Jove
Visualize
Contact Us

Related Concept Videos

Difference from Background: Limit of Detection01:05

Difference from Background: Limit of Detection

The limit of detection (LOD) is the smallest amount of analyte that can be distinguished from the background noise. The LOD value corresponds to the concentration at which the analyte signal is three times larger than the standard deviation of the blank signal. Below this value, the analyte signal cannot be differentiated from the background noise. It is calculated by dividing the calibration slope by 3 times the standard deviation of the blank signals.
The LOD indicates the presence or absence...
Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
In contrast, nonlinear systems do not inherently possess these properties. However, for small deviations around an operating point, a nonlinear system can often be approximated as linear.
Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

Nonlinear systems often require sophisticated approaches for accurate modeling and analysis, with state-space representation being particularly effective. This method is especially useful for systems where variables and parameters vary with time or operating conditions, such as in a simple pendulum or a translational mechanical system with nonlinear springs.
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length, the...
Application of Linearization and Approximation01:29

Application of Linearization and Approximation

A drone flying through complex terrain often relies on more than one sensing method to estimate small changes in altitude. Along with direct measurements, air pressure provides a useful indirect indicator of vertical movement. Atmospheric pressure decreases as altitude increases, and this relationship is commonly described using an exponential model. Although accurate, converting pressure measurements into altitude values requires calculations that are too complex to perform repeatedly during...
Sampling Continuous Time Signal01:11

Sampling Continuous Time Signal

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...
Sampling Methods: Overview01:06

Sampling Methods: Overview

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 sampling...

You might also read

Related Articles

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

Sort by
Same author

Encouraging Student Attendance and Engagement in Lectures & Workshops in the Pre- and Post-Covid World.

Chimia·2023
Same author

Comprehensive evaluations of a prototype full field-of-view photon counting CT system through phantom studies.

Physics in medicine and biology·2023
Same author

Management of warm autoimmune hemolytic anemia related to band 3-positive colon carcinoma.

Annals of hematology·2021
Same author

Soft-sensor development for monitoring the lysine fermentation process.

Journal of bioscience and bioengineering·2021
Same author

Active Learning: From Flipped Lectures to the Covid-19 Era.

Chimia·2021
Same author

Data science-based modeling of the lysine fermentation process.

Journal of bioscience and bioengineering·2020
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 Experiment Video

Updated: Jul 14, 2026

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

Developing nonstationary noise estimation for application in edge and corner detection.

Paul Wyatt1, Hiroaki Nakai

  • 1Toshiba Research Laboratory, Komukai-Toshiba-cho, Saiwai-ku, Kawasaki 212-8582, Japan. wyatt@eel.rdc.toshiba.co.jp

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|July 4, 2007
PubMed
Summary

Accurate noise and signal power estimation is vital for vision applications. This study introduces fast, noniterative methods for nonstationary noise estimation, improving image processing tasks like edge detection.

Related Experiment Videos

Last Updated: Jul 14, 2026

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

Area of Science:

  • Computer Vision
  • Image Processing
  • Signal Processing

Background:

  • Accurate noise and signal power estimation is fundamental in vision applications.
  • Thresholding and decision-making processes critically depend on this estimation.
  • Nonstationary noise poses a significant challenge in image analysis.

Purpose of the Study:

  • To propose novel methods for estimating nonstationary noise.
  • To develop algorithms that locally separate signal from noise using image structure models.
  • To evaluate the performance of these methods against existing techniques.

Main Methods:

  • Development of two noniterative algorithms for nonstationary noise estimation.
  • Utilizing models of image structure to distinguish signal from noise.
  • Comparative analysis of proposed methods with existing techniques.

Main Results:

  • The proposed methods provide accurate estimation of nonstationary noise.
  • Algorithms are noniterative, offering computational speed advantages.
  • Demonstrated improvement in the stability of edge and corner detection algorithms.

Conclusions:

  • The proposed noise estimation models enhance image processing stability.
  • Effective in handling contrast changes and nonstationary noise.
  • Offers a robust solution for critical vision application tasks.