惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

博客园 - 司徒正美
月光博客
月光博客
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
美团技术团队
WordPress大学
WordPress大学
罗磊的独立博客
Last Week in AI
Last Week in AI
人人都是产品经理
人人都是产品经理
爱范儿
爱范儿
MongoDB | Blog
MongoDB | Blog
J
Java Code Geeks
H
Hackread – Cybersecurity News, Data Breaches, AI and More
H
Help Net Security
S
SegmentFault 最新的问题
C
Check Point Blog
酷 壳 – CoolShell
酷 壳 – CoolShell
腾讯CDC
Engineering at Meta
Engineering at Meta
The GitHub Blog
The GitHub Blog
F
Fortinet All Blogs
D
DataBreaches.Net
雷峰网
雷峰网
GbyAI
GbyAI
宝玉的分享
宝玉的分享

eess.SP updates on arXiv.org

ECG-biometrics-bench: A Unified Framework for Reproducible Benchmarking of ECG Biometrics Physiology-Aware Masked Cross-Modal Reconstruction for Biosignal Representation Learning Towards Improving Speaker Distance Estimation through Generative Impulse Response Augmentation Federated Learning with Hypergradient-based Online Update of Aggregation Weights Soft Graph Diffusion Transformer for MIMO Detection SPLICE: Latent Diffusion over JEPA Embeddings for Conformal Time-Series Inpainting Sequential Inference for Gaussian Processes: A Signal Processing Perspective Statistical Channel Fingerprint Construction for Massive MIMO: A Unified Tensor Learning Framework Recent Advances in mm-Wave and Sub-THz/THz Oscillators for FutureG Technologies Cross-Subject Generalization for EEG Decoding: A Survey of Deep Learning Methods Super-resolution Multi-signal Direction-of-Arrival Estimation by Hankel-structured Sensing and Decomposition Hankel and Toeplitz Rank-1 Decomposition of Arbitrary Matrices with Applications to Signal Direction-of-Arrival Estimation Adaptive Transform Coding for Semantic Compression EdgeSpike: Spiking Neural Networks for Low-Power Autonomous Sensing in Edge IoT Architectures Sparse Graph Learning from Sparse Data via Fiedler Number Maximization A Deep Learning Model for Battery State Prediction towards Intelligent Energy Management Transfer Learning for Tonal Noise Prediction in VRF Units Using Thermodynamic and Vibration Signals EVT-Based Generative AI for Tail-Aware Channel Estimation Monitoring exposure-length variations in submarine power cables using distributed fiber-optic sensing BandRouteNet: An Adaptive Band Routing Neural Network for EEG Artifact Removal Phase-Separated Complex Hilbert PCA on Markerless 3D Pose Estimation Data: A Global Phase Network and Its Extension to a Continuous Field on the Body Surface Selective Correlation Based Knowledge Distillation for Ground Reaction Force Estimation Deep Learning-Enabled Dissolved Oxygen Sensing in Biofouling Environments for Ocean Monitoring Speech Enhancement Based on Drifting Models Robust and Clinically Reliable EEG Biomarkers: A Cross Population Framework for Generalizable Parkinson's Disease Detection An AI-Based Supervisory Measurement Integrity Validation Layer for Cyber-Resilient AC/DC Protection in Inverter-Based Microgrids Explainable AI in Speaker Recognition -- Making Latent Representations Understandable Time-Localized Parametric Decomposition of Respiratory Airflow for Sub-Breath Analysis NAKUL-Med: Spectral-Graph State Space Models with Dynamics Kernels for Medical Signals An Algorithm for On-Sensor Agnostic Detection of Changes in Human Activity for Ultra-Low-Power Applications
Empirical characterization of the Translational acoustic-...
[Submitted on 24 Jun 2026] · 2026-06-25 · via eess.SP updates on arXiv.org

View PDF HTML (experimental)

Abstract:Translational acoustic-radio frequency (TARF) communication paves the way for translating information from an underwater acoustic signal to the over-the-air (OTA) electromagnetic receiver through the medium interface. The study and characterization of the channel is essential for establishing a reliable communication link. Although channel modeling has been extensively studied for OTA and underwater channels, the amplitude characteristics of the TARF cross-medium channel have not been investigated in comparison with well-known distributions to date. In this work, we first refine the signal model to incorporate the effects of the wavefront-water surface interactions. With the help of numerical and graphical methods, we then attempt to characterize the cross-medium channel with empirical data using existing models developed for OTA and underwater channels. We further evaluate channel linearity and time invariance empirically. Observations from these studies over multiple experiments are detailed with additional discussions that enable better channel characterization to develop reliable and consistent cross-medium TARF communication in challenging scenarios.

Submission history

From: Ann Pamela Mathew [view email]
[v1] Wed, 24 Jun 2026 11:21:00 UTC (453 KB)