Name: dtfs for periodic signal analysis
Uploaded: 2024-09-26T13:17:01.000Z
Duration: 1 min 20 s
Channel: JoVE

K12 Standards

Next Generation Science Standards

High School

Physical Science

waves-and-their-applications-in-technologies-for-information-transfer

Waves and Their Applications in Technologies for Information Transfer

HS-PS4-2

Discrete-Time Fourier Series

סדרת פורייה בדידה-טיים היא מקבילה להרחבת סדרת פורייה של אותות מחזוריים רציפים.
חישוב מקדמי ההתפשטות של DTFS כרוך בסיכומים ולא באינטגרלים. התגובה של מערכת ליניארית בזמן אינווריאנטי לאות מחזורי בדיד-זמן יכולה להיקבע באמצעות תהליך שלב אחר שלב.
ראשית, יש לחשב את ה- DTFS של אות הקלט. תגובת הפלט לכל מונח DTFS מחושבת באמצעות תגובת התדר של המערכת. לבסוף, תוצאות אלה מסוכמות כדי לקבל את אות הפלט הכולל.
לאות מחזורי בזמן רציף יש תקופה עם תדרים מעגליים וזוויתיים, המיוצגת על ידי סדרת פורייה מרוכבת-מעריכית. באופן דומה, לאות מחזורי בדיד-זמן יש תדר זוויתי בסיסי.
הרחבת DTFS מורכבת ממונחים סופיים, בניגוד לסדרת פורייה לזמן רציף, המורכבת ממספר אינסופי של מונחים.
בעיבוד אותות דיגיטליים, DTFS מסייע בבדיקת דגימות תקופתיות, איתור תדרים ספציפיים וסינון יעיל של רעשים בלתי רצויים.

טור פורייה בזמן בדיד (dfts)

<div style="direction: rtl;">
 טור פורייה בזמן בדיד (DTFS) הוא מושג בסיסי בעיבוד אותות, המשמש כגרסה בזמן בדיד של טור פורייה בזמן רציף. הוא מאפשר ייצוג וניתוח של אותות מחזוריים בזמן בדיד במונחים של רכיבי התדר שלהם. בניגוד לגרסה הרציפה, שבה נעשה שימוש באינטגרלים, חישוב המקדמים של הרחבת DTFS כרוך בסכימות, בשל אופיו הבדיד של האות.
 
 עבור אות מחזורי בזמן בדיד [x[n עם מחזור N_0, מקדמי ה-X[k] DFTS מחושבים באמצעות הנוסחה:
 
 <img alt="Equation1" src="/files/ftp_upload/16065/16065_PT%20EQUATION_1.svg" style="text-align: center; height: 48px;">
 כאשר k=0,1,2,...,N_0−1. מקדמים אלו [X[k מייצגים את האות במישור התדר, ומשקפים את המשרעת והמופע של כל רכיב תדר.
 
 כדי לקבוע את התגובה של מערכת ליניארית בלתי תלויה בזמן (LTI) לאות מחזורי בזמן בדיד, מבוצעים מספר שלבים:
 
 <ol style="display: flex; flex-direction: column; gap: 10px;">
 <li style="text-align: right;">חישוב ה-DTFS של אות הקלט: חשב את מקדמי ה-X[k] DFTS עבור אות הקלט [x[n.
 </li>
 <li style="text-align: right;">חישוב תגובת המערכת עבור כל רכיב DTFS: השתמש בתגובת התדר של המערכת (H(e^jΩ) כדי לקבוע את התגובה עבור כל רכיב תדר. מקדמי ה-DTFS של הפלט [Y[k ניתנים על ידי: 
 <img alt="Equation2" src="/files/ftp_upload/16065/16065_PT%20EQUATION_2.svg" style="text-align: center; height: 36px;">
 Ω = 2πk/N כאשר
 </li>
 <li style="text-align: right;">סכום התגובות: לבסוף, סכם את התרומות של כל רכיבי ה-DTFS כדי לקבל את אות הפלט הכולל במישור הזמן.
 </li>
 </ol>

 באותות בזמן רציף, המחזוריות מוגדרת ביחס למחזור T, בהתאם לתדירות מעגלית וזוויתית. עבור אות בזמן בדיד, המחזוריות מקושרת לתדירות זוויתית בסיסית 2πk/N_0 = Ω_k כאשר N_0 הוא המחזור של האות הבדיד. הרחבת ה-DTFS היא סופית, וכוללת N רכיבים, בניגוד לסדרה האינסופית של טור פורייה בזמן רציף.
 
 
 ל-DTFS תפקיד חשוב בעיבוד אותות דיגיטליים (DSP), במיוחד בניתוח ומניפולציה של אותות מחזוריים שמקורם בנתונים דגומים. הטור הוא כלי מרכזי במשימות כמו זיהוי תדרים ספציפיים באותות קול, הגברה או דיכוי של רכיבי תדר מסוימים, וסינון רעשים לא רצויים. על ידי המרת האותות למישור התדר, DTFS מאפשר ניתוח ועיבוד אותות יעילים, ומשפר ביצועים ביישומים שונים כגון תקשורת, הנדסת שמע ומערכות בקרה.
 
</div>

טור פורייה בזמן בדיד (DFTS)

סדרת פורייה בדידה-טיים היא מקבילה להרחבת סדרת פורייה של אותות מחזוריים רציפים.
חישוב ההרחבה ...

Education

JoVE Core

Electrical Engineering

הנדסה חשמלית

Unit 1

יחידה 1

Fourier Series

טורי פורייה

KEY TERMS AND CONCEPTS

מונחי מפתח ומושגים

dtfs for periodic signal analysis

Discrete-Time Fourier Series (DTFS) helps analyze periodic signals in discrete time by breaking them into frequency components. It is the discrete-time counterpart of the continuous-time Fourier series. Instead of using integrals, DTFS uses summations because the signal is discrete.

For a discrete-time periodic signal x[n] with period N0, the DTFS coefficients X[k] are found from the DTFS formula. The index k runs from 0 to N0 − 1. These coefficients describe the signal in the frequency domain and show the amplitude and phase of each frequency component.

DTFS is also useful for finding the response of a linear time-invariant (LTI) system to a periodic input. First, compute the DTFS coefficients of the input signal x[n]. Then use the system frequency response H(e^jΩ) to find the output for each frequency term. The output coefficients Y[k] are determined from that frequency response, and the total time-domain output is found by summing all DTFS terms.

In discrete-time signals, periodicity is tied to the fundamental angular frequency Ωk = 2πk/N0, where N0 is the signal period. The DTFS expansion has a finite number of terms, unlike the infinite series used for continuous-time Fourier series. This makes DTFS an important tool in digital signal processing for studying sampled periodic signals, identifying frequencies in audio, and helping enhance or suppress frequency components. It is also used to filter unwanted noise and support applications in telecommunications, audio engineering, and control systems.

DTFS for Periodic Signal Analysis

trigonometric fourier series

Fourier Coefficients and Harmonic Terms

Fourier series describe periodic functions by writing them as sums of sine and cosine waves. The trigonometric Fourier series uses a function with period T and represents it with simple harmonic terms. This approach helps break a complex repeating signal into parts that are easier to study. In the standard form, the series includes a0, an, and bn. The term a0 gives the average value of the function over one period. The coefficients an and bn show how much each cosine and sine term contributes...

exponential fourier series

Fourier Series in Audio Signal Processing

Fourier series are used in audio signal processing to break complex sounds into simpler sinusoidal parts. This helps explain how musical notes and other periodic signals can be analyzed and rebuilt from frequency components. The exponential Fourier series writes a periodic signal as a sum of complex exponentials at positive and negative harmonic frequencies. These harmonic terms capture the repeated pattern in the signal. Euler's identity connects the exponential form to cosine and sine, which...

properties of fourier series i

Fourier Series Properties in Signal Processing

Fourier series properties help describe periodic signals in signal processing and communications. A Fourier series breaks a signal into sine and cosine parts. These properties make it easier to study how signals behave and how they can be handled in real systems. Linearity is one of the most important properties. If two periodic signals are added together or combined in a linear way, the new signal has Fourier coefficients that are the same linear combination of the original coefficients. This...

properties of fourier series ii

Fourier Series Symmetry and Time Scaling

Fourier series change in useful ways when a signal is time-scaled or when it has symmetry. Time scaling changes the fundamental frequency, but the Fourier series coefficients stay the same. This idea helps show how a signal is represented over time in signal processing. Symmetry also makes Fourier series easier to work with. A function is even if f(t) = f(-t). For even functions, the sine terms disappear because sine is an odd function, and the integral of an odd function over a symmetric...

parseval's theorem

Parseval's Theorem in Signal Power

Parseval's theorem links signal power to Fourier series coefficients. For a periodic function, it says the average power over one period equals the sum of the squared magnitudes of its complex Fourier coefficients. This makes the theorem useful for studying how energy is distributed in a signal. The theorem also works for the trigonometric form of the Fourier series. In that form, a function is written using sine and cosine terms. The Fourier coefficients can be related to the trigonometric...

convergence of fourier series

Fourier Series Truncation and Gibbs Ripples

Fourier series can represent a periodic signal as an infinite sum of complex exponentials. For practical use, that infinite series is cut off after a finite number of terms. The result is a partial sum that gives a workable approximation of the original signal. Truncating the series creates a challenge near discontinuities. The approximation develops the Gibbs phenomenon, which is the set of persistent oscillations and overshoots around a jump in the signal. These high-frequency ripples do not...

discrete-time fourier series

Discrete-Time Fourier Series (DTFS) helps analyze periodic signals in discrete time by breaking them into frequency components. It is the discrete-time counterpart of the continuous-time Fourier series. Instead of using integrals, DTFS uses summations because the signal is discrete. For a discrete-time periodic signal x[n] with period N0, the DTFS coefficients X[k] are found from the DTFS formula. The index k runs from 0 to N0 − 1. These coefficients describe the signal in the frequency domain...

key terms and concepts

Learn how DTFS represents discrete-time periodic signals, finds frequency components, and helps analyze LTI system responses in digital signal processing.

DTFS for Periodic Signal Analysis | 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

HS-PS4-5

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HS-PS4-4

DTFS for Periodic Signal Analysis

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