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相关概念视频

Upsampling01:22

Upsampling

246
Managing signal sampling rates is essential in digital signal processing to maintain signal integrity. A decimated signal, characterized by a reduced frequency range due to its lower sampling rate, can be upsampled by inserting zeros between each sample. This upsampling process expands the original spectrum and introduces repeated spectral replicas at intervals dictated by the new Nyquist frequency. To refine this zero-inserted sequence, it is passed through a lowpass filter with a cutoff...
246
Sampling Methods: Overview01:06

Sampling Methods: Overview

360
A sample refers to a smaller subset representative of a larger population. In analytical chemistry, studying or analyzing an entire population is often impractical or impossible. Therefore, samples are used to draw inferences and generalize the whole population. The sampling method selects individuals or items from a population to create a sample. Standard sampling methods include random, judgemental, systematic, stratified, and cluster sampling. 
In analytical chemistry, the choice of...
360
Aliasing01:18

Aliasing

146
Accurate signal sampling and reconstruction are crucial in various signal-processing applications. A time-domain signal's spectrum can be revealed using its Fourier transform. When this signal is sampled at a specific frequency, it results in multiple scaled replicas of the original spectrum in the frequency domain. The spacing of these replicas is determined by the sampling frequency.
If the sampling frequency is below the Nyquist rate, these replicas overlap, preventing the original...
146
Sampling Methods: Sample Types01:18

Sampling Methods: Sample Types

252
Sampling materials are classified into three main types: solid, liquid, and gas.
Solid samples include a variety of substances, such as sediments from water bodies, soil, metals, and biological tissues. Two standard methods for extracting sediments from water bodies are grab sampling and piston coring. Grab sampling involves using a device to collect a discrete sediment sample from the bottom of a water body with minimal disturbance. Grab samples do not always represent the entire area due to...
252
Bandpass Sampling01:17

Bandpass Sampling

190
In signal processing, bandpass sampling is an effective technique for sampling signals that have most of their energy concentrated within a narrow frequency band. This type of signal is known as a bandpass signal. The key principle of bandpass sampling involves sampling the signal at a rate that is greater than twice the signal's bandwidth to prevent aliasing.
A bandpass signal has a spectrum with a lower frequency limit, denoted as ω1, and an upper frequency limit, denoted as ω2....
190
Sampling Theorem01:15

Sampling Theorem

359
In signal processing, the analysis of continuous-time signals, denoted as x(t), often involves sampling techniques to convert these signals into discrete-time signals. This process is essential for digital representation and manipulation. A critical component in sampling is the train of impulses, characterized by the sampling interval and the sampling frequency. The relationship between these parameters and the original signal's properties dictates the success of the sampling process.
359

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相关实验视频

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Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment
06:49

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FreqSense:根据可调节的计算预算,基于传感器识别人类活动的自适应采样率.

Guangyu Yang, Lei Zhang, Can Bu

    IEEE journal of biomedical and health informatics
    |October 4, 2023
    PubMed
    概括

    本研究引入了用于人类活动识别 (HAR) 的自适应解决网络. 它通过使用低频特征来高效地处理传感器数据,用于简单的活动和详细的信息来进行艰难的活动,优化计算成本.

    科学领域:

    • 计算机科学 计算机科学
    • 机器学习 机器学习
    • 信号处理 信号处理

    背景情况:

    • 深度卷积网络在基于传感器的人类活动识别 (HAR) 中实现了高精度.
    • 实际的HAR部署受到可靠预测的可变计算需求的阻碍.
    • 现有的方法通常需要固定的计算预算,无论样本的复杂性如何.

    研究的目的:

    • 为 HAR 开发一种适应性推理方法,以优化计算资源配置.
    • 为了利用信号频率特征,有效地识别活动.
    • 在不牺牲预测准确性的情况下降低计算成本.

    主要方法:

    • 提出了一个适应性解决网络,结合了亚抽样和有条件的早期退出策略.
    • 实现了多分辨率子网络架构,更容易的样本被较低分辨率子网络分类.
    • 基于信心值的动态选择的抽样率,使简单活动的提前终止成为可能.

    主要成果:

    • 在四个不同的HAR基准数据集中证明了自适应解决方案网络的有效性.
    • 实现了有利的准确性-成本权衡,根据样本复杂性调整计算负载.
    • 在真实硬件上的基准平均延迟,验证实际性能.

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    结论:

    • 拟议的自适应分辨率网络为基于传感器的HAR提供了一种高效的方法.
    • 根据信号频率和样本难度动态调整计算力度,优化了性能.
    • 这种方法为具有不同计算预算的 HAR 系统提供了灵活的解决方案.