多モード光ファイバセンシングの基本から最新展開までを俯瞰した王さん（元研究員）の招待総説論文が、Measurement Science and Technology に掲載されました。26ページに及ぶ渾身の力作です。
K. Wang, Y. Mizuno, X. Dong, W. Kurz, M. Köhler, P. Kienle, H. Lee, M. Jakobi, and A. W. Koch, “Multimode optical fiber sensors: from conventional to machine learning-assisted,” Meas. Sci. Technol., vol. 35, no. 2, 022002 (2023) <invited review>.
Multimode fiber (MMF) sensors have been extensively developed and utilized in various sensing applications for decades. Traditionally, the performance of MMF sensors was improved by conventional methods that focused on structural design and specialty fibers. However, in recent years, the blossom of machine learning techniques has opened up new avenues for enhancing MMF sensors. Machine learning techniques offer a promising alternative to conventional methods in the field of MMF sensors, as they do not necessarily require complex structures or rare specialty fibers. This not only reduces fabrication difficulties but also lowers costs. In this review, we provide an overview of the latest developments in MMF sensors, from those based on conventional methods to those assisted by machine learning. This article begins by categorizing MMF sensors based on their sensing applications, including temperature and strain sensors, displacement sensors, refractive index sensors, curvature sensors, bio/chemical sensors, and other sensors. Their distinct sensor structures and sensing properties are reviewed in detail. Subsequently, the machine learning-assisted MMF sensors that have been recently reported are analyzed and categorized into two groups: learning the specklegrams and learning the spectra. In conclusion, a comprehensive discussion and outlook on MMF sensors are presented. This review will serve as a valuable guide for the future advancement of MMF sensors using conventional or machine learning methods. Multimode fiber sensors are anticipated to be utilized in a wide range of applications.