Determination of Neural Fiber Connections Based on Data Structure Algorithm

2016 ◽  
pp. 42-52
Keyword(s):  
Data Structure ◽  
Structure Algorithm ◽  
Neural Fiber

A Data Abstraction Alternative to Data Structure/Algorithm Modularization

2000 ◽  
pp. 102-113 ◽  
Author(s):  
Murali Sitaraman ◽  
Bruce W. Weide ◽  
Timothy J. Long ◽  
William F. Ogden
Keyword(s):  
Data Structure ◽  
Data Abstraction ◽  
Structure Algorithm

scholarly journals PIPELINE TRANSPORT GEOSPACES

2021 ◽  
Vol 26 (1) ◽  
pp. 76-85
Author(s):  
Daniil V. Dolgopolov ◽  
◽  
Keyword(s):  
Data Structure ◽  
Spatial Data ◽  
Spatial Information ◽  
Information Support ◽  
Pipeline Transport ◽  
Safe Operation ◽  
Pipeline Systems ◽  
Production Problems ◽  
Theoretical Foundations

One of the important tasks of safe operation of pipeline systems is information support of produc-tion units. The purpose of this work was to determine approaches to the formation of geoinformation space to ensure the operation of complex man-made complexes of pipeline systems. The methodolog-ical basis of the research is based on the theoretical foundations and methodological developments of scientists devoted to the collection and processing of spatial information, presented in the works of A. P. Karpik, V. B. Zharnikov, H. K. Yambaev, A. A. Varlamov, V. G. Bondur, D. V. Lisitsky, G. A. Ustavich, A. T. Zverev, V. A. Malinnikov, A. P. Sizov, V. A. Melkiy and other well-known scientists. To create a GIS of the current pipeline transport system there was defined the procedure of forming geo-information space, including the determination of the list of production problems associated with the use of spatial information, spatial data structure, needs in aerospace survey, performance require-ments and frequency of updates of spatial information.

A Fast Template Matching Algorithm Based on Wavelet Coefficient Projection Transform

2011 ◽  
Vol 48-49 ◽  
pp. 21-24 ◽  
Author(s):  
Xian Ping Fu ◽  
Sheng Long Liao
Keyword(s):  
Computer Vision ◽  
Data Structure ◽  
Template Matching ◽  
Performance Enhancement ◽  
Wavelet Coefficient ◽  
Target Position ◽  
Similarity Matching ◽  
Structure Algorithm ◽  
Searching Ability ◽  
Resolution Data

As the electronic industry advances rapidly toward automatic manufacturing smaller, faster, and cheaper products, computer vision play more important role in IC packaging technology than before. One of the important tasks of computer vision is finding target position through similarity matching. Similarity matching requires distance computation of feature vectors for each target image. In this paper we propose a projection transform of wavelet coefficient based multi resolution data-structure algorithm for faster template matching, a position sequence of local sharp variation points in such signals is recorded as features. The proposed approach reduces the number of computation by around 70% over multi resolution data structure algorithm. We use the proposed approach to match similarity between wavelet parameters histograms for image matching. It is noticeable that the proposed fast algorithm provides not only the same retrieval results as the exhaustive search, but also a faster searching ability than existing fast algorithms. The proposed approach can be easily combined with existing algorithms for further performance enhancement.

WAVELET-BASED MULTIRESOLUTION HISTOGRAM FOR FAST IMAGE RETRIEVAL

2004 ◽  
Vol 02 (01) ◽  
pp. 59-73 ◽  
Author(s):  
PAWAN JAIN ◽  
S. N. MERCHANT
Keyword(s):  
Data Structure ◽  
Image Retrieval ◽  
Retrieval System ◽  
Performance Enhancement ◽  
Image Database ◽  
Retrieval Systems ◽  
Candidate Image ◽  
Image Retrieval System ◽  
Resolution Level ◽  
Structure Algorithm

Most of the content-based image retrieval systems require a distance computation of feature vectors for each candidate image in the image database. This exhaustive search is highly time-consuming and inefficient. This limits the usefulness of such system. Thus there is a growing need for a fast image retrieval system. Multiresolution data-structure algorithm provides a good solution to the above problem. In this paper we propose a wavelet-based multiresolution data-structure algorithm. Wavelet-based multiresolution data-structure further reduce the number of computation by around 50%. In the proposed approach we reuse the information obtained at lower resolution levels to calculate the distance at a higher resolution level. Apart from this, the proposed structure saves memory overheads by about 50% over multiresolution data-structure algorithm. The proposed algorithm can be easily combined with other algorithms for performance enhancement.4 In this paper we use the proposed technique to match luminance histogram for image retrieval. Fuzzy histograms enhances performance by considering the similarity between neighboring bins. We have extended the proposed approach to fuzzy histograms for better performance.

scholarly journals EVALUATING THE REVERSING DATA STRUCTURE ALGORITHM OF LINKED LIST

2020 ◽  
Vol 11 (1) ◽  
pp. 57-65
Author(s):  
Rwan F. Al-Rashed ◽  
Atheer S. Al-Mutiri ◽  
Wadha M. Al-Marrai ◽  
Badriayh G. Al-Mutiri ◽  
Hanan F. Al-Qahtani ◽  
...  
Keyword(s):  
Data Structure ◽  
Linked List ◽  
Structure Algorithm

scholarly journals MODELING OF THE INTELLECTUAL SYSTEM’S WORK FOR SUPPORTING DECISIONS MAKING ON TECHNICAL REGULATION IN BUILDING UNDER UNCERTAINTY CONDITIONS

2019 ◽  
Vol 2 ◽  
pp. 3-9
Author(s):  
Dmytro Isaienko ◽  
Volodymyr Scochko
Keyword(s):  
Data Structure ◽  
City Planning ◽  
Main Attention ◽  
Technical Regulation ◽  
Normative Documents ◽  
Planning Activity ◽  
Intellectual System ◽  
Different Types ◽  
The City

The main aim of the work is to elaborate algorithms and structure of data of the intellectual system of decisions making, able to provide functioning of the organizational-technical system of technical regulation in building under uncertainty conditions. The ability of intellectual systems to function under uncertainty conditions of different types is provided by using methods of indistinct mathematics and organization of a data structure, formed for revealing uncertainty automatically. The main attention in the article is paid to forming a data structure and algorithm for revealing uncertainty, caused by excessive information. The material of the research is information, contained in normative documents, according to which the technical regulation of the Ukrainian building branch is realized. It is especially urgent to reveal conflicts of parameters and rules, connected with reformation of the legislation in a part, related to the technical regulation of the branch. The automatic revelation of uncertainty that results in a discordance or conflict of parameters and rules, essentially facilitates elaboration of project documents and realization of expert evaluations at stages of building and exploitation of unique objects. But introduction of such intellectual systems allows to raise a speed and reliability of the work of other systems of the documents’ circulation under transformation conditions essentially. The scientific novelty of the work is in using models of indistinct mathematics at formalizing norms and rules of the technical regulation in building and elaborating methods of their processing by the intellectual system of making decisions. The results of the work are demonstrated on the example of determination of a technical regulation parameter at excessive information, manifested in the presence of more than one normative document with instructions for determination of one regulation parameter. The probation and adaptation of norms and rules are realized in the legislative regulation of the activity in the Ukrainian building branch under conditions of changes, directed on improving the city-planning activity according to world standards.

Fast Determination of Items Support Technique from Enhanced Tree Data Structure

2014 ◽  
Vol 8 (1) ◽  
pp. 21-32 ◽  
Author(s):  
Zailani Abdullah ◽  
Tutut Herawan ◽  
A. Noraziah ◽  
Mustafa Mat Deris
Keyword(s):  
Data Structure ◽  
Fast Determination ◽  
Tree Data ◽  
Tree Data Structure
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