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In any LTI (Linear Time-Invariant) system, the convolution of two signals is denoted using a convolution operator, assuming all initial conditions are zero. The convolution integral can be divided into two parts: the zero-input or natural response and the zero-state or forced response, with t0 indicating the initial time.
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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.
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相关实验视频

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Constructing and Visualizing Models using Mime-based Machine-learning Framework
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灵活的视觉安全图像加密与元学习压缩和混乱系统.

Wei Chen1, Yichuan Wang2, Cheng Shi1

  • 1School of Computer Science and Engineering, Xi'an University of Technology, Xi'an 710048, China.

Neural networks : the official journal of the International Neural Network Society
|July 6, 2025
PubMed
概括
此摘要是机器生成的。

本研究介绍了一种使用元学习和混乱系统的新型图像加密方案,以确保安全,高质量的视觉数据. 这种灵活的方法在广泛的应用中平衡了运行时间和图像质量.

关键词:
一个混乱的系统.动态辅助输入 动态辅助输入图像加密 图像加密超学习压缩压缩.传统的深度学习.

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科学领域:

  • 计算机科学 计算机科学
  • 信息安全 信息安全
  • 人工智能的人工智能

背景情况:

  • 越来越多的人在各种应用中对安全的数字图像加密的需求.
  • 现有方法的局限性包括安全性不足和解密图像质量差.
  • 需要先进的技术,整合视觉数据的压缩和加密.

研究的目的:

  • 提出一个灵活而安全的图像加密方案.
  • 为了提高解密的图像质量和加密安全性.
  • 为图像安全利用元学习,混乱系统和深度学习.

主要方法:

  • 开发了一个超学习压缩重建网络,具有动态辅助输入,用于高质量的图像压缩.
  • 通过将2D-IS混乱系统与图像加密的深度学习网络相结合,构建了一个新的IS-DP混乱系统.
  • 实现了无损LSB-2^k校正嵌入方法,将秘密图像嵌入到载体图像中.

主要成果:

  • 实现了高质量的压缩和数字图像的视觉安全加密.
  • 证明了拟议的IS-DP混乱系统和元学习方法的有效性.
  • 验证了深度学习方法在集成加密和压缩任务中的可行性.

结论:

  • 拟议的方案提供了一个灵活的解决方案,用于视觉安全的图像加密.
  • 超级学习提供了适应性,允许用户平衡性能和质量.
  • 深度学习和混乱系统的集成显示了高级图像安全应用的巨大潜力.