Wataru Kohno NEC Labs AmericaWataru Kohno is a Researcher in the Optical Networking and Sensing Department at NEC Laboratories America. He earned his Ph.D. in Physics from Hokkaido University in Japan, where he built a strong foundation in the physics and engineering principles underlying optical communications.

He has also authored a number of research papers in condensed matter physics, where he investigated fundamental problems in electronic structures, correlated electron systems, and quantum materials, contributing to a deeper theoretical understanding of how microscopic physical principles give rise to novel material properties; a selection of these works can be found on his Google Scholar profile. With his background in fundamental theoretical condensed matter physics, his work focuses on distributed acoustic sensing (DAS) and fiber-optic communication technologies, where he develops methods to transform optical fibers into highly sensitive, passive sensors capable of detecting vibration, movement, and pressure across vast geographic areas. These innovations enable real-time situational awareness without requiring active electronics at the sensing points, making large-scale monitoring systems both scalable and unobtrusive. His research supports a range of critical applications, including perimeter security, seismic monitoring, and infrastructure protection. His academic training continues to inform his applied research at NEC, where he bridges theoretical advances in photonics with real-world sensing challenges.

At NEC Laboratories America, he has contributed to multiple cutting-edge projects that expand the capabilities of distributed acoustic sensing. His recent work includes the development of advanced vibrometry techniques, recognition systems that adapt fiber sensing for downstream applications, and AI-enhanced methods for real-time detection in critical infrastructure such as power grids. By combining deep expertise in optics with practical engineering approaches, his research is helping create intelligent sensing platforms that can deliver reliable, real-time monitoring solutions for global security and infrastructure needs.

Posts

CLAP-S: Support Set Based Adaptation for Downstream Fiber-optic Acoustic Recognition

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Contrastive Language-Audio Pretraining (CLAP) models have demonstrated unprecedented performance in various acoustic signal recognition tasks. Fiber-optic-based acoustic recognition is one of the most important downstream tasks and plays a significant role in environmental sensing. Adapting CLAP for fiber-optic acoustic recognition has become an active research area. As a non-conventional acoustic sensor, fiberoptic acoustic recognition presents a challenging, domain-specific, low-shot deployment environment with significant domain shifts due to unique frequency response and noise characteristics. To address these challenges, we propose a support-based adaptation method, CLAP-S, which linearly interpolates a CLAP Adapter with the Support Set, leveraging both implicit knowledge through fine-tuning and explicit knowledge retrieved from memory for cross-domain generalization. Experimental results show that our method delivers competitive performance on both laboratory recorded fiber-optic ESC-50 datasets and a real-world fiber optic gunshot-firework dataset. Our research also provides valuable insights for other downstream acoustic recognition tasks.

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Underwater Acoustic OFDM Transmission over Optical Fiber with Distributed Acoustic Sensing

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We demonstrate fiber-optic acoustic data transmission using distributed acoustic sensing technology in an underwater environment. An acoustic orthogonal frequencydivisionmultiplexing (OFDM) signal transmitted through a fiber-optic cable deployed in a standard 40-meter-scale underwater testbed.

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Multi-Event Distributed Forwarding Sensing with Dual-Sensor Adaptive Beamforming

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We present adaptive beamforming techniques to forward-transmission multi-event vibration sensing in environments with interference and jamming. Experimental validation over 100km fiber demonstrates significant improvements on signal reconstruction, noise reduction, and interference rejection from other locations.

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CLAP-S: Support Set Based Adaptation for Downstream Fiber-optic Acoustic Recognition

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Contrastive Language-Audio Pretraining (CLAP) models have demonstrated unprecedented performance in various acoustic signal recognition tasks. Fiber optic-based acoustic recognition is one of the most important downstream tasks and plays a significant role in environmental sensing. Adapting CLAP for fiber-optic acoustic recognition has become an active research area. As a non-conventional acoustic sensor, fiber-optic acoustic recognition presents a challenging, domain-specific, low-shot deployment environment with significant domain shifts due to unique frequency response and noise characteristics. To address these challenges, we propose a support-based adaptation method, CLAP-S, which linearly interpolates a CLAP Adapter with the Support Set, leveraging both implicit knowledge through fine-tuning and explicit knowledge retrieved from memory for cross-domain generalization. Experimental results show that our method delivers competitive performance on both laboratory-recorded fiber-optic ESC-50 datasets and a real-world fiber-optic gunshot-firework dataset. Our research also provides valuable insights for other downstream acoustic recognition tasks.

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Remote Sensing for Power Grid Fuse Tripping Using AI-Based Fiber Sensing with Aerial Telecom Cables

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For the first time, we demonstrate remote sensing of pole-mounted fuse-cutout blowing in a power grid setup using telecom fiber cable. The proposed frequency-based AI model achieves over 98% detection accuracy using distributed fiber sensing data.

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Low-rank Constrained Multichannel Signal Denoising Considering Channel-dependent Sensitivity Inspired by Self-supervised Learning for Optical Fiber Sensing

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Optical fiber sensing is a technology wherein audio, vibrations, and temperature are detected using an optical fiber; especially the audio/vibrations-aware sensing is called distributed acoustic sensing (DAS). In DAS, observed data, which is comprised of multichannel data, has suffered from severe noise levels because of the optical noise or the installation methods. In conventional methods for denoising DAS data, signal-processing- or deep-neural-network (DNN)-based models have been studied. The signal-processing-based methods have the interpretability, i.e., non-black box. The DNN-based methods are good at flexibility designing network architectures and objective functions, that is, priors. However, there is no balance between the interpretability and the flexibility of priors in the DAS studies. The DNN-based methods also require a large amount of training data in general. To address the problems, we propose a DNN-structure signal-processing-based denoising method in this paper. As the priors of DAS, we employ spatial knowledge; low rank and channel-dependent sensitivity using the DNN-based structure.The result of fiber-acoustic sensing shows that the proposed method outperforms the conventional methods and the robustness to the number of the spatial ranks. Moreover, the optimized parameters of the proposed method indicate the relationship with the channel sensitivity; the interpretability.

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Distributed Fiber-Optic Sensor as an Acoustic Communication Receiver Array

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A novel acoustic transmission technique using distributed acoustic sensors is introduced. By choosing better incident angles for smaller fading and employing an 8- channel beamformer, over 10KB data is transmitted at a 6.4kbps data rate.

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OFDM Signal Transmission Using Distributed Fiber-Optic Acoustic Sensing

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Acoustic data transmission with the Orthogonal Frequency Division Multiplexing (OFDM) signal has been demonstrated using a Distributed Acoustic Sensor (DAS) based on Phase-sensitive Optical Time-Domain Reflectometry (?-OTDR).

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Template Matching Method with Distributed Acoustic Sensing Data and Simulation Data

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We propose a new method to detect acoustic signals by matching distributed acoustic sensing data with simulation. In the simulation of the dynamic strain on an optical fiber, the optical fiber layouts and the gauge length are properly incorporated. We apply the proposed method to the acoustic-source localization and demonstrate the method localizes the source accurately even under the layouts which include the straight optical fiber for the sensing points with the large gauge-length settings.

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