High-performance fast-axial-flow radio-frequency discharge-excited CO laser

1994 ◽  
Author(s):  
Kouki Shimizu ◽  
Shunichi Sato ◽  
Kojiro Shimamoto ◽  
Yasunobu Takahashi
Keyword(s):  
Radio Frequency ◽  
High Performance ◽  
Axial Flow ◽  
Co Laser ◽  
Radio Frequency Discharge

1-kW, room-temperature, fast-axial-flow CO laser excited by a radio-frequency discharge

1996 ◽  
Vol 21 (2) ◽  
pp. 125 ◽  
Author(s):  
Kouki Shimizu ◽  
Manabu Taniwaki ◽  
Shunichi Sato
Keyword(s):  
Radio Frequency ◽  
Room Temperature ◽  
Axial Flow ◽  
Co Laser ◽  
Radio Frequency Discharge

Optimization of operating conditions for a fast-axial-flow, radio frequency discharge-excited, room-temperature CO laser

2000 ◽  
Vol 71 (5) ◽  
pp. 1987-1990
Author(s):  
Kouki Shimizu ◽  
Manabu Taniwaki ◽  
Shunichi Sato ◽  
Hiroshi Nagano ◽  
Kazuo Maeno
Keyword(s):  
Radio Frequency ◽  
Room Temperature ◽  
Axial Flow ◽  
Operating Conditions ◽  
Co Laser ◽  
Radio Frequency Discharge

Sitsen: Passive sitting posture sensing based on wireless devices

2021 ◽  
Vol 17 (7) ◽  
pp. 155014772110248
Author(s):  
Miaoyu Li ◽  
Zhuohan Jiang ◽  
Yutong Liu ◽  
Shuheng Chen ◽  
Marcin Wozniak ◽  
...  
Keyword(s):  
Radio Frequency ◽  
Radio Frequency Identification ◽  
High Performance ◽  
Learning Algorithm ◽  
Low Cost ◽  
Recognition System ◽  
Sitting Posture ◽  
Average Accuracy ◽  
Phase Variations ◽  
Window Approach

Physical health diseases caused by wrong sitting postures are becoming increasingly serious and widespread, especially for sedentary students and workers. Existing video-based approaches and sensor-based approaches can achieve high accuracy, while they have limitations like breaching privacy and relying on specific sensor devices. In this work, we propose Sitsen, a non-contact wireless-based sitting posture recognition system, just using radio frequency signals alone, which neither compromises the privacy nor requires using various specific sensors. We demonstrate that Sitsen can successfully recognize five habitual sitting postures with just one lightweight and low-cost radio frequency identification tag. The intuition is that different postures induce different phase variations. Due to the received phase readings are corrupted by the environmental noise and hardware imperfection, we employ series of signal processing schemes to obtain clean phase readings. Using the sliding window approach to extract effective features of the measured phase sequences and employing an appropriate machine learning algorithm, Sitsen can achieve robust and high performance. Extensive experiments are conducted in an office with 10 volunteers. The result shows that our system can recognize different sitting postures with an average accuracy of 97.02%.

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