Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Sampling Plans01:23

Sampling Plans

825
Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
Random sampling is a method where each member of the population has an equal chance of being selected for the sample. It involves selecting individuals randomly, often using random number generators or lottery-type methods. For example, when analyzing the properties of a...
825
Cluster Sampling Method01:20

Cluster Sampling Method

13.9K
Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
13.9K
Sampling Methods: Overview01:06

Sampling Methods: Overview

1.9K
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...
1.9K
Convenience Sampling Method00:55

Convenience Sampling Method

10.8K
Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population.
Convenience sampling is a non-random method of sample selection; this method selects individuals that are easily accessible and may result in biased data. For example, a marketing...
10.8K
State Space Representation01:27

State Space Representation

472
The frequency-domain technique, commonly used in analyzing and designing feedback control systems, is effective for linear, time-invariant systems. However, it falls short when dealing with nonlinear, time-varying, and multiple-input multiple-output systems. The time-domain or state-space approach addresses these limitations by utilizing state variables to construct simultaneous, first-order differential equations, known as state equations, for an nth-order system.
Consider an RLC circuit, a...
472
Bandpass Sampling01:17

Bandpass Sampling

435
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....
435

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Corrigendum to: Advancing Alzheimer's Disease Diagnosis Using VGG19 and XGBoost: A Neuroimaging-Based Method.

Current Alzheimer research·2026
Same author

Dietary protein source mediates colitis pathogenesis through bacterial modulation of bile acids.

Cellular and molecular gastroenterology and hepatology·2026
Same author

Multistep Regulation of Ionic Liquids in Photocatalytic CO<sub>2</sub> Reduction: From Capture, Preactivation, Carrier to Pathway Control.

Chemical record (New York, N.Y.)·2026
Same author

[Cessation intention and behavior of e-cigarettes and associated factors among 18-44 Chinese adults: Based on protection motivation theory].

Beijing da xue xue bao. Yi xue ban = Journal of Peking University. Health sciences·2026
Same author

Global characterization of extrachromosomal circular DNA in cerebrospinal fluid of lung adenocarcinoma.

Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico·2026
Same author

Consistency in Advance Care Planning Awareness, Attitudes, and Engagement Among Older Adults With Chronic Diseases and Their Families in China: A Mixed Methods Study.

Journal of gerontological nursing·2026
Same journal

RETRACTED: Zhang et al. A Novel Framework for Reconstruction and Imaging of Target Scattering Centers via Wide-Angle Incidence in Radar Networks. <i>Sensors</i> 2025, <i>25</i>, 6802.

Sensors (Basel, Switzerland)·2026
Same journal

Enhancing Unsupervised Multi-Source Domain Adaptation for Person Re-Identification via Mixture of Experts and Graph-Based Relation.

Sensors (Basel, Switzerland)·2026
Same journal

Development of an Instrumented Glove for Palmar Pressure Assessment in Kayakers.

Sensors (Basel, Switzerland)·2026
Same journal

Development and Experimental Validation of an Autonomous IoT-Based Monitoring System for Real-Time Water Quality Assessment in the Amazon River.

Sensors (Basel, Switzerland)·2026
Same journal

Semi-Supervised Adversarial Learning Framework for Controller Area Network Bus Intrusion Detection.

Sensors (Basel, Switzerland)·2026
Same journal

Smart Optimization Method for Safety Signs in Innovative Manufacturing Environments Integrating Industrial Field IoT Sensors and Knowledge Graphs.

Sensors (Basel, Switzerland)·2026
See all related articles

Related Experiment Video

Updated: Dec 28, 2025

In Situ Soil Moisture Sensors in Undisturbed Soils
08:20

In Situ Soil Moisture Sensors in Undisturbed Soils

Published on: November 18, 2022

7.2K

A Subspace Approach to Sparse Sampling based Data Gathering in Wireless Sensor Networks.

Jingfei He1, Xiaoyue Zhang1, Yatong Zhou1

  • 1Tianjin Key Laboratory of Electronic Materials and Devices, School of Electronics and Information Engineering, Hebei University of Technology, 5340 Xiping Road, Beichen District, Tianjin 300401, China.

Sensors (Basel, Switzerland)
|February 16, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces an efficient data gathering method for Wireless Sensor Networks (WSNs) using sparse sampling. The approach conserves energy and extends network life by intelligently managing node sleep cycles and reconstructing data effectively.

Keywords:
data gatheringdata reconstructionsparse samplingsubspacewireless sensor networks

More Related Videos

Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles
11:54

Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles

Published on: March 13, 2017

9.7K
Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

1.0K

Related Experiment Videos

Last Updated: Dec 28, 2025

In Situ Soil Moisture Sensors in Undisturbed Soils
08:20

In Situ Soil Moisture Sensors in Undisturbed Soils

Published on: November 18, 2022

7.2K
Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles
11:54

Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles

Published on: March 13, 2017

9.7K
Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

1.0K

Area of Science:

  • Computer Science
  • Electrical Engineering
  • Network Engineering

Background:

  • Data gathering is critical for Wireless Sensor Networks (WSNs).
  • Energy consumption and network lifetime are key challenges in WSNs.
  • Efficient data collection and reconstruction are needed for WSN performance.

Purpose of the Study:

  • To propose an efficient data gathering method for clustered WSNs.
  • To reduce energy consumption and prolong network lifetime.
  • To achieve accurate data reconstruction from sparse samples.

Main Methods:

  • A sparse sampling data gathering scheme where a fixed percentage of nodes remain in sleep mode.
  • A subspace approach for data reconstruction, enforcing a low-rank constraint.
  • Estimation of spatial data distributions and incorporation of total variation constraint for efficient reconstruction.

Main Results:

  • The proposed method significantly reduces energy consumption in WSNs.
  • Network lifetime is prolonged through efficient data gathering strategies.
  • Satisfying recovery accuracy is achieved in data reconstruction from sparse samples.

Conclusions:

  • The developed method offers an efficient solution for data gathering in WSNs.
  • The approach balances energy conservation with data recovery accuracy.
  • This technique contributes to more sustainable and longer-operating WSNs.