Abstract: This research presents the main ideas to implement an
intelligent system composed by communicating wireless sensors
measuring environmental data linked to drought indicators (such as
air temperature, soil moisture , etc...). On the other hand, the setting
up of a spatio temporal database communicating with a Web mapping
application for a monitoring in real time in activity 24:00 /day, 7
days/week is proposed to allow the screening of the drought
parameters time evolution and their extraction. Thus this system
helps detecting surfaces touched by the phenomenon of drought.
Spatio-temporal conceptual models seek to answer the users who
need to manage soil water content for irrigating or fertilizing or other
activities pursuing crop yield augmentation. Effectively, spatiotemporal
conceptual models enable users to obtain a diagram of
readable and easy data to apprehend. Based on socio-economic
information, it helps identifying people impacted by the phenomena
with the corresponding severity especially that this information is
accessible by farmers and stakeholders themselves. The study will be
applied in Siliana watershed Northern Tunisia.
Abstract: In order to help the expert to validate association rules
extracted from data, some quality measures are proposed in the
literature. We distinguish two categories: objective and subjective
measures. The first one depends on a fixed threshold and on data
quality from which the rules are extracted. The second one consists
on providing to the expert some tools in the objective to explore and
visualize rules during the evaluation step. However, the number of
extracted rules to validate remains high. Thus, the manually mining
rules task is very hard. To solve this problem, we propose, in this
paper, a semi-automatic method to assist the expert during the
association rule's validation. Our method uses rule-based
classification as follow: (i) We transform association rules into
classification rules (classifiers), (ii) We use the generated classifiers
for data classification. (iii) We visualize association rules with their
quality classification to give an idea to the expert and to assist him
during validation process.
Abstract: Feature selection has been used in many fields such as
classification, data mining and object recognition and proven to be
effective for removing irrelevant and redundant features from the
original dataset. In this paper, a new design of distributed intrusion
detection system using a combination feature selection model based
on bees and decision tree. Bees algorithm is used as the search
strategy to find the optimal subset of features, whereas decision tree
is used as a judgment for the selected features. Both the produced
features and the generated rules are used by Decision Making Mobile
Agent to decide whether there is an attack or not in the networks.
Decision Making Mobile Agent will migrate through the networks,
moving from node to another, if it found that there is an attack on one
of the nodes, it then alerts the user through User Interface Agent or
takes some action through Action Mobile Agent. The KDD Cup 99
dataset is used to test the effectiveness of the proposed system. The
results show that even if only four features are used, the proposed
system gives a better performance when it is compared with the
obtained results using all 41 features.
Abstract: The design of Reverse logistics Network has attracted
growing attention with the stringent pressures from both
environmental awareness and business sustainability. Reverse
logistical activities include return, remanufacture, disassemble and
dispose of products can be quite complex to manage. In addition,
demand can be difficult to predict, and decision making is one of the
challenges task in such network. This complexity has amplified the
need to develop an integrated architecture for product return as an
enterprise system. The main purpose of this paper is to design Multi
Agent System (MAS) architecture using the Prometheus
methodology to efficiently manage reverse logistics processes. The
proposed MAS architecture includes five types of agents: Gate
keeping Agent, Collection Agent, Sorting Agent, Processing Agent
and Disposal Agent which act respectively during the five steps of
reverse logistics Network.
Abstract: Live video streaming is one of the most widely used
service among end users, yet it is a big challenge for the network
operators in terms of quality. The only way to provide excellent
Quality of Experience (QoE) to the end users is continuous
monitoring of live video streaming. For this purpose, there are several
objective algorithms available that monitor the quality of the video in
a live stream. Subjective tests play a very important role in fine
tuning the results of objective algorithms. As human perception is
considered to be the most reliable source for assessing the quality of a
video stream subjective tests are conducted in order to develop more
reliable objective algorithms. Temporal impairments in a live video
stream can have a negative impact on the end users. In this paper we
have conducted subjective evaluation tests on a set of video
sequences containing temporal impairment known as frame freezing.
Frame Freezing is considered as a transmission error as well as a
hardware error which can result in loss of video frames on the
reception side of a transmission system. In our subjective tests, we
have performed tests on videos that contain a single freezing event
and also for videos that contain multiple freezing events. We have
recorded our subjective test results for all the videos in order to give a
comparison on the available No Reference (NR) objective
algorithms. Finally, we have shown the performance of no reference
algorithms used for objective evaluation of videos and suggested the
algorithm that works better. The outcome of this study shows the
importance of QoE and its effect on human perception. The results
for the subjective evaluation can serve the purpose for validating
objective algorithms.
Abstract: This paper presents an approach of on-line control of
the state of technosphere and environment objects based on the
integration of Data Warehouse, OLAP and Expert systems
technologies. It looks at the structure and content of data warehouse
that provides consolidation and storage of monitoring data. There is a
description of OLAP-models that provide a multidimensional
analysis of monitoring data and dynamic analysis of principal
parameters of controlled objects. The authors suggest some criteria of
emergency risk assessment using expert knowledge about danger
levels. It is demonstrated now some of the proposed solutions could
be adopted in territorial decision making support systems.
Operational control allows authorities to detect threat, prevent natural
and anthropogenic emergencies and ensure a comprehensive safety of
territory.
Abstract: In this paper, we present a four-step ortho-rectification
procedure for real-time geo-referencing of video data from a low-cost
UAV equipped with a multi-sensor system. The basic procedures for
the real-time ortho-rectification are: (1) decompilation of the video
stream into individual frames; (2) establishing the interior camera
orientation parameters; (3) determining the relative orientation
parameters for each video frame with respect to each other; (4)
finding the absolute orientation parameters, using a self-calibration
bundle and adjustment with the aid of a mathematical model. Each
ortho-rectified video frame is then mosaicked together to produce a
mosaic image of the test area, which is then merged with a well
referenced existing digital map for the purpose of geo-referencing
and aerial surveillance. A test field located in Abuja, Nigeria was
used to evaluate our method. Video and telemetry data were collected
for about fifteen minutes, and they were processed using the four-step
ortho-rectification procedure. The results demonstrated that the
geometric measurement of the control field from ortho-images is
more accurate when compared with those from original perspective
images when used to pin point the exact location of targets on the
video imagery acquired by the UAV. The 2-D planimetric accuracy
when compared with the 6 control points measured by a GPS receiver
is between 3 to 5 metres.
Abstract: Background subtraction and temporal difference are
often used for moving object detection in video. Both approaches are
computationally simple and easy to be deployed in real-time image
processing. However, while the background subtraction is highly
sensitive to dynamic background and illumination changes, the
temporal difference approach is poor at extracting relevant pixels of
the moving object and at detecting the stopped or slowly moving
objects in the scene. In this paper, we propose a simple moving object
detection scheme based on adaptive background subtraction and
temporal difference exploiting dynamic background updates. The
proposed technique consists of histogram equalization, a linear
combination of background and temporal difference, followed by the
novel frame-based and pixel-based background updating techniques.
Finally, morphological operations are applied to the output images.
Experimental results show that the proposed algorithm can solve the
drawbacks of both background subtraction and temporal difference
methods and can provide better performance than that of each method.
Abstract: This paper aims to project the construction of a
prototype azimuthal thruster, mounted with materials of low cost and
easy access, testing in a controlled environment to measure their
performance, characteristics and feasibility of future projects. The
construction of the simulation of dynamic positioning software,
responsible for simulating a vessel and reposition it when necessary.
Validation tests were performed in the form of partial or complete
system. These tests validate the system manually or automatically.
The system provides an interface to the user and simulates the
conditions unfavorable positioning of a vessel, accurately calculates
the azimuth angle, the direction of rotation of the helix and the time
that this should be turned on so that the vessel back to position
original. A serial communication connects the Simulation Dynamic
Positioning System with Embedded System causing the usergenerated
data to simulate the DP system arrives in the form of
control signals to the motors of the propellant. This article addresses
issues in the marine industry employees.
Abstract: The work aims to develop a robot in the form of
autonomous vehicle to detect, inspection and mapping of
underground pipelines through the ATmega328 Arduino platform.
Hardware prototyping is very similar to C / C ++ language that
facilitates its use in robotics open source, resembles PLC used in
large industrial processes. The robot will traverse the surface
independently of direct human action, in order to automate the
process of detecting buried pipes, guided by electromagnetic
induction. The induction comes from coils that send the signal to the
Arduino microcontroller contained in that will make the difference in
intensity and the treatment of the information, and then this
determines actions to electrical components such as relays and
motors, allowing the prototype to move on the surface and getting the
necessary information. This change of direction is performed by a
stepper motor with a servo motor. The robot was developed by
electrical and electronic assemblies that allowed test your application.
The assembly is made up of metal detector coils, circuit boards and
microprocessor, which interconnected circuits previously developed
can determine, process control and mechanical actions for a robot
(autonomous car) that will make the detection and mapping of buried
pipelines plates. This type of prototype can prevent and identifies
possible landslides and they can prevent the buried pipelines suffer an
external pressure on the walls with the possibility of oil leakage and
thus pollute the environment.
Abstract: In this paper, we propose moving object detection
method which is helpful for driver to safely take his/her car out of
parking lot. When moving objects such as motorbikes, pedestrians,
the other cars and some obstacles are detected at the rear-side of host
vehicle, the proposed algorithm can provide to driver warning. We
assume that the host vehicle is just before departure. Gaussian
Mixture Model (GMM) based background subtraction is basically
applied. Pre-processing such as smoothing and post-processing as
morphological filtering are added. We examine “which color space
has better performance for detection of moving objects?” Three color
spaces including RGB, YCbCr, and Y are applied and compared, in
terms of detection rate. Through simulation, we prove that RGB
space is more suitable for moving object detection based on
background subtraction.
Abstract: The new era of digital communication has brought up
many challenges that network operators need to overcome. The high
demand of mobile data rates require improved networks, which is a
challenge for the operators in terms of maintaining the quality of
experience (QoE) for their consumers. In live video transmission,
there is a sheer need for live surveillance of the videos in order to
maintain the quality of the network. For this purpose objective
algorithms are employed to monitor the quality of the videos that are
transmitted over a network. In order to test these objective algorithms,
subjective quality assessment of the streamed videos is required, as the
human eye is the best source of perceptual assessment. In this paper we
have conducted subjective evaluation of videos with varying spatial
and temporal impairments. These videos were impaired with frame
freezing distortions so that the impact of frame freezing on the quality
of experience could be studied. We present subjective Mean Opinion
Score (MOS) for these videos that can be used for fine tuning the
objective algorithms for video quality assessment.
Abstract: Model predictive control is a kind of optimal feedback
control in which control performance over a finite future is optimized
with a performance index that has a moving initial time and a moving
terminal time. This paper examines the stability of model predictive
control for linear discrete-time systems with additive stochastic
disturbances. A sufficient condition for the stability of the closed-loop
system with model predictive control is derived by means of a linear
matrix inequality. The objective of this paper is to show the results
of computational simulations in order to verify the effectiveness of
the obtained stability condition.
Abstract: Information generated from various computerization processes is a potential rich source of knowledge for its designated community. To pass this information from generation to generation without modifying the meaning is a challenging activity. To preserve and archive the data for future generations it’s very essential to prove the authenticity of the data. It can be achieved by extracting the metadata from the data which can prove the authenticity and create trust on the archived data. Subsequent challenge is the technology obsolescence. Metadata extraction and standardization can be effectively used to resolve and tackle this problem. Metadata can be categorized at two levels i.e. Technical and Domain level broadly. Technical metadata will provide the information that can be used to understand and interpret the data record, but only this level of metadata isn’t sufficient to create trustworthiness. We have developed a tool which will extract and standardize the technical as well as domain level metadata. This paper is about the different features of the tool and how we have developed this.
Abstract: This paper focuses on a critical component of the
situational awareness (SA), the control of autonomous vertical flight
for vectored thrust aerial vehicle (VTAV). With the SA strategy, we
proposed a neural network motion control procedure to address the
dynamics variation and performance requirement difference of flight
trajectory for a VTAV. This control strategy with using of NARMAL2
neurocontroller for chosen model of VTAV has been verified by
simulation of take-off and forward maneuvers using software
package Simulink and demonstrated good performance for fast
stabilization of motors, consequently, fast SA with economy in
energy can be asserted during search-and-rescue operations.
Abstract: Model transformation, as a pivotal aspect of Modeldriven
engineering, attracts more and more attentions both from
researchers and practitioners. Many domains (enterprise engineering,
software engineering, knowledge engineering, etc.) use model
transformation principles and practices to serve to their domain
specific problems; furthermore, model transformation could also be
used to fulfill the gap between different domains: by sharing and
exchanging knowledge. Since model transformation has been widely
used, there comes new requirement on it: effectively and efficiently
define the transformation process and reduce manual effort that
involved in. This paper presents an automatic model transformation
methodology based on semantic and syntactic comparisons, and
focuses particularly on granularity issue that existed in transformation
process. Comparing to the traditional model transformation
methodologies, this methodology serves to a general purpose: crossdomain
methodology. Semantic and syntactic checking
measurements are combined into a refined transformation process,
which solves the granularity issue. Moreover, semantic and syntactic
comparisons are supported by software tool; manual effort is replaced
in this way.