Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Applications of Integration to Probability Density Functions

Applications of Integration to Probability Density Functions

Continuous probability distributions are used to model random variables that can take on any real value within a specified range. These variables do not take on isolated or countable values but rather exist on a continuum. For example, the height of an individual can be measured with increasing precision—such as 163.5 or 165.25 centimeters—demonstrating that height is a continuous random variable.The behavior of such variables is described using a probability density function (PDF), which...

Probability Distributions

Probability Distributions

The probability of a random variable x is the likelihood of its occurrence. A probability distribution represents the probabilities of a random variable using a formula, graph, or table. There are two types of probability distribution– discrete probability distribution and continuous probability distribution.
A discrete probability distribution is a probability distribution of discrete random variables. It can be categorized into binomial probability distribution and Poisson probability...

Sampling Continuous Time Signal

Sampling Continuous Time Signal

In signal processing, a continuous-time signal can be sampled using an impulse-train sampling technique, followed by the zero-order hold method. Impulse-train sampling involves the use of a periodic impulse train, which consists of a series of delta functions spaced at regular intervals determined by the sampling period. When a continuous-time signal is multiplied by this impulse train, it generates impulses with amplitudes corresponding to the signal's values at the sampling points.
In the...

Basic Continuous Time Signals

Basic Continuous Time Signals

Basic continuous-time signals include the unit step function, unit impulse function, and unit ramp function, collectively referred to as singularity functions. Singularity functions are characterized by discontinuities or discontinuous derivatives.
The unit step function, denoted u(t), is zero for negative time values and one for positive time values, exhibiting a discontinuity at t=0. This function often represents abrupt changes, such as the step voltage introduced when turning a car's...

Continuous -time Fourier Transform

Continuous -time Fourier Transform

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...

Classification of Signals

Classification of Signals

In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...

You might also read

Related Articles

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

Sort by

Same author

Biophysical mechanisms underlying the generation and maintenance of rule-learning engram.

Communications biology·2026

Same author

Quantum Elastica.

Entropy (Basel, Switzerland)·2025

Same author

Non-invasive positive pressure ventilation for acute asthma in children.

The Cochrane database of systematic reviews·2024

Same author

A Fast and Reliable Solution to PnP, Using Polynomial Homogeneity and a Theorem of Hilbert.

Sensors (Basel, Switzerland)·2023

Same author

Drug interventions for prevention of COVID-19 progression to severe disease in outpatients: a systematic review with meta-analyses and trial sequential analyses (The LIVING Project).

BMJ open·2023

Same author

An Approach to Robust ICP Initialization.

IEEE transactions on pattern analysis and machine intelligence·2023

Search research articles

Related Experiment Video

Updated: May 26, 2026

Cross-Modal Multivariate Pattern Analysis

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

A probabilistic approach to pattern matching in the continuous domain.

Daniel Keren¹, Michael Werman, Joshua Feinberg

¹Department of Computer Science, University of Haifa, Haifa 31905, Israel. dkeren@cs.haifa.ac.il

IEEE Transactions on Pattern Analysis and Machine Intelligence

|January 4, 2012

Summary

This study computes the probability distribution of a continuous signal's distance from a template using noisy samples. Path integration techniques provide an accurate solution and an efficient approximation scheme.

More Related Videos

Electronic Tongue Generating Continuous Recognition Patterns for Protein Analysis

Electronic Tongue Generating Continuous Recognition Patterns for Protein Analysis

Published on: September 16, 2014

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

Related Experiment Videos

Last Updated: May 26, 2026

Cross-Modal Multivariate Pattern Analysis

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

Electronic Tongue Generating Continuous Recognition Patterns for Protein Analysis

Electronic Tongue Generating Continuous Recognition Patterns for Protein Analysis

Published on: September 16, 2014

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

Area of Science:

Signal Processing
Computational Physics
Probability Theory

Background:

Estimating signal properties from discrete, noisy data is a fundamental challenge.
Traditional signal restoration focuses on single optimal signal reconstruction.
Calculating probability distributions requires integrating over all possible signal configurations.

Purpose of the Study:

To compute the probability distribution of a continuous signal's distance from a fixed template, given discrete noisy samples.
To move beyond single-signal restoration towards a full probabilistic characterization.
To develop and apply advanced mathematical techniques for this complex problem.

Main Methods:

Application of path integration techniques to solve the problem.
Analysis in both one and two dimensions.
Development of an accurate solution and an efficient approximation scheme.

Main Results:

An accurate method for computing the probability distribution of signal-template distance was developed.
An efficient approximation scheme was successfully devised.
The approach was validated in one and two-dimensional signal spaces.

Conclusions:

Path integration offers a powerful framework for signal probability distribution computation.
The developed methods provide accurate and efficient tools for analyzing noisy signals.
This work advances the understanding of signal properties in the presence of uncertainty.