2017 RIMS Joint Research
Advanced Innovation powered by Mathematics Platform

Signal analysis and time-frequency analysis
Organizer: Ryuichi Ashino(Mathematics and Information Sciences, Osaka Kyoiku University)
Period: 2017-10-23–2017-10-24
Location: Research Institute for Mathematical Sciences, Room 110
Kyoto University, Kyoto 606-8502, Japan

Program
October 23, Monday
13:00 – 14:00 Hongmei Zhu Department of Mathematics & Statistics, York University, Toronto
From the Fourier transform to the S-transform, and beyond
The S-transform is an effective time-frequency analysis technique that canprovide a time-frequency representation of a nonstationary signal. In addition, its multi-scale analysis allows more accurate detection of subtle signal
changes while interpretation in a time-frequency domain is easy to understand. It has been used successfully in various fields including ocean analysis,
medical and industrial application. In this talk, we will overview the theoryof the ST and introduce the various forms of the S-transforms.
14:15 – 15:15 Kensuke Fujinoki Department of Mathematical Sciences, Tokai University
Multi-lapped directional wavelet transforms and their applications toimage analysis
Various types of framelets have been developed and their redundant transforms proven to be a useful tool for applications such as image and video
processing. While high redundancy rate provides significant flexibility inwavelet design, computational cost increases exponentially as the dimension
increases. In this talk, we introduce a two-dimensional redundant wavelet
transform that offers a good tradeoff between redundancy and computationalcost. We show how our redundancy makes the directional selectivity of thewavelet transform improve and how it leads to better performance in image
processing tasks.
15:45 – 16:45 Hisashi Yoshida Department of Computational Systems Biology, Kindai University
Application of time-frequency analysis in biomedical signals and thechallenges
Biomedical signals, such as EEG, ECG and EMG usually have time-varying
characteristic. Time-frequency representation is effective way to track thechange but it is often too much information for automatic diagnosis. In thistalk, we introduce information theoretic instantaneous bandwidths which
is defined on positive time-frequency distribution and show some results ofbiomedical signal applications.

October 24, Tuesday
9:30 – 10:30 Hongmei Zhu Department of Mathematics & Statistics, York University, Toronto
Looking inside of biomedical signals using the S-transform
Biomedical signals are typically finite duration, dynamic and non-stationary
processes whose frequency characteristics vary over time or space. This
often requires algorithms capable of locally analyzing and processing signals.
The S-transform (ST) combines the time-frequency representation of thewindowed Fourier transform with the multi-scale analysis of the wavelet
transforms. It is easy interpretation and multi-scale analysis make it popularin various applications. In this talk, we illustrate its eﬀectiveness of the Stransform in biomedical applications.
10:45 – 11:45 Kazuaki Nakane Graduate School of Medicine, Osaka University
A tissue image analysis method via the homology concept
With the development of imaging devices in recent years, many tissue images have been taken as data. For clearly shaped images, analysis is being
carried out by using deep learning and other techniques. Eﬀective methods
have not yet been developed, however, for very complex images like medical
tissue images. For this reason, many tissue image data have been hoarded
without being utilized. These can also be called “image data type big data”.
Developing a method to analyze such images is important issue. In this talk,
we introduce the imaging method via the homology concept and several applied examples. This method has been applied to analyze the colon cancer
tissue images and got significant results.
13:15 – 14:15 Kohei Arai Saga University
Phytoplankton and zooplankton identification with microscopic images
using shape feature extracted by wavelet descriptor
Method for phytoplankton and zooplankton identification with microscopic
images using shape feature extracted by wavelet descriptor is proposed.
Planktons have a plenty of shape features. Therefore, shape features areeﬀective for plankton identification rather than tone and color features aswell as textural features. The eﬀectiveness of these features is compared forplankton image identifications.
14:30 – 15:30 Kiyoshi Mizohata Department of Mathematical Sciences, Doshisha University
The analysis of big data and applications of wavelets
The amount of social media data is now growing exponentially. Such data isnow called ‘Big Data’. In this talk, we shall show several interesting resultson comments of niconico-douga (famous social media in Japan) obtained byusing Hadoop system and wavelets.
For more information, visit our website at◃ http://www.osaka-kyoiku.ac.jp/~ashino/rims2017/
Contact: Ryuichi Ashino

Tel: 072-978-3685

ashino@cc.osaka-kyoiku.ac.jp