相关实验视频

Updated: Jul 20, 2025

Functional Near-Infrared Spectroscopy Hyperscanning Study in Psychological Counseling
06:04

Functional Near-Infrared Spectroscopy Hyperscanning Study in Psychological Counseling

Published on: January 17, 2025

619

富里埃域中的非同寻常的时间和记忆效率大规模正规相关性分析:从浅到深

Xiang-Jun Shen, Zhaorui Xu, Liangjun Wang

    IEEE transactions on neural networks and learning systems
    |August 1, 2023
    PubMed
    概括

    对于大型数据集,正规相关性分析 (CCA) 现已成为现实. 一种新的福里埃域方法显著加快了训练速度,减少了内存的使用,比现有技术快1000倍的速度达到可比准确度.

    更多相关视频

    A Method for Investigating Age-related Differences in the Functional Connectivity of Cognitive Control Networks Associated with Dimensional Change Card Sort Performance09:01

    A Method for Investigating Age-related Differences in the Functional Connectivity of Cognitive Control Networks Associated with Dimensional Change Card Sort Performance

    Published on: May 7, 2014

    10.2K
    Author Spotlight: Unlocking New Insights in fNIRS Studies - A Novel Framework for Inter-Brain Synchrony Analysis05:59

    Author Spotlight: Unlocking New Insights in fNIRS Studies - A Novel Framework for Inter-Brain Synchrony Analysis

    Published on: October 6, 2023

    2.6K

    相关概念视频

    Fast Fourier Transform01:10

    Fast Fourier Transform

    The Fast Fourier Transform (FFT) is a computational algorithm designed to compute the Discrete Fourier Transform (DFT) efficiently. By breaking down the calculations into smaller, manageable sections, the FFT significantly reduces the computational complexity involved. Direct computation of an N-point DFT requires N2 complex multiplications, whereas the FFT algorithm needs only (N/2)log⁡2N multiplications, offering a much faster performance.
    The computational efficiency of the FFT becomes...
    388
    Continuous -time Fourier Transform01:11

    Continuous -time Fourier Transform

    The Fourier series is instrumental in representing periodic functions, offering a powerful method to decompose such functions into a sum of sinusoids. This technique, however, necessitates modification when applied to nonperiodic functions. Consider a pulse-train waveform consisting of a series of rectangular pulses. When these pulses have a finite period, they can be accurately represented by a Fourier series. Yet, as the period approaches infinity, resulting in a single, isolated pulse, the...
    348
    Calculating and Interpreting the Linear Correlation Coefficient01:11

    Calculating and Interpreting the Linear Correlation Coefficient

    The correlation coefficient, r, developed by Karl Pearson in the early 1900s, is numerical and provides a measure of strength and direction of the linear association between the independent variable, x, and the dependent variable, y. Hence, it is also known as the Pearson product-moment correlation coefficient. It can be calculated using the following equation:
    6.0K
    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...
    324
    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....
    111
    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...
    377