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Technical category
    • Application of inorganic nanofiber technology to promote the development of biotechnology

      Smart machinerynovel materials FutureTech Application of inorganic nanofiber technology to promote the development of biotechnology

      Inorganic porous nanofibers with surfaceinterface defects are prepared through humidity-controlled electrospinninghigh-temperature annealing technology. Under the irradiation of light sources of different wavelengths (380~780 nm), the bound electrons stored in the valence band can be excited to the conduction band to form free electrons on the surface of the material, generating different intensities of microcurrents, light sensitivitymicrocurrent changes. Because the "inorganic nanofiber" technology has high uniquenesshigh product compatibility, it can be applied to a wide range of markets.
    • (test)Application of inorganic nanofiber technology to promote the development of biotechnology

      Smart machinerynovel materials FutureTech (test)Application of inorganic nanofiber technology to promote the development of biotechnology

      Inorganic porous nanofibers with surfaceinterface defects are prepared through humidity-controlled electrospinninghigh-temperature annealing technology. Under the irradiation of light sources of different wavelengths (380~780 nm), the bound electrons stored in the valence band can be excited to the conduction band to form free electrons on the surface of the material, generating different intensities of microcurrents, light sensitivitymicrocurrent changes. Because the "inorganic nanofiber" technology has high uniquenesshigh product compatibility, it can be applied to a wide range of markets.
    • 基於深度強化學習,智慧化商用Wi-Fi裝置增強通訊效能

      FutureTech 基於深度強化學習,智慧化商用Wi-Fi裝置增強通訊效能

      We develop an asynchronous framework across userkernel spaces for deep learning applications on the improvement of Wi-Fi performanceimplement 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 patternenvironment.