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Dr. Fei-Yun Wu from School of Marine Science and Technology published a paper in a top Journal IEEE Transactions on Industrial Informatics
2017-10-30 17:28 Feiyun Wu  审核人:


Dr. Fei-Yun Wu, along with co-authors Kunde Yang and Zhi Yang published the paper “Compressed Acquisition and Denoising Recovery of EMGdi Signal in WSNs and IoT” in "IEEE Transactions on Industrial Informatics", one of the world’s largest research journals. Congratulations to Dr. Wu on this fantastic achievement!


About the author

Dr. Fei-Yun Wu is an Assistant Professor in the School of Marine Science and Technology, Northwestern Polytechnical University (NPU), Xi’an, China, joining the Department of Acoustics and Information Engineering, National Key Discipline of Underwater Acoustic Engineering (in NPU) in 2016. He has been supported by the National Natural Science Foundation of China (Project 61701405). Dr. Wu received the Ph.D. degree in ocean physics from Xiamen University in the same year and was a visiting scholar in University of Delaware during 2013 and 2015. Dr. Wu’s research is mainly in the areas of adaptive signal processing, compressed sensing, independent component analysis (ICA), and wavelet algorithms and their applications. His work appears in leading international journals, including IEEE Transactions on Industrial Informatics, IEEE Communications Letters, Signal Processing, Applied Soft Computing and Neurocomuting.



About the paper

Signal telemonitoring in Wireless Sensor Networks (WSNs) and Internet-of-Things (IoT) holds the promise to be an evolving direction in personalized medicine. The WSNs and IoT enable information telemonitoring and communications technologies play important roles in the future life. However, when design such a system, one should consider the required functionality, miniaturization, energy efficiency and etc., to make fewer resources required in WSNs and IoT. Conventional methods of data-acquisition cannot energy-effectively compress data with reduced device costs. Different with the traditional compression methods, compressed sensing (CS) takes promising steps towards these challenges. Unfortunately, the data is not sparse in time domain. Hence, current CS algorithms are extremely difficult to use directly for recovering the required signal. In order to satisfy the requirements of applications in WSNs and IoT, this study proposes an approximated l0 (AL0) norm based method to search the solution via the gradient descent method, then projects the searched solution to the reconstruction feasible set. Meanwhile, this study adopts a new wavelet threshold (NWT) based method to denoise the interference. Experimental results are provided to testify the performance of the proposed methods. The scheme if shown in Fig.1, and the compression and basis matrices are shown in Fig.2.


About the publisher

IEEE Transactions on Industrial Informatics (IEEE TII) publishes high quality, original papers that including more knowledge-based production and systems organization and considers production from a more holistic perspective, encompassing not only hardware and software, but also people and the way in which they learn and share knowledge. Papers published in this journal demonstrate a high level of originality through the industrial applications with interesting new aspects or by providing fresh insights leading to a successful implementation. 

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