• Skip to primary navigation
  • Skip to main content
  • Skip to primary sidebar

ALLSIGNALPROCESSING.COM

Learn signal processing online

  • Home
  • Courses
  • Courses
  • About
  • FAQ
  • My Account
  • Blog
  • News
  • Contact
  • Login
  • Logout
  • Get All Access

The Fourier Series: Continuous-Time Periodic Signals

February 8, 2019 by 3200 Creative

Fourier Series

The Fourier series represents periodic, continuous-time signals as a weighted sum of continuous-time sinusoids. It is widely used to analyze and synthesize periodic signals. This lesson shows you how to compute the Fourier series coefficients, or weights, from the signal. It also introduces you to the conditions that must be met for a signal to have a convergent representation. You will learn both the general and trigonometric forms. The general form expresses the signal as a weighted sum of harmonically related complex sinusoids. The trigonometric form expresses real-valued signals as weighted sums of harmonically related sines and cosines.

The Fourier series is an essential tool and will enable you to work effectively with periodic signals in the frequency domain.

Prerequisites

  • The Four Fourier Representations

Key Concepts and Screenshots

Concepts and Screenshots for The Fourier Series: Continuous-Time Periodic Signals

Supplementary Materials

Table of Fourier Series Pairs

Table of Fourier Series Pairs
QuizzesStatus
1

The Fourier Series Continuous Time Periodic Signals


Next Lesson →

Filed Under: Uncategorized

Primary Sidebar

Course Lessons

  • The Fourier Series: Continuous-Time Periodic Signals

  • Square Wave Fourier Series and the Sinc Function

  • Fourier Series Properties

  • The Fourier Transform: Linking Time and Frequency Domains

  • Properties of the Fourier Transform

  • The Discrete-Time Fourier Transform

  • Discrete-Time Fourier Transform Properties

  • Fourier Transforms and Discrete-Time Fourier Transforms for Periodic Signals

  • Frequency-Domain Descriptions for Continuous-Time Linear Time-Invariant Systems

  • Frequency-Domain Descriptions for Discrete-Time Linear Time-Invariant Systems

  • Two-Dimensional Signal Processing: Continuous Space

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

Copyright © 2023 ALLSIGNALPROCESSING.COM | Site Design by 3200 Creative

  • Terms of Service
  • Privacy Policy
  • Contact