scholarly journals Continuous Flow Liquid–Liquid Separation Using a Computer-Vision Control System: The Bromination of Enaminones with N-Bromosuccinimide

Synlett ◽  
2015 ◽  
Vol 27 (01) ◽  
pp. 164-168 ◽  
Author(s):  
Matthew O’Brien ◽  
Dennis Cooper
Keyword(s):  
Computer Vision ◽  
Control System ◽  
Continuous Flow ◽  
Liquid Separation ◽  
Vision Control

Computer Vision Control System for Food Industry

2018 ◽  
pp. 852-872
Author(s):  
Hadj Baraka Ibrahim ◽  
Oussama Aiadi ◽  
Yassir Zardoua ◽  
Mohamed Jbilou ◽  
Benaissa Amami
Keyword(s):  
Computer Vision ◽  
Control System ◽  
Food Industry ◽  
Vision Control

scholarly journals A Survey on FPGA-Based Sensor Systems: Towards Intelligent and Reconfigurable Low-Power Sensors for Computer Vision, Control and Signal Processing

Sensors ◽  
2014 ◽  
Vol 14 (4) ◽  
pp. 6247-6278 ◽  
Author(s):  
Gabriel García ◽  
Carlos Jara ◽  
Jorge Pomares ◽  
Aiman Alabdo ◽  
Lucas Poggi ◽  
...  
Keyword(s):  
Computer Vision ◽  
Signal Processing ◽  
Low Power ◽  
Sensor Systems ◽  
Vision Control

Abstract 15064: Control of Left Ventricular Load and Pulsatility by a Continuous-flow Left Ventricular Assist Device With a Native Heart Load Control System in Chronic Awake Phase

Circulation ◽  
2014 ◽  
Vol 130 (suppl_2) ◽  
Author(s):  
Kazuma Date ◽  
Takashi Nishimura ◽  
Satoru Kishimoto ◽  
Yoshiaki Takewa ◽  
Minoru Ono ◽  
...  
Keyword(s):  
Control System ◽  
Continuous Flow ◽  
Aortic Pressure ◽  
Pulse Mode ◽  
Left Ventricular ◽  
Load Control ◽  
Continuous Mode ◽  
Assist Device ◽  
Ventricular Assist ◽  
Left Ventricular Assist

Introduction: We have previously developed a Native Heart Load Control System for a continuous-flow left ventricular assist device (EVAHEART) and demonstrated that the rotational speed (RS) in synchronization with the cardiac cycle can alter left ventricular (LV) load and pulsatility under general anesthesia. In this study, we assessed this system in chronic awake phase. Methods: We implanted EVAHEART in five goats (59.4±5.6kg) with a normal heart. Two weeks after the implantation, we examined the effects of the continuous mode (constant RS), counter-pulse mode (increase RS in diastolic phase) and co-pulse mode (increase RS in systolic phase) of this system on LV load and pulsatility in 100%, 75% and 50% bypass. We used two parameters to evaluate LV load, including stroke work (SW) and end-diastolic volume (LVEDV) determined from LV pressure-volume loops. About pulsatility, we used pulse pressure (PP) and mean dP/dt max of aortic pressure. Results: The co-pulse mode created greater LV load and pulsatility than the continuous mode in all bypass rates. In contrast, LV load and pulsatility in the counter-pulse mode were smaller than those in the continuous mode. There were statistically significant differences between values about LV load and pulsatility in counter-pulse and co-pulse mode; continuous/counter-pulse/co-pulse SW (ml·mmHg); 2601.71±2208.50/2127.88±1745.11/2845.54±2230.82 (100%): 2968.05±2265.75/2485.79±1714.07/3133.92±2203.64 (75%): 3134.42±2319.75/2927.76±2111.76/3313.13±2350.49 (50%), LVEDV (ml); 52.79±26.43/46.95±20.31/56.32±25.43 (100%): 57.22±28.19/51.38±21.83/59.06±26.63 (75%), 59.59±28.24/56.02±25.40/61.88±28.61 (50%), PP (mmHg); 30.09±10.01/22.63±8.09/36.79±9.32 (100%): 32.88±9.88/26.66±10.80/40.69±7.83 (75%): 37.30±10.86/32.76±12.43/41.97±9.59 (50%), mean dP/dt max of aortic pressure (mmHg/s); 979.89±369.20/687.98±230.12/1165.67±391.36 (100%): 1321.25±446.14/1047.78±393.29/1474.99±474.53 (75%): 1511.06±519.18/1332.6±471.91/1666.59±511.55 (50%). Conclusions: Our newly developed system could control LV load and pulsatility in the chronic awake phase. This system may provide the most favorable LV loading conditions for the recovery of the native heart.

scholarly journals Badminton motion capture with visual image detection of picking robotics

2020 ◽  
Vol 17 (6) ◽  
pp. 172988142096907
Author(s):  
Changxin Li
Keyword(s):  
Image Processing ◽  
Control System ◽  
Motion Capture ◽  
High Precision ◽  
Target Recognition ◽  
Vision System ◽  
Recognition Rate ◽  
Motion Capture System ◽  
Vision Control ◽  
Edge Features

In the process of strawberry easily broken fruit picking, in order to reduce the damage rate of the fruit, improves accuracy and efficiency of picking robot, field put forward a motion capture system based on international standard badminton edge feature detection and capture automation algorithm process of night picking robot badminton motion capture techniques training methods. The badminton motion capture system can analyze the game video in real time and obtain the accuracy rate of excellent badminton players and the technical characteristics of badminton motion capture through motion capture. The purpose of this article is to apply the high-precision motion capture vision control system to the design of the vision control system of the robot in the night picking process, so as to effectively improve the observation and recognition accuracy of the robot in the night picking process, so as to improve the degree of automation of the operation. This paper tests the reliability of the picking robot vision system. Taking the environment of picking at night as an example, image processing was performed on the edge features of the fruits picked by the picking robot. The results show that smooth and enhanced image processing can successfully extract edge features of fruit images. The accuracy of the target recognition rate and the positioning ability of the vision system of the picking robot were tested by the edge feature test. The results showed that the accuracy of the target recognition rate and the positioning ability of the motion edge of the vision system were far higher than 91%, satisfying the automation demand of the picking robot operation with high precision.

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