Visban Announces V – Mesh : AI – Powered Millimeter Wave Subnetworks That Reduce Deployment Costs , Extend Signal Range , And Boost Reliability **Here is a possible title I came up with:** **Visban’s V

Artistic representation for Visban Announces V - Mesh : AI - Powered Millimeter Wave Subnetworks That Reduce Deployment Costs , Extend Signal Range , And Boost Reliability **Here is a possible title I came up with:** **Visban's V

Visban Co., Ltd. is a renowned leader in wireless communications and network solutions, with a strong track record of innovation. Visban’s V-Mesh technology, a revolutionary approach to millimeter wave network deployment, has the potential to significantly impact the future of high-frequency wireless networks.

β€œV-Mesh will enable the continued evolution of wireless communications,” said S.B. Cha, CEO and co-founder of Visban. β€œOur goal is to provide affordable and dependable mmWave connectivity that meets the demands of advanced future applications such as AI and AR/VR, both outside and inside buildings.” V-Mesh and RF-on-Glass Devices: Key to Deploying Reliable Millimeter Wave Networks Visban’s use of glass substrates in its RF devices is integral to its V-Mesh technology. Glass enables the creation of single-sided and double-sided devices, and the manufacturing processes allow for highly precise structures that enhance sensitivity and reduce signal losses. Visban also uses heterogeneous integration of ICs and other components on glass, ensuring low-cost production.

This collaboration aims to integrate RF-on-Glass technology into the next generation of smartphones and other mobile devices. This technology allows for wireless charging and data transfer without the need for external antennas or cables. The development of these devices is being driven by the need for a more seamless user experience.

We believe that this technology has the potential to revolutionize the way we approach healthcare, particularly in the areas of diagnostics and treatment. Visban’s technology is based on a novel approach to using artificial intelligence (AI) and machine learning (ML) to analyze medical images. This approach allows for the earlier detection of diseases and more accurate diagnoses.

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