Perception and Signal Processing

LECTURES:

1. Introduction to embedded systems: sensing the environment, processing data. Investigating a typical signal path: sensors, perception and signal conditioning, sampling, data processing. Typical embedded signal processing architectures: microcontroller, DSP, FPGA, GPU.

2. Temperature sensors: e.g., thermocouple, NTC/PTC, infra, semiconductor. Measurement of light intensity, e.g. photodiode, phototransistor, photo resistor, photovoltaic. Specific properties and applications of sensors.

3. Vibroacoustic sensors: MEMS and conventional analog sensors (piezo, electret, geophone). Charge and voltage output devices, signal conditioning issues, typical specifications.

4. Measurement of position, displacement, rotation: incremental transducers, LVDT, optical sensors, time-of-flight sensors, Hall-sensors and magneto-resistive sensors, inductive sensors. Force and torque measurement: strain gauges, piezo, force-sensitive resistor.

5. Measurement of current: shunt resistance (bottom and top), current transformer/Rogowski coil, magnetic field based (Hall sensor, fluxgate, magneto-resistive) sensors. Measurement of ECG and photoplethysmographic signals.

6. Interpretation of DFT for periodic and stochastic signals. Calculation of equivalent noise bandwidth, signal to noise ratio. Coherent/non-coherent sampling, distortion effects. Alternative interpretations of the discrete Fourier transform (DFT): matrix transform, filter bank, LS estimation (generalization to sine components of arbitrary frequency).

7. DFT applications: convolution acceleration, real DFT computation using complex DFT, cepstrum computation. Wavelet transform, description of wavelets, implementation. Discrete cosine transform.

8. Classification of digital filters. Overview of the properties of IIR and FIR filters. Amplitude and phase characteristics. Types of filters: FIR: LS and smooth wave; IIR: Butterworth, Chebyshev, elliptic, Bessel-Thomson.

9. Filter design procedures for FIR and IIR filters (LS, Parks-McClellan, windowing, bilinear transform, pulse invariant transform). Realization forms of digital filters, biquad implementation.

10. Specialties of fixed point fractional representation, performing operations with fixed point fractional numbers, design difficulties. Nonlinear filters and outlier detection: median filter and variants, Hampel filter.

11. An overview of problems stemming from different sampling frequencies. Implementation of decimation and interpolation in time and frequency domains. Decimation and interpolation filter design, polyphase filter. Polynomial interpolation.

12. Numerical optimization problems: root locus search, extreme value search. Formulating a mathematical problem, types of cost function, interpretation. One and multi-parameter problems, conditional search for extreme values. Methods using first and second order derivatives.

13. Optimization methods based on different heuristics. The problem of local extrema. Convergence problems, ill-conditioned cases. Illustration of numerical optimization using the Least-Mean Square (LMS) algorithm as a data-invariant filter for real-time embedded systems.

14. Consultation, extra space for missing lessons due to holidays, examples of applications.

PRACTICES:

1. Characterization of systematic and random static errors: error analysis of complex circuits, contribution of different passive and active components to the error and non-linearity of the signal path. Reducing the errors with special components and designs. 

2. Noise analysis: noise of circuit components (amplifiers, passive components, power supplies), noise suppression/immunity, ambient noise/disturbance, shielding, design solutions. Jitter and its effects.

3. Introduction to some basic signal processing steps through a real application. Block and sample processing: difference between real-time embedded processing and offline processing. Interpretation of timings. Implementing simple signal processing algorithms, e.g. measuring signal parameters (frequency, amplitude...).

4. Introduction of filter design software packages, design and test FIR and IIR filters with different specifications on real signals.

5. Spectrum analysis examples. Fault detection in embedded systems based on spectral imagery: independent periodic, modulating errors, random errors/jitter, distortion, and their derivation from software and hardware properties of embedded systems.

6. Examples for designing decimating and interpolating filters for different tasks. Synchronization of different sampling frequencies in distributed embedded systems.