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

Reconstruction of Signal using Interpolation

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...

Linear Approximation in Time Domain

Linear Approximation in Time Domain

Nonlinear systems often require sophisticated approaches for accurate modeling and analysis, with state-space representation being particularly effective. This method is especially useful for systems where variables and parameters vary with time or operating conditions, such as in a simple pendulum or a translational mechanical system with nonlinear springs.
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length,...

Convolution: Math, Graphics, and Discrete Signals

Convolution: Math, Graphics, and Discrete Signals

In any LTI (Linear Time-Invariant) system, the convolution of two signals is denoted using a convolution operator, assuming all initial conditions are zero. The convolution integral can be divided into two parts: the zero-input or natural response and the zero-state or forced response, with t0 indicating the initial time.
To simplify the convolution integral, it is assumed that both the input signal and impulse response are zero for negative time values. The graphical convolution process...

Linear Approximation in Frequency Domain

Linear Approximation in Frequency Domain

Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
In contrast, nonlinear systems do not inherently possess these properties. However, for small deviations around an operating point, a nonlinear system can often be approximated as linear....

You might also read

Related Articles

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

Sort by

Same author

Novel Bio-Inspired Physics-Based Learning and Evolutionary Guidance for Dynamic Multi-Objective Cold Chain Routings.

Biomimetics (Basel, Switzerland)·2026

Same author

Oxidative Stress Induced Senescent Macrophage-Driven Squamous Cell Carcinoma Invasion via Glutamine Metabolic Reprogramming.

Aging cell·2026

Same author

Biodegradable plastic mulches: Distinct effects on microbial communities but no impact on soil multifunctionality during cabbage production.

Journal of hazardous materials·2026

Same author

AbCVista: a deep learning framework for predicting antibody conformational ensembles.

Protein & cell·2026

Same author

Perineural invasion in gastrointestinal malignancies: From neurotrophic signaling to targetable tumor-microenvironment crosstalk.

Biochimica et biophysica acta. Reviews on cancer·2026

Same author

Polymeric Nanomaterials for Atherosclerosis Diagnosis and Treatment.

Polymer science & technology (Washington, D.C.)·2026

Same journal

Multiphysics Investigation on Thermal Characteristics of Internal Bio-Inspired V-Ribbed Cooling Channels for Outer Rotor PMSM.

Biomimetics (Basel, Switzerland)·2026

Same journal

Smart Logistics Model for Supply Chain Management via Brain-Inspired Geometric Deep Networks.

Biomimetics (Basel, Switzerland)·2026

Same journal

A Systematic Taxonomy of the Sunflower Optimization Algorithm: Variants, Hybridization Strategies, Applications, and Research Directions.

Biomimetics (Basel, Switzerland)·2026

Same journal

Toward a Compositional Theory of Trust in Embodied Intelligence: A QNLP Framework for Modeling Context, Interaction, and Trustworthiness.

Biomimetics (Basel, Switzerland)·2026

Same journal

Empirical Logic for Bio-Inspired Soft Computing: Illustrative Applications in Control Engineering and Cluster Analysis.

Biomimetics (Basel, Switzerland)·2026

Same journal

A Modified Multi-Strategy Dhole Optimization Algorithm and Its Engineering Applications.

Biomimetics (Basel, Switzerland)·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Feb 27, 2026

Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform

Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform

Published on: February 12, 2014

An Evolutionary-Algorithm-Driven Efficient Temporal Convolutional Network for Radar Image Extrapolation.

Peiyang Wei^1,2,3, Changyuan Fan⁴, Yuyan Wang¹

¹School of Software Engineering, Chengdu University of Information Technology, Chengdu 610225, China.

Biomimetics (Basel, Switzerland)

|February 26, 2026

Summary

This summary is machine-generated.

This study introduces E-HEOA, a deep learning model for radar image extrapolation, significantly improving short-term weather forecasting accuracy. The enhanced model overcomes limitations of traditional methods, offering better prediction fidelity and reliability.

Keywords:

adaptive hyperparameter optimization associated algorithm convolutional neural network deep learning evolutionary algorithm radar extrapolation

Related Experiment Videos

Last Updated: Feb 27, 2026

Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform

Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform

Published on: February 12, 2014

Area of Science:

Meteorology
Artificial Intelligence
Computer Vision

Background:

Radar image extrapolation is crucial for short-term weather forecasting.
Conventional methods suffer from image degradation and artifacts, limiting reliability.
Deep learning offers potential for improved spatiotemporal sequence analysis.

Purpose of the Study:

Introduce E-HEOA, an enhanced deep learning architecture for radar image extrapolation.
Improve prediction fidelity, convergence efficiency, and structural similarity in forecasts.
Address limitations of conventional forecasting methods.

Main Methods:

Developed E-HEOA, a deep learning framework with integrated hyperparameter optimization.
Implemented a hybrid metaheuristic optimizer (Gaussian-mutated ESOA and Cauchy-mutated HEOA) for autonomous optimization.
Utilized embedded ConvLSTM2D modules for enhanced spatiotemporal feature preservation.

Main Results:

E-HEOA demonstrated superior performance over baseline models.
Achieved considerable enhancements in prediction fidelity, convergence efficiency, and structural similarity.
Established new state-of-the-art benchmarks in radar echo forecasting.

Conclusions:

The proposed E-HEOA framework significantly advances radar image extrapolation.
The hybrid optimizer and ConvLSTM2D modules effectively improve forecasting accuracy.
E-HEOA offers a more reliable and efficient solution for operational meteorological forecasting.