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相关概念视频

Veins of Upper Limbs01:17

Veins of Upper Limbs

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The human circulatory system, a marvel of biological engineering, is a complex network of vessels that transport blood throughout the body. Among these, the veins responsible for carrying blood from the upper limbs are divided into two categories: deep and superficial.
The deep venous system is primarily composed of the ulnar and radial veins. The ulnar vein, which drains the fingers through the superficial palmar venous arches, and the radial vein, which serves the palms via the deep palmar...
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IR Frequency Region: Fingerprint Region01:03

IR Frequency Region: Fingerprint Region

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IR spectra are divided into two main regions: the diagnostic region and the fingerprint region. The diagnostic region of the spectrum lies above 1500 cm−1. The absorptions resulting from single-bond vibrations of the N–H, C–H, and O–H stretch at higher wavenumbers and appear on the left side of the spectrum. The stretching absorptions of the C≡C and C≡N occur between 2100–2300 cm−1. In contrast, those arising from stretching absorptions of the...
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Veins of Lower Limbs01:15

Veins of Lower Limbs

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The human body consists of an intricate network of veins responsible for the crucial task of blood drainage from the lower limbs. These veins can be categorized into two main types: deep veins and superficial veins.
Formed by the union of the medial and lateral plantar veins, the posterior tibial vein, rising through the calf muscle, assimilates the fibular vein. The anterior tibial vein, a superior extension of the foot's dorsalis pedis vein, merges with the posterior tibial vein at the...
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Veins01:17

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Veins are an integral part of our circulatory system, serving as the blood vessels that transport blood from all body regions to the heart. They are a network of hollow tubes that carry blood low in oxygen from the body's cells back to the heart for reoxygenation. Veins are crucial for maintaining the body's overall fluid balance and the continuous circulation of blood.
Structure of Veins:
The structure of veins is specifically designed to assist in the low-pressure transportation of...
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Veins of Head and Neck01:19

Veins of Head and Neck

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The blood drainage from the head and neck is primarily managed by three pairs of veins: the external jugular, internal jugular, and vertebral veins. The external jugular veins drain superficial scalp and face structures, passing over the sternocleidomastoid muscles to empty into the subclavian veins.
On the other hand, the vertebral veins, unlike their arterial counterparts, are not primarily responsible for brain drainage. Instead, they drain the cervical vertebrae, spinal cord, and some small...
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Force Classification01:22

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Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...
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相关实验视频

Updated: Jul 18, 2025

Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision
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改进的轻量级卷积神经网络用于手指静脉识别系统.

Chih-Hsien Hsia1,2, Liang-Ying Ke1, Sheng-Tao Chen3

  • 1Department of Computer Science and Information Engineering, National Ilan University, Yilan County 26047, Taiwan.

Bioengineering (Basel, Switzerland)
|August 26, 2023
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种改进的轻型卷积神经网络 (ILCNN),用于准确识别手指静脉. 新型模型增强了特征提取,实现了高精度,更少的参数用于现实世界的应用.

关键词:
生物识别信息 生物识别信息识别手指静脉的使用方法人机交互的人机交互轻量级网络是轻量级的网络.

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科学领域:

  • 生物工程是生物工程.
  • 计算机视觉 计算机视觉
  • 生物识别信息 生物识别信息

背景情况:

  • 计算机视觉 (CV) 和卷积神经网络 (CNN) 在生物工程中的特征提取方面表现出色.
  • 传统的CNN在手指静脉成像中难以翻译,阻碍了实时,准确的识别.
  • CNNs拥有众多参数,缺乏固有的转移不变特征,限制了它们的实际应用.

研究的目的:

  • 开发一个改进的轻量级卷积神经网络 (ILCNN),用于增强手指静脉识别.
  • 解决现有的CNN在处理翻译和生物识别参数效率方面的局限性.
  • 提高指纹静脉识别系统在不同环境中的准确性和稳定性.

主要方法:

  • 引入了一种改进的轻质卷积神经网络 (ILCNN),包含多样化的分支区块 (DBB).
  • 集成自适应多相采样 (APS) 和协调注意力机制 (CoAM),以促进特征识别.
  • 马来西亚大学科学院 (FV-USM) 和PLUSVein背手掌指脉 (PLUSVein-FV3) 公共数据库使用手指脉的评估.

主要成果:

  • ILCNN模型在FV-USM上实现了99.82%的高识别准确率,在PLUSVein-FV3.3上达到95.90%.
  • 拟议的模型使用了显著减少的参数数量,仅为123万.
  • 对比分析表明,ILCNN的性能优于以前的手指静脉识别方法.

结论:

  • 开发的ILCNN为指纹静脉识别提供了高度准确和参数高效的解决方案.
  • 整合DBB,APS和CoAM有效地提高了模型捕获歧视性手指静脉特征的能力.
  • 这项研究为实时和可靠的生物识别系统带来了有前途的进展.