Jove
Visualize
Contact Us

Related Concept Videos

Wald-Wolfowitz Runs Test I01:17

Wald-Wolfowitz Runs Test I

The Wald-Wolfowitz test, also known as the runs test, is a nonparametric statistical test used to assess the randomness of a sequence of two different types of elements (e.g., positive/negative values, successes/failures). It examines whether the order of the elements in a sequence is random or if there is a pattern or trend present. This nonparametric test applies to any ordered data despite the population and sample data distribution, even if a higher sample size is available.
The test works...
Fast Fourier Transform01:10

Fast Fourier Transform

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...
Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

Nonlinear systems often require sophisticated approaches for accurate modeling and analysis, with state-space representation being particularly effective. This method is especially useful for systems where variables and parameters vary with time or operating conditions, such as in a simple pendulum or a translational mechanical system with nonlinear springs.
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length, the...
Deconvolution01:20

Deconvolution

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...
Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

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.
Convolution: Math, Graphics, and Discrete Signals01:24

Convolution: Math, Graphics, and Discrete Signals

In any LTI (Linear Time-Invariant) system, the convolution of two signals is denoted using a convolution operator, assuming all initial conditions are zero. The convolution integral can be divided into two parts: the zero-input or natural response and the zero-state or forced response, with t0 indicating the initial time.
To simplify the convolution integral, it is assumed that both the input signal and impulse response are zero for negative time values. The graphical convolution process...

You might also read

Related Articles

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

Sort by
Same author

Improving protein and protein interactions using pseudo-dimers derived from monomeric proteins.

Nature communications·2026
Same author

CRA5 a high-fidelity compressed reanalysis atmospheric dataset for weather and climate research.

Scientific data·2026
Same author

CrystalX: High-Accuracy Crystal Structure Analysis Using Deep Learning.

Journal of the American Chemical Society·2026
Same author

Evidential deep learning for interatomic potentials.

Nature communications·2025
Same author

Glasses-free 3D display with ultrawide viewing range using deep learning.

Nature·2025
Same author

TripoSG: High-Fidelity 3D Shape Synthesis Using Large-Scale Rectified Flow Models.

IEEE transactions on pattern analysis and machine intelligence·2025
Same journal

Relation DETR+: Exploring Explicit Position Relation Prior for Dense Prediction.

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

RBF++: Quantifying and Optimizing Reasoning Boundaries across Measurable and Unmeasurable Capabilities for Chain-of-Thought Reasoning.

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

CAFE: Cross-View Adaptive Fusion and Cluster Center Enhancement for Robust Multi-View Clustering.

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

DIVER: Reinforced Diffusion Breaks Imitation Bottlenecks in End-to-End Autonomous Driving.

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

Ethics-Aware Safe Reinforcement Learning for Rare-Event Risk Control in Interactive Urban Driving.

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

Learning Shape Anchors for Holistic Indoor Scene Understanding.

IEEE transactions on pattern analysis and machine intelligence·2026
See all related articles
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 Experiment Videos

Fast algorithm for Walsh Hadamard Transform on sliding windows.

Wanli Ouyang1, Wai-Kuen Cham

  • 1Department of Electronic Engineering, The Chinese University of Hong Kong, Ho Sin Hang Engineering Building, Hong Kong, Shatin, NT, Hong Kong. wlouyang@ee.cuhk.edu.hk

IEEE Transactions on Pattern Analysis and Machine Intelligence
|November 21, 2009
PubMed
Summary
This summary is machine-generated.

This study introduces a rapid Walsh-Hadamard Transform algorithm for sliding windows, enhancing pattern matching efficiency. Its computational cost is remarkably low, making it the most efficient fast algorithm available.

Related Experiment Videos

Area of Science:

  • Digital Signal Processing
  • Computer Vision
  • Algorithm Optimization

Background:

  • Efficient computation of transforms is crucial for real-time pattern matching.
  • Sliding window algorithms are widely used in signal and image processing.
  • Existing fast Walsh-Hadamard Transform algorithms have limitations in computational efficiency.

Purpose of the Study:

  • To propose a novel fast algorithm for Walsh-Hadamard Transform on sliding windows.
  • To achieve highly efficient pattern matching using the proposed transform.
  • To minimize the computational requirements of the transform.

Main Methods:

  • Development of a fast algorithm specifically for Walsh-Hadamard Transform on sliding windows.
  • Analysis of the computational complexity in terms of additions per projection vector per sample.
  • Comparison with existing fast algorithms for Walsh-Hadamard Transform on sliding windows.

Main Results:

  • The proposed algorithm achieves approximately 1.5 additions per projection vector per sample.
  • This computational requirement is lower than all previously reported fast algorithms for sliding window Walsh-Hadamard Transform.
  • The algorithm enables highly efficient implementation of pattern matching.

Conclusions:

  • The developed fast Walsh-Hadamard Transform algorithm offers significant computational savings.
  • This advancement facilitates more efficient pattern matching in various applications.
  • The algorithm represents a new state-of-the-art in fast transforms for sliding windows.