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  1. ; ; ; ; ; ( , IEEE Conference on Computer Vision and Pattern Recognition)
    Free, publicly-accessible full text available June 19, 2022
  2. ; ; ; ; ( , International FLAIRS Conference (FLAIRS))
    Accepted Manuscript Full Text Available
  3. ; ; ; ; ; ; ( , Operations research)
    Accepted Manuscript Full Text Available
  4. ; ; ; ; ; ; ; ; ; ; et al ( , Geschaftsbericht fur das Geschaftsjahr)
    Siciliano, B. ; Laschi, C. ; Khatib, O. (Ed.)
    Full Text Available 
  5. ; ; ; ; ; ( , IEEE 2020 High Performance Extreme Computing Conference)
    Accepted Manuscript Full Text Available
  6. ; ; ; ; ; ; ; ( , Proceedings of the IEEERSJ International Conference on Intelligent Robots and Systems)
    In this paper, we propose a real-time deep-learning approach for determining the 6D relative pose of Autonomous Underwater Vehicles (AUV) from a single image. A team of autonomous robots localizing themselves, in a communicationconstrained underwater environment, is essential for many applications such as underwater exploration, mapping, multirobot convoying, and other multi-robot tasks. Due to the profound difficulty of collecting ground truth images with accurate 6D poses underwater, this work utilizes rendered images from the Unreal Game Engine simulation for training. An image translation network is employed to bridge the gap between the rendered and the real images producing synthetic imagesmore »for training. The proposed method predicts the 6D pose of an AUV from a single image as 2D image keypoints representing 8 corners of the 3D model of the AUV, and then the 6D pose in the camera coordinates is determined using RANSACbased PnP. Experimental results in underwater environments (swimming pool and ocean) with different cameras demonstrate the robustness of the proposed technique, where the trained system decreased translation error by 75.5\% and orientation error by 64.6\% over the state-of-the-art methods.« less
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  7. ( , Nature astronomy)
    Full Text Available 
  8. ; ; ; ; ; ; ; ; ; ; et al ( , Energy and buildings)
    Heating, ventilation and air-conditioning (HVAC) systems have been adopted to create comfortable, healthy and safe indoor environments. In the control loop, the technical feature of the human demand-oriented supply can help operate HVAC effectively. Among many technical options, real time monitoring based on feedback signals from end users has been frequently reported as a critical technology to confirm optimizing building performance. Recent studies have incorporated human thermal physiologysignals and thermal comfort/discomfort status as real-time feedback signals. A series of human subject experiments used to be conducted by primarily adopting subjective questionnaire surveys in a lab-setting study, which is limited inmore »the application for reality. With the help of advanced technologies, physiological signals have been detected, measured and processed by using multiple technical formats, such as wearable sensors. Nevertheless, they mostly require physical contacts with the skin surface in spite of the small physical dimension and compatibility with other wearable accessories, such as goggles, and intelligent bracelets. Most recently, a low cost small infrared camera has been adopted for monitoring human facial images, which could detect the facial skin temperature and blood perfusion in a contactless way. Also, according to latest pilot studies, a conventional digital camera can generate infrared images with the help of new methods, such as the Euler video magnification technology. Human thermal comfort/discomfort poses can also be detected by video methods without contacting human bodies and be analyzed by the skeleton keypoints model. In this review, new sensing technologies were summarized, their cons and pros were discussed, and extended applications for the demand-oriented ventilation were also reviewed as potential development and applications.« less
    Full Text Available 
  9. ; ; ; ; ; ; ; ; ; ; et al ( , 53rd IEEE/ACM International Symposium on Microarchitecture (MICRO))
    Accepted Manuscript Full Text Available
  10. ; ; ; ; ( , IEEE International Conference on Tools with Artificial Intelligence (ICTAI))
    Accepted Manuscript Full Text Available