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Featured Article: "A machine-learning phase classification scheme for anomaly detection in signals with periodic characteristics"

New Content Item (2)In this paper, Lia Ahrens, Julian Ahrens and Hans D. Schotten proposes a novel approach to detecting anomalies in time series exhibiting periodic characteristics, where we applied deep convolutional neural networks for phase classification and automated phase similarity tagging. They evaluated their approach on three example datasets corresponding to the domains of cardiology, industry, and signal processing, confirming that their method is feasible in a number of contexts.



Special Issues

Artificial Intelligence and Cognitive Computing for Smart Internet of Vehicle
Edited by:  Xin Liu, Hongjian Sun, Zhengguo Sheng, Edith C.H., and Qiuming Zhu
Collection published: 17 September 2021

Edge Intelligent Sensors for Infectious Diseases
Edited by: Kaijian Xia, Adel M. Alimi, Wenbing Zhao, James J. (Jong Hyuk) Park
Collection published: 27 July 2021

Integrated Space-Ground-Sea Wireless Networks for B5G
Edited by: Chunguo Li, Hyun Jong Yang, Yunfei Chen, Ning Zhang, Weizhi Meng
Collection published: 5 August 2021

Intelligent Sensors trends in Real-time Signal Processing
Edited by: Gopal Chaudhary, Manju Khari, Bharat Rawal, Rubén González Crespo
Collection published: 5 August 2021

Emerging Trends, Issues, and Challenges in Information Fusion and Its Applications for Massive RF Data in Smart Environments
Edited by: Mu Zhou, Chengpeng Hao, Jianxin Li, Ying-Ren Chien, Hongying Meng, Xin Ge
Collection published: 29 July 2021

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The EURASIP Journal on Advances in Signal Processing (JASP) welcomes Special Issues on timely topics related to the field of signal processing at large. The objective of Special Issues is to bring together recent and high quality works in a research domain, to promote key advances in signal processing, and to provide overviews of the state-of-the-art in emerging domains, to the general signal processing audience.

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The European Association for Signal Processing (EURASIP) was founded on 1 September 1978 to improve communication between groups and individuals that work within the multidisciplinary, fast growing field of signal processing in Europe and elsewhere, and to exchange and disseminate information in this field all over the world. The association exists to further the efforts of researchers by providing a learned and professional platform for dissemination and discussion of all aspects of signal processing including continuous- and discrete-time signal theory, applications of signal processing, systems and technology, speech communication, and image processing and communication.
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