RESUMO
The intricate optical distortions that occur when light interacts with complex media, such as few- or multi-mode optical fiber, often appear random in origin and are a fundamental source of error for communication and sensing systems. We propose the use of orbital angular momentum (OAM) feature extraction to mitigate phase-noise and allow for the use of intermodal-coupling as an effective tool for fiber sensing. OAM feature extraction is achieved by passive all-optical OAM demultiplexing, and we demonstrate fiber bend tracking with 94.1% accuracy. Conversely, an accuracy of only 14% was achieved for determining the same bend positions when using a convolutional-neural-network trained with intensity measurements of the output of the fiber. Further, OAM feature extraction used 120 times less information for training compared to intensity image based measurements. This work indicates that structured light enhanced machine learning could be used in a wide range of future sensing technologies.
RESUMO
Bismuth ferrite nanoparticles with an average particle diameter of 45 nm and spatial symmetry R3c were obtained by combustion of organic nitrate precursors. BiFeO3-silicone nanocomposites with various concentrations of nanoparticles were obtained by mixing with a solution of M10 silicone. Models of piezoelectric generators were made by applying nanocomposites on a glass substrate and using aluminum foil as contacts. The thickness of the layers was about 230 µm. There was a proportional relationship between the different concentrations of nanoparticles and the detected potential. The output voltages were 0.028, 0.055, and 0.17 V with mass loads of 10, 30, and 50 mass%, respectively.