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

Properties of DTFT I01:24

Properties of DTFT I

339
In signal processing, Discrete-Time Fourier Transforms (DTFTs) play a critical role in analyzing discrete-time signals in the frequency domain. Various properties of the DTFTs such as linearity, time-shifting, frequency-shifting, time reversal, conjugation, and time scaling help understand and manipulate these signals for different applications.
The linearity property of DTFTs is fundamental. If two discrete-time signals are multiplied by constants a and b respectively, and then combined to...
339
Properties of DTFT II01:24

Properties of DTFT II

173
In the study of discrete-time signal processing, understanding the properties of the Discrete-Time Fourier Transform (DTFT) is crucial for analyzing and manipulating signals in the frequency domain. Several properties, including frequency differentiation, convolution, accumulation, and Parseval's relation, offer powerful tools for signal analysis.
The frequency differentiation property is illustrated by considering a DTFT pair and differentiating both sides with respect to ω.
173
Discrete-time Fourier transform01:26

Discrete-time Fourier transform

247
The Discrete-Time Fourier Transform (DTFT) is an essential mathematical tool for analyzing discrete-time signals, converting them from the time domain to the frequency domain. This transformation allows for examining the frequency components of discrete signals, providing insights into their spectral characteristics. In the DTFT, the continuous integral used in the continuous-time Fourier transform is replaced by a summation to accommodate the discrete nature of the signal.
One of the notable...
247

You might also read

Related Articles

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

Sort by
Same author

Simultaneous inhibition of PARP/AKT to intercept nascent BRCA1/2<sup>mut</sup> breast tumors.

NPJ breast cancer·2026
Same author

Integrating single cell- and spatial- resolved transcriptomics unravels the inter-tumor heterogeneity and immunosuppressive landscape in HBV- and Clonorchis sinensis-associated hepatocellular carcinoma.

Molecular cancer·2026
Same author

DPM-UNet: A Mamba-Based Network with Dynamic Perception Feature Enhancement for Medical Image Segmentation.

Sensors (Basel, Switzerland)·2025
Same author

Proteolysis-Targeting Chimera (PROTAC): Current Applications and Future Directions.

MedComm·2025
Same author

Masculinizing Testosterone Therapy Reduces the Incidence of PIK3CA-Mutant/ER⁺ Breast Cancer but Not BRCA1-Associated Triple-Negative Breast Cancer.

medRxiv : the preprint server for health sciences·2025
Same author

Targeting transcription factors through an IMiD independent zinc finger domain.

EMBO molecular medicine·2025
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: May 21, 2025

A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers
12:39

A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers

Published on: January 18, 2020

7.5K

A Method Combining Discrete Cosine Transform with Attention for Multi-Temporal Remote Sensing Image Matching.

Qinyan Zeng1,2,3, Bin Hui1,2, Zhaoji Liu1,2

  • 1Key Laboratory of Opto-Electronic Information Processing, Chinese Academy of Sciences, Shenyang 110016, China.

Sensors (Basel, Switzerland)
|March 17, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a new method for matching multi-temporal remote sensing images, improving accuracy despite temporal differences. The approach uses Discrete Cosine Transform (DCT) and attention mechanisms for robust and efficient image analysis.

Keywords:
channel attentiondiscrete cosine transformationimage matchingremote sensingsparse attention

More Related Videos

Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform
06:25

Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform

Published on: February 12, 2014

8.4K
Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments
13:00

Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments

Published on: January 23, 2017

9.8K

Related Experiment Videos

Last Updated: May 21, 2025

A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers
12:39

A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers

Published on: January 18, 2020

7.5K
Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform
06:25

Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform

Published on: February 12, 2014

8.4K
Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments
13:00

Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments

Published on: January 23, 2017

9.8K

Area of Science:

  • Remote Sensing
  • Computer Vision
  • Geospatial Analysis

Background:

  • Multi-temporal remote sensing image matching is vital for applications like drone navigation, disaster monitoring, and land-cover change detection.
  • Significant temporal differences in images often degrade the performance of conventional matching methods.

Purpose of the Study:

  • To develop a robust and efficient image matching method for multi-temporal remote sensing data.
  • To overcome challenges posed by temporal variations in image datasets.

Main Methods:

  • Introduced Discrete Cosine Transform (DCT) for frequency analysis tailored to remote sensing images.
  • Proposed a network combining DCT with attention mechanisms for multi-scale feature matching.
  • Utilized DCT-enhanced channel attention for richer feature extraction and DCT-guided sparse attention for coarse-scale matching refinement.

Main Results:

  • Achieved correct keypoint percentages of 81.92% (DSIFN) and 88.48% (LEVIR-CD).
  • Recorded average corner errors of 4.27 pixels (DSIFN) and 2.98 pixels (LEVIR-CD).
  • Demonstrated high inference speed and outperformed state-of-the-art methods in robustness and efficiency.

Conclusions:

  • The proposed DCT-based attention network effectively addresses challenges in multi-temporal remote sensing image matching.
  • The method offers superior accuracy, robustness, and efficiency compared to existing approaches.
  • This technique holds significant potential for various remote sensing applications requiring precise image analysis over time.