Abstract: The idea of cropping-system is a method used by
farmers. It is an environmentally-friendly method, protecting the
natural resources (soil, water, air, nutritive substances) and increase
the production at the same time, taking into account some crop
particularities. The combination of this powerful method with the
concepts of genetic algorithms results into a possibility of generating
sequences of crops in order to form a rotation. The usage of this type
of algorithms has been efficient in solving problems related to
optimization and their polynomial complexity allows them to be used
at solving more difficult and various problems. In our case, the
optimization consists in finding the most profitable rotation of
cultures. One of the expected results is to optimize the usage of the
resources, in order to minimize the costs and maximize the profit. In
order to achieve these goals, a genetic algorithm was designed. This
algorithm ensures the finding of several optimized solutions of
cropping-systems possibilities which have the highest profit and,
thus, which minimize the costs. The algorithm uses genetic-based
methods (mutation, crossover) and structures (genes, chromosomes).
A cropping-system possibility will be considered a chromosome and
a crop within the rotation is a gene within a chromosome. Results
about the efficiency of this method will be presented in a special
section. The implementation of this method would bring benefits into
the activity of the farmers by giving them hints and helping them to
use the resources efficiently.
Abstract: Software vulnerabilities are increasing and not only impact services and processes availability as well as information confidentiality, integrity and privacy, but also cause changes that interfere in the development process. Security test could be a solution to reduce vulnerabilities. However, the variety of test techniques with the lack of real case studies of applying tests focusing on software development life cycle compromise its effective use. This paper offers an overview of how a Systematic Mapping Study (MS) about security verification, validation and test (VVT) was performed, besides presenting general results about this study.
Abstract: One of the major developments in machine learning in the past decade is the ensemble method, which finds highly accurate classifier by combining many moderately accurate component classifiers. In this research work, new ensemble classification methods are proposed with homogeneous ensemble classifier using bagging and heterogeneous ensemble classifier using arcing and their performances are analyzed in terms of accuracy. A Classifier ensemble is designed using Radial Basis Function (RBF) and Support Vector Machine (SVM) as base classifiers. The feasibility and the benefits of the proposed approaches are demonstrated by the means of standard datasets of intrusion detection. The main originality of the proposed approach is based on three main parts: preprocessing phase, classification phase, and combining phase. A wide range of comparative experiments is conducted for standard datasets of intrusion detection. The performance of the proposed homogeneous and heterogeneous ensemble classifiers are compared to the performance of other standard homogeneous and heterogeneous ensemble methods. The standard homogeneous ensemble methods include Error correcting output codes, Dagging and heterogeneous ensemble methods include majority voting, stacking. The proposed ensemble methods provide significant improvement of accuracy compared to individual classifiers and the proposed bagged RBF and SVM performs significantly better than ECOC and Dagging and the proposed hybrid RBF-SVM performs significantly better than voting and stacking. Also heterogeneous models exhibit better results than homogeneous models for standard datasets of intrusion detection.
Abstract: In recent years, a multi-antenna system is actively used
to improve the performance of the communication. A MIMO-OFDM
system can provide multiplexing gain or diversity gain. These gains
are obtained in proportion to the increase of the number of antennas.
In order to provide the optimal gain of the MIMO-OFDM system,
various transmission and reception schemes are presented. This paper
aims to propose a hybrid scheme that base station provides both
diversity gain and multiplexing gain at the same time.
Abstract: Since Google launched Google Glass in 2012, numbers of commercial wearable devices were released, such as smart belt, smart band, smart shoes, smart clothes ... etc. However, most of these devices perform as sensors to show the readings of measurements and few of them provide the interactive feedback to the user. Furthermore, these devices are single task devices which are not able to communicate with each other. In this paper a new health promotion system, Ubiquitous Life People Informatics Engine (U-Life PIE), will be presented. This engine consists of People Informatics Engine (PIE) and the interactive user interface. PIE collects all the data from the compatible devices, analyzes this data comprehensively and communicates between devices via various application programming interfaces. All the data and informations are stored on the PIE unit, therefore, the user is able to view the instant and historical data on their mobile devices any time. It also provides the real-time hands-free feedback and instructions through the user interface visually, acoustically and tactilely. These feedback and instructions suggest the user to adjust their posture or habits in order to avoid the physical injuries and prevent illness.
Abstract: In this paper, we present a fast and efficient mesh coarsening algorithm for 3D triangular meshes. Theis approach can be applied to very complex 3D meshes of arbitrary topology and with millions of vertices. The algorithm is based on the clustering of the input mesh elements, which divides the faces of an input mesh into a given number of clusters for clustering purpose by approximating the Centroidal Voronoi Tessellation of the input mesh. Once a clustering is achieved, it provides us an efficient way to construct uniform tessellations, and therefore leads to good coarsening of polygonal meshes. With proliferation of 3D scanners, this coarsening algorithm is particularly useful for reverse engineering applications of 3D models, which in many cases are dense, non-uniform, irregular and arbitrary topology. Examples demonstrating effectiveness of the new algorithm are also included in the paper.
Abstract: Virtualization technologies are experiencing a renewed interest as a way to improve system reliability, and availability, reduce costs, and provide flexibility. This paper presents the development on leverage existing cloud infrastructure and virtualization tools. We adopted some virtualization technologies which improve portability, manageability and compatibility of applications by encapsulating them from the underlying operating system on which they are executed. Given the development of application virtualization, it allows shifting the user’s applications from the traditional PC environment to the virtualized environment, which is stored on a remote virtual machine rather than locally. This proposed effort has the potential to positively provide an efficient, resilience and elastic environment for online cloud service. Users no longer need to burden the platform maintenances and drastically reduces the overall cost of hardware and software licenses. Moreover, this flexible and web-based application virtualization service represents the next significant step to the mobile workplace, and it lets user executes their applications from virtually anywhere.
Abstract: Analysing unbalanced datasets is one of the challenges that practitioners in machine learning field face. However, many researches have been carried out to determine the effectiveness of the use of the synthetic minority over-sampling technique (SMOTE) to address this issue. The aim of this study was therefore to compare the effectiveness of the SMOTE over different models on unbalanced datasets. Three classification models (Logistic Regression, Support Vector Machine and Nearest Neighbour) were tested with multiple datasets, then the same datasets were oversampled by using SMOTE and applied again to the three models to compare the differences in the performances. Results of experiments show that the highest number of nearest neighbours gives lower values of error rates.
Abstract: A methodology based on wavelets is proposed for the automatic location and delimitation of defects in limestone plates. Natural defects include dark colored spots, crystal zones trapped in the stone, areas of abnormal contrast colors, cracks or fracture lines, and fossil patterns. Although some of these may or may not be considered as defects according to the intended use of the plate, the goal is to pair each stone with a map of defects that can be overlaid on a computer display. These layers of defects constitute a database that will allow the preliminary selection of matching tiles of a particular variety, with specific dimensions, for a requirement of N square meters, to be done on a desktop computer rather than by a two-hour search in the storage park, with human operators manipulating stone plates as large as 3 m x 2 m, weighing about one ton. Accident risks and work times are reduced, with a consequent increase in productivity. The base for the algorithm is wavelet decomposition executed in two instances of the original image, to detect both hypotheses – dark and clear defects. The existence and/or size of these defects are the gauge to classify the quality grade of the stone products. The tuning of parameters that are possible in the framework of the wavelets corresponds to different levels of accuracy in the drawing of the contours and selection of the defects size, which allows for the use of the map of defects to cut a selected stone into tiles with minimum waste, according the dimension of defects allowed.
Abstract: This paper explores efficient ways to implement various
media-updating features like news aggregation, video conversion,
and bulk email handling. All of these jobs share the property
that they are periodic in nature, and they all benefit from being
handled in a distributed fashion. The data for these jobs also often
comes from a social or collaborative source. We isolate the class of
periodic, one round map reduce jobs as a useful setting to describe
and handle media updating tasks. As such tasks are simpler than
general map reduce jobs, programming them in a general map
reduce platform could easily become tedious. This paper presents
a MediaUpdater module of the Yioop Open Source Search Engine
Web Portal designed to handle such jobs via an extension of a
PHP class. We describe how to implement various media-updating
tasks in our system as well as experiments carried out using these
implementations on an Amazon Web Services cluster.
Abstract: Feature Selection is significant in order to perform constructive classification in the area of cancer diagnosis. However, a large number of features compared to the number of samples makes the task of classification computationally very hard and prone to errors in microarray gene expression datasets. In this paper, we present an innovative method for selecting highly informative gene subsets of gene expression data that effectively classifies the cancer data into tumorous and non-tumorous. The hybrid gene selection technique comprises of combined Mutual Information and Fisher score to select informative genes. The gene selection is validated by classification using Support Vector Machine (SVM) which is a supervised learning algorithm capable of solving complex classification problems. The results obtained from improved Mutual Information and F-Score with SVM as a classifier has produced efficient results.
Abstract: This paper presents a computationally efficient method
for the modeling of robot manipulators with flexible links and
joints. This approach combines the Discrete Time Transfer Matrix
Method with the Finite Segment Method, in which the flexible
links are discretized by a number of rigid segments connected by
torsion springs; and the flexibility of joints are modeled by torsion
springs. The proposed method avoids the global dynamics and has the
advantage of modeling non-uniform manipulators. Experiments and
simulations of a single-link flexible manipulator are conducted for
verifying the proposed methodologies. The simulations of a three-link
robot arm with links and joints flexibility are also performed.
Abstract: There are about 1% of the world population suffering
from the hidden disability known as epilepsy and major developing
countries are not fully equipped to counter this problem. In order to
reduce the inconvenience and danger of epilepsy, different methods
have been researched by using a artificial neural network (ANN)
classification to distinguish epileptic waveforms from normal brain
waveforms. This paper outlines the aim of achieving massive
ANN parallelization through a dedicated hardware using bit-serial
processing. The design of this bit-serial Neural Processing Element
(NPE) is presented which implements the functionality of a complete
neuron using variable accuracy. The proposed design has been tested
taking into consideration non-idealities of a hardware ANN. The NPE
consists of a bit-serial multiplier which uses only 16 logic elements
on an Altera Cyclone IV FPGA and a bit-serial ALU as well as a
look-up table. Arrays of NPEs can be driven by a single controller
which executes the neural processing algorithm. In conclusion, the
proposed compact NPE design allows the construction of complex
hardware ANNs that can be implemented in a portable equipment
that suits the needs of a single epileptic patient in his or her daily
activities to predict the occurrences of impending tonic conic seizures.
Abstract: Ubiquity of natural disasters during last few decades
have risen serious questions towards the prediction of such events
and human safety. Every disaster regardless its proportion has a
precursor which is manifested as a disruption of some environmental
parameter such as temperature, humidity, pressure, vibrations and
etc. In order to anticipate and monitor those changes, in this paper
we propose an overall system for disaster prediction and monitoring,
based on wireless sensor network (WSN). Furthermore, we introduce
a modified and simplified WSN routing protocol built on the top
of the trickle routing algorithm. Routing algorithm was deployed
using the bluetooth low energy protocol in order to achieve low
power consumption. Performance of the WSN network was analyzed
using a real life system implementation. Estimates of the WSN
parameters such as battery life time, network size and packet delay are
determined. Based on the performance of the WSN network, proposed
system can be utilized for disaster monitoring and prediction due to
its low power profile and mesh routing feature.
Abstract: In this paper, we describe a Mixed-Initiative Operational
Model (MIOM) which directly intervenes on the state of the
functionalities embedded into a robot for Urban Search&Rescue
(USAR) domain applications. MIOM extends the reasoning
capabilities of the vehicle, i.e. mapping, path planning, visual
perception and trajectory tracking, with operator knowledge.
Especially in USAR scenarios, this coupled initiative has the main
advantage of enhancing the overall performance of a rescue mission.
In-field experiments with rescue responders have been carried out to
evaluate the effectiveness of this operational model.
Abstract: In this work, we present a Bayesian non-parametric
approach to model the motion control of ATVs. The motion control
model is based on a Dirichlet Process-Gaussian Process (DP-GP)
mixture model. The DP-GP mixture model provides a flexible
representation of patterns of control manoeuvres along trajectories
of different lengths and discretizations. The model also estimates the
number of patterns, sufficient for modeling the dynamics of the ATV.
Abstract: Due to the rapid growth in modern communication systems, fault tolerance and data security are two important issues in a secure transaction. During the transmission of data between the sender and receiver, errors may occur frequently. Therefore, the sender must re-transmit the data to the receiver in order to correct these errors, which makes the system very feeble. To improve the scalability of the scheme, we present a secure proxy signature scheme with fault tolerance over an efficient and secure authenticated key agreement protocol based on RSA system. Authenticated key agreement protocols have an important role in building a secure communications network between the two parties.
Abstract: In this paper, we first construct a new state and disturbance estimator using discrete-time proportional plus integral observer to estimate the system state and the unknown external disturbance for the discrete-time system with an input-to-output direct-feedthrough term. Then, the generalized optimal linear quadratic digital tracker design is applied to construct a proportional plus integral observer-based tracker for the system with an unknown external disturbance to have a desired tracking performance. Finally, a numerical simulation is given to demonstrate the effectiveness of the new application of our proposed approach.
Abstract: This paper describes the proficient way of choosing the cluster head based on dominating set algorithm in a wireless sensor network (WSN). The algorithm overcomes the energy deterioration problems by this selection process of cluster heads. Clustering algorithms such as LEACH, EEHC and HEED enhance scalability in WSNs. Dominating set algorithm keeps the first node alive longer than the other protocols previously used. As the dominating set of cluster heads are directly connected to each node, the energy of the network is saved by eliminating the intermediate nodes in WSN. Security and trust is pivotal in network messaging. Cluster head is secured with a unique key. The member can only connect with the cluster head if and only if they are secured too. The secured trust model provides security for data transmission in the dominated set network with the group key. The concept can be extended to add a mobile sink for each or for no of clusters to transmit data or messages between cluster heads and to base station. Data security id preferably high and data loss can be prevented. The simulation demonstrates the concept of choosing cluster heads by dominating set algorithm and trust evaluation using DSTE. The research done is rationalized.
Abstract: The Flow Shop Scheduling Problem (FSSP) is a typical problem that is faced by production planning managers in Flexible Manufacturing Systems (FMS). This problem consists in finding the optimal scheduling to carry out a set of jobs, which are processed in a set of machines or shared resources. Moreover, all the jobs are processed in the same machine sequence. As in all the scheduling problems, the makespan can be obtained by drawing the Gantt chart according to the operations order, among other alternatives. On this way, an FMS presenting the FSSP can be modeled by Petri nets (PNs), which are a powerful tool that has been used to model and analyze discrete event systems. Then, the makespan can be obtained by simulating the PN through the token game animation and incidence matrix. In this work, we present an adaptive PN to obtain the makespan of FSSP by applying PN analytical tools.