Biometric technologies in Japan, such as the adoption of vein authentication devices in ATMs (automated teller machines) in 2004, the e-passport in 2006, and the implementation of face data in the IC driver's license card in 2007, have been expanded steadily in the social infrastructure system. A paradigm shift in the biometric market in the United States called “Post 9.11” has occurred since 2010. There is a possibility of expanding the market towards the next stage including, for example, administrative services and civilian use. Thus, it is more important than ever to provide countermeasures to solve the problems specific to biometrics. One example is the protection of privacy. In this paper, I describe the commercialization of biometric technologies and the technologies that must be developed in the future.

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The basic principles of a new methodology named resilience engineering are reviewed and guidelines for implementing the methodology are described in this article. The eventual goal of resilience engineering is to significantly enhance the resilience of sociotechnical systems. Four capabilities and supplementary requirements are introduced as the key constituents of resilience engineering. A new concept of safety pursued in resilience engineering named Safety-II is also described since it is different from the conventional static definition of safety named Safety-I, implying that no undesirable events occur. The applicability of resilience engineering is demonstrated by several examples.

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This paper focuses on the care support of elderly persons in a superaging society, and describes a proposed sleep-monitoring agent and its use to ensure the comfortable and healthy life of aged persons. A sleep-monitoring agent is provided to each aged person to estimate his/her sleep stage from heartbeat and body movement data acquired by an unconstrained-type sensor. The obtained information is remotely displayed in the tablets and/or PCs of care workers and informs them when an aged person sleepwalks or falls from his/her bed. In this paper, we introduce the sleep-monitoring agent and its system, and describe a new sleep-stage estimation method in detail. This paper also provides the results of experiments on human subjects, which show the effectiveness of the sleep-monitoring agent. Finally, future works are summarized.

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Neurons and neural networks in the brain, which also interest many people in the field of engineering, are nonlinear systems. Neurons fire not only regularly but also irregularly depending on the membrane potential, stimulus intensity, and stimulus frequency. This irregular firing is nonperiodic oscillation, which is subject to a dynamical law referred to as chaos. In neural networks, the field potential, which reflects the mass activity of neurons, causes phase locking and chaotic responses to periodic stimulation. That is, the degree of synchronization and the number of synchronous neurons fluctuate owing to periodic stimulation. Here, the nonlinear dynamical features of neurons and neural networks are first reviewd. It is then demonstrated that stochastic resonance (SR) occurs in the hippocampal CA3-CA1 network model, and memory patterns embedded in Schaffer collateral synapses in CA1 can be recalled via SR, as an example of research on information-processing mechanisms in the brain.

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