Takamichi Nakamoto

It's long been possible to give users outside an actual environment that environment's visual and auditory information and thus contribute to establishing presence. However, we've yet to establish much presence when users require olfactory information - such as in environments focused on foods, flowers, perfumes, or, in some cases, more offensive smells. Recently, several VR researchers have become interested in olfaction and olfactory displays that present smells in virtual environments (VEs).

The authors analyzed the behavior of a quartz crystal microbalance (QCM) in the experimental environments of air and liquid using a Mason equivalent circuit. It was found that the mass loading effect of QCM could be regarded as an inductance increase, and the analytical equation of the frequency shift, which is valid over the wide range of the thickness of a loading film, was derived. Utilizing this equation, the frequency shift can be predicted without solving the transcendental equation. Furth

The research on olfactory sense in virtual reality has gradually expanded even though the technology is still premature. We have developed an olfactory display composed of multiple solenoid valves. In the present study, an extended olfactory display, where 32 component odors can be blended in any recipe, is described; the previous version has only 8 odor components. The size was unchanged even though the number of odor components was four times larger than that in the previous display. The compl

Quartz Crystal Microbalance (QCM) is one of the many acoustic transducers. It is the most popular and widely used acoustic transducer for sensor applications. It has found wide applications in chemical and biosensing fields owing to its high sensitivity, robustness, small sized-design, and ease of integration with electronic measurement systems. However, it is necessary to coat QCM with a sensing film. Without coating materials, its selectivity and sensitivity are not obtained. At present, this

ADVERTISEMENT RETURN TO ISSUEPREVArticleNEXTChemical Sensing in Spatial/Temporal DomainsTakamichi Nakamoto and Hiroshi IshidaView Author Information Graduate School of Science and Engineering, Tokyo Institute of Technology, 2-12-1 Ookayama, Meguro-ku, Tokyo 152-8552, Japan, and Department of Mechanical Systems Engineering, Tokyo University of Agriculture and Technology, 2-24-16 Nakacho, Kogahei, Tokyo 184-8588, Japan Cite this: Chem. Rev. 2008, 108, 2, 680–704Publication Date (Web):January 26, 2

Recent studies on machine learning technology have reported successful performances in some visual and auditory recognition tasks, while little has been reported in the field of olfaction. In this paper we report computational methods to predict the odor impression of a chemical from its physicochemical properties. Our predictive model utilizes nonlinear dimensionality reduction on mass spectra data and performs the clustering of descriptors by natural language processing. Sensory evaluation is

An experiment of odor identification using a neural network is described. A quartz-resonator array with different coating films was used, and its output pattern was recognized using a neural network. Various odors of commercially available liquors were identifiable by the network following training. The sensing system was adaptive to environmental variations during the cyclic process of the data sampling and training. High recognition probability was maintained even under temperature variations.