Lecture Signals & systems - Lesson 1: Introduction to signals

Lecture Signals, systems & inference – Lecture 3: Energy spectral density

18 p | 8 | 2

Lecture Signals, systems & inference – Lecture 23: Neyman-Pearson testing. Signal detection

6 p | 11 | 1

Lecture Signals, systems & inference – Lecture 18: Power Spectral Density (PSD)

9 p | 19 | 1

Lecture Signals, systems & inference – Lecture 16: Wide-sense stationary processes; LTI filtering of WSS processes

10 p | 6 | 1

Lecture Signals, systems & inference – Lecture 11: State feedback, observer-based feedback

8 p | 9 | 1

Lecture Signals, systems & inference – Lecture 9: Observers for state estimation

12 p | 9 | 1

Lecture Signals, systems & inference – Lecture 7: Full modal solution, asymptotic stability, reachability and observability

14 p | 13 | 1

Lecture Signals, systems & inference – Lecture 5: State-space models, equilibrium, linearization

11 p | 11 | 1

Lecture Signals, systems & inference – Lecture 2: Transforms

10 p | 8 | 1