Assia Hamzaoui
Assia Hamzaoui has a total of 5 co-authors. Here are the top co-authors by number of shared publications:
- Abdelaziz Aouiche
LSSS Laboratory, Department of Electronics and Communications, Echahid Cheikh Larbi Tebessi University, Tebessa 12002, Algeria
2 shared publications Soraya Gouder
Department of Electronics and Telecommunications, Faculty of Science and Technology, Echahid Cheikh Larbi Tebessi University, Tebessa, Algeria
2 shared publicationsAhmed Belaadi
Département de Génie Mécanique, Faculté de Technologie, Université 20 Août 1955, Skikda, Algérie
1 shared publicationHoussam Eddine Abdellatif
Département de Génie Mécanique, Faculté de Technologie, Université 20 Août 1955, Skikda, Algérie
1 shared publicationShan Ali Khan
School of Energy and Power Engineering, Xi’an Jiaotong University, Xi’an , China
1 shared publication
Latest Publications
- 4 June 2025
- Published by Springer Nature in Journal of Fluorescence
- Vol. 35 (11) , 11409-11422
Machine Learning-Enhanced Surface Plasmon Resonance Sensor with D-Shaped Dual-Core Photonic Crystal Fiber Design
A D-shaped dual-core surface plasmon resonance (SPR) sensor based on photonic crystal fibers (PCFs) has been created, and its sensing capabilities were evaluated through the finite element method (FEM). The design features a square lattice arrangement of air holes, with two central holes removed to form a D-shaped dual-core structure. The sensor's performance was assessed using both wavelength and amplitude interrogation approaches. It achieved a maximum wavelength sensitivity of 16,000 nm/RIU for y-polarized light at an analyte refractive index (RI) of 1.38, along with a peak amplitude sensitivity of 765.21 RIU−1 at the same RI, and a wavelength resolution of 2.5 × 10–6 RIU. Furthermore, machine learning (ML) techniques, particularly artificial neural networks (ANN), were employed to predict confinement loss (CL) with high accuracy, without the need for the imaginary component of the effective RI. For an RI of 1.32, the ANN model achieved a mean squared error (MSE) of 3.5363 × 10–6, showcasing the model's reliability in forecasting sensor performance.
- 4 June 2025
- Published by Springer Nature in Journal of Fluorescence
- Vol. 35 (11) , 11409-11422
Machine Learning-Enhanced Surface Plasmon Resonance Sensor with D-Shaped Dual-Core Photonic Crystal Fiber Design
A D-shaped dual-core surface plasmon resonance (SPR) sensor based on photonic crystal fibers (PCFs) has been created, and its sensing capabilities were evaluated through the finite element method (FEM). The design features a square lattice arrangement of air holes, with two central holes removed to form a D-shaped dual-core structure. The sensor's performance was assessed using both wavelength and amplitude interrogation approaches. It achieved a maximum wavelength sensitivity of 16,000 nm/RIU for y-polarized light at an analyte refractive index (RI) of 1.38, along with a peak amplitude sensitivity of 765.21 RIU−1 at the same RI, and a wavelength resolution of 2.5 × 10–6 RIU. Furthermore, machine learning (ML) techniques, particularly artificial neural networks (ANN), were employed to predict confinement loss (CL) with high accuracy, without the need for the imaginary component of the effective RI. For an RI of 1.32, the ANN model achieved a mean squared error (MSE) of 3.5363 × 10–6, showcasing the model's reliability in forecasting sensor performance.
- 10 February 2025
- Published by Springer Nature in Sensing and Imaging
- Vol. 26 (1) , 1-24
Design and Development of a Nature-Inspired SPR-PCF Sensor Based on Lotus Root Geometry
- 10 February 2025
- Published by Springer Nature in Sensing and Imaging
- Vol. 26 (1) , 1-24
Design and Development of a Nature-Inspired SPR-PCF Sensor Based on Lotus Root Geometry