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

¹³C NMR: Distortionless Enhancement by Polarization Transfer (DEPT)01:20

¹³C NMR: Distortionless Enhancement by Polarization Transfer (DEPT)

When proton-coupled carbon-13 spectra are simplified by a broadband proton decoupling technique, structural information about the coupled protons is lost. Distortionless enhancement by polarization transfer (DEPT) is a technique that provides information on the number of hydrogens attached to each carbon in a molecule. While the DEPT experiment utilizes complex pulse sequences, the pulse delay and flip angle are specifically manipulated. The resulting signals have different phases depending on...
Bandpass Sampling01:17

Bandpass Sampling

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. The spectrum...
Reconstruction of Signal using Interpolation01:10

Reconstruction of Signal using Interpolation

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 sampling...
Habitat Fragmentation02:31

Habitat Fragmentation

Habitat fragmentation describes the division of a more extensive, continuous habitat into smaller, discontinuous areas. Human activities such as land conversion, as well as slower geological processes leading to changes in the physical environment, are the two leading causes of habitat fragmentation. The fragmentation process typically follows the same steps: perforation, dissection, fragmentation, shrinkage, and attrition.
Aliasing01:18

Aliasing

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 signal...
¹³C NMR: ¹H–¹³C Decoupling01:04

¹³C NMR: ¹H–¹³C Decoupling

The probability of having two carbon-13 atoms next to each other is negligible because of the low natural abundance of carbon-13. Consequently, peak splitting due to carbon-carbon spin-spin coupling is not observed in spectra. However, protons up to three sigma bonds away split the carbon signal according to the n+1 rule, resulting in complicated spectra.
A broadband decoupling technique is used to simplify these complex, sometimes overlapping, signals. Broadband decoupling relies on a...

You might also read

Related Articles

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

Sort by
Same author

SIC50: Determining drug inhibitory concentrations using a vision transformer and an optimized Sobel operator.

Patterns (New York, N.Y.)·2023
Same author

Steering CO<sub>2</sub> electroreduction pathway toward ethanol via surface-bounded hydroxyl species-induced noncovalent interaction.

Proceedings of the National Academy of Sciences of the United States of America·2023
Same author

CHD8 mutations increase gliogenesis to enlarge brain size in the nonhuman primate.

Cell discovery·2023
Same author

Effects of Different Types of LAB on Dynamic Fermentation Quality and Microbial Community of Native Grass Silage during Anaerobic Fermentation and Aerobic Exposure.

Microorganisms·2023
Same author

Malic Enzyme 1 as a Novel Anti-Ferroptotic Regulator in Hepatic Ischemia/Reperfusion Injury.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2023
Same author

Long-term exposure to elemental components of fine particulate matter and all-natural and cause-specific mortality in a Danish nationwide administrative cohort study.

Environmental research·2023

Related Experiment Videos

Path connectivity based spectral defragmentation in flexible bandwidth networks.

Ying Wang1, Jie Zhang, Yongli Zhao

  • 1State Key Laboratory of Information Photonics and Optical Communication, Beijing University of Posts and Telecommunications, No.10 Xitucheng Road, Beijing 100876, China.

Optics Express
|February 8, 2013
PubMed
Summary
This summary is machine-generated.

Two new spectrum defragmentation algorithms, Maximum Path Connectivity (MPC) and Path Connectivity Triggering (PCT), improve optical network efficiency. These methods enhance resource utilization and reduce connection blocking probability in flexible bandwidth networks.

Related Experiment Videos

Area of Science:

  • Optical networking
  • Telecommunications
  • Computer networks

Background:

  • Flexible bandwidth optical networks offer efficient resource utilization for diverse demands.
  • Spectrum defragmentation is crucial for optimizing these networks.
  • Existing methods may not fully address the complexities of dynamic bandwidth allocation.

Purpose of the Study:

  • To introduce two novel spectrum defragmentation algorithms: Maximum Path Connectivity (MPC) and Path Connectivity Triggering (PCT).
  • To define and utilize the concept of Path Connectivity for flexible bandwidth networks.
  • To evaluate the profitability and performance of spectrum defragmentation.

Main Methods:

  • Development of the Maximum Path Connectivity (MPC) algorithm.
  • Development of the Path Connectivity Triggering (PCT) algorithm.
  • Creation of a cost-performance-ratio based profitability model.
  • Comparative analysis of MPC, PCT, and a non-defragmentation approach using blocking probability.

Main Results:

  • The proposed MPC and PCT algorithms demonstrate potential for improved performance over non-defragmentation strategies.
  • Path Connectivity is established as a key metric for evaluating defragmentation effectiveness.
  • The cost-performance-ratio model provides a framework for assessing the economic viability of defragmentation.

Conclusions:

  • Spectrum defragmentation, particularly using MPC and PCT, can significantly enhance optical network efficiency.
  • The choice between MPC and PCT depends on specific network conditions and profitability considerations.
  • Further research into defragmentation profitability analysis is warranted for advanced optical networks.