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

¹H NMR: Complex Splitting

¹H NMR: Complex Splitting

A proton M that is coupled to a proton X results in doublet signals for M. However, NMR-active nuclei can be simultaneously coupled to more than one nonequivalent nucleus. When M is coupled to a second proton A, such as in styrene oxide, each peak in the doublet is split into another doublet.
Splitting diagrams or splitting tree diagrams are routinely used to depict such complex couplings. While drawing splitting diagrams, the splitting with the larger coupling constant is usually applied...

You might also read

Related Articles

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

Sort by

Same author

CARS_SPA optimized UV-Vis spectroscopy for rapid and robust COD prediction in water samples.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy·2026

Same author

Licochalcone a Disrupted Mitochondrial Function to Promote Cuproptosis in Glioblastoma Through Regulating RAS/MAPK Pathway.

Applied biochemistry and biotechnology·2026

Same author

Bidirectional effect modification between diurnal temperature range and particulate matter on acute myocardial infarction risk.

iScience·2026

Same author

6H2L, a novel derivative of bifendate, dually suppresses glycolysis and glutaminolysis in hepatocellular carcinoma via inhibiting YAP stability.

Chemico-biological interactions·2026

Same author

The effect of web-based learning with peer-facilitated discussion on nursing students' knowledge and practices in work-related biological exposure: a quasi-experimental study.

BMC medical education·2026

Same author

Sodiophilic Hosts With Pseudocapacitive Kinetics for Robust Anode-Free Sodium Metal Batteries.

Angewandte Chemie (International ed. in English)·2026

Same journal

Correction: Kang et al. Fluid Flow to Electricity: Capturing Flow-Induced Vibrations with Micro-Electromechanical-System-Based Piezoelectric Energy Harvester. <i>Micromachines</i> 2024, <i>15</i>, 581.

Micromachines·2026

Same journal

Femtosecond Laser Texturing of Wood Coatings with Bio-Based Epoxy and Wax Additives for Enhanced Hydrophobicity.

Micromachines·2026

Same journal

Engineering of Optoelectronic Devices for Renewable Energy Applications.

Micromachines·2026

Same journal

Phase Transformation and Electrochemical Behavior of Hexagonal TiO<sub>2</sub> Nanotubes Under Different Annealing Temperatures and Heating Rates.

Micromachines·2026

Same journal

Process Optimization and Predictive Modeling of Femtosecond Laser Precision Milling for Commercial PMMA Slices.

Micromachines·2026

Same journal

A Hybrid Preprocessing Multi-Objective Surrogate Model for Thermal MEMS Actuators.

Micromachines·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jul 18, 2025

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

Improved Empirical Formula Modeling Method Using Neuro-Space Mapping for Coupled Microstrip Lines.

Shuxia Yan^1,2, Fengqi Qian¹, Chenglin Li¹

¹School of Electronics and Information Engineering, Tiangong University, Tianjin 300387, China.

|August 26, 2023

Summary

This summary is machine-generated.

This study introduces an improved empirical formula for modeling coupled microstrip lines using neuro-space mapping (Neuro-SM). The method enhances accuracy and speeds up optimization, offering better compatibility than current simulation software.

Keywords:

coupled microstrip lines mapping neural networks microwave devices modeling optimization

More Related Videos

Targeting Neuronal Fiber Tracts for Deep Brain Stimulation Therapy Using Interactive, Patient-Specific Models

Targeting Neuronal Fiber Tracts for Deep Brain Stimulation Therapy Using Interactive, Patient-Specific Models

Published on: August 12, 2018

Brain Mapping Using a Graphene Electrode Array

Brain Mapping Using a Graphene Electrode Array

Published on: October 20, 2023

Related Experiment Videos

Last Updated: Jul 18, 2025

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

Targeting Neuronal Fiber Tracts for Deep Brain Stimulation Therapy Using Interactive, Patient-Specific Models

Targeting Neuronal Fiber Tracts for Deep Brain Stimulation Therapy Using Interactive, Patient-Specific Models

Published on: August 12, 2018

Brain Mapping Using a Graphene Electrode Array

Brain Mapping Using a Graphene Electrode Array

Published on: October 20, 2023

Area of Science:

Electrical Engineering
Computational Electromagnetics
Microwave Engineering

Background:

Coupled microstrip lines are fundamental components in high-frequency circuits.
Accurate modeling is crucial for device performance and simulation efficiency.
Existing empirical models often involve slow trial-and-error processes.

Purpose of the Study:

To propose an improved empirical formula modeling method for coupled microstrip lines.
To enhance model accuracy and reduce variable dependency using mapping neural networks (MNNs).
To accelerate the optimization process through integrated sensitivity analysis.

Main Methods:

Utilizing neuro-space mapping (Neuro-SM) for empirical formula modeling.
Employing mapping neural networks (MNNs) with geometric and frequency variables.
Incorporating simple sensitivity analysis expressions into the training process.

Main Results:

The proposed Neuro-SM model accurately reflects the performance of coupled microstrip lines.
The model achieves high accuracy with fewer variables compared to traditional methods.
The integrated sensitivity analysis significantly accelerates the optimization process.

Conclusions:

The developed Neuro-SM model provides an accurate and efficient method for coupled microstrip line analysis.
The model demonstrates superior compatibility compared to existing simulation software.
This approach offers a promising alternative for high-frequency circuit design and simulation.