Parallelizing a Coarse Grain Graph Search Problem Based upon LDPC Codes on a Supercomputer

This paper presents a unique approach for the dichotomy between useful and adverse variations of key-point descriptors, namely the identity and the expression variations in the descriptor (feature) space. The descriptors variations are learned from training examples. Based on labels of the training data, the equivalence relations among the descriptors are established. Both types of descriptor variations are represented by a graph embedded in the descriptor manifold. Invariant recognition is then conducted as a graph search problem. A heuristic graph search algorithm suitable for the recognition under this setup was devised. The proposed approach was tested on the FRGC v2.0, the Bosphorus and the 3D TEC datasets. It has shown to enhance the recognition performance, under expression variations, by considerable margins.

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Gastroscopic Image Graph: Application to Noninvasive Multitarget Tracking under Gastroscopy

Computational and Mathematical Methods in Medicine ◽

10.1155/2014/974038 ◽

2014 ◽

Vol 2014 ◽

pp. 1-9 ◽

Cited By ~ 3

Author(s):

Bin Wang ◽

Weiling Hu ◽

Jiquan Liu ◽

Jianmin Si ◽

Huilong Duan

Keyword(s):

Disease Diagnosis ◽

Optical Biopsy ◽

Graph Search ◽

Gastric Disease ◽

Multitarget Tracking ◽

Search Problem ◽

Stomach Body ◽

Attractive Candidate ◽

The Mean

Gastroscopic examination is one of the most common methods for gastric disease diagnosis. In this paper, a multitarget tracking approach is proposed to assist endoscopists in identifying lesions under gastroscopy. This approach analyzes numerous preobserved gastroscopic images and constructs a gastroscopic image graph. In this way, the deformation registration between gastroscopic images is regarded as a graph search problem. During the procedure, the endoscopist marks suspicious lesions on the screen and the graph is utilized to locate and display the lesions in the appropriate frames based on the calculated registration model. Compared to traditional gastroscopic lesion surveillance methods (e.g., tattooing or probe-based optical biopsy), this approach is noninvasive and does not require additional instruments. In order to assess and quantify the performance, this approach was applied to stomach phantom data andin vivodata. The clinical experimental results demonstrated that the accuracy at angularis, antral, and stomach body was 6.3 ± 2.4 mm, 7.6 ± 3.1 mm, and 7.9 ± 1.6 mm, respectively. The mean accuracy was 7.31 mm, average targeting time was 56 ms, and thePvalue was 0.032, which makes it an attractive candidate for clinical practice. Furthermore, this approach provides a significant reference for endoscopic target tracking of other soft tissue organs.

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Efficient Breadth-First Reduct Search

Mathematics ◽

10.3390/math8050833 ◽

2020 ◽

Vol 8 (5) ◽

pp. 833

Author(s):

Veera Boonjing ◽

Pisit Chanvarasuth

Keyword(s):

Information System ◽

Search Strategy ◽

Search Space ◽

Graph Search ◽

Search Problem ◽

Breadth First Search ◽

Time And Space ◽

First Order ◽

Pruning Rule

This paper formulates the problem of determining all reducts of an information system as a graph search problem. The search space is represented in the form of a rooted graph. The proposed algorithm uses a breadth-first search strategy to search for all reducts starting from the graph root. It expands nodes in breadth-first order and uses a pruning rule to decrease the search space. It is mathematically shown that the proposed algorithm is both time and space efficient.

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A Swarm Robotics Approach to Decontamination

Mobile Ad Hoc Robots and Wireless Robotic Systems - Advances in Computational Intelligence and Robotics ◽

10.4018/978-1-4666-2658-4.ch006 ◽

2013 ◽

pp. 107-122

Author(s):

Daniel S. F. Alves ◽

E. Elael M. Soares ◽

Guilherme C. Strachan ◽

Guilherme P. S. Carvalho ◽

Marco F. S. Xaud ◽

...

Keyword(s):

Swarm Robotics ◽

Target Space ◽

Graph Search ◽

Search Problem ◽

Ground Floor ◽

Chemical Decontamination ◽

Minimum Number

Many interesting and difficult practical problems need to be tackled in the areas of firefighting, biological and/or chemical decontamination, tactical and/or rescue searches, and Web spamming, among others. These problems, however, can be mapped onto the graph decontamination problem, also called the graph search problem. Once the target space is mapped onto a graph G(N,E), where N is the set of G nodes and E the set of G edges, one initially considers all nodes in N to be contaminated. When a guard, i.e., a decontaminating agent, is placed in a node i ??N, i becomes (clean and) guarded. In case such a guard leaves node i, it can only be guaranteed that i will remain clean if all its neighboring nodes are either clean or clean and guarded. The graph decontamination/search problem consists of determining a sequence of guard movements, requiring the minimum number of guards needed for the decontamination of G. This chapter presents a novel swarm robotics approach to firefighting, a conflagration in a hypothetical apartment ground floor. The mechanism has been successfully simulated on the Webots platform, depicting a firefighting swarm of e-puck robots.

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Graph Based Path Planning

Advances in Computational Intelligence and Robotics - Intelligent Planning for Mobile Robotics ◽

10.4018/978-1-4666-2074-2.ch002 ◽

2012 ◽

pp. 26-53

Keyword(s):

Motion Planning ◽

Graph Search ◽

Robot Motion ◽

Search Problem ◽

A Algorithm ◽

Daily Lives ◽

Advantages And Disadvantages ◽

Graph Search Algorithms ◽

Key Steps ◽

Standard Graph

Graphs are keenly studied by people of numerous domains as most of the applications we encounter in our daily lives can be easily given a graph-based representation. All the problems may then be easily studied as grap-based problems. In this chapter, the authors study the problem of robot motion planning as a graph search problem. The key steps involve the representation of the problem as a graph and solving the problem as a standard graph search problem. A number of graph search algorithms exist, each having its own advantages and disadvantages. In this chapter, the authors explain the concept, working methodology, and issues associated with some of these algorithms. The key algorithms under discussion include Breadth First Search, Depth First Search, A* Algorithm, Multi Neuron Heuristic Search, Dijkstra’s Algorithm, D* Algorithm, etc. Experimental results of some of these algorithms are also discussed. The chapter further presents the advantages and disadvantages of graph-based motion planning.

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On the performance of Hopfield network for graph search problem

Neurocomputing ◽

10.1016/s0925-2312(96)00037-9 ◽

1997 ◽

Vol 14 (4) ◽

pp. 365-381 ◽

Cited By ~ 10

Author(s):

Gursel Serpen ◽

Azadeh Parvin

Keyword(s):

Hopfield Network ◽

Graph Search ◽

Search Problem

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Ontology based Indoor Navigation Service for the ILONA System

Infocommunications journal ◽

10.36244/icj.2018.3.4 ◽

2018 ◽

pp. 20-29

Author(s):

Dániel Péter Kun ◽

Erika Baksáné Varga ◽

Zsolt Tóth

Keyword(s):

Shortest Path ◽

Indoor Environment ◽

Real Life ◽

Indoor Navigation ◽

Graph Search ◽

Search Problem ◽

Shortest Path Algorithm ◽

Complex Queries ◽

Way Finding ◽

Navigation Service

An ontology based way finding algorithm is presented in this paper that allows route generation between two separate parts of an indoor environment. The presented ontology provides a flexible way to describe and model the indoor environment, in addition it fits and extends the existing model of the ILONA System. Ontology reasoners provide an efficient way to perform complex queries over the knowledge base. The instances, that are queried by the reasoner, are used to initialize the graph which represents an indoor environment. Due to parameterization of the reasoner, different graphs can be generated from the ontology which makes the way finding algorithm flexible. Thus, the task of indoor way finding was converted into a well-known graph search problem. Dijkstra’s shortest path algorithm is used for route generation in the graph yielded. The algorithm was implemented and tested in the ILONA System and its functioning is demonstrated by real-life scenarios.

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Incremental Symmetry Breaking Constraints for Graph Search Problems

Proceedings of the AAAI Conference on Artificial Intelligence ◽

10.1609/aaai.v34i02.5513 ◽

2020 ◽

Vol 34 (02) ◽

pp. 1536-1543

Author(s):

Avraham Itzhakov ◽

Michael Codish

Keyword(s):

Problem Solving ◽

Symmetry Breaking ◽

Special Property ◽

Graph Search ◽

Search Problem ◽

Search Problems ◽

Canonical Order ◽

Parallel Graph ◽

Canonical Graph

This paper introduces incremental symmetry breaking constraints for graph search problems which are complete and compact. We show that these constraints can be computed incrementally: A symmetry breaking constraint for order n graphs can be extended to one for order n + 1 graphs. Moreover, these constraints induce a special property on their canonical solutions: An order n canonical graph contains a canonical subgraph on the first k vertices for every 1 ≤ k ≤ n. This facilitates a “generate and extend” paradigm for parallel graph search problem solving: To solve a graph search problem φ on order n graphs, first generate the canonical graphs of some order k < n. Then, compute canonical solutions for φ by extending, in parallel, each canonical order k graph together with suitable symmetry breaking constraints. The contribution is that the proposed symmetry breaking constraints enable us to extend the order k canonical graphs to order n canonical solutions. We demonstrate our approach through its application on two hard graph search problems.

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