進階篩選

Technical category
技術研發單位
    • 5D智慧城市─SmartES平台

      AI & IOT Application FutureTech 5D智慧城市─SmartES平台

      NCREE has originally developed 5D digital space—on the basis of 3D city modelsconnections of different kinds of sensors around the world— is an online to offline virtual space with a combination of rising 5G technology advantages. Collectingreorganizing various 3D cartographic data with Building Information Modeling (BIM), satellite imagery, UAV 3D modeling, LiDar point cloud data, etc., can increase the diversity of the buildinglandscape. 5D+5G smart city platform would accelerate the 5D smart city to become the digital twin of a real city.
    • 突破物聯網的最後一哩:無晶片射頻辨識

      Electronic & Optoelectronics FutureTech 突破物聯網的最後一哩:無晶片射頻辨識

      The major cost of conventional RFID tags comes from a microchip. To reduce the start-up cost, we develop a chipless RFID system that eliminates the use of the chip. This research organize the real-world issues of chipless RFID into four subjects, including the design of chipless tags, chipless RFID readers, reader signal processing,wireless propagation channel. We successfully integrate the four topics, developing a calibration-free, orientation-insensitive,high-capacity chipless RFID system with multi-tag detection capability.
    • Ted-ICU AI Platform

      AI & IOT Application FutureTech Ted-ICU AI Platform

      Ted-ICU AI Platform: (1)Provide a single view of patients' EMRsvital signs (2)Support remote ICU monitoring (3)Online labeling tool & Expandable AI algorithms repository (4)Disease-specific prediction models (5)Standardized EMR templates
    • AI深度壓縮工具鏈及混合定點數CNN運算加速器

      FutureTech AI深度壓縮工具鏈及混合定點數CNN運算加速器

      Assisted by in-house AI deep compression toolchain (ezLabel, ezModel, ezQUANT, ezHybrid-M), the proposed technology supports automatic AI model designoptimization with the integrated performance of 120x model size reduction70x power reduction in 2D CNN model,develops a world-first 1/2/4/8-bit CNN model realized by the developed high efficiency Hybrid fixed point CNN NPU (Hybrid-NPU), which has been verified in Xilinx ZCU102 FPGAachieves the performance up to 2.5 TOPS(8-b)/ 20TOPS(1-b)@28nm technology running at 550MHz4TOPS/W energy efficiency.
    • 深度學習視訊/毫米波雷達感測器融合 RSU 系統

      FutureTech 深度學習視訊/毫米波雷達感測器融合 RSU 系統

      Based on deep learning camera/radar object detectiontracking technology, the proposed road side unit (RSU) system has achieved over 95 vehicle detection accuracy within 100m detection range in the processing performance of 10fps under nVidia Jetson Xavier platform. Compared to the 32-beam lidar based RSU, the proposed RSU achieves 97 reduction of sensor cost that exhibits high competitiveness in deployment cost. The proposed RSU system has been verified in fieldswe are now cooperating with an industry partner to deploy the RSU system in both TainanTao-Yuan cities.