Machine‐learning‐enhanced nanosensors are rapidly emerging as a promising solution in
the field of sensor technology, as traditional sensors encounter limitations of data analysis in …

Over the past decade, deep learning (DL) has been applied in a large number of optical
sensors applications. DL algorithms can improve the accuracy and reduce the noise level in …

Low-loss photonic integrated circuits (PICs) are the key elements in future quantum
technologies, nonlinear photonics and neural networks. The low-loss photonic circuits …

Mixed-potential electrochemical sensor arrays consisting of indium tin oxide (ITO), La 0.87
Sr 0.13 CrO 3, Au, and Pt electrodes can detect the leaks from natural gas infrastructure …

Fiber optic sensors are applied in industry, remote sensing, environmental monitoring and
healthcare. A special place is occupied by tilted fiber Bragg gratings, which can significantly …

Multimode fiber (MMF) specklegram sensors have recently drawn significant attention due to
the incorporation of machine learning (ML) algorithms in detecting different sensing …

Deep learning models, with a prerequisite of large databases, are common approaches in
applying machine learning for inverse design in photonics. For these models, less …

The electronic nose, popularly known as the E-nose, that combines gas sensor arrays
(GSAs) with machine learning has gained a strong foothold in gas sensing technology. The …

Whispering gallery mode (WGM) resonators provide an important platform for fine
measurement thanks to their small size, high sensitivity, and fast response time …

When talking about nanophotonics, quantum computing and sensing high-quality factor
plasmonic devices are playing crucial role. In most cases for making such devices one has …