Next Article in Journal
Fiber Bragg Grating Sensor to Monitor Stress Kinetics in Drying Process of Commercial Latex Paints
Previous Article in Journal
Biosensing for the Environment and Defence: Aqueous Uranyl Detection Using Bacterial Surface Layer Proteins
Previous Article in Special Issue
Recent Advances in Sensing Oropharyngeal Swallowing Function in Japan
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Editorial

Introduction to the Special Issue on “State-of-the-Art Sensor Technology in Japan”

Department of Knowledge-Based Information Engineering, Toyohashi University of Technology; 1-1, Tempaku, Toyohashi, Aichi 441-8580, Japan
Sensors 2010, 10(5), 4756-4760; https://doi.org/10.3390/s100504756
Submission received: 30 April 2010 / Accepted: 10 May 2010 / Published: 10 May 2010
(This article belongs to the Special Issue State-of-the-Art Sensors Technology in Japan)
The combination of sensing technology with information and communication technology (ICT) could serve both as global eyes that monitor the environment for environmental issues, and as local eyes that monitor humans for aging society issues. System technology is also required to form such global and local eyes. This special issue, “State-of-the-Art Sensor Technology in Japan”, contains articles and reviews related to the monitoring of humans and the environment, and the integration of sensor systems.

Sensing Technologies: A Perspective for Japan

It is often pointed out that Japan faces both human issues and environmental issues, the former originating from the domestic situation and the latter from international circumstances. In terms of human issues, Japan’s demographics are rapidly changing as the population ages. Japan has become a nation of elderly people, with the proportion of people over sixty-five exceeding 21% in 2007. As for environmental issues, Japan needs to play a leadership role, not only in international politics, but also in industrial and academic fields.
Sensing technology and information and communication technology (ICT) can help tackle these two issues. For example, sensing technology could play a primary role in gathering data into the global network. Environmental issues together with ICT [1,2] and aging society issues [2] have often been pointed out, and we focus on them from the viewpoint of sensing technologies. Although security is also important [2,3], we must leave it to a future special issue.
Because sensing technology is related to the gateway to the network, we also examine intelligent information processing to realize sensor fusion, intelligent sensing, sensor networks and sensor systems. Both environmental monitoring and human monitoring require flexibility and adaptation, hence a third category is the integration of sensor systems and networks that involve machine learning and adaptation technology.

Sensing Technology for Human Monitoring

Sensors for monitoring humans and bio-systems require several technologies. Optic technologies [4,5] and carbon nanotubes [6] are promising and essential technologies, and have been attracting much attention not only for biosensors, but also other areas including damage detection [7]. Other technologies such as mass spectrometry [8] have been attracting attention for biosensors. CMOS technology [9,10] allows small size and low power consumption, enabling the sensors to be implanted in humans and animals.
For the aged society, human monitoring in pioneering fields is required, such as oropharyngeal swallowing [11] and finger tapping movement [12], as well as the monitoring of vital signs [13] and specific activities such as farming operations [14]. Wearable sensors are also needed for monitoring elderly people.
Sensing technology can also be used to understand human sensors [15] or even to support human sensors [9]. Finally, further studies on sensing technology in relatively unexplored areas such as the senses of smell [15,16] and taste [17] are needed.

Sensing Technology for Monitoring the Environment

Sensing technology for monitoring the environment requires global monitoring involving sensor systems (including sensor networks), as described in the next section, and infrastructure such as satellites [1820] and submarine stations [21].
The specific sensing technologies required depend on the target, which includes land surface temperature [18], near-surface seawater temperature [22], seismic and tectonic activities [21], and aeolian sand transport [23]. Even when monitoring the same or similar measurements, regional characteristics mean that there are region-specific problems in monitoring, so field studies will be essential [19,21].
Global-scale monitoring is important for environmental monitoring, but it involves systems science and technologies other than earth science and sensing technologies. The difficulty of environmental monitoring comes from not only its large scale but also its complex system aspects (entanglement of cause and effect; mixture of slow and rapid processes; inextricable linkage between humans and natural environment).

Intelligent Information Processing for Sensor Systems

Sensor system technologies need to involve many technologies to allow the integration of multiple and multimodal sensors (i.e., sensor fusion), a network of autonomous sensor units (i.e., sensor network), and post-processing of sensed data (i.e., intelligent sensing). The next generation of sensor system technologies must be more flexible to attain more efficient, intelligent and autonomous sensor systems.
Fortunately, intelligent information processing technologies such as artificial intelligence (AI) and machine learning (ML) are sufficiently mature to provide such flexibility. These include feature extraction [24]; classification of sensed events [12,19,25]; cooperative communications in networking sensor nodes [26]; adaptability to deal with the trade-off between false alarms and missed alarms [27]; and adaptability to allow automatic weighting of important and credible sensors [28].

Sensing Technology: A Challenge

Globalization has been driven by the explosive expansion of the information network involving the Internet, satellites, and sensor networks. Since the network now covers the globe, the next stage is to organize the functions and integration for each level, place, and situation.
Network expansion and globalization can be considered to be the development phase of the central nervous system of the earth. In the next phase, the network should be integrated with sensor and motor systems.
Sensing technology R&D requires many disciplines: sciences and humanities; science and engineering; and many sections within each branch of science and engineering. Although it is usually convenient and necessary to work within a discipline, it is also necessary to conduct studies spanning different fields. Sensing technology and science inherently require cross-sectional studies, and articles of this special issue indeed show the importance of such studies.
Although globalization with ICT encourages such cross-sectional studies by enabling a cooperative R&D involving intensive computing and networking (as found within the frameworks of e-Science and cyberinfrastructure), it is a challenge to manage and implement them for the cross-sectional studies of sensing technology.

Acknowledgments

We are grateful to Matthias Burkhalter and Shu-Kun Lin for giving us the opportunity to publish this special issue. We are indebted to Laura Simon, Kathy Lai, Laura Li, Iris Li, Zeno Schumacher, Dietrich Rordorf, Maggie Sun, Hibby Li, Yolanda Ughini, Ophelia Han and all the staff of MDPI for their great support in managing the review process and organization of this special issue. Finally, we sincerely thank all the authors who submitted their important work to this special issue.

References

  1. Komiyama, H.; Kraines, S. Vision 2050: Roadmap for a Sustainable Earth; Springer: Tokyo, Japan, 2008. [Google Scholar]
  2. Science Council of Japan. Japan Vision 2050: Principles of Strategic Science and Technology Policy Toward 2020. 2005. available online: http://www.scj.go.jp/en/vision2050.pdf (accessed on 10 May 2010).
  3. Kawaguchi, T.; Shankaran, D.R.; Kim, S.J.; Matsumoto, K.; Toko, K.; Miura, N. Surface Plasmon Resonance Immunosensor Using Au Nanoparticle for Detection of TNT. Sens. Actuat. B 2008, 133, 467–472. [Google Scholar]
  4. Anne, M.L.; Keirsse, J.; Nazabal, V.; Hyodo, K.; Inoue, S.; Boussard-Pledel, C.; Lhermite, H.; Charrier, J.; Yanakata, K.; Loreal, O.; Person, J.L.; Colas, F.; Compère, C.; Bureau, B. Chalcogenide Glass Optical Waveguides for Infrared Biosensing. Sensors 2009, 9, 7398–7411. [Google Scholar]
  5. Francois, A.; Himmelhaus, M. Optical Sensors Based on Whispering Gallery Modes in Fluorescent Microbeads: Size Dependence and Influence of Substrate. Sensors 2009, 9, 6836–6852. [Google Scholar]
  6. Maehashi, K.; Matsumoto, K. Label-Free Electrical Detection Using Carbon Nanotube-Based Biosensors. Sensors 2009, 9, 5368–5378. [Google Scholar]
  7. Li, F.; Murayama, H.; Kageyama, K.; Shirai, T. Guided Wave and Damage Detection in Composite Laminates Using Different Fiber Optic Sensors. Sensors 2009, 9, 4005–4021. [Google Scholar]
  8. Hashimoto, S.; Isobe, T.; Natsume, T. Biomolecular Interaction Analysis Coupled with Mass Spectrometry to Detect Interacting Proteins. In The Proteomics Protocols Handbook; Humana Press: Totowa, NJ, USA, 2005; pp. 689–698. available online: http://www.springerprotocols.com/Abstract/doi/10.1385/1-59259-890-0:689 (accessed on 10 May 2010).
  9. Ohta, J.; Tokuda, T.; Sasagawa, K.; Noda, T. Implantable CMOS Biomedical Devices. Sensors 2009, 9, 9073–9093. [Google Scholar]
  10. Kim, J.W.; Takao, H.; Sawada, K.; Ishida, M. Integrated Inductors for RF Transmitters in CMOS/MEMS Smart Microsensor Systems. Sensors 2007, 8, 1387–1398. [Google Scholar]
  11. Ono, T.; Hori, K.; Masuda, Y.; Hayashi, T. Recent Advances in Sensing Oropharyngeal Swallowing Function in Japan. Sensors 2010, 10, 176–202. [Google Scholar]
  12. Shima, K.; Tsuji, T.; Kandori, A.; Yokoe, M.; Sakoda, S. Measurement and Evaluation of Finger Tapping Movements Using Log-linearized Gaussian Mixture Networks. Sensors 2009, 9, 2187–2201. [Google Scholar]
  13. Adnane, M.; Jiang, Z.; Choi, S.; Jang, H. Detecting Specific Health-Related Events Using an Integrated Sensor System for Vital Sign Monitoring. Sensors 2009, 9, 6897–6912. [Google Scholar]
  14. Fukatsu, T.; Nanseki, T. Monitoring System for Farming Operations with Wearable Devices Utilized Sensor Networks. Sensors 2009, 9, 6171–6184. [Google Scholar]
  15. Sugawara, Y.; Sugimoto, C.; Minabe, S.; Iura, Y.; Okazaki, M.; Nakagawa, N.; Seto, M.; Maruyama, S.; Hirano, M.; Kitayama, I. Use of Human Senses as Sensors. Sensors 2009, 9, 3184–3204. [Google Scholar]
  16. Izumi, R.; Abe, H.; Hayashi, K.; Toko, K. Odor Quantification of Aromatic. Alcohols Using Artificial Olfactory Epithelium. Sensor. Mater 2007, 19, 299–307. [Google Scholar]
  17. Cui, H.; Habara, M.; Ikezaki, H.; Toko, K. Study of Surface-Modified Lipid /Polymer Membranes for Detecting Sweet Taste Substances. Proceedings of 3rd International Conference on Sensing Technology, Tainan, Taiwan, October 2008; pp. 610–614.
  18. Liu, Y.; Noumi, Y.; Yamaguchi, Y. Discrepancy between ASTER- and MODIS- Derived Land Surface Temperatures: Terrain Effects. Sensors 2009, 9, 1054–1066. [Google Scholar]
  19. Kato, S.; Yamaguchi, Y.; Liu, C.C.; Sun, C.Y. Surface Heat Balance Analysis of Tainan City on March 6, 2001 Using ASTER and Formosat-2 Data. Sensors 2008, 8, 6026–6044. [Google Scholar]
  20. Watari, S.; Tokumitsu, M.; Kitamura, K.; Ishida, Y. Forecast of High-energy Electron Flux at Geostationary Orbit Using Neural Network. Transactions of the Japan Society for Aeronautical and Space Sciences, Space Technology Japan. 2009, 7, Tr. 2,. pp. 47–51. available online: http://www.jstage.jst.go.jp/article/tstj/7/ists26/7_Tr_2_47/_article (accessed on 10 May 2010).
  21. Kasaya, T.; Mitsuzawa, K.; Goto, T.; Iwase, R.; Sayanagi, K.; Araki, E.; Asakawa, K.; Mikada, H.; Watanabe, T.; Takahashi, I.; Nagao, T. Trial of Multidisciplinary Observation at an Expandable Sub-Marine Cabled Station “Off-Hatsushima Island Observatory” in Sagami Bay, Japan. Sensors 2009, 9, 9241–9254. [Google Scholar]
  22. Kawai, Y.; Ando, K.; Kawamura, H. Distortion of Near-Surface Seawater Temperature Structure by a Moored-Buoy Hull and Its Effect on Skin Temperature and Heat Flux Estimates. Sensors 2009, 9, 6119–6130. [Google Scholar]
  23. Udo, K. New Method for Estimation of Aeolian Sand Transport Rate Using Ceramic Sand Flux Sensors (UD-101). Sensors 2009, 9, 9058–9072. [Google Scholar]
  24. Wang, H.; Chen, P. A Feature Extraction Method Based on Information Theory for Fault Diagnosis of Reciprocating Machinery. Sensors 2009, 9, 2415–2436. [Google Scholar]
  25. Bagan, H.; Takeuchi, W.; Yamagata, Y.; Wang, X.; Yasuoka, Y. Extended Averaged Learning Subspace Method for Hyperspectral Data Classification. Sensors 2009, 9, 4247–4270. [Google Scholar]
  26. Treeprapin, K.; Kanzaki, A.; Hara, T.; Nishio, S. An Effective Mobile Sensor Control Method for Sparse Sensor Networks. Sensors 2009, 9, 327–354. [Google Scholar]
  27. Okamoto, T.; Ishida, Y. An Immunity-Based Anomaly Detection System with Sensor Agents. Sensors 2009, 9, 9175–9195. [Google Scholar]
  28. Ishida, Y.; Tokumitsu, M. Adaptive Sensing Based on Profiles for Sensor Systems. Sensors 2009, 9, 8422–8437. [Google Scholar]

Share and Cite

MDPI and ACS Style

Ishida, Y. Introduction to the Special Issue on “State-of-the-Art Sensor Technology in Japan”. Sensors 2010, 10, 4756-4760. https://doi.org/10.3390/s100504756

AMA Style

Ishida Y. Introduction to the Special Issue on “State-of-the-Art Sensor Technology in Japan”. Sensors. 2010; 10(5):4756-4760. https://doi.org/10.3390/s100504756

Chicago/Turabian Style

Ishida, Yoshiteru. 2010. "Introduction to the Special Issue on “State-of-the-Art Sensor Technology in Japan”" Sensors 10, no. 5: 4756-4760. https://doi.org/10.3390/s100504756

Article Metrics

Back to TopTop