相关实验视频

Updated: Jun 5, 2025

Demonstration of Equal-Intensity Beam Generation by Dielectric Metasurfaces
09:33

Demonstration of Equal-Intensity Beam Generation by Dielectric Metasurfaces

Published on: June 7, 2019

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超表面空间过器用于多个波信号

Daeik Kim1, Mai Anh Nguyen1, Gangil Byun1

  • 1Department of Electrical Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan 44919, Republic of Korea.

Nanophotonics (Berlin, Germany)
|December 5, 2024
PubMed
概括

本研究引入了超表面空间波器,以将基本和波频率与非线性频率混合区分开来. 这使宽带多频源能够同时使用.

关键词:
灯束转向的方向盘.和频率是指和频率的和频率.metasurfaces 是一个表层.空间过器是一个空间过器.

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Properties of Fourier Transform II01:24

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The Fourier Transform (FT) is an essential mathematical tool in signal processing, transforming a time-domain signal into its frequency-domain representation. This transformation elucidates the relationship between time and frequency domains through several properties, each revealing unique aspects of signal behavior.
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Accurate signal sampling and reconstruction are crucial in various signal-processing applications. A time-domain signal's spectrum can be revealed using its Fourier transform. When this signal is sampled at a specific frequency, it results in multiple scaled replicas of the original spectrum in the frequency domain. The spacing of these replicas is determined by the sampling frequency.
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Managing signal sampling rates is essential in digital signal processing to maintain signal integrity. A decimated signal, characterized by a reduced frequency range due to its lower sampling rate, can be upsampled by inserting zeros between each sample. This upsampling process expands the original spectrum and introduces repeated spectral replicas at intervals dictated by the new Nyquist frequency. To refine this zero-inserted sequence, it is passed through a lowpass filter with a cutoff...
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Properties of Fourier series II01:21

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Time scaling of signals is a crucial concept in signal processing that affects the Fourier series representation without altering its coefficients. The process modifies the fundamental frequency, thereby changing how the series represents the signal over time. This principle is essential in various applications, including audio and image processing, where signal manipulation is frequent. Understanding function symmetries is fundamental to simplifying the Fourier series.
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