Automatic Integration of HDL IPs in Simulink Using FMI and S-Function Interfaces

2018 ◽  
pp. 1-23
Author(s):  
Stefano Centomo ◽  
Michele Lora ◽  
Antonio Portaluri ◽  
Francesco Stefanni ◽  
Franco Fummi
Keyword(s):  
Automatic Integration

ConnectOpen - automatic integration of IoT devices

2015 ◽  
Author(s):  
Nuria Pazos ◽  
Michael Muller ◽  
Marco Aeberli ◽  
Nabil Ouerhani
Keyword(s):  
Automatic Integration ◽  
Iot Devices

scholarly journals Conception of Geomagnetic Data Integrated Space

2019 ◽  
Vol 18 (2) ◽  
pp. 390-415
Author(s):  
Andrei Vorobev ◽  
Gulnara Vorobeva ◽  
Nafisa Yusupova
Keyword(s):  
Time Series ◽  
Relevant Information ◽  
Data Sets ◽  
Spatial Anisotropy ◽  
Automatic Integration ◽  
Final Sample ◽  
Geomagnetic Data ◽  
Consolidation Model ◽  
Discretization Step ◽  
Original Time

. As is known, today the problem of geomagnetic field and its variations parameters monitoring is solved mainly by a network of magnetic observatories and variational stations, but a significant obstacle in the processing and analysis of the data thus obtained, along with their spatial anisotropy, are omissions or reliable inconsistency with the established format. Heterogeneity and anomalousness of the data excludes (significantly complicates) the possibility of their automatic integration and the application of frequency analysis tools to them. Known solutions for the integration of heterogeneous geomagnetic data are mainly based on the consolidation model and only partially solve the problem. The resulting data sets, as a rule, do not meet the requirements for real-time information systems, may include outliers, and omissions in the time series of geomagnetic data are eliminated by excluding missing or anomalous values from the final sample, which can obviously lead to both to the loss of relevant information, violation of the discretization step, and to heterogeneity of the time series. The paper proposes an approach to creating an integrated space of geomagnetic data based on a combination of consolidation and federalization models, including preliminary processing of the original time series with an optionally available procedure for their recovery and verification, focused on the use of cloud computing technologies and hierarchical format and processing speed of large amounts of data and, as a result, providing users with better and more homogeneous data.

scholarly journals Improved Crop Classification with Rotation Knowledge using Sentinel-1 and -2 Time Series

2020 ◽  
Vol 86 (7) ◽  
pp. 431-441 ◽  
Author(s):  
Sébastien Giordano ◽  
Simon Bailly ◽  
Loic Landrieu ◽  
Nesrine Chehata
Keyword(s):  
Time Series ◽  
Conditional Random Field ◽  
Temporal Structure ◽  
Linear Chain ◽  
Agricultural Practices ◽  
Annual Crops ◽  
Automatic Integration ◽  
Crop Type ◽  
Crop Types ◽  
Type Classification

Leveraging the recent availability of accurate, frequent, and multimodal (radar and optical) Sentinel-1 and -2 acquisitions, this paper investigates the automation of land parcel identi- fication system (LPIS ) crop type classification. Our approach allows for the automatic integration of temporal knowledge, i.e., crop rotations using existing parcel-based land cover databases and multi-modal Sentinel-1 and -2 time series. The temporal evolution of crop types was modeled with a linear- chain conditional random field, trained with time series of multi-modal (radar and optical) satellite acquisitions and associated LPIS. Our model was tested on two study areas in France (≥ 1250 km2) which show different crop types, various parcel sizes, and agricultural practices: . the Seine et Marne and the Alpes de Haute-Provence classified accordingly to a fine national 25-class nomenclature. We first trained a Random Forest classifier without temporal structure to achieve 89.0% overall accuracy in Seine et Marne (10 classes) and 73% in Alpes de Haute-Provence (14 classes). We then demonstrated experimentally that taking into account the temporal structure of crop rotation with our model resulted in an increase of 3% to +5% in accuracy. This increase was especially important (+12%) for classes which were poorly classified without using the temporal structure. A stark posi- tive impact was also demonstrated on permanent crops, while it was fairly limited or even detrimental for annual crops.

Laboratory chromatograph with automatic integration

1965 ◽  
Vol 8 (11) ◽  
pp. 1060-1061
Author(s):  
L. N. Gal'perin ◽  
L. B. Mashkinov ◽  
D. N. Sokolov
Keyword(s):  
Automatic Integration

Algorithm 22 Algorithm for the automatic integration of highly oscillatory functions

Computing ◽  
1974 ◽  
Vol 13 (2) ◽  
pp. 183-193 ◽  
Author(s):  
R. Piessens ◽  
Ann Haegemans
Keyword(s):  
Automatic Integration ◽  
Highly Oscillatory

Automatic Integration and Querying of Semantic Rich Heterogeneous Data

2017 ◽  
pp. 251-273 ◽  
Author(s):  
Muhammad Rizwan Saeed ◽  
Charalampos Chelmis ◽  
Viktor K. Prasanna
Keyword(s):  
Heterogeneous Data ◽  
Automatic Integration
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