Name: fourier transform signals in time and frequency
Uploaded: 2024-09-26T13:17:03.000Z
Duration: 1 min 7 s
Channel: JoVE

Parmi les éléments clés de la transformée de Fourier, la fonction sinc est unique en ce sens qu’elle est égale à 1 lorsque son argument est nul et présente une symétrie régulière.
Dans le domaine fréquentiel, la transformée de Fourier d’une impulsion rectangulaire se transforme en cette fonction sinc, qui présente une symétrie avec un pic à l’origine et des lobes de plus en plus petits de chaque côté.
Le signal exponentiel, une fonction à valeurs complexes, représente une oscillation sinusoïdale à une fréquence spécifique. Lorsqu’il est soumis à une transformée de Fourier, son contenu fréquentiel devient une impulsion unique située à une fréquence particulière. 
Cela indique qu’une onde carrée parfaite incorpore un nombre infini de fréquences harmoniques représentées par les lobes de la fonction sinc.
La fonction delta est nulle partout sauf à zéro, où elle est infinie. La transformée de Fourier d’une fonction delta est une constante, ce qui signifie qu’une fonction delta contient toutes les fréquences d’amplitude égale.

signaux de base de la transformée de fourier

La transformée de Fourier est un outil mathématique essentiel dans le traitement du signal, qui permet la transformation des signaux du domaine temporel en leurs représentations dans le domaine fréquentiel. Parmi les nombreux éléments de ce domaine, certaines fonctions comme la fonction sinc, la fonction delta et les signaux exponentiels revêtent une importance significative en raison de leurs propriétés et implications uniques.

La fonction sinc, définie comme sinc(x) = sin(πx)/(πx), est particulièrement intéressante pour sa symétrie et son comportement à zéro. Elle atteint une valeur de un lorsque son argument est nul et présente une symétrie uniforme autour de l'axe des y. Cette fonction apparaît clairement dans le domaine fréquentiel comme la transformée de Fourier d'une impulsion rectangulaire. Une impulsion rectangulaire, caractérisée par son amplitude constante sur un intervalle spécifique, se transforme en une fonction sinc. La fonction sinc résultante est symétrique avec un pic prononcé à l'origine, et ses lobes diminuent en amplitude à mesure qu'ils s'éloignent du centre. Cette transformation montre qu'une impulsion rectangulaire dans le domaine temporel est composée d'une série infinie de fréquences harmoniques.

La fonction delta, ou fonction delta de Dirac, est un autre élément essentiel dans l'étude des transformées de Fourier. Elle est définie comme nulle partout sauf à zéro, où elle est infiniment grande de sorte que son intégrale sur toute la ligne réelle est égale à un. La transformée de Fourier d'une fonction delta donne une valeur constante sur toutes les fréquences, ce qui indique que la fonction delta englobe toutes les fréquences de même amplitude. Cette propriété fait de la fonction delta un outil essentiel pour l'analyse et la synthèse des signaux, car elle sert de base à la construction d'autres fonctions par convolution.

Les signaux exponentiels, représentés par des fonctions à valeurs complexes de la forme e^jωt, sont fondamentaux pour décrire les oscillations sinusoïdales à des fréquences spécifiques. Lorsqu'un signal exponentiel subit une transformation de Fourier, le résultat est une impulsion unique à la fréquence correspondante dans le domaine fréquentiel. Cette transformation met en évidence le contenu en fréquence pure du signal exponentiel, illustrant le fait qu’il est constitué d'une seule composante de fréquence sans aucune harmonique.

Signaux de base de la transformée de Fourier

Parmi les éléments clés de la transformée de Fourier, la fonction sinc est unique en ce sens qu’elle est égale à 1 lorsque son argument est nul et présente une symétrie régulière.
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Education

Enseignement

JoVE Core

Electrical Engineering

Génie électrique

Unit 1

Unité 1

The Fourier Transform

La transformée de Fourier

KEY TERMS AND CONCEPTS

TERMES ET CONCEPTS CLÉS

Basic signals of Fourier Transform

fourier transform signals in time and frequency

The Fourier Transform links time-domain signals to frequency-domain signals. It is a key tool in signal processing. It helps show how a waveform can be described by frequency parts instead of only by time changes.

The sinc function is one of the basic signals in this topic. It is written as sinc(x) = sin(πx)/(πx). It equals 1 when x is 0, and it has even symmetry about the y-axis. In the frequency domain, it appears as the Fourier transform of a rectangular pulse.

A rectangular pulse has a constant amplitude over a limited interval. When it is transformed, the result is a sinc function with a strong peak at the center. Its side lobes get smaller as they move away from the origin. This shows that a rectangular pulse in time is made of an infinite set of harmonic frequencies.

The delta function, also called the Dirac delta function, is another important signal. It is zero everywhere except at zero, where it is infinitely large in a way that makes its total area equal to one. Its Fourier transform is a constant across all frequencies. That means the delta function contains equal magnitude at every frequency and is useful for signal analysis and synthesis through convolution.

Exponential signals are also fundamental in Fourier analysis. They are complex-valued functions written in the form e^jωt and describe sinusoidal oscillations at a specific frequency. When an exponential signal is transformed, it becomes a single impulse at that same frequency in the frequency domain. This shows that the signal has one pure frequency component and no harmonics.

Fourier Transform Signals in Time and Frequency

fourier-transform-signals-in-time-and-frequency

continuous -time fourier transform

From Fourier Series to Fourier Transform

The Fourier transform extends Fourier series ideas to nonperiodic functions. A Fourier series breaks a periodic signal into a sum of sines and cosines. When the period becomes infinite, that discrete sum changes into a continuous integral. A pulse-train waveform shows this change clearly. With a finite period, the repeating rectangular pulses can be represented by a Fourier series. As the period grows without bound, the waveform becomes a single isolated pulse, and the description shifts to...

basic signals of fourier transform

The Fourier Transform links time-domain signals to frequency-domain signals. It is a key tool in signal processing. It helps show how a waveform can be described by frequency parts instead of only by time changes. The sinc function is one of the basic signals in this topic. It is written as sinc(x) = sin(πx)/(πx). It equals 1 when x is 0, and it has even symmetry about the y-axis. In the frequency domain, it appears as the Fourier transform of a rectangular pulse. A rectangular pulse has a...

properties of fourier transform i

Fourier Transform in Radio Broadcasting

Fourier Transform properties help radio broadcasting manage signals in the time and frequency domains. These properties are useful for sending multiple audio channels at once, changing clip speed, adding broadcast delays, adjusting audio frequencies, and demodulating signals. They make it easier to transmit and receive audio clearly. When broadcasters combine several audio signals, the Fourier Transform simplifies the process. If f(t) and g(t) have Fourier Transforms F(ω) and G(ω), then the...

properties of fourier transform ii

Fourier Transform Rules in Signal Analysis

The Fourier Transform helps analyze signals by linking the time domain and the frequency domain. Its properties show how a signal changes when it is shifted, differentiated, or combined with another signal. These rules are important in signal processing, radio broadcasting, and audio work. The frequency shifting property shows that moving a signal in frequency causes a phase change in time. If x(t) has a Fourier Transform X(f), then multiplying x(t) by e^j2πf0t gives a transform of X(f−f0).

parseval's theorem for fourier transform

Signal Energy in Time and Frequency

Parseval's theorem links signal energy in the time domain and the frequency domain. In signal processing, it shows that the same energy can be calculated in either view. This makes it an important idea for comparing time-based and frequency-based analysis. To find signal energy, the standard approach uses a resistor value of 1 ohm. In that case, power equals the square of the signal's voltage or current. This gives a simple way to relate power to energy before moving into Fourier analysis. <br...

discrete-time fourier transform

DTFT and Frequency Analysis of Digital Signals

The Discrete-Time Fourier Transform (DTFT) shows how a discrete-time signal can be analyzed in the frequency domain. It takes a signal from the time domain and reveals its frequency components and spectral features. In the DTFT, the integral used for continuous-time signals is replaced by a summation to match the discrete form of the signal. The DTFT spectrum X(Ω) is periodic with a period of 2π. This periodicity means that X(Ω) can be written as a Fourier series, which helps with analysis and...

properties of dtft i

DTFT Signal Properties and Frequency Effects

Discrete-Time Fourier Transforms (DTFTs) help describe discrete-time signals in the frequency domain. The main DTFT properties include linearity, time shifting, frequency shifting, time reversal, conjugation, and time scaling. These rules make it easier to analyze and change signals in signal processing. Linearity is the most basic property. If two discrete-time signals are multiplied by constants a and b and then added together, the DTFT of the result is the weighted sum of the two individual...

properties of dtft ii

DTFT Rules for Differentiation and Energy

The DTFT has useful rules for frequency differentiation, convolution, accumulation, and energy checks. These rules help students see how a discrete-time signal changes in the frequency domain. For frequency differentiation, start with a DTFT pair and differentiate both sides with respect to ω. After multiplying by j, the result gives the DTFT of n x[n]. This property is useful for studying how the slope of a spectrum relates to the signal in time. Convolution brings out another key DTFT rule.

discrete fourier transform

DFT for Finding Signal Frequencies

The Discrete Fourier Transform (DFT) turns a finite set of time-domain samples into frequency components. It is a key tool in signal processing because it evaluates a discrete signal at evenly spaced frequency intervals. The result shows the magnitude and phase of each complex sinusoid in the signal. The DFT also follows several important properties. It is linear, so the DFT of a sum of sequences equals the sum of their individual DFTs. When a sequence shifts in time, its DFT changes by a...

fast fourier transform

Fast Fourier Transform for Signal Analysis

The Fast Fourier Transform (FFT) is a computational algorithm that makes the Discrete Fourier Transform (DFT) faster to calculate. It works by splitting the calculation into smaller parts. This reduces the amount of work needed to move data between the time domain and the frequency domain. A direct N-point DFT needs N² complex multiplications. The FFT lowers that cost to about (N/2)log₂N multiplications. That difference becomes more important as N gets larger, because the FFT cuts the number...

key terms and concepts

Learn how the Fourier Transform connects time and frequency through sinc, delta, and exponential signals. Built for grades 9–12 science students.

Fourier Transform Signals in Time and Frequency | JoVE K12

JoVE Core Electrical Engineering offers easy-to-understand animated videos that cover fundamental concepts of electric circuits along with circuit analysis techniques. It incorporates first-order and second-order circuits using operational amplifiers and circuit transformation concepts. The inclusion of practical design examples and real-world applications through scientist-in-action videos adds significant educational value, making complex concepts accessible.

electrical engineering

Fourier Transform Signals in Time and Frequency

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