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

腾讯CDC
博客园 - Franky
MyScale Blog
MyScale Blog
L
LangChain Blog
Martin Fowler
Martin Fowler
Recent Announcements
Recent Announcements
Stack Overflow Blog
Stack Overflow Blog
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
博客园 - 司徒正美
量子位
A
About on SuperTechFans
C
Check Point Blog
大猫的无限游戏
大猫的无限游戏
Last Week in AI
Last Week in AI
小众软件
小众软件
Apple Machine Learning Research
Apple Machine Learning Research
I
InfoQ
V
Visual Studio Blog
Vercel News
Vercel News
B
Blog
爱范儿
爱范儿
aimingoo的专栏
aimingoo的专栏
U
Unit 42

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
Bi-Phasic Quasistatic Brain Communication for Fully Untet...
Baibhab Chatterjee, Mayukh Nath, Gaurav Kumar K, Shulan Xiao, Kr · 2022-05-18 · via eess.SP updates on arXiv.org

Wireless communication using electro-magnetic (EM) fields acts as the backbone for information exchange among wearable devices around the human body. However, for Implanted devices, EM fields incur high amount of absorption in the tissue, while alternative modes of transmission including ultrasound, optical and magneto-electric methods result in large amount of transduction losses due to conversion of one form of energy to another, thereby increasing the overall end-to-end energy loss. To solve the challenge of powering and communication in a brain implant with low end-end channel loss, we present Bi-Phasic Quasistatic Brain Communication (BP-QBC), achieving < 60dB worst-case end-to-end channel loss at a channel length of 55mm, by avoiding the transduction losses during field-modality conversion. BP-QBC utilizes dipole coupling based signal transmission within the brain tissue using differential excitation in the transmitter and differential signal pick-up at the receiver, and offers 41X lower power w.r.t. traditional Galvanic Human Body Communication at a carrier frequency of 1MHz, by blocking any DC current paths through the brain tissue. Since the electrical signal transfer through the human tissue is electro-quasistatic up to several 10's of MHz range, BP-QBC allows a scalable (bps-10Mbps) duty-cycled uplink from the implant to an external wearable. The power consumption in the BP-QBC TX is only 0.52uW at 1Mbps (with 1% duty cycling), which is within the range of harvested body-coupled power in the downlink from an external wearable to the brain implant. Furthermore, BP-QBC eliminates the need for sub-cranial repeaters, as it utilizes quasi-static electrical signals, thereby avoiding any transduction losses. Such low end-to-end channel loss with high data rates would find applications in neuroscience, brain-machine interfaces, electroceuticals and connected healthcare.