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We have been developing a liquid-phase detection technique of biological targets using magnetic markers. In this method, markers bound to the targets (bound markers) and unbound (free) markers are magnetically differentiated by using the Brownian relaxation of free markers. Although highly sensitive detection of targets was demonstrated, blank signal from the free markers remained an issue for further improvement [1].

We developed improved methods of liquid-phase detection of biological targets using HTS SQUID and Brownian relaxation of magnetic markers. In a real sample, the aggregation and precipitation of free markers deteriorate their Brownian relaxation and cause so-called blank signals. In order to solve this problem, we developed two methods. First, we introduced a reaction field Bre during the binding reaction between the markers and targets. Using this method, we could obtain a strong signal from the bound markers without increasing the aggregation of the free markers. Next, we introduced a field-reversal method in the measurement procedure in order to differentiate the signal from the markers in suspension from that from the precipitated markers. Using this procedure, we could eliminate the signal from the precipitated markers. Then, we detected biotin molecules by using these methods. The biotins were immobilized on the surfaces of large polymer beads with diameters of 3.3 μm and were detected with streptavidin-conjugated magnetic markers. The minimum detectable molecular number concentration was 1.8×10^(-19) mol/ml, indicating the high sensitivity of the present method.

For solving the “Digital divide” problem and developing a system which understands human, the data mining technology is being developed. Moreover, within the concept of Kukanchi, the system can not only collect information, but also provide service to user. In this paper, the authors show two experiments which constructed based on “Kukanchi”. These Kukanchi systems include both techniques of action recognition and mining in time-series motion logs. In the first experiment, the authors constructed a service robot system, which makes networked robots exchange and provide the information to customers by recognizing their actions, to show the basis concept of the paper. In the second experiment, the authors show the possibility of using interactive data mining, which uses visualization techniques to help human add judgments, to improve the results of the first experiment. The two experiments show the utility and possibility of Kukanchi and mining in time-series motion logs which supports the system to understand human and help people directly.

This paper shows service robot system, which provides information considering user's personal attribute. In this research we focused on two attributes, "Preference attribute information and Body attribute information". We defined that "Preference attribute information" was information about preference, which was obtained in daily life such as favorite color; "Body attribute information " was information based on person's bodily features such as sex. We called them "Personal attribute information". The service robot system was implemented in a store based on a new concept "ambient intelligence". The networked robots shared personal attribute information. This paper shows an experiment that the system provides information about clothes by using "preference attribute information and body attribute information" for the user in a clothing store. Finally authors discussed the utility of the system and summarized our service robot system.