Parallel Algorithm for Run Length Encoding

2006 ◽  
Author(s):  
N. Manchev
Keyword(s):  
Parallel Algorithm ◽  
Run Length

scholarly journals Partitionable Bus-based String-matching Algorithm for Run-length Coded Strings With VLDCs

VLSI Design ◽  
1999 ◽  
Vol 9 (1) ◽  
pp. 55-67
Author(s):  
Hsiu-Niang Chen ◽  
Kuo-Liang Chung
Keyword(s):  
Parallel Algorithm ◽  
String Matching ◽  
Vlsi Implementation ◽  
Finite Alphabet ◽  
Matching Algorithm ◽  
Run Length ◽  
Special Symbol ◽  
Reconfigurable Mesh

String matching (SM) problem is to find the occurrences of a pattern within a text. A vanable length don't care (VLDC) is a special symbol, not belonging to a finite alphabet ∑ but in ∑*. Each VLDC in the pattern can match any substring in the text. Given a run-length coded text of length 2n over ∑ and a run-length coded pattern of length 2m over ∑*, this paper first presents an O(1) time parallel SM algorithm for run-length coded strings with VLDCs on a reconfigurable mesh (RM) using O(nm) processors. Consider the hardware limitation in VLSI implementation. In order to be suitable for VLSI modular implementation, a partitionable parallel algorithm on the RM with limited processors is further presented. For N < n and M < m, the SM for run-length coded strings with VLDCs can be solved in O(X^Y^) time on the RM using O(NM)(= O((nm)/((X^Y^))) processors, where X^ = [(n – 1)/(N – 1)] and Y^ = [(m – 1)/(M – 1)].

Characterization of Pancreas at Diabetic Patients in CT images using Texture Analysis

2017 ◽  
Vol 7 (7) ◽  
pp. 8
Author(s):  
Mona E. Elbashier ◽  
Suhaib Alameen ◽  
Caroline Edward Ayad ◽  
Mohamed E. M. Gar-Elnabi
Keyword(s):  
Size Distribution ◽  
Texture Analysis ◽  
Classification Accuracy ◽  
Ct Images ◽  
Diabetic Patients ◽  
Gray Level ◽  
Grey Level ◽  
Run Length ◽  
Interactive Data

This study concern to characterize the pancreas areato head, body and tail using Gray Level Run Length Matrix (GLRLM) and extract classification features from CT images. The GLRLM techniques included eleven’s features. To find the gray level distribution in CT images it complements the GLRLM features extracted from CT images with runs of gray level in pixels and estimate the size distribution of thesubpatterns. analyzing the image with Interactive Data Language IDL software to measure the grey level distribution of images. The results show that the Gray Level Run Length Matrix and  features give classification accuracy of pancreashead 89.2%, body 93.6 and the tail classification accuracy 93.5%. The overall classification accuracy of pancreas area 92.0%.These relationships are stored in a Texture Dictionary that can be later used to automatically annotate new CT images with the appropriate pancreas area names.

Line interpolation algorithm based on diagonal run-length

2009 ◽  
Vol 28 (9) ◽  
pp. 2270-2273
Author(s):  
Xiao-tong YE ◽  
Yun DENG
Keyword(s):  
Interpolation Algorithm ◽  
Run Length

Parallel algorithm for Turbo codes

2010 ◽  
Vol 24 (7) ◽  
pp. 638-642
Author(s):  
Linli Cui ◽  
Fan Yang ◽  
Qicong Peng
Keyword(s):  
Parallel Algorithm ◽  
Turbo Codes
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