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With the spread of the Internet, e-learning which is a way of learning using information technology has become popular. Although lecture videos in e-learning are designed so that many learners can understand, they do not take into consideration individual differences of learners. To address this problem, we designed a system that can grasp the concentration state of learners using electroencephalogram and dynamically provide contents according to the state. Moreover, we defined a metric using alpha waves in brain waves acquired from the occipital region as an index of changing learning materials used in the system. It enables providing suitable contents to each learner. In this research, we investigated the usefulness of the index and compared it with other brain waves such as theta waves and beta waves. As a result, it was confirmed that the proposed index was useful to extract learners who are relatively concentrating and not relatively concentrating. Moreover, it was suggested that alpha waves were more useful as the index for concentrating state than other waves.

This paper deals with map-construction problems for visual localization. Basically, the map is an aggregation of visual landmarks, and it is desirable that such landmarks exist densely and permanently for long-term localization. However, there are no landmarks with these attributes at the same time. In order to solve this problem, we propose a hybrid map with permanent landmarks and temporal landmarks. As a permanent landmark, we employ 3D wireframe which can be easily obtained from architectural CAD. For a temporal landmark, we use line segments which are visually detected in images captured by a camera. To handle these two types of landmarks on the same map, we develop two algorithms. One is to extract temporal line segments from images containing two mixed landmarks, and the other is to reconstruct them into the 3D wireframe map. We experimentally demonstrated that the proposed hybrid map outperformed the 3D wireframe map in terms of localization accuracy.

On-surface chemical reactions provide an attractive route for making tailored nanostructures. Here the authors present a thermally-controlled sequential on-surface transformation of a hydrocarbon molecule, characterized via high-resolution atomic force microscopy and density functional theory calculations.

In order to reduce a road monitoring cost, we propose a system to monitor extensively road condition by cyclists with a smartphone. In this paper, we propose two methods towards road monitoring. First is to classify road signals to four road conditions. Second is to extract road signal from a smartphone's accelerometer in three positions: pants' side pocket, chest pocket and a bag in a front basket. In pants' side pocket, road signal is extracted by Independent Component Analysis. In chest pocket and bag in a front basket, road signal is extracted by selecting 1-axis affected from gravitational acceleration. In the experiment of the classification method, overall accuracy was 75%. The experimental results of the extraction methods with correlation coefficient showed the overall accuracy were more than 0.7 in pants' side pocket and chest pocket, the overall accuracy was less than 0.3 in bag in a front basket.

Temperature in an urban area exhibits a complicated pattern due to complexity of infrastructure. Despite geographical proximity, structures of a group of buildings and streets affect changes in temperature. To investigate the pattern of fine-grained distribution of temperature, we installed a densely distributed sensor network called UScan. In this paper, we describe the system architecture of UScan as well as experience learned from installing 200 sensors in downtown Tokyo. The field experiment of UScan system operated for two months to collect long-term urban temperature data. To analyze the collected data in an efficient manner, we propose a lightweight clustering methodology to study the correlation between the pattern of temperature and various environmental factors including the amount of sunshine, the width of streets, and the existence of trees. The analysis reveals meaningful results and asserts the necessity of fine-grained deployment of sensors in an urban area.

Rotaxanes consisting of achiral axle and achiral ring components can possess supramolecular chirality due to their unique geometrical architectures. To synthesize such chiral rotaxanes, we adapted a prerotaxane method based on aminolysis of a metacyclophane type prerotaxane that had planar chirality, which is composed of an achiral stopper unit and a crown ether type ring component. The prerotaxanes were well resolved using chiral HPLC into a pair of enantiomerically pure prerotaxanes, which were transferred into corresponding chiral rotaxanes, respectively. Obtained chiral rotaxanes were revealed to have considerable enantioselectivity.

This paper describes an autonomous deployment and restoration of a Wireless Sensor Network (WSN) using mobile robots. The authors have been developing an information-gathering system using mobile robots and WSNs in underground spaces in post-disaster environments. In our system, mobile robots carry wireless sensor nodes (SN) and deploy them into the environment while measuring Received Signal Strength Indication (RSSI) values to ensure communication, thereby enabling the WSN to be deployed and restored autonomously. If the WSN is disrupted, mobile robots restore the communication route by deploying additional or alternate SNs to suitable positions. Utilizing the proposed method, a mobile robot can deploy a WSN and gather environmental information via the WSN. Experimental results using a verification system equipped with a SN deployment and retrieval mechanism are presented.