Smart Sensors: 1D-CNNs and Signal Processing for Spectroscopy

To equip researchers with AI-driven techniques using 1D-CNNs and signal processing to enhance spectral data analysis, improving classification, denoising, and trace detection in Raman, Fluorescence, and IR spectroscopy.

📅 Day 1 — Spectral Preprocessing & Augmentation

• Lecture: Overview of spectral data cleaning—baseline correction, smoothing, and the importance of data augmentation for improving model robustness.
• Lab: Hands-on experience with Python (scipy.signal) to preprocess raw Raman/Fluorescence spectra and apply augmentation techniques like synthetic noise and shifts.
• Deliverable: Preprocessed, augmented spectral dataset ready for deep learning model training and analysis.

📅 Day 2 — 1D-Convolutional Neural Networks (CNNs) for Spectral Classification

• Lecture: Understanding the advantages of 1D-CNNs over traditional PCA for spectral classification. Learn how CNNs can automatically extract spectral features (e.g., peaks, shoulders) without manual intervention.
• Lab: Practical session on training a 1D-CNN to classify spectral data, such as Acidic vs. Basic or Toxin Present vs. Absent, using raw spectral fingerprints.
• Deliverable: Trained 1D-CNN model capable of classifying spectral data based on learned features.

📅 Day 3 — Denoising Autoencoders for Spectral Data Enhancement

• Lecture: Introduction to super-resolution for spectroscopy. Learn how Autoencoders can be applied to remove noise and reconstruct high-fidelity signals from low-quality spectra.
• Lab: Hands-on training on using Autoencoders to denoise spectra and reconstruct clean data for trace detection applications in spectroscopy.
• Deliverable: Trained Autoencoder model that enhances noisy spectra, improving detection limits and data quality.

• Doctoral Scholars & Researchers: PhD candidates seeking to integrate computational workflows into their molecular research.
• Postdoctoral Fellows: Early-career scientists aiming to enhance their data-driven publication profile.
• University Faculty: Professors and HODs interested in modern bioinformatics pedagogy and tool mastery.
• Industry Scientists: R&D professionals from the Biotechnology and Pharmaceutical sectors transitioning to genomic-driven discovery.
• Postgraduate Students: Final-year PG students looking for specialized research-grade exposure beyond standard curricula.

• Expertise in 1D-CNNs for spectral classification.
• Ability to denoise spectral data for precise trace detection.
• Mastery of spectral preprocessing and data augmentation techniques.
• Skills in automated feature extraction from spectral data.
• Advanced capability in sensor data analysis across chemical sensing, biotech, and environmental monitoring.