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

Martin Fowler
Martin Fowler
博客园 - 【当耐特】
GbyAI
GbyAI
M
MIT News - Artificial intelligence
Microsoft Azure Blog
Microsoft Azure Blog
A
About on SuperTechFans
罗磊的独立博客
Apple Machine Learning Research
Apple Machine Learning Research
腾讯CDC
F
Fortinet All Blogs
IT之家
IT之家
WordPress大学
WordPress大学
Cyber Security Advisories - MS-ISAC
Cyber Security Advisories - MS-ISAC
Last Week in AI
Last Week in AI
Google DeepMind News
Google DeepMind News
Jina AI
Jina AI
让小产品的独立变现更简单 - ezindie.com
让小产品的独立变现更简单 - ezindie.com
D
DataBreaches.Net
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
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
H
Help Net Security
V
Visual Studio Blog
小众软件
小众软件
Y
Y Combinator 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
On Properties of Phase-Conjugation Focusing for Large Int...
Jiawang Li · 2025-05-15 · via eess.SP updates on arXiv.org

Large intelligent surface (LIS) is one promising path to leverage 6G performance in sub-10 GHz bands. This two-part paper explores the properties of phase-conjugation focusing for a simplified LIS setup with a two-dimensional (2D) circular antenna array and a user antenna located within the array aperture in the same plane. In Part I of this article, we assume vertical polarization for all antenna elements, whereas Part II assumes horizontal polarization. In Part I, we focus on the effect of array radius on the peak gain, 3 dB focusing width, and sidelobes for two types of circular arrays. The numerical results show that the gain minimum is located at the array center. The peak gain varies by less than 0.5 dB for focal points located within 2 from the array center. Similarly, the focal width and sidelobe level are also stable within this region, irrespective of array radius. From 2 from the center to the array edge, the closer proximity of the focal points to some array elements than other elements results in more drastic changes in these NFF properties. Finally, full-wave simulation using Ansys HFSS is used to partially validate the numerical results.