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相关概念视频

Reconstruction of Signal using Interpolation01:10

Reconstruction of Signal using Interpolation

174
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...
174
Cascaded Op Amps01:16

Cascaded Op Amps

590
Operational amplifiers (op-amps) are versatile electronic components that can be interconnected in a cascade - one after another in a linear sequence. This cascading is possible due to their infinite input resistance and zero output resistance, allowing them to maintain their input-output relationships even when connected in series.
In a cascaded system, each op-amp is referred to as a stage. The output of one stage drives the input of the subsequent stage. As the input signal passes through...
590
Deconvolution01:20

Deconvolution

133
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...
133

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相关实验视频

Updated: Jun 8, 2025

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
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通过自适应子频段分解进行结构规范化的CNN架构.

Pavel Sinha, Ioannis Psaromiligkos, Zeljko Zilic

    IEEE transactions on neural networks and learning systems
    |November 8, 2024
    PubMed
    概括

    一个新的卷积神经网络 (CNN) 架构将信号分解为子带,提高分类准确性和稳定性. 这种方法显著降低了计算成本,同时保持了图像数据集的最新性能.

    科学领域:

    • 计算机科学 计算机科学
    • 人工智能的人工智能
    • 机器学习 机器学习

    背景情况:

    • 卷积神经网络 (CNN) 是图像分类的强大工具.
    • 传统的CNN处理全频段信号,这可能是计算密集的.
    • 现有的架构可能缺乏对噪声和量子化的强度.

    研究的目的:

    • 提出一个通用的CNN架构,将输入信号分解为子带.
    • 为了提高CNN的计算效率和稳定性.
    • 与现有方法相比,保持或提高分类准确性.

    主要方法:

    • 一个新的CNN架构,采用适应性波器银行进行子频段分解.
    • 使用卷积层从每个子带提取独立的特征.
    • 通过完全连接的层来进行分类的特征组合.
    • 从数据中分解子带的端到端学习.

    主要成果:

    • 拟议的架构通过将子带CNN限制在特定信号频谱上来证明结构规范化.
    • 在MNIST,CIFAR-10/100,Caltech-101和ImageNet-2012数据集上实现了最先进的分类准确性.
    • 在ImageNet-2012上获得了前5名 (86.91%) 和前1名 (69.73%) 的精度.

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  • 显著降低计算成本:在推断方面超过90%,在反向传播方面约75% (单层分解).
  • 对输入和重量和偏差量化噪声的稳定性.
  • 结论:

    • 拟议的子频段分解CNN架构为传统的全频段CNN提供了一个计算效率高且强大的替代方案.
    • 它在各种基准数据集中实现了优越或具有竞争力的准确性.
    • 该方法提供了大量的计算节省,特别有利于大规模的深度学习应用程序.