Modulation Representing digital signals as analog waveforms Baseband signals Signals whose frequency components are concentrated around zero Passband signals Signals whose frequency components are centered at some frequency fc away from zero Baseband signals can be converted to passband signals through modulation Multiplication by a sinusoid with frequency fc Eytan Modiano

73.1 Signal Detection General Considerations. Detection of Known Signals.Detection of Parametrized Signals. Detection of Random Signals Deciding A mong Multiple Signals. Detection of Signals in More General noise rocessesRobust and Nonparametric Detection Distributed and Sequential Detection. Detection with Continuous-Time Measurements 73.2 Noise Statistics of Noise. Noise Pow owerect of Linear Transformations on Autocorrelation and Power Spectral Density White, Gaussian

§1.1 Introduction §1.2 Continue-time Signal §1.3 signal representation based on δ(t) §1.4 Linear time-invariant system §1.5 System unit impulse response §1.6 Fourier series of periodic signals §1.7 Fourier analyses of non-periodic signals—Fourier Transform §1.8 Fourier Transform of typical signals §1.9 Fourier Transform of typical signals §1.10 Properties of Fourier Transform §1.11 Fourier analyses of linear system

Question: Are there sets of basic\ signals so that a) We can represent rich classes of signals as linear combinations of these building block signals b) The response of ltI Systems to these basic signals are both simp and insightful Fact: For LtI Systems(CT or dt) there are two natural choices for these building blocks Focus for now:

§5.1 Digital Processing of Continuous-Time Signals §5.2 Sampling of Continuous-time Signals §5.3 Effect of Sampling in the Frequency Domain §5.4 Recovery of the Analog Signal §5.5 Implication of the Sampling Process §5.6 Sampling of Bandpass Signals §5.7 Analog Lowpass Filter Specifications §5.8 Analog Lowpass Filter Design

◆2.1 Discrete-Time Signals: Sequences ◆High and Low Frequencies in Discrete-time signal ◆2.2 Discrete-Time Systems ◆Memoryless (memory); Linear; Time-Invariant; Causality; Stability(BIBO) ◆2.3 Linear Time-Invariant (LTI) Systems ◆LTI Systems:Convolution( 系统适用吗?) ◆2.4 Properties of LTI Systems ◆Stability and Causality of LTI systems;FIR and IIR systems ◆2.5 Linear Constant-Coefficient Difference Equations ◆The output for a given input is not uniquely specified. Auxiliary conditions are required; initial-rest conditions ◆2.6 Frequency-Domain Representation of Discrete￾Time Signals and systems ◆ Eigenfunction and Eigenvalue for LTI systems ◆2.7 Representation of Sequences by Fourier Transforms ◆2.8 Symmetry Properties of the Fourier Transform ◆2.9 Fourier Transform Theorems ◆2.10 Discrete-Time Random Signals

§5.1 Digital Processing of Continuous-Time Signals §5.2 Sampling of Continuous-time Signals §5.3 Effect of Sampling in the Frequency Domain §5.4 Recovery of the Analog Signal §5.6 Sampling of Bandpass Signals §5.7 Analog Lowpass Filter Specifications §5.8 Analog Lowpass Filter Design

2.0 Introduction 2.1 Discrete-Time Signals: Sequences 2.2 Discrete-Time Systems 2.3 Linear Time-Invariant (LTI) Systems 2.4 Properties of LTI Systems 2.5 Linear Constant-Coefficient Difference Equations 2.6 Frequency-Domain Representation of Discrete-Time Signals and systems 2.7 Representation of Sequences by Fourier Transforms 2.8 Symmetry Properties of the Fourier Transform 2.9 Fourier Transform Theorems 2.10 Discrete-Time Random Signals 2.11 Summary

◆2.1 Discrete-Time Signals: Sequences ◆2.2 Discrete-Time Systems ◆2.3 Linear Time-Invariant (LTI) Systems ◆2.4 Properties of LTI Systems ◆2.5 Linear Constant-Coefficient Difference Equations ◆2.6 Frequency-Domain Representation of Discrete￾Time Signals and systems ◆2.7 Representation of Sequences by Fourier Transforms ◆2.8 Symmetry Properties of the Fourier Transform ◆2.9 Fourier Transform Theorems ◆2.10 Discrete-Time Random Signals

SAMPLING We live in a continuous-time world most of the signals we encounter are CT signals, e.g. x(). How do we convert them into Dt signals x[n? Sampling, taking snap shots of x(t) every Seconds