SEFDM-signals Euclidean Distance Analysis

2018 ◽  
Author(s):  
Dmitry Vasilyev ◽  
Andrey Rashich
Keyword(s):  
Euclidean Distance ◽  
Distance Analysis

Regency grouping in East Java based on Variable Type of Agriculture uses Hybrid Hierarchical Clustering Via Mutual Cluster Method

2020 ◽  
Vol 2 (1) ◽  
pp. 51-58
Author(s):  
Sulthan Fikri Mu'afa ◽  
Nurissaidah Ulinnuha
Keyword(s):  
Standard Deviation ◽  
Hierarchical Clustering ◽  
Analysis Of Variance ◽  
Euclidean Distance ◽  
Cluster Method ◽  
Top Down ◽  
Bottom Up ◽  
Distance Analysis ◽  
Shortest Distance ◽  
The Government

AbstractEast Java Province is one of the provinces that has the largest agricultural resources in Indonesia. The Government of East Java needs to produce superior commodities in each region. This study aims to group districts in East Java Province based on variable types of agriculture with the hybrid hierarchical clustering via mutual cluster method that combines the merging of bottom-up clustering advantages and top-down clustering advantages. Mutual cluster is a grouping with the largest distance between small groups of the shortest distance for each point outside the group. In this research, the calculation uses Euclidean distance. The data used in this study are from the East Java Central Statistics Agency (BPS) in 2017. The division calculation is obtained by finding the minimum  (standard deviation of intra cluster) value and the maximum  (standard deviation of inter clusters) value and using the analysis of variance calculation. The grouping results obtained were nine groups with  value of 725.934,  value of 1.475.978 and  value of 7,908.Keywords: agriculture; Hybrid Hierarchical Clustering; mutual cluster; Euclidean distance; analysis of variance. AbstrakProvinsi Jawa Timur merupakan salah satu provinsi yang memiliki sumber daya pertanian terbesar di Indonesia. Pemerintah Jawa Timur perlu mengembangkan komoditi unggulan di tiap daerah di Jawa Timur. Penelitian ini bertujuan untuk mengelompokkan kabupaten di Provinsi Jawa Timur berdasarkan variabel jenis pertanian dengan metode hybrid hierarchical clustering via mutual cluster yaitu menggabungkan kelebihan bottom-up clustering dan kelebihan top-down clustering. Mutual cluster yakni pengelompokkan dengan jarak terbesar antara bagian dalam kelompok yang kecil dari jarak yang terpendek kepada tiap titik di luar kelompok. Dalam penelitian ini, perhitungan jarak menggunakan jarak Euclidean. Data yang digunakan dalam penelitian ini dari Badan Pusat Statistik Jawa Timur tahun 2017. Perhitungan pembagian didapat dengan mencari nilai (simpangan baku dalam klaster) yang minimal dan nilai  (simpangan baku antar klaster) yang maksimal, serta digunakan perhitungan analyze of varians. Hasil pengelompokkan yang diperoleh didapatkan sebanyak sembilan kelompok dengan nilai  sebesar 725.934, nilai sebesar 1.475.978 dan nilai  sebesar 7,908.Kata Kunci: pertanian; Hybrid Hierarchical Clustering; mutual cluster; jarak Euclid; analisis variansi.

Quantifying the association of chipped stone crescents with wetlands and paleoshorelines of western North America

2016 ◽  
Vol 38 (2) ◽  
pp. 107-137 ◽  
Author(s):  
Gabriel M Sanchez ◽  
Jon M Erlandson ◽  
Nicholas Tripcevich
Keyword(s):  
Euclidean Distance ◽  
Strong Association ◽  
Western North America ◽  
Plant Resources ◽  
Chipped Stone ◽  
Distance Analysis ◽  
The Pacific ◽  
Pacific Flyway ◽  
Terminal Pleistocene ◽  
Wetland Habitats

In America’s Far West, chipped stone crescents dating between approximately 12,000 to 8000 cal BP are often found associated with Western Stemmed Tradition points. Crescent function is debated, but scholars have suggested that they are closely associated with wetland habitats, an association that has never been systematically investigated. Using a geographic information system-based Euclidean distance analysis, we compared a sample of 100 geolocated crescent-bearing sites in Washington, Oregon, Idaho, Nevada, Utah, and California with reconstructed paleoshorelines. We confirmed a strong association of crescents with wetlands—94 of the 100 sites and approximately 99% of crescents themselves were located within 10 km of reconstructed paleoshorelines. Our results provide quantitative and region-wide support for a strong association of crescents with terminal Pleistocene and early Holocene wetland habitats. The diversity of aquatic habitats crescents are associated with, along with their morphology, suggests an association with faunal rather than plant resources, possibly birds of the Pacific Flyway.

scholarly journals Morphometric diversity and phenotypic relationship among indigenous buffaloes of Banten, Indonesia

2020 ◽  
Vol 21 (3) ◽  
Author(s):  
Dewi Murni ◽  
UMIE LESTARI ◽  
SRI ENDAH INDRIWATI ◽  
ACHMAD EFENDI ◽  
NANI MARYANI ◽  
...  
Keyword(s):  
Discriminant Analysis ◽  
Correlation Coefficient ◽  
Body Length ◽  
Euclidean Distance ◽  
Genetic Distances ◽  
The Body ◽  
Chest Circumference ◽  
Morphometric Characters ◽  
Distance Analysis ◽  
Multivariate Discriminant

Abstract. Murni D, Lestari U, Indriwati SE, Efendi A, Maryani N, Amin M. 2020. Morphometric diversity and phenotypic relationship among indigenous buffaloes of Banten, Indonesia. Biodiversitas 21: 933-940. This study aimed to describe the morphometric diversity and phenotypic relationship among indigenous buffaloes of Banten, Indonesia. In this study, 125 buffaloes from six regions were investigated based on 11 morphometric characters. Morphometric diversity was analyzed using multivariate discriminant analysis. The Euclidean genetic distances were used to estimate the phenotypic relationship among the buffalo populations. The indigenous buffaloes of Banten have high morphometric diversity, with a coefficient from 2.83 to 41.43%. The body length and chest circumference can be used as a morphometric marker to determine potential indigenous buffaloes as their high correlation coefficient value (0.506). The Serang district buffaloes have the highest mean of body length and chest circumference, which shows that this population is potential compared to the populations from other regions. The morphometric of buffalo population from Serang City, Cilegon City, Serang District, and Pandeglang District tend to be homogenous. Meanwhile, Lebak and Tangerang District population tends to heterogeneous. According to Euclidean distance analysis, the proximate phenotypic relationship was between Serang and Pandeglang District's buffalo populations. Our results indicated that morphometric diversity and phenotypic relationships of the populations were related to geographical origins and can be used to determine the potential indigenous of buffaloes.

scholarly journals Fast and In-Situ Identification of Archaeometallurgical Collections in the Museum of Malaga Using Laser-Induced Breakdown Spectroscopy and a New Mathematical Algorithm

Heritage ◽  
2020 ◽  
Vol 3 (4) ◽  
pp. 1330-1343
Author(s):  
Francisco J. Fortes ◽  
Luisa M. Cabalín ◽  
Javier J. Laserna
Keyword(s):  
Euclidean Distance ◽  
Correlation Method ◽  
Laser Induced Breakdown Spectroscopy ◽  
Archaeological Sites ◽  
Mathematical Algorithm ◽  
Distance Analysis ◽  
Breakdown Spectroscopy ◽  
Laser Induced Breakdown

This paper reports the use of an advanced statistical algorithm for the recognition and classification of a set of 30 archaeological metallic objects from the Museum of Malaga. In-situ laser-induced breakdown spectrometry (LIBS) analysis was performed using a portable analyzer. The coordinate-obtaining method provided the statistical weights of each element in the sample. A comparative study between the coordinate-obtaining method and the linear correlation method is also discussed in order to corroborate the applicability of the proposed approach to the field of cultural heritage. The possibility of fast identification based on the simultaneous comparison of all the spectra in the reference LIBS library while allowing the analysis of heterogeneous materials is the main advantage of the method. In addition, statistical analysis (Euclidean distance analysis and binary diagrams) suggested that differentiating between archaeological sites is feasible.

scholarly journals Euclidean Distance Analysis Enables Nucleotide Skew Analysis in Viral Genomes

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Formijn van Hemert ◽  
Maarten Jebbink ◽  
Andries van der Ark ◽  
Frits Scholer ◽  
Ben Berkhout
Keyword(s):  
Euclidean Distance ◽  
Classical Model ◽  
Simple Calculation ◽  
Nucleotide Composition ◽  
Characteristic Sequence ◽  
Distance Analysis ◽  
Difficult Time ◽  
Nucleotide Skew ◽  
Skew Data ◽  
Sequence Signatures

Nucleotide skew analysis is a versatile method to study the nucleotide composition of RNA/DNA molecules, in particular to reveal characteristic sequence signatures. For instance, skew analysis of the nucleotide bias of several viral RNA genomes indicated that it is enriched in the unpaired, single-stranded genome regions, thus creating an even more striking virus-specific signature. The comparison of skew graphs for many virus isolates or families is difficult, time-consuming, and nonquantitative. Here, we present a procedure for a more simple identification of similarities and dissimilarities between nucleotide skew data of coronavirus, flavivirus, picornavirus, and HIV-1 RNA genomes. Window and step sizes were normalized to correct for differences in length of the viral genome. Cumulative skew data are converted into pairwise Euclidean distance matrices, which can be presented as neighbor-joining trees. We present skew value trees for the four virus families and show that closely related viruses are placed in small clusters. Importantly, the skew value trees are similar to the trees constructed by a “classical” model of evolutionary nucleotide substitution. Thus, we conclude that the simple calculation of Euclidean distances between nucleotide skew data allows an easy and quantitative comparison of characteristic sequence signatures of virus genomes. These results indicate that the Euclidean distance analysis of nucleotide skew data forms a nice addition to the virology toolbox.

The Earth Mover’s Distance as a Metric for the Space of Inorganic Compositions

2020 ◽  
Author(s):  
Cameron Hargreaves ◽  
Matthew Dyer ◽  
Michael Gaultois ◽  
Vitaliy Kurlin ◽  
Matthew J Rosseinsky
Keyword(s):  
Euclidean Distance ◽  
Nearest Neighbor ◽  
Nearest Neighbor Search ◽  
Inorganic Crystal Structure Database ◽  
Earth Mover’S Distance ◽  
Chemical Similarity ◽  
Earth Mover's Distance ◽  
Neighbor Search ◽  
The Earth ◽  
Binary Compounds

It is a core problem in any field to reliably tell how close two objects are to being the same, and once this relation has been established we can use this information to precisely quantify potential relationships, both analytically and with machine learning (ML). For inorganic solids, the chemical composition is a fundamental descriptor, which can be represented by assigning the ratio of each element in the material to a vector. These vectors are a convenient mathematical data structure for measuring similarity, but unfortunately, the standard metric (the Euclidean distance) gives little to no variance in the resultant distances between chemically dissimilar compositions. We present the Earth Mover’s Distance (EMD) for inorganic compositions, a well-defined metric which enables the measure of chemical similarity in an explainable fashion. We compute the EMD between two compositions from the ratio of each of the elements and the absolute distance between the elements on the modified Pettifor scale. This simple metric shows clear strength at distinguishing compounds and is efficient to compute in practice. The resultant distances have greater alignment with chemical understanding than the Euclidean distance, which is demonstrated on the binary compositions of the Inorganic Crystal Structure Database (ICSD). The EMD is a reliable numeric measure of chemical similarity that can be incorporated into automated workflows for a range of ML techniques. We have found that with no supervision the use of this metric gives a distinct partitioning of binary compounds into clear trends and families of chemical property, with future applications for nearest neighbor search queries in chemical database retrieval systems and supervised ML techniques.

Level Set Method Optimized with the Euclidean Distance Transform

2019 ◽  
Author(s):  
Luis Fernando Segalla ◽  
Alexandre Zabot ◽  
Diogo Nardelli Siebert ◽  
Fabiano Wolf
Keyword(s):  
Level Set Method ◽  
Level Set ◽  
Euclidean Distance ◽  
Distance Transform ◽  
Euclidean Distance Transform

Improving the Computational Cost for Copied Region Detection in Forensic Images

2016 ◽  
Vol 2 (1) ◽  
pp. 55
Author(s):  
Tu Huynh-Kha ◽  
Thuong Le-Tien ◽  
Synh Ha ◽  
Khoa Huynh-Van
Keyword(s):  
Wavelet Transform ◽  
Euclidean Distance ◽  
Research Work ◽  
Computational Cost ◽  
Correlation Coefficients ◽  
Zernike Moments ◽  
Computational Time ◽  
Discrete Wavelet ◽  
Feature Vectors ◽  
Region Detection

This research work develops a new method to detect the forgery in image by combining the Wavelet transform and modified Zernike Moments (MZMs) in which the features are defined from more pixels than in traditional Zernike Moments. The tested image is firstly converted to grayscale and applied one level Discrete Wavelet Transform (DWT) to reduce the size of image by a half in both sides. The approximation sub-band (LL), which is used for processing, is then divided into overlapping blocks and modified Zernike moments are calculated in each block as feature vectors. More pixels are considered, more sufficient features are extracted. Lexicographical sorting and correlation coefficients computation on feature vectors are next steps to find the similar blocks. The purpose of applying DWT to reduce the dimension of the image before using Zernike moments with updated coefficients is to improve the computational time and increase exactness in detection. Copied or duplicated parts will be detected as traces of copy-move forgery manipulation based on a threshold of correlation coefficients and confirmed exactly from the constraint of Euclidean distance. Comparisons results between proposed method and related ones prove the feasibility and efficiency of the proposed algorithm.

IMPLEMENTASI METODE EUCLIDEAN DISTANCE UNTUK EKSTRAKSI FITUR JARAK PADA CITRA SKELETON

2019 ◽  
Vol 24 (2) ◽  
pp. 134-139
Author(s):  
Miftahul Jannah ◽  
Nurul Humaira
Keyword(s):  
Euclidean Distance

Gait adalah cara atau sikap berjalan kaki seseorang. Tiap orang memiliki cara berjalan yang berbeda, sehingga gerak jalan seseorang sulit untuk disembunyikan ataupun direkayasa. Analisis gait adalah ilmu pengetahuan yang mempelajari tentang kemampuan atau cara bergerak manusia. Dalam bidang kedokteran, analisis gait digunakan untuk menentukan penanganan dan terapi bagi pasien rehabilitasi medik. Dalam penelitian ini digunakan fitur jarak pada citra skeleton. Ekstraksi fitur jarak pada citra skeleton menggunakan metode euclidean distance terbagi dalam beberapa tahapan, dimulai dengan mengambil citra skeleton, konversi citra RGB menjadi citra Biner, proses menemukan titik koordinat dari titik akhir dan titik percabangan, dan ekstraksi fitur pada skeleton. Metode yang digunakan menghasilkan persentase tingkat keberhasilan sebesar 87.84%.

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