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

Sound Intensity Level00:53

Sound Intensity Level

4.9K
Humans perceive sound by hearing. The human ear helps sound waves reach the brain, which then interprets the waves and creates the perception of hearing. The loudness of the environment in which a person is located determines whether they can distinguish between different sound sources.
The human ear can perceive an extensive range of sound intensity, necessitating the use of the logarithmic scale to define a physical quantity—the intensity level. It is a ratio of two intensities and...
4.9K
Ratio Level of Measurement00:54

Ratio Level of Measurement

21.5K
The way a set of data is measured is called its level of measurement. Correct statistical procedures depend on a researcher being familiar with levels of measurement. For analysis, data are classified into four levels of measurement—nominal, ordinal, interval, and ratio.
A set of data measured using the ratio scale takes care of the ratio problem and provides complete information. Ratio scale data are like interval scale data, except they have a zero point and ratios can be calculated....
21.5K
Ordinal Level of Measurement00:55

Ordinal Level of Measurement

34.0K
The way a set of data is measured is called its level of measurement. Correct statistical procedures depend on a researcher being familiar with levels of measurement. For analysis, data are classified into four levels of measurement—nominal, ordinal, interval, and ratio.
Data measured using an ordinal scale are similar to nominal scale data, but there is one major difference. The ordinal scale data can be ordered. An example of ordinal scale data is a list of the top five national parks...
34.0K
Interval Level of Measurement00:55

Interval Level of Measurement

19.2K
For effective statistical analysis, data are classified into four levels of measurement—nominal, ordinal, interval, and ratio.
Data measured using the interval scale are similar to ordinal level data because they have a definite arrangement. However, in the interval level of measurement, the differences between data values are meaningful even though the data does not have a starting point.
Temperature is measured using the interval scale. It is measurable data, and the difference between...
19.2K
Nominal Level of Measurement00:56

Nominal Level of Measurement

39.1K
The way a set of data is measured is called its level of measurement. Correct statistical procedures depend on a researcher being familiar with levels of measurement. Not every statistical operation can be used with every set of data. For analysis, data are classified into four levels of measurement—nominal, ordinal, interval, and ratio.
The data that cannot be measured but can be grouped into categories fall under the nominal level of measurement. Data that is measured using a nominal...
39.1K
Korotkoff Sounds01:12

Korotkoff Sounds

8.5K
Korotkoff sounds are the specific sounds heard while measuring blood pressure using a sphygmomanometer, typically with a stethoscope or a Doppler device. They are named after Russian physician Nikolai Korotkov, who first described them in 1905. These sounds correspond to turbulent blood flow in the artery as the blood pressure cuff is gradually released after inflation.
During blood pressure assessment, inflating the cuff 30 millimeters of mercury above the patient's systolic blood pressure...
8.5K

You might also read

Related Articles

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

Sort by
Same author

Noise Reduction with Recursive Filtering for More Accurate Parameter Identification of Electrochemical Sources and Interfaces.

Sensors (Basel, Switzerland)·2025
Same author

An Approximate GEMM Unit for Energy-Efficient Object Detection.

Sensors (Basel, Switzerland)·2021
Same author

On the Design of an Energy Efficient Digital IIR A-Weighting Filter Using Approximate Multiplication.

Sensors (Basel, Switzerland)·2021
Same author

Data Transmission Efficiency in Bluetooth Low Energy Versions.

Sensors (Basel, Switzerland)·2019

Related Experiment Video

Updated: Feb 7, 2026

In Vitro Application of a Wireless Sensor in Flexion-Extension Gap Balance of Unicompartmental Knee Arthroplasty
07:33

In Vitro Application of a Wireless Sensor in Flexion-Extension Gap Balance of Unicompartmental Knee Arthroplasty

Published on: May 5, 2023

1.1K

Accurate Indoor Sound Level Measurement on a Low-Power and Low-Cost Wireless Sensor Node.

Vladimir Risojević1, Robert Rozman2, Ratko Pilipović3

  • 1Faculty of Electrical Engineering, University of Banja Luka, 78000 Banja Luka, Bosnia and Herzegovina. vladimir.risojevic@etf.unibl.org.

Sensors (Basel, Switzerland)
|July 22, 2018
PubMed
Summary
This summary is machine-generated.

This study presents a low-cost wireless sensor node for monitoring indoor noise pollution. The device accurately measures sound pressure levels, offering a promising solution for continuous environmental monitoring.

Keywords:
A-weightingenvironmental noise monitoringhardware platformnoise sensingwireless sensor network

More Related Videos

Construction of a Wireless-Enabled Endoscopically Implantable Sensor for pH Monitoring with Zero-Bias Schottky Diode-based Receiver
08:25

Construction of a Wireless-Enabled Endoscopically Implantable Sensor for pH Monitoring with Zero-Bias Schottky Diode-based Receiver

Published on: August 27, 2021

3.0K
A Human-machine-interface Integrating Low-cost Sensors with a Neuromuscular Electrical Stimulation System for Post-stroke Balance Rehabilitation
11:06

A Human-machine-interface Integrating Low-cost Sensors with a Neuromuscular Electrical Stimulation System for Post-stroke Balance Rehabilitation

Published on: April 12, 2016

10.9K

Related Experiment Videos

Last Updated: Feb 7, 2026

In Vitro Application of a Wireless Sensor in Flexion-Extension Gap Balance of Unicompartmental Knee Arthroplasty
07:33

In Vitro Application of a Wireless Sensor in Flexion-Extension Gap Balance of Unicompartmental Knee Arthroplasty

Published on: May 5, 2023

1.1K
Construction of a Wireless-Enabled Endoscopically Implantable Sensor for pH Monitoring with Zero-Bias Schottky Diode-based Receiver
08:25

Construction of a Wireless-Enabled Endoscopically Implantable Sensor for pH Monitoring with Zero-Bias Schottky Diode-based Receiver

Published on: August 27, 2021

3.0K
A Human-machine-interface Integrating Low-cost Sensors with a Neuromuscular Electrical Stimulation System for Post-stroke Balance Rehabilitation
11:06

A Human-machine-interface Integrating Low-cost Sensors with a Neuromuscular Electrical Stimulation System for Post-stroke Balance Rehabilitation

Published on: April 12, 2016

10.9K

Area of Science:

  • Environmental Science
  • Acoustics
  • Wireless Sensor Networks

Background:

  • Wireless sensor networks (WSNs) offer flexible noise pollution measurement but face challenges with data processing on resource-constrained nodes.
  • Limited power, low-performance microcontrollers, and memory restrict complex signal processing in WSN nodes.
  • Efficient indoor noise monitoring requires solutions that overcome these hardware limitations.

Purpose of the Study:

  • To propose a novel sensor node for effective indoor ambient noise monitoring.
  • To develop a low-power, low-cost solution for continuous noise level measurement.
  • To address the computational and communication constraints of resource-limited sensor nodes.

Main Methods:

  • Designed a sensor node utilizing a hardware platform with limited computational resources.
  • Implemented signal processing simplifications for approximating complex stages.
  • Performed digital A-weighting filtering and non-calibrated sound pressure level calculation on the node to reduce communication and power consumption.
  • Utilized IEEE 802.15.4 (ZigBee) for wireless communication.

Main Results:

  • The proposed sensor node accurately measures noise levels up to 100 dB.
  • Experimental results show a mean difference of less than 2 dB compared to a Class 1 sound level meter.
  • The device demonstrated continuous indoor noise monitoring capability for several days.
  • Achieved significant reduction in communication and power consumption.

Conclusions:

  • The developed sensor node is a viable low-cost and low-power solution for indoor ambient noise monitoring.
  • The simplifications in signal processing and on-node calculations effectively manage resource constraints.
  • The system provides accurate noise level measurements suitable for practical applications.
  • This approach offers a promising alternative for widespread environmental noise surveillance.