Deep learning for classifying quantum emission signals in WS2 monolayers using wavelet transform / Hossein Najafzadeh, Zahra Raissi, Shole Golmohammady, Parivash Safari Kaji, Mahdad Esmaeili. In: Scientific Reports. Paderborn : Universitätsbibliothek, 2026
Inhalt
- Dataset characteristics and acquisition protocol
- Experimental setup and data collection
- Spectral band identification and characterization
- Data quality assessment and validation
- Comprehensive preprocessing pipeline
- Spectral band extraction and temporal segmentation
- Noise reduction and signal enhancement
- Intensity normalization and standardization
- Continuous wavelet transform implementation
- Image generation and standardization
- Model architecture
- Evaluation of pre-trained models: VGG16, ResNet50, and Xception
- Learning parameters and model configurations
- Model evaluation
- Result
- Statistical validation of performance metrics
- Discussion
- Summary of results and performance analysis
- Comparative analysis with existing literature
- Physical interpretation of classification results
- Computational implementation and training performance
- Critical analysis of results in context of quantum materials characterization
- Potential applications in quantum technologies
- Current limitations and future research directions
- Methodological contributions and technical advances
- Transfer learning applications in quantum emission analysis
- Conclusion
- References