Analysis of the Block Segmentation Method of the Licklider Transmission Protocol

2020 ◽  
pp. 14-26
Author(s):  
Ricardo Lent
Keyword(s):  
Transmission Protocol ◽  
Segmentation Method ◽  
Block Segmentation

DEVELOPMENT OF COMMUNICATION DATA TRANSMISSION PROTOCOL IN DISTRIBUTED SYSTEMS

2014 ◽  
pp. 209-214
Author(s):  
P. Ilin ◽  
◽  
J. Sieck ◽  
V. Brovkov ◽  
M. Pashkovskyi
Keyword(s):  
Distributed Systems ◽  
Data Transmission ◽  
Transmission Protocol

Degree-of-Freedom Based Transmission Protocol Design with Optimization for Multi-Cell Multi-User Green Systems

2014 ◽  
Vol E97.B (12) ◽  
pp. 2784-2789
Author(s):  
Chunguo LI ◽  
Yongping ZHANG ◽  
John M. CIOFFI ◽  
Luxi YANG
Keyword(s):  
Protocol Design ◽  
Degree Of Freedom ◽  
Transmission Protocol

Mask R-CNN based segmentation method for satellite imagery of photovoltaics generation systems

2020 ◽  
Author(s):  
SiMing Liang ◽  
FengYang Qi ◽  
YiFan Ding ◽  
Rui Cao ◽  
Qiang Yang ◽  
...  
Keyword(s):  
Satellite Imagery ◽  
Segmentation Method

Segmentation In Medical Resonance images to extract the cancerous nodule for early diagnosis on cancer

2012 ◽  
Vol 3 (2) ◽  
pp. 253-255
Author(s):  
Raman Brar
Keyword(s):  
Human Lung ◽  
Research Work ◽  
Vital Role ◽  
Watershed Algorithm ◽  
Segmentation Method ◽  
Resonance Imaging ◽  
Segmentation Methods ◽  
Segmentation Accuracy ◽  
Background Segmentation ◽  
Lung Mri

Image segmentation plays a vital role in several medical imaging programs by assisting the delineation of physiological structures along with other parts. The objective of this research work is to segmentize human lung MRI (Medical resonance Imaging) images for early detection of cancer.Watershed Transform Technique is implemented as the Segmentation method in this work. Some comparative experiments using both directly applied watershed algorithm and after marking foreground and computed background segmentation methods show the improved lung segmentation accuracy in some image cases.

Multiresolution Bio-Medical Image Segmentation using Fuzzy C-Means Clustering

2019 ◽  
Vol 8 (4) ◽  
pp. 9548-9551
Keyword(s):  
Image Segmentation ◽  
Medical Image Segmentation ◽  
Experimental Result ◽  
Segmentation Method ◽  
Fuzzy C Means ◽  
Fuzzy C Means Clustering ◽  
Robust Image ◽  
Multiple Clusters ◽  
Single Pixel ◽  
Popular Image

Fuzzy c-means clustering is a popular image segmentation technique, in which a single pixel belongs to multiple clusters, with varying degree of membership. The main drawback of this method is it sensitive to noise. This method can be improved by incorporating multiresolution stationary wavelet analysis. In this paper we develop a robust image segmentation method using Fuzzy c-means clustering and wavelet transform. The experimental result shows that the proposed method is more accurate than the Fuzzy c-means clustering.

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