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相关概念视频

Reconstruction of Signal using Interpolation01:10

Reconstruction of Signal using Interpolation

194
Signal processing techniques are essential for accurately converting continuous signals to digital formats and vice versa. When a continuous signal is sampled with a period T, the resulting sampled signal exhibits replicas of the original spectrum in the frequency domain, spaced at intervals equal to the sampling frequency. To handle this sampled signal, a zero-order hold method can be applied, which creates a piecewise constant signal by retaining each sample's value until the next...
194
Convolution: Math, Graphics, and Discrete Signals01:24

Convolution: Math, Graphics, and Discrete Signals

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In any LTI (Linear Time-Invariant) system, the convolution of two signals is denoted using a convolution operator, assuming all initial conditions are zero. The convolution integral can be divided into two parts: the zero-input or natural response and the zero-state or forced response, with t0 indicating the initial time.
To simplify the convolution integral, it is assumed that both the input signal and impulse response are zero for negative time values. The graphical convolution process...
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Deconvolution01:20

Deconvolution

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Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...
154

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相关实验视频

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Experimental Investigation of Secondary Flow Structures Downstream of a Model Type IV Stent Failure in a 180° Curved Artery Test Section
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卷积神经网络用于改进基于事件的Shack-Hartmann波浪前沿重建.

Mitchell Grose, Jason D Schmidt, Keigo Hirakawa

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    此摘要是机器生成的。

    本研究介绍了一个基于事件的波浪前线网络 (EBWFNet),用于更快,更准确的Shack-Hartmann波浪前线传感 (SHWFS). 在现实世界中,新型CNN在现实条件下实现了亚像素精度,超过了现有的基于事件的方法.

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    科学领域:

    • 光学和光子学 在光学和光子学.
    • 机器学习 机器学习
    • 适应光学适应光学

    背景情况:

    • 沙克-哈特曼波面传感 (SHWFS) 传统上使用基于的摄像头进行偏差测量.
    • 传统方法受到固定采样率和低效的像素使用限制.
    • 基于事件的摄像头为SHWFS提供异步,高速的数据采集.

    研究的目的:

    • 开发一种新的卷积神经网络 (CNN),用于基于事件的SHWFS的实时,准确的点心部估计.
    • 在现实场景中评估拟议的EBWFNet的性能.
    • 将EBWFNet与基于事件的SHWFS技术进行比较.

    主要方法:

    • 开发一个定制的SHWFS硬件与同步和基于事件的摄像头.
    • 使用CNN架构实现基于事件的波浪网络 (EBWFNet).
    • 使用基于的摄像头数据进行EBWFNet的无监督培训和测试.
    • 实地测试和废弃性研究,以评估性能和组件影响.

    主要成果:

    • 在现实世界条件下,EBWFNet实现了高度准确的,分像素点心脏估计.
    • 与现有的基于事件的先进SHWFS方法相比,已显著改进.
    • 一个未经优化的MATLAB实现在单个GPU上实现了超过800Hz的速度.

    结论:

    • 拟议的EBWFNet显著提高了基于事件的SHWFS的准确性和速度.
    • 基于事件的摄像头与CNN相结合,代表了适应光学的一个有希望的进步.
    • 该EBWFNet提供了一个强大的和高效的解决方案,用于实时波浪偏差测量.