New trends in modelling and control of medical systems
Eva Henrietta Dulf, Cristina Ioana Muresan
Code ti1a6
Novel technologies in medical devices, rapidly expanding knowledge in science and biomedical engineering has led the speed in innovation of new medical ideas. Next generation image-guided systems; advanced non-invasive brain-computer interfaces; wearable health devices for monitoring; and, display visualization techniques are some examples to name a few. On the other side control theory is not only a fundamental notion for understanding feedback paths in physiological systems, but also a particular concept for building artificial organs and in controlling artificial devices (pacemakers, insulin injection devices, anesthesia). Control engineering also profoundly impacts the everyday lives of a large part of the human population including the disabled and the elderly who use assistive and rehabilitation robots for improving the quality of their lives and increasing their independence. The present special session intends to curate novel advances in the development and application of control engineering techniques in medicine to address ever-present challenges of healtcare. Contributions are invited in topics that include, but are not limited to:
• Machine learning methods for modeling, control and optimization
• Theoretical and implementation challenges which arise in medical systems
• Control engineering tools for solving specific system design problems in medicine
Real time process control and diagnosis
Ciprian Lupu, Dumitru Popescu
Code m5yj7
The session is opened for research presentations that bring interesting, relevant scientific work and innovative contributions in the field of modern process control and system diagnosis. The session provides opportunities for researchers and specialists to offer their recent developments and results in control and diagnosis, applied in different domains with technical and economical interest: Energy, Chemistry, Petro-chemistry, Aerospace, Transport, Automotive, Bio-technology. Communications etc. We evaluate and discuss during this session about performance and security issues that arise in process exploitation and management and optimization in real-time applications, by means of automatic control resources and information support. Appreciated papers should offer modern solutions related to control and diagnosis of systems structures, supported by an adequate theoretical background, implemented and validated in simulation and on industrial applications.
Complex data processing for monitoring, diagnosis, and control
Loretta Ichim, Dan Popescu
Code 9d967
The session aims to underline the intrinsic connection between complex data processing, on one hand, and two important actions in different fields: monitoring and control, on the other hand. The applications of complex images (like texture and fractals), time series, and neural networks in many domains (industry, medicine, agriculture, environment, transportation, and so on) needs interdisciplinary knowledge and effectively solve many encountered problems. This special session at the 25th International Conference on System Theory, Control and Computing (ICSTCC 2021) provides a forum for researchers and practitioners to present and discuss advances in the research and development of intelligent systems for complex data processing and interpretation based on efficient feature selection and neural networks in the field of monitoring, control and diagnosis. All session papers need to have a high scientific level and will be selected based on their relevance to the session topics. The included topics are the following (but not limited): Criteria for feature selection, Image processing for real time control, Traffic control based on images, Medical diagnostic systems based on complex data processing, Assistive technologies based on data processing, UAV and robot guidance based on image interpretation, Quality control based on image processing, Texture analysis, Parallel processing of data, Neural networks for data classification and prediction.