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Sinusoidal Signals

February 8, 2019 by 3200 Creative

Sinusoids are arguably the most important type of signal used in signal processing. In this lesson you will learn why sinusoids play such a prominent role, important differences between continuous- and discrete-time sinusoids, and how sampling relates continuous- and discrete-time frequency. Knowledge of sinusoidal signals will allow you to understand important signal processing concepts such as filtering and Fourier analysis.

This topic is divided into three lessons. The first contains the theory pertaining to sinusoidal signals. The next lesson illustrates use of sinusoids in two applications drawn from music. The third lesson introduces complex-valued sinusoids.

Prerequisites

  • Signals: The Basics

Key Concepts and Screenshots

Concepts and Screenshots for Sinusoidal Signals

Supplementary Material

  • m-file: Sinusoidal Signals
QuizzesStatus
1

Sinusoidal Signals

2

Sinusoidal Signals Exercises


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Filed Under: Uncategorized

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Course Lessons

  • Signals Everywhere

  • Ever-Present Noise

  • Models, Math, and Real-World Signals

  • Four Signal-Processing Themes

  • Jupyter Notebook: Explore FIR Filtering

  • Jupyter Notebook: Explore Image Filtering

  • Building Signals with Blocks: Basis Expansions

  • Signals: The Basics

  • Sinusoidal Signals

  • Sinusoidal Signals Examples

  • Complex Sinusoids

  • Exponential, Step, and Impulse Signals

  • Introduction to Linear, Time-Invariant Systems

  • Introduction to Difference Equation System Descriptions

  • Impulse Response Descriptions for LTI Systems

  • Frequency Response Descriptions for LTI Systems

  • Introduction to the System Function and System Poles and Zeros

  • The Four Fourier Representations

  • Summary Problems for Foundations

Courses

  • Foundations

  • Time Domain LTI Systems

  • Fourier Series and Transforms

  • Sampling and Reconstruction

  • The DFT and Applications

  • The Z-Transform

  • Intro to Filter Design

  • IIR Filter Design

  • FIR Filter Design

  • Random Signal Characterization

  • Basis Representations of Signals

  • Estimation of Power Spectra and Coherence

  • Introduction to Signal Estimation and Detection Theory

  • MMSE Filtering and Least-Squares Problems

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