Deep Learning for Sensors and Actuators

Dear Colleagues,

The deep learning boom has been unabated since 2012. Approaches based on neural networks currently dominate all benchmarks. These approaches are particularly successful for tasks in the areas of pattern recognition, such as computer vision, audio signal processing, and natural language processing (NLP), but a combination of classical approaches with deep learning methods, such as deep reinforcement learning, are also currently leading in the areas of action selection for agents such as robots.

The Special Issue titled “Deep Learning for Sensors and Actuators” aims to present current concrete work that successfully uses deep learning approaches in sensor data processing or actuator control. These can be generic approaches and concrete applications. Possible contributions for this Special Issue could come from the following subject areas, for example:

• Deep learning for sensor data time series analysis
• Deep learning for heterogeneous sensor networks (i.e., sensors of different types)
• Deep learning for multimodal sensor data fusion
• Deep learning for the control of robots and other actuator systems
• Deep learning for the control of systems/machines
• Deep learning for embedded systems with sensors and actuators
• Surveys/overviews on the topic of “deep learning for sensor data processing”
• Surveys/overviews on the topic of “deep learning for the control of actuators”

By means of selected contributions from these areas, this Special Issue is intended to provide readers with an insight into the current state of research and the application of deep learning approaches in sensor data processing and actuator control by means of concrete examples.

This Special Issue is based on contributions to the Deep Learning Update (DLU) 2021 Conference (see http://www.deep-learning-update.org/). Authors of selected outstanding papers from the conference are invited to submit an extended version of their work. Furthermore, completely new submissions from the community are also welcome.

Prof. Dr. Jürgen Brauer
Guest Editor

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 submissions that pass pre-check are 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. Journal of Sensor and Actuator Networks 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 2000 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.

• deep learning
• neural networks
• machine learning
• sensor data processing
• actuator control