Abstract: Optical Bursts Switching (OBS) is a relatively new
optical switching paradigm. Contention and burst loss in OBS
networks are major concerns. To resolve contentions, an interesting
alternative to discarding the entire data burst is to partially drop the
burst. Partial burst dropping is based on burst segmentation concept
that its implementation is constrained by some technical challenges,
besides the complexity added to the algorithms and protocols on both
edge and core nodes. In this paper, the burst segmentation concept is
investigated, and an implementation scheme is proposed and
evaluated. An appropriate dropping policy that effectively manages
the size of the segmented data bursts is presented. The dropping
policy is further supported by a new control packet format that
provides constant transmission overhead.
Abstract: This work presents a low-cost and eco-friendly
building material named Agrostone panel. Africa-s urban population
is growing at an annual rate of 2.8% and 62% of its population will
live in urban areas by 2050. As a consequence, many of the least
urbanized and least developed African countries- will face serious
challenges in providing affordable housing to the urban dwellers.
Since the cost of building materials accounts for the largest
proportion of the overall construction cost, innovating low-cost
building material is vital. Agrostone panel is used in housing projects
in Ethiopia. It uses raw materials of agricultural/industrial wastes
and/or natural minerals as a filler, magnesium-based chemicals as a
binder and fiberglass as reinforcement. Agrostone panel reduces the
cost of wall construction by 50% compared with the conventional
building materials. The pros and cons of Agrostone panel as well as
the use of other waste materials as a raw material to make the panel
more sustainable, low-cost and better properties are discussed.
Abstract: Amazing development of the information technology,
communications and internet expansion as well as the requirements
of the city managers to new ideas to run the city and higher
participation of the citizens encourage us to complete the electronic
city as soon as possible. The foundations of this electronic city are in
information technology. People-s participation in metropolitan
management is a crucial topic. Information technology does not
impede this matter. It can ameliorate populace-s participation and
better interactions between the citizens and the city managers.
Citizens can proffer their ideas, beliefs and votes through digital
mass media based upon the internet and computerization plexuses on
the topical matters to receive appropriate replies and services. They
can participate in urban projects by becoming cognizant of the city
views. The most significant challenges are as follows: information
and communicative management, altering citizens- views, as well as
legal and office documents
Electronic city obstacles have been identified in this research. The
required data were forgathered through questionnaires to identify the
barriers from a statistical community comprising specialists and
practitioners of the ministry of information technology and
communication, the municipality information technology
organization.
The conclusions demonstrate that the prioritized electronic city
application barriers in Iran are as follows:
The support quandaries (non-financial ones), behavioral, cultural
and educational plights, the security, legal and license predicaments,
the hardware, orismological and infrastructural curbs, the software
and fiscal problems.
Abstract: Sedimentation formation is a complex hydraulic phenomenon that has emerged as a major operational and maintenance consideration in modern hydraulic engineering in general and river engineering in particular. Sediments accumulation along the river course and their eventual storage in a form of islands affect water intake in the canal systems that are fed by the storage reservoirs. Without proper management, sediment transport can lead to major operational challenges in water distribution system of arid regions like the Dez and Hamidieh command areas. The paper aims to investigate sedimentation in the Western Canal of Dez Diversion Weir using the SHARC model and compare the results with the two intake structures of the Hamidieh dam in Iran using SSIIM model. The objective was to identify the factors which influence the process, check reliability of outcome and provide ways in which to mitigate the implications on operation and maintenance of the structures. Results estimated sand and silt bed loads concentrations to be 193 ppm and 827ppm respectively. This followed ,ore or less similar pattern in Hamidieh where the sediment formation impeded water intake in the canal system. Given the available data on average annual bed loads and average suspended sediment loads of 165ppm and 837ppm in the Dez, there was a significant statistical difference (16%) between the sand grains, whereas no significant difference (1.2%) was find in the silt grain sizes. One explanation for such finding being that along the 6 Km river course there was considerable meandering effects which explains recent shift in the hydraulic behavior along the stream course under investigation. The sand concentration in downstream relative to present state of the canal showed a steep descending curve. Sediment trapping on the other hand indicated a steep ascending curve. These occurred because the diversion weir was not considered in the simulation model. The comparative study showed very close similarities in the results which explains the fact that both software can be used as accurate and reliable analytical tools for simulation of the sedimentation in hydraulic engineering.
Abstract: The standard investigational method for obstructive
sleep apnea syndrome (OSAS) diagnosis is polysomnography (PSG),
which consists of a simultaneous, usually overnight recording of
multiple electro-physiological signals related to sleep and
wakefulness. This is an expensive, encumbering and not a readily
repeated protocol, and therefore there is need for simpler and easily
implemented screening and detection techniques. Identification of
apnea/hypopnea events in the screening recordings is the key factor
for the diagnosis of OSAS. The analysis of a solely single-lead
electrocardiographic (ECG) signal for OSAS diagnosis, which may
be done with portable devices, at patient-s home, is the challenge of
the last years. A novel artificial neural network (ANN) based
approach for feature extraction and automatic identification of
respiratory events in ECG signals is presented in this paper. A
nonlinear principal component analysis (NLPCA) method was
considered for feature extraction and support vector machine for
classification/recognition. An alternative representation of the
respiratory events by means of Kohonen type neural network is
discussed. Our prospective study was based on OSAS patients of the
Clinical Hospital of Pneumology from Iaşi, Romania, males and
females, as well as on non-OSAS investigated human subjects. Our
computed analysis includes a learning phase based on cross signal
PSG annotation.
Abstract: Discovering new biological knowledge from the highthroughput biological data is a major challenge to bioinformatics today. To address this challenge, we developed a new approach for protein classification. Proteins that are evolutionarily- and thereby functionally- related are said to belong to the same classification. Identifying protein classification is of fundamental importance to document the diversity of the known protein universe. It also provides a means to determine the functional roles of newly discovered protein sequences. Our goal is to predict the functional classification of novel protein sequences based on a set of features extracted from each protein sequence. The proposed technique used datasets extracted from the Structural Classification of Proteins (SCOP) database. A set of spectral domain features based on Fast Fourier Transform (FFT) is used. The proposed classifier uses multilayer back propagation (MLBP) neural network for protein classification. The maximum classification accuracy is about 91% when applying the classifier to the full four levels of the SCOP database. However, it reaches a maximum of 96% when limiting the classification to the family level. The classification results reveal that spectral domain contains information that can be used for classification with high accuracy. In addition, the results emphasize that sequence similarity measures are of great importance especially at the family level.
Abstract: Kuwait-s electric power system is vertically integrated
organization owned and operated by the government. For more than
five decades, the government of Kuwait has provided relatively
reliable electric services to consumers with subsidized electric
service fees. Given the country-s rapid socio-economical
development and consequently the increase of electricity demand, a
question that inflicts itself: Is it necessary to reform the power system
to face the fast growing demand? This paper recommends that the
government should consider the private sector as a partner in
operating the power system. Therefore, power system restructuring is
needed to allow such partnership. There are challenges that prevent
such restructuring. Abstract recommendations toward resolving these
challenges are proposed.
Abstract: Wireless sensor network is formed with the combination of sensor nodes and sink nodes. Recently Wireless sensor network has attracted attention of the research community. The main application of wireless sensor network is security from different attacks both for mass public and military. However securing these networks, by itself is a critical issue due to many constraints like limited energy, computational power and lower memory. Researchers working in this area have proposed a number of security techniques for this purpose. Still, more work needs to be done.In this paper we provide a detailed discussion on security in wireless sensor networks. This paper will help to identify different obstacles and requirements for security of wireless sensor networks as well as highlight weaknesses of existing techniques.
Abstract: Drilling is the most common machining operation and it forms the highest machining cost in many manufacturing activities including automotive engine production. The outcome of this operation depends upon many factors including utilization of proper cutting tool geometry, cutting tool material and the type of coating used to improve hardness and resistance to wear, and also cutting parameters. With the availability of a large array of tool geometries, materials and coatings, is has become a challenging task to select the best tool and cutting parameters that would result in the lowest machining cost or highest profit rate. This paper describes an algorithm developed to help achieve good performances in drilling operations by automatically determination of proper cutting tools and cutting parameters. It also helps determine machining sequences resulting in minimum tool changes that would eventually reduce machining time and cost where multiple tools are used.
Abstract: Optimal load shedding (LS) design as an emergency plan is one of the main control challenges posed by emerging new uncertainties and numerous distributed generators including renewable energy sources in a modern power system. This paper presents an overview of the key issues and new challenges on optimal LS synthesis concerning the integration of wind turbine units into the power systems. Following a brief survey on the existing LS methods, the impact of power fluctuation produced by wind powers on system frequency and voltage performance is presented. The most LS schemas proposed so far used voltage or frequency parameter via under-frequency or under-voltage LS schemes. Here, the necessity of considering both voltage and frequency indices to achieve a more effective and comprehensive LS strategy is emphasized. Then it is clarified that this problem will be more dominated in the presence of wind turbines.
Abstract: The aim of this research is to design a collaborative
framework that integrates risk analysis activities into the geospatial
database design (GDD) process. Risk analysis is rarely undertaken
iteratively as part of the present GDD methods in conformance to
requirement engineering (RE) guidelines and risk standards.
Accordingly, when risk analysis is performed during the GDD, some
foreseeable risks may be overlooked and not reach the output
specifications especially when user intentions are not systematically
collected. This may lead to ill-defined requirements and ultimately in
higher risks of geospatial data misuse. The adopted approach consists
of 1) reviewing risk analysis process within the scope of RE and
GDD, 2) analyzing the challenges of risk analysis within the context
of GDD, and 3) presenting the components of a risk-based
collaborative framework that improves the collection of the
intended/forbidden usages of the data and helps geo-IT experts to
discover implicit requirements and risks.
Abstract: Image enhancement is the most important challenging preprocessing for almost all applications of Image Processing. By now, various methods such as Median filter, α-trimmed mean filter, etc. have been suggested. It was proved that the α-trimmed mean filter is the modification of median and mean filters. On the other hand, ε-filters have shown excellent performance in suppressing noise. In spite of their simplicity, they achieve good results. However, conventional ε-filter is based on moving average. In this paper, we suggested a new ε-filter which utilizes α-trimmed mean. We argue that this new method gives better outcomes compared to previous ones and the experimental results confirmed this claim.
Abstract: A challenging problem in radar signal processing is to
achieve reliable target detection in the presence of interferences. In
this paper, we propose a novel algorithm for automatic censoring of
radar interfering targets in log-normal clutter. The proposed
algorithm, termed the forward automatic censored cell averaging
detector (F-ACCAD), consists of two steps: removing the corrupted
reference cells (censoring) and the actual detection. Both steps are
performed dynamically by using a suitable set of ranked cells to
estimate the unknown background level and set the adaptive
thresholds accordingly. The F-ACCAD algorithm does not require
any prior information about the clutter parameters nor does it require
the number of interfering targets. The effectiveness of the F-ACCAD
algorithm is assessed by computing, using Monte Carlo simulations,
the probability of censoring and the probability of detection in
different background environments.
Abstract: Querying a data source and routing data towards sink
becomes a serious challenge in static wireless sensor networks if sink
and/or data source are mobile. Many a times the event to be observed
either moves or spreads across wide area making maintenance of
continuous path between source and sink a challenge. Also, sink can
move while query is being issued or data is on its way towards sink.
In this paper, we extend our already proposed Grid Based Data
Dissemination (GBDD) scheme which is a virtual grid based
topology management scheme restricting impact of movement of
sink(s) and event(s) to some specific cells of a grid. This obviates the
need for frequent path modifications and hence maintains continuous
flow of data while minimizing the network energy consumptions.
Simulation experiments show significant improvements in network
energy savings and average packet delay for a packet to reach at sink.
Abstract: The greenhouse effect and limitations on carbon
dioxide emissions concern engine maker and the future of the
internal combustion engines should go toward substantially and
improved thermal efficiency engine. Homogeneous charge
compression ignition (HCCI) is an alternative high-efficiency
technology for combustion engines to reduce exhaust emissions and
fuel consumption. However, there are still tough challenges in the
successful operation of HCCI engines, such as controlling the
combustion phasing, extending the operating range, and high
unburned hydrocarbon and CO emissions. HCCI and the exploitation
of ethanol as an alternative fuel is one way to explore new frontiers
of internal combustion engines with an eye towards maintaining its
sustainability. This study was done to extend database knowledge
about HCCI with ethanol a fuel.
Abstract: How to coordinate the behaviors of the agents through
learning is a challenging problem within multi-agent domains.
Because of its complexity, recent work has focused on how
coordinated strategies can be learned. Here we are interested in using
reinforcement learning techniques to learn the coordinated actions of a
group of agents, without requiring explicit communication among
them. However, traditional reinforcement learning methods are based
on the assumption that the environment can be modeled as Markov
Decision Process, which usually cannot be satisfied when multiple
agents coexist in the same environment. Moreover, to effectively
coordinate each agent-s behavior so as to achieve the goal, it-s
necessary to augment the state of each agent with the information
about other existing agents. Whereas, as the number of agents in a
multiagent environment increases, the state space of each agent grows
exponentially, which will cause the combinational explosion problem.
Profit sharing is one of the reinforcement learning methods that allow
agents to learn effective behaviors from their experiences even within
non-Markovian environments. In this paper, to remedy the drawback
of the original profit sharing approach that needs much memory to
store each state-action pair during the learning process, we firstly
address a kind of on-line rational profit sharing algorithm. Then, we
integrate the advantages of modular learning architecture with on-line
rational profit sharing algorithm, and propose a new modular
reinforcement learning model. The effectiveness of the technique is
demonstrated using the pursuit problem.
Abstract: Banishing hunger from the face of earth has been
frequently expressed in various international, national and regional
level conferences since 1974. Providing food security has become
important issue across the world particularly in developing countries.
In a developing country like India, where growth rate of population is
more than that of the food grains production, food security is a
question of great concern. According to the International Food Policy
Research Institute's Global Hunger Index, 2011, India ranks 67 of the
81 countries of the world with the worst food security status. After
Green Revolution, India became a food surplus country. Its
production has increased from 74.23 million tonnes in 1966-67 to
257.44 million tonnes in 2011-12. But after achieving selfsufficiency
in food during last three decades, the country is now
facing new challenges due to increasing population, climate change,
stagnation in farm productivity. Therefore, the main objective of the
present paper is to examine the food security situation at national
level in the country and further to explain the paradox of food
insecurity in a food surplus state of India i.e in Punjab at micro level.
In order to achieve the said objectives, secondary data collected from
the Ministry of Agriculture and the Agriculture department of Punjab
State was analyzed. The result of the study showed that despite
having surplus food production the country is still facing food
insecurity problem at micro level. Within the Kandi belt of Punjab
state, the area adjacent to plains is food secure while the area along
the hills falls in food insecure zone.
The present paper is divided into following three sections (i)
Introduction, (ii) Analysis of food security situation at national level
as well as micro level (Kandi belt of Punjab State) (iii) Concluding
Observations
Abstract: Gene expression profiling is rapidly evolving into a
powerful technique for investigating tumor malignancies. The
researchers are overwhelmed with the microarray-based platforms
and methods that confer them the freedom to conduct large-scale
gene expression profiling measurements. Simultaneously,
investigations into cross-platform integration methods have started
gaining momentum due to their underlying potential to help
comprehend a myriad of broad biological issues in tumor diagnosis,
prognosis, and therapy. However, comparing results from different
platforms remains to be a challenging task as various inherent
technical differences exist between the microarray platforms. In this
paper, we explain a simple ratio-transformation method, which can
provide some common ground for cDNA and Affymetrix platform
towards cross-platform integration. The method is based on the
characteristic data attributes of Affymetrix- and cDNA- platform. In
the work, we considered seven childhood leukemia patients and their
gene expression levels in either platform. With a dataset of 822
differentially expressed genes from both these platforms, we carried
out a specific ratio-treatment to Affymetrix data, which subsequently
showed an improvement in the relationship with the cDNA data.
Abstract: This paper presents key challenges reported by a
group of Australian undergraduate Physical Education students in
conducting a program for persons with an intellectual disability.
Strategies adopted to address these challenges are presented together
with representative feedback given by the Physical Education
students at the completion of the program. The significance of the
program’s findings is summarized.
Abstract: In recent years, IT convergence technology has been developed to get creative solution by combining robotics or sports science technology. Object detection and recognition have mainly applied to sports science field that has processed by recognizing face and by tracking human body. But object detection and recognition using vision sensor is challenge task in real world because of illumination. In this paper, object detection and recognition using vision sensor applied to sports simulator has been introduced. Face recognition has been processed to identify user and to update automatically a person athletic recording. Human body has tracked to offer a most accurate way of riding horse simulator. Combined image processing has been processed to reduce illumination adverse affect because illumination has caused low performance in detection and recognition in real world application filed. Face has recognized using standard face graph and human body has tracked using pose model, which has composed of feature nodes generated diverse face and pose images. Face recognition using Gabor wavelet and pose recognition using pose graph is robust to real application. We have simulated using ETRI database, which has constructed on horse riding simulator.