Special Issue for APSIPA Transactions on Signal and Information Processing
Brief description:
Data-driven research based on signal processing analysis has shown remarkable technological advances in the fields of computer vision, image analysis, and speech analysis, close to human recognition ability or even beyond. In addition, utilizing this methodology as leverage, researchers have been studying on human emotion, perception, recognition, preference, and even the art world, and moving beyond the imagination to new creative areas. As a result, the accuracy of signal processing and computer vision has been rapidly grown up, and the barriers of each field have collapsed in the past, and the union of different fields is becoming easier and faster. This technology is very important to collect and analyze efficient data, and recent advances in deep-learning technology have made it possible to more accurately analyze human ability beyond cognitive ability. It is so difficult to predict the boundary enough to question the extent to which it is a technical limit. These fields are really diverse, encompassing engineering, medicine, aesthetics, psychology, humanities, and theology, and it is difficult to define the diversity as a word of acknowledgement. In addition, through data-driven learning, not only in the engineering field, but also in researchers in other fields, research that has been difficult to summarize has begun to be summarized. This tells us very clearly how the analysis of the information in the data breaks down the barriers between fields. The aim of this special issue is to provide researchers and professionals with high-quality tutorial-style papers addressing the latest advances in the design, development, and deployment of data-driven based signal processing analysis technologies for various applications. A special emphasis will also be devoted to not only cover the current state-of-the-art, but also new and emerging trends. Prospective authors are invited to submit tutorial-style papers on topics related to data-driven based signal processing
analysis including but not limited to the following:
* Current and emerging multimedia contents analysis
* Data-driven signal analysis for cross-over applications
* Performance improvement of computer vision field via artificial intelligence
* Deep-learning technology for signal processing analysis
* Validation, verification, and performance assessment methodologies of signal processing/computer vision algorithms
* Signal processing analysis for medicine, aesthetics, psychology, humanities, and theology and arts.
Editor in Chief APSIPA T-SIP
Tatsuya Kawahara, Professor, School of Informatics, Kyoto University, Japan
Editors of the special issue:
Chia-Hung Yeh, National Taiwan Normal University, Taiwan (Lead Guest Editor) <yeh@mail.ee.nsysu.edu.tw>
Yuichi Tanaka, Tokyo University of Agriculture and Technology, Japan <ytnk@cc.tuat.ac.jp>
Huihui Bai, Beijing Jiaotong University, China <hhbai@bjtu.edu.cn>
Prospective authors:
Jingliang Peng, Shandong Univ., China <jpeng@sdu.edu.cn>
Shogo Muramatsu, Niigata Univ., Japan <shogo@eng.niigata-u.ac.jp>
Sanghoon Lee, Yonsei Univ., Korea <slee@yonsei.ac.kr>
Tentative schedule of submission and publication:
Submission deadline: June 30, 2019
Publication date: December 31, 2019
Papers are published upon acceptance, regardless of the Special Issue publication date.
Please refer to the journal website for paper submission procedure.
https://www.cambridge.org/core/journals/apsipa-transactions-on-signal-and-information-processing