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Introduction to Communication, Control, and Signal Processing >> Content Detail



Calendar / Schedule



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
L1Introduction and Overview

Basics of Probability (Optional Review Lecture)
L2Random Processes: Stationarity
L3Correlation Functions

LTI Systems, CT and DT Fourier Transforms (Optional Review Lecture)
L4Random Processes through LTI Systems
L5Power Spectral Density
L6Time Versus Ensemble Averages
L7Sampling of Random Processes

Basic Matrix Notions, Linear Algebra (Optional Review Lecture)
L8State-Space Models
L9Zero Input Response, Zero State Response, Stability
L10Modal Analysis, Hidden Modes
Q1Quiz 1
L11Noise-Free State Reconstruction (Observers)
L12State Feedback
L13Observer-Based Feedback
L14Signal Estimation: Filtering, Prediction, Interpolation
L15Linear Minimum-Mean-Square-Error Estimation
L16Non-Causal Wiener Filters
L17Pulse Amplitude Modulation (PAM), Intersymbol Interference
Q2Quiz 2
L18Group Delay
L19Binary PAM-Hypothesis Testing
L20Receiver Operating Characteristics
L21Matched Filters in White Noise
L22Matched Filters in Colored Noise, On/Off Versus Antipodal Signalling
L23Final Lecture
Final Exam



Spring 2005 Calendar



SES #Topics
L1Introduction and Overview: Signals, Systems, Uncertainty/Randomness
L2New Kinds of Signals/Signal Properties: Random Processes, Stationarity, Mean Value
L3Correlation and Covariance Functions, Wide-sense Stationarity
L4New Kinds of Signal Processing (Inference): Simple Linear Minimum Mean-square-error (LMMSE) Estimation, Orthogonality Principle
L5LTI Filtering of Wide-sense Stationary (WSS) Processes
L6Exponentials as Eigenfunctions of LTI Systems, Fourier Transforms (Optional Review)
L7More on Fourier Transforms, Energy Spectral Density
L8Power Spectral Density of WSS Processes

New Representations of Signals: "Shaping" or "Modeling" Filters
L9Ergodicity, Periodogram Averaging
L10More LMMSE Estimation: Noncausal Wiener Filters
L11FIR Wiener Filtering, Normal Equations
L12Causal Wiener Filtering
Q1Quiz 1
L13New Kinds of System Descriptions: State-space Models for Causal Systems
L14LTI State-space Models: Modes, Stability
L15Reachability, Observability, Hidden Modes
L16State Estimation, Observers
L17Control Design using State-space Models: State Feedback, Observer-based Control
L18New Combinations of DT and CT: Sampled Data Control
L19DT Processing of CT Signals
L20More on DT Processing of CT Signals
Q2Quiz 2
L21CT Communication of DT Signals using Pulse-amplitude Modulation (PAM)
L22Noise in PAM

QAM, Modems
L23Matched Filtering for SNR-optimum Processing of Noise-corrupted PAM
L24New Kinds of Inference from Signals: Optimal (Minimum Probability of Error, MPE) Detection/Hypothesis Testing
L25Neyman-Pearson Detection, Receiver Operating Characteristic
L26Matched Filtering for MPE-optimal Detection of DT Signals in WGN
Final Exam

 








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