Turbidity Sensor

Turbidity sensors measure the cloudiness of a fluid, a crucial parameter for monitoring water quality and various biological processes. Current research focuses on improving accuracy and efficiency through machine learning algorithms like CatBoost and neural networks (including LSTMs and deep spiking neural networks), often integrating data from remote sensing (e.g., Sentinel-2) or surrogate sensors to reduce costs and increase spatial coverage. These advancements enable more precise and cost-effective monitoring of water quality in diverse environments, with applications ranging from environmental management to medical imaging (e.g., monitoring blood flow). The development of novel computational methods, such as neuromorphic approaches, further enhances the ability to extract meaningful information from highly scattering media.