| Autor: |
Al-Ashwal NH; Department of Physics, The American University in Cairo, New Cairo 11835, Egypt.; Department of Electrical Engineering, Ibb University, Ibb City 00967, Yemen., Al Soufy KAM; Department of Physics, The American University in Cairo, New Cairo 11835, Egypt.; Department of Electrical Engineering, Ibb University, Ibb City 00967, Yemen., Hamza ME; Department of Physics, The American University in Cairo, New Cairo 11835, Egypt., Swillam MA; Department of Physics, The American University in Cairo, New Cairo 11835, Egypt. |
| Abstrakt: |
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 optical sensors. Optical sensors are considered as a promising technology for modern intelligent sensing platforms. These sensors are widely used in process monitoring, quality prediction, pollution, defence, security, and many other applications. However, they suffer major challenges such as the large generated datasets and low processing speeds for these data, including the high cost of these sensors. These challenges can be mitigated by integrating DL systems with optical sensor technologies. This paper presents recent studies integrating DL algorithms with optical sensor applications. This paper also highlights several directions for DL algorithms that promise a considerable impact on use for optical sensor applications. Moreover, this study provides new directions for the future development of related research. |
