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: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.
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.
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: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: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...
The paper addresses the spectral bias of Fourier Neural Operators (FNO) in predicting underwater acoustic transmission loss. It introduces a Spectral‑Spatial Residual Learning (S2RL) framework that first uses a spectral global propagator for coarse predictions and then a spatial local refiner to recover high‑frequency details. Experiments on a South China Sea dataset show that S2RL outperforms FNO baselines while keeping inference times in the millisecond range.
arXiv:2602. 11834v2 Announce Type: replace-cross Abstract: While machine learning (ML)-based receiver algorithms have received a great deal of attention in the recent literature, they often suffer from poor scaling with increasing spatial multiplexing order and lack of explainability and generalization.
arXiv:2507. 09627v3 Announce Type: replace-cross Abstract: Next-generation wireless technologies such as 6G aim to meet demanding requirements such as ultra-high data rates, low latency, and enhanced connectivity.
arXiv:2606. 02341v1 Announce Type: cross Abstract: Underwater acoustic classification has a wide array of oceanic applications, but faces challenges due to an increasingly complex acoustic environment.
The paper introduces Wave-BLS, a robust Broad Learning System that replaces the traditional squared error loss with an asymmetric, bounded, and smooth wave loss function. This change allows controlled penalization of large errors and eliminates the need for matrix inversion by using a Nesterov accelerated gradient scheme. Experiments on 30 UCI datasets show that Wave-BLS consistently outperforms classical BLS and other robust variants, with statistical tests confirming the significance of the improvements and demonstrating greater resilience to noise and outliers.
arXiv:2607. 00860v1 Announce Type: cross Abstract: Millimeter-wave (mmWave) beam alignment plays a critical role in next-generation wireless systems, yet its efficient implementation remains challenging.