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相关概念视频

Discrete-Time Fourier Series01:20

Discrete-Time Fourier Series

229
The Discrete-Time Fourier Series (DTFS) is a fundamental concept in signal processing, serving as the discrete-time counterpart to the continuous-time Fourier series. It allows for the representation and analysis of discrete-time periodic signals in terms of their frequency components. Unlike its continuous counterpart, which utilizes integrals, the calculation of DTFS expansion coefficients involves summations due to the discrete nature of the signal.
For a discrete-time periodic signal x[n]...
229
Time-Series Graph00:54

Time-Series Graph

4.3K
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...
133
Noncompartmental Analysis: Mean Residence Time01:05

Noncompartmental Analysis: Mean Residence Time

106
According to statistical moment theory, mean residence time (MRT) is an important measure in pharmacokinetics. MRT can be defined as the expected mean of a probability density function distribution. It provides valuable insights into drug disposition in the body.
After the administration of a drug through intravenous bolus injection, the drug molecules are distributed throughout the body and remain there for varying periods. The MRT represents the average time these drug molecules stay in the...
106
Deconvolution01:20

Deconvolution

137
Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...
137
Discrete Fourier Transform01:15

Discrete Fourier Transform

225
The Discrete Fourier Transform (DFT) is a fundamental tool in signal processing, extending the discrete-time Fourier transform by evaluating discrete signals at uniformly spaced frequency intervals. This transformation converts a finite sequence of time-domain samples into frequency components, each representing complex sinusoids ordered by frequency. The DFT translates these sequences into the frequency domain, effectively indicating the magnitude and phase of each frequency component present...
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相关实验视频

Updated: Jun 9, 2025

Trajectory Data Analyses for Pedestrian Space-time Activity Study
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Trajectory Data Analyses for Pedestrian Space-time Activity Study

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基于时空密度和时间序列分析的事件流否定方法.

Haiyan Jiang1, Xiaoshuang Wang1, Wei Tang1

  • 1College of Intelligent Equipment, Shandong University of Science and Technology, Tai'an 271000, China.

Sensors (Basel, Switzerland)
|October 26, 2024
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种新的算法,用于减少事件摄像头数据中的噪声. 该方法有效地过噪音并产生清晰的事件框架,改善神经模拟传感器的数据质量.

关键词:
拒绝的意思是拒绝.事件摄像机事件摄像机事件流可视化事件流可视化

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相关实验视频

Last Updated: Jun 9, 2025

Trajectory Data Analyses for Pedestrian Space-time Activity Study
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Trajectory Data Analyses for Pedestrian Space-time Activity Study

Published on: February 25, 2013

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Spatial Temporal Analysis of Fieldwise Flow in Microvasculature
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科学领域:

  • 神经形态工程的神经形态工程
  • 计算机视觉 计算机视觉
  • 信号处理 信号处理

背景情况:

  • 事件摄像机提供高动态范围,时间分辨率和低功耗,模仿人类视网膜成像.
  • 由于硬件/软件因素,事件流往往含有显著的噪音,使传统的无声化方法变得无效.
  • 强大的降噪对于利用事件摄像机功能至关重要.

研究的目的:

  • 开发一个事件流降噪和可视化算法,适应各种噪音类型.
  • 为了提高从噪音事件流中生成的事件框架的清晰度和连贯性.
  • 为了解决现有的事件摄像头数据无声化技术的局限性.

主要方法:

  • 一种两阶段的过方法:对背景/添加 (BA) 噪声进行初始基于时空密度的过,然后进行细.
  • 精细过采用事件像素及其邻居的时间序列分析来消除热噪声.
  • 一个可视化算法适应地重叠事件基于密度差异的框架连贯性.

主要成果:

  • 拟议的算法有效地减少了来自现实世界的场景和公共数据集的事件流中的噪音.
  • 清晰和连贯的事件框架被成功地生成,即使在不同的事件密度和噪音水平下.
  • 实验验证证证实了算法的稳定性和有效性在denoising和可视化.

结论:

  • 开发的算法提供了一种有效的解决方案,用于降低事件摄像头数据中的噪声.
  • 该方法提高了事件流的质量,使得下游应用程序更可靠.
  • 这项工作有助于在各种条件下实践使用事件摄像机.