We develop an asynchronous framework across user and kernel spaces for deep learning applications on the improvement of Wi-Fi performance and implement it in the driver of commodity Intel Wi-Fi cards. Under this framework, we apply deep reinforcement learning to developing an intelligent rate adaptation (RA) algorithm (DRL-RA), which can achieve the highest throughput in varying channel conditions given many rate options of current Wi-Fi technologies. Its on-learning capability can learn how to efficiently approach the best rate from the experiences of its common usage pattern and environment.
Intelligent Non-Invasive LVH Risk Prediction Techniques based on ECG Deep Learning
i-Dris: Intelligent Rapid Deployment for Reconfigurable Intelligent Surface in Millimeter Wave Communication System
An Artificial Intelligence Medicine Recognition and Verification System in Hospital Dispensing Room
Development of AI assisted assessment and intervention system based on the culture contextualization for care of people with neurocognitive disoder.
Technology maturity:Experiment stage
Exhibiting purpose:Display of scientific results
Trading preferences:Technical license/cooperation、Negotiate by self
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