Jove
Visualize
Contact Us

Related Concept Videos

Even and Odd Signals01:17

Even and Odd Signals

891
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
891
Difference from Background: Limit of Detection01:05

Difference from Background: Limit of Detection

6.4K
The limit of detection (LOD) is the smallest amount of analyte that can be distinguished from the background noise. The LOD value corresponds to the concentration at which the analyte signal is three times larger than the standard deviation of the blank signal. Below this value, the analyte signal cannot be differentiated from the background noise. It is calculated by dividing the calibration slope by 3 times the standard deviation of the blank signals.
The LOD indicates the presence or absence...
6.4K
Classification of Signals01:30

Classification of Signals

505
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...
505
Methods of Classification and Identification01:28

Methods of Classification and Identification

32
Bacterial identification relies on a diverse array of techniques to classify and understand microorganisms, each tailored to uncover specific characteristics. Traditional morphological approaches, while still valuable, are limited for closely related or structurally simple organisms. Modern methods integrate biochemical, serological, genetic, and advanced molecular tools to achieve greater accuracy.Morphological and Biochemical TechniquesMorphological characteristics, such as cell shape and...
32
Determination of Expected Frequency01:08

Determination of Expected Frequency

2.2K
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...
2.2K
IR Spectrum Peak Splitting: Symmetric vs Asymmetric Vibrations01:08

IR Spectrum Peak Splitting: Symmetric vs Asymmetric Vibrations

1.1K
Identical bonds within a polyatomic group can stretch symmetrically (in-phase) or asymmetrically (out-of-phase). Similar to hydrogen bonding, these vibrations also influence the shape of the IR peak. Generally, asymmetric stretching frequencies are higher than symmetric stretching frequencies. For example, primary amines exhibit two distinct IR peaks between 3300–3500 cm−1 corresponding to the symmetric and asymmetric N-H stretching, while secondary amines exhibit a single...
1.1K

You might also read

Related Articles

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

Sort by
Same author

The State-Dependent Channel with a Rate-Limited Cribbing Helper.

Entropy (Basel, Switzerland)·2024
Same author

The Listsize Capacity of the Gaussian Channel with Decoder Assistance.

Entropy (Basel, Switzerland)·2022
Same author

Conditional Rényi Divergences and Horse Betting.

Entropy (Basel, Switzerland)·2020
Same author

Guessing with Distributed Encoders.

Entropy (Basel, Switzerland)·2020
Same author

Two Measures of Dependence.

Entropy (Basel, Switzerland)·2020
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
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 Video

Updated: Jul 15, 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

9.3K

Assisted Identification over Modulo-Additive Noise Channels.

Amos Lapidoth1, Baohua Ni1

  • 1Signal and Information Processing Laboratory, ETH Zurich, 8092 Zurich, Switzerland.

Entropy (Basel, Switzerland)
|September 28, 2023
PubMed
Summary
This summary is machine-generated.

This study examines identification capacity in modulo-additive noise channels using rate-limited noise descriptions. Both versions of capacity, with and without missed identifications, are shown to be equal.

Keywords:
erasures-only capacityhelperidentification capacitymodulo-additive noiserate-limitedzero-undetected-error capacity

More Related Videos

Transmission of Multiple Signals through an Optical Fiber Using Wavefront Shaping
09:43

Transmission of Multiple Signals through an Optical Fiber Using Wavefront Shaping

Published on: March 20, 2017

9.9K
Simulating Imaging of Large Scale Radio Arrays on the Lunar Surface
06:14

Simulating Imaging of Large Scale Radio Arrays on the Lunar Surface

Published on: July 30, 2020

4.9K

Related Experiment Videos

Last Updated: Jul 15, 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

9.3K
Transmission of Multiple Signals through an Optical Fiber Using Wavefront Shaping
09:43

Transmission of Multiple Signals through an Optical Fiber Using Wavefront Shaping

Published on: March 20, 2017

9.9K
Simulating Imaging of Large Scale Radio Arrays on the Lunar Surface
06:14

Simulating Imaging of Large Scale Radio Arrays on the Lunar Surface

Published on: July 30, 2020

4.9K

Area of Science:

  • Information Theory
  • Communication Systems

Background:

  • Modulo-additive noise channels are fundamental in communication systems.
  • Understanding channel capacity is crucial for efficient data transmission.

Purpose of the Study:

  • To investigate the identification capacity of modulo-additive noise channels.
  • To analyze the impact of rate-limited noise descriptions on channel capacity.

Main Methods:

  • Studying the classical Ahlswede-Dueck capacity.
  • Analyzing the Ahlswede-Cai-Ning-Zhang capacity, which disallows missed identifications.
  • Comparing capacities with descriptions provided to the receiver, transmitter, or both.

Main Results:

  • The identification capacity gain from rate-limited noise descriptions was quantified.
  • Both the Ahlswede-Dueck and Ahlswede-Cai-Ning-Zhang capacities were found to coincide.
  • The capacities were shown to be equal to the helper-assisted Shannon capacity.

Conclusions:

  • Rate-limited noise descriptions do not alter the fundamental identification capacity.
  • The helper-assisted Shannon capacity provides a unified measure for these channels.
  • The location of the noise description (receiver/transmitter) does not affect the capacity.