NEC Corporation is a global leader in IT and network technologies, providing advanced solutions in AI, biometrics, smart cities, and communications. It drives innovation for social value creation and infrastructure resilience. As part of the broader NEC family, NECLA frequently collaborates with NEC Corporation on next-generation networking, AI, and secure computing systems. Our joint efforts span fundamental research to real-world deployments, including innovations in optical networks, data science platforms, and trusted AI frameworks. Please read about our latest news and collaborative publications with NEC Corporation.

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GFF-Agnostic Black Box Gain Model for non-Flat Input Spectrum

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We present a simple and accurate semi-analytical model predicting the gain of a single-stage erbium-doped fiber amplifier (EDFA) embedded with an unknown gain flattening filter (GFF). Characteristic wavelength-dependent gain coefficients and their scaling laws are extracted with a limited set of simple flat input spectrum measurements at variable temperatures and pump powers. Based on a black box approach, the proposed model provides a precise gain profile estimation of GFF-embedded EDFA for non-flat input spectra in variable temperature and pump power conditions. The accuracy of the presented methodology is validated on an extensive experimental dataset and compared with state-of-the-art gain models based on semi-analytic and solutions.

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Optical Flow Processing for Chirp-Pulse Coherent OTDR

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We propose a novel optical flow processing technique for distributed temperature and strain sensing with the chirped-pulse coherent OTDR. Unlike conventional 1-dimensional cross-correlation methods, the technique treats the 2-dimensional waterfall data as sequential video frames, estimating local shifts through optical flow. The weighted least square approach with adaptive window size enables pixel-level optical flow calculation, providing accurate local shifts via accumulative tracks with enhanced spatial resolution. Preliminary experimental results over 20km fiber demonstrate its effectiveness for dynamic temperature and strain sensing, addressing limitations of traditional methods and improving sensing capabilities.

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DISC: Dynamic Decomposition Improves LLM Inference Scaling (SSI-FM)

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Inference scaling methods often rely on decomposing problems into steps, followed by sampling and selecting the best next steps. However, these steps and their sizes are typically fixed or depend on domain knowledge. We propose dynamic decomposition, a method that adaptively and automatically breaks down solution and reasoning traces into manageable steps during inference. By allocating compute more effectively, particularly by subdividing challenging steps and sampling them more frequently, dynamic decomposition significantly enhances inference efficiency. Experiments on benchmarks such as APPS, MATH, and LiveCodeBench demonstrate that dynamic decomposition outperforms static approaches, including token-level, sentence-level, and single-step decompositions. These findings highlight the potential of dynamic decomposition to improve a wide range of inference scaling techniques.

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DISC: Dynamic Decomposition Improves LLM Inference Scaling (DL4C)

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Inference scaling methods often rely on decomposing problems into steps, followed by sampling and selecting the best next steps. However, these steps and their sizes are typically fixed or depend on domain knowledge. We propose dynamic decomposition, a method that adaptively and automatically breaks down solution and reasoning traces into manageable steps during inference. By allocating compute more effectively—particularly by subdividing challenging steps and sampling them more frequently—dynamic decomposition significantly enhances inference efficiency. Experiments on benchmarks such as APPS, MATH, and LiveCodeBench demonstrate that dynamic decomposition outperforms static approaches, including token-level, sentence-level, and single-step decompositions. These findings highlight the potential of dynamic decomposition to improve a wide range of inference scaling techniques.

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Text-guided Device-realistic Sound Generation for Fiber-based Sound Event Classification

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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, caused by differences in frequency response and noise characteristics from the original training data. In this study, we introduce a text-guided framework for generating new datasets to retrain models specifically for these non-conventional sensors efficiently. Our approach integrates text-conditional audio generative models with two additional steps: (1) selecting audio samples based on text input to match the desired sounds, and (2) applying domain transfer techniques using recorded impulse responses and background noise to simulate the characteristics of the sensors. We demonstrate this process by generating emulated signals for fiber-optic Distributed Acoustic Sensors (DAS), creating datasets similar to the recorded ESC-50 dataset. The generated signals are then used to train a classifier, which outperforms few-shot learning approaches in environmental sound classification.

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Trainingless Adaptation of Pretrained Models for Environmental Sound Classification

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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, are used with rich computing resources, e.g., the graphical processing unit (GPU). However, it is becoming more difficult to keep up with research trends for those who have poor computing resources because state-of-the-art models are becoming computationally resource-intensive. In this paper, we propose a trainingless adaptation method for pretrained models for environmental sound classification. To introduce the trainingless adaptation method, we first propose an operation of recovering time–frequency-ish (TF-ish) structures in intermediate layers of DNN models. We then propose the trainingless frequency filtering method for domain adaptation, which is not a gradient-based optimization widely used. The experiments conducted using the ESC-50 dataset show that the proposed adaptation method improves the classification accuracy by 20.40 percentage points compared with the conventional method.

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1.2 Tb/s/l Real Time Mode Division Multiplexing Free Space Optical Communication with Commercial 400G Open and Disaggregated Transponders

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We experimentally demonstrate real time mode division multiplexing free space optical communication with commercial 400G open and disaggregated transponders. As proof of concept,using HG00, HG10, and HG01 modes, we transmit 1.2 Tb/s/l (3´1l´400Gb/s) error free.

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Field Tests of AI-Driven Road Deformation Detection Leveraging Ambient Noise over Deployed Fiber Networks

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This study demonstrates an AI-driven method for detecting road deformations using Distributed Acoustic Sensing (DAS) over existing telecom fiber networks. Utilizingambient traffic noise, it enables real-time, long-term, and scalable monitoring for road safety.

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Field Trials of Manhole Localization and Condition Diagnostics by Using Ambient Noise and Temperature Data with AI in a Real-Time Integrated Fiber Sensing System

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Field trials of ambient noise-based automated methods for manhole localization and condition diagnostics using a real-time DAS/DTS integrated system were conducted. Crossreferencingmultiple sensing data resulted in a 94.7% detection rate and enhanced anomaly identification.

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High-Sensitivity Forward-Transmission Vibration Sensing for Real-World Event Detection in Urban Fiber Networks

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Publication Date: 4/3/2025 Event: OFC 2025 Reference: Th4C.2: 1-3, 2025 Authors: Jian Fang, NEC Laboratories America, Inc.; Ming-Fang Huang, NEC Laboratories America, Inc.; Scott Kotrla, Verizon; Tiejun J. Xia, Verizon; Glenn A. Wellbrock, Verizon; Jeffrey A Mundt, Verizon; Ting Wang, NEC Laboratories America, Inc.; Yoshiaki Aono, NEC Corporation Abstract: We demonstrated a high-sensitivity forwarding-transmission vibration […]

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