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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
Body-Resonance Human Body Communication
Samyadip Sarkar, Qi Huang, Sarthak Antal, Mayukh Nath, Shreyas S · 2024-11-17 · via eess.SP updates on arXiv.org

Seamless interaction between Humans and AI-empowered battery-operated miniaturized electronic devices, exponentially transforming the wearable technology industry while forming an anthropomorphic artificial nervous system for distributed computing around the human body, demands high-speed low-power connectivity. If interconnected via radio frequency (RF) based wireless communication techniques, that being radiative, incur substantial absorption losses from the body during non-line-of-sight scenarios and consume higher power (more than 10s of mW). Although as a promising alternative with its non-radiative nature that resulted in 100X improvement in energy efficiency (sub-10 pJ/bit) and better signal confinement, Electro-Quasistatic Human Body Communication (EQS HBC) incurs moderate path loss (60-70 dB), limited data rate (less than 20 Mbps), making it less suitable for applications demanding fast connectivity like HD audio-video streaming, AR-VR-based products, distributed computing with wearable AI devices. Hence, to meet the requirement of energy-efficient connectivity at 100s of Mbps between wearables, we propose Body-Resonance (BR) HBC, which operates in the near-intermediate field and utilizes the transmission-line-like behavior of the body channel to offer 30X improvement in channel capacity. Our work sheds new light on the wireless communication system for wearables with potential to increase the channel gain by 20 dB with a 10X improvement in bandwidth compared to the EQS HBC for communication over on-body channels (whole-body coverage area). Experimentally demonstrating BR HBC, we presented low-loss (40-50 dB) and wide-band (hundreds of MHz) body channels that are 10X less leaky than radiative wireless communication, hence, can revolutionize the design of wireless communication system for several applications with wearables from healthcare, defense, to consumer electronics.