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Multi-input and Multi-variable systems

Multi-input and Multi-variable systems

Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
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Related Experiment Video

Updated: Dec 14, 2025

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

A Novel Multi-Focus Image Fusion Network with U-Shape Structure.

Tao Pan¹, Jiaqin Jiang¹, Jian Yao^1,2

¹School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430000, China.

Sensors (Basel, Switzerland)

|July 17, 2020

Summary

This summary is machine-generated.

This study introduces a novel U-shape network for accurate multi-focus image fusion. The proposed method enhances focus region detection, leading to superior all-in-focus image generation.

Keywords:

Siamese encoder U-shape network hybrid loss multi-focus image fusion spatial pyramid pooling

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Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

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Published on: December 15, 2023

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Last Updated: Dec 14, 2025

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

Area of Science:

Computer Vision
Image Processing

Background:

Multi-focus image fusion aims to create an all-in-focus image from multiple images.
Existing methods struggle with accurate focus region detection, limiting fusion performance.

Purpose of the Study:

To propose a novel U-shape network for accurate decision map generation in multi-focus image fusion.
To improve the detection of focus regions for enhanced all-in-focus image synthesis.

Main Methods:

A Siamese encoder preserves low-level and high-level information from source images.
ResBlocks expand the receptive field for better focus/defocus distinction.
Spatial pyramid pooling captures global context in the bridge stage.
A hybrid loss (binary cross-entropy and structural similarity) is used for supervision.

Main Results:

The proposed U-shape network generates accurate decision maps.
The method effectively distinguishes between focus and defocus regions.
State-of-the-art performance in multi-focus image fusion is achieved.

Conclusions:

The novel U-shape network significantly improves multi-focus image fusion.
Accurate focus region detection is crucial for high-quality all-in-focus images.
The proposed method offers a robust solution for practical image processing tasks.