FFT的音標(biāo)為/?f?ft/，基本翻譯為“快速”，速記技巧為：發(fā)此音時(shí)，要快速發(fā)音，不必過(guò)分關(guān)注音標(biāo)的形狀。

Fast Fourier Transform (FFT) 的英文詞源可以追溯到數(shù)學(xué)領(lǐng)域，它是由“fast”（快速）和“Fourier Transform”（傅立葉變換）兩個(gè)詞組合而成的。

變化形式：FFT 在英語(yǔ)中是一個(gè)名詞，沒(méi)有明顯的變化形式。

相關(guān)單詞：

1. Fast Fourier Transform (FFT)：快速傅立葉變換，是一種在數(shù)學(xué)和工程領(lǐng)域廣泛應(yīng)用的算法，用于在頻率域中快速計(jì)算離散傅立葉變換（DFT）。

2. transform：這個(gè)詞在此上下文中，除了作為動(dòng)詞表示變換外，還可以作為名詞表示變換。

3. frequency domain：頻率域，是信號(hào)分析中的一個(gè)術(shù)語(yǔ)，指的是傅立葉變換的結(jié)果，其中包含了信號(hào)的頻率成分和相應(yīng)的幅度。

4. time domain：時(shí)間域，是信號(hào)分析中的一個(gè)術(shù)語(yǔ)，與頻率域相對(duì)，表示信號(hào)的實(shí)際時(shí)間域特性。

5. inverse FFT：逆傅立葉變換，是FFT的反向過(guò)程，用于從傅立葉變換后的結(jié)果中恢復(fù)原始信號(hào)。

6. computational efficiency：計(jì)算效率，F(xiàn)FT算法的高效性使其在許多計(jì)算任務(wù)中成為首選。

7. signal processing：信號(hào)處理，F(xiàn)FT是信號(hào)處理中的一個(gè)重要工具，用于分析和重建信號(hào)的頻譜。

8. discrete Fourier transform (DFT)：離散傅立葉變換，是連續(xù)傅立葉變換的離散化版本，是FFT的基礎(chǔ)。

9. spectral analysis：譜分析，F(xiàn)FT和DFT都是譜分析中的工具，用于分析信號(hào)的頻率成分。

10. sampling theorem：采樣定理，是信號(hào)處理中的一個(gè)重要定理，它闡明了在何種條件下可以從離散信號(hào)中恢復(fù)原始信號(hào)。

常用短語(yǔ)：

1. Fast Fourier Transform (FFT)

2. time-domain

3. frequency-domain

4. signal processing

5. data analysis

6. spectral analysis

7. frequency spectrum

8. time-frequency representation

雙語(yǔ)例句：

1. 通過(guò)FFT，我們可以快速分析信號(hào)的頻率成分。

In the process of signal analysis, FFT can quickly reveal the frequency components of a signal.

2. 頻率域是信號(hào)處理的一個(gè)重要領(lǐng)域。

The frequency domain is an important field in signal processing.

3. 數(shù)據(jù)分析中，頻譜分析是常用的工具。

In data analysis, spectral analysis is a commonly used tool.

4. 信號(hào)在時(shí)間和頻率上的表現(xiàn)是相互關(guān)聯(lián)的。

The performance of a signal in time and frequency is interrelated.

5. 頻率譜是頻率分析的重要手段。

Frequency spectrum is an important tool for frequency analysis.

6. 時(shí)間-頻率表示法是一種強(qiáng)大的技術(shù)，可用于實(shí)時(shí)信號(hào)處理。

Time-frequency representation is a powerful technique used for real-time signal processing.

7. FFT算法在數(shù)據(jù)壓縮和通信領(lǐng)域有廣泛應(yīng)用。

FFT algorithm has wide applications in data compression and communication fields.

英文小作文：

Title: The Power of FFT: A Journey into the Frequency Domain

In signal processing, the Fast Fourier Transform (FFT) is a transform that allows us to move from the time domain to the frequency domain, providing valuable insights into the behavior of signals. Through FFT, we can quickly analyze the frequency components of a signal, revealing patterns and trends that would otherwise be difficult or impossible to see in the time domain.

The frequency domain is a powerful tool for data analysis, as it allows us to see patterns in data that are otherwise hidden. From data compression to communication systems, FFT has found numerous applications, making it a crucial tool in the arsenal of signal processing engineers and scientists.

Moreover, FFT is not just limited to static analysis; it can also be used for real-time signal processing, where it provides a time-frequency representation of signals, allowing us to see how signals are changing over time, and how their behavior changes with respect to frequency. This allows for more accurate and effective decision-making in a wide range of applications, from audio processing to medical imaging.

In conclusion, the Fast Fourier Transform is a transform that has revolutionized the way we think about signals and data analysis. It provides us with a window into the frequency domain, revealing patterns and trends that would otherwise be hidden from view. With its wide range of applications and versatility, FFT continues to be a crucial tool in the toolbox of signal processing engineers and scientists.