The Spring 2004 version of the class was taught by Prof. Oppenheim and the Spring 2005 version was taught by Prof. Verghese. Separate calendars are provided for each class. The Spring 2005 calendar is available below.
The calendars below provide information on the course's lecture (L) and quiz (Q) sessions.
Spring 2004 Calendar
| SES # | Topics |
|---|---|
L1 |
Introduction and Overview Basics of Probability (Optional Review Lecture) |
L2 |
Random Processes: Stationarity |
L3 |
Correlation Functions LTI Systems, CT and DT Fourier Transforms (Optional Review Lecture) |
L4 |
Random Processes through LTI Systems |
L5 |
Power Spectral Density |
L6 |
Time Versus Ensemble Averages |
L7 |
Sampling of Random Processes Basic Matrix Notions, Linear Algebra (Optional Review Lecture) |
L8 |
State-Space Models |
L9 |
Zero Input Response, Zero State Response, Stability |
L10 |
Modal Analysis, Hidden Modes |
Q1 |
Quiz 1 |
L11 |
Noise-Free State Reconstruction (Observers) |
L12 |
State Feedback |
L13 |
Observer-Based Feedback |
L14 |
Signal Estimation: Filtering, Prediction, Interpolation |
L15 |
Linear Minimum-Mean-Square-Error Estimation |
L16 |
Non-Causal Wiener Filters |
L17 |
Pulse Amplitude Modulation (PAM), Intersymbol Interference |
Q2 |
Quiz 2 |
L18 |
Group Delay |
L19 |
Binary PAM-Hypothesis Testing |
L20 |
Receiver Operating Characteristics |
L21 |
Matched Filters in White Noise |
L22 |
Matched Filters in Colored Noise, On/Off Versus Antipodal Signalling |
L23 |
Final Lecture |
Final Exam |
Spring 2005 Calendar
| SES # | Topics |
|---|---|
L1 |
Introduction and Overview: Signals, Systems, Uncertainty/Randomness |
L2 |
New Kinds of Signals/Signal Properties: Random Processes, Stationarity, Mean Value |
L3 |
Correlation and Covariance Functions, Wide-sense Stationarity |
L4 |
New Kinds of Signal Processing (Inference): Simple Linear Minimum Mean-square-error (LMMSE) Estimation, Orthogonality Principle |
L5 |
LTI Filtering of Wide-sense Stationary (WSS) Processes |
L6 |
Exponentials as Eigenfunctions of LTI Systems, Fourier Transforms (Optional Review) |
L7 |
More on Fourier Transforms, Energy Spectral Density |
L8 |
Power Spectral Density of WSS Processes New Representations of Signals: "Shaping" or "Modeling" Filters |
L9 |
Ergodicity, Periodogram Averaging |
L10 |
More LMMSE Estimation: Noncausal Wiener Filters |
L11 |
FIR Wiener Filtering, Normal Equations |
L12 |
Causal Wiener Filtering |
Q1 |
Quiz 1 |
L13 |
New Kinds of System Descriptions: State-space Models for Causal Systems |
L14 |
LTI State-space Models: Modes, Stability |
L15 |
Reachability, Observability, Hidden Modes |
L16 |
State Estimation, Observers |
L17 |
Control Design using State-space Models: State Feedback, Observer-based Control |
L18 |
New Combinations of DT and CT: Sampled Data Control |
L19 |
DT Processing of CT Signals |
L20 |
More on DT Processing of CT Signals |
Q2 |
Quiz 2 |
L21 |
CT Communication of DT Signals using Pulse-amplitude Modulation (PAM) |
L22 |
Noise in PAM QAM, Modems |
L23 |
Matched Filtering for SNR-optimum Processing of Noise-corrupted PAM |
L24 |
New Kinds of Inference from Signals: Optimal (Minimum Probability of Error, MPE) Detection/Hypothesis Testing |
L25 |
Neyman-Pearson Detection, Receiver Operating Characteristic |
L26 |
Matched Filtering for MPE-optimal Detection of DT Signals in WGN |
Final Exam |