scholarly journals Optimal Minimum Euclidean Distance-Based Precoder for NOMA With Finite-Alphabet Inputs

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 45123-45136 ◽  
Author(s):  
Wenlong Xia ◽  
Yuanping Zhou ◽  
Guide Yang ◽  
Ray T. Chen
Keyword(s):  
Euclidean Distance ◽  
Finite Alphabet ◽  
Minimum Euclidean Distance

Computed Tomography Brain Images Semantic Segmentation

2017 ◽  
pp. 77-104
Author(s):  
Poonam Fauzdar ◽  
Sarvesh Kumar
Keyword(s):  
Computed Tomography ◽  
Positive Predictive Value ◽  
Euclidean Distance ◽  
Global Analysis ◽  
Semantic Segmentation ◽  
Brain Images ◽  
Minimum Euclidean Distance ◽  
Computed Tomography Images ◽  
Multi Level ◽  
Reduced Time

In this paper we applianced an approach for segmenting brain tumour regions in a computed tomography images by proposing a multi-level fuzzy technique with quantization and minimum computed Euclidean distance applied to morphologically divided skull part. Since the edges identified with closed contours and further improved by adding minimum Euclidean distance, that is why the numerous results that are analyzed are very assuring and algorithm poses following advantages like less cost, global analysis of image, reduced time, more specificity and positive predictive value.

A Novel Distribution Parameter Fitting Algorithm of Correlation Noise Model for Distributed Video Coding

2014 ◽  
Vol 599-601 ◽  
pp. 1360-1363
Author(s):  
Xiang Yan Liang ◽  
Zhen Hua Tang ◽  
Ya Dan Luo ◽  
Tuan Fa Qin
Keyword(s):  
Video Coding ◽  
Euclidean Distance ◽  
Rate Distortion ◽  
Distributed Video Coding ◽  
Distribution Parameter ◽  
Noise Model ◽  
Correlated Noise ◽  
Minimum Euclidean Distance ◽  
Parameter Fitting ◽  
Fitting Algorithm

In order to improve the accuracy of correlated noise (CN) model for distributed video coding (DVC), this paper proposes a novel distribution parameter fitting algorithm based on the minimum Euclidean distance. The presented method can obtain the final fitted distribution parameter by using the minimum Euclidean distance to compare the Laplace probability density function (PDF) with the PDF computed utilizing the actual residual frame data. Experiment results show that the proposed distribution parameter fitting algorithm can improve the rate-distortion (R-D) performance of DVC significantly.

scholarly journals Implementasi Algoritma GLCM Dan MED pada Aplikasi Pendeteksi Kolesterol Melalui Iris Mata

MIND Journal ◽  
2018 ◽  
Vol 2 (2) ◽  
pp. 23-42
Author(s):  
Mira Musrini B ◽  
Andri Ana ◽  
Ari Seisar Hidayat
Keyword(s):  
Euclidean Distance ◽  
Gray Level ◽  
Minimum Euclidean Distance ◽  
Occurrence Matrix

Berdasarkan penelitian yang telah dilakukan dengan menggunakan gambar iris normal dan kolesterol sebagai input, metode GLCM (Gray Level Co-Occurrence Matrix) dapat digunakan pada aplikasi pendeteksi kolesterol melalui iris mata. Karena GLCM mampu memperoleh nilai fitur yang terdiri dari energi, entropi, homogenitas dan kontras dengan cara menghitung nilai probabilitas dari hasil perhitungan kemunculan matriks yang sama dalam piksel gambar dengan sudut(θ) 0◦ dan jarak(d) 1. Metode MED (Minimum Euclidean Distance) dapat digunakan untuk mengklasifikasi gambar iris normal dan kolesterol dengan menghitung nilai jarak Euclidean berdasarkan dari nilai fitur GLCM. Pembuatan aplikasi ini bertujuan untuk membantu orang-orang dengan mudah mendeteksi kolesterol secara cepat dan praktis tanpa harus pergi ke klinik atau rumah sakit. Pengujian yang dilakukan ke 24 orang dari gambar iris normal dan kolesterol dengan intensitas cahaya yang berbeda tingkat akurasi mencapai 70,83%.

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