Abstract: With the advent of three-dimension (3D) technology, there are lots of research in converting 2D images to 3D images. The main difference between 2D and 3D is the visual illusion of depth in 3D images. In the recent era, there are more depth estimation techniques. The objective of this paper is to convert 2D images to 3D images with less computation time. For this, the input image is divided into blocks from which the depth information is obtained. Having the depth information, a depth map is generated. Then the 3D image is warped using the original image and the depth map. The proposed method is tested on Make3D dataset and NYU-V2 dataset. The experimental results are compared with other recent methods. The proposed method proved to work with less computation time and good accuracy.
Abstract: Two consensus problems are considered in this
paper. One is the consensus of linear multi-agent systems with
weakly connected directed communication topology. The other
is the consensus of nonlinear multi-agent systems with strongly
connected directed communication topology. For the first problem,
a simplified consensus protocol is designed: Each child agent can
only communicate with one of its neighbors. That is, the real
communication topology is a directed spanning tree of the original
communication topology and without any cycles. Then, the necessary
and sufficient condition is put forward to the multi-agent systems can
be reached consensus. It is worth noting that the given conditions do
not need any eigenvalue of the corresponding Laplacian matrix of the
original directed communication network. For the second problem,
the feedback gain is designed in the nonlinear consensus protocol.
Then, the sufficient condition is proposed such that the systems can
be achieved consensus. Besides, the consensus interval is introduced
and analyzed to solve the consensus problem. Finally, two numerical
simulations are included to verify the theoretical analysis.
Abstract: The traditional k-means algorithm has been widely used as a simple and efficient clustering method. However, the algorithm often converges to local minima for the reason that it is sensitive to the initial cluster centers. In this paper, an algorithm for selecting initial cluster centers on the basis of minimum spanning tree (MST) is presented. The set of vertices in MST with same degree are regarded as a whole which is used to find the skeleton data points. Furthermore, a distance measure between the skeleton data points with consideration of degree and Euclidean distance is presented. Finally, MST-based initialization method for the k-means algorithm is presented, and the corresponding time complexity is analyzed as well. The presented algorithm is tested on five data sets from the UCI Machine Learning Repository. The experimental results illustrate the effectiveness of the presented algorithm compared to three existing initialization methods.
Abstract: Wireless Sensor Network (WSN) clustering architecture enables features like network scalability, communication overhead reduction, and fault tolerance. After clustering, aggregated data is transferred to data sink and reducing unnecessary, redundant data transfer. It reduces nodes transmitting, and so saves energy consumption. Also, it allows scalability for many nodes, reduces communication overhead, and allows efficient use of WSN resources. Clustering based routing methods manage network energy consumption efficiently. Building spanning trees for data collection rooted at a sink node is a fundamental data aggregation method in sensor networks. The problem of determining Cluster Head (CH) optimal number is an NP-Hard problem. In this paper, we combine cluster based routing features for cluster formation and CH selection and use Minimum Spanning Tree (MST) for intra-cluster communication. The proposed method is based on optimizing MST using Simulated Annealing (SA). In this work, normalized values of mobility, delay, and remaining energy are considered for finding optimal MST. Simulation results demonstrate the effectiveness of the proposed method in improving the packet delivery ratio and reducing the end to end delay.
Abstract: This paper presents an optimal broadcast algorithm
for the hypercube networks. The main focus of the paper is the
effectiveness of the algorithm in the presence of many node faults.
For the optimal solution, our algorithm builds with spanning tree
connecting the all nodes of the networks, through which messages
are propagated from source node to remaining nodes. At any given
time, maximum n − 1 nodes may fail due to crashing. We show
that the hypercube networks are strongly fault-tolerant. Simulation
results analyze to accomplish algorithm characteristics under many
node faults. We have compared our simulation results between our
proposed method and the Fu’s method. Fu’s approach cannot tolerate
n − 1 faulty nodes in the worst case, but our approach can tolerate
n − 1 faulty nodes.
Abstract: For a given a simple connected graph, we present
some new bounds via a new approach for a special topological index
given by the sum of the real number power of the non-zero
normalized Laplacian eigenvalues. To use this approach presents an
advantage not only to derive old and new bounds on this topic but
also gives an idea how some previous results in similar area can be
developed.
Abstract: In Knowledge and Data Engineering field, relational
database is the best repository to store data in a real world. It has
been using around the world more than eight decades. Normalization
is the most important process for the analysis and design of relational
databases. It aims at creating a set of relational tables with minimum
data redundancy that preserve consistency and facilitate correct
insertion, deletion, and modification. Normalization is a major task in
the design of relational databases. Despite its importance, very few
algorithms have been developed to be used in the design of
commercial automatic normalization tools. It is also rare technique to
do it automatically rather manually. Moreover, for a large and
complex database as of now, it make even harder to do it manually.
This paper presents a new complete automated relational database
normalization method. It produces the directed graph and spanning
tree, first. It then proceeds with generating the 2NF, 3NF and also
BCNF normal forms. The benefit of this new algorithm is that it can
cope with a large set of complex function dependencies.
Abstract: The travelling salesman problem (TSP) is a combinatorial optimization problem in which the goal is to find the shortest path between different cities that the salesman takes. In other words, the problem deals with finding a route covering all cities so that total distance and execution time is minimized. This paper adopts the nearest neighbor and minimum spanning tree algorithm to solve the well-known travelling salesman problem. The algorithms were implemented using java programming language. The approach is tested on three graphs that making a TSP tour instance of 5-city, 10 –city, and 229–city. The computation results validate the performance of the proposed algorithm.
Abstract: This work provides a practical method for the
development of rural road networks in rural areas of developing
countries. The proposed methodology enables to determine
obligatory points in the rural road network maximizing the number of
settlements that have access to basic services within a given
maximum distance. The proposed methodology is simple and
practical, hence, highly applicable to real-world scenarios, as
demonstrated in the definition of the road network for the rural areas
of Nepal.
Abstract: Opportunistic Data Forwarding (ODF) has drawn much attention in mobile adhoc networking research in recent years. The effectiveness of ODF in MANET depends on a suitable routing protocol which provides a powerful source routing services. PLSR is featured by source routing, loop free and small routing overhead. The update messages in PLSR are integrated into a tree structure and no need to time stamp routing updates which reduces the routing overhead.
Abstract: Independent spanning trees (ISTs) provide a number of advantages in data broadcasting. One can cite the use in fault tolerance network protocols for distributed computing and bandwidth. However, the problem of constructing multiple ISTs is considered hard for arbitrary graphs. In this paper we present an efficient algorithm to construct ISTs on hypercubes that requires minimum resources to be performed.
Abstract: The vertex connectivity of a graph is the smallest number of vertices whose deletion separates the graph or makes it trivial. This work is devoted to the problem of vertex connectivity test of graphs in a distributed environment based on a general and a constructive approach. The contribution of this paper is threefold. First, using a preconstructed spanning tree of the considered graph, we present a protocol to test whether a given graph is 2-connected using only local knowledge. Second, we present an encoding of this protocol using graph relabeling systems. The last contribution is the implementation of this protocol in the message passing model. For a given graph G, where M is the number of its edges, N the number of its nodes and Δ is its degree, our algorithms need the following requirements: The first one uses O(Δ×N2) steps and O(Δ×logΔ) bits per node. The second one uses O(Δ×N2) messages, O(N2) time and O(Δ × logΔ) bits per node. Furthermore, the studied network is semi-anonymous: Only the root of the pre-constructed spanning tree needs to be identified.
Abstract: Finding the shortest path between two positions is a
fundamental problem in transportation, routing, and communications
applications. In robot motion planning, the robot should pass around
the obstacles touching none of them, i.e. the goal is to find a
collision-free path from a starting to a target position. This task has
many specific formulations depending on the shape of obstacles,
allowable directions of movements, knowledge of the scene, etc.
Research of path planning has yielded many fundamentally different
approaches to its solution, mainly based on various decomposition
and roadmap methods. In this paper, we show a possible use of
visibility graphs in point-to-point motion planning in the Euclidean
plane and an alternative approach using Voronoi diagrams that
decreases the probability of collisions with obstacles. The second
application area, investigated here, is focused on problems of finding
minimal networks connecting a set of given points in the plane using
either only straight connections between pairs of points (minimum
spanning tree) or allowing the addition of auxiliary points to the set
to obtain shorter spanning networks (minimum Steiner tree).
Abstract: Due to heavy energy constraints in WSNs clustering is
an efficient way to manage the energy in sensors. There are many
methods already proposed in the area of clustering and research is
still going on to make clustering more energy efficient. In our paper
we are proposing a minimum spanning tree based clustering using
divide and conquer approach. The MST based clustering was first
proposed in 1970’s for large databases. Here we are taking divide and
conquer approach and implementing it for wireless sensor networks
with the constraints attached to the sensor networks. This Divide and
conquer approach is implemented in a way that we don’t have to
construct the whole MST before clustering but we just find the edge
which will be the part of the MST to a corresponding graph and
divide the graph in clusters there itself if that edge from the graph can
be removed judging on certain constraints and hence saving lot of
computation.
Abstract: A spanning tree of a connected graph is a tree which
consists the set of vertices and some or perhaps all of the edges from
the connected graph. In this paper, a model for spanning tree
transformation of connected graphs into single-row networks, namely
Spanning Tree of Connected Graph Modeling (STCGM) will be
introduced. Path-Growing Tree-Forming algorithm applied with
Vertex-Prioritized is contained in the model to produce the spanning
tree from the connected graph. Paths are produced by Path-Growing
and they are combined into a spanning tree by Tree-Forming. The
spanning tree that is produced from the connected graph is then
transformed into single-row network using Tree Sequence Modeling
(TSM). Finally, the single-row routing problem is solved using a
method called Enhanced Simulated Annealing for Single-Row
Routing (ESSR).
Abstract: Graph decompositions are vital in the study of
combinatorial design theory. A decomposition of a graph G is a
partition of its edge set. An n-sun graph is a cycle Cn with an edge
terminating in a vertex of degree one attached to each vertex. In this
paper, we define n-sun decomposition of some even order graphs
with a perfect matching. We have proved that the complete graph
K2n, complete bipartite graph K2n, 2n and the Harary graph H4, 2n have
n-sun decompositions. A labeling scheme is used to construct the n-suns.
Abstract: Graph decompositions are vital in the study of combinatorial design theory. Given two graphs G and H, an H-decomposition of G is a partition of the edge set of G into disjoint isomorphic copies of H. An n-sun is a cycle Cn with an edge terminating in a vertex of degree one attached to each vertex. In this paper we have proved that the complete graph of order 2n, K2n can be decomposed into n-2 n-suns, a Hamilton cycle and a perfect matching, when n is even and for odd case, the decomposition is n-1 n-suns and a perfect matching. For an odd order complete graph K2n+1, delete the star subgraph K1, 2n and the resultant graph K2n is decomposed as in the case of even order. The method of building n-suns uses Walecki's construction for the Hamilton decomposition of complete graphs. A spanning tree decomposition of even order complete graphs is also discussed using the labeling scheme of n-sun decomposition. A complete bipartite graph Kn, n can be decomposed into n/2 n-suns when n/2 is even. When n/2 is odd, Kn, n can be decomposed into (n-2)/2 n-suns and a Hamilton cycle.
Abstract: Most of fuzzy clustering algorithms have some
discrepancies, e.g. they are not able to detect clusters with convex
shapes, the number of the clusters should be a priori known, they
suffer from numerical problems, like sensitiveness to the
initialization, etc. This paper studies the synergistic combination of
the hierarchical and graph theoretic minimal spanning tree based
clustering algorithm with the partitional Gath-Geva fuzzy clustering
algorithm. The aim of this hybridization is to increase the robustness
and consistency of the clustering results and to decrease the number
of the heuristically defined parameters of these algorithms to
decrease the influence of the user on the clustering results. For the
analysis of the resulted fuzzy clusters a new fuzzy similarity measure
based tool has been presented. The calculated similarities of the
clusters can be used for the hierarchical clustering of the resulted
fuzzy clusters, which information is useful for cluster merging and
for the visualization of the clustering results. As the examples used
for the illustration of the operation of the new algorithm will show,
the proposed algorithm can detect clusters from data with arbitrary
shape and does not suffer from the numerical problems of the
classical Gath-Geva fuzzy clustering algorithm.