Research on Data Fusion Algorithm Based on Fuzzy Mathematics and Principal Component Analysis

2019 ◽  
Vol 08 (05) ◽  
pp. 953-957
Author(s):  
晚霞 徐
Keyword(s):  
Principal Component Analysis ◽  
Data Fusion ◽  
Principal Component ◽  
Component Analysis ◽  
Fuzzy Mathematics ◽  
Fusion Algorithm

scholarly journals Image Fusion Based on Principal Component Analysis and Slicing Image Transformation

2018 ◽  
Vol 210 ◽  
pp. 04020
Author(s):  
Leonardo Acho ◽  
Pablo Buenestado
Keyword(s):  
Principal Component Analysis ◽  
Data Fusion ◽  
Image Fusion ◽  
Computing Time ◽  
Principal Component ◽  
Component Analysis ◽  
Image Transformation ◽  
Source Information ◽  
Fusion Algorithm ◽  
Neural Data

Image fusion deals with the ability to integrate data from image sensors at different instants when the source information is uncertain. Although there exist many techniques on the subject, in this paper, we develop two originative techniques based on principal component analysis and slicing image transformation to efficiently fuse a small set of noisy images. For instance, in neural data fusion, this approach requires a considerable number of corrupted images to efficiently produce the desired outcome and also requiring a considerable computing time because of the dynamics involved in the fusion data process. In our approaches, the computation time is considerably smaller. This results appealing to increasing feasibility, for instance, in remote sensing or wireless sensor network. Moreover, and according to our numerical experiments, when our methods are compared against the neural data fusion algorithm, they present better performance.

scholarly journals Ancient human bones studied and compared by near infrared spectroscopy, thermogravimetry and chemometrics

2018 ◽  
Vol 27 (1) ◽  
pp. 6-14 ◽  
Author(s):  
Alessandra Biancolillo ◽  
Mauro Tomassetti ◽  
Remo Bucci ◽  
Simona Izzo ◽  
Francesca Candilio ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Infrared Spectroscopy ◽  
Data Fusion ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Principal Component ◽  
Component Analysis ◽  
Human Bones ◽  
Fusion Approach ◽  
Ancient Human Bones

Near infrared spectroscopy and thermogravimetry have been coupled with chemometric exploratory methods in order to investigate ancient (pre-Roman/Roman) human bones from two different necropolises in Central-South Italy (Cavo degli Zucchi and Elea Velia). These findings have been investigated by principal component analysis and they have also been compared with ancient human bones from two Sudanese necropolises (Saggai and Geili). Samples coming from African and European necropolises, mainly differ in two aspects: the burial procedures and their historical period. The ritual applied in the European region involved cremation, while the one applied in the African necropolises did not. Bones from Italian sites (Cavo degli Zucchi and Elea Velia) are Pre-Roman/Roman while the others (from middle Nile) come from the Prehistoric, Meroitic, and Christian Sudanese age. Near infrared spectroscopy and thermogravimetric measures have been analysed either individually or by a mid-level data-fusion approach. Principal component analysis of the near infrared spectroscopy data allowed differentiation between burnt and unburnt samples, while from the scores plots extracted from the principal component analysis model based on the entire derived thermograms, it was possible to recognize the different clusters related to the various dating of samples. The data-fusion analysis led to considerations similar to those obtained from the model based on thermogravimetry data. Finally, instead of inspecting the entire thermogravimetry curves, principal component analysis was carried out on carbonates, total collagen and water losses only. In this case, the data-fusion approach has led to extremely interesting results; in fact, this model clearly shows that samples group in separate clusters in agreement with their age and the different burial rituals.

Assessing mineral profiles for rice flour fraud detection by principal component analysis based data fusion

2021 ◽  
Vol 339 ◽  
pp. 128125
Author(s):  
Michael Pérez-Rodríguez ◽  
Pamela Maia Dirchwolf ◽  
Zenaida Rodríguez-Negrín ◽  
Roberto Gerardo Pellerano
Keyword(s):  
Principal Component Analysis ◽  
Data Fusion ◽  
Fraud Detection ◽  
Principal Component ◽  
Rice Flour ◽  
Component Analysis

SAR Data Fusion Using Nonlinear Principal Component Analysis

2020 ◽  
Vol 17 (9) ◽  
pp. 1543-1547
Author(s):  
Luca Fasano ◽  
Daniele Latini ◽  
Alina Machidon ◽  
Chiara Clementini ◽  
Giovanni Schiavon ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Data Fusion ◽  
Principal Component ◽  
Component Analysis ◽  
Nonlinear Principal Component Analysis ◽  
Sar Data
Sign in / Sign up

Export Citation Format

Share Document