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Graphene-Based Sensor Data Analytics

Graphene-Based Sensor Data Analytics

Original price was: USD $99.00.Current price is: USD $49.00.

Unlocking the Power of Graphene Sensors: Advanced Analytics for Gas Detection and Anomaly Detection

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About This Course

This course dives into advanced data analytics techniques for enhancing the performance of graphene-based Surface Acoustic Wave (SAW) gas sensors. Participants will learn essential signal preprocessing methods such as noise filtering and drift correction, and gain experience with anomaly detection using autoencoders to identify sensor faults. Additionally, the course covers transfer learning, helping you adapt models to new gas analytes. With hands-on experience, you’ll be equipped to develop real-time, adaptive sensor systems for a variety of applications.

Aim

The goal of this course is to teach participants advanced data analytics techniques for graphene-based SAW gas sensors, focusing on signal preprocessing, anomaly detection with autoencoders, and the use of transfer learning to adapt models to new gas analytes.

Course Structure

📅 Module 1 – Signal Preprocessing for SAW Gas Sensors

  • Overview of Surface Acoustic Wave (SAW) Gas Sensors: Understand the fundamentals of SAW gas sensors and how graphene plays a key role in their functionality.
  • Signal Characteristics of Graphene-Based Sensing Devices: Learn about the unique signal characteristics of graphene-based sensors.
  • Preprocessing Needs: Explore signal preprocessing techniques such as noise filtering, drift correction, and baseline alignment to improve sensor readings.
  • Algorithms for Signal Smoothing and Normalization: Study algorithms used for signal smoothing, peak alignment, and normalization to ensure clean data.
  • Discussion: Dive into current sensor research and the challenges of signal preprocessing in real-world applications.

📅 Module 2 – Anomaly Detection Using Autoencoders

  • Unsupervised Anomaly Detection: Gain insight into how unsupervised anomaly detection can be applied in sensor systems to identify faults.
  • Autoencoders: Learn about the architecture and workflow of autoencoders, which can be used for detecting anomalies in sensor data.
  • Sensor-Specific Anomalies: Understand common sensor faults such as drift, spikes, and signal loss, and how autoencoders can help detect them.
  • Research Trends: Explore current trends in anomaly detection, including denoising autoencoders and reconstruction error analysis.
  • Application of Autoencoders: Apply autoencoders in fault detection systems and develop early warning systems for real-time sensor performance monitoring.

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

  • Quick Recap: Refresh your knowledge of signal preprocessing and autoencoder workflows covered in the earlier modules.
  • Transfer Learning Introduction: Learn how transfer learning can be used to generalize models across different gas analytes, making them adaptable to new data.
  • Hands-On Activities:
    • Visualize and load preprocessed SAW sensor data.
    • Use a pretrained autoencoder for anomaly detection on the data.
    • Fine-tune the model using data from a new gas analyte and evaluate its performance.
  • Evaluate Model Adaptation Performance: Assess how well the model adapts to the new analyte and identify areas for further optimization.

Who Should Enrol?

  • Researchers and Professionals: In sensor technology, data science, and materials science who want to apply AI to sensor performance optimization.
  • Students: With an interest in sensor technology, data analytics, and machine learning applications in real-time sensor systems.
  • Individuals Interested in SAW Gas Sensor Optimization: Looking to learn how advanced data analytics and AI techniques can improve sensor systems.
  • Those with a Basic Understanding of Machine Learning, Python, and Sensor Technologies: Who want to deepen their knowledge and practical skills in sensor optimization.

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