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

Design Example: Alignment of a Road Line Using GIS01:17

Design Example: Alignment of a Road Line Using GIS

350
The alignment of a road line using Geographic Information Systems (GIS) is a critical process in civil engineering, combining advanced technology with practical decision-making. This methodology begins with the collection of geospatial data, including information on land cover, geomorphology, drainage patterns, slope, and contour details. Such data is typically acquired through satellite imagery and GIS tools, offering a comprehensive understanding of the terrain.Once the data is gathered, it...
350
Self-Evaluation: Self-Enhancement and Self-Verification03:00

Self-Evaluation: Self-Enhancement and Self-Verification

5.8K
Social psychologists have documented that feeling good about ourselves and maintaining positive self-esteem is a powerful motivator of human behavior (Tavris & Aronson, 2008). In the United States, members of the predominant culture typically think very highly of themselves and view themselves as good people who are above average on many desirable traits (Ehrlinger, Gilovich, & Ross, 2005). Often, our behavior, attitudes, and beliefs are affected when we experience a threat to our...
5.8K
Nursing Evaluation01:15

Nursing Evaluation

4.4K
The evaluation stage signals the end of the nursing process. The nurse gathers evaluative data to assess whether or not the patient has attained the expected results. Whereas the nurse collects data in the nursing assessment to identify the patient's health concerns, the evaluation stage data determines if the indicated health issues are resolved. Evaluative data collection includes two sections: the data acquired to evaluate patient outcomes and the time criteria for data collection.
4.4K
Self-Evaluation Maintenance Model01:29

Self-Evaluation Maintenance Model

325
The Self-Evaluation Maintenance (SEM) model offers a psychological framework to understand how individuals’ self-esteem is influenced by the achievements of others, particularly those with whom they share close personal bonds. The SEM model operates when personal rather than social identity guides individuals. Central to this model is the notion that individuals have an inherent desire to preserve a favorable self-image, which is continuously shaped by interpersonal comparisons and...
325
Dysrhythmias V: Evaluating Dysrhythmias01:30

Dysrhythmias V: Evaluating Dysrhythmias

383
Dysrhythmias, also known as arrhythmias, are disturbances in the heart's rhythm that range from benign to life-threatening. A thorough evaluation is crucial for appropriate management and involves a comprehensive medical history, physical examination, and various diagnostic tests.Medical HistorySymptoms: Collect detailed information on palpitations, dizziness, syncope, chest pain, and fatigue. Note their onset, frequency, and triggers.Previous Cardiac Issues: Document any history of heart...
383
Detection of Gross Error: The Q Test01:00

Detection of Gross Error: The Q Test

7.0K
When one or more data points appear far from the rest of the data, there is a need to determine whether they are outliers and whether they should be eliminated from the data set to ensure an accurate representation of the measured value. In many cases, outliers arise from gross errors (or human errors) and do not accurately reflect the underlying phenomenon. In some cases, however, these apparent outliers reflect true phenomenological differences. In these cases, we can use statistical methods...
7.0K

You might also read

Related Articles

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

Sort by
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: Feb 8, 2026

Three-Dimensional Printing of a Complex Aortic Anomaly
03:40

Three-Dimensional Printing of a Complex Aortic Anomaly

Published on: November 1, 2018

7.1K

Road Anomalies Detection System Evaluation.

Nuno Silva1, Vaibhav Shah2, João Soares3

  • 1Information Systems Department, University of Minho, 4800-058 Guimarães, Portugal. nuno.silva@dsi.uminho.pt.

Sensors (Basel, Switzerland)
|June 24, 2018
PubMed
Summary
This summary is machine-generated.

This study compares road anomaly detection systems in controlled versus real-world settings, finding performance drops in the latter. Insights aim to improve future road condition monitoring services for drivers and governments.

Keywords:
Fi-WarePCAcollaborative mobile sensingdata-miningroad anomalies

More Related Videos

Evaluating the Effect of Roadside Parking on a Dual-Direction Urban Street
14:55

Evaluating the Effect of Roadside Parking on a Dual-Direction Urban Street

Published on: January 20, 2023

4.3K
Evaluation of Colorectal Cancer Risk and Prevalence by Stool DNA Integrity Detection
07:35

Evaluation of Colorectal Cancer Risk and Prevalence by Stool DNA Integrity Detection

Published on: June 8, 2020

7.5K

Related Experiment Videos

Last Updated: Feb 8, 2026

Three-Dimensional Printing of a Complex Aortic Anomaly
03:40

Three-Dimensional Printing of a Complex Aortic Anomaly

Published on: November 1, 2018

7.1K
Evaluating the Effect of Roadside Parking on a Dual-Direction Urban Street
14:55

Evaluating the Effect of Roadside Parking on a Dual-Direction Urban Street

Published on: January 20, 2023

4.3K
Evaluation of Colorectal Cancer Risk and Prevalence by Stool DNA Integrity Detection
07:35

Evaluation of Colorectal Cancer Risk and Prevalence by Stool DNA Integrity Detection

Published on: June 8, 2020

7.5K

Area of Science:

  • Civil Engineering
  • Computer Science
  • Transportation Engineering

Background:

  • Road anomalies cause vehicle damage, accidents, and significant maintenance costs.
  • Current road maintenance strategies lead to traffic congestion and driver inconvenience.
  • Effective road anomaly detection is crucial for infrastructure management and public safety.

Purpose of the Study:

  • To analyze the performance disparity of a road anomaly detection system between controlled and real-world environments.
  • To identify key challenges and insights for improving road anomaly detection system design and deployment.
  • To lay the groundwork for a road anomaly detection service providing real-time road condition information.

Main Methods:

  • Comparative analysis of system performance in "conditioned" versus real-world setups.
  • System performance evaluation through training dataset analysis.
  • Attribute complexity assessment using Principal Component Analysis (PCA).
  • Distribution analysis of anomaly classes using acceleration standard deviation attributes.

Main Results:

  • The road anomaly detection system exhibited reduced performance in real-world conditions compared to controlled environments.
  • Analysis revealed insights into training data characteristics and attribute complexity influencing system performance.
  • Acceleration standard deviation attributes showed distinct distributions for different anomaly types.

Conclusions:

  • Real-world deployment presents significant challenges for road anomaly detection systems compared to controlled setups.
  • Understanding attribute complexity and data distribution is vital for enhancing system accuracy.
  • Further iterations are needed to develop a robust road anomaly detection service for drivers and government entities.