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

4.2K
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...
4.2K
Upsampling01:22

Upsampling

730
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...
730
Sampling Methods: Sample Types01:18

Sampling Methods: Sample Types

3.8K
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...
3.8K
Downsampling01:20

Downsampling

822
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...
822
Sampling Plans01:23

Sampling Plans

1.4K
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...
1.4K
Sampling Continuous Time Signal01:11

Sampling Continuous Time Signal

893
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...
893

You might also read

Related Articles

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

Sort by
Same author

FEMBA on the Edge: Physiologically-Aware Pre-Training, Quantization, and Deployment of a Bidirectional Mamba EEG Foundation Model on an Ultra-Low Power Microcontroller.

IEEE transactions on bio-medical engineering·2026
Same author

ChemoNETosis in Cancer: A Comprehensive Review of Treatment-Induced NET Formation and Therapeutic Consequences.

Cells·2026
Same author

Current Trends in Ultrasound Wearables: Spotlight on System Architecture.

IEEE reviews in biomedical engineering·2026
Same author

Experimental Investigation of the Flexural Performance of Continuous Self-Compacting Concrete Beams with Natural and Recycled Aggregates.

Materials (Basel, Switzerland)·2026
Same author

BioGAP-Ultra: A Modular Edge-AI Platform for Wearable Multimodal Biosignal Acquisition and Processing.

IEEE transactions on biomedical circuits and systems·2026
Same author

Nrf2 as a Molecular Guardian of Redox Balance and Barrier Integrity in IBD.

Antioxidants (Basel, Switzerland)·2025
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: Apr 16, 2026

Electroencephalographic Signal Acquisition Framework for Neurodiverse: A Case Study of Dolphin-Assisted Therapy
07:21

Electroencephalographic Signal Acquisition Framework for Neurodiverse: A Case Study of Dolphin-Assisted Therapy

Published on: June 27, 2025

610

Sub-sampling framework comparison for low-power data gathering: a comparative analysis.

Bojan Milosevic1,2, Carlo Caione3, Elisabetta Farella4,5

  • 1DEI, University of Bologna, 40123 Bologna, Italy. bojan.milosevic@unibo.it.

Sensors (Basel, Switzerland)
|March 5, 2015
PubMed
Summary
This summary is machine-generated.

This study compares compressive sensing (CS) and latent variable (LV) models for wireless sensor networks (WSNs) in historical buildings. CS offers superior data reconstruction and energy efficiency for long-term monitoring.

More Related Videos

Data Acquisition Protocol for Determining Embedded Sensitivity Functions
07:46

Data Acquisition Protocol for Determining Embedded Sensitivity Functions

Published on: April 20, 2016

6.6K
Performing Behavioral Tasks in Subjects with Intracranial Electrodes
12:10

Performing Behavioral Tasks in Subjects with Intracranial Electrodes

Published on: October 2, 2014

12.0K

Related Experiment Videos

Last Updated: Apr 16, 2026

Electroencephalographic Signal Acquisition Framework for Neurodiverse: A Case Study of Dolphin-Assisted Therapy
07:21

Electroencephalographic Signal Acquisition Framework for Neurodiverse: A Case Study of Dolphin-Assisted Therapy

Published on: June 27, 2025

610
Data Acquisition Protocol for Determining Embedded Sensitivity Functions
07:46

Data Acquisition Protocol for Determining Embedded Sensitivity Functions

Published on: April 20, 2016

6.6K
Performing Behavioral Tasks in Subjects with Intracranial Electrodes
12:10

Performing Behavioral Tasks in Subjects with Intracranial Electrodes

Published on: October 2, 2014

12.0K

Area of Science:

  • * Wireless Sensor Networks (WSNs)
  • * Data Acquisition and Signal Processing
  • * Energy Efficiency in IoT

Background:

  • * Balancing data resolution and energy consumption is critical for WSN deployment.
  • * Long-term monitoring of historical buildings presents unique WSN challenges.
  • * Existing WSN solutions often struggle with energy efficiency and data fidelity.

Purpose of the Study:

  • * To develop and compare two energy-efficient WSN approaches for historical building monitoring.
  • * To evaluate the trade-offs between data reconstruction accuracy and energy consumption.
  • * To assess the effectiveness of compressive sensing (CS) and latent variable (LV) models.

Main Methods:

  • * Development and comparison of CS-based and LV-based WSN data acquisition strategies.
  • * Implementation of sub-sampling at sub-Nyquist levels leveraging multivariate data.
  • * Experimental analysis of network-level energy reduction and signal reconstruction performance.

Main Results:

  • * Compressive sensing (CS) demonstrated superior reconstruction accuracy and overall energy efficiency.
  • * Latent variable (LV) models showed promise but were outperformed by CS in most scenarios.
  • * Performance varied with sub-sampling policies, with aggressive policies impacting accuracy.

Conclusions:

  • * CS is a highly effective approach for energy-efficient WSNs in historical building monitoring.
  • * The choice of sub-sampling strategy significantly impacts both data accuracy and energy savings.
  • * WSNs can achieve a good balance between data resolution and energy consumption using advanced signal processing techniques.