Verizon and NEC Laboratories America turned live telecom fiber into a city-wide sensing network. By repurposing existing optical infrastructure, the system detects vibrations along fiber routes, cutting cable damage incidents and improving response times. The approach also enables AI models that adapt to new, unseen routes without manual labeling.
Mix-CLAP from NEC Laboratories America delivers near-Transformer accuracy for sound event classification at a fraction of the compute cost, using dual lightweight encoders and adaptive, noise-aware inference for real-world edge deployment.
We demonstrate a low-cost wavelength selectable distributed vibration sensor to monitor the whole routes in a 1×32 PON architecture with a unified sensitivity. The results obtained from the lab and a field testbed are presented.
Distributed Acoustic Sensing may suffer severe cross-day domain shift. We bridge this gap by encoding 1D phasesignals into physics-informed 3-channel gradient tensors andadapting Vision Foundation Models (ViT-B/16), achieving superior threat detection over baselines.
We present field trial on urban monitoring over telecom networks using Rayleigh-based distributed temperature/ strain sensor (DTSS), showing the capability of detecting subtle infrastructure signatures including manhole locations, road traffic, sprinklers and underground leakage events.
We propose to jointly optimize path and gateway selection to achieve load balancing for mobile orbital domain-based hierarchical routing in satellite networks. The load at bottleneck gateway satellites can be reduced by 22% on average.
We demonstrate distributed fiber-optic acoustic sensing that employs a mechanical synchronizer to achieve sample-level synchronization of signals across independent sensors.This enables phase-noise suppression and bandwidth extension in standard multi-fiber optical cables without hardware modification.
NEC Laboratories America is proud to participate in OECC 2026, the 31st Opto-Electronics and Communications Conference, taking place in Busan, South Korea. We look forward to connecting with the international photonics and communications community and sharing the work we’re doing to shape the next generation of optical networks.
Real-world deployment requires sound event and acoustic scene classification systems to remain reliable in noisy, diverse environments on resource-constrained devices. Although contrastive language-audio pretraining (CLAP) models with Transformer-based audio encoders achieve strong zero-shot performance, their computational cost hinders deployment. In this paper, we propose Mix-CLAP, a computationally efficient, noise-aware CLAP model with knowledge-distilled audio encoders. Our method includes: (1) a two-stage knowledge distillation from teacher embeddings to two lightweight student encoders?one on clean audio, the other on noisy audio, and (2) adaptive inference that combines their embeddings together with a fusion parameter and minimizes the parameterized entropy at test time. Experiments show that Mix-CLAP with MobileNetV3-based audio encoders greatly improves computational efficiency, while achieving a comparable average accuracy of 52.58% to the Transformer-based CLAP model at 52.83% on the recorded ESC50 datasets with different devices including microphones and fiber-optic distributed acoustic sensors under diverse conditions, making it suitable for real-world, resource-constrained applications.
Distributed fiber optic sensing (DFOS) has emerged as a promising technology for wide-area monitoring by utilizing existing telecom cables as large-scale sensing media. This paper explores three sensing modalities, backscattering-based sensing, forward-transmission-based sensing, and hybrid sensing, and discusses their respective benefits, challenges, and application domains. Backscattering sensing demonstrates strong potential for applications such as road traffic monitoring, pavement condition assessment, intrusion detection, and cabledamage prevention but is constrained in amplified dense wavelength division multiplexing (DWDM) networks. Forward-transmission sensing enables sensing over operational telecom links with in-line amplification, extending sensing reach, although it involves trade-offs in spatial resolution and localization accuracy. To address these challenges, a hybrid sensing architecture that integrates backscattering and forward-transmission techniques is introduced, achieving enhanced sensing distance while maintaining high sensitivity and localization performance.In addition, this work incorporates artificial intelligence (AI) through a locally adaptive anomaly detection (LAAD) framework based on self-supervised representation learning. By leveraging location-based pretext tasks and unlabeled data, the proposed AI approach enables efficient adaptation across heterogeneous fiber routes and operational environments, significantly reducing reliance on labeled data while improving cross-domain generalization. Field trials over deployed telecom networks validate the feasibility and effectiveness of the proposedsensing and AI framework, demonstrating scalable, telecom-compatible DFOS for real-world infrastructure monitoring and intelligent network operations.
