Abstract: All-to-all personalized communication, also known as complete exchange, is one of the most dense communication patterns in parallel computing. In this paper, we propose new indirect algorithms for complete exchange on all-port ring and torus. The new algorithms fully utilize all communication links and transmit messages along shortest paths to completely achieve the theoretical lower bounds on message transmission, which have not be achieved among other existing indirect algorithms. For 2D r × c ( r % c ) all-port torus, the algorithm has time complexities of optimal transmission cost and O(c) message startup cost. In addition, the proposed algorithms accommodate non-power-of-two tori where the number of nodes in each dimension needs not be power-of-two or square. Finally, the algorithms are conceptually simple and symmetrical for every message and every node so that they can be easily implemented and achieve the optimum in practice.
Abstract: IEEE 802.16 is a new wireless technology standard, it
has some advantages, including wider coverage, higher bandwidth,
and QoS support. As the new wireless technology for last mile
solution, there are designed two models in IEEE 802.16 standard. One
is PMP (point to multipoint) and the other is Mesh. In this paper we
only focus on IEEE 802.16 Mesh model. According to the IEEE
802.16 standard description, Mesh model has two scheduling modes,
centralized and distributed. Considering the pros and cons of the two
scheduling, we present the combined scheduling QoS framework that
the BS (Base Station) controls time frame scheduling and selects the
shortest path from source to destination directly. On the other hand, we
propose the Expedited Queue mechanism to cut down the transmission
time. The EQ mechanism can reduce a lot of end-to-end delay in our
QoS framework. Simulation study has shown that the average delay is
smaller than contrasts. Furthermore, our proposed scheme can also
achieve higher performance.
Abstract: This paper presents a solution for the behavioural
animation of autonomous virtual agent navigation in virtual environments.
We focus on using Dempster-Shafer-s Theory of Evidence
in developing visual sensor for virtual agent. The role of the visual
sensor is to capture the information about the virtual environment
or identifie which part of an obstacle can be seen from the position
of the virtual agent. This information is require for vitual agent to
coordinate navigation in virtual environment. The virual agent uses
fuzzy controller as a navigation system and Fuzzy α - level for
the action selection method. The result clearly demonstrates the path
produced is reasonably smooth even though there is some sharp turn
and also still not diverted too far from the potential shortest path.
This had indicated the benefit of our method, where more reliable
and accurate paths produced during navigation task.
Abstract: Feature selection has recently been the subject of intensive research in data mining, specially for datasets with a large number of attributes. Recent work has shown that feature selection can have a positive effect on the performance of machine learning algorithms. The success of many learning algorithms in their attempts to construct models of data, hinges on the reliable identification of a small set of highly predictive attributes. The inclusion of irrelevant, redundant and noisy attributes in the model building process phase can result in poor predictive performance and increased computation. In this paper, a novel feature search procedure that utilizes the Ant Colony Optimization (ACO) is presented. The ACO is a metaheuristic inspired by the behavior of real ants in their search for the shortest paths to food sources. It looks for optimal solutions by considering both local heuristics and previous knowledge. When applied to two different classification problems, the proposed algorithm achieved very promising results.
Abstract: Route bus system is the fundamental public transportation
system and has an important role in every province. To improve
the usability of it greatly, we develop an AR application for "Bus-
Net". The Bus-Net system is the shortest path planning system.
Bus-Net supports bus users to make a plan to change buses by
providing them with information about the direction. However, with
Bus-Net, these information are provided in text-base. It is difficult
to understand them for the person who does not know the place. We
developed the AR application for Bus-Net. It supports the action of
a bus user in an innovative way by putting information on a camera
picture and leading the way to a bus stop. The application also inform
the user the correct bus to get, the direction the bus takes and the
fare, which ease many anxieties and worries people tend to feel when
they take buses.
Abstract: An accident is an unexpected and unplanned situation
that happens and affects human in a negative outcome. The accident
can cause an injury to a human biological organism. Thus, the
provision of initial care for an illness or injury is very important
move to prepare the patients/victims before sending to the doctor. In
this paper, a First Aid Application is developed to give some
directions for preliminary taking care of patient/victim via Android
mobile device. Also, the navigation function using Google Maps API
is implemented in this paper for searching a suitable path to the
nearest hospital. Therefore, in the emergency case, this function can
be activated and navigate patients/victims to the hospital with the
shortest path.
Abstract: In this paper, we deal with the Steiner tree problem
(STP) on a graph in which a fuzzy number, instead of a real number,
is assigned to each edge. We propose a modification of the shortest
paths approximation based on the fuzzy shortest paths (FSP)
evaluations. Since a fuzzy min operation using the extension
principle leads to nondominated solutions, we propose another
approach to solving the FSP using Cheng's centroid point fuzzy
ranking method.
Abstract: Many multimedia communication applications require a
source to transmit messages to multiple destinations subject to quality
of service (QoS) delay constraint. To support delay constrained
multicast communications, computer networks need to guarantee an
upper bound end-to-end delay from the source node to each of
the destination nodes. This is known as multicast delay problem.
On the other hand, if the same message fails to arrive at each
destination node at the same time, there may arise inconsistency and
unfairness problem among users. This is related to multicast delayvariation
problem. The problem to find a minimum cost multicast
tree with delay and delay-variation constraints has been proven to
be NP-Complete. In this paper, we propose an efficient heuristic
algorithm, namely, Economic Delay and Delay-Variation Bounded
Multicast (EDVBM) algorithm, based on a novel heuristic function,
to construct an economic delay and delay-variation bounded multicast
tree. A noteworthy feature of this algorithm is that it has very high
probability of finding the optimal solution in polynomial time with
low computational complexity.
Abstract: This paper presents Qmulus- a Cloud Based GPS
Model. Qmulus is designed to compute the best possible route which
would lead the driver to the specified destination in the shortest time
while taking into account real-time constraints. Intelligence
incorporated to Qmulus-s design makes it capable of generating and
assigning priorities to a list of optimal routes through customizable
dynamic updates. The goal of this design is to minimize travel and
cost overheads, maintain reliability and consistency, and implement
scalability and flexibility. The model proposed focuses on
reducing the bridge between a Client Application and a Cloud
service so as to render seamless operations. Qmulus-s system
model is closely integrated and its concept has the potential to be
extended into several other integrated applications making it capable
of adapting to different media and resources.
Abstract: This paper presents an overview of the multiobjective shortest path problem (MSPP) and a review of essential and recent issues regarding the methods to its solution. The paper further explores a multiobjective evolutionary algorithm as applied to the MSPP and describes its behavior in terms of diversity of solutions, computational complexity, and optimality of solutions. Results show that the evolutionary algorithm can find diverse solutions to the MSPP in polynomial time (based on several network instances) and can be an alternative when other methods are trapped by the tractability problem.
Abstract: The shortest path routing problem is a multiobjective nonlinear optimization problem with constraints. This problem has been addressed by considering Quality of service parameters, delay and cost objectives separately or as a weighted sum of both objectives. Multiobjective evolutionary algorithms can find multiple pareto-optimal solutions in one single run and this ability makes them attractive for solving problems with multiple and conflicting objectives. This paper uses an elitist multiobjective evolutionary algorithm based on the Non-dominated Sorting Genetic Algorithm (NSGA), for solving the dynamic shortest path routing problem in computer networks. A priority-based encoding scheme is proposed for population initialization. Elitism ensures that the best solution does not deteriorate in the next generations. Results for a sample test network have been presented to demonstrate the capabilities of the proposed approach to generate well-distributed pareto-optimal solutions of dynamic routing problem in one single run. The results obtained by NSGA are compared with single objective weighting factor method for which Genetic Algorithm (GA) was applied.
Abstract: When binary decision diagrams are formed from
uniformly distributed Monte Carlo data for a large number of
variables, the complexity of the decision diagrams exhibits a
predictable relationship to the number of variables and minterms. In
the present work, a neural network model has been used to analyze the
pattern of shortest path length for larger number of Monte Carlo data
points. The neural model shows a strong descriptive power for the
ISCAS benchmark data with an RMS error of 0.102 for the shortest
path length complexity. Therefore, the model can be considered as a
method of predicting path length complexities; this is expected to lead
to minimum time complexity of very large-scale integrated circuitries
and related computer-aided design tools that use binary decision
diagrams.
Abstract: This paper presents a solution for the behavioural animation of autonomous virtual agent navigation in virtual environments. We focus on using Dempster-Shafer-s Theory of Evidence in developing visual sensor for virtual agent. The role of the visual sensor is to capture the information about the virtual environment or identifie which part of an obstacle can be seen from the position of the virtual agent. This information is require for vitual agent to coordinate navigation in virtual environment. The virual agent uses fuzzy controller as a navigation system and Fuzzy α - level for the action selection method. The result clearly demonstrates the path produced is reasonably smooth even though there is some sharp turn and also still not diverted too far from the potential shortest path. This had indicated the benefit of our method, where more reliable and accurate paths produced during navigation task.
Abstract: Proteomics is one of the largest areas of research for
bioinformatics and medical science. An ambitious goal of proteomics
is to elucidate the structure, interactions and functions of all proteins
within cells and organisms. Predicting Protein-Protein Interaction
(PPI) is one of the crucial and decisive problems in current research.
Genomic data offer a great opportunity and at the same time a lot of
challenges for the identification of these interactions. Many methods
have already been proposed in this regard. In case of in-silico
identification, most of the methods require both positive and negative
examples of protein interaction and the perfection of these examples
are very much crucial for the final prediction accuracy. Positive
examples are relatively easy to obtain from well known databases. But
the generation of negative examples is not a trivial task. Current PPI
identification methods generate negative examples based on some
assumptions, which are likely to affect their prediction accuracy.
Hence, if more reliable negative examples are used, the PPI prediction
methods may achieve even more accuracy. Focusing on this issue, a
graph based negative example generation method is proposed, which
is simple and more accurate than the existing approaches. An
interaction graph of the protein sequences is created. The basic
assumption is that the longer the shortest path between two
protein-sequences in the interaction graph, the less is the possibility of
their interaction. A well established PPI detection algorithm is
employed with our negative examples and in most cases it increases
the accuracy more than 10% in comparison with the negative pair
selection method in that paper.
Abstract: Buildings with floating column are highly undesirable built in seismically active areas. Many urban multi-storey buildings today have floating column buildings which are adopted to accommodate parking at ground floor or reception lobbies in the first storey. The earthquake forces developed at different floor levels in a building need to be brought down along the height to the ground by the shortest path; any deviation or discontinuity in this load transfer path results in poor performance of the building. Floating column buildings are severely damaged during earthquake. Damage on this structure can be reduce by taking the effect of infill wall. This paper presents the effect of stiffness of infill wall to the damage occurred in floating column building when ground shakes. Modelling and analysis are carried out by non linear analysis programme IDARC-2D. Damage occurred in beams, columns, storey are studied by formulating modified Park & Ang model to evaluate damage indices. Overall structural damage indices in buildings due to shaking of ground are also obtained. Dynamic response parameters i.e. lateral floor displacement, storey drift, time period, base shear of buildings are obtained and results are compared with the ordinary moment resisting frame buildings. Formation of cracks, yield, plastic hinge, are also observed during analysis.
Abstract: The Ant Colony Optimization (ACO) is a metaheuristic inspired by the behavior of real ants in their search for the shortest paths to food sources. It has recently attracted a lot of attention and has been successfully applied to a number of different optimization problems. Due to the importance of the feature selection problem and the potential of ACO, this paper presents a novel method that utilizes the ACO algorithm to implement a feature subset search procedure. Initial results obtained using the classification of speech segments are very promising.
Abstract: The study of proteomics reached unexpected levels of
interest, as a direct consequence of its discovered influence over some
complex biological phenomena, such as problematic diseases like
cancer. This paper presents the latest authors- achievements regarding
the analysis of the networks of proteins (interactome networks), by
computing more efficiently the betweenness centrality measure. The
paper introduces the concept of betweenness centrality, and then
describes how betweenness computation can help the interactome net-
work analysis. Current sequential implementations for the between-
ness computation do not perform satisfactory in terms of execution
times. The paper-s main contribution is centered towards introducing
a speedup technique for the betweenness computation, based on
modified shortest path algorithms for sparse graphs. Three optimized
generic algorithms for betweenness computation are described and
implemented, and their performance tested against real biological
data, which is part of the IntAct dataset.
Abstract: Travelling salesman problem (TSP) is a combinational
optimization problem and solution approaches have been applied
many real world problems. Pure TSP assumes the cities to visit are
fixed in time and thus solutions are created to find shortest path
according to these point. But some of the points are canceled to visit
in time. If the problem is not time crucial it is not important to
determine new routing plan but if the points are changing rapidly and
time is necessary do decide a new route plan a new approach should
be applied in such cases. We developed a route plan transfer method
based on transfer learning and we achieved high performance against
determining a new model from scratch in every change.
Abstract: Feature selection is an important step in many pattern
classification problems. It is applied to select a subset of features,
from a much larger set, such that the selected subset is sufficient to
perform the classification task. Due to its importance, the problem of
feature selection has been investigated by many researchers. In this
paper, a novel feature subset search procedure that utilizes the Ant
Colony Optimization (ACO) is presented. The ACO is a
metaheuristic inspired by the behavior of real ants in their search for
the shortest paths to food sources. It looks for optimal solutions by
considering both local heuristics and previous knowledge. When
applied to two different classification problems, the proposed
algorithm achieved very promising results.
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).