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相关概念视频

Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

86
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....
86
Linear time-invariant Systems01:23

Linear time-invariant Systems

221
A system is linear if it displays the characteristics of homogeneity and additivity, together termed the superposition property. This principle is fundamental in all linear systems. Linear time-invariant (LTI) systems include systems with linear elements and constant parameters.
The input-output behavior of an LTI system can be fully defined by its response to an impulsive excitation at its input. Once this impulse response is known, the system's reaction to any other input can be...
221
Basic signals of Fourier Transform01:07

Basic signals of Fourier Transform

475
The Fourier Transform is a pivotal mathematical tool in signal processing, enabling the transformation of time-domain signals into their frequency-domain representations. Among the numerous elements within this domain, certain functions like the sinc function, delta function, and exponential signals hold significant importance due to their unique properties and implications.
The sinc function, defined as sinc(x) = sin(πx)/(πx), is particularly notable for its symmetry and behavior at...
475
Basic Operations on Signals01:22

Basic Operations on Signals

358
Basic signal operations include time reversal, time scaling, time shifting, and amplitude transformations. These operations are fundamental in signal processing and analysis.
Time Reversal mirrors a continuous-time signal about the vertical axis at t=0. This is achieved by substituting t with −t. For example, if a signal x(t) is considered, the time-reversed signal is x(−t). This operation can be graphically represented, showing the mirrored signal.
358
Superposition Theorem for AC Circuits01:13

Superposition Theorem for AC Circuits

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Consider encountering a circuit in a steady state where all its inputs are sinusoidal, yet they do not all possess the same frequency. Such a circuit is not classified as an alternating current (AC) circuit, and consequently, its currents and voltages will not exhibit sinusoidal behavior. However, this circuit can be analyzed using the principle of superposition.
The principle of superposition stipulates that the output of a linear circuit with several concurrent inputs is equivalent to the...
620
Properties of Laplace Transform-I01:15

Properties of Laplace Transform-I

360
The Laplace transform is a powerful mathematical tool used to convert functions from the time domain into the frequency domain, greatly simplifying the analysis and solution of linear time-invariant systems. This transformation is facilitated by several universal properties: Linearity, Time-Scaling, Time-Shifting, and Frequency Shifting.
The Linearity property is foundational to the Laplace transform. It states that the transform of a linear combination of functions is equivalent to the same...
360

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相关实验视频

Updated: Jun 9, 2025

Shaping the Amplitude and Phase of Laser Beams by Using a Phase-only Spatial Light Modulator
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一个基于值激活的简化Lv的变换算法,用于短暂的多组件线性频率调制信号分析.

Maolin Lei, Peng Ye, Chengyang Li

    The Review of scientific instruments
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    PubMed
    概括
    此摘要是机器生成的。

    一个新的简化Lv的转换 (SLVT) 算法有效地分析短暂的信号,仅在信号到达时激活. 这种方法显著降低了计算负载,并与现有技术相比提高了准确性.

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    科学领域:

    • 数字信号处理 数字信号处理
    • 算法开发 算法开发
    • 过渡信号分析 过渡信号分析

    背景情况:

    • 由于采样率高,现代数字系统产生了大量数据,这带来了计算挑战.
    • 分析短暂的多元件线性频率调制 (LFM) 信号需要高效的处理方法.
    • 现有的信号处理技术对于稀疏的短暂信号可能是计算密集的.

    研究的目的:

    • 提出一种计算效率高的算法来分析短暂的LFM信号.
    • 为了减少与高采样率数字系统相关的计算负担.
    • 为了提高短暂信号分析的准确性和速度.

    主要方法:

    • 基于值激活的简化Lv的变换 (SLVT) 算法的开发.
    • 只有在信号到达时,SLVT才能触发分析,利用信号稀疏性.
    • 使用Bluestein chirp-z算法实现拉伸基石转换,删除冗余计算.
    • 与离散里埃变换 (DFT) 和其他先进方法进行比较.

    主要成果:

    • SLVT 将原始Lv的变换 (LVT) 的计算复杂度降低至少30.8%.
    • 该算法在参数提取准确度,计算复杂性和执行时间方面表现出优异的性能,与离散的波里叶变换,分数里叶变换和拉登维格纳变换相比.
    • 现场可编程网关阵列 (FPGA) 实现加速了SLVT计算的速度,比CPU加快了116倍.

    结论:

    • 拟议的SLVT算法为暂时LFM信号分析的效率和有效性提供了显著的改进.
    • 通过稀疏的信号处理,SLVT有效地解决了高采样率系统的计算负担.
    • 该算法的增强性能和速度使其适合实时应用程序和高级信号处理任务.