| _version_ | 1866901163810488320 |
|---|---|
| author | E.Suresh, E.Dhanasri, M.Manasa, G.Srinu, A.Madhusilpa and P.Vishupriya |
| author_facet | E.Suresh, E.Dhanasri, M.Manasa, G.Srinu, A.Madhusilpa and P.Vishupriya |
| contents | <p>The transition to sixth- generation (6G) communication networks is unlocking new horizons for applications including immersive extended reality, autonomous mobility, holographic telepresence, and massive Internet of Things (IoT). Such demands can be addressed with antenna systems that provide increased efficiency, faster agility, and consistent performance across difficult environments. Conventional design techniques, although successful in previous generations, are increasingly constricted when dealing with the intricacy of multi-band, reconfigurable, and high-frequency antennas. Here, we discuss how artificial intelligence (AI) can transform the antenna design process. With the integration of data-driven learning and physics-driven understanding, AI methods such as machine learning and reinforcement learning can accelerate design cycles, enhance precision, and optimize factors such as gain, beamforming, and radiation patterns. We also present case studies at millimeter-wave (mmWave) and terahertz (THz) frequencies that show quantifiable improvements over traditional methods. The research indicates that AI-based approaches not only improve performance but also offer adaptable and scalable solutions that best fit the changing demands of 6G and beyond.</p> <p> </p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_21474_IJAR01_22243 |
| institution | Zenodo |
| language | |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | THE WEB AND IOT-BASED SMART NOTICE SYSTEM FOR PLACEMENT ELIGIBILITY FILTERING E.Suresh, E.Dhanasri, M.Manasa, G.Srinu, A.Madhusilpa and P.Vishupriya DeepfakeDetection Digital Forensics CNN Error Level Analysis Lip- Sync Analysis Multimedia Authentication Cybersecurity. <p>The transition to sixth- generation (6G) communication networks is unlocking new horizons for applications including immersive extended reality, autonomous mobility, holographic telepresence, and massive Internet of Things (IoT). Such demands can be addressed with antenna systems that provide increased efficiency, faster agility, and consistent performance across difficult environments. Conventional design techniques, although successful in previous generations, are increasingly constricted when dealing with the intricacy of multi-band, reconfigurable, and high-frequency antennas. Here, we discuss how artificial intelligence (AI) can transform the antenna design process. With the integration of data-driven learning and physics-driven understanding, AI methods such as machine learning and reinforcement learning can accelerate design cycles, enhance precision, and optimize factors such as gain, beamforming, and radiation patterns. We also present case studies at millimeter-wave (mmWave) and terahertz (THz) frequencies that show quantifiable improvements over traditional methods. The research indicates that AI-based approaches not only improve performance but also offer adaptable and scalable solutions that best fit the changing demands of 6G and beyond.</p> <p> </p> |
| title | THE WEB AND IOT-BASED SMART NOTICE SYSTEM FOR PLACEMENT ELIGIBILITY FILTERING |
| topic | DeepfakeDetection Digital Forensics CNN Error Level Analysis Lip- Sync Analysis Multimedia Authentication Cybersecurity. |
| url | https://doi.org/10.21474/IJAR01/22243 |