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Wavelet Analysis with MATLAB (NEW)
Next level after the fundamental
Wavelet Analysis with MATLAB
Learn wavelet techniques to solve issue in signals with different time-frequency resolutions.
Complimentary Services: Post training email support & 1-hr consultation session within 1 month after the course completion!
Course
Highlights
Signals obtained from many real-world phenomenon often have features occurring at different scales or resolutions. For instance, a vibration signal may have components that oscillate slowly punctuated with abrupt transients at regular intervals. The abrupt transients often carry a lot of meaning and obtaining a joint time-frequency analysis would be helpful in revealing interesting characteristics.
Short-Time Fourier Transform (STFT) is effective as long as the signals does not include structures having different time-frequency resolutions, some being very localised in time and others very localized in frequency. Wavelet address this issue by changing the time and frequency resolution.
This one-day course shows how to use Wavelet ToolboxTM to perform wavelet analysis on signals. After motivating time-frequency analysis of time-varying signals using wavelets, both continuous and discrete wavelet-based techniques are covered. Topics include:
- Performing time-frequency analysis using continuous wavelet transform
- Building and using time-localized filters
- Implementing multiresolution analysis using discrete wavelet transform
- Denoising signals using wavelets
Who Should
Attend
The course is intended for MATLAB users who work in data preparation of signals with different scales or resolution for data analytics and artificial intelligent workflow in MATLAB.
Course
Prerequisites
MATLAB Fundamentals or equivalent experience using MATLAB®; signal processing background is highly recommended.
Course
Benefits
Upon the completion of the course, the participants will be able to:
- Obtain joint time-frequency estimates of a real-world signal
- Remove unwanted components in signals using time-frequency filtering
- Reconstruct oscillatory modes in signal using Wavelet Synchrosqueezing-based ridge extraction techniques
- Find coherent oscillations in two signals
- Perform multi-level decomposition and reconstruction using Discrete Wavelet Transform
- Remove noise using wavelet denoising technique
- Perform non-decimated Discrete Wavelet Transform
- Control signal extension mode for Discrete Wavelet Transform
Partners
Upcoming Program
- Please keep me posted on the next schedule
- Please contact me to arrange customized/ in-house training
Course Outline
Continuous Wavelet Analysis
Objective: Learn the motivation for using wavelets to analyze signals and the fundamentals of wavelet analysis.
- Interactive and programmatic time-frequency analysis using continuous wavelet transform
- Time-localized filtering/li>
- Time-frequency resolution using wavelet synchrosqueezing and ridge extraction
- Wavelet coherence to find coherent time-varying oscillations in two signals
Discrete Wavelet Analysis
Objective: Learn how multiresolution analysis can be used to detect patterns in signals and reconstruct signals that retain only desired features.
- Multiresolution analysis
- Decimated and nondecimated discrete wavelet transforms
- Wavelet denoising
- Wavelet packet transform