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相关概念视频

Neural Control of Respiration01:18

Neural Control of Respiration

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The neural regulation of respiration is a meticulously coordinated process primarily controlled by the respiratory centers located within the brainstem. These centers, composed of specialized neurons, transmit nerve impulses that control the contraction and relaxation of our respiratory muscles.
Respiratory Centers in the Brainstem
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Digestion begins with a cephalic phase that prepares the digestive system to receive food. When our brain processes visual or olfactory information about food, it triggers impulses in the cranial nerves innervating the salivary glands and stomach to prepare for food.
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Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
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Active filters are electronic circuits that use operational amplifiers (op-amps), resistors, and capacitors to filter out unwanted frequency components from a signal. A first-order low-pass active filter is designed to pass signals with a frequency lower than a certain cutoff frequency and attenuate frequencies higher than that cutoff frequency. The transfer function for a first-order low-pass active filter is:
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The human brain perceives pitch through two primary mechanisms reflected in place theory and frequency theory. Each mechanism describes how sound waves are interpreted as specific pitches by the brain, offering insights into the intricate processes of auditory perception.
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Measurement of Neurophysiological Signals of Ignoring and Attending Processes in Attention Control
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低延迟主动噪声控制使用注意力循环网络.

Hao Zhang1, Ashutosh Pandey1, DeLiang Wang2

  • 1Department of Computer Science and Engineering, Ohio State University, Columbus, OH 43210-1277 USA.

IEEE/ACM transactions on audio, speech, and language processing
|September 25, 2023
PubMed
概括
此摘要是机器生成的。

本研究介绍了深度主动噪声控制 (ANC) 策略,以尽量减少处理延迟. 通过使用注意力循环网络 (ARN) 和延迟补偿训练,研究人员实现了接近零的算法延迟,缓解了ANC系统约束.

关键词:
这就是ARN ARN.活动噪音控制 活动噪音控制算法延迟时间算法延迟时间在深度ANC ANC中.低延迟的低延迟时间

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科学领域:

  • 声学和信号处理
  • 人工智能和机器学习

背景情况:

  • 由于因果关系约束,处理延迟是主动噪声控制 (ANC) 系统的一个关键挑战.
  • 对ANC的深度学习方法经常引入额外的延迟,加剧了这个问题.

研究的目的:

  • 开发和评估基于深度学习的战略,以实现低延迟主动噪声控制.
  • 通过减少算法延迟来减轻ANC系统中的因果关系约束.

主要方法:

  • 采用时间域专注循环网络 (ARN) 进行深度ANC,缩小尺寸.
  • 引入了延迟补偿培训,以使ANC使用预测噪声进行操作.
  • 使用了修订后的重叠加方法,以防止信号重合成期间的延迟.

主要成果:

  • 证明了拟议策略在实现低延迟深度ANC方面的有效性.
  • 结合的策略导致了零甚至负算法延迟.
  • 观察到对ANC整体表现的影响最小.

结论:

  • 拟议的策略显著减少了深层ANC系统中的算法延迟.
  • 实现接近零或负延迟可以缓解ANC设计中的关键因果关系约束.
  • 这项工作为更高效,更有效的实时ANC应用铺平了道路.