Abstract: This paper presents the development of a robot car
that can track the motion of an object by detecting its color through
an Android device. The employed computer vision algorithm uses the
OpenCV library, which is embedded into an Android application of a
smartphone, for manipulating the captured image of the object. The
captured image of the object is subjected to color conversion and is
transformed to a binary image for further processing after color
filtering. The desired object is clearly determined after removing
pixel noise by applying image morphology operations and contour
definition. Finally, the area and the center of the object are
determined so that object’s motion to be tracked. The smartphone
application has been placed on a robot car and transmits by Bluetooth
to an Arduino assembly the motion directives so that to follow
objects of a specified color. The experimental evaluation of the
proposed algorithm shows reliable color detection and smooth
tracking characteristics.
Abstract: In order to avoid self-collision of space manipulators
during operation process, a real-time detection method is proposed in
this paper. The manipulator is fitted into a cylinder-enveloping
surface, and then, a kind of detection algorithm of collision between
cylinders is analyzed. The collision model of space manipulator
self-links can be detected by using this algorithm in real-time detection
during the operation process. To ensure security of the operation, a
safety threshold is designed. The simulation and experiment results
verify the effectiveness of the proposed algorithm for a 7-DOF space
manipulator.
Abstract: Design concepts of real-time embedded system can be
realized initially by introducing novel design approaches. In this
literature, model based design approach and in-the-loop testing were
employed early in the conceptual and preliminary phase to formulate
design requirements and perform quick real-time verification. The
design and analysis methodology includes simulation analysis, model
based testing, and in-the-loop testing. The design of conceptual driveby-
wire, or DBW, algorithm for electronic control unit, or ECU, was
presented to demonstrate the conceptual design process, analysis, and
functionality evaluation. The concepts of DBW ECU function can be
implemented in the vehicle system to improve electric vehicle, or EV,
conversion drivability. However, within a new development process,
conceptual ECU functions and parameters are needed to be evaluated.
As a result, the testing system was employed to support conceptual
DBW ECU functions evaluation. For the current setup, the system
components were consisted of actual DBW ECU hardware, electric
vehicle models, and control area network or CAN protocol. The
vehicle models and CAN bus interface were both implemented as
real-time applications where ECU and CAN protocol functionality
were verified according to the design requirements. The proposed
system could potentially benefit in performing rapid real-time
analysis of design parameters for conceptual system or software
algorithm development.
Abstract: This study investigates the use of a time-series of
MODIS NDVI data to identify agricultural land cover change on an
annual time step (2007 - 2012) and characterize the trend. Following
an ISODATA classification of the MODIS imagery to selectively
mask areas not agriculture or semi-natural, NDVI signatures were
created to identify areas cereals and vineyards with the aid of
ancillary, pictometry and field sample data for 2010. The NDVI
signature curve and training samples were used to create a decision
tree model in WEKA 3.6.9 using decision tree classifier (J48)
algorithm; Model 1 including ISODATA classification and Model 2
not. These two models were then used to classify all data for the
study area for 2010, producing land cover maps with classification
accuracies of 77% and 80% for Model 1 and 2 respectively. Model 2
was subsequently used to create land cover classification and change
detection maps for all other years. Subtle changes and areas of
consistency (unchanged) were observed in the agricultural classes
and crop practices. Over the years as predicted by the land cover
classification. Forty one percent of the catchment comprised of
cereals with 35% possibly following a crop rotation system.
Vineyards largely remained constant with only one percent
conversion to vineyard from other land cover classes.
Abstract: This work is the first dowel in a rather wide research
activity in collaboration with Euro Mediterranean Center for Climate
Changes, aimed at introducing scalable approaches in Ocean
Circulation Models. We discuss designing and implementation of
a parallel algorithm for solving the Variational Data Assimilation
(DA) problem on Graphics Processing Units (GPUs). The algorithm
is based on the fully scalable 3DVar DA model, previously proposed
by the authors, which uses a Domain Decomposition approach
(we refer to this model as the DD-DA model). We proceed with
an incremental porting process consisting of 3 distinct stages:
requirements and source code analysis, incremental development of
CUDA kernels, testing and optimization. Experiments confirm the
theoretic performance analysis based on the so-called scale up factor
demonstrating that the DD-DA model can be suitably mapped on
GPU architectures.
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: Quality of Service (QoS) attributes as part of the
service description is an important factor for service attribute. It is not
easy to exactly quantify the weight of each QoS conditions since
human judgments based on their preference causes vagueness. As
web services selection requires optimization, evolutionary computing
based on heuristics to select an optimal solution is adopted. In this
work, the evolutionary computing technique Particle Swarm
Optimization (PSO) is used for selecting a suitable web services
based on the user’s weightage of each QoS values by optimizing the
QoS weight vector and thereby finding the best weight vectors for
best services that is being selected. Finally the results are compared
and analyzed using static inertia weight and deterministic inertia
weight of PSO.
Abstract: The Scheduling and mapping of tasks on a set of
processors is considered as a critical problem in parallel and
distributed computing system. This paper deals with the problem of
dynamic scheduling on a special type of multiprocessor architecture
known as Linear Crossed Cube (LCQ) network. This proposed
multiprocessor is a hybrid network which combines the features of
both linear types of architectures as well as cube based architectures.
Two standard dynamic scheduling schemes namely Minimum
Distance Scheduling (MDS) and Two Round Scheduling (TRS)
schemes are implemented on the LCQ network. Parallel tasks are
mapped and the imbalance of load is evaluated on different set of
processors in LCQ network. The simulations results are evaluated
and effort is made by means of through analysis of the results to
obtain the best solution for the given network in term of load
imbalance left and execution time. The other performance matrices
like speedup and efficiency are also evaluated with the given
dynamic algorithms.
Abstract: This paper shows in detail the mathematical model of
direct and inverse kinematics for a robot manipulator (welding type)
with four degrees of freedom. Using the D-H parameters, screw
theory, numerical, geometric and interpolation methods, the
theoretical and practical values of the position of robot were
determined using an optimized algorithm for inverse kinematics
obtaining the values of the particular joints in order to determine the
virtual paths in a relatively short time.
Abstract: In the past few years, the amount of malicious software
increased exponentially and, therefore, machine learning algorithms
became instrumental in identifying clean and malware files through
(semi)-automated classification. When working with very large
datasets, the major challenge is to reach both a very high malware
detection rate and a very low false positive rate. Another challenge
is to minimize the time needed for the machine learning algorithm to
do so. This paper presents a comparative study between different
machine learning techniques such as linear classifiers, ensembles,
decision trees or various hybrids thereof. The training dataset consists
of approximately 2 million clean files and 200.000 infected files,
which is a realistic quantitative mixture. The paper investigates the
above mentioned methods with respect to both their performance
(detection rate and false positive rate) and their practicability.
Abstract: One of the crucial parameters of digital cryptographic
systems is the selection of the keys used and their distribution. The
randomness of the keys has a strong impact on the system’s security
strength being difficult to be predicted, guessed, reproduced, or
discovered by a cryptanalyst. Therefore, adequate key randomness
generation is still sought for the benefit of stronger cryptosystems.
This paper suggests an algorithm designed to generate and test
pseudo random number sequences intended for cryptographic
applications. This algorithm is based on mathematically manipulating
a publically agreed upon information between sender and receiver
over a public channel. This information is used as a seed for
performing some mathematical functions in order to generate a
sequence of pseudorandom numbers that will be used for
encryption/decryption purposes. This manipulation involves
permutations and substitutions that fulfill Shannon’s principle of
“confusion and diffusion”. ASCII code characters were utilized in the
generation process instead of using bit strings initially, which adds
more flexibility in testing different seed values. Finally, the obtained
results would indicate sound difficulty of guessing keys by attackers.
Abstract: Grid is an environment with millions of resources
which are dynamic and heterogeneous in nature. A computational
grid is one in which the resources are computing nodes and is meant
for applications that involves larger computations. A scheduling
algorithm is said to be efficient if and only if it performs better
resource allocation even in case of resource failure. Resource
allocation is a tedious issue since it has to consider several
requirements such as system load, processing cost and time, user’s
deadline and resource failure. This work attempts in designing a
resource allocation algorithm which is cost-effective and also targets
at load balancing, fault tolerance and user satisfaction by considering
the above requirements. The proposed Budget Constrained Load
Balancing Fault Tolerant algorithm with user satisfaction (BLBFT)
reduces the schedule makespan, schedule cost and task failure rate
and improves resource utilization. Evaluation of the proposed
BLBFT algorithm is done using Gridsim toolkit and the results are
compared with the algorithms which separately concentrates on all
these factors. The comparison results ensure that the proposed
algorithm works better than its counterparts.
Abstract: Association rule mining is one of the most important fields of data mining and knowledge discovery. In this paper, we propose an efficient multiple support frequent pattern growth algorithm which we called “MSFP-growth” that enhancing the FPgrowth algorithm by making infrequent child node pruning step with multiple minimum support using maximum constrains. The algorithm is implemented, and it is compared with other common algorithms: Apriori-multiple minimum supports using maximum constraints and FP-growth. The experimental results show that the rule mining from the proposed algorithm are interesting and our algorithm achieved better performance than other algorithms without scarifying the accuracy.
Abstract: Wavelength Division Multiplexing (WDM) is the dominant transport technology used in numerous high capacity backbone networks, based on optical infrastructures. Given the importance of costs (CapEx and OpEx) associated to these networks, resource management is becoming increasingly important, especially how the optical circuits, called “lightpaths”, are routed throughout the network. This requires the use of efficient algorithms which provide routing strategies with the lowest cost. We focus on the lightpath routing and wavelength assignment problem, known as the RWA problem, while optimizing wavelength fragmentation over the network. Wavelength fragmentation poses a serious challenge for network operators since it leads to the misuse of the wavelength spectrum, and then to the refusal of new lightpath requests. In this paper, we first establish a new Integer Linear Program (ILP) for the problem based on a node-link formulation. This formulation is based on a multilayer approach where the original network is decomposed into several network layers, each corresponding to a wavelength. Furthermore, we propose an efficient heuristic for the problem based on a greedy algorithm followed by a post-treatment procedure. The obtained results show that the optimal solution is often reached. We also compare our results with those of other RWA heuristic methods
Abstract: In this contribution a structure for high level lateral vehicle tracking control based on the disturbance observer is presented. The structure is characterized by stationary compensating side forces disturbances and guaranteeing a cooperative behavior at the same time. Driver inputs are not compensated by the disturbance observer. Moreover the structure is especially useful as it robustly stabilizes the vehicle. Therefore the parameters are selected using the Parameter Space Approach. The implemented algorithms are tested in real world scenarios.
Abstract: This paper is concerned with knowledge representation
and extraction of fuzzy if-then rules using Interval Type-2
Context-based Fuzzy C-Means clustering (IT2-CFCM) with the aid of
fuzzy granulation. This proposed clustering algorithm is based on
information granulation in the form of IT2 based Fuzzy C-Means
(IT2-FCM) clustering and estimates the cluster centers by preserving
the homogeneity between the clustered patterns from the IT2 contexts
produced in the output space. Furthermore, we can obtain the
automatic knowledge representation in the design of Radial Basis
Function Networks (RBFN), Linguistic Model (LM), and Adaptive
Neuro-Fuzzy Networks (ANFN) from the numerical input-output data
pairs. We shall focus on a design of ANFN in this paper. The
experimental results on an estimation problem of energy performance
reveal that the proposed method showed a good knowledge
representation and performance in comparison with the previous
works.
Abstract: Subspace channel estimation methods have been
studied widely, where the subspace of the covariance matrix is
decomposed to separate the signal subspace from noise subspace. The
decomposition is normally done by using either the eigenvalue
decomposition (EVD) or the singular value decomposition (SVD) of
the auto-correlation matrix (ACM). However, the subspace
decomposition process is computationally expensive. This paper
considers the estimation of the multipath slow frequency hopping
(FH) channel using noise space based method. In particular, an
efficient method is proposed to estimate the multipath time delays by
applying multiple signal classification (MUSIC) algorithm which is
based on the null space extracted by the rank revealing LU (RRLU)
factorization. As a result, precise information is provided by the
RRLU about the numerical null space and the rank, (i.e., important
tool in linear algebra). The simulation results demonstrate the
effectiveness of the proposed novel method by approximately
decreasing the computational complexity to the half as compared
with RRQR methods keeping the same performance.
Abstract: Taiwan is a hyper endemic area for the Hepatitis B
virus (HBV). The estimated total number of HBsAg carriers in the
general population who are more than 20 years old is more than 3
million. Therefore, a case record review is conducted from January
2003 to June 2007 for all patients with a diagnosis of acute hepatitis
who were admitted to the Emergency Department (ED) of a
well-known teaching hospital. The cost for the use of medical
resources is defined as the total medical fee. In this study, principal
component analysis (PCA) is firstly employed to reduce the number of
dimensions. Support vector regression (SVR) and artificial neural
network (ANN) are then used to develop the forecasting model. A total
of 117 patients meet the inclusion criteria. 61% patients involved in
this study are hepatitis B related. The computational result shows that
the proposed PCA-SVR model has superior performance than other
compared algorithms. In conclusion, the Child-Pugh score and
echogram can both be used to predict the cost of medical resources for
patients with acute hepatitis in the ED.
Abstract: The aim of this paper is to present the optimization
methodology developed in the frame of a Coastal Transport
Information System. The system will be used for the effective design
of coastal transportation lines and incorporates subsystems that
implement models, tools and techniques that may support the design
of improved networks. The role of the optimization and decision
subsystem is to provide the user with better and optimal scenarios
that will best fulfill any constrains, goals or requirements posed. The
complexity of the problem and the large number of parameters and
objectives involved led to the adoption of an evolutionary method
(Genetic Algorithms). The problem model and the subsystem
structure are presented in detail, and, its support for simulation is also
discussed.
Abstract: In order to protect data privacy, image with sensitive or
private information needs to be encrypted before being outsourced to
the cloud. However, this causes difficulties in image retrieval and data
management. A secure image retrieval method based on orthogonal
decomposition is proposed in the paper. The image is divided into two
different components, for which encryption and feature extraction are
executed separately. As a result, cloud server can extract features from
an encrypted image directly and compare them with the features of the
queried images, so that the user can thus obtain the image. Different
from other methods, the proposed method has no special requirements
to encryption algorithms. Experimental results prove that the proposed
method can achieve better security and better retrieval precision.