An estimated 17.5 million people died from a cardiovascular disease in 2012, representing 31% of all global deaths. Most acute coronary events result from rupture of the protective fibrous cap overlying an atherosclerotic plaque. The task of early identification of plaque types that can potentially rupture is, therefore, of great importance. The state-ofthe-art approach to imaging blood vessels is intravascular optical coherence tomography (IVOCT). However, currently, this is an offline approach where the images are first collected and then manually analyzed a frame at a time to identify regions at risk of thrombosis. This process is extremely laborious, time consuming and prone to human error. We are building a system that, when complete, will provide interactive 3D visualization of a blood vessel as an IVOCT is in progress. The visualization will highlight different plaque types and enable quick identification of regions at risk for thrombosis. In this paper, we describe our approach, focusing on machine learning methods that are a key enabling technology. Our empirical results using real OCT data show that our approach can identify different plaque types efficiently with high accuracy across multiple patients.

So far, we have investigated a unified wide area disaster information network (WDN) using the Internet based on the combination of both wired and wireless networks. In this paper, we introduce resource management system (RMS) for WDN to recover from system failure with serious damage when the disaster happened. The system configuration and architecture are described and their functions are precisely explained. A prototyped system based on our suggested WDN was built to demonstrate its functionality.

In this paper, we develop the on-line design method and the hierarchical design method of dynamics based information processing system for the robot intelligence. By using the forgetting parameter, dynamics memorizes a new robot motion forgetting an old motion, which means the plasticity of the system. The hierarchical structure enables information processing for complex and continuous environment. We implement the proposed method to a humanoid robot and realize the motion generation and transition.

The style of cryptography in which important data are embedded into image data not to be noticed by outsiders is called video steganography. This paper proposes a new method for embedding data into pictorial images. In this method, the amount of locally embedded data is adaptively varied according to the local characteristics of the original image, and more data will be embedded into the image with less distortion. Using the discrete cosine transform (DCT), data are mainly embedded into high frequency components of the image, which have little influence on the quality of the image. The amount of embedded data is varied depending on DCT components of adjacent areas not to weaken edge lines in the image. We also show the effectiveness of our method by subjective evaluation.