OPTICAL SENSING & SOLUTIONS
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Optical Sensing & Solutions
Wataru Kohno is a Researcher in the Optical Sensing & Solutions 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.
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 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
Knowledge distillation establishes a learning paradigm that leverages both data supervision and teacher guidance. However, determining the optimal balance between learning from data and learning from the teacher is challenging, as some samples may be noisy while others are subject to teacher uncertainty.
NEC Labs America headed to Denver for CVPR 2026, one of the most prestigious gatherings in computer vision, machine learning, and pattern recognition. The IEEE/CVF Conference on Computer Vision and Pattern Recognition brought innovators from around the world to share breakthroughs.
Machine learning is shifting from learning from data alone to learning from both data and teacher models. Beta-KD uses uncertainty-aware Bayesian weighting to train compact multimodal AI without blindly trusting every teacher signal.
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,
Distributed multichannel acoustic sensing (DMAS) enables large-scale sound event classification (SEC), but performance drops when many channels are degraded and when sensor layouts at test time differ from training layouts. We propose a learning-free, physics-informed inpainting frontend based on reverse
Knowledge distillation establishes a learning paradigm that leverages both data supervision and teacher guidance. However, determining the optimal balance between learning from data and learning from the teacher is challenging, as some samples may be noisy while others are subject to teacher uncertainty.
Distributed Fiber-Optic Sensing (DFOS) is a promising technique for large-scale acoustic monitoring. However, its wide variation in installation environments and sensor characteristics causes spatial heterogeneity. This heterogeneity makes it difficult to collect representative training data. It also
We propose a hybrid remote and distributed vibration sensing system based on phase-sensitive optical time-domain reflectometry with collimator-based sensor heads. We demonstrate dual-laser vibrometers that detects nm-scale displacements of remote targets.
Deep neural network (DNN)-based models for environmental sound classification are not robust against a domain to which training data do not belong, that is, out-of-distribution or unseen data. To utilize pretrained models for the unseen domain, adaptation methods, such as finetuning and transfer learning,
Recent advancements in unique acoustic sensing devices and large-scale audio recognition models have unlocked new possibilities for environmental sound monitoring and detection. However, applying pretrained models to non-conventional acoustic sensors results in performance degradation due to domain shifts,
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
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.
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
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
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.
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
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.
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).
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
