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

J
Java Code Geeks
Engineering at Meta
Engineering at Meta
GbyAI
GbyAI
MongoDB | Blog
MongoDB | Blog
Blog — PlanetScale
Blog — PlanetScale
腾讯CDC
U
Unit 42
freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More
Apple Machine Learning Research
Apple Machine Learning Research
M
MIT News - Artificial intelligence
人人都是产品经理
人人都是产品经理
Hugging Face - Blog
Hugging Face - Blog
MyScale Blog
MyScale Blog
小众软件
小众软件
博客园 - 三生石上(FineUI控件)
N
Netflix TechBlog - Medium
阮一峰的网络日志
阮一峰的网络日志
博客园 - Franky
Recent Announcements
Recent Announcements
A
About on SuperTechFans
Stack Overflow Blog
Stack Overflow Blog
The GitHub Blog
The GitHub Blog
D
Docker
H
Hackread – Cybersecurity News, Data Breaches, AI and More

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
Radar Cross Section Reduction of Microstrip Patch Antenna...
Syamly S. B, Job Chunkath · 2023-06-30 · via eess.SP updates on arXiv.org

Radar cross section (RCS) reduction has become one of the critical research areas in recent years. The RCS of the target should be small to avoid detection. Different methods are used to reduce RCS, but the major challenge with many RCS minimization methodologies is that, it may deteriorate some antenna parameters. When antenna mode RCS is considered; structural mode RCS, and antenna parameters are critical, as the structure should be an antenna and a RCS reducing structure simultaneously. The techniques like applying Radar Absorption Material (RAM) entirely over the target, deployment of Energy Band Gap (EBG) structures, the use of passive, active cancellation, and polarization conversion are prevalent methods to reduce RCS. The manifestation of metamaterial property in an antenna results in the antenna's electromagnetic characteristics becoming negative for a particular bandwidth. Thus the RCS of the antenna can be reduced to a minimum range by loading the metamaterial structures. This paper discusses the application of polarization conversion method (PCM), L-structured and Square-structured fractal metamaterial antenna for RCS reduction. This paper reports the simulation, fabrication, and testing of the above antennas with their performance comparison. The antennas are designed for 4.3GHz frequency with a total dimension of 80mmx80mmx1.6mm. Antenna parameters like return loss, gain, radiation pattern, and bandwidth are analyzed along with the RCS. The L-structured metamaterial antenna implemented has a 29.37% larger bandwidth than the reference patch antenna with a gain of 2.94dB with a return loss of -28.28dB.