Integrated Software MEGMRIAn for the Analysis and Modeling of the Magnetic Encephalography Data

2013 ◽  
Vol 8 (2) ◽  
pp. 691-707 ◽  
Author(s):  
М.Н. Устинин ◽  
M.N. Ustinin
Keyword(s):  
Magnetic Encephalography ◽  
Integrated Software

Recent Patents on Integrated Software / Hardware GMR - Based Systems and Applications for PCB Inspection

2009 ◽  
Vol 2 (2) ◽  
pp. 82-91 ◽  
Author(s):  
Giuseppe Megali ◽  
Matteo Cacciola ◽  
Diego Pellicano ◽  
Francesco Morabito
Keyword(s):  
Integrated Software

scholarly journals Deep Learning Techniques for Grape Plant Species Identification in Natural Images

Sensors ◽  
2019 ◽  
Vol 19 (22) ◽  
pp. 4850 ◽  
Author(s):  
Carlos S. Pereira ◽  
Raul Morais ◽  
Manuel J. C. S. Reis
Keyword(s):  
Transfer Learning ◽  
Climatic Conditions ◽  
Fine Tuning ◽  
Variety Identification ◽  
Test Accuracy ◽  
Accuracy Score ◽  
Learning Techniques ◽  
Four Corners ◽  
Integrated Software ◽  
Grape Varieties

Frequently, the vineyards in the Douro Region present multiple grape varieties per parcel and even per row. An automatic algorithm for grape variety identification as an integrated software component was proposed that can be applied, for example, to a robotic harvesting system. However, some issues and constraints in its development were highlighted, namely, the images captured in natural environment, low volume of images, high similarity of the images among different grape varieties, leaf senescence, and significant changes on the grapevine leaf and bunch images in the harvest seasons, mainly due to adverse climatic conditions, diseases, and the presence of pesticides. In this paper, the performance of the transfer learning and fine-tuning techniques based on AlexNet architecture were evaluated when applied to the identification of grape varieties. Two natural vineyard image datasets were captured in different geographical locations and harvest seasons. To generate different datasets for training and classification, some image processing methods, including a proposed four-corners-in-one image warping algorithm, were used. The experimental results, obtained from the application of an AlexNet-based transfer learning scheme and trained on the image dataset pre-processed through the four-corners-in-one method, achieved a test accuracy score of 77.30%. Applying this classifier model, an accuracy of 89.75% on the popular Flavia leaf dataset was reached. The results obtained by the proposed approach are promising and encouraging in helping Douro wine growers in the automatic task of identifying grape varieties.

scholarly journals AssociationViewer: a scalable and integrated software tool for visualization of large-scale variation data in genomic context

2009 ◽  
Vol 25 (5) ◽  
pp. 662-663 ◽  
Author(s):  
Olivier Martin ◽  
Armand Valsesia ◽  
Amalio Telenti ◽  
Ioannis Xenarios ◽  
Brian J. Stevenson
Keyword(s):  
Large Scale ◽  
Software Tool ◽  
Genomic Context ◽  
Variation Data ◽  
Integrated Software ◽  
Scale Variation

Building integrated software development environments. Part I

1992 ◽  
Vol 1 (2) ◽  
pp. 135-167 ◽  
Author(s):  
G. Engels ◽  
C. Lewerentz ◽  
M. Nagl ◽  
W. Schäfer ◽  
A. Schürr
Keyword(s):  
Software Development ◽  
Integrated Software ◽  
Software Development Environments
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