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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 11, 2025

Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities

Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities

Published on: October 27, 2023

Multi-modality medical image fusion technique using multi-objective differential evolution based deep neural

Manjit Kaur^1,2, Dilbag Singh^3,2

¹Department of Computer and Communication Engineering, Manipal University Jaipur, Jaipur, India.

Journal of Ambient Intelligence and Humanized Computing

|August 25, 2020

Summary

This summary is machine-generated.

This study introduces a novel multi-modality image fusion method using non-subsampled contourlet transform (NSCT) and Xception for enhanced medical image analysis. The approach successfully integrates information from multiple sources, improving diagnostic capabilities.

Keywords:

CNN Diagnosis Differential evolution Fusion Multi-modality

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Area of Science:

Medical Imaging
Computer Vision
Signal Processing

Background:

Automated diagnostic tools increasingly rely on medical images.
Multi-modality imaging offers richer information than traditional methods but presents fusion challenges.

Purpose of the Study:

To develop an effective approach for fusing multi-modality medical images.
To enhance the information content of medical images for improved diagnostics.

Main Methods:

Image decomposition into sub-bands using non-subsampled contourlet transform (NSCT).
Feature extraction using an extreme version of Inception (Xception).
Optimal feature selection via multi-objective differential evolution and image fusion using coefficient of determination and energy loss functions.

Main Results:

The proposed method successfully fuses multi-modality medical images.
Experimental results demonstrate superior performance compared to existing fusion techniques.

Conclusions:

The developed NSCT and Xception-based fusion approach is effective for multi-modality medical image analysis.
This method offers a significant advancement over current multi-modality image fusion techniques.