Spectral Demodulation & FBG Sensor Networks | Land Monitoring Innovation #Sciencefather #Researcherawards




Introduction

Fiber Bragg grating (FBG) sensing technology has emerged as a cornerstone for distributed optical sensing applications, but overlapping spectral responses continue to hinder scalability. Traditional demodulation methods struggle under conditions where multiple sensors share narrow wavelength ranges, reducing system density and limiting monitoring potential. To overcome these challenges, researchers are increasingly exploring machine learning and cloud-based approaches for intelligent spectral demodulation.

Transformer-based architecture for spectral resolution

This study presents a novel Transformer-driven neural network capable of resolving complex spectral overlaps in both uniform and mixed-linewidth FBG sensor arrays. Unlike conventional peak-finding algorithms, the Transformer model captures intricate spectral dependencies, ensuring high-accuracy demodulation even under bidirectional drift. Its attention mechanism enables effective feature learning, creating new pathways for addressing one of the most persistent limitations in FBG sensor systems.

Dual-linewidth configuration and mode fusion

A unique contribution of this work is the integration of dual-linewidth sensor configurations, coupled with reflection and transmission mode fusion. This hybrid setup significantly enhances sensing capacity by enabling the system to extract complementary information from both modes. The combination not only improves overall accuracy but also increases the robustness of spectral analysis under extreme congestion.

Cloud-enabled scalable deployment

By leveraging cloud computing, the proposed demodulation framework provides near-real-time inference while maintaining scalability for large sensor networks. This capability supports massive deployments for applications such as soil stability, groundwater detection, and maritime surveillance. Cloud-based infrastructure also ensures cost efficiency, flexibility, and adaptability for long-term monitoring projects.

Resilience and self-healing functionality

Another breakthrough feature of this system is its self-healing ability, achieved through dynamic switching between spectral modes in the event of fiber breaks. This ensures continuity of sensing operations, maintaining high resilience against network disruptions. Such robustness positions the system as a reliable tool for mission-critical applications where uninterrupted monitoring is essential.

Evaluation under drift scenarios

Comprehensive testing across twelve drift scenarios validates the effectiveness of the proposed method. Results demonstrate exceptional demodulation performance under conditions that severely challenge existing algorithms. These evaluations underscore the potential of Transformer-based architectures to set new benchmarks in high-density, distributed sensing networks, establishing a paradigm shift for next-generation FBG monitoring systems.

Global Particle Physics Excellence Awards

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Hashtags

#Sciencefather, #Reseachawards, #FiberBraggGrating, #FBGSensors, #SpectralDemodulation, #TransformerAI, #NeuralNetworks, #OpticalSensing, #SmartAgriculture, #SoilMonitoring, #GroundwaterDetection, #MaritimeSurveillance, #CloudComputing, #DistributedSensing, #SpectralOverlap, #BidirectionalDrift, #MixedLinewidth, #ModeFusion, #SelfHealingNetworks, #SmartInfrastructure, #AIinSensing, #LandMonitoring,

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