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相关概念视频

Bandpass Sampling01:17

Bandpass Sampling

265
In signal processing, bandpass sampling is an effective technique for sampling signals that have most of their energy concentrated within a narrow frequency band. This type of signal is known as a bandpass signal. The key principle of bandpass sampling involves sampling the signal at a rate that is greater than twice the signal's bandwidth to prevent aliasing.
A bandpass signal has a spectrum with a lower frequency limit, denoted as ω1, and an upper frequency limit, denoted as ω2....
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IR Spectrum01:19

IR Spectrum

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When infrared (IR) radiation passes through a molecule, the bonds stretch or bend by absorbing the radiation. This absorption creates the molecule's absorption spectrum, which is the plot of its percentage transmittance versus wavenumber.
Transmittance is defined as the ratio of the radiant power passing through a sample to that from the radiation's source. Multiplying the transmittance by 100 gives the percent transmittance (%T), which varies between 100% (no absorption) and 0%...
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IR Spectrum Peak Splitting: Symmetric vs Asymmetric Vibrations01:08

IR Spectrum Peak Splitting: Symmetric vs Asymmetric Vibrations

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Identical bonds within a polyatomic group can stretch symmetrically (in-phase) or asymmetrically (out-of-phase). Similar to hydrogen bonding, these vibrations also influence the shape of the IR peak. Generally, asymmetric stretching frequencies are higher than symmetric stretching frequencies. For example, primary amines exhibit two distinct IR peaks between 3300–3500 cm−1 corresponding to the symmetric and asymmetric N-H stretching, while secondary amines exhibit a single...
1.2K
Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

137
Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
In contrast, nonlinear systems do not inherently possess these properties. However, for small deviations around an operating point, a nonlinear system can often be approximated as linear....
137
Aliasing01:18

Aliasing

238
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.
If the sampling frequency is below the Nyquist rate, these replicas overlap, preventing the original...
238
Attenuated Total Reflectance (ATR) Infrared Spectroscopy: Overview01:13

Attenuated Total Reflectance (ATR) Infrared Spectroscopy: Overview

580
Attenuated total reflectance (ATR) infrared spectroscopy is a powerful analytical technique used to study the composition of materials. It is widely employed in chemistry, materials science, forensic science, and other fields where sample characterization is required. ATR has several advantages over traditional transmission IR spectroscopy, including the requirement of little to no sample preparation and the ability to analyze a wide range of samples.
The ATR process begins by directing a beam...
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Computer-based Multitaper Spectrogram Program for Electroencephalographic Data
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格拉米安角场和卷积神经网络用于认知无线电网络中的实时多频谱传感.

Yanqueleth Molina-Tenorio1, Alfonso Prieto-Guerrero1, Enrique Rodriguez-Colina1

  • 1Electrical Engineering Department, Universidad Autónoma Metropolitana-Iztapalapa, Av. Ferrocarril San Rafael Atlixco 186, Mexico City 09310, Mexico.

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概括

本研究介绍了格拉米安的角度场图像,用于认知无线电网络 (CRN) 的合作频谱传感. 这种方法与卷积神经网络 (CNN) 相结合,在检测频谱占用率方面达到99.6%的准确性.

关键词:
认知无线电网络是一种认知无线电网络.卷积神经网络是一种卷积神经网络.格拉米安的角度场.多频谱频谱传感器

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相关实验视频

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科学领域:

  • 无线通信无线通信
  • 信号处理 信号处理
  • 机器学习 机器学习

背景情况:

  • 认知无线电网络 (CRN) 需要高效的频谱传感,以实现动态资源管理.
  • 传统的频谱传感技术在复杂的实时环境中面临着挑战.

研究的目的:

  • 在合作CRN中开发多频段频谱传感的新框架.
  • 提高检测主要用户活动和频谱占用率的准确性和效率.

主要方法:

  • 利用格拉米安角场 (GAF) 总和,将时间序列功率光谱密度数据转换为图像表示.
  • 集成GAF图像与卷积神经网络 (CNN) 进行分类.
  • 采用了合作感应方法,一个中央实体从分布式二级用户收集数据.

主要成果:

  • 在确定频谱占用率方面取得了99.6%的准确性.
  • 与传统的频谱传感技术相比,其表现优越.
  • 在动态的实时环境中验证了框架的有效性.

结论:

  • 基于GAF的图像表示为CRN中的合作频谱传感提供了一个强大的新方法.
  • 拟议的CNN框架允许精确和实时检测频谱占用情况.
  • 这项研究显著推进了CRN中的频谱资源管理.