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State Space Representation

State Space Representation

The frequency-domain technique, commonly used in analyzing and designing feedback control systems, is effective for linear, time-invariant systems. However, it falls short when dealing with nonlinear, time-varying, and multiple-input multiple-output systems. The time-domain or state-space approach addresses these limitations by utilizing state variables to construct simultaneous, first-order differential equations, known as state equations, for an nth-order system.
Consider an RLC circuit, a...

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Related Experiment Video

Updated: Jun 24, 2025

Measuring Spatially- and Directionally-varying Light Scattering from Biological Material

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Published on: May 20, 2013

Non-Invasive Self-Adaptive Information States' Acquisition inside Dynamic Scattering Spaces.

Ruifeng Li^1,2, Jinyan Ma^1,2, Da Li^1,2

¹Zhejiang University-University of Illinois at Urbana-Champaign Institute, Zhejiang University, Haining 314400, China.

Research (Washington, D.C.)

|June 3, 2024

Summary

This summary is machine-generated.

This study demonstrates non-invasive acquisition of information states in dynamic scattering environments using a novel adversarial network. This breakthrough achieves the Fisher information limit for multi-target scenarios, enhancing measurement precision.

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Area of Science:

Physics
Information Theory
Machine Learning

Background:

Achieving high information acquisition efficiency is crucial for reaching measurement precision limits in scattering media.
While engineered modes can maximize information states, they often require partial system intrusion.
Non-invasive methods for dynamic scattering spaces are challenging due to complex mapping problems, especially for multi-target scenarios.

Purpose of the Study:

To experimentally demonstrate the feasibility of non-invasive information state acquisition in dynamic scattering spaces.
To address the challenges posed by the non-unique mapping problem in multi-target scenarios.
To achieve the ultimate measurement precision limit without disturbing the system.

Main Methods:

Introduction of a tandem-generated adversarial network framework.
Utilizing the external scattering matrix of the system.
Experimental validation in dynamic scattering environments.

Main Results:

Successful non-invasive acquisition of information states in dynamic scattering spaces.
Demonstration of efficient information state acquisition for multi-target scenarios.
Achievement of the Fisher information limit solely through system's scattering matrix.

Conclusions:

The developed adversarial network framework enables non-invasive information state acquisition in dynamic scattering systems.
This approach overcomes previous limitations in multi-target scenarios and complex scattering environments.
The findings offer new perspectives for high-precision measurements in dynamic complex systems.