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相关概念视频

Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

64
Nonlinear systems often require sophisticated approaches for accurate modeling and analysis, with state-space representation being particularly effective. This method is especially useful for systems where variables and parameters vary with time or operating conditions, such as in a simple pendulum or a translational mechanical system with nonlinear springs.
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length,...
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Linear time-invariant Systems01:23

Linear time-invariant Systems

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A system is linear if it displays the characteristics of homogeneity and additivity, together termed the superposition property. This principle is fundamental in all linear systems. Linear time-invariant (LTI) systems include systems with linear elements and constant parameters.
The input-output behavior of an LTI system can be fully defined by its response to an impulsive excitation at its input. Once this impulse response is known, the system's reaction to any other input can be...
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Time-Series Graph00:54

Time-Series Graph

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A time-series graph is a line graph with repeated measurements taken at successive intervals of time. It is also called a time series chart. To construct a time-series graph, one must look at both pieces of a paired data set. The horizontal axis is used to plot the time increments, and the vertical axis is used to plot the values of the variable that one is measuring. By using the axes in this way, each point on the graph will correspond to time and a measured quantity. The points on the graph...
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Downsampling01:20

Downsampling

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When considering a sampled sequence with zero values between sampling instants, one can replace it by taking every N-th value of the sequence. At these integer multiples of N, the original and sampled sequences coincide. This process, known as decimation, involves extracting every N-th sample from a sequence, thereby creating a more efficient sequence.
The Fourier transform of the decimated sequence reveals a combination of scaled and shifted versions of the original spectrum. This...
130
Sampling Continuous Time Signal01:11

Sampling Continuous Time Signal

205
In signal processing, a continuous-time signal can be sampled using an impulse-train sampling technique, followed by the zero-order hold method. Impulse-train sampling involves the use of a periodic impulse train, which consists of a series of delta functions spaced at regular intervals determined by the sampling period. When a continuous-time signal is multiplied by this impulse train, it generates impulses with amplitudes corresponding to the signal's values at the sampling points.
In the...
205
Continuous -time Fourier Transform01:11

Continuous -time Fourier Transform

289
The Fourier series is instrumental in representing periodic functions, offering a powerful method to decompose such functions into a sum of sinusoids. This technique, however, necessitates modification when applied to nonperiodic functions. Consider a pulse-train waveform consisting of a series of rectangular pulses. When these pulses have a finite period, they can be accurately represented by a Fourier series. Yet, as the period approaches infinity, resulting in a single, isolated pulse, the...
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相关实验视频

Updated: Jun 6, 2025

Author Spotlight: Automated Deep Brain Stimulation for Parkinson's Disease - Exploring the Possibilities and Challenges of Home Monitoring
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一个不断发展的多变量时间序列压缩算法用于物联网应用程序.

Hagi Costa1, Marianne Silva1,2, Ignacio Sánchez-Gendriz3

  • 1UFRN-PPgEEC, Postgraduate Program in Electrical and Computer Engineering, Federal University of Rio Grande do Norte, Natal 59078-970, Brazil.

Sensors (Basel, Switzerland)
|November 27, 2024
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概括
此摘要是机器生成的。

本研究介绍了用于车辆监控中的微型机器学习 (TinyML) 的两种数据压缩方法. 这些方法可以减少物联网 (IoT) 设备的延迟和能源消耗.

关键词:
这就是为什么物联网物联网物联网.在OBD-II边缘.在TinyML中使用TinyML.数据压缩数据压缩.演变的算法正在演变.

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科学领域:

  • 计算机科学 计算机科学
  • 嵌入式系统 嵌入式系统
  • 数据压缩数据压缩

背景情况:

  • 物联网 (IoT) 系统由于大量实时数据传输而面临高延迟和能源消耗的挑战.
  • 微型机器学习 (TinyML) 通过在资源受限的嵌入式设备上启用机器学习来提供解决方案.
  • 物联网中的车辆监控应用程序产生大量数据,加剧了传输问题.

研究的目的:

  • 为TinyML应用程序开发和评估两个新的在线多变量数据压缩方法.
  • 为了利用典型性和特异性数据分析 (TEDA) 框架进行数据压缩.
  • 为了优化压缩性能,而不依赖预定义的数学模型或数据分布假设.

主要方法:

  • 在TEDA框架内开发了两种基于数据偏心的在线多变量压缩技术.
  • 将压缩方法应用于OBD-II Freematics ONE+数据集用于车辆监控.
  • 评估了并行和顺序的压缩策略.

主要成果:

  • 两种拟议的压缩方法都显示了执行时间的显著改善.
  • 这些方法实现了显著的压缩误差减少.
  • 这项研究证实了TinyML.基于偏心的压缩对TinyML的有效性.

结论:

  • 开发的压缩方法提高了嵌入式物联网系统的性能,特别是在车辆应用中.
  • 这些发现有助于提高实时数据处理在资源有限的环境中的效率和可持续性.
  • TEDA框架为为TinyML开发先进的数据压缩技术提供了坚实的基础.