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

G
Google Developers Blog
有赞技术团队
有赞技术团队
WordPress大学
WordPress大学
博客园 - 司徒正美
D
Docker
B
Blog
V
Visual Studio Blog
Blog — PlanetScale
Blog — PlanetScale
U
Unit 42
S
SegmentFault 最新的问题
小众软件
小众软件
J
Java Code Geeks
美团技术团队
腾讯CDC
MyScale Blog
MyScale Blog
爱范儿
爱范儿
H
Help Net Security
宝玉的分享
宝玉的分享
Microsoft Azure Blog
Microsoft Azure Blog
A
About on SuperTechFans
Google DeepMind News
Google DeepMind News
钛媒体:引领未来商业与生活新知
钛媒体:引领未来商业与生活新知
博客园 - 三生石上(FineUI控件)
博客园 - 【当耐特】

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
EMC Limit Level Guidelines for In-System Interference wit...
Giorgi Tsintsadze, Haran Manoharan, Aaron Harmon, Daniel Commero · 2025-09-16 · via eess.SP updates on arXiv.org

Because GPS signals are weak, electronic systems and components that are placed near GPS receivers can easily cause disruptive electromagnetic interference through their unintended radiated emissions. In this paper, EMC limit level guidelines are presented for electronics that are intended to be placed near to GPS receivers, as often happens in automotive and other applications. One of the challenges of defining limit-levels for systems intended to be integrated with GPS receivers is that the impact of noise at the input of the receiver may vary substantially depending on the form of the noise due to the correlator function implemented by GPS receiver. The quality of the correlated signal is typically represented using the carrier-to-noise ratio ($C / N_0$). A theoretical model predicting the degredation of the carrier-to-noise ratio with radio frequency interference is presented in this paper and is validated with realistic noise sources. The model is then used to develop guidelines to assess the impact of unintended emissions from electronic devices on nearby GPS receivers based on the frequency, bandwidth, and magnitude of the noise. These guidelines provide a more nuanced method of evaluating emissions than simple limit lines that are used by many emissions standards.