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相关概念视频

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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Force Classification01:22

Force Classification

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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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Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

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Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence...
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Assessment of blood pressure in brachial artery(two-step method)01:23

Assessment of blood pressure in brachial artery(two-step method)

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Measuring blood pressure is a fundamental skill in healthcare that aids in diagnosing and monitoring hypertension and other cardiovascular conditions. An aneroid sphygmomanometer, commonly used in clinical settings, offers a manual and precise method for blood pressure measurement. The technique for using this instrument involves specific steps that must be carefully executed to ensure accuracy. The following detailed description outlines a two-step technique for assessing blood pressure using...
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Fast Decoupled and DC Powerflow01:24

Fast Decoupled and DC Powerflow

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The fast decoupled power flow method addresses contingencies in power system operations, such as generator outages or transmission line failures. This method provides quick power flow solutions, essential for real-time system adjustments. Fast decoupled power flow algorithms simplify the Jacobian matrix by neglecting certain elements, leading to two sets of decoupled equations:
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Assessment of blood pressure in brachial artery(one-step method)01:15

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This procedural guide systematically measures blood pressure using an oscillometric digital sphygmomanometer, emphasizing accuracy, patient safety, and comfort.
Prepare for the Procedure:
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相关实验视频

Updated: Jun 17, 2025

Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision
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DDP-FedFV:双解个性化联合学习框架,用于识别手指静脉.

Zijie Guo1,2, Jian Guo1,2, Yanan Huang2,3

  • 1School of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing 210023, China.

Sensors (Basel, Switzerland)
|August 10, 2024
PubMed
概括

这项研究引入了双解个性化联合学习框架 (DDP-FedFV) 用于指纹静脉识别. 该方法提高了通用性和个性化,优于没有隐私风险的集中式模型.

关键词:
双重脱的双重脱方式识别手指静脉的使用方法个性化的联合学习.两个阶段的培训培训.

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科学领域:

  • 生物识别信息 生物识别信息
  • 机器学习 机器学习
  • 数据 隐私 数据 隐私 数据

背景情况:

  • 指纹识别为身份验证提供了很高的准确性,但在集中式方法中面临隐私问题.
  • 联合学习 (FL) 通过培训没有数据共享的模型来解决数据隐私问题,但其性能受到数据集异质性的影响.
  • 现有的FL方法难以平衡全球模型通用性与个人客户模型个性化.

研究的目的:

  • 提出一个新的联合学习框架,DDP-FedFV,专门设计用于指纹静脉识别.
  • 提高全球模型的通用性和客户端模型在分布式环境中的个性化.
  • 解决生物识别应用中的数据异质性引起的联合学习的性能限制.

主要方法:

  • 引入了双解机制 (模型和特征解),以优化特征表示和全球模型通用性.
  • 实施了个性化权重聚合方法 (FedPWRR),以根据数据分布量身定制客户模型.
  • 通过对六个公开的手指静脉数据集进行理论分析和实验,评估了DDP-FedFV框架.

主要成果:

  • DDP-FedFV框架有效地结合了指纹静脉识别的概括和个性化.
  • 双解机制改善了特征表示和全球模型通用性.
  • 通过优化基于数据分布的参数聚合,FedPWRR增强了客户端模型个性化.
  • 实验结果显示,与传统的集中式培训模式相比,其表现优越.

结论:

  • 拟议的DDP-FedFV框架为手指静脉识别提供了一种保护隐私和有效的解决方案.
  • 双解和个性化聚合策略成功地解决了在异质生物识别数据中的FL挑战.
  • 在不影响数据隐私或增加通信开销的情况下,DDP-FedFV实现了高精度.