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    • Ckip Lab

      Ckip Lab

      Textual Advertisement Generator: Given any limited specifics of any product, AI Advertisement Producer can automatically generate tons of top-quality descriptions and advertisements for the product in just one second. And not just one copy is produced. With deep learning and natural language processing technologies learned from millions of existing samples, our AI model can produce various styles of advertisements at the same time for users to select. It will be a big helper or a virtual brainstorming partner for any brands or advertisers to create their advertisements.
    • AI農情調查之UAV群眾協作平台

      AI農情調查之UAV群眾協作平台

      AI農情調查之UAV群眾協作平台後台支援自動鑲嵌建模,更具備四項突破技術:(1)巨量影像格化技術;(2)平行運算技術;(3)任務規格標準化;(4)UAV任務媒合。致力於打造空中UBER協作服務,未來能應用於農作物分佈調查、大範圍災情調查、農業保險、農地違法使用調查與休耕補助調查等面向。
    • Computer Vision Research Center, National Yang-Ming Chiao-Tung university

      Computer Vision Research Center, National Yang-Ming Chiao-Tung university

      Development of AI Platform for Smart Drone - Intelligent Flight: Due to its high mobility and the ability to fly in the sky, the drone has inspired more and more innovative applications/services in recent years. The goal of this project is to resolve the problem of blindly flying an unmanned aerial vehicle (UAV, which a drone in our case) when it is out of human sight or the range of wireless communication, and three major research and development directions will be considered in this project. Three artificial intelligence (AI) technologies, namely, smart sensing, smart control, and smart simulation, are applied in this project. Smart sensing - a flight system is developed, which can avoid the obstacles, complete a flight mission, and land safely. Smart control - an intelligence flight control system and a light-weighted somatosensory vest are developed. Smart simulation - a cost-effective training system and a 3D model simplification method are designed.
    • Indoor Radar Sensing System

      Indoor Radar Sensing System

      The proposed indoor radar sensing system includes two enabling technology, activity recognition and respiration rate estimation. In the former, the proposed framework consists of four major components: denosing, enhanced voxelization, data augmentation, and dual-view machine-learning to lead to accurate and efficient human-activity recognition. In the latter, the proposed system leverages the variation of the phase information of a specific frequency bin of the range profiles, and proposes a dynamic adaptive respiration waveform filtering algorithm to improve accuracy.
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