相关实验视频

Updated: Jun 16, 2025

Optical Recording of Electrical Activity in Guinea-pig Enteric Networks using Voltage-sensitive Dyes
14:23

Optical Recording of Electrical Activity in Guinea-pig Enteric Networks using Voltage-sensitive Dyes

Published on: December 4, 2009

10.0K

在180Gb/s的净比特率IMDD短距离光学系统中,FPGA实现了轻功率的Volterra启发的神经网络等效器

Wenqing Jiang, Xiaokai Guan, Luyao Huang

    Optics letters
    |August 15, 2024
    PubMed
    概括

    一个新的Volterra启发的神经网络 (VINN) 均衡器可以降低计算复杂性,同时保持非线性补偿. 这种功率轻平衡器在强度调制和直接检测系统中实现了创纪录的180 Gb/s净速率.

    更多相关视频

    Quasi-light Storage for Optical Data Packets07:45

    Quasi-light Storage for Optical Data Packets

    Published on: February 6, 2014

    10.8K
    Optogenetic Entrainment of Hippocampal Theta Oscillations in Behaving Mice07:33

    Optogenetic Entrainment of Hippocampal Theta Oscillations in Behaving Mice

    Published on: June 29, 2018

    11.7K

    相关概念视频

    Linear Approximation in Frequency Domain01:26

    Linear Approximation in Frequency Domain

    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....
    88
    Reconstruction of Signal using Interpolation01:10

    Reconstruction of Signal using Interpolation

    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...
    183
    Linear Approximation in Time Domain01:21

    Linear Approximation in Time Domain

    Nonlinear systems often require sophisticated approaches for accurate modeling and analysis, with state-space representation being particularly effective. This method is especially useful for systems where variables and parameters vary with time or operating conditions, such as in a simple pendulum or a translational mechanical system with nonlinear springs.
    For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length,...
    75
    Discrete-time Fourier transform01:26

    Discrete-time Fourier transform

    The Discrete-Time Fourier Transform (DTFT) is an essential mathematical tool for analyzing discrete-time signals, converting them from the time domain to the frequency domain. This transformation allows for examining the frequency components of discrete signals, providing insights into their spectral characteristics. In the DTFT, the continuous integral used in the continuous-time Fourier transform is replaced by a summation to accommodate the discrete nature of the signal.
    One of the notable...
    288
    Integrator and Differentiator01:13

    Integrator and Differentiator

    Op-amp circuits have significant applications in various fields, including automotive engineering. One such application is cruise control systems in cars, where op-amp circuits are integral for maintaining a constant speed. In these systems, op-amps function as both integrators and differentiators.
    An integrator within an op-amp circuit produces an output directly proportional to the integral of the input signal. This is achieved by replacing the feedback resistor in a typical inverting...
    792
    Discrete Fourier Transform01:15

    Discrete Fourier Transform

    The Discrete Fourier Transform (DFT) is a fundamental tool in signal processing, extending the discrete-time Fourier transform by evaluating discrete signals at uniformly spaced frequency intervals. This transformation converts a finite sequence of time-domain samples into frequency components, each representing complex sinusoids ordered by frequency. The DFT translates these sequences into the frequency domain, effectively indicating the magnitude and phase of each frequency component present...
    234