Frequency assignment modeling and optimization from the perspective of graph theory

2014 ◽  
Author(s):  
Wenzhun Huang
Keyword(s):  
Graph Theory ◽  
Frequency Assignment ◽  
Modeling And Optimization

scholarly journals Joint Polarization and Frequency Assignment Algorithm Based on Graph Theory

2016 ◽  
Vol 41 (8) ◽  
pp. 954-957
Author(s):  
Bonhong Koo ◽  
Chan-Byoung Chae ◽  
Sung-Ho Park ◽  
Hwi-Sung Park ◽  
Jae-Hyun Ham
Keyword(s):  
Graph Theory ◽  
Frequency Assignment ◽  
Assignment Algorithm

Semi-Definite positive Programming Relaxations for Graph Kn-Coloring in Frequency Assignment

2001 ◽  
Vol 35 (2) ◽  
pp. 211-228 ◽  
Author(s):  
Philippe Meurdesoif ◽  
Benoît Rottembourg
Keyword(s):  
Frequency Assignment

Pattern Recognition in Histo-Pathology: Basic Considerations

1982 ◽  
Vol 21 (01) ◽  
pp. 15-22 ◽  
Author(s):  
W. Schlegel ◽  
K. Kayser
Keyword(s):  
Pattern Recognition ◽  
Graph Theory ◽  
Normal Tissue ◽  
Diagnostic Procedures ◽  
Minimal Distance ◽  
Malignant Growth ◽  
Colon Tissue ◽  
First Results ◽  
Tissue Structures ◽  
The Given

A basic concept for the automatic diagnosis of histo-pathological specimen is presented. The algorithm is based on tissue structures of the original organ. Low power magnification was used to inspect the specimens. The form of the given tissue structures, e. g. diameter, distance, shape factor and number of neighbours, is measured. Graph theory is applied by using the center of structures as vertices and the shortest connection of neighbours as edges. The algorithm leads to two independent sets of parameters which can be used for diagnostic procedures. First results with colon tissue show significant differences between normal tissue, benign and malignant growth. Polyps form glands that are twice as wide as normal and carcinomatous tissue. Carcinomas can be separated by the minimal distance of the glands formed. First results of pattern recognition using graph theory are discussed.

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