arXiv Machine Learning
Machine Learning for Underwater Optical Wireless Communication Systems: A Comprehensive Survey
Read the original on arXiv Machine Learning →The Flow has not summarised this story yet — read it at arXiv Machine Learning.
Related stories
arXiv Machine Learning
Jul 24
Cross-Domain Generalization in Optical Networks via Joint Contrastive and Classification Learning
arXiv:2607. 20666v1 Announce Type: cross Abstract: The robustness of machine learning techniques across heterogeneous network domains remains an open challenge in optical networks.
arXiv Machine Learning
Jul 31
A Distributed Acoustic Sensing Dataset for Vessel Detection and Localization in Submarine Cable Protection
arXiv:2607. 28306v1 Announce Type: cross Abstract: Recent incidents of accidental damage and suspected sabotage to submarine telecommunication and power cables, particularly in the Baltic Sea, have underscored their vulnerability and the need for continuous monitoring solutions.
arXiv Machine Learning
Sep 25
Towards Deployable Underwater Vessel Classification
The paper presents a compact underwater acoustic classification framework that integrates multi-representation feature engineering, temporal statistical pooling, and lightweight convolutional architectures for acoustic time-frequency and cochlear representations. Experiments on the ShipsEar dataset show a two-layer CNN achieving a macro F1 of 0.9918 and an RBF-SVM reaching 0.9883, but recording provenance issues limit verification of generalisation. When evaluated on the DeepShip dataset with recording-level partitioning, a 157K-parameter CNN attains a macro F1 of 0.7226, while a larger ResNet18 does not improve validation performance, underscoring the need for representation-aware design and rigorous evaluation for deployable systems.
arXiv Machine Learning
Aug 28
Over-The-Air Extreme Learning Machines with Nonlinear Stacked Intelligent Metasurfaces
The paper proposes an eXtremely Large MIMO system that functions as an Extreme Learning Machine for over‑the‑air binary classification. It uses cascaded metasurfaces, with a front layer providing a fixed nonlinear activation and subsequent tunable linear layers implementing trained weights directly in the wave domain. Numerical results on various datasets show that this low‑complexity, wave‑domain architecture achieves classification accuracy comparable to ideal digital models.
arXiv Computer Vision
Sep 15
UniqueShip: Mitigating Data Leakage in Acoustic Ship Classification Benchmark Datasets
arXiv:2609.13659v1 Announce Type: cross Abstract: Underwater Acoustic Target Recognition (UATR) of ships is well-suited for machine learning, yet its progress is hindered by the lack of large, divers...
arXiv Machine Learning
Sep 23
Linear probing enables Ship-Radiated Noise recognition with pretrained audio embeddings
arXiv:2601.08358v2 Announce Type: replace Abstract: Even though the ocean covers the majority of the planet's surface, it remains the least explored ecosystem. As light and radio waves do not propaga...