Graphene-Based Sensor Data Analytics

Aim

Syllabus

📅 Day 1 – Signal Preprocessing for SAW Gas Sensors

• Overview of Surface Acoustic Wave (SAW) Gas Sensors
• Signal characteristics of graphene-based sensing devices
• Preprocessing needs: Noise filtering, drift correction, baseline alignment
• Algorithms for signal smoothing, peak alignment, and normalization
• Discussion on preprocessing pipelines in current sensor research

📅 Day 2 – Anomaly Detection Using Autoencoders

• Introduction to unsupervised anomaly detection in sensor systems
• Autoencoders: Concept, architecture, and training workflow
• Sensor-specific anomaly types: drift, spikes, signal loss
• Research trends: Denoising autoencoders, reconstruction error analysis
• Application of autoencoders in fault detection and early warning systems

📅 Day 3 – Hands-On: Transfer Learning & Adaptation to New Analytes

• Quick recap of signal preprocessing and autoencoder workflow
• Introduction to transfer learning for cross-analyte generalization
• ⚙️ Hands-On Activities:
• Load and visualize preprocessed SAW sensor data
• Use a pretrained autoencoder for anomaly detection
• Fine-tune model on data from a new gas analyte
• Evaluate model adaptation performance

Who Should Enrol?

Course Outcomes