A quality analysis of the Vectrino II instrument using a new open-source MATLAB toolbox and 2D ARMA models to detect and replace spikes

2014 ◽  
pp. 1951-1959 ◽  
Author(s):  
B MacVicar ◽  
Scott Dilling ◽  
J Lacey ◽  
K Hipel
Author(s):  
Alexander Boll ◽  
Florian Brokhausen ◽  
Tiago Amorim ◽  
Timo Kehrer ◽  
Andreas Vogelsang

AbstractSimulink is an example of a successful application of the paradigm of model-based development into industrial practice. Numerous companies create and maintain Simulink projects for modeling software-intensive embedded systems, aiming at early validation and automated code generation. However, Simulink projects are not as easily available as code-based ones, which profit from large publicly accessible open-source repositories, thus curbing empirical research. In this paper, we investigate a set of 1734 freely available Simulink models from 194 projects and analyze their suitability for empirical research. We analyze the projects considering (1) their development context, (2) their complexity in terms of size and organization within projects, and (3) their evolution over time. Our results show that there are both limitations and potentials for empirical research. On the one hand, some application domains dominate the development context, and there is a large number of models that can be considered toy examples of limited practical relevance. These often stem from an academic context, consist of only a few Simulink blocks, and are no longer (or have never been) under active development or maintenance. On the other hand, we found that a subset of the analyzed models is of considerable size and complexity. There are models comprising several thousands of blocks, some of them highly modularized by hierarchically organized Simulink subsystems. Likewise, some of the models expose an active maintenance span of several years, which indicates that they are used as primary development artifacts throughout a project’s lifecycle. According to a discussion of our results with a domain expert, many models can be considered mature enough for quality analysis purposes, and they expose characteristics that can be considered representative for industry-scale models. Thus, we are confident that a subset of the models is suitable for empirical research. More generally, using a publicly available model corpus or a dedicated subset enables researchers to replicate findings, publish subsequent studies, and use them for validation purposes. We publish our dataset for the sake of replicating our results and fostering future empirical research.


2020 ◽  
Vol 196 ◽  
pp. 105716
Author(s):  
Zachary A. Vesoulis ◽  
Paul G. Gamble ◽  
Siddharth Jain ◽  
Nathalie M. El Ters ◽  
Steve M. Liao ◽  
...  

2017 ◽  
Vol 65 (3) ◽  
Author(s):  
Wolfgang Doneit ◽  
Ralf Mikut ◽  
Lutz Gröll ◽  
Tim Pychynski ◽  
Markus Reischl
Keyword(s):  

ZusammenfassungIn diesem Beitrag wird DaMoQ vorgestellt, eine Erweiterung zur MATLAB-Toolbox SciXMiner, um die Datenqualität von Eingangsdaten für Regressionen zu bewerten. Bei SciXMiner handelt es sich um eine Open-Source-MATLAB-Toolbox zur automatisierten Bild- und Datenanalyse. In DaMoQ werden verschiedene Muster ungleichmäßiger Verteilungen in Datensätzen quantifiziert und visualisiert. Während die Visualisierungen dem Anwender einen schnellen Einblick in den vorliegenden Datensatz geben, wird die Quantifizierung für eine aggregierte Bewertung der einzelnen Eingangsgrößen sowie des Datensatzes genutzt. Anhand Benchmark-Datensätze und einer Anwendung für Labyrinthdichtungen wird gezeigt, dass die Kriterien und Visualisierungen von DaMoQ nicht nur ähnliche Ergebnisse wie eine visuelle Analyse der Streuwolkendiagramme liefern, sondern auch visuell schwer erkennbare Phänomene identifizieren.


2018 ◽  
Vol 159 (4) ◽  
pp. 1105-1111 ◽  
Author(s):  
Sándor Zsebők ◽  
György Blázi ◽  
Miklós Laczi ◽  
Gergely Nagy ◽  
Éva Vaskuti ◽  
...  

2015 ◽  
Vol 192 ◽  
pp. 348-362 ◽  
Author(s):  
Bertrand Thierry ◽  
Xavier Antoine ◽  
Chokri Chniti ◽  
Hasan Alzubaidi

Cells ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 397
Author(s):  
Boyoung Kim

To investigate the cellular structure, biomedical researchers often obtain three-dimensional images by combining two-dimensional images taken along the z axis. However, these images are blurry in all directions due to diffraction limitations. This blur becomes more severe when focusing further inside the specimen as photons in deeper focus must traverse a longer distance within the specimen. This type of blur is called depth-variance. Moreover, due to lens imperfection, the blur has asymmetric shape. Most deconvolution solutions for removing blur assume depth-invariant or x-y symmetric blur, and presently, there is no open-source for depth-variant asymmetric deconvolution. In addition, existing datasets for deconvolution microscopy also assume invariant or x-y symmetric blur, which are insufficient to reflect actual imaging conditions. DVDeconv, that is a set of MATLAB functions with a user-friendly graphical interface, has been developed to address depth-variant asymmetric blur. DVDeconv includes dataset, depth-variant asymmetric point spread function generator, and deconvolution algorithms. Experimental results using DVDeconv reveal that depth-variant asymmetric deconvolution using DVDeconv removes blurs accurately. Furthermore, the dataset in DVDeconv constructed can be used to evaluate the performance of microscopy deconvolution to be developed in the future.


2021 ◽  
Vol 10 (4) ◽  
pp. 267
Author(s):  
Inder Tecuapetla-Gómez ◽  
Gerardo López-Saldaña ◽  
María Isabel Cruz-López ◽  
Rainer Ressl

Earth observation (EO) data play a crucial role in monitoring ecosystems and environmental processes. Time series of satellite data are essential for long-term studies in this context. Working with large volumes of satellite data, however, can still be a challenge, as the computational environment with respect to storage, processing and data handling can be demanding, which sometimes can be perceived as a barrier when using EO data for scientific purposes. In particular, open-source developments which comprise all components of EO data handling and analysis are still scarce. To overcome this difficulty, we present Tools for Analyzing Time Series of Satellite Imagery (TATSSI), an open-source platform written in Python that provides routines for downloading, generating, gap-filling, smoothing, analyzing and exporting EO time series. Since TATSSI integrates quality assessment and quality control flags when generating time series, data quality analysis is the backbone of any analysis made with the platform. We discuss TATSSI’s 3-layered architecture (data handling, engine and three application programming interfaces (API)); by allowing three APIs (a native graphical user interface, some Jupyter Notebooks and the Python command line) this development is exceptionally user-friendly. Furthermore, to demonstrate the application potential of TATSSI, we evaluated MODIS time series data for three case studies (irrigation area changes, evaluation of moisture dynamics in a wetland ecosystem and vegetation monitoring in a burned area) in different geographical regions of Mexico. Our analyses were based on methods such as the spatio-temporal distribution of maxima over time, statistical trend analysis and change-point decomposition, all of which were implemented in TATSSI. Our results are consistent with other scientific studies and results in these areas and with related in-situ data.


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