Studies on Context-Aware Notification Systems for Designing Living Environments with Citizens

学位の種別: 課程博士審査委員会委員 : （主査）東京大学准教授 木實 新一, 東京大学教授 出口 敦, 東京大学教授 浅見 泰司, 東京大学教授 有川 正俊, 東京大学准教授 福永 真弓University of Tokyo(東京大学

3D Shape Reconstruction of Japanese Traditional Puppet Head from CT Images

In this paper, we discuss a 3D shape reconstruction method of Japanese traditional puppet heads for a digital archiving. Especially, to reconstruct an inner shape of head, we use CT images. First, we divide four regions (wood, hair, paint, and air) by thresholds based on manual directed regions. After that, we divide these regions by a graph cut method. And we also present a method to estimate 3D shape of parts in puppet head. This method is also based on a graph cut method. Moreover, we also discuss a method to distinguish material of puppet head by machine learning. Here we use “U-Net” to extract wood parts of puppet head from its CT images. And we show experimental results by these methods

3D shape reconstruction of Japanese traditional puppet head from CT images by graph cut and machine learning methods

In this study, we discuss the digital archiving of Japanese traditional puppets. We propose two methods for extracting the puppet head shape from computed tomography (CT) images. The first is the graph cut method, and the second is a machine learning method based on U-Net. According to the experimental results of the extraction of puppet heads from CT images, the U-Net-based method can extract puppet heads more accurately than the graph cut method. Moreover, the U-Net-based method can extract puppet heads with multiple materials. However, the extraction of metal parts is inaccurate because of metal artefacts in the X-ray CT images and insufficient learning data

Exploring the use of ambient WiFi signals to find vacant houses

AbstractIn many countries, the population is either declining or rapidly concentrating in big cities, which causes problems in the form of vacant houses in many local communities. It is often challenging to keep track of the locations and the conditions of vacant houses, and for example in Japan, costly manual field studies are employed to map the occupancy situation. In this paper, we propose a technique to infer the locations of occupied houses based on ambient WiFi signals. Our technique collects RSSI (Received Signal Strength Indicator) data based on opportunistic smartphone sensing, constructs hybrid networks of WiFi access points, and analyzes their geospatial patterns based on statistical shape modeling. We show that the technique can successfully infer occupied houses in a suburban residential community, and argue that it can substantially reduce the cost of field surveys to find vacant houses as the number of potential houses to be inspected decreases

Community Reminder:participatory contextual reminder environments for local communities

Abstract Many projects have looked at how communities can co-design shared online repositories, such as Wikimapia and Wikipedia. However, little work has examined how local communities can give advice and support to their members by creating context-aware reminders that may include advice, tips and small requests. We developed the Community Reminder environment, a smartphone-based platform that supports community members to design and use context-aware reminders. We have conducted a one-month field study of Community Reminder to crowdsource and deliver safety-relevant information in a local community. The results show the benefits of involving community members in reminder design and connecting different perspectives. We also show that the proposed approach can broaden participation in local communities