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

Course Overview

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

Professional Certification Program

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FormatLive + Recorded

📅

Duration8 Weeks

📜

CertificationVerified

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Program Syllabus

Module 1

About This Course

Smart Sensors: 1D-CNNs and Signal Processing for Spectroscopy teaches AI-driven techniques to enhance Raman, Fluorescence, and IR spectroscopy. Learn to use 1D-CNNs for classification, denoising, and spectral preprocessing, improving data quality for trace detection. Ideal for researchers in chemical sensing, biotech, and environmental monitoring, this hands-on workshop provides tools for advanced spectral analysis.

Module 2

Aim

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.

Module 3

Workshop Objectives

Classify Spectra with 1D-CNNs for enhanced accuracy.
Denoise Spectral Data using AI techniques for better trace detection.
Optimize Preprocessing with methods like baseline correction and data augmentation.
Automate Feature Extraction from spectral data.
Enhance Sensor Data Analysis in chemical sensing, biotech, and environmental monitoring.

Module 4

Workshop Structure

Module 5

📅 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.

Module 6

📅 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.

Module 7

📅 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.

Module 8

Who Should Enrol?

Researchers/R&D working with Raman, IR/FTIR, fluorescence, SERS, or sensor time-series data.
PhD/postdoc/faculty/industry scientists in spectroscopy/sensing labs.
Those moving beyond peak fitting/PCA to 1D-CNN classification + denoising.
Recommended: CSV/Excel + basic Python/Jupyter (no DL required).

Module 9

Important Dates

Module 10

Registration Ends

02/12/2026
IST 04:30 PM

Module 11

Workshop Dates

02/12/2026 – 02/14/2026
IST 05: 30PM

Module 12

Workshop Outcomes

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.

Module 13

Fee Structure

Module 14

Student

₹2499 | $75

Module 15

Ph.D. Scholar / Researcher

₹3499 | $85

Module 16

Academician / Faculty

₹4499 | $105

Module 17

Industry Professional

₹6499 | $120

Module 18

What You’ll Gain

Live & recorded sessions
e-Certificate upon completion
Post-workshop query support
Hands-on learning experience

Module 19

Join Our Hall of Fame!

Take your research to the next level with NanoSchool.

Module 20

Publication Opportunity

Get published in a prestigious open-access journal.

Module 21

Centre of Excellence

Become part of an elite research community.

Module 22

Networking & Learning

Connect with global researchers and mentors.

Module 23

Global Recognition

Worth ₹20,000 / $1,000 in academic value.

Module 24

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

Course Overview

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

Program Syllabus

About This Course

Aim

Workshop Objectives

Workshop Structure

📅 Day 1 — Spectral Preprocessing & Augmentation

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

📅 Day 3 — Denoising Autoencoders for Spectral Data Enhancement

Who Should Enrol?

Important Dates

Registration Ends

Workshop Dates

Workshop Outcomes

Fee Structure

Student

Ph.D. Scholar / Researcher

Academician / Faculty

Industry Professional

What You’ll Gain

Join Our Hall of Fame!

Publication Opportunity

Centre of Excellence

Networking & Learning

Global Recognition

Need Help?

Lead Instructor

Dr. Sarah Chen

Need Guidance?

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

Course Overview

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

Program Syllabus

About This Course

Aim

Workshop Objectives

Workshop Structure

📅 Day 1 — Spectral Preprocessing &amp; Augmentation

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

📅 Day 3 — Denoising Autoencoders for Spectral Data Enhancement

Who Should Enrol?

Important Dates

Registration Ends

Workshop Dates

Workshop Outcomes

Fee Structure

Student

Ph.D. Scholar / Researcher

Academician / Faculty

Industry Professional

What You&#8217;ll Gain

Join Our Hall of Fame!

Publication Opportunity

Centre of Excellence

Networking &#038; Learning

Global Recognition

Need Help?

Lead Instructor

Dr. Sarah Chen

Need Guidance?

📅 Day 1 — Spectral Preprocessing & Augmentation

What You’ll Gain

Networking & Learning