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

Transformers in Distribution System01:27

Transformers in Distribution System

99
Transformers in distribution systems can be broadly categorized into distribution substation transformers and other distribution transformers. They are crucial for stepping down high transmission voltages to levels suitable for distribution and end-user applications.
Distribution substation transformers come in various ratings and typically use mineral oil for insulation and cooling. To prevent moisture and air from entering the oil, some transformers use an inert gas like nitrogen to fill the...
99
Types Of Transformers01:16

Types Of Transformers

951
Transformers can provide desired voltages to a circuit by modifying the number of turns in the secondary windings.
If the ratio of the number of turns in the secondary winding to that of the primary winding is greater than one, then the transformer is said to be a step-up transformer. In a step-up transformer, the voltage at the secondary winding is greater than the voltage applied at the primary winding.
However, if this ratio is less than one, the transformer is said to be a step-down...
951
Transformers with Off-Nominal Turns Ratios01:25

Transformers with Off-Nominal Turns Ratios

141
In scenarios involving parallel transformers with disparate ratings, developing per-unit models requires accommodating off-nominal turns ratios. This situation arises when the selected base voltages are not proportional to the transformer’s voltage ratings. Consider a transformer where the rated voltages are related by the term a. If the chosen voltage bases satisfy a relationship involving term b, term c is defined as the ratio of these bases. This ratio is then substituted into the...
141
The Ideal Transformer01:26

The Ideal Transformer

358
In single-phase two-winding transformers, two windings are coiled around a magnetic core characterized by cross-sectional area A and magnetic permeability μ. A phasor current i1 enters the left winding while i2 exits the right winding, establishing the fundamental working of the transformer through electromagnetic principles.
Ampere's Law forms the basis of understanding the magnetic field within the transformer. It states that the integral of the magnetic field intensity's...
358
Depth Perception and Spatial Vision01:15

Depth Perception and Spatial Vision

601
Depth perception is the ability to perceive objects three-dimensionally. It relies on two types of cues: binocular and monocular. Binocular cues depend on the combination of images from both eyes and how the eyes work together. Since the eyes are in slightly different positions, each eye captures a slightly different image. This disparity between images, known as binocular disparity, helps the brain interpret depth. When the brain compares these images, it determines the distance to an object.
601
Deconvolution01:20

Deconvolution

137
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...
137

You might also read

Related Articles

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

Sort by
Same author

Comparison of frequency-resolved optical polarization gating induced by molecular alignment and Kerr effects.

Optics letters·2012
Same author

Direct transformation of simple enals to 3,4-disubstituted benzaldehydes under mild reaction conditions via an organocatalytic regio- and chemoselective dimerization cascade.

Chemistry (Weinheim an der Bergstrasse, Germany)·2012
Same author

[Digital anatomy of the perforator flap in the thigh].

Zhonghua zheng xing wai ke za zhi = Zhonghua zhengxing waike zazhi = Chinese journal of plastic surgery·2012
Same author

[Value of methylation-specific mutiplex ligation-dependent probe in the diagnosis of Prader-Willi syndrome].

Zhongguo dang dai er ke za zhi = Chinese journal of contemporary pediatrics·2012
Same author

Elevated local TGF-β1 level predisposes a closed bone fracture to tuberculosis infection.

Medical hypotheses·2012
Same author

Modulation of P-glycoprotein expression by triptolide in adriamycin-resistant K562/A02 cells.

Oncology letters·2012
Same journal

Granular Ball-Based Noise-Resistant Fuzzy Multineighborhood Feature Selection via Label Enhancement and Feature Graph.

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

Fighting Evolving Spam With ARTMAP Models: A Noise-Resilient Online Detection Framework.

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

HyperSAT: Unsupervised Hypergraph Neural Networks for Weighted MaxSAT Problems.

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

Negation of Basic Belief Assignment in Multisource Information Fusion on Dempster-Shafer Theory With Applications in Pattern Classification.

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

Related Experiment Video

Updated: Jun 10, 2025

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

376

STFormer: Spatial-Temporal-Aware Transformer for Video Instance Segmentation.

Hao Li, Wei Wang, Mengzhu Wang

    IEEE Transactions on Neural Networks and Learning Systems
    |October 17, 2024
    PubMed
    Summary
    This summary is machine-generated.

    STFormer enhances video instance segmentation (VIS) by using high-resolution features and location-guided queries. This approach improves accuracy, especially for small objects, and speeds up convergence in complex video analysis.

    More Related Videos

    A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
    04:23

    A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

    Published on: April 21, 2023

    1.8K
    Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
    04:48

    Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

    Published on: November 30, 2022

    2.7K

    Related Experiment Videos

    Last Updated: Jun 10, 2025

    Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
    04:48

    Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

    Published on: July 5, 2024

    376
    A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
    04:23

    A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

    Published on: April 21, 2023

    1.8K
    Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
    04:48

    Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

    Published on: November 30, 2022

    2.7K

    Area of Science:

    • Computer Vision
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Video instance segmentation (VIS) combines object classification, segmentation, and tracking.
    • Current Transformer-based VIS methods struggle with low-resolution decoder inputs, losing fine details and misidentifying small objects.
    • Existing methods lack location information in instance queries, impacting convergence and localization accuracy.

    Purpose of the Study:

    • To introduce STFormer, a novel VIS approach addressing limitations of existing methods.
    • To improve the handling of fine-grained information, background interference, and small objects in VIS.
    • To enhance convergence efficiency and object instance localization accuracy.

    Main Methods:

    • Developed a spatial-temporal feature aggregation (STFA) module for efficient, high-resolution feature extraction.
    • Introduced a spatial-temporal-aware Transformer (STT) incorporating location-guided instance queries (LGIQ).
    • STFA provides robust features for the decoder, while LGIQ refines initial instance queries.

    Main Results:

    • STFormer preserves more fine-grained details compared to existing VIS methods.
    • The approach demonstrates improved convergence efficiency and accurate object instance localization.
    • Experiments on YouTube-VIS 2019, YouTube-VIS 2021, and OVIS datasets show superior performance.

    Conclusions:

    • STFormer effectively overcomes the limitations of low-resolution features and uninformative queries in VIS.
    • The proposed method achieves state-of-the-art results on benchmark datasets.
    • STFormer offers a significant advancement in video instance segmentation technology.