Aim & Scope
This Journal addresses miniaturized sensor, instrumentation, control, and power systems for small air and space platforms and applications.
The IEEE Journal on Miniaturization for Air and Space Systems (J-MASS) is a new technical journal devoted to covering the rapidly evolving field of small air and space systems such as drones and small satellites. These platforms offer new, low-cost ways to accomplish a wide range of sensing functions for applications ranging from agriculture to land use and ocean surveys.
The overall aim of this special issue is to collect state-of-the-art research efforts on the latest development, up-to-date issues, and challenges in the analysis and processing of UAV data using different kinds of computing architectures, with particular emphasis on advanced machine (and deep) learning algorithms and scalable implementations. Submissions to the special issue should describe original, innovative and in-depth research that makes significant theoretical, methodological or practical contributions to this specific field. Potential topics of interest include, but are not limited to the following areas:
- Advanced algorithms for UAV data interpretation
- Machine learning based UAV data processing
- Deep learning based UAV data processing and its lightweight design
- Parallel processing for efficient analysis of UAV data
- Specialized architectures for real-time processing of UAV data
- Cloud computing technologies for UAV data processing
- Distributed storage solutions for UAV data
- Stochastic optimization for UAV data processing
- New techniques and applications for fusion of UAV data with other data sources, including optical (e.g., hyperspectral), radar (e.g., synthetic aperture radar) and light detection and ranging (LiDAR)
- Classification strategies for UAV data
- Change detection for UAV data
Submission Deadline: June 30, 2020