scholarly journals Efficient plotting the functions with discontinuities based on combined sampling

2018 ◽  
Vol 17 (2) ◽  
pp. 9-30
Author(s):  
Tomáš Bayer
Keyword(s):  
Adaptive Sampling ◽  
Automated Detection ◽  
Polygonal Approximation ◽  
Map Projection ◽  
Function Representation ◽  
Contour Lines ◽  
Efficient Sampling ◽  
Adjacent Segments

This article presents a new algorithm for interval plotting of the function y = f(x) based on combined sampling. The proposed method synthesizes the uniform and adaptive sampling approaches and provides a more compact and efficient function representation. During the combined sampling, the polygonal approximation with a given threshold α between the adjacent segments is constructed. The automated detection and treatment of the discontinuities based on the LR criterion are involved. Two implementations, the recursive-based and stack-based, are introduced. Finally, several tests of the proposed algorithms for the different functions involving the discontinuities and several map projection graticules are presented. The proposed method may be used for more efficient sampling the curves (map projection graticules, contour lines, or buffers) in geoinformatics.

scholarly journals Plotting the map projection graticule involving discontinuities based on combined sampling

2018 ◽  
Vol 17 (2) ◽  
pp. 31-64
Author(s):  
Tomáš Bayer
Keyword(s):  
Adaptive Sampling ◽  
Sampling Technique ◽  
Central Meridian ◽  
Polygonal Approximation ◽  
Map Projection ◽  
Good Data ◽  
Projection Constant ◽  
Coordinate Functions ◽  
Adjacent Segments ◽  
The Given

This article presents  new algorithm for interval plotting the projection graticule on the interval $\varOmega=\varOmega_{\varphi}\times\varOmega_{\lambda}$ based on the combined sampling technique. The proposed method synthesizes uniform and adaptive sampling approaches and treats discontinuities of the coordinate functions $F,G$. A full set of the projection constant values represented by the projection pole $K=[\varphi_{k},\lambda_{k}]$, two standard parallels $\varphi_{1},\varphi_{2}$ and the central meridian shift $\lambda_{0}^{\prime}$ are supported. In accordance with the discontinuity direction it utilizes a subdivision of the given latitude/longitude intervals $\varOmega_{\varphi}=[\underline{\varphi},\overline{\varphi}]$, $\varOmega_{\lambda}=[\underline{\lambda},\overline{\lambda}]$ to the set of disjoint subintervals $\varOmega_{k,\varphi}^{g},$$\varOmega_{k,\lambda}^{g}$ forming tiles without internal singularities, containing only "good" data; their parameters can be easily adjusted. Each graticule tile borders generated over $\varOmega_{k}^{g}=\varOmega_{k,\varphi}^{g}\times\varOmega_{k,\lambda}^{g}$ run along singularities. For combined sampling with the given threshold $\overline{\alpha}$ between adjacent segments of the polygonal approximation the recursive approach has been used; meridian/parallel offsets are $\Delta\varphi,\Delta\lambda$. Finally, several tests of the proposed algorithms are involved.

scholarly journals Automated Detection of Arrhythmia for Hybrid Neural Network of LSTM-Residual with Multi-Information Fusion

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Liang Tao ◽  
Baoning Liu ◽  
Wei Liang
Keyword(s):  
Short Term Memory ◽  
Classification Performance ◽  
Automated Detection ◽  
Ecg Signal ◽  
Model Classification ◽  
Temporal Features ◽  
Proposed Model ◽  
The Time Domain ◽  
Adjacent Segments ◽  
Electrocardiogram Ecg

Arrhythmia is a common cardiovascular disease; the electrocardiogram (ECG) is widely used as an effective tool for detecting arrhythmia. However, real-time arrhythmia detection monitoring is difficult, so this study proposes a long short-term memory-residual model. Individual beats provide morphological features and combined with adjacent segments provide temporal features. Our proposed model captures the time-domain and morphological ECG signal information simultaneously and fuses the two information types. At the same time, the attention block is applied to the network to further strengthen the useful information, capture the hidden information in the ECG signal, and improve the model classification performance. Our model was finally trained and tested on the MIT-BIH arrhythmia database, and the entire dataset was divided into intrapatient and interpatient modes. Accuracies of 99.11% and 85.65%, respectively, were obtained under the two modes. Experimental results demonstrate that our proposed method is an efficient automated detection method.

A Photographic Map of the Moon

1962 ◽  
Vol 14 ◽  
pp. 113-115
Author(s):  
D. W. G. Arthur ◽  
E. A. Whitaker
Keyword(s):  
Lunar Surface ◽  
The Moon ◽  
Map Projection

The cartography of the lunar surface can be split into two operations which can be carried on quite independently. The first, which is also the most laborious, is the interpretation of the lunar photographs into the symbolism of the map, with the addition of fine details from telescopic sketches. An example of this kind of work is contained in Johann Krieger'sMond Atlaswhich consists of photographic enlargements in which Krieger has sharpened up the detail to accord with his telescopic impressions. Krieger did not go on either to convert the photographic picture into the line symbolism of a map, or to place this picture on any definite map projection.

Automated detection of epithelial changes in colorectal carcinoma

2012 ◽  
Vol 50 (05) ◽  
Author(s):  
G Valcz ◽  
I Bándi ◽  
B Wichmann ◽  
A Patai ◽  
D Szabó ◽  
...  
Keyword(s):  
Colorectal Carcinoma ◽  
Automated Detection

Machine Learning in Agriculture

2018 ◽  
Vol 8 (6) ◽  
pp. 26 ◽  
Author(s):  
Matthew N. O. Sadiku ◽  
Chandra M. M Kotteti ◽  
Sarhan M. Musa
Keyword(s):  
Climate Change ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Disease Diagnosis ◽  
Automated Detection ◽  
Disease Prediction ◽  
Paper Briefly ◽  
Agriculture Sector ◽  
Modern Agriculture ◽  
Crop Disease

Machine learning is an emerging field of artificial intelligence which can be applied to the agriculture sector. It refers to the automated detection of meaningful patterns in a given data.  Modern agriculture seeks ways to conserve water, use nutrients and energy more efficiently, and adapt to climate change.  Machine learning in agriculture allows for more accurate disease diagnosis and crop disease prediction. This paper briefly introduces what machine learning can do in the agriculture sector.

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