scholarly journals Real‐time surgical instrument detection in robot‐assisted surgery using a convolutional neural network cascade

2019 ◽  
Vol 6 (6) ◽  
pp. 275-279 ◽  
Author(s):  
Zijian Zhao ◽  
Tongbiao Cai ◽  
Faliang Chang ◽  
Xiaolin Cheng
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Real Time ◽  
Surgical Instrument ◽  
Robot Assisted ◽  
Robot Assisted Surgery

scholarly journals Telemanipulation of an Articulated Robotic Arm using a Commercial Virtual Reality Controller

2020 ◽  
Vol 6 (3) ◽  
pp. 127-130
Author(s):  
Max B. Schäfer ◽  
Kent W. Stewart ◽  
Nico Lösch ◽  
Peter P. Pott
Keyword(s):  
Virtual Reality ◽  
Real Time ◽  
Robotic Arm ◽  
Input Device ◽  
Robot Assisted ◽  
Promising Alternative ◽  
Input Devices ◽  
Current Input ◽  
Current Systems ◽  
Robot Assisted Surgery

AbstractAccess to systems for robot-assisted surgery is limited due to high costs. To enable widespread use, numerous issues have to be addressed to improve and/or simplify their components. Current systems commonly use universal linkage-based input devices, and only a few applicationoriented and specialized designs are used. A versatile virtual reality controller is proposed as an alternative input device for the control of a seven degree of freedom articulated robotic arm. The real-time capabilities of the setup, replicating a system for robot-assisted teleoperated surgery, are investigated to assess suitability. Image-based assessment showed a considerable system latency of 81.7 ± 27.7 ms. However, due to its versatility, the virtual reality controller is a promising alternative to current input devices for research around medical telemanipulation systems.

scholarly journals Real Time Multipurpose Smart Waste Classification Model for Efficient Recycling in Smart Cities Using Multilayer Convolutional Neural Network and Perceptron

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4916
Author(s):  
Ali Usman Gondal ◽  
Muhammad Imran Sadiq ◽  
Tariq Ali ◽  
Muhammad Irfan ◽  
Ahmad Shaf ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Real Time ◽  
Multilayer Perceptron ◽  
Smart Cities ◽  
Classification Model ◽  
Rapid Urbanization ◽  
Metal Waste ◽  
Waste Classification ◽  
The City

Urbanization is a big concern for both developed and developing countries in recent years. People shift themselves and their families to urban areas for the sake of better education and a modern lifestyle. Due to rapid urbanization, cities are facing huge challenges, one of which is waste management, as the volume of waste is directly proportional to the people living in the city. The municipalities and the city administrations use the traditional wastage classification techniques which are manual, very slow, inefficient and costly. Therefore, automatic waste classification and management is essential for the cities that are being urbanized for the better recycling of waste. Better recycling of waste gives the opportunity to reduce the amount of waste sent to landfills by reducing the need to collect new raw material. In this paper, the idea of a real-time smart waste classification model is presented that uses a hybrid approach to classify waste into various classes. Two machine learning models, a multilayer perceptron and multilayer convolutional neural network (ML-CNN), are implemented. The multilayer perceptron is used to provide binary classification, i.e., metal or non-metal waste, and the CNN identifies the class of non-metal waste. A camera is placed in front of the waste conveyor belt, which takes a picture of the waste and classifies it. Upon successful classification, an automatic hand hammer is used to push the waste into the assigned labeled bucket. Experiments were carried out in a real-time environment with image segmentation. The training, testing, and validation accuracy of the purposed model was 0.99% under different training batches with different input features.

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