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

Vision01:24

Vision

55.3K
Vision is the result of light being detected and transduced into neural signals by the retina of the eye. This information is then further analyzed and interpreted by the brain. First, light enters the front of the eye and is focused by the cornea and lens onto the retina—a thin sheet of neural tissue lining the back of the eye. Because of refraction through the convex lens of the eye, images are projected onto the retina upside-down and reversed.
55.3K
Light Acquisition02:16

Light Acquisition

8.6K
In order to produce glucose, plants need to capture sufficient light energy. Many modern plants have evolved leaves specialized for light acquisition. Leaves can be only millimeters in width or tens of meters wide, depending on the environment. Due to competition for sunlight, evolution has driven the evolution of increasingly larger leaves and taller plants, to avoid shading by their neighbors with contaminant elaboration of root architecture and mechanisms to transport water and nutrients.
8.6K
Uniform Depth Channel Flow01:27

Uniform Depth Channel Flow

144
Uniform depth channel flow keeps fluid depth consistent along channels such as irrigation canals. In natural channels, such as rivers, approximate uniform flow is often assumed. This condition occurs when the channel’s bottom slope matches the energy slope, balancing potential energy lost from gravity with head loss due to shear stress. This balance prevents depth changes along the channel length, resulting in a steady, uniform flow.Uniform flow in open channels with a constant cross-section...
144
Force Classification01:22

Force Classification

1.6K
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,...
1.6K
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

149
Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence...
149
Deconvolution01:20

Deconvolution

247
Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...
247

You might also read

Related Articles

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

Sort by
Same author

Cross-Domain Generalization for LiDAR-Based 3D Object Detection in Infrastructure and Vehicle Environments.

Sensors (Basel, Switzerland)·2025
Same author

Nursing home adjustment in China: mediating and moderating effects.

BMC geriatrics·2023
Same author

Psychometric properties of the Chinese version of quality of life in life-threatening illness-family carer version.

Frontiers in psychology·2022
Same author

Research on Video Captioning Based on Multifeature Fusion.

Computational intelligence and neuroscience·2022
Same author

Volatile composition changes of fruits in a biopolymer-coated polyethylene active packaging: Effects of modified atmosphere and packaging-shaped bacterial community.

Food research international (Ottawa, Ont.)·2022
Same author

A systematic review and meta-analysis of the influence of case analysis teaching in clinical anesthesia education.

Annals of palliative medicine·2022
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: Sep 9, 2025

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

EdgeVidCap: A Channel-Spatial Dual-Branch Lightweight Video Captioning Model for IoT Edge Cameras.

Lan Guo1, Xuyang Li1, Jinqiang Wang1

  • 1School of Information Science and Engineering, Lanzhou University, Lanzhou 730000, China.

Sensors (Basel, Switzerland)
|August 28, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces EdgeVidCap, a lightweight video captioning model for IoT edge cameras. It achieves efficient video understanding and accurate description generation on resource-constrained devices.

Keywords:
IOTattention mechanismedge computinglightweight neural networksstate space modelsvideo captioning

More Related Videos

Capturing Representative Hand Use at Home Using Egocentric Video in Individuals with Upper Limb Impairment
06:25

Capturing Representative Hand Use at Home Using Egocentric Video in Individuals with Upper Limb Impairment

Published on: December 23, 2020

2.6K
A View of Their Own: Capturing the Egocentric View of Infants and Toddlers with Head-Mounted Cameras
03:56

A View of Their Own: Capturing the Egocentric View of Infants and Toddlers with Head-Mounted Cameras

Published on: October 5, 2018

7.6K

Related Experiment Videos

Last Updated: Sep 9, 2025

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
Capturing Representative Hand Use at Home Using Egocentric Video in Individuals with Upper Limb Impairment
06:25

Capturing Representative Hand Use at Home Using Egocentric Video in Individuals with Upper Limb Impairment

Published on: December 23, 2020

2.6K
A View of Their Own: Capturing the Egocentric View of Infants and Toddlers with Head-Mounted Cameras
03:56

A View of Their Own: Capturing the Egocentric View of Infants and Toddlers with Head-Mounted Cameras

Published on: October 5, 2018

7.6K

Area of Science:

  • Artificial Intelligence
  • Computer Vision
  • Internet of Things

Background:

  • Intelligent edge cameras with edge computing and IoT integration enable local video understanding.
  • Existing video captioning models are computationally intensive, hindering deployment on resource-constrained IoT devices.

Purpose of the Study:

  • To develop a lightweight video captioning model, EdgeVidCap, for efficient real-time processing on IoT edge cameras.
  • To address the limitations of high computational complexity and large parameter counts in current video captioning solutions.

Main Methods:

  • Proposed a Synergetic Attention State Mamba (SASM) module combining channel attention and State Space Models (SSMs) for efficient spatiotemporal feature modeling.
  • Developed an adaptive attention-guided LSTM decoder for auto-regressive caption generation with dynamic feature weighting.
  • Implemented a streamlined frame filtering mechanism for enhanced processing efficiency.

Main Results:

  • EdgeVidCap demonstrated enhanced precision compared to existing video captioning methods on MSR-VTT and MSVD datasets.
  • The model achieved greater processing efficiency due to its streamlined frame filtering.
  • Generated more dependable textual descriptions following frame selection.

Conclusions:

  • EdgeVidCap offers an effective solution for lightweight video captioning on IoT edge devices.
  • The proposed SASM module and adaptive LSTM decoder contribute to efficient and accurate video understanding.
  • The model meets the real-time processing requirements for resource-constrained edge computing environments.