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相关概念视频

Depth Perception and Spatial Vision01:15

Depth Perception and Spatial Vision

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Depth perception is the ability to perceive objects three-dimensionally. It relies on two types of cues: binocular and monocular. Binocular cues depend on the combination of images from both eyes and how the eyes work together. Since the eyes are in slightly different positions, each eye captures a slightly different image. This disparity between images, known as binocular disparity, helps the brain interpret depth. When the brain compares these images, it determines the distance to an object.
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Observational Learning01:12

Observational Learning

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Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...
802
Uniform Depth Channel Flow: Problem Solving01:18

Uniform Depth Channel Flow: Problem Solving

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To calculate the flow rate for a trapezoidal channel, first, identify the bottom width, side slope, and flow depth of the channel. The cross-sectional area (A) corresponding to the depth of flow (y), channel bottom width (B), and side slope (θ) is determined by:Next, calculate the wetted perimeter, which includes the bottom width and the sloped side lengths in contact with the water. Using the values of the cross-sectional area and the wetted perimeter, determine the hydraulic radius by...
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Understanding Memory01:19

Understanding Memory

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Memory is the retention of information or experiences over time, facilitated through three main processes: encoding, storage, and retrieval. Encoding is the process of inputting information into the memory system. For instance, when listening to a lecture, watching a play, reading a book, or having a conversation, the brain is actively encoding information. This initial stage involves transforming sensory input into a form that can be processed and stored by the brain. Various factors, such as...
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Reducing Line Loss01:18

Reducing Line Loss

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In a three-phase circuit, line loss is an indicator of energy dissipated as heat due to the resistance of transmission lines. To address this, incorporating transformers into the system—a step-up transformer at the source and a step-down transformer at the load—is a strategic solution. Two three-phase transformers are introduced to improve this.
With a step-up transformer at the source, the voltage is increased, thereby reducing the current in the transmission lines since power loss in...
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Differential Leveling01:12

Differential Leveling

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Differential leveling is a precise method in surveying used to determine the elevation difference between two points. Its primary goal is to establish accurate vertical measurements to create level surfaces or grade lines critical for designing and constructing infrastructures such as roads, bridges, and buildings.The procedure for differential leveling begins with setting up and leveling the instrument at a point where the benchmark can be seen. The level rod is held on the benchmark (BM), and...
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相关实验视频

Updated: Jan 10, 2026

Author Spotlight: Automated Deep Brain Stimulation for Parkinson's Disease - Exploring the Possibilities and Challenges of Home Monitoring
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基于深度学习的缓存优化,用于车辆边缘计算中的VR 360°视频.

Shahbaz Khan1, Jinling Zhang2, Kamlesh Kumar Soothar1

  • 1School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing, 100876, China.

Scientific reports
|November 25, 2025
PubMed
概括
此摘要是机器生成的。

DeepEdge360优化了使用深度学习在车辆边缘计算 (VEC) 中的虚拟现实 (VR) 360°视频缓存. 它显著提高了缓存命中率,并减少了在移动车辆中无VR流媒体的延迟.

关键词:
360°视频缓存可以使用.深度强化学习学习 (deep reinforcement learning) 是一种深度强化学习的方法.车辆网络 车辆网络视窗预测的预测

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An Open-Source Virtual Reality System for the Measurement of Spatial Learning in Head-Restrained Mice
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An Open-Source Virtual Reality System for the Measurement of Spatial Learning in Head-Restrained Mice

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相关实验视频

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科学领域:

  • 计算机科学 计算机科学
  • 电气工程 电气工程
  • 多媒体系统 多媒体系统

背景情况:

  • 虚拟现实 (VR) 和360度视频流在车辆边缘计算 (VEC) 中存在重大挑战,原因是高带宽需求和超低延迟要求.
  • 现有的缓存策略 (LFU,LRU) 和视图端口意识方法无法充分解决VEC环境中固有的时空动态和移动性.
  • 无VR体验需要先进的解决方案,可以动态地适应用户行为和车辆运动.

研究的目的:

  • 提出DeepEdge360,一个基于深度学习的新框架,用于优化VEC系统中360°视频的缓存.
  • 通过解决与带宽,延迟和用户视图端口动态相关的挑战,提高VR流媒体体验的质量.
  • 开发一个主动和智能缓存策略,适应实时车辆移动和用户查看模式.

主要方法:

  • 实现了基于的自适应细分和请求机制,使用长短期内存 (LSTM) 进行人气预测.
  • 开发了针对车辆和路边单位 (RSU) 的主动缓存策略,以优化基于用户行为和移动性的存储.
  • 利用深度Q网络 (DQN) 进行智能缓存驱逐策略,以平衡性能指标.

主要成果:

  • 实现了82%的缓存命中率,证明了卓越的缓存效率.
  • 将端到端延迟降低到45ms,这对于沉浸式VR体验至关重要.
  • 通过智能缓存和预检,将带宽利用率优化至76%.

结论:

  • DeepEdge360框架有效地支持动态车辆网络中的高质量VR流媒体.
  • 基于深度学习的缓存在VEC环境中明显优于传统和最先进的方法.
  • DeepEdge360的模块化设计可确保在边缘辅助系统中实现实际部署,以增强VR服务.