Special Issue "Recent Advances in Artificial Intelligence and Deep Learning for Sensor Information Fusion"
Deadline for manuscript submissions: closed (30 January 2019)
Artificial Intelligence (AI) has attracted the attention of almost every researcher in computer science since AlphaGo Master continuously beat the current world number one ranking Go player three times. A few months later, DeepMind announced AlphaGo Zero which is a new version of AlphaGo Master created without using any human knowledge. AlphaGo Zero exceeded the Master after 40 days of reinforcement learning in its neural network. The brilliant performance and outstanding breakthroughs in AI brought machine learning and deep learning back to focus.
On the other hand, the number of sensors has been increased incrementally due to the rapid development of the Internet of Things (IoT) in the past decade. The numerous sensors and Internet-connected devices generate a vast quantity of data that needs to be tackled. During the past few years, a great number of systems, algorithms, mechanisms and methodologies have been proposed for sensor information fusion. However, sensor networks and information fusion are still searching for more intelligent and learning-based fusion technique, system architecture, sensor chip, fusion processing, data analysis, message control algorithm, sensing method, and so on.
This Special Issue will focus on AI and deep learning for sensor information fusion. It will also present a holistic view of research challenges and opportunities in the emerging area of deep learning and machine learning for sensor information fusion. Research papers that describe innovative ideas and solutions for intelligent and learning-based sensor information fusion are solicited.
Topics of interest include, but are not limited to:
- AI and deep learning for sensor information fusion system
- System architecture of AI sensors and multi-sensors
- Learning model for sensor information fusion
- Intelligent and learning-based fusion techniques for multi-sensor system
- AI and deep learning for sensor fusion decision
- AI and deep learning for sensor applications
- Deep learning and machine learning for sensor message control
- Intelligent and learning-based sensor communication technology
- Learning-based fusion processing for sensor and multi-sensor system
- Intelligent data analysis for sensor information fusion
- AI and deep learning for data mining in IoT
- AI chips for sensors, UAVs, home appliances and mobile devices
Prof. Dr. Han-Chieh Chao
Dr. Chi-Yuan Chen
Dr. Fan-Hsun Tseng
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. Sensors 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.
- Artificial Intelligence
- Deep Learning
- Sensor Networks
- Information Fusion