Experimental Behavior and Performance Evaluation of a Large-Scale Composite MRF System under Seismic Loading

2007 ◽  
Author(s):  
James Ricles ◽  
Richard Sause ◽  
Ricardo Herrera
Keyword(s):  
Performance Evaluation ◽  
Large Scale ◽  
Seismic Loading ◽  
Experimental Behavior ◽  
And Performance

scholarly journals Sharing Pandemic Vaccination Certificates through Blockchain: Case Study and Performance Evaluation

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
José L. Hernández-Ramos ◽  
Georgios Karopoulos ◽  
Dimitris Geneiatakis ◽  
Tania Martin ◽  
Georgios Kambourakis ◽  
...  
Keyword(s):  
European Union ◽  
Performance Evaluation ◽  
Large Scale ◽  
Evaluation Criteria ◽  
Use Case ◽  
The European Union ◽  
Satisfactory Performance ◽  
And Performance ◽  
Network Response Time

During 2021, different worldwide initiatives have been established for the development of digital vaccination certificates to alleviate the restrictions associated with the COVID-19 pandemic to vaccinated individuals. Although diverse technologies can be considered for the deployment of such certificates, the use of blockchain has been suggested as a promising approach due to its decentralization and transparency features. However, the proposed solutions often lack realistic experimental evaluation that could help to determine possible practical challenges for the deployment of a blockchain platform for this purpose. To fill this gap, this work introduces a scalable, blockchain-based platform for the secure sharing of COVID-19 or other disease vaccination certificates. As an indicative use case, we emulate a large-scale deployment by considering the countries of the European Union. The platform is evaluated through extensive experiments measuring computing resource usage, network response time, and bandwidth. Based on the results, the proposed scheme shows satisfactory performance across all major evaluation criteria, suggesting that it can set the pace for real implementations. Vis-à-vis the related work, the proposed platform is novel, especially through the prism of a large-scale, full-fledged implementation and its assessment.

scholarly journals Evaluation of Bag of Visual Words for Category Level Object Recognition

2013 ◽  
pp. 224-230
Author(s):  
K. S. Sujatha ◽  
G. M. Karthiga ◽  
B. Vinod
Keyword(s):  
Performance Evaluation ◽  
Object Recognition ◽  
Large Scale ◽  
Recognition Performance ◽  
Bag Of Visual Words ◽  
Classification Problems ◽  
Visual Words ◽  
Knn Classifier ◽  
Real World Datasets ◽  
And Performance

Object recognition in a large scale collection of images has become an important application in machine vision. The recent advances in the object or image recognition for classification of objects shows that Bag-of-visual words approach is a better method for image classification problems. In this work, the effect of different possible parameters and performance evaluation of Bag of visual words approach in terms of their recognition performance such as Accuracy rate, Precision and F1 measure using 8 different classes of real world datasets that are commonly used in restaurant applications is explored. The system presented here is based on visual vocabulary. Features are extracted, clustered, trained and evaluated on an image database of 1600 images of different categories. To validate the obtained results,a performance evaluation on vehicle datasetsunder SURF and SIFT descriptors with Kmeans and K-medoid clustering and KNN classifier has been made. Among these SURF K-means performs better.

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