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

Direction of Acceleration Vectors01:10

Direction of Acceleration Vectors

21.4K
Acceleration occurs when velocity changes in magnitude (an increase or decrease in speed), direction, or both. Although acceleration is in the direction of the change in velocity, it is not always in the direction of motion. When an object slows down, its acceleration is opposite to the direction of its motion. This is commonly referred to as deceleration. However, the term deceleration can cause confusion in analysis because it is not a vector; it does not point to a specific direction with...
21.4K
Direction Cosines of a Vector01:29

Direction Cosines of a Vector

1.4K
Direction cosines, which help describe the orientation of a vector with respect to the coordinate axes, are an essential concept in the field of vector calculus. Consider vector A that is expressed in terms of the Cartesian vector form using i, j, and k unit vectors. The magnitude of vector A is defined as the square root of the sum of the squares of its components. The direction of this vector with respect to the x, y, and z axes is defined by the coordinate direction angles α, β, and γ,...
1.4K
What Are Outliers?01:12

What Are Outliers?

4.9K
Outliers are observed data points that are far from the least squares line. They have unusual values and need to be examined carefully. Though an outlier may result from erroneous data, at other times, it may hold valuable information about the population under study and should be included in the data. Hence, it is crucial to examine what causes a data point to be an outlier.
The z score is used to find outliers or unusual values. It should be noted that any values beyond -2 and +2 are...
4.9K
Outliers and Influential Points01:08

Outliers and Influential Points

6.2K
An outlier is an observation of data that does not fit the rest of the data. It is sometimes called an extreme value. When you graph an outlier, it will appear not to fit the pattern of the graph. Some outliers are due to mistakes (for example, writing down 50 instead of 500), while others may indicate that something unusual is happening. Outliers are present far from the least squares line in the vertical direction. They have large "errors," where the "error" or residual is the...
6.2K
Quantifying and Rejecting Outliers: The Grubbs Test01:02

Quantifying and Rejecting Outliers: The Grubbs Test

3.7K
Sometimes, a data set can have a recorded numerical observation that greatly  deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier.  To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This...
3.7K
Protein Networks02:26

Protein Networks

4.5K
An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
4.5K

You might also read

Related Articles

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

Sort by
Same author

Redox-inactive metal ions promoted the catalytic reactivity of non-heme manganese complexes towards oxygen atom transfer.

Dalton transactions (Cambridge, England : 2003)·2015
Same author

A reliable, high-resolution and high-throughput genotyping method for HLA-DRB1.

Human immunology·2015
Same author

Implicit processing of heroin and emotional cues in abstinent heroin users: early and late event-related potential effects.

The American journal of drug and alcohol abuse·2015
Same author

Expression and methylation of DNA repair genes in lens epithelium cells of age-related cataract.

Mutation research·2015
Same author

Rapid detection and identification of infectious pathogens based on high-throughput sequencing.

Chinese medical journal·2015
Same author

Synchrotron-based and globar-sourced molecular (micro)spectroscopy contributions to advances in new hulless barley (with structure alteration) research on molecular structure, molecular nutrition, and nutrient delivery.

Critical reviews in food science and nutrition·2015
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: Jan 26, 2026

Analyzing the Size, Shape, and Directionality of Networks of Coupled Astrocytes
10:10

Analyzing the Size, Shape, and Directionality of Networks of Coupled Astrocytes

Published on: October 4, 2018

9.3K

Specific Direction-Based Outlier Detection Approach for GNSS Vector Networks.

Yufeng Nie1, Ling Yang2, Yunzhong Shen3

  • 1College of Surveying and Geo-informatics, Tongji University, Shanghai 200092, China. yufeng.nie@tongji.edu.cn.

Sensors (Basel, Switzerland)
|April 20, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces a Specific Direction (SD) outlier detection method for Global Navigation Satellite System (GNSS) networks. The SD approach effectively identifies antenna height errors in GNSS baselines, simplifying outlier detection.

Keywords:
GNSS networksantenna heightbaseline vectoroutlier detection

More Related Videos

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

5.3K
Generation of Transgenic Rats using a Lentiviral Vector Approach
09:07

Generation of Transgenic Rats using a Lentiviral Vector Approach

Published on: May 17, 2020

9.2K

Related Experiment Videos

Last Updated: Jan 26, 2026

Analyzing the Size, Shape, and Directionality of Networks of Coupled Astrocytes
10:10

Analyzing the Size, Shape, and Directionality of Networks of Coupled Astrocytes

Published on: October 4, 2018

9.3K
Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

5.3K
Generation of Transgenic Rats using a Lentiviral Vector Approach
09:07

Generation of Transgenic Rats using a Lentiviral Vector Approach

Published on: May 17, 2020

9.2K

Area of Science:

  • Geomatics Engineering
  • Satellite Navigation Systems
  • Geodetic Surveying

Background:

  • Outlier detection is crucial for maintaining the integrity of Global Navigation Satellite System (GNSS) vector networks.
  • Traditional methods for outlier detection in 3D space can be complex and computationally intensive.
  • Identifying specific error sources, such as antenna height measurement anomalies, requires precise detection techniques.

Purpose of the Study:

  • To propose and validate a novel outlier detection approach for GNSS vector networks based on a specific direction (SD approach).
  • To demonstrate the mathematical equivalence between the proposed SD approach and the conventional three-dimensional (3D) outlier detection methods.
  • To assess the efficacy of the SD approach in detecting abnormal antenna height measurements in GNSS data.

Main Methods:

  • Derivation of the unit vector representing the specific direction of maximum test statistic.
  • Mathematical proof establishing the equivalence between the SD approach and the 3D approach.
  • Application and validation using a real GNSS network dataset.
  • Numerical simulations to evaluate the detection of antenna height errors in multiple baseline solutions.

Main Results:

  • The unit vector in the SD approach aligns with the direction determined by 3D outlier estimates.
  • The maximum test statistic in the SD direction follows a square root of Chi-squared distribution.
  • The SD approach yields equivalent results to the 3D approach for outlier elimination.
  • The SD approach effectively detects GNSS baselines affected by receiver antenna height errors.

Conclusions:

  • The proposed SD approach offers a mathematically equivalent and potentially more efficient alternative to 3D outlier detection in GNSS networks.
  • The SD method is particularly effective for identifying and mitigating errors associated with abnormal antenna height measurements.
  • This approach enhances the reliability and accuracy of GNSS data processing and analysis.