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

Signal Sequences and Sorting Receptors01:41

Signal Sequences and Sorting Receptors

12.9K
Signal sequences are short amino acid sequences that guide newly synthesized proteins to their proper location within the cell. Classical signal sequences are fifteen to sixty amino acids long and present at the N-terminus of a polypeptide chain. Each signal sequence has a conserved segment of basic residues towards their N terminus, a hydrophobic core, and a C-terminus rich in polar residues. The C-terminus also contains a signal cleavage site and features a -3 -1 sequence motif. The -3-1...
12.9K
Classification of Signals01:30

Classification of Signals

1.2K
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...
1.2K
Even and Odd Signals01:17

Even and Odd Signals

1.8K
An even signal, whether in continuous-time or discrete-time, is defined by its symmetry with its time-reversed version. Mathematically, this is represented as
1.8K
Basic Discrete Time Signals01:16

Basic Discrete Time Signals

510
The unit step sequence is defined as 1 for zero and positive values of the integer n. This sequence can be graphically displayed using a set of eight sample points, showing a step function starting from n=0 and remaining constant thereafter.
The unit impulse or sample sequence is mathematically expressed as zero for all n values except at n=0, where it is one. The unit impulse sequence, denoted by δ(n), is the first difference of the unit step sequence, while the unit step sequence u(n) is the...
510
Reconstruction of Signal using Interpolation01:10

Reconstruction of Signal using Interpolation

566
Signal processing techniques are essential for accurately converting continuous signals to digital formats and vice versa. When a continuous signal is sampled with a period T, the resulting sampled signal exhibits replicas of the original spectrum in the frequency domain, spaced at intervals equal to the sampling frequency. To handle this sampled signal, a zero-order hold method can be applied, which creates a piecewise constant signal by retaining each sample's value until the next...
566
Sampling Continuous Time Signal01:11

Sampling Continuous Time Signal

541
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...
541

You might also read

Related Articles

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

Sort by
Same author

Haphazard Intentional Sampling in Survey and Allocation Studies on COVID-19 Prevalence and Vaccine Efficacy.

Entropy (Basel, Switzerland)·2022
Same author

Cointegration and Unit Root Tests: A Fully Bayesian Approach.

Entropy (Basel, Switzerland)·2020
Same author

Classical-Equivalent Bayesian Portfolio Optimization for Electricity Generation Planning.

Entropy (Basel, Switzerland)·2020
Same author

A Bayesian binary algorithm for root mean squared-based acoustic signal segmentation.

The Journal of the Acoustical Society of America·2019
Same author

A multi-exonic SPG4 duplication underlies sex-dependent penetrance of hereditary spastic paraplegia in a large Brazilian pedigree.

European journal of human genetics : EJHG·2007
Same journal

Research on a Regional Availability Evaluation Model for Road-Area High-Entropy Energy Based on Synergy Factors.

Entropy (Basel, Switzerland)·2026
Same journal

Atmospheric Turbulence Channel Modeling and Performance Analysis of a CO-ZP-OFDM Coherent Optical Communication System for UAV Air-to-Ground Scenarios.

Entropy (Basel, Switzerland)·2026
Same journal

Information Geometry and Asymptotic Theory for SMML Estimators.

Entropy (Basel, Switzerland)·2026
Same journal

Correlation Entropy and Power-Law Kinetics.

Entropy (Basel, Switzerland)·2026
Same journal

Research on the Contagion of Systemic Financial Risk Under the Impact of Climate Risks-From the Perspective of Complex Networks and Machine Learning.

Entropy (Basel, Switzerland)·2026
Same journal

The Statistical-Mechanical Meaning of the Wave Function of Quantum Mechanics.

Entropy (Basel, Switzerland)·2026
See all related articles

Related Experiment Video

Updated: Nov 27, 2025

Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles
11:54

Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles

Published on: March 13, 2017

9.6K

A Sequential Algorithm for Signal Segmentation.

Paulo Hubert1, Linilson Padovese2, Julio Michael Stern1

  • 1Instituto de Matemática e Estatística, University of São Paulo (IME-USP), São Paulo 05508-090, Brazil.

Entropy (Basel, Switzerland)
|December 3, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces a novel Bayesian sequential algorithm for event detection in noisy acoustic signals. The method identifies significant events without needing prior event forms or annotated data, focusing on energy alterations.

Keywords:
audio segmentationbayesian methodshypothesis testingsignal detection

More Related Videos

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

3.2K
Substructure Analyzer: A User-Friendly Workflow for Rapid Exploration and Accurate Analysis of Cellular Bodies in Fluorescence Microscopy Images
14:28

Substructure Analyzer: A User-Friendly Workflow for Rapid Exploration and Accurate Analysis of Cellular Bodies in Fluorescence Microscopy Images

Published on: July 15, 2020

8.2K

Related Experiment Videos

Last Updated: Nov 27, 2025

Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles
11:54

Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles

Published on: March 13, 2017

9.6K
Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

3.2K
Substructure Analyzer: A User-Friendly Workflow for Rapid Exploration and Accurate Analysis of Cellular Bodies in Fluorescence Microscopy Images
14:28

Substructure Analyzer: A User-Friendly Workflow for Rapid Exploration and Accurate Analysis of Cellular Bodies in Fluorescence Microscopy Images

Published on: July 15, 2020

8.2K

Area of Science:

  • Signal Processing
  • Acoustics
  • Statistical Inference

Background:

  • Event detection in noisy signals is crucial across various applications.
  • Traditional methods often require known event forms or labeled data, which are not always available.
  • Challenges arise when dealing with unknown event characteristics in complex signal environments.

Purpose of the Study:

  • To develop a robust method for separating significant event segments from noisy acoustic signals.
  • To address event detection scenarios lacking functional event descriptions or annotated training samples.
  • To analyze 15-minute acoustic signal samples for event identification.

Main Methods:

  • Application of a sequential algorithm based on Bayesian principles.
  • The core assumption is that events cause detectable alterations in signal energy.
  • Analysis of acoustic signal segments to isolate potential event occurrences.

Main Results:

  • Successfully separated signal segments likely containing significant events.
  • Demonstrated the efficacy of the Bayesian sequential approach in unsupervised event detection.
  • Validated the method's performance on 15-minute acoustic signal samples.

Conclusions:

  • The proposed Bayesian sequential algorithm offers a viable strategy for event detection when labeled data is absent.
  • Signal energy alteration is a sufficient basis for event separation in noisy acoustic data.
  • This approach provides a foundation for further research in unsupervised event characterization.