| Project/Area Number |
05044088
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| Research Category |
Grant-in-Aid for international Scientific Research
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| Allocation Type | Single-year Grants |
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| Section | Joint Research |
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| Research Institution | Tokyo Institute of Technology |
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Principal Investigator |
SATO Takuso Tokyo Institute of Technology, 大学院・総合理工学研究科, 教授 (20016760)
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| Co-Investigator(Kenkyū-buntansha) |
SASE Mikiya Tokyo Institute of Technology, 大学院・総合理工学研究科, 助手 (20225907)
KAMEYAMA Keisuke Tokyo Institute of Technology, 大学院・総合理工学研究科, 助手 (40242309)
KOSUGI Yukio Tokyo Institute of Technology, 大学院・総合理工学研究科, 助教授 (30108237)
ROMAN Kuc エール大学, 工学部, 教授
KUC Roman Yale University
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| Project Period (FY) |
1993 – 1994
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| Project Status |
Completed (Fiscal Year 1994)
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| Budget Amount *help |
¥5,500,000 (Direct Cost: ¥5,500,000)
Fiscal Year 1994: ¥2,500,000 (Direct Cost: ¥2,500,000)
Fiscal Year 1993: ¥3,000,000 (Direct Cost: ¥3,000,000)
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| Keywords | Ultra Sound / Intelligent / Sensor Fusion / Neural Networks / Higher-Order Correlation / Medical Images / Nonlinear Effects / Position Estimation / 位置計測 / 医用診断 |
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| Research Abstract |
The objective of our research is to investigate the new technologies for intelligent sensors including ultrasonic sensors for biomedical and robotic applications, optical sensors for robotics and positron-emission sensors for biomedical diagnosis. This fiscal year, based on the discussion among the Japanese and US project participants, we mostly concerned with the development of new technologies for fusing sensory information given by peripheral pre-processors attached to each transducer.
These technologies include fusion of ultrasonic nonlinear parameters such as the phase-shift parameter and structure-change parameter for tissue characterization and clinical diagnosis, fusion of positron emission tomographic data with magnetic resonance images in a layred neural network and the one to fuse multi-channel evoked potential waveform to localize the neuro-bundle position in the living body.
Through the experiments regarding the above applications, we showed that the hierarchically arranged intelligent processors, from the peripheral to the central, just like the biological nervous systems, extremely improve the performance of the sensing system especially for diagnostic use.
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