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

Random and Systematic Errors01:20

Random and Systematic Errors

14.2K
Scientists always try their best to record measurements with the utmost accuracy and precision. However, sometimes errors do occur. These errors can be random or systematic. Random errors are observed due to the inconsistency or fluctuation in the measurement process, or variations in the quantity itself that is being measured. Such errors fluctuate from being greater than or less than the true value in repeated measurements. Consider a scientist measuring the length of an earthworm using a...
14.2K
Calibration Curves: Linear Least Squares01:20

Calibration Curves: Linear Least Squares

4.0K
A calibration curve is a plot of the instrument's response against a series of known concentrations of a substance. This curve is used to set the instrument response levels, using the substance and its concentrations as standards. Alternatively, or additionally, an equation is fitted to the calibration curve plot and subsequently used to calculate the unknown concentrations of other samples reliably.
For data that follow a straight line, the standard method for fitting is the linear...
4.0K
Residuals and Least-Squares Property01:11

Residuals and Least-Squares Property

8.9K
The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
8.9K
Distance Corrections01:15

Distance Corrections

243
To achieve precise distance measurements, especially in surveying and construction, certain corrections must be applied to account for potential sources of error like the standardization errors, temperature variations, and slope adjustments.Standardization error emerges when measurement equipment undergoes changes, such as wear, repairs, or weather impacts. To address this, surveyors compare the equipment’s readings to a standard. This process identifies any deviation that might lead to...
243
Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

1.6K
An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
1.6K
Uniform Depth Channel Flow: Problem Solving01:18

Uniform Depth Channel Flow: Problem Solving

404
To calculate the flow rate for a trapezoidal channel, first, identify the bottom width, side slope, and flow depth of the channel. The cross-sectional area (A) corresponding to the depth of flow (y), channel bottom width (B), and side slope (θ) is determined by:Next, calculate the wetted perimeter, which includes the bottom width and the sloped side lengths in contact with the water. Using the values of the cross-sectional area and the wetted perimeter, determine the hydraulic radius by...
404

You might also read

Related Articles

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

Sort by
Same author

Thermal Ablation Therapy Integrated with Microenvironment Regulation and Anti-Inflammatory Effects for Choroidal Neovascularization.

ACS applied materials & interfaces·2026
Same author

Gas-Assisted Steam Explosion Enables Targeted Regulation of Nutritional and Flavor Quality in <i>Pleurotus eryngii</i> via Microstructural Remodeling and Metabolite Modulation.

Foods (Basel, Switzerland)·2026
Same author

Single-exposure holographic lithography of ultra-high aspect-ratio microstructures.

Nature communications·2026
Same author

Decoupled Seg Tokens Make Stronger Reasoning Video Segmenter and Grounder.

IEEE transactions on pattern analysis and machine intelligence·2026
Same author

Video Decoupling Networks for Accurate, Efficient, Generalizable, and Robust Video Object Segmentation.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same author

Single-atom Pt doped nanoceria for enhanced cell phagocytosis and nanozyme activities in keratitis immune regulation.

Journal of nanobiotechnology·2026
Same journal

Intervention Feasible Region and Driver Risk Capacity Aware Human-Machine Collaborative Safe Trajectory Planning.

IEEE transactions on neural networks and learning systems·2026
Same journal

A Unified Differential Denoising Learning Framework With a Pre-Trained Model and Fuzzy Graph Networks for Drug-Drug Interaction Prediction.

IEEE transactions on neural networks and learning systems·2026
Same journal

Self-Supervised Continuous Dynamic Graph Representation Learning via Hawkes Processes.

IEEE transactions on neural networks and learning systems·2026
Same journal

cPU: Consistent Risk Estimator for Positive-Unlabeled Learning.

IEEE transactions on neural networks and learning systems·2026
Same journal

Tuning-Free Latent Diffusion Models for Ultrahigh-Resolution Image Editing.

IEEE transactions on neural networks and learning systems·2026
Same journal

Hidden Data Recovery and Forecasting via Next-Generation Reservoir Computing With Multiscale Delay Selection.

IEEE transactions on neural networks and learning systems·2026
See all related articles

Related Experiment Video

Updated: Jan 4, 2026

Author Spotlight: Deciphering Electrical Networks Behind Complex Brain Activities and Disorders
05:49

Author Spotlight: Deciphering Electrical Networks Behind Complex Brain Activities and Disorders

Published on: November 1, 2024

1.2K

Laplacian-Uniform Mixture-Driven Iterative Robust Coding With Applications to Face Recognition Against Dense Errors.

Huicheng Zheng, Dajun Lin, Lina Lian

    IEEE Transactions on Neural Networks and Learning Systems
    |November 13, 2019
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a novel sparse representation method for robust face recognition. The new algorithm effectively handles dense gross errors, significantly improving recognition accuracy in challenging conditions.

    Related Experiment Videos

    Last Updated: Jan 4, 2026

    Author Spotlight: Deciphering Electrical Networks Behind Complex Brain Activities and Disorders
    05:49

    Author Spotlight: Deciphering Electrical Networks Behind Complex Brain Activities and Disorders

    Published on: November 1, 2024

    1.2K

    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Image Processing

    Background:

    • Face recognition systems struggle with outliers like occlusion and pixel corruption.
    • Current sparse representation methods lack robustness against dense, gross errors.

    Purpose of the Study:

    • To develop a more robust sparse representation for face recognition.
    • To address the limitations of existing methods in handling significant data corruption.

    Main Methods:

    • Modeled coding residual distribution using a Laplacian-uniform mixture.
    • Developed an iterative robust coding algorithm via local linear approximation.
    • Employed iteratively reweighted L1 minimization for error term optimization.

    Main Results:

    • Achieved significantly higher robustness against dense gross errors compared to prior methods.
    • Demonstrated effective cooperative error detection and correction within the coding process.
    • Validated improved performance through extensive experimental verification.

    Conclusions:

    • The proposed method offers superior robustness in face recognition under severe outlier conditions.
    • The novel iterative algorithm effectively handles non-differentiable objective functions.
    • This approach advances the field of robust sparse representation for image analysis.