Shouhei Kidera

Innovative microwave imaging method by incorporating super-accurate imaging and polarimetric based permittivity estimation

Researcher

Shouhei Kidera

Outline

This research aims at creating innovative multi-functional image analysis method for microwave or THz wave sensors. Based on the original range points migration (RPM) method, this research incorporates polarimetric, multi scattering and Doppler data into imaging analysis to extract permittivity, motion or other physical state of object.

Takashi Kimura

Extension of X-ray Laser Diffraction Imaging Technique by Big-Data Approach

Researcher

Takashi Kimura

Outline

X-ray free-electron lasers (XFELs) with angstrom wavelength and femtosecond pulse duration enable us to reveal high-resolution sample structure before the onset of radiation damage. Radiation-damage-free measurement by XFELs is especially effective for fragile nanostructures in solution, which suffer from serious damage from high-energy radiation. This project aims to develop a new XFEL imaging technique that can capture dynamics of various samples in solution using big data and machine learning approach.

Yoshinori Nakanishi

Development of resampling-based data-driven diagnostics for informatics-aided measurement

Researcher

Yoshinori Nakanishi

Outline

Data-driven diagnostics to determine the limits of informatics-aided measurement will be developed using resampling. Resampling is a category of traditional statistical methods such as cross validation and bootstrap, in which repeatedly sampling from available data enables advanced information processing. People who address unexplained natural phenomena on the frontiers of measurement science are likely to be too skeptical or too confident of novel facts. When the purpose of the study is achieved, it will progressively innovate experimental design keeping a balanced perspective.

Kazuyuki Nakamura

Development of automation principles of data assimilation modeling for high level prediction and knowledge discovery

Researcher

Kazuyuki Nakamura

Outline

Data assimilation is the method which enables us to obtain new fact findings and good prediction by combining measurements with simulations. However, problem specific modeling is required and application fields are limited. To overcome these problems, this research aims at generating objective and automatic modeling principle for data assimilation by combining statistical and mathematical methods such as Bayesian statistics and nonlinear mathematical analysis. This principle will enable us to perform high level data assimilation with advanced measurements that will realize new knowledge discoveries.

Norio Narita

Searching Earth-like Exoplanets via Multi-color Simultaneous Photometry and High-Precision Analyses

Researcher

Norio Narita

Outline

Recent discoveries of new exoplanets have been advanced by space-based satellite missions and ground-based observations. In the course of studies, time-correlated systematic effects caused by various reasons remain an issue which prevents high-precision analyses for high signal-to-noise ratio time-series datasets. In this research, I will search for Earth-like exoplanets from candidates found by the Transiting Exoplanet Survey Satellite (TESS) mission via high-precision multi-color simultaneous photometry and high-precision analyses enabled by recent astrostatistics.

Manabu Hoshino

Statistical Estimation of High-Resolution X-ray Diffraction Intensities for Ultrahigh-Definition X-ray Crystal Structure Analysis

Researcher

Manabu Hoshino

Outline

X-ray crystal structure analysis provides a defined (atomically resolved) molecular structure and enables detailed evaluation of electron distribution and correlation in a subject material. However, accumulation of radiation damage in a sample crystal or fewness of incident X-ray photons lowers data resolution and definition of structure in the analysis. In this project, statistical analysis for estimation of high-resolution X-ray diffraction data faded by the above reasons is developed to accomplish ultrahigh-definition (definition unreachable by measurement only) X-ray crystal structure analysis of proteins and molecular dynamics.

Daisuke Matsuoka

Prediction of extreme weather events from climate big data -from tropical cyclone to heavy rain-

Researcher

Daisuke Matsuoka

Outline

Predicting extreme weather events such as tropical cyclone and heavy rain before their occurrence has tremendous social and academic significance. In this project, we use long-term high-resolution simulation and observational data (climate big data) and detect the precursors of such extreme event using deep neural networks. By applying generated deep classifiers to real-time satellite data, we try to predict tropical cyclone and heavy rain occurrence seven and one days before, simultaneously.

Yoichi Miyawaki

Dynamics of real world recognition under natural conditions using neural information analysis at high spatio-temporal resolution

Researcher

Yoichi Miyawaki

Outline

Visual recognition of the real world is an important function contributing for survival, reproduction, and culture of the human being. This study performs integrative analysis of human behavior and brain activity measured under natural conditions as close as possible to our living environment, challenging to overcome the current limitation of spatio-temporal resolution of human brain activity measurement effectively and to understand neural mechanisms underlying our real world recognition.

Yoshihiro Morishita

Reconstruction, prediction, and manipulation of organ developmental processes using advanced information processing technologies

Researcher

Yoshihiro Morishita

Outline

In this project, I develop intelligent measurement-analysis methods for estimating and predicting unmeasurable states/variables from limited observed data on cellular collective motions during organ morphogenesis and spatial patterning.

Yuichi Yamasaki

Coherent Soft X-ray Operando Measurement by Sparse Phase Retrieval Analysis

Researcher

Yuichi Yamasaki

Outline

A coherent soft x-ray diffraction imaging (CDI) is an x-ray microscope method for imaging of electronic state or magnetic structure by a phase retrieval algorithm, an iterative Fourier transformation. In this research, I will perform an operand measurement using CDI method to elucidate the mechanism of characteristics in devices and functional materials. In order to efficiently extract features from measurement data with poor statistical accuracy, I will develop a novel phase retrieval algorithm based on the sparse modeling and the machine learning techniques.