Signal processing algorithms in embedded systems

The course (self-standing module) aims to introduce the most frequent signal processing algorithms implemented in embedded systems. The course thus focuses on processing time domain waveforms representing physical processes.

It contains 6 parts with 16 lecture videos together with their handouts, and 3 Matlab exercises. The modules cover the following topics:

• Introduction to embedded systems (architecture, components, role of signal processing)
• Filtering: linear filtering (FIR and IIR filters, design methods, properties, realization considerations)
• Spectral analysis: Discrete Fourier Transform (interpretation, properties, coherent/noncoherent sampling, different forms).
• Increasing the accuracy and precision of measurements in embedded systems
• Sensor fusion: complementary filtering, Kalman filter for sensor fusion
• Inverse filtering: numerical correction of static and dynamic distortion