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Calendar

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