Program Objectives:

• Bridge the gap between sensor hardware innovation and intelligent data use

• Promote cross-disciplinary learning between nanoscience and AI

• Empower participants to contribute to next-gen sensor networks and smart systems

• Foster innovation in sustainable, scalable, and real-time sensing platforms

• Prepare researchers to publish or commercialize sensor-based data solutions

What you will learn?

Day 1: Signal Preprocessing for SAW Gas Sensors

• Introduction to SAW Gas Sensors

  • Working principles and applications

  • Common signal characteristics and noise sources

• Signal Preprocessing Techniques

  • Filtering: Low-pass, band-pass, median filters

  • Baseline correction and normalization

• Feature Extraction & Dimensionality Reduction

  • Time-domain and frequency-domain features

  • PCA, FFT, and wavelet transforms for SAW data

Day 2: Anomaly Detection Using Autoencoders

• Basics of Autoencoders

  • Architecture: Encoder, bottleneck, decoder

  • Training for reconstruction accuracy

• Autoencoders for Anomaly Detection

  • Loss-based detection of anomalous gas responses

  • Performance evaluation metrics (AUC, precision-recall)

• Implementation & Case Study

  • Building an autoencoder model in Python (Keras/PyTorch)

  • Real SAW data analysis with labeled anomalies

Day 3: Transfer Learning for New Analytes

• Introduction to Transfer Learning

  • What is transfer learning and why it matters

  • Types: Feature-based, fine-tuning, domain adaptation

• Applying Transfer Learning to Sensor Data

  • Transferring models across sensor types or analyte classes

  • Handling distribution shift and domain generalization

• Advanced Techniques & Future Directions

  • Meta-learning, few-shot learning, and continual learning

  • Preparing your model for deployment in dynamic environments