scholarly journals Extraction of Coal and Gangue Geometric Features with Multifractal Detrending Fluctuation Analysis

2018 ◽  
Vol 8 (3) ◽  
pp. 463 ◽  
Author(s):  
Kai Liu ◽  
Xi Zhang ◽  
YangQuan Chen
Keyword(s):  
Fluctuation Analysis ◽  
Geometric Features ◽  
Detrending Fluctuation Analysis

Detrending fluctuation analysis based on moving average filtering

2005 ◽  
Vol 354 ◽  
pp. 199-219 ◽  
Author(s):  
Jose Alvarez-Ramirez ◽  
Eduardo Rodriguez ◽  
Juan Carlos Echeverría
Keyword(s):  
Moving Average ◽  
Fluctuation Analysis ◽  
Detrending Fluctuation Analysis

Scaling properties of image textures: A detrending fluctuation analysis approach

2006 ◽  
Vol 361 (2) ◽  
pp. 677-698 ◽  
Author(s):  
Jose Alvarez-Ramirez ◽  
Eduardo Rodriguez ◽  
Ilse Cervantes ◽  
Juan Carlos Echeverria
Keyword(s):  
Analysis Approach ◽  
Fluctuation Analysis ◽  
Scaling Properties ◽  
Detrending Fluctuation Analysis

Detrending fluctuation analysis of gastric pH reveals long-term correlations accentuated by acid suppression

2003 ◽  
Vol 124 (4) ◽  
pp. A444-A445
Author(s):  
Shingo Yuki ◽  
David Amstrong ◽  
J.S. Capogna ◽  
Francesco Viani ◽  
Andre L. Blum ◽  
...  
Keyword(s):  
Gastric Ph ◽  
Acid Suppression ◽  
Fluctuation Analysis ◽  
Detrending Fluctuation Analysis

Abstract P422: Two-kidney One Clip (2k1c) Hypertension Reduces Complexity of Heart Rate Variability in Mice

Hypertension ◽  
2017 ◽  
Vol 70 (suppl_1) ◽  
Author(s):  
Fernanda L Rodrigues ◽  
Luiz E Silva ◽  
Carlos A Silva ◽  
Fernando S Carneiro ◽  
Rita C Tostes ◽  
...  
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Multiscale Entropy ◽  
Left Renal Artery ◽  
Fluctuation Analysis ◽  
Detrending Fluctuation Analysis ◽  
Interval Time Series ◽  
Electrocardiogram Ecg ◽  
2K1c Hypertension

Introduction: Hypertension is the most common chronic cardiovascular disease, being multifactorial in origin and an important cause of morbidity and mortality worldwide. Complex behaviors of heart rate series have been widely recognized and the loss of complexity in heart rate variability (HRV) has been shown to predict adverse cardiovascular outcomes. We hypothesized that two-kidney one clip (2K1C) hypertension reduces the HRV complexity in mice. Methods and Results: C57BL/6 mice were anesthetized with isoflurane and submitted to 2K1C hypertension by placing a silver clip (0.12 mm) around left renal artery. After 4 weeks, mice were implanted with subcutaneous electrocardiogram (ECG) electrodes and allowed to recover for 48 h. On the day of the experiment, the ECG was recorded for 30 minutes in conscious, unrestrained mice. At the end of the recording, arterial pressure (AP) was directly measured in each mouse under isoflurane anesthesia. RR interval time series were generated and the complexity of HRV was determined using detrending fluctuation analysis (DFA) and multiscale entropy (MSE). Mean AP was higher in 2K1C mice (133±2 vs 93±4 mmHg) while the HR was similar between groups. DFA scaling exponents were calculated in short (5 to 15), mid (30 to 200) and long (200 to 1500) window sizes, but only the long-term exponent was different between groups (1.27±0.09 vs 0.89±0.08 in 2K1C and sham mice, respectively). MSE was calculated up to scale 20 and averaged in short (1 to 5) and long (6 to 20) time scales. In both short (0.75±0.16 vs 1.25±0.11) and long (0.76±0.17 vs 1.22±0.09) ranges, entropy is lower in hypertensive mice. Conclusions: The complexity of HRV dynamics was found lower in renovascular hypertensive mice. Both sympathetic and vagal control of the heart seems to be involved in this process, as predictability (MSE) and fractality (DFA) is affected in various temporal scales. Nevertheless, the greatest entropy difference between groups is found at scale 6, which is closely related to respiration.

Detrending fluctuation analysis based on high-pass filtering

2007 ◽  
Vol 375 (2) ◽  
pp. 699-708 ◽  
Author(s):  
Eduardo Rodriguez ◽  
Juan Carlos Echeverría ◽  
Jose Alvarez-Ramirez
Keyword(s):  
Fluctuation Analysis ◽  
Detrending Fluctuation Analysis ◽  
High Pass

Face Completion Using Semantic Segmentation and Geometric Features

2018 ◽  
Vol 11 (6) ◽  
pp. 304
Author(s):  
Javier Pinzon-Arenas ◽  
Robinson Jimenez-Moreno ◽  
Ruben Hernandez-Beleno
Keyword(s):  
Semantic Segmentation ◽  
Geometric Features

OFFLINE YORÙBÁ HANDWRITTEN WORD RECOGNITION USING GEOMETRIC FEATURE EXTRACTION AND SUPPORT VECTOR MACHINE CLASSIFIER

2020 ◽  
Vol 5 (2) ◽  
pp. 504
Author(s):  
Matthias Omotayo Oladele ◽  
Temilola Morufat Adepoju ◽  
Olaide ` Abiodun Olatoke ◽  
Oluwaseun Adewale Ojo
Keyword(s):  
Support Vector Machine ◽  
Feature Extraction ◽  
Word Recognition ◽  
Support Vector Machine Classifier ◽  
Recognition Accuracy ◽  
Recognition System ◽  
Support Vector ◽  
Geometric Features ◽  
Total Length ◽  
Yoruba Language

Yorùbá language is one of the three main languages that is been spoken in Nigeria. It is a tonal language that carries an accent on the vowel alphabets. There are twenty-five (25) alphabets in Yorùbá language with one of the alphabets a digraph (GB). Due to the difficulty in typing handwritten Yorùbá documents, there is a need to develop a handwritten recognition system that can convert the handwritten texts to digital format. This study discusses the offline Yorùbá handwritten word recognition system (OYHWR) that recognizes Yorùbá uppercase alphabets. Handwritten characters and words were obtained from different writers using the paint application and M708 graphics tablets. The characters were used for training and the words were used for testing. Pre-processing was done on the images and the geometric features of the images were extracted using zoning and gradient-based feature extraction. Geometric features are the different line types that form a particular character such as the vertical, horizontal, and diagonal lines. The geometric features used are the number of horizontal lines, number of vertical lines, number of right diagonal lines, number of left diagonal lines, total length of all horizontal lines, total length of all vertical lines, total length of all right slanting lines, total length of all left-slanting lines and the area of the skeleton. The characters are divided into 9 zones and gradient feature extraction was used to extract the horizontal and vertical components and geometric features in each zone. The words were fed into the support vector machine classifier and the performance was evaluated based on recognition accuracy. Support vector machine is a two-class classifier, hence a multiclass SVM classifier least square support vector machine (LSSVM) was used for word recognition. The one vs one strategy and RBF kernel were used and the recognition accuracy obtained from the tested words ranges between 66.7%, 83.3%, 85.7%, 87.5%, and 100%. The low recognition rate for some of the words could be as a result of the similarity in the extracted features.

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