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 Methods: Overview01:06

Sampling Methods: Overview

2.0K
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...
2.0K
Reconstruction of Signal using Interpolation01:10

Reconstruction of Signal using Interpolation

640
Signal processing techniques are essential for accurately converting continuous signals to digital formats and vice versa. When a continuous signal is sampled with a period T, the resulting sampled signal exhibits replicas of the original spectrum in the frequency domain, spaced at intervals equal to the sampling frequency. To handle this sampled signal, a zero-order hold method can be applied, which creates a piecewise constant signal by retaining each sample's value until the next...
640
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
Downsampling01:20

Downsampling

551
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...
551
Sampling Plans01:23

Sampling Plans

832
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...
832
Stratified Sampling Method01:16

Stratified Sampling Method

14.4K
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. The sampling method ensures 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 stratified sample, divide the population into groups called strata and then take a...
14.4K

You might also read

Related Articles

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

Sort by
Same author

Tourism and Livable Towns Beyond the Coronavirus Disease 2019: A Case Study for Chongqing, China.

Frontiers in public health·2021
Same author

Trends of HIV, hepatitis C virus and syphilis seroprevalence among injection and non-injection drug users in southwestern China, 2010-2017.

AIDS care·2020
Same author

Chandler-Loop surveyed blood compatibility and dynamic blood triggered degradation behavior of Zn-4Cu alloy and Zn.

Materials science & engineering. C, Materials for biological applications·2020
Same author

Knockdown of Parkinson's disease-related gene ATP13A2 reduces tumorigenesis via blocking autophagic flux in colon cancer.

Cell & bioscience·2020
Same author

Diagnostics of skin features through 3D skin mapping based on electro-controlled deposition of conducting polymers onto metal-sebum modified surfaces and their possible applications in skin treatment.

Analytica chimica acta·2020
Same author

Spongy <i>p</i>-Toluenesulfonic Acid-doped Polypyrrole with Extraordinary Rate Performance as Durable Anodes of Sodium-Ion Batteries at Different Temperatures.

Langmuir : the ACS journal of surfaces and colloids·2020
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 31, 2025

High-resolution, High-speed, Three-dimensional Video Imaging with Digital Fringe Projection Techniques
11:34

High-resolution, High-speed, Three-dimensional Video Imaging with Digital Fringe Projection Techniques

Published on: December 3, 2013

16.0K

JsrNet: A Joint Sampling-Reconstruction Framework for Distributed Compressive Video Sensing.

Can Chen1, Yutong Wu2, Chao Zhou1

  • 1College of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China.

Sensors (Basel, Switzerland)
|January 8, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces JsrNet, a novel framework for distributed compressive video sensing (DCVS) that enhances reconstruction quality and reduces computational load. JsrNet effectively leverages spatial-temporal correlations for efficient video processing in resource-limited environments.

Keywords:
deep convolutional neural networksdistributed compressive video sensingvideo signal processing

More Related Videos

A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers
12:39

A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers

Published on: January 18, 2020

8.1K
Lensless Fluorescent Microscopy on a Chip
11:23

Lensless Fluorescent Microscopy on a Chip

Published on: August 17, 2011

18.1K

Related Experiment Videos

Last Updated: Dec 31, 2025

High-resolution, High-speed, Three-dimensional Video Imaging with Digital Fringe Projection Techniques
11:34

High-resolution, High-speed, Three-dimensional Video Imaging with Digital Fringe Projection Techniques

Published on: December 3, 2013

16.0K
A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers
12:39

A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers

Published on: January 18, 2020

8.1K
Lensless Fluorescent Microscopy on a Chip
11:23

Lensless Fluorescent Microscopy on a Chip

Published on: August 17, 2011

18.1K

Area of Science:

  • Computer Vision
  • Signal Processing
  • Machine Learning

Background:

  • Distributed compressive video sensing (DCVS) is crucial for source-limited scenarios like wireless video sensor networks (WVSNs) due to large video data challenges.
  • Traditional optimization-based methods for DCVS are computationally intensive and unsuitable for real-time applications.
  • Deep convolutional neural networks (DCNNs) offer a promising alternative for efficient DCVS.

Purpose of the Study:

  • To propose a novel joint sampling-reconstruction framework for DCVS named JsrNet.
  • To improve video reconstruction quality and reduce computational complexity in DCVS.
  • To enable real-time video processing in resource-constrained environments.

Main Methods:

  • JsrNet employs a joint sampling-reconstruction strategy utilizing the entire group of frames for reference.
  • It incorporates learnable convolutions in the encoder for simultaneous joint sampling of multiple frames.
  • The framework fully exploits spatial-temporal correlations during both sampling and reconstruction phases.

Main Results:

  • JsrNet achieves competitive performance in reconstruction quality compared to existing methods.
  • The proposed framework demonstrates reduced computational complexity.
  • It proves effective in fully exploiting spatial-temporal correlations for enhanced DCVS.

Conclusions:

  • JsrNet presents a powerful and efficient solution for DCVS in source-limited, real-time applications.
  • The framework's ability to utilize all frames as references and its joint sampling approach offer significant advantages.
  • JsrNet represents a promising advancement in video sensing and processing technologies.