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information sciences期刊multi-modal sensor fusion专栏征稿
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Special Issue on Multi-modal Information Fusion and Perception for Real-World Applications With the rapid development of advanced multi-disciplinary technologies for acquiring, storing and transmitting massive Big Data, multi-modal information processing has attracted growing attention recently, in both academia and industry. Multi-modal big data research challenges, particularly related to fusion and perception, are ubiquitous in diverse domains such as Internet of Things, robotics, manufacturing, engineering, Natural Language Processing (NLP) and medical informatics applications. In practice, humans make extensive use of real-time Big data simultaneously sourced from multiple cognitive sensory modalities such as sight, sound, touch, smell, self-motion and taste, for both perceiving and interacting with the world. Next-generation cognitive agents will require to be appropriately equipped with multi-modal information fusion and perception capabilities to carry out cognitive tasks such as perception, action, affective and cognitive learning and memory, attention, decision making and control, social cognition, language processing and communication, reasoning, problem solving, and consciousness. Despite recent significant progress in the multi-disciplinary area of multi-modal fusion, there remain significant outstanding challenges for effectively exploiting multi-modal information processing in practical environments, in particular for untapped real-world applications in diverse disciplines. This timely special issue aims to provide a platform for bringing together world-class multi-disciplinary research in innovative methodologies and applications of multi-modal information fusion. In particular, we solicit reviews and original research works reporting practical applications of multi-modal fusion and perception technologies. TOPICS OF INTEREST Topics include: Architectures for multi-modal data fusion Efficient coding and learning of multi-modal big data Deep and sparse learning for multi-modal big data fusion Spatial and spatio-temporal multi-modal data mining Dynamic multi-modal fusion Cross-modal perception and learning of big data Simulated and real-world applications of multi-modal fusion, such as Big Data, NLP, internet of things, engineering, medical informatics, robotics, human computer interaction IMPORTANT DATES Submission deadline: December 15th, 2016 （投稿系统延期一周关闭） First round notification: February 28th, 2017 Revised version due: April 30th, 2017 Final notification: June 30th, 2017 Publication tentative date: TBD |
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