles will benefit from advances in deep learning as a subfield of machine learning able to endow these vehicles with different capabilities such as perception, situation awareness, planning, and intelligent control. Deep learning models also have the ability to generate actionable insights into the complex structures of large data sets.
In recent years, deep learning research has received increasing attention from researchers in academia, government laboratories, and industry. These research activities have borne some fruit in tackling some of the remaining challenging problems of manned and unmanned ground, aerial, and marine vehicles. Moreover, deep learning methods have recently been actively developed in other areas of machine learning, including reinforcement training and transfer/meta-learning, whereas standard deep learning methods such as RNN (recent neural network) and CNN (coevolutionary neural networks).
The purpose of this Special Issue is to report recent applications of deep learning approaches in manned and unmanned ground, aerial, and marine vehicles. Topics include but are not limited to:
- Cognitive data collection;
- Data cleansing;
- Data compression;
- Multisensor data fusion;
- Vehicle localization;
- Perception systems;
- AI for automation systems;
- Object detection, localization, and tracking;
- Situation awareness;
- Vehicle control;
- Autonomous vehicles;
- Connected vehicles;
- Self-driving cars;
- Generative adversarial networks (GANs);
- Collective intelligence;
- Multiagent systems;
- Platooning, flocking, and self-organization;
- Applications: unmanned aerial vehicles (UAVs), unmanned ground vehicles (UGVs), unmanned underwater vehicles (UUVs), and unmanned surface vehicles (USVs), self-driving cars, delivery robots, search and rescue, reconnaissance, surveillance, swarm robotics, etc.
Prof. Dr. Ahmad Taher Azar
Prof. Dr. Anis Koubaa
Prof. Dr. Alaa Khamis
Prof. Dr. Ibrahim A. Hameed
Dr. Gabriella Casalino
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access semimonthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.