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

Discrete-Time Fourier Series01:20

Discrete-Time Fourier Series

385
The Discrete-Time Fourier Series (DTFS) is a fundamental concept in signal processing, serving as the discrete-time counterpart to the continuous-time Fourier series. It allows for the representation and analysis of discrete-time periodic signals in terms of their frequency components. Unlike its continuous counterpart, which utilizes integrals, the calculation of DTFS expansion coefficients involves summations due to the discrete nature of the signal.
For a discrete-time periodic signal x[n]...
385
Fast Fourier Transform01:10

Fast Fourier Transform

507
The Fast Fourier Transform (FFT) is a computational algorithm designed to compute the Discrete Fourier Transform (DFT) efficiently. By breaking down the calculations into smaller, manageable sections, the FFT significantly reduces the computational complexity involved. Direct computation of an N-point DFT requires N2 complex multiplications, whereas the FFT algorithm needs only (N/2)log⁡2N multiplications, offering a much faster performance.
The computational efficiency of the FFT becomes...
507
Discrete-time Fourier transform01:26

Discrete-time Fourier transform

525
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...
525
Continuous -time Fourier Transform01:11

Continuous -time Fourier Transform

449
The Fourier series is instrumental in representing periodic functions, offering a powerful method to decompose such functions into a sum of sinusoids. This technique, however, necessitates modification when applied to nonperiodic functions. Consider a pulse-train waveform consisting of a series of rectangular pulses. When these pulses have a finite period, they can be accurately represented by a Fourier series. Yet, as the period approaches infinity, resulting in a single, isolated pulse, the...
449
Discrete Fourier Transform01:15

Discrete Fourier Transform

448
The Discrete Fourier Transform (DFT) is a fundamental tool in signal processing, extending the discrete-time Fourier transform by evaluating discrete signals at uniformly spaced frequency intervals. This transformation converts a finite sequence of time-domain samples into frequency components, each representing complex sinusoids ordered by frequency. The DFT translates these sequences into the frequency domain, effectively indicating the magnitude and phase of each frequency component present...
448
Relation of DFT to z-Transform01:20

Relation of DFT to z-Transform

516
The Discrete Fourier Transform (DFT) is a crucial tool for analyzing the frequency content of discrete-time signals. It converts a sequence of N samples from the time domain into its corresponding sequence in the frequency domain, where each sample represents a specific frequency component.
To understand how the DFT works, it's helpful to consider the z-transform, which is a method for representing discrete sequences in the complex frequency domain. The z-transform involves summing the...
516

You might also read

Related Articles

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

Sort by
Same author

Efficacy of van Beek headgear activator in adolescents with UARS and hyperdivergent skeletal class II malocclusion: dentofacial morphology, airway, and growth.

BMC oral health·2026
Same author

Big Endothelin-1 Predicts the long-term survival in patients undergoing septal myectomy.

International journal of cardiology. Heart & vasculature·2026
Same author

Image Quality and Diagnostic Accuracy of Third-Generation Dual-Source CT With High-Pitch Scanning for Coronary CTA in Patients With Atrial Fibrillation and Breath-Holding Inability.

Journal of computer assisted tomography·2026
Same author

Investigating the shared genetic architecture and causality of breast cancer and thyroid cancer: genome-wide cross trait analysis and bi-directional Mendelian randomization study.

Breast cancer (Tokyo, Japan)·2026
Same author

Biwt-UNet: lung nodule segmentation via wavelet transform and multi-scale feature fusion.

Biomedical physics & engineering express·2026
Same author

Proteomic profiling identifies molecular subtypes and unveils mechanistic insights into clinical features of hypertrophic cardiomyopathy.

Journal of translational medicine·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
Same journal

Self-Supervised Continuous Dynamic Graph Representation Learning via Hawkes Processes.

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

cPU: Consistent Risk Estimator for Positive-Unlabeled Learning.

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

Tuning-Free Latent Diffusion Models for Ultrahigh-Resolution Image Editing.

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

Hidden Data Recovery and Forecasting via Next-Generation Reservoir Computing With Multiscale Delay Selection.

IEEE transactions on neural networks and learning systems·2026
See all related articles

Related Experiment Video

Updated: Sep 29, 2025

Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases
09:33

Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases

Published on: July 28, 2013

28.6K

TFA-Net: A Deep Learning-Based Time-Frequency Analysis Tool.

Pingping Pan, Yunjian Zhang, Zhenmiao Deng

    IEEE Transactions on Neural Networks and Learning Systems
    |March 21, 2022
    PubMed
    Summary
    This summary is machine-generated.

    A new deep learning method, TFA-Net, improves time-frequency analysis (TFA) for complex signals. It overcomes limitations of synchrosqueezing transform (SST) methods, offering better TF representation for nonstationary signals.

    More Related Videos

    Analyzing Neural Activity and Connectivity Using Intracranial EEG Data with SPM Software
    06:50

    Analyzing Neural Activity and Connectivity Using Intracranial EEG Data with SPM Software

    Published on: October 30, 2018

    9.6K
    Combined Invasive Subcortical and Non-invasive Surface Neurophysiological Recordings for the Assessment of Cognitive and Emotional Functions in Humans
    08:25

    Combined Invasive Subcortical and Non-invasive Surface Neurophysiological Recordings for the Assessment of Cognitive and Emotional Functions in Humans

    Published on: May 19, 2016

    10.8K

    Related Experiment Videos

    Last Updated: Sep 29, 2025

    Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases
    09:33

    Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases

    Published on: July 28, 2013

    28.6K
    Analyzing Neural Activity and Connectivity Using Intracranial EEG Data with SPM Software
    06:50

    Analyzing Neural Activity and Connectivity Using Intracranial EEG Data with SPM Software

    Published on: October 30, 2018

    9.6K
    Combined Invasive Subcortical and Non-invasive Surface Neurophysiological Recordings for the Assessment of Cognitive and Emotional Functions in Humans
    08:25

    Combined Invasive Subcortical and Non-invasive Surface Neurophysiological Recordings for the Assessment of Cognitive and Emotional Functions in Humans

    Published on: May 19, 2016

    10.8K

    Area of Science:

    • Signal Processing
    • Machine Learning
    • Time-Frequency Analysis

    Background:

    • Synchrosqueezing transform (SST) methods offer concentrated time-frequency representations (TFRs).
    • SST methods struggle with adjacent/intersecting instantaneous frequencies and require extensive parameter tuning.
    • Existing methods lack optimal TFRs for complex, nonstationary signals.

    Purpose of the Study:

    • To address the limitations of SST-based time-frequency analysis (TFA).
    • To develop an end-to-end deep learning model for improved TFA.
    • To enhance the concentration and accuracy of time-frequency representations (TFRs).

    Main Methods:

    • Analysis of TFR concentration in SST-based methods.
    • Proposal of TFA-Net, a deep learning (DL) based end-to-end replacement for SST.
    • Utilizing learned basis functions and 2-D filter kernels for energy concentration.

    Main Results:

    • TFA-Net learns basis functions beneficial for energy concentration.
    • The end-to-end architecture integrates TF feature extraction and energy concentration.
    • Demonstrated effectiveness through comprehensive numerical experiments.

    Conclusions:

    • TFA-Net offers a superior alternative to traditional SST methods for TFA.
    • The method shows significant potential for analyzing nonstationary signals.
    • Successful application to vital signs, undersea voices, and micro-Doppler signatures.