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

Sampling Theorem01:15

Sampling Theorem

1.7K
In signal processing, the analysis of continuous-time signals, denoted as x(t), often involves sampling techniques to convert these signals into discrete-time signals. This process is essential for digital representation and manipulation. A critical component in sampling is the train of impulses, characterized by the sampling interval and the sampling frequency. The relationship between these parameters and the original signal's properties dictates the success of the sampling process.
1.7K
Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

502
Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
In contrast, nonlinear systems do not inherently possess these properties. However, for small deviations around an operating point, a nonlinear system can often be approximated as linear....
502
Discrete-Time Fourier Series01:20

Discrete-Time Fourier Series

967
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]...
967
Aliasing01:18

Aliasing

945
Accurate signal sampling and reconstruction are crucial in various signal-processing applications. A time-domain signal's spectrum can be revealed using its Fourier transform. When this signal is sampled at a specific frequency, it results in multiple scaled replicas of the original spectrum in the frequency domain. The spacing of these replicas is determined by the sampling frequency.
If the sampling frequency is below the Nyquist rate, these replicas overlap, preventing the original...
945
Determination of Expected Frequency01:08

Determination of Expected Frequency

1.7K
Suppose one wants to test independence between the two variables of a contingency table. The values in the table constitute the observed frequencies of the dataset. But how does one determine the expected frequency of the dataset? One of the important assumptions is that the two variables are independent, which means the variables do not influence each other. For independent variables, the statistical probability of any event involving both variables is calculated by multiplying the individual...
1.7K
Bandpass Sampling01:17

Bandpass Sampling

681
In signal processing, bandpass sampling is an effective technique for sampling signals that have most of their energy concentrated within a narrow frequency band. This type of signal is known as a bandpass signal. The key principle of bandpass sampling involves sampling the signal at a rate that is greater than twice the signal's bandwidth to prevent aliasing.
A bandpass signal has a spectrum with a lower frequency limit, denoted as ω1, and an upper frequency limit, denoted as ω2....
681

You might also read

Related Articles

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

Sort by
Same author

An Efficient Regenerated Cross-Modal Hashing: Improving Existing Hash Codes with the Arbitrary Length.

IEEE transactions on pattern analysis and machine intelligence·2026
Same author

Adapting Domain-Aware Knowledge to Vision-Language Model for Zero-Shot Anomaly Detection.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same author

Predefined-Time Dynamic Self-Triggered Approximate Optimal Control of Autonomous Surface Vehicles With Disturbances.

IEEE transactions on cybernetics·2025
Same author

RaLo: Rank-aware low-rank adaptation for pre-trained foundation models.

Neural networks : the official journal of the International Neural Network Society·2025
Same author

Central similarity joint-learning for cross-domain retrieval.

Neural networks : the official journal of the International Neural Network Society·2025
Same author

Multi-view subspace tensorization with attentive clustering embedding.

Neural networks : the official journal of the International Neural Network Society·2025
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: Apr 30, 2026

Computer-based Multitaper Spectrogram Program for Electroencephalographic Data
04:13

Computer-based Multitaper Spectrogram Program for Electroencephalographic Data

Published on: November 13, 2019

13.5K

Time-frequency approach to underdetermined blind source separation.

Shengli Xie, Liu Yang, Jun-Mei Yang

    IEEE Transactions on Neural Networks and Learning Systems
    |May 9, 2014
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a novel time-frequency approach for separating non-stationary sources from mixed signals. The method accurately identifies source components using Wigner-Ville distribution and Khatri-Rao product, outperforming existing algorithms.

    More Related Videos

    Infant Auditory Processing and Event-related Brain Oscillations
    06:34

    Infant Auditory Processing and Event-related Brain Oscillations

    Published on: July 1, 2015

    17.3K
    Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging
    11:28

    Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging

    Published on: June 30, 2018

    11.5K

    Related Experiment Videos

    Last Updated: Apr 30, 2026

    Computer-based Multitaper Spectrogram Program for Electroencephalographic Data
    04:13

    Computer-based Multitaper Spectrogram Program for Electroencephalographic Data

    Published on: November 13, 2019

    13.5K
    Infant Auditory Processing and Event-related Brain Oscillations
    06:34

    Infant Auditory Processing and Event-related Brain Oscillations

    Published on: July 1, 2015

    17.3K
    Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging
    11:28

    Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging

    Published on: June 30, 2018

    11.5K

    Area of Science:

    • Signal Processing
    • Time-Frequency Analysis
    • Blind Source Separation

    Background:

    • Underdetermined blind source separation (BSS) is challenging, especially with non-stationary signals.
    • Existing methods often struggle with accurate mixing matrix estimation and source extraction.

    Purpose of the Study:

    • To develop a novel time-frequency (TF) based underdetermined BSS approach.
    • To improve the estimation of the mixing matrix by considering the negative auto-Wigner-Ville distribution (WVD) of sources.
    • To accurately extract auto-term TF points for source separation.

    Main Methods:

    • Utilizing Wigner-Ville distribution (WVD) for time-frequency representation.
    • Employing the Khatri-Rao product for matrix factorization.
    • Developing an improved method for mixing matrix estimation by analyzing negative auto-WVD values.
    • Extracting auto-term TF points to determine source WVD values.

    Main Results:

    • The proposed approach accurately estimates the mixing matrix by incorporating negative auto-WVD.
    • Auto-term TF points are effectively extracted, enabling precise WVD value determination for sources.
    • The algorithm demonstrates superiority over existing methods in numerical simulations, particularly for N ≤ 2M-1 sources.

    Conclusions:

    • The novel TF-based underdetermined BSS approach offers improved performance.
    • Accurate separation of non-stationary sources is achievable with the proposed WVD and Khatri-Rao product method.
    • The algorithm shows significant advantages in scenarios with more sources than mixtures (N > M).