Docent Signal Processing en de wiskunde- en natuurkundemaand
Docent Signal Processing en de wiskunde- en natuurkundemaand
Training content
The training course consists of 11 evening sessions of three hours.
Each session combines theory with practical assignments in Python. Under the guidance of trainer Shahrad “Sadra” Ghanbari, you will work on industrial cases such as vibration analysis of motors, resonance in structures and noise reduction in measurement systems.
You will learn:
The fundamentals of Fourier, Laplace and wavelet transforms
How to analyse signals in the frequency domain
Noise reduction and fault detection in Python
How to build spectral filters and analysis tools
How to link data to physical processes and design decisions
During the training course, you will build your own analysis tool with a graphical user interface, which you can take with you after the course and apply in your own work.
The programme
- Evening 1: Introduction, Python for Engineers, Vector Spaces and Signals
- Evening 2: Fourier Series and Data Decomposition, including Singular Value Decomposition in Python
- Evening 3: Fourier Transforms, FFT and Python Applications
- Evening 4: Laplace and Z-Transforms, Filter Design in the Time and Frequency Domain, including IIR vs FIR Filters in Python and Microcontrollers
- Evening 5: Time-Frequency Analysis, including STFT, Gabor and Wavelet Transforms in Python, and an Introduction to the Dosign Signal Analysis Application
- Evening 6: Probability in Signals and the Wiener Filter, including the mathematically optimal filter, adaptive filtering, Python and microcontrollers
- Evening 7: Linear Algebra in Signal Processing, Sparsity and Compressed Sensing, including how sparse signals can be reconstructed below the conventional Nyquist sampling rate using Python
- Evening 8: Control Theory and Nonlinear Filtering, including damping of oscillations and Python implementation
- Evening 9: Adaptive Sparsity and Filtering, including Python implementation
Evening 10: Connecting the Concepts, including the implementation of multi-purpose algorithms in Python
The training course also includes four practical case studies and a final project.
The target audience
Who is this training intended for?
This training course is intended for engineers who work with measurement data and sensors and want to understand what their data really means.
The training course is suitable for:
- instrumentation engineers
- automation engineers
- mechanical engineers working with vibration or dynamic behaviour
- engineers in R&D, maintenance or asset management
- engineers who work with Python and want to deepen their analysis skills
The training course requires technical understanding, but no academic background in signal theory. Mathematics is used functionally and is always linked to practical applications.
The fee and duration of the training
Practical information
The Signal Processing for Engineers training course consists of 11 evening sessions from 17:30 to 22:00. A hot evening meal, course materials, an analysis tool and a certificate of participation are included.
The fee is €2,646 per person, excluding VAT.
Investment: €2,646 per person, excluding VAT
Duration: 11 evenings
Start data
Time
- hrs
Location
Rotterdam
Trainers
Language
Dutch, English
The location
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