labview labview2012 (National Instruments Inc)
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90
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National Instruments Inc
labview labview2012
Labview Labview2012, supplied by National Instruments Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/labview2012/labview+2013/10__2525_slash_ecb__58__105-76-30-32
Average 90 stars, based on 1 article reviews
Labview Labview2012, supplied by National Instruments Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/labview2012/labview+2013/10__2525_slash_ecb__58__105-76-30-32
Average 90 stars, based on 1 article reviews
labview labview2012 - by Bioz Stars,
2026-09
90/100 stars
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Control:Article Title: Multi-class biological tissue classification based on a multi-classifier: Preliminary study of an automatic output power control for ultrasonic surgical units. Article Snippet: Ultrasonic surgical units (USUs) have the advantage of minimizing tissue damage during surgeries that require tissue dissection by reducing problems such as coagulation and unwanted carbonization, but the disadvantage of requiring manual adjustment of power output according to the target tissue.. In order to overcome this limitation, it is necessary to determine the properties of in vivo tissues automatically.. We propose a multi-classifier that can accurately classify tissues based on the unique impedance of each tissue. Software:Article Title: Multi-class biological tissue classification based on a multi-classifier: Preliminary study of an automatic output power control for ultrasonic surgical units. Article Snippet: Ultrasonic surgical units (USUs) have the advantage of minimizing tissue damage during surgeries that require tissue dissection by reducing problems such as coagulation and unwanted carbonization, but the disadvantage of requiring manual adjustment of power output according to the target tissue.. In order to overcome this limitation, it is necessary to determine the properties of in vivo tissues automatically.. We propose a multi-classifier that can accurately classify tissues based on the unique impedance of each tissue. |