Low level data fusion of laser and monocular color camera using occupancy grid framework

2010 ◽  
Author(s):  
Qadeer Baig ◽  
Olivier Aycard
Keyword(s):  
Data Fusion ◽  
Low Level ◽  
Occupancy Grid ◽  
Level Data

scholarly journals Assessing Geographical Origin of Gentiana Rigescens Using Untargeted Chromatographic Fingerprint, Data Fusion and Chemometrics

Molecules ◽  
2019 ◽  
Vol 24 (14) ◽  
pp. 2562 ◽  
Author(s):  
Shen ◽  
Yu ◽  
Wang
Keyword(s):  
Data Fusion ◽  
Geographical Origin ◽  
Data Sets ◽  
Classification Models ◽  
Hplc Fingerprint ◽  
Low Level ◽  
Level Data ◽  
Stems And Leaves ◽  
Gentiana Rigescens ◽  
Fingerprint Data

Gentiana rigescens Franchet, which is famous for its bitter properties, is a traditional drug of chronic hepatitis and important raw materials for the pharmaceutical industry in China. In the study, high-performance liquid chromatography (HPLC), coupled with diode array detector (DAD) and chemometrics, were used to investigate the chemical geographical variation of G. rigescens and to classify medicinal materials, according to their grown latitudes. The chromatographic fingerprints of 280 individuals and 840 samples from rhizomes, stems, and leaves of four different latitude areas were recorded and analyzed for tracing the geographical origin of medicinal materials. At first, HPLC fingerprints of underground and aerial parts were generated while using reversed-phase liquid chromatography. After the preliminary data exploration, two supervised pattern recognition techniques, random forest (RF) and orthogonal partial least-squares discriminant analysis (OPLS-DA), were applied to the three HPLC fingerprint data sets of rhizomes, stems, and leaves, respectively. Furthermore, fingerprint data sets of aerial and underground parts were separately processed and joined while using two data fusion strategies (“low-level” and “mid-level”). The results showed that classification models that are based OPLS-DA were more efficient than RF models. The classification models using low-level data fusion method built showed considerably good recognition and prediction abilities (the accuracy is higher than 99% and sensibility, specificity, Matthews correlation coefficient, and efficiency range from 0.95 to 1.00). Low-level data fusion strategy combined with OPLS-DA could provide the best discrimination result. In summary, this study explored the latitude variation of phytochemical of G. rigescens and developed a reliable and accurate identification method for G. rigescens that were grown at different latitudes based on untargeted HPLC fingerprint, data fusion, and chemometrics. The study results are meaningful for authentication and the quality control of Chinese medicinal materials.

A Threat Assessment Method Based on Hierarchies and Modules

2014 ◽  
Vol 18 (1) ◽  
pp. 93-99
Author(s):  
Fang Deng ◽  
◽  
Xinan Liu ◽  
Zhihong Peng ◽  
Jie Chen
Keyword(s):  
Data Fusion ◽  
Threat Assessment ◽  
Assessment Method ◽  
Fusion Technology ◽  
Low Level ◽  
Information Classification ◽  
Level Data ◽  
High Level

With the development of low-level data fusion technology, threat assessment, which is a part of high-level data fusion, is recognized by an increasing numbers of people. However, the method to solve the problem of threat assessment for various kinds of targets and attacks is unknown. Hence, a threat assessment method is proposed in this paper to solve this problem. This method includes tertiary assessments: information classification, reorganization, and summary. In the tertiary assessments model, various threats with multi-class targets and attacks can be comprehensively assessed. A case study with specific algorithms and scenarios is shown to prove the validity and rationality of this method.

Calibration and low-level data fusion algorithms for a parallel 2D/3D-camera

2011 ◽  
Vol 12 (1) ◽  
pp. 37-47 ◽  
Author(s):  
T. Hanning ◽  
A. Lasaruk ◽  
T. Tatschke
Keyword(s):  
Data Fusion ◽  
Low Level ◽  
Level Data ◽  
3D Camera

scholarly journals Data Fusion for the Prediction of Elemental Concentrations in Polymetallic Sulphide Ore Using Mid-Wave Infrared and Long-Wave Infrared Reflectance Data

Minerals ◽  
2020 ◽  
Vol 10 (3) ◽  
pp. 235 ◽  
Author(s):  
Feven Desta ◽  
Mike Buxton ◽  
Jeroen Jansen
Keyword(s):  
Data Fusion ◽  
Multivariate Data ◽  
Infrared Reflectance ◽  
Low Level ◽  
Elemental Concentrations ◽  
Long Wave ◽  
Level Data ◽  
Long Wave Infrared ◽  
Plsr Model ◽  
Reflectance Data

The increasing availability of complex multivariate data yielded by sensor technologies permits qualitative and quantitative data analysis for material characterization. Multivariate data are hard to understand by visual inspection and intuition. Thus, data-driven models are required to derive study-specific insights from large datasets. In the present study, a partial least squares regression (PLSR) model was used for the prediction of elemental concentrations using the mineralogical techniques mid-wave infrared (MWIR) and long-wave infrared (LWIR) combined with data fusion approaches. In achieving the study objectives, the usability of the individual MWIR and LWIR datasets for the prediction of the concentration of elements in a polymetallic sulphide deposit was assessed, and the results were compared with the outputs of low- and mid-level data fusion methods. Prior to low-level data fusion implementation, data filtering techniques were applied to the MWIR and LWIR datasets. The pre-processed data were concatenated and a PLSR model was developed using the fused data. The mid-level data fusion was implemented by extracting features using principal component analysis (PCA) scores. As the models were applied to the MWIR, LWIR, and fused datasets, an improved prediction was achieved using the low-level data fusion approach. Overall, the acquired results indicate that the MWIR data can be used to reliably predict a combined Pb–Zn concentration, whereas LWIR data has a good correlation with the Fe concentration. The proposed approach could be extended for generating indicative element concentrations in polymetallic sulphide deposits in real-time using infrared reflectance data. Thus, it is beneficial in providing elemental concentration insights in mining operations.

scholarly journals Low-level detection of language-level data races with LARD

2014 ◽  
Vol 42 (1) ◽  
pp. 671-686 ◽  
Author(s):  
Benjamin P. Wood ◽  
Luis Ceze ◽  
Dan Grossman
Keyword(s):  
Low Level ◽  
Language Level ◽  
Level Data ◽  
Data Races

Image retrieval of MRI brain tumour images based on SVM and FCM approaches

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Sonia Bansal ◽  
Vineet Mehan
Keyword(s):  
Image Retrieval ◽  
Medical Image ◽  
Elevated Level ◽  
Critical Level ◽  
Support Vector ◽  
Resonance Imaging ◽  
Power Structures ◽  
Low Level ◽  
Mri Brain ◽  
Level Data

Abstract Objectives The key test in Content-Based Medical Image Retrieval (CBMIR) frameworks for MRI (Magnetic Resonance Imaging) pictures is the semantic hole between the low-level visual data caught by the MRI machine and the elevated level data seen by the human evaluator. Methods The conventional component extraction strategies centre just on low-level or significant level highlights and utilize some handmade highlights to diminish this hole. It is important to plan an element extraction structure to diminish this hole without utilizing handmade highlights by encoding/consolidating low-level and elevated level highlights. The Fleecy gathering is another packing technique, which is applied in plan depiction here and SVM (Support Vector Machine) is applied. Remembering the predefinition of bunching amount and enlistment cross-section is until now a significant theme, a new predefinition advance is extended in this paper, in like manner, and another CBMIR procedure is suggested and endorsed. It is essential to design a part extraction framework to diminish this opening without using painstakingly gathered features by encoding/joining low-level and critical level features. Results SVM and FCM (Fuzzy C Means) are applied to the power structures. Consequently, the incorporate vector contains all the objectives of the image. Recuperation of the image relies upon the detachment among request and database pictures called closeness measure. Conclusions Tests are performed on the 200 Image Database. Finally, exploratory results are evaluated by the audit and precision.

scholarly journals Low-level detection of language-level data races with LARD

2014 ◽  
Vol 49 (4) ◽  
pp. 671-686 ◽  
Author(s):  
Benjamin P. Wood ◽  
Luis Ceze ◽  
Dan Grossman
Keyword(s):  
Low Level ◽  
Language Level ◽  
Level Data ◽  
Data Races
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