The PolySense and Polysense Innovations teams have just published a new article on Sensors and Actuators B Chemical Journal (IF 7.7) reporting on a novel signal-denoising framework, termed U-LSTM, is proposed by integrating the symmetric encoder-decoder architecture of a U-shaped neural network (U-Net) with Long Short-Term Memory (LSTM) modules. This hybrid architecture enables the simultaneous extraction of local signal features and the modeling of global temporal dependencies, thereby attaining superior suppression of complex, nonlinear noise. The results demonstrate that the proposed U-LSTM model substantially outperforms conventional filtering methods.The proposed method provides a robust and effective denoising solution for PAS- based trace-gas detection and significantly improves detection accuracy and system reliability in noisy environments.
The following link provides access to the article:
Photoacoustic carbon monoxide gas sensor using deep neural networks for nonlinear noise suppression
