相关实验视频

Updated: Jul 3, 2025

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
11:18

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

10.3K

非高斯噪声对神经网络均等系统中的GMI和LDPC性能的影响

Weiqi Lu, Zexu Liu, Zhaopeng Xu

    Optics letters
    |February 15, 2024
    PubMed
    概括

    一种新的查找表方法提高了50Gb/s PAM4系统的低密度平价检查代码 (LDPC) 在非高斯噪声的性能. 这种方法在人工神经网络等效器系统中提高了0.6dB的灵敏度.

    更多相关视频

    Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy11:15

    Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy

    Published on: June 27, 2013

    33.7K
    Stochastic Noise Application for the Assessment of Medial Vestibular Nucleus Neuron Sensitivity In Vitro00:06

    Stochastic Noise Application for the Assessment of Medial Vestibular Nucleus Neuron Sensitivity In Vitro

    Published on: August 28, 2019

    5.1K

    相关概念视频

    Propagation of Uncertainty from Random Error00:59

    Propagation of Uncertainty from Random Error

    An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
    684
    Propagation of Uncertainty from Systematic Error01:10

    Propagation of Uncertainty from Systematic Error

    The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this...
    519
    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....
    91
    Difference from Background: Limit of Detection01:05

    Difference from Background: Limit of Detection

    The limit of detection (LOD) is the smallest amount of analyte that can be distinguished from the background noise. The LOD value corresponds to the concentration at which the analyte signal is three times larger than the standard deviation of the blank signal. Below this value, the analyte signal cannot be differentiated from the background noise. It is calculated by dividing the calibration slope by 3 times the standard deviation of the blank signals.
    The LOD indicates the presence or absence...
    6.4K
    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,...
    81
    Linear time-invariant Systems01:23

    Linear time-invariant Systems

    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...
    258