2024.1.29 10:00 N613 

Microwave imaging holds significant relevance across a spectrum of application domains, encompassing radar imaging, non-destructive evaluation, autonomous driving, and biomedical imaging. This presentation initially categorizes the commonly used microwave imaging methodologies based on the electrical length of the object. Subsequently, the presentation introduces the fundamental inverse scattering approach, accompanied by an exploration of the advantage and disadvantage of machine learning techniques for resolving inverse scattering problems. The discourse then proceeds to address three distinct problems: the imaging of objects within inhomogeneous backgrounds, the resolution of mixed boundary problems, and the anisotropic scatterer imaging. In each of these scenarios, the effectiveness of the machine learning approach in resolving these problems is demonstrated. Lastly, a biomedical imaging system will be introduced, where machine learning approach is used in both the data processing and imaging. Some simulation and experimental result will be given to show the effectiveness of the machine learning approach.