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

The GitHub Blog
The GitHub Blog
Jina AI
Jina AI
月光博客
月光博客
博客园 - Franky
小众软件
小众软件
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
V
Visual Studio Blog
有赞技术团队
有赞技术团队
V
V2EX
IT之家
IT之家
阮一峰的网络日志
阮一峰的网络日志
Stack Overflow Blog
Stack Overflow Blog
H
Help Net Security
Apple Machine Learning Research
Apple Machine Learning Research
腾讯CDC
D
DataBreaches.Net
Hugging Face - Blog
Hugging Face - Blog
Martin Fowler
Martin Fowler
罗磊的独立博客
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
WordPress大学
WordPress大学
C
Check Point Blog
Microsoft Azure Blog
Microsoft Azure Blog
Microsoft Security Blog
Microsoft Security Blog

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
Feed-forward active magnetic shielding
Alain de Cheveigné · 2024-06-20 · via eess.SP updates on arXiv.org

Magnetic fields from the brain are tiny relative to ambient fields which therefore need to be suppressed. The common solution of passive shielding is expensive, bulky and insufficiently effective, thus motivating research into the alternative of active shielding which comes in two flavours: feed-back and feed-forward. In feed-back designs (the most common), corrective fields are created by coils driven from sensors within the area that they correct, for example from the main sensors of an MEG device. In feed-forward designs (less common), corrective fields are driven from dedicated reference sensors outside the area they correct. Feed-forward can achieve better performance than feed-back, in principle, however its implementation is hobbled by an unavoidable coupling between coils and reference sensors, which reduces the effectiveness of the shielding and may affect stability, complicating the design. This paper suggests a solution that relies on a ``decoupling matrix," inserted in the signal pathway between sensors and corrective coils, to counteract the spurious coupling. This allows feed-forward shielding do reduce the ambient field to zero across the full frequency range, in principle, although performance may be limited by other factors such as current noise. The solution, which is fully data-driven and does not require geometric calculations, high-tolerance fabrication, or physical calibration, has been evaluated by simulation, but not implemented in hardware. It might contribute to the deployment of a new generation of measurement systems based on optically-pumped magnetometers (OPM). The lower cost and reduced constraints of those systems are a strong incentive to likewise reduce the cost and constraints of the shielding required to operate them, hence the appeal of active shielding.