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

Improving Translational Accuracy02:07

Improving Translational Accuracy

11.8K
Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
11.8K
Difference from Background: Limit of Detection01:05

Difference from Background: Limit of Detection

6.7K
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...
6.7K
IR Frequency Region: Fingerprint Region01:03

IR Frequency Region: Fingerprint Region

1.0K
IR spectra are divided into two main regions: the diagnostic region and the fingerprint region. The diagnostic region of the spectrum lies above 1500 cm−1. The absorptions resulting from single-bond vibrations of the N–H, C–H, and O–H stretch at higher wavenumbers and appear on the left side of the spectrum. The stretching absorptions of the C≡C and C≡N occur between 2100–2300 cm−1. In contrast, those arising from stretching absorptions of the...
1.0K
Force Classification01:22

Force Classification

1.4K
Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...
1.4K
Design Example: Identifying the Locations of Monuments in the Field Using Global Positioning System Device01:30

Design Example: Identifying the Locations of Monuments in the Field Using Global Positioning System Device

155
Surveyors use Global Positioning System (GPS) technology to measure the precise location and elevation of points on Earth. In a recent survey, GPS receivers were used to determine the coordinates and elevations of two park monuments. The process involved careful mission planning, data collection, and correction to ensure accuracy. The survey began with mission planning to identify optimal satellite visibility and minimize Position Dilution of Precision (PDOP). A geodetic control point...
155
Reducing Line Loss01:18

Reducing Line Loss

185
In a three-phase circuit, line loss is an indicator of energy dissipated as heat due to the resistance of transmission lines. To address this, incorporating transformers into the system—a step-up transformer at the source and a step-down transformer at the load—is a strategic solution. Two three-phase transformers are introduced to improve this.
With a step-up transformer at the source, the voltage is increased, thereby reducing the current in the transmission lines since power loss...
185

You might also read

Related Articles

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

Sort by
Same author

Hard Lewis Base-Driven Crystallization Control for Efficient All-Perovskite Tandem Solar Cells.

Advanced materials (Deerfield Beach, Fla.)·2026
Same author

MVGFormer: Multi-view perspective with graph-guided transformer for cryo-ET segmentation.

Knowledge-based systems·2026
Same author

Construction of 2D-3D Halide Perovskite Heterojunctions with the Amine Vapor Replacement Reaction.

ACS omega·2026
Same author

Homogenizing interfacial assembly via indole-mediated binary monolayers for perovskite solar cells.

Nature communications·2026
Same author

Rare-Earth Element-Induced Charge Redistribution in High-Entropy Alloys toward Highly Stable Oxygen Evolution Catalysis.

ACS nano·2026
Same author

A self-amplifying cuproptosis nanomedicine to overcome immunosuppression by blocking tumor-derived exosomes for enhancing lung cancer immunotherapy.

Journal of controlled release : official journal of the Controlled Release Society·2026
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: Aug 16, 2025

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
08:20

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

1.6K

Robust Iris-Localization Algorithm in Non-Cooperative Environments Based on the Improved YOLO v4 Model.

Qi Xiong1,2, Xinman Zhang1, Xingzhu Wang2

  • 1MOE Key Lab for Intelligent Networks and Network Security, School of Automation Science and Engineering, Faculty of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an 710049, China.

Sensors (Basel, Switzerland)
|December 23, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces an improved You Only Look Once v4 (YOLO v4) model for precise iris localization in challenging, non-cooperative environments. The novel algorithm significantly enhances accuracy for iris boundary detection, outperforming traditional methods.

Keywords:
Daugman’s operatorMobileNetYOLObiometriciris localizationiris recognitionmodified integro-differential operatorradial gradient amplitude

More Related Videos

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
08:25

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

9.1K
Biomolecular Detection employing the Interferometric Reflectance Imaging Sensor IRIS
11:04

Biomolecular Detection employing the Interferometric Reflectance Imaging Sensor IRIS

Published on: May 3, 2011

14.7K

Related Experiment Videos

Last Updated: Aug 16, 2025

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
08:20

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

1.6K
Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
08:25

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

9.1K
Biomolecular Detection employing the Interferometric Reflectance Imaging Sensor IRIS
11:04

Biomolecular Detection employing the Interferometric Reflectance Imaging Sensor IRIS

Published on: May 3, 2011

14.7K

Area of Science:

  • Computer Vision
  • Biometrics
  • Artificial Intelligence

Background:

  • Accurate iris localization is crucial for reliable iris recognition systems.
  • Traditional methods struggle in non-cooperative environments, facing challenges like varying distances and occlusions (e.g., glasses).
  • The You Only Look Once (YOLO) model offers robustness, making it a suitable foundation for improved iris detection.

Purpose of the Study:

  • To develop a novel and robust iris localization algorithm for non-cooperative environments.
  • To enhance the accuracy of detecting iris inner and outer boundaries.
  • To outperform existing iris localization techniques, including traditional YOLO and Daugman's algorithm.

Main Methods:

  • A modified You Only Look Once v4 (YOLO v4) model was designed for initial iris detection and pupil center approximation.
  • A modified integro-differential operator was employed for precise localization of iris inner and outer boundaries.
  • The algorithm was evaluated on multiple datasets, including scenarios with and without glasses.

Main Results:

  • The modified YOLO v4 model achieved a high iris detection accuracy of 99.83%.
  • Iris boundary localization accuracy reached 97.72% (short distance) and 98.32% (long distance) without glasses.
  • Accuracy with glasses was recorded at 93.91% (short distance) and 84% (long distance).
  • The proposed method demonstrated significantly higher accuracy compared to the traditional Daugman's algorithm.

Conclusions:

  • The novel iris localization algorithm effectively addresses challenges in non-cooperative environments.
  • The modified YOLO v4 approach provides superior accuracy and robustness for iris boundary detection.
  • This method offers a significant advancement for practical iris recognition systems.