MUMBAI, India, May 1 -- Intellectual Property India has published a patent application (202641049330 A) filed by R. M. K Engineering College, Chennai, Tamil Nadu, on April 17, for 'energy-optimized neuromorphic quantum learning system for secure brain signal transmission in next-generation wireless networks.'
Inventor(s) include Dr. K. Saravanan.
The application for the patent was published on May 1, under issue no. 18/2026.
According to the abstract released by the Intellectual Property India: "The rapid evolution of next-generation wireless networks demands highly efficient, secure, and intelligent systems capable of processing complex biological signals in real time. This paper introduces an energy-optimized neuromorphic quantum learning system designed for secure brain signal transmission in advanced wireless environments. The proposed framework integrates neuromorphic computing principles with quantum learning algorithms to mimic biological neural architectures while leveraging quantum parallelism for enhanced processing capabilities. By incorporating spiking neural networks and quantum-inspired optimization techniques, the system significantly reduces computational overhead and energy consumption compared to conventional AI-based transmission models. Furthermore, a secure transmission protocol is embedded using quantum cryptographic methods, ensuring robust protection against data breaches and unauthorized access. The architecture supports real-time brain-computer interface (BCI) applications, enabling seamless interaction between human neural systems and wireless communication networks. Experimental evaluations demonstrate improved transmission efficiency, reduced latency, and enhanced signal fidelity under varying network conditions. The proposed model holds promise for applications in healthcare monitoring, neuroprosthetics, cognitive communication systems, and intelligent IoT ecosystems. Overall, this research contributes to bridging neuroscience, quantum computing, and wireless communication, paving the way for sustainable and secure brain signal transmission in future network paradigms. Keywords Neuromorphic computing, Quantum learning, Brain-computer interface, Secure wireless communication, Energy optimization, Quantum cryptography."
Disclaimer: Curated by HT Syndication.
