Call for Papers

The recent advances of machine learning have brought about tremendous development to many areas in signal analysis and processing, including image/video processing,biometrics and medical data processing, acoustic/vibration signal processing and recognition etc. The data-driven or application-oriented signal analysis and processing applications not only benefit tremendously from machine learning based theories and methods, but also promote the development of intelligent algorithms, including the approximation theory, learning algorithms, optimization approaches, real-time processing/hardware realization and implementations, etc. In the past decade, a great number of achievements in machine learning as well as its applications to signal analysis and processing have been witnessed in the research community. It therefore becomes vital to report the very recent progress in advanced machine learning methodologies for signal analysis and processing.

Mechatronics, such as aircraft, spacecrafts, launch vehicles, missiles, robot manipulators, electronic vehicles, and unmanned aerial vehicles, has played a very important role in modern industry-related applications. Actually, mechatronics are highly multidisciplinary which demands an effective integration of mechanical, electrical, control, and information disciplines. To accomplish planned tasks, there is an ever-increasing demand for advanced mechatronic systems with enhanced performance. This is particularly important for mechanical systems. However, due to the effects of external disturbances, system uncertainties, component failures, or sensor noises, conventional control such as PID may lead to an unsatisfactory performance. Faced with this problem, issues like designing advanced control and signal processing approaches have received major attention both in engineering application and academic research domains. The main objective of this Special Issue is to create a platform for scientists, engineers, and practitioners to present their latest theoretical and technological advancements in the field of sophisticated/advanced control and signal processing for mechatronic systems.

With the development and integration of theories of information, communication and control, large-scale and spatially distributed control systems over wireless digital channels are now widely applied in intelligent transportation, industrial automation and advanced manufacturing, for instance. Integrating wireless networks in control systems allows users to take benefit from modular and flexible system design, simple and fast implementation and reduced installation and maintenance costs in order to realize distributed processing and interoperability and reduce wiring and powerful configuration tools. However, the analysis and design of such systems pose new challenges due to packet loss, data quantization, channel fading, communication delay, and asynchronous sampling. In the past decades, although some fundamental aspects have been studied, there are several open issues that still need to be addressed such as the performance analysis of networked systems, the modeling and congestion control of wireless networks, co-design of wireless networked control systems, distributed estimation and control.