Abstract: Decision Feedback equalizers (DFEs) usually outperform linear equalizers for channels with intersymbol interference. However, the DFE performance is highly dependent on the availability of reliable past decisions. Hence, in coded systems, where reliable decisions are only available after decoding the full block, the performance of the DFE will be affected. A symbol based DFE is a DFE that only uses the decision after the block is decoded. In this paper we derive the optimal settings of both the feedforward and feedback taps of the symbol based equalizer. We present a novel symbol based DFE filterbank, and derive its taps optimal settings. We also show that it outperforms the classic DFE in terms of complexity and/or performance.
Abstract: This paper proposes an effective adaptation learning
algorithm based on artificial neural networks for speed control of an
induction motor assumed to operate in a high-performance drives
environment. The structure scheme consists of a neural network
controller and an algorithm for changing the NN weights in order that
the motor speed can accurately track of the reference command. This
paper also makes uses a very realistic and practical scheme to
estimate and adaptively learn the noise content in the speed load
torque characteristic of the motor. The availability of the proposed
controller is verified by through a laboratory implementation and
under computation simulations with Matlab-software. The process is
also tested for the tracking property using different types of reference
signals. The performance and robustness of the proposed control
scheme have evaluated under a variety of operating conditions of the
induction motor drives. The obtained results demonstrate the
effectiveness of the proposed control scheme system performances,
both in steady state error in speed and dynamic conditions, was found
to be excellent and those is not overshoot.
Abstract: The article deals with the classification of alternative water resources in terms of potential risks which is the prerequisite for incorporating these water resources to the emergency plans. The classification is based on the quantification of risks resulting from possible damage, disruption or total destruction of water resource caused by natural and anthropogenic hazards, assessment of water quality and availability, traffic accessibility of the assessed resource and finally its water yield. The aim is to achieve the development of an integrated rescue system, which will be capable of supplying the population with drinking water on the whole stricken territory during the states of emergency.
Abstract: CloudSim is a useful tool to simulate the cloud
environment. It shows the service availability, the power consumption,
and the network traffic of services on the cloud environment.
Moreover, it supports to calculate a network communication delay
through a network topology data easily. CloudSim allows inputting a
file of topology data, but it does not provide any generating process.
Thus, it needs the file of topology data generated from some other
tools. The BRITE is typical network topology generator. Also, it
supports various type of topology generating algorithms. If CloudSim
can include the BRITE, network simulation for clouds is easier than
existing version. This paper shows the potential of connection between
BRITE and CloudSim. Also, it proposes the direction to link between
them.
Abstract: How to efficiently assign system resource to route the
Client demand by Gateway servers is a tricky predicament. In this
paper, we tender an enhanced proposal for autonomous recital of
Gateway servers under highly vibrant traffic loads. We devise a
methodology to calculate Queue Length and Waiting Time utilizing
Gateway Server information to reduce response time variance in
presence of bursty traffic.
The most widespread contemplation is performance, because
Gateway Servers must offer cost-effective and high-availability
services in the elongated period, thus they have to be scaled to meet
the expected load. Performance measurements can be the base for
performance modeling and prediction. With the help of performance
models, the performance metrics (like buffer estimation, waiting
time) can be determined at the development process.
This paper describes the possible queue models those can be
applied in the estimation of queue length to estimate the final value
of the memory size. Both simulation and experimental studies using
synthesized workloads and analysis of real-world Gateway Servers
demonstrate the effectiveness of the proposed system.
Abstract: In this work, we present a novel active learning approach
for learning a visual object detection system. Our system
is composed of an active learning mechanism as wrapper around
a sub-algorithm which implement an online boosting-based learning
object detector. In the core is a combination of a bootstrap procedure
and a semi automatic learning process based on the online boosting
procedure. The idea is to exploit the availability of classifier during
learning to automatically label training samples and increasingly
improves the classifier. This addresses the issue of reducing labeling
effort meanwhile obtain better performance. In addition, we propose
a verification process for further improvement of the classifier.
The idea is to allow re-update on seen data during learning for
stabilizing the detector. The main contribution of this empirical study
is a demonstration that active learning based on an online boosting
approach trained in this manner can achieve results comparable or
even outperform a framework trained in conventional manner using
much more labeling effort. Empirical experiments on challenging data
set for specific object deteciton problems show the effectiveness of
our approach.
Abstract: In this paper, an artificial neural network simulator is
employed to carry out diagnosis and prognosis on electric motor as
rotating machinery based on predictive maintenance. Vibration data
of the primary failed motor including unbalance, misalignment and
bearing fault were collected for training the neural network. Neural
network training was performed for a variety of inputs and the motor
condition was used as the expert training information. The main
purpose of applying the neural network as an expert system was to
detect the type of failure and applying preventive maintenance. The
advantage of this study is for machinery Industries by providing
appropriate maintenance that has an essential activity to keep the
production process going at all processes in the machinery industry.
Proper maintenance is pivotal in order to prevent the possible failures
in operating system and increase the availability and effectiveness of
a system by analyzing vibration monitoring and developing expert
system.
Abstract: The electromagnetic spectrum is a natural resource
and hence well-organized usage of the limited natural resources is the
necessities for better communication. The present static frequency
allocation schemes cannot accommodate demands of the rapidly
increasing number of higher data rate services. Therefore, dynamic
usage of the spectrum must be distinguished from the static usage to
increase the availability of frequency spectrum. Cognitive radio is not
a single piece of apparatus but it is a technology that can incorporate
components spread across a network. It offers great promise for
improving system efficiency, spectrum utilization, more effective
applications, reduction in interference and reduced complexity of
usage for users. Cognitive radio is aware of its environmental,
internal state, and location, and autonomously adjusts its operations
to achieve designed objectives. It first senses its spectral environment
over a wide frequency band, and then adapts the parameters to
maximize spectrum efficiency with high performance. This paper
only focuses on the analysis of Bit-Error-Rate in cognitive radio by
using Particle Swarm Optimization Algorithm. It is theoretically as
well as practically analyzed and interpreted in the sense of
advantages and drawbacks and how BER affects the efficiency and
performance of the communication system.
Abstract: In the present study, development of salbutamol
sulphate nanoparticles that adhere to gastric mucus was investigated.
Salbutamol sulphate has low bioavailability due to short transit time in
gastric. It also has a positive surface charge that provides hurdles to be
encapsulated by the positively strong mucoadhesive polymer of
chitosan. To overcome the difficulties, the surface charge of active
ingredient was modified using several nonionic and anionic
stomach-specific polymers. The nanoparticles were prepared using
ionotropic gelation technique. The evaluation involved determination
of particle size, zeta potential, entrapment efficiency, in vitro drug
release and in vitro mucoadhesion test. Results exhibited that the use
of anionic alginate polymer was more satisfactory than that of
nonionic polymer. Characteristics of the particles was nano-size, high
encapsulation efficiency, fulfilled the drug release requirements and
adhesive towards stomach for around 11 hours. This result shows that
the salbutamol sulphate nanoparticles can be utilized for improvement
its delivery.
Abstract: The paper presents a modelling methodology for
small scale multi-source renewable energy systems. Using historical
site-specific weather data, the relationships of cost, availability and
energy form are visualised as a function of the sizing of photovoltaic
arrays, wind turbines, and battery capacity. The specific dependency
of each site on its own particular weather patterns show that unique
solutions exist for each site. It is shown that in certain cases the
capital component cost can be halved if the desired theoretical
demand availability is reduced from 100% to 99%.
Abstract: The abundance and availability of rice husk, an agricultural waste, make them as a good source for precursor of activated carbon. In this work, rice husk-based activated carbons were prepared via base treated chemical activation process prior the carbonization process. The effect of carbonization temperatures (400, 600 and 800oC) on their pore structure was evaluated through morphology analysis using scanning electron microscope (SEM). Sample carbonized at 800oC showed better evolution and development of pores as compared to those carbonized at 400 and 600oC. The potential of rice husk-based activated carbon as an alternative adsorbent was investigated for the removal of Ni(II), Zn(II) and Pb(II) from single metal aqueous solution. The adsorption studies using rice husk-based activated carbon as an adsorbent were carried out as a function of contact time at room temperature and the metal ions were analyzed using atomic absorption spectrophotometer (AAS). The ability to remove metal ion from single metal aqueous solution was found to be improved with the increasing of carbonization temperature. Among the three metal ions tested, Pb(II) ion gave the highest adsorption on rice husk-based activated carbon. The results obtained indicate the potential to utilize rice husk as a promising precursor for the preparation of activated carbon for removal of heavy metals.
Abstract: Avoidable unscheduled maintenance events and unnecessary
spare parts deliveries are mostly caused by an incorrect choice
of the underlying maintenance strategy. For a faster and more efficient
supply of spare parts for aircrafts of an airline we examine options for
improving the underlying logistics network integrated in an existing
aviation industry network. This paper presents a dynamic prediction
model as decision support for maintenance method selection considering
requirements of an entire flight network. The objective is
to guarantee a high supply of spare parts by an optimal interaction
of various network levels and thus to reduce unscheduled maintenance
events and minimize total costs. By using a prognostics-based
preventive maintenance strategy unscheduled component failures are
avoided for an increase in availability and reliability of the entire
system. The model is intended for use in an aviation company that
utilizes a structured planning process based on collected failures data
of components.
Abstract: Developing an accurate classifier for high dimensional microarray datasets is a challenging task due to availability of small sample size. Therefore, it is important to determine a set of relevant genes that classify the data well. Traditionally, gene selection method often selects the top ranked genes according to their discriminatory power. Often these genes are correlated with each other resulting in redundancy. In this paper, we have proposed a hybrid method using feature ranking and wrapper method (Genetic Algorithm with multiclass SVM) to identify a set of relevant genes that classify the data more accurately. A new fitness function for genetic algorithm is defined that focuses on selecting the smallest set of genes that provides maximum accuracy. Experiments have been carried on four well-known datasets1. The proposed method provides better results in comparison to the results found in the literature in terms of both classification accuracy and number of genes selected.
Abstract: Large scale systems such as computational Grid is
a distributed computing infrastructure that can provide globally
available network resources. The evolution of information processing
systems in Data Grid is characterized by a strong decentralization of
data in several fields whose objective is to ensure the availability and
the reliability of the data in the reason to provide a fault tolerance
and scalability, which cannot be possible only with the use of the
techniques of replication. Unfortunately the use of these techniques
has a height cost, because it is necessary to maintain consistency
between the distributed data. Nevertheless, to agree to live with
certain imperfections can improve the performance of the system by
improving competition. In this paper, we propose a multi-layer protocol
combining the pessimistic and optimistic approaches conceived
for the data consistency maintenance in large scale systems. Our
approach is based on a hierarchical representation model with tree
layers, whose objective is with double vocation, because it initially
makes it possible to reduce response times compared to completely
pessimistic approach and it the second time to improve the quality
of service compared to an optimistic approach.
Abstract: Use of the Internet and the World-Wide-Web
(WWW) has become widespread in recent years and mobile agent
technology has proliferated at an equally rapid rate. In this scenario
load balancing becomes important for P2P systems. Beside P2P
systems can be highly heterogeneous, i.e., they may consists of peers
that range from old desktops to powerful servers connected to
internet through high-bandwidth lines. There are various loads
balancing policies came into picture. Primitive one is Message
Passing Interface (MPI). Its wide availability and portability make it
an attractive choice; however the communication requirements are
sometimes inefficient when implementing the primitives provided by
MPI. In this scenario we use the concept of mobile agent because
Mobile agent (MA) based approach have the merits of high
flexibility, efficiency, low network traffic, less communication
latency as well as highly asynchronous. In this study we present
decentralized load balancing scheme using mobile agent technology
in which when a node is overloaded, task migrates to less utilized
nodes so as to share the workload. However, the decision of which
nodes receive migrating task is made in real-time by defining certain
load balancing policies. These policies are executed on PMADE (A
Platform for Mobile Agent Distribution and Execution) in
decentralized manner using JuxtaNet and various load balancing
metrics are discussed.
Abstract: Biogas, a clean renewable energy, is attracting a growing
concern of researchers and professionals in many fields. Based
on the natural and climatic conditions in semi-arid regions of northwestern
China, the present study introduces a specifically-designed
family-size biogas plant (with a digester of 10m3) with manure
and urine of animals and humanity as raw materials. The biogas
plant is applicable to areas with altitudes of more than 2000 meters
in northwestern China. In addition to the installation cost, a little
operational expenditure, structure, characteristics, benefits of this
small-scale biogas plant, this article introduces a wide range of
specific popularization methods such as training, financial support,
guided tour to the biogas plant, community-based group study and
delivery of operational manuals. The feasibility of the biogas plant is
explored on the basis of the availability of the raw materials. Simple
operations contained in the current work increase the possibility of
the wide use of this small-scale biogas plant in similar regions of the
world.
Abstract: The work presented in this study is related to an
energy system analysis based on passive cooling system for
dwellings. It consists to solar chimney energy performances
determination versus geometrical and environmental considerations
as the size and inlet width conditions of the chimney. Adrar site
located in the southern region of Algeria is chosen for this study
according to ambient temperature and solar irradiance technical data
availability. Obtained results are related to the glazing temperature
distributions, the chimney air flow and internal wall temperatures.
The air room change per hour (ACH) parameter, the outlet air
velocity and mass air flow rate are also determined. It is shown that
the chimney width has a significant effect on energy performances
compared to its entry size. A good agreement is observed between
these results and those obtained by others from the literature.
Abstract: Frequent machine breakdowns, low plant availability and increased overtime are a great threat to a manufacturing plant as they increase operating costs of an industry. The main aim of this study was to improve Overall Equipment Effectiveness (OEE) at a manufacturing company through the implementation of innovative maintenance strategies. A case study approach was used. The paper focuses on improving the maintenance in a manufacturing set up using an innovative maintenance regime mix to improve overall equipment effectiveness. Interviews, reviewing documentation and historical records, direct and participatory observation were used as data collection methods during the research. Usually production is based on the total kilowatt of motors produced per day. The target kilowatt at 91% availability is 75 Kilowatts a day. Reduced demand and lack of raw materials particularly imported items are adversely affecting the manufacturing operations. The company had to reset its targets from the usual figure of 250 Kilowatt per day to mere 75 per day due to lower availability of machines as result of breakdowns as well as lack of raw materials. The price reductions and uncertainties as well as general machine breakdowns further lowered production. Some recommendations were given. For instance, employee empowerment in the company will enhance responsibility and authority to improve and totally eliminate the six big losses. If the maintenance department is to realise its proper function in a progressive, innovative industrial society, then its personnel must be continuously trained to meet current needs as well as future requirements. To make the maintenance planning system effective, it is essential to keep track of all the corrective maintenance jobs and preventive maintenance inspections. For large processing plants these cannot be handled manually. It was therefore recommended that the company implement (Computerised Maintenance Management System) CMMS.
Abstract: Marketing is an essential issue to the survival of any
real estate company in Turkey. There are some factors which are
constraining the achievements of the marketing and sales strategies in
the Turkey real estate industry. This study aims to identify and
prioritise the most significant constraints to marketing in real estate
sector and new strategies based on those constraints. This study is
based on survey method, where the respondents such as credit
counsellors, real estate investors, consultants, academicians and
marketing representatives in Turkey were asked to rank forty seven
sub-factors according to their levels of impact. The results of Multiattribute
analytical technique indicated that the main subcomponents
having impact on marketing in real estate sector are interest rates, real
estate credit availability, accessibility, company image and consumer
real income, respectively. The identified constraints are expected to
guide the marketing team in a sales-effective way.
Abstract: This paper discusses the performance modeling and availability analysis of Yarn Dyeing System of a Textile Industry. The Textile Industry is a complex and repairable engineering system. Yarn Dyeing System of Textile Industry consists of five subsystems arranged in series configuration. For performance modeling and analysis of availability, a performance evaluating model has been developed with the help of mathematical formulation based on Markov-Birth-Death Process. The differential equations have been developed on the basis of Probabilistic Approach using a Transition Diagram. These equations have further been solved using normalizing condition in order to develop the steady state availability, a performance measure of the system concerned. The system performance has been further analyzed with the help of decision matrices. These matrices provide various availability levels for different combinations of failure and repair rates for various subsystems. The findings of this paper are therefore, considered to be useful for the analysis of availability and determination of the best possible maintenance strategies which can be implemented in future to enhance the system performance.