Abstract: This paper studies the dependability of componentbased
applications, especially embedded ones, from the diagnosis
point of view. The principle of the diagnosis technique is to
implement inter-component tests in order to detect and locate the
faulty components without redundancy. The proposed approach for
diagnosing faulty components consists of two main aspects. The first
one concerns the execution of the inter-component tests which
requires integrating test functionality within a component. This is the
subject of this paper. The second one is the diagnosis process itself
which consists of the analysis of inter-component test results to
determine the fault-state of the whole system. Advantage of this
diagnosis method when compared to classical redundancy faulttolerant
techniques are application autonomy, cost-effectiveness and
better usage of system resources. Such advantage is very important
for many systems and especially for embedded ones.
Abstract: The adaptive power control of Code Division Multiple
Access (CDMA) communications using Remote Radio Head
(RRH) between multiple Unmanned Aerial Vehicles (UAVs) with
a link-budget based Signal-to-Interference Ratio (SIR) estimate is
applied to four inner loop power control algorithms. It is concluded
that Base Station (BS) can calculate not only UAV distance using
linearity between speed and Consecutive Transmit-Power-Control
Ratio (CTR) of Adaptive Step-size Closed Loop Power Control (ASCLPC),
Consecutive TPC Ratio Step-size Closed Loop Power Control
(CS-CLPC), Fixed Step-size Power Control (FSPC), but also UAV
position with Received Signal Strength Indicator (RSSI) ratio of
RRHs.
Abstract: Most simple nonlinear thresholding rules for
wavelet- based denoising assume that the wavelet coefficients are independent. However, wavelet coefficients of natural images have significant dependencies. This paper attempts to give a recipe for selecting one of the popular image-denoising algorithms based
on VisuShrink, SureShrink, OracleShrink, BayesShrink and BiShrink and also this paper compares different Bivariate models used for image denoising applications. The first part of the paper
compares different Shrinkage functions used for image-denoising.
The second part of the paper compares different bivariate models
and the third part of this paper uses the Bivariate model with modified marginal variance which is based on Laplacian assumption. This paper gives an experimental comparison on six 512x512 commonly used images, Lenna, Barbara, Goldhill,
Clown, Boat and Stonehenge. The following noise powers 25dB,26dB, 27dB, 28dB and 29dB are added to the six standard images and the corresponding Peak Signal to Noise Ratio (PSNR) values
are calculated for each noise level.
Abstract: The numerous qualities of squirrel cage induction
machines enhance their use in industry. However, various faults can
occur, such as stator short-circuits and rotor failures.
In this paper, we use a technique based on the spectral analysis of
stator current in order to detect the fault in the machine: broken rotor
bars. Thus, the number effect of the breaks has been highlighted. The
effect is highlighted by considering the machine controlled by the
Direct Torque Control (DTC). The key to fault detection is the
development of a simplified dynamic model of a squirrel cage
induction motor taking account the broken bars fault and the stator
current spectrum analysis (FFT).
Abstract: This document describes an electronic speed control designed to drive two DC motors from a 6 V battery pack to be controlled by a commercial universal infrared remote control hand set. Conceived for a tank-like vehicle, one motor drives the left side wheels or tracks and the other motor drives the right side. As it is shown here, there is a left-right steering input and a forward– backward throttles input, like would be used on a model car. It is designed using a microcontroller PIC16F873A.
Abstract: Supply Chain Management (SCM) is the integration
between manufacturer, transporter and customer in order to form one
seamless chain that allows smooth flow of raw materials, information
and products throughout the entire network that help in minimizing
all related efforts and costs. The main objective of this paper is to
develop a model that can accept a specified number of spare-parts
within the supply chain, simulating its inventory operations
throughout all stages in order to minimize the inventory holding
costs, base-stock, safety-stock, and to find the optimum quantity of
inventory levels, thereby suggesting a way forward to adapt some
factors of Just-In-Time to minimizing the inventory costs throughout
the entire supply chain. The model has been developed using Micro-
Soft Excel & Visual Basic in order to study inventory allocations in
any network of the supply chain. The application and reproducibility
of this model were tested by comparing the actual system that was
implemented in the case study with the results of the developed
model. The findings showed that the total inventory costs of the
developed model are about 50% less than the actual costs of the
inventory items within the case study.
Abstract: Plants are commonly known for its positive
correlation in reducing temperature. Since it can benefit buildings by
modifying the microclimate, it-s also believed capable of reducing
the internal temperature. Various experiments have been done in
Universiti Sains Malaysia, Penang to investigate the comparison in
thermal benefits between two rooms, one being a typical control
room (exposed wall) and the other a biofacade room (plant shaded
wall). The investigations were conducted during non-rainy season for
approximately a month. Climbing plant Psophocarpus
tetrogonobulus from legume species was selected as insulation for
the biofacade wall. Conclusions were made on whether the biofacade
can be used to tackle the energy efficiency, based on the parameters
taken into consideration.
Abstract: The promises of component-based technology can only be fully realized when the system contains in its design a necessary level of separation of concerns. The authors propose to focus on the concerns that emerge throughout the life cycle of the system and use them as an architectural foundation for the design of a component-based framework. The proposed model comprises a set of superimposed views of the system describing its functional and non-functional concerns. This approach is illustrated by the design of a specific framework for data analysis and data acquisition and supplemented with experiences from using the systems developed with this framework at the Fermi National Accelerator Laboratory.
Abstract: Lanthanide-doped upconversion nanoparticles which can convert near-infrared lights to visible lights have attracted growing interest because of their great potentials in fluorescence imaging. Upconversion fluorescence imaging technique with excitation in the near-infrared (NIR) region has been used for imaging of biological cells and tissues. However, improving the detection sensitivity and decreasing the absorption and scattering in biological tissues are as yet unresolved problems. In this present study, a novel NIR-reflected multispectral imaging system was developed for upconversion fluorescent imaging in small animals. Based on this system, we have obtained the high contrast images without the autofluorescence when biocompatible UCPs were injected near the body surface or deeply into the tissue. Furthermore, we have extracted respective spectra of the upconversion fluorescence and relatively quantify the fluorescence intensity with the multispectral analysis. To our knowledge, this is the first time to analyze and quantify the upconversion fluorescence in the small animal imaging.
Abstract: Using spatial models as a shared common basis of
information about the environment for different kinds of contextaware
systems has been a heavily researched topic in the last years.
Thereby the research focused on how to create, to update, and to
merge spatial models so as to enable highly dynamic, consistent and
coherent spatial models at large scale. In this paper however, we
want to concentrate on how context-aware applications could use this
information so as to adapt their behavior according to the situation
they are in. The main idea is to provide the spatial model
infrastructure with a situation recognition component based on
generic situation templates. A situation template is – as part of a
much larger situation template library – an abstract, machinereadable
description of a certain basic situation type, which could be
used by different applications to evaluate their situation. In this
paper, different theoretical and practical issues – technical, ethical
and philosophical ones – are discussed important for understanding
and developing situation dependent systems based on situation
templates. A basic system design is presented which allows for the
reasoning with uncertain data using an improved version of a
learning algorithm for the automatic adaption of situation templates.
Finally, for supporting the development of adaptive applications, we
present a new situation-aware adaptation concept based on
workflows.
Abstract: Nuclear energy sources have been widely used in the
past decades in order to power spacecraft subsystems. Nevertheless,
their use has attracted controversy because of the risk of harmful
material released into the atmosphere if an accident were to occur
during the launch phase of the mission, leading to the general
adoption of photovoltaic systems.
As compared to solar cells, wind turbines have a great advantage
on Mars, as they can continuously produce power both during dust
storms and at night-time: this paper focuses on the potential of a wind
energy conversion system (WECS) considering the atmospheric
conditions on Mars. Wind potential on Martian surface has been
estimated, as well as the average energy requirements of a Martian
probe or surface rover. Finally, the expected daily energy output of
the WECS has been computed on the basis of both the swept area of
the rotor and the equivalent wind speed at the landing site.
Abstract: The wireless sensor networks have been extensively
deployed and researched. One of the major issues in wireless sensor
networks is a developing energy-efficient clustering protocol.
Clustering algorithm provides an effective way to prolong the lifetime
of a wireless sensor networks. In the paper, we compare several
clustering protocols which significantly affect a balancing of energy
consumption. And we propose an Energy-Efficient Distributed
Unequal Clustering (EEDUC) algorithm which provides a new way of
creating distributed clusters. In EEDUC, each sensor node sets the
waiting time. This waiting time is considered as a function of residual
energy, number of neighborhood nodes. EEDUC uses waiting time to
distribute cluster heads. We also propose an unequal clustering
mechanism to solve the hot-spot problem. Simulation results show that
EEDUC distributes the cluster heads, balances the energy
consumption well among the cluster heads and increases the network
lifetime.
Abstract: Traditional higher-education classrooms allow lecturers to observe students- behaviours and responses to a particular pedagogy during learning in a way that can influence changes to the pedagogical approach. Within current e-learning systems it is difficult to perform continuous analysis of the cohort-s behavioural tendency, making real-time pedagogical decisions difficult. This paper presents a Virtual Learning Process Environment (VLPE) based on the Business Process Management (BPM) conceptual framework. Within the VLPE, course designers can model various education pedagogies in the form of learning process workflows using an intuitive flow diagram interface. These diagrams are used to visually track the learning progresses of a cohort of students. This helps assess the effectiveness of the chosen pedagogy, providing the information required to improve course design. A case scenario of a cohort of students is presented and quantitative statistical analysis of their learning process performance is gathered and displayed in realtime using dashboards.
Abstract: This study has investigated the antidiabetic and
antioxidant potential of Pseudovaria macrophylla bark extract on
streptozotocin–nicotinamide induced type 2 diabetic rats. LCMSQTOF
and NMR experiments were done to determine the chemical
composition in the methanolic bark extract. For in vivo experiments,
the STZ (60 mg/kg/b.w, 15 min after 120 mg/kg/1 nicotinamide, i.p.)
induced diabetic rats were treated with methanolic extract of
Pseuduvaria macrophylla (200 and 400 mg/kg·bw) and
glibenclamide (2.5 mg/kg) as positive control respectively.
Biochemical parameters were assayed in the blood samples of all
groups of rats. The pro-inflammatory cytokines, antioxidant status
and plasma transforming growth factor βeta-1 (TGF-β1) were
evaluated. The histological study of the pancreas was examined and
its expression level of insulin was observed by
immunohistochemistry. In addition, the expression of glucose
transporters (GLUT 1, 2 and 4) were assessed in pancreas tissue by
western blot analysis. The outcomes of the study displayed that the
bark methanol extract of Pseuduvaria macrophylla has potentially
normalized the elevated blood glucose levels and improved serum
insulin and C-peptide levels with significant increase in the
antioxidant enzyme, reduced glutathione (GSH) and decrease in the
level of lipid peroxidation (LPO). Additionally, the extract has
markedly decreased the levels of serum pro-inflammatory cytokines
and transforming growth factor beta-1 (TGF-β1). Histopathology
analysis demonstrated that Pseuduvaria macrophylla has the
potential to protect the pancreas of diabetic rats against peroxidation
damage by downregulating oxidative stress and elevated
hyperglycaemia. Furthermore, the expression of insulin protein,
GLUT-1, GLUT-2 and GLUT-4 in pancreatic cells was enhanced.
The findings of this study support the anti-diabetic claims of
Pseudovaria macrophylla bark.
Abstract: Cosmic showers, during the transit through space, produce
sub - products as a result of interactions with the intergalactic
or interstellar medium which after entering earth generate secondary
particles called Extensive Air Shower (EAS). Detection and analysis
of High Energy Particle Showers involve a plethora of theoretical and
experimental works with a host of constraints resulting in inaccuracies
in measurements. Therefore, there exist a necessity to develop a
readily available system based on soft-computational approaches
which can be used for EAS analysis. This is due to the fact that soft
computational tools such as Artificial Neural Network (ANN)s can be
trained as classifiers to adapt and learn the surrounding variations. But
single classifiers fail to reach optimality of decision making in many
situations for which Multiple Classifier System (MCS) are preferred
to enhance the ability of the system to make decisions adjusting
to finer variations. This work describes the formation of an MCS
using Multi Layer Perceptron (MLP), Recurrent Neural Network
(RNN) and Probabilistic Neural Network (PNN) with data inputs
from correlation mapping Self Organizing Map (SOM) blocks and
the output optimized by another SOM. The results show that the setup
can be adopted for real time practical applications for prediction
of primary energy and location of EAS from density values captured
using detectors in a circular grid.
Abstract: In this paper we suggest a method for setting
electronic credits for the customers. In this method banks and
market-sites help each other to make doing large shopping through
internet so easy. By developing this system, the people who have less
money to buy most of the things they want, become able to buy all of
them just through a credit. This credit is given by market-sites
through a banking control on it. The method suggested can stop
being imprisoned because of banking debts.
Abstract: Since communications between tag and reader in RFID
system are by radio, anyone can access the tag and obtain its any
information. And a tag always replies with the same ID so that it is
hard to distinguish between a real and a fake tag. Thus, there are many
security problems in today-s RFID System. Firstly, unauthorized
reader can easily read the ID information of any Tag. Secondly,
Adversary can easily cheat the legitimate reader using the collected
Tag ID information, such as the any legitimate Tag. These security
problems can be typically solved by encryption of messages
transmitted between Tag and Reader and by authentication for Tag.
In this paper, to solve these security problems on RFID system, we
propose the Tag Authentication Scheme based on self shrinking
generator (SSG). SSG Algorithm using in our scheme is proposed by
W.Meier and O.Staffelbach in EUROCRYPT-94. This Algorithm is
organized that only one LFSR and selection logic in order to generate
random stream. Thus it is optimized to implement the hardware logic
on devices with extremely limited resource, and the output generating
from SSG at each time do role as random stream so that it is allow our
to design the light-weight authentication scheme with security against
some network attacks. Therefore, we propose the novel tag
authentication scheme which use SSG to encrypt the Tag-ID
transmitted from tag to reader and achieve authentication of tag.
Abstract: In this paper, an efficient local appearance feature
extraction method based the multi-resolution Curvelet transform is
proposed in order to further enhance the performance of the well
known Linear Discriminant Analysis(LDA) method when applied
to face recognition. Each face is described by a subset of band
filtered images containing block-based Curvelet coefficients. These
coefficients characterize the face texture and a set of simple statistical
measures allows us to form compact and meaningful feature vectors.
The proposed method is compared with some related feature extraction
methods such as Principal component analysis (PCA), as well
as Linear Discriminant Analysis LDA, and independent component
Analysis (ICA). Two different muti-resolution transforms, Wavelet
(DWT) and Contourlet, were also compared against the Block Based
Curvelet-LDA algorithm. Experimental results on ORL, YALE and
FERET face databases convince us that the proposed method provides
a better representation of the class information and obtains much
higher recognition accuracies.
Abstract: The intention of this study to design the probability optimized sewing sack-s workstation based on ergonomics for productivity improvement and decreasing musculoskeletal disorders. The physical dimensions of two workers were using to design the new workstation. The physical dimensions are (1) sitting height, (2) mid shoulder height sitting, (3) shoulder breadth, (4) knee height, (5) popliteal height, (6) hip breadth and (7) buttock-knee length. The 5th percentile of buttock knee length sitting (51 cm), the 50th percentile of mid shoulder height sitting (62 cm) and the 95th percentile of popliteal height (43 cm) and hip breadth (45 cm) applied to design the workstation for sewing sack-s operator and the others used to adjust the components of this workstation. The risk assessment by RULA before and after using the probability optimized workstation were 7 and 7 scores and REBA scores were 11 and 5, respectively. Body discomfort-abnormal index was used to assess muscle fatigue of operators before adjustment workstation found that neck muscles, arm muscles area, muscles on the back and the lower back muscles fatigue. Therefore, the extension and flexion exercise was applied to relief musculoskeletal stresses. The workers exercised 15 minutes before the beginning and the end of work for 5 days. After that, the capability of flexion and extension muscles- workers were increasing in 3 muscles (arm, leg, and back muscles).
Abstract: In this paper the problem of estimating the time delay
between two spatially separated noisy sinusoidal signals by system
identification modeling is addressed. The system is assumed to be
perturbed by both input and output additive white Gaussian noise. The
presence of input noise introduces bias in the time delay estimates.
Normally the solution requires a priori knowledge of the input-output
noise variance ratio. We utilize the cascade of a self-tuned filter with
the time delay estimator, thus making the delay estimates robust to
input noise. Simulation results are presented to confirm the superiority
of the proposed approach at low input signal-to-noise ratios.