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Related Concept Videos

Network Function of a Circuit01:25

Network Function of a Circuit

1.1K
Frequency response analysis in electrical circuits provides vital insights into a circuit's behavior as the frequency of the input signal changes. The transfer function, a mathematical tool, is instrumental in understanding this behavior. It defines the relationship between phasor output and input and comes in four types: voltage gain, current gain, transfer impedance, and transfer admittance. The critical components of the transfer function are the poles and zeros.
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State Space Representation01:27

State Space Representation

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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: May 5, 2026

Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time
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Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time

Published on: July 1, 2014

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Time-Aware Graph Neural Network for Asynchronous Multi-Station Integrated Sensing and Communications Fusion in Open

Zhiqiang Shen1, Wooseok Shin2, Jitae Shin2

  • 1Department of Computer Science and Engineering, Sungkyunkwan University, Suwon 16419, Republic of Korea.

Sensors (Basel, Switzerland)
|May 4, 2026
PubMed
Summary
This summary is machine-generated.

We introduce Age-of-Sensing (AoS) to fuse asynchronous sensing data in Open RAN (O-RAN) networks. Our AoS-aware graph neural network (GNN) method, TA-Fusion, improves accuracy and resilience against network jitter for Integrated Sensing and Communication (ISAC) services.

Keywords:
6GAge-of-Sensing (AoS)Graph Neural Networks (GNN)Integrated Sensing and Communication (ISAC)Near-RT RICOpen RAN (O-RAN)asynchronous data fusionnetwork-native sensingphysics-informed learning

Related Experiment Videos

Last Updated: May 5, 2026

Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time
07:12

Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time

Published on: July 1, 2014

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Area of Science:

  • Wireless communication networks
  • Graph neural networks
  • Signal processing

Background:

  • Open RAN (O-RAN) Near-RT RIC faces challenges with out-of-order telemetry due to network jitter.
  • Traditional spatial fusion methods are invalidated by this temporal scrambling.
  • Asynchronous sensing reports require new reliability metrics.

Purpose of the Study:

  • To introduce Age-of-Sensing (AoS) as a dynamic reliability metric for asynchronous sensing data.
  • To develop an AoS-aware graph neural network (GNN) paradigm for robust sensing fusion.
  • To present Time-Aware Fusion (TA-Fusion) for prioritizing fresh telemetry and suppressing stale data.

Main Methods:

  • Developed an AoS-aware GNN paradigm incorporating sensing freshness into graph-based fusion.
  • Introduced a TA-Gate mechanism within TA-Fusion to recalibrate node trust before graph aggregation.
  • Utilized a standardized O-RAN benchmark for performance evaluation.

Main Results:

  • TA-Fusion achieved a root mean square error (RMSE) of 12.22 m.
  • Demonstrated a 21.7% reduction in Mean Absolute Error (MAE) compared to the AoS-aware GNN baseline.
  • Maintained robustness in extreme jitter scenarios, outperforming traditional linear methods.

Conclusions:

  • The proposed AoS-aware GNN paradigm offers a resilient spatial foundation for delay-critical Integrated Sensing and Communication (ISAC) services.
  • TA-Fusion provides real-time feasibility for 6G orchestration under substantial network jitter.
  • The framework ensures consistent error bounds across diverse base station geometries without recalibration.