The aim of this study is to investigate the use of the Bayesian inference and to restore noise-degraded images under conditions of spatially correlated noise. The generative statistical models used for the original image and the noise were assumed to obey multi-dimensional Gaussian distributions whose covariance matrices are translational invariant. This study derives an exact description to be used as the expectation for the restored image by the Fourier transformation and restored an image distorted by spatially correlated noise by using a spatially uncorrelated noise model.

In this study, various mechatronics devices and control systems with pneumatic and electric actuators such as non-wearing type power assist devices, electric motorcycles, and mobile robots were developed based on MPU (embedded controller) and RTAI. The tested power assist device was attached a subject and the muscle burden was evaluated by using myoelectric sensors and a musculoskeletal simulator. In addition, in the study of electric motorcycles and mobile robots, these control systems were designed based on the analytical simulation. Through these studies, I’m going to submit papers and give presentations in domestic/international conferences.

We develop multi-precision computational routines with automatic result verification.
These routines, tracing error bounds of each steps in computation, finally give an interval in which the exact result exists.
Also, we study on tonal music generating system using MIDI interface as an application of symbolic computation.