Research

In the field of information science and engineering, many problems appear, such as the problem of designing a mathematical system that provides a desired result, the problem of identifying a mathematical model that gives a given input and output, and the inverse problem of estimating unknown inputs from a system and observations. When dealing with these problems, by dividing the analysis into conditions specific to each problem and mathematical models independent of each problem, it becomes possible to theoretically determine the performance and limitations, and to also expand the analysis horizontally to problems that can be described by similar mathematical models. With this unique approach, we have developed various methodological constructs and application techniques in machine learning problems including image and color restoration, separation of individual sounds in acoustic signals, pattern recognition, and sampling theory. By applying our methodology to today's rapidly developing and diverse measurement technologies, we expect to develop a variety of application techniques based on theory.