Abstract: Ant Colony Optimization (ACO) is a promising
modern approach to the unused combinatorial optimization. Here
ACO is applied to finding the shortest during communication link
failure. In this paper, the performances of the prim’s and ACO
algorithm are made. By comparing the time complexity and program
execution time as set of parameters, we demonstrate the pleasant
performance of ACO in finding excellent solution to finding shortest
path during communication link failure.
Abstract: A knowledge base stores facts and rules about the
world that applications can use for the purpose of reasoning. By
applying the concept of granular computing to a knowledge base,
several advantages emerge. These can be harnessed by applications
to improve their capabilities and performance. In this paper, the
concept behind such a construct, called a granular knowledge cube,
is defined, and its intended use as an instrument that manages to
cope with different data types and detect knowledge domains is
elaborated. Furthermore, the underlying architecture, consisting of the
three layers of the storing, representing, and structuring of knowledge,
is described. Finally, benefits as well as challenges of deploying it
are listed alongside application types that could profit from having
such an enhanced knowledge base.
Abstract: The rapid growth of multimedia technology demands
the secure and efficient access to information. This fast growing lose
the confidence of unauthorized duplication. Henceforth the protection
of multimedia content is becoming more important. Watermarking
solves the issue of unlawful copy of advanced data. In this paper,
blind video watermarking technique has been proposed. A luminance
layer of selected frames is interlaced into two even and odd rows of
an image, further it is deinterlaced and equalizes the coefficients of
the two shares. Color watermark is split into different blocks, and the
pieces of block are concealed in one of the share under the wavelet
transform. Stack the two images into a single image by introducing
interlaced even and odd rows in the two shares. Finally, chrominance
bands are concatenated with the watermarked luminance band. The
safeguard level of the secret information is high, and it is
undetectable. Results show that the quality of the video is not
changed also yields the better PSNR values.
Abstract: In this paper a novel color image compression
technique for efficient storage and delivery of data is proposed. The
proposed compression technique started by RGB to YCbCr color
transformation process. Secondly, the canny edge detection method is
used to classify the blocks into the edge and non-edge blocks. Each
color component Y, Cb, and Cr compressed by discrete cosine
transform (DCT) process, quantizing and coding step by step using
adaptive arithmetic coding. Our technique is concerned with the
compression ratio, bits per pixel and peak signal to noise ratio, and
produce better results than JPEG and more recent published schemes
(like CBDCT-CABS and MHC). The provided experimental results
illustrate the proposed technique that is efficient and feasible in terms
of compression ratio, bits per pixel and peak signal to noise ratio.
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: 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: 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: Artificial neural networks have gained a lot of interest
as empirical models for their powerful representational capacity,
multi input and output mapping characteristics. In fact, most feedforward
networks with nonlinear nodal functions have been proved to
be universal approximates. In this paper, we propose a new
supervised method for color image classification based on selforganizing
feature maps (SOFM). This algorithm is based on
competitive learning. The method partitions the input space using
self-organizing feature maps to introduce the concept of local
neighborhoods. Our image classification system entered into RGB
image. Experiments with simulated data showed that separability of
classes increased when increasing training time. In additional, the
result shows proposed algorithms are effective for color image
classification.
Abstract: DNA Barcode provides good sources of needed
information to classify living species. The classification problem has
to be supported with reliable methods and algorithms. To analyze
species regions or entire genomes, it becomes necessary to use the
similarity sequence methods. A large set of sequences can be
simultaneously compared using Multiple Sequence Alignment which
is known to be NP-complete. However, all the used methods are still
computationally very expensive and require significant computational
infrastructure. Our goal is to build predictive models that are highly
accurate and interpretable. In fact, our method permits to avoid the
complex problem of form and structure in different classes of
organisms. The empirical data and their classification performances
are compared with other methods. Evenly, in this study, we present
our system which is consisted of three phases. The first one, is called
transformation, is composed of three sub steps; Electron-Ion
Interaction Pseudopotential (EIIP) for the codification of DNA
Barcodes, Fourier Transform and Power Spectrum Signal Processing.
Moreover, the second phase step is an approximation; it is
empowered by the use of Multi Library Wavelet Neural Networks
(MLWNN). Finally, the third one, is called the classification of DNA
Barcodes, is realized by applying the algorithm of hierarchical
classification.
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: 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: A robust sequential nonparametric method is proposed
for adaptation to background noise parameters for real-time. The
distribution of background noise was modelled like to Huber
contamination mixture. The method is designed to operate as an
adaptation-unit, which is included inside a detection subsystem of an
integrated multichannel monitoring system. The proposed method
guarantees the given size of a nonasymptotic confidence set for noise
parameters. Properties of the suggested method are rigorously
proved. The proposed algorithm has been successfully tested in real
conditions of a functioning C-OTDR monitoring system, which was
designed to monitor railways.
Abstract: The enormous amount of information stored on the
web increases from one day to the next, exposing the web currently
faced with the inevitable difficulties of research pertinent information
that users really want. The problem today is not limited to expanding
the size of the information highways, but to design a system for
intelligent search. The vast majority of this information is stored in
relational databases, which in turn represent a backend for managing
RDF data of the semantic web. This problem has motivated us to
write this paper in order to establish an effective approach to support
semantic transformation algorithm for SPARQL queries to SQL
queries, more precisely SPARQL SELECT queries; by adopting this
method, the relational database can be questioned easily with
SPARQL queries maintaining the same performance.
Abstract: Most of the existing video streaming protocols
provide video services without considering security aspects in
decentralized mobile ad-hoc networks. The security policies adapted
to the currently existing non-streaming protocols, do not comply with
the live video streaming protocols resulting in considerable
vulnerability, high bandwidth consumption and unreliability which
cause severe security threats, low bandwidth and error prone
transmission respectively in video streaming applications. Therefore
a synergized methodology is required to reduce vulnerability and
bandwidth consumption, and enhance reliability in the video
streaming applications in MANET. To ensure the security measures
with reduced bandwidth consumption and improve reliability of the
video streaming applications, a Secure Low-bandwidth Video
Streaming through Reliable Multipath Propagation (SLVRMP)
protocol architecture has been proposed by incorporating the two
algorithms namely Secure Low-bandwidth Video Streaming
Algorithm and Reliable Secure Multipath Propagation Algorithm
using Layered Video Coding in non-overlapping zone routing
network topology. The performances of the proposed system are
compared to those of the other existing secure multipath protocols
Sec-MR, SPREAD using NS 2.34 and the simulation results show
that the performances of the proposed system get considerably
improved.
Abstract: Based on application requirements, nodes are static or
mobile in Wireless Sensor Networks (WSNs). Mobility poses
challenges in protocol design, especially at the link layer requiring
mobility adaptation algorithms to localize mobile nodes and predict
link quality to be established with them. This study implements
XMAC and Berkeley Media Access Control (BMAC) routing
protocols to evaluate performance under WSN’s static and mobility
conditions. This paper gives a comparative study of mobility-aware
MAC protocols. Routing protocol performance, based on Average
End to End Delay, Average Packet Delivery Ratio, Average Number
of hops, and Jitter is evaluated.
Abstract: Voting algorithms are extensively used to make
decisions in fault tolerant systems where each redundant module
gives inconsistent outputs. Popular voting algorithms include
majority voting, weighted voting, and inexact majority voters. Each
of these techniques suffers from scenarios where agreements do not
exist for the given voter inputs. This has been successfully overcome
in literature using fuzzy theory. Our previous work concentrated on a
neuro-fuzzy algorithm where training using the neuro system
substantially improved the prediction result of the voting system.
Weight training of Neural Network is sub-optimal. This study
proposes to optimize the weights of the Neural Network using
Artificial Bee Colony algorithm. Experimental results show the
proposed system improves the decision making of the voting
algorithms.
Abstract: Mobile Adhoc Networks (MANETs) are
infrastructure-less, dynamic network of collections of wireless mobile
nodes communicating with each other without any centralized
authority. A MANET is a mobile device of interconnections through
wireless links, forming a dynamic topology. Routing protocols have a
big role in data transmission across a network. Routing protocols,
two major classifications are unipath and multipath. This study
evaluates performance of an on-demand multipath routing protocol
named Adhoc On-demand Multipath Distance Vector routing
(AOMDV). This study proposes Energy Aware AOMDV (EAAOMDV)
an extension of AOMDV which decreases energy
consumed on a route.
Abstract: Nowadays, several research studies point up that an
active lifestyle is essential for physical and mental health benefits.
Mobile phones have greatly influenced people’s habits and attitudes
also in the way they exercise. Our research work is mainly focused on
investigating how to exploit mobile technologies to favour people’s
exertion experience. To this end, we developed an exertion framework
users can exploit through a real world mobile application, called
EverywhereSport Run (EWRun), designed to act as a virtual personal
trainer to support runners during their trainings. In this work, inspired
by both previous findings in the field of interaction design for people
with visual impairments, feedback gathered from real users of our
framework, and positive results obtained from two experimentations,
we present some new interaction facilities we designed to enhance
the interaction experience during a training. The positive obtained
results helped us to derive some interaction design recommendations
we believe will be a valid support for designers of future mobile
systems conceived to be used in circumstances where there are limited
possibilities of interaction.
Abstract: This paper presents the local mesh co-occurrence
patterns (LMCoP) using HSV color space for image retrieval system.
HSV color space is used in this method to utilize color, intensity and
brightness of images. Local mesh patterns are applied to define the
local information of image and gray level co-occurrence is used to
obtain the co-occurrence of LMeP pixels. Local mesh co-occurrence
pattern extracts the local directional information from local mesh
pattern and converts it into a well-mannered feature vector using gray
level co-occurrence matrix. The proposed method is tested on three
different databases called MIT VisTex, Corel, and STex. Also, this
algorithm is compared with existing methods, and results in terms of
precision and recall are shown in this paper.
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.