Real-Time High-Accuracy Micropipette Aspiration for Characterizing Mechanical Properties of Biological Cells

2007 ◽  
Author(s):  
Xinyu Liu ◽  
Yifei Wang ◽  
Yu Sun
Keyword(s):  
Mechanical Properties ◽  
Real Time ◽  
High Accuracy ◽  
Micropipette Aspiration ◽  
Biological Cells

Real Time Person Recognition and Attendance Marking Using Holistic Based Approach

2017 ◽  
Vol 7 (7) ◽  
pp. 302
Author(s):  
Reshma P ◽  
Muneer VK ◽  
Muhammed Ilyas P
Keyword(s):  
Face Recognition ◽  
Real Time ◽  
High Accuracy ◽  
Efficient Technique ◽  
Human Face ◽  
Traditional Methods ◽  
Person Recognition ◽  
The Face

Face recognition is a challenging task for the researches. It is very useful for personal verification and recognition and also it is very difficult to implement due to all different situation that a human face can be found. This system makes use of the face recognition approach for the computerized attendance marking of students or employees in the room environment without lectures intervention or the employee. This system is very efficient and requires very less maintenance compared to the traditional methods. Among existing methods PCA is the most efficient technique. In this project Holistic based approach is adapted. The system is implemented using MATLAB and provides high accuracy.

scholarly journals Augmented Reality Maintenance Assistant Using YOLOv5

2021 ◽  
Vol 11 (11) ◽  
pp. 4758
Author(s):  
Ana Malta ◽  
Mateus Mendes ◽  
Torres Farinha
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Object Recognition ◽  
Augmented Reality ◽  
Real Time ◽  
Recognition System ◽  
High Accuracy ◽  
Video Streams ◽  
The Neural Network ◽  
Deep Learning Neural Network

Maintenance professionals and other technical staff regularly need to learn to identify new parts in car engines and other equipment. The present work proposes a model of a task assistant based on a deep learning neural network. A YOLOv5 network is used for recognizing some of the constituent parts of an automobile. A dataset of car engine images was created and eight car parts were marked in the images. Then, the neural network was trained to detect each part. The results show that YOLOv5s is able to successfully detect the parts in real time video streams, with high accuracy, thus being useful as an aid to train professionals learning to deal with new equipment using augmented reality. The architecture of an object recognition system using augmented reality glasses is also designed.

scholarly journals High-Accuracy Tracking Using Ultrawideband Signals for Enhanced Safety of Cyclists

2017 ◽  
Vol 2017 ◽  
pp. 1-13 ◽  
Author(s):  
Davide Dardari ◽  
Nicoló Decarli ◽  
Anna Guerra ◽  
Ashraf Al-Rimawi ◽  
Víctor Marín Puchades ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Real Time ◽  
High Accuracy ◽  
Experimental Results ◽  
Localization Error ◽  
Localization System ◽  
Dynamic Conditions ◽  
The Road ◽  
Road Users

In this paper, an ultrawideband localization system to improve the cyclists’ safety is presented. The architectural solutions proposed consist of tags placed on bikes, whose positions have to be estimated, and anchors, acting as reference nodes, located at intersections and/or on vehicles. The peculiarities of the localization system in terms of accuracy and cost enable its adoption with enhanced risk assessment units situated on the infrastructure/vehicle, depending on the architecture chosen, as well as real-time warning to the road users. Experimental results reveal that the localization error, in both static and dynamic conditions, is below 50 cm in most of the cases.

Vision Based Real-time High-accuracy Automatic Counting with Applications for Smart Pharmacy

2021 ◽  
Author(s):  
Haotian Wu ◽  
YuRan Wang ◽  
Hongbin Ma ◽  
Baokui Li ◽  
Ying Jin
Keyword(s):  
Real Time ◽  
High Accuracy ◽  
Automatic Counting

Electrostatic Sensor for Real–Time Mass Flow Rate Measurement of Particle Conveying in Pneumatic Pipeline

2012 ◽  
Author(s):  
Mohd. Fua’ad Rahmat ◽  
Wee Lee Yaw
Keyword(s):  
Real Time ◽  
Mass Flow Rate ◽  
Mass Flow ◽  
Flow Rate ◽  
Cross Correlation ◽  
High Accuracy ◽  
Industrial Processes ◽  
Rate Measurement ◽  
Flow Rate Measurement ◽  
Controlled Flow

This paper discussed the electrostatic sensors that have been constructed for real–time mass flow rate measurement of particle conveying in a Pneumatic pipeline. Many industrial processes require continuous, smooth, and consistent delivery of solids materials with a high accuracy of controlled flow rate. This requirement can only be achieved by installing a proper measurement system. Electrostatic sensor offers the most inexpensive and simplest means of measuring solids flows in pipes. Key words: Electrostatic sensor, cross-correlation, peripheral velocity

scholarly journals Assessment of the Accuracy of a Multi-Beam LED Scanner Sensor for Measuring Olive Canopies

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4406 ◽  
Author(s):  
Rafael Sola-Guirado ◽  
Sergio Bayano-Tejero ◽  
Antonio Rodríguez-Lizana ◽  
Jesús Gil-Ribes ◽  
Antonio Miranda-Fuentes
Keyword(s):  
Laboratory Test ◽  
Real Time ◽  
Relative Error ◽  
Laser Scanner ◽  
Field Tests ◽  
High Accuracy ◽  
Field Conditions ◽  
Canopy Trees ◽  
Canopy Volume

Canopy characterization has become important when trying to optimize any kind of agricultural operation in high-growing crops, such as olive. Many sensors and techniques have reported satisfactory results in these approaches and in this work a 2D laser scanner was explored for measuring canopy trees in real-time conditions. The sensor was tested in both laboratory and field conditions to check its accuracy, its cone width, and its ability to characterize olive canopies in situ. The sensor was mounted on a mast and tested in laboratory conditions to check: (i) its accuracy at different measurement distances; (ii) its measurement cone width with different reflectivity targets; and (iii) the influence of the target’s density on its accuracy. The field tests involved both isolated and hedgerow orchards, in which the measurements were taken manually and with the sensor. The canopy volume was estimated with a methodology consisting of revolving or extruding the canopy contour. The sensor showed high accuracy in the laboratory test, except for the measurements performed at 1.0 m distance, with 60 mm error (6%). Otherwise, error remained below 20 mm (1% relative error). The cone width depended on the target reflectivity. The accuracy decreased with the target density.

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