Abstract: Moving into a new era of healthcare, new tools and
devices are developed to extend and improve health services, such as
remote patient monitoring and risk prevention. In this concept,
Internet of Things (IoT) and Cloud Computing present great
advantages by providing remote and efficient services, as well as
cooperation between patients, clinicians, researchers and other health
professionals. This paper focuses on patients suffering from bipolar
disorder, a brain disorder that belongs to a group of conditions
called affective disorders, which is characterized by great mood
swings. We exploit the advantages of Semantic Web and Cloud
Technologies to develop a patient monitoring system to support
clinicians. Based on intelligently filtering of evidence-knowledge and
individual-specific information we aim to provide treatment
notifications and recommended function tests at appropriate times or
concluding into alerts for serious mood changes and patient’s nonresponse
to treatment. We propose an architecture as the back-end
part of a cloud platform for IoT, intertwining intelligence devices
with patients’ daily routine and clinicians’ support.
Abstract: We have conducted the optimal synthesis of rootmean-
squared objective filter to estimate the state vector in the case if
within the observation channel with memory the anomalous noises
with unknown mathematical expectation are complement in the
function of the regular noises. The synthesis has been carried out for
linear stochastic systems of continuous - time.
Abstract: We present a framework of researcher knowledge
development in conducting a study in mathematics education. The
key components of the framework are: knowledge germane to
conducting a particular study, processes of knowledge accumulation,
and catalyzing filters that influence a researcher decision making.
The components of the framework originated from a confluence
between constructs and theories in Mathematics Education, Higher
Education and Sociology. Drawing on a self-reflective interview with
a leading researcher in mathematics education, Professor Michèle
Artigue, we illustrate how the framework can be utilized in data
analysis. Criteria for framework evaluation are discussed.
Abstract: For optimal unbiased filter as mean-square and in the
case of functioning anomalous noises in the observation memory
channel, we have proved insensitivity of filter to inaccurate
knowledge of the anomalous noise intensity matrix and its
equivalence to truncated filter plotted only by non anomalous
components of an observation vector.
Abstract: Nanofibers are effective materials which have
frequently been investigated to produce high quality air filters. As an
environmental approach our aim is to achieve nanofibers by melting.
In spun-bond systems extruder, spin-pump, nozzle package and
attenuator are used. Molten polymer which flows from extruder is
made steady by spin-pump. Regular melt passes through nozzle holes
and forms fibers under high pressure. The fibers pulled from nozzle
are shrunk to micron size by an attenuator; after solidification, they
are collected on a conveyor. In this research different designs of
attenuator system have been studied; and also CFD analysis has been
done on these different designs. Afterwards, one of these designs
tested and finally some optimizations have been done to reduce
pressure loss and increase air velocity.
Abstract: The formulated problem of optimization of the
technological process of water treatment for thermal power plants is
considered in this article. The problem is of multiparametric nature.
To optimize the process, namely, reduce the amount of waste water, a
new technology was developed to reuse such water. A mathematical
model of the technology of wastewater reuse was developed.
Optimization parameters were determined. The model consists of a
material balance equation, an equation describing the kinetics of ion
exchange for the non-equilibrium case and an equation for the ion
exchange isotherm. The material balance equation includes a
nonlinear term that depends on the kinetics of ion exchange. A direct
problem of calculating the impurity concentration at the outlet of the
water treatment plant was numerically solved. The direct problem
was approximated by an implicit point-to-point computation
difference scheme. The inverse problem was formulated as relates to
determination of the parameters of the mathematical model of the
water treatment plant operating in non-equilibrium conditions. The
formulated inverse problem was solved. Following the results of
calculation the time of start of the filter regeneration process was
determined, as well as the period of regeneration process and the
amount of regeneration and wash water. Multi-parameter
optimization of water treatment process for thermal power plants
allowed decreasing the amount of wastewater by 15%.
Abstract: In this paper, the problem of fault detection and
isolation in the attitude control subsystem of spacecraft formation
flying is considered. In order to design the fault detection method, an
extended Kalman filter is utilized which is a nonlinear stochastic state
estimation method. Three fault detection architectures, namely,
centralized, decentralized, and semi-decentralized are designed based
on the extended Kalman filters. Moreover, the residual generation
and threshold selection techniques are proposed for these
architectures.
Abstract: The edges of low contrast images are not clearly
distinguishable to human eye. It is difficult to find the edges and
boundaries in it. The present work encompasses a new approach for
low contrast images. The Chebyshev polynomial based fractional
order filter has been used for filtering operation on an image. The
preprocessing has been performed by this filter on the input image.
Laplacian of Gaussian method has been applied on preprocessed
image for edge detection. The algorithm has been tested on two test
images.
Abstract: The goal of image segmentation is to cluster pixels
into salient image regions. Segmentation could be used for object
recognition, occlusion boundary estimation within motion or stereo
systems, image compression, image editing, or image database lookup.
In this paper, we present a color image segmentation using
support vector machine (SVM) pixel classification. Firstly, the pixel
level color and texture features of the image are extracted and they
are used as input to the SVM classifier. These features are extracted
using the homogeneity model and Gabor Filter. With the extracted
pixel level features, the SVM Classifier is trained by using FCM
(Fuzzy C-Means).The image segmentation takes the advantage of
both the pixel level information of the image and also the ability of
the SVM Classifier. The Experiments show that the proposed method
has a very good segmentation result and a better efficiency, increases
the quality of the image segmentation compared with the other
segmentation methods proposed in the literature.
Abstract: ESPRIT-TLS method appears a good choice for high
resolution fault detection in induction machines. It has a very high
effectiveness in the frequency and amplitude identification.
Contrariwise, it presents a high computation complexity which
affects its implementation in real time fault diagnosis. To avoid this
problem, a Fast-ESPRIT algorithm that combined the IIR band-pass
filtering technique, the decimation technique and the original
ESPRIT-TLS method was employed to enhance extracting accurately
frequencies and their magnitudes from the wind stator current with
less computation cost. The proposed algorithm has been applied to
verify the wind turbine machine need in the implementation of an online,
fast, and proactive condition monitoring. This type of remote
and periodic maintenance provides an acceptable machine lifetime,
minimize its downtimes and maximize its productivity. The
developed technique has evaluated by computer simulations under
many fault scenarios. Study results prove the performance of Fast-
ESPRIT offering rapid and high resolution harmonics recognizing
with minimum computation time and less memory cost.
Abstract: This paper presents two types of microstrip bandpass
filter (BPF) at microwave frequencies. The first one is a tunable BPF
using planar patch resonators based on a varactor diode. The filter is
formed by a triple mode circular patch resonator with two pairs of
slots, in which the varactor diodes are connected. Indeed, this filter is
initially centered at 2.4 GHz; the center frequency of the tunable
patch filter could be tuned up to 1.8 GHz simultaneously with the
bandwidth, reaching high tuning ranges. Lossless simulations were
compared to those considering the substrate dielectric, conductor
losses and the equivalent electrical circuit model of the tuning
element in order to assess their effects. Within these variations,
simulation results showed insertion loss better than 2 dB and return
loss better than 10 dB over the passband. The second structure is a
BPF for ultra-wideband (UWB) applications based on multiple-mode
resonator (MMR) and rectangular-shaped defected ground structure
(DGS). This filter, which is compact size of 25.2 x 3.8 mm2, provides
in the pass band an insertion loss of 0.57 dB and a return loss greater
than 12 dB. The proposed filters presents good performances and the
simulation results are in satisfactory agreement with the
experimentation ones reported elsewhere.
Abstract: The development of the United Arab Emirates (UAE)
into a regional trade, tourism, finance and logistics hub has
transformed its real estate markets. However, speculative activity and
price volatility remain concerns. UAE residential market values
(MV) are exposed to fluctuations in capital flows and migration
which, in turn, are affected by geopolitical uncertainty, oil price
volatility and global investment market sentiment. Internally, a
complex interplay between administrative boundaries, land tenure,
building quality and evolving location characteristics fragments UAE
residential property markets. In short, the UAE Residential Valuation
System (UAE-RVS) confronts multiple challenges to collect, filter
and analyze relevant information in complex and dynamic spatial and
capital markets. A robust (RVS) can mitigate the risk of unhelpful
volatility, speculative excess or investment mistakes. The research
outlines the institutional, ontological, dynamic and epistemological
issues at play. We highlight the importance of system capabilities,
valuation standard salience and stakeholders trust.
Abstract: Collection of storm water runoff and forcing it into the
groundwater is the need of the hour to sustain the ground water table.
However, the runoff entraps various types of sediments and other
floating objects whose removal are essential to avoid pollution of
ground water and blocking of pores of aquifer. However, it requires
regular cleaning and maintenance due to problem of clogging. To
evaluate the performance of filter system consisting of coarse sand
(CS), gravel (G) and pebble (P) layers, a laboratory experiment was
conducted in a rectangular column. The effect of variable thickness
of CS, G and P layers of the filtration unit of the recharge shaft on the
recharge rate and the sediment concentration of effluent water were
evaluated.
Medium sand (MS) of three particle sizes, viz. 0.150–0.300 mm
(T1), 0.300–0.425 mm (T2) and 0.425–0.600 mm of thickness 25 cm,
30 cm and 35 cm respectively in the top layer of the filter system and
having seven influent sediment concentrations of 250–3,000 mg/l
were used for experimental study. The performance was evaluated in
terms of recharge rates and clogging time. The results indicated that
100 % suspended solids were entrapped in the upper 10 cm layer of
MS, the recharge rates declined sharply for influent concentrations of
more than 1,000 mg/l. All treatments with higher thickness of MS
media indicated recharge rate slightly more than that of all treatment
with lower thickness of MS media respectively. The performance of
storm water infiltration systems was highly dependent on the
formation of a clogging layer at the filter. An empirical relationship
has been derived between recharge rates, inflow sediment load, size
of MS and thickness of MS with using MLR.
Abstract: Modelling of the earth's surface and evaluation of
urban environment, with 3D models, is an important research topic.
New stereo capabilities of high resolution optical satellites images,
such as the tri-stereo mode of Pleiades, combined with new image
matching algorithms, are now available and can be applied in urban
area analysis. In addition, photogrammetry software packages gained
new, more efficient matching algorithms, such as SGM, as well as
improved filters to deal with shadow areas, can achieve more dense
and more precise results.
This paper describes a comparison between 3D data extracted
from tri-stereo and dual stereo satellite images, combined with pixel
based matching and Wallis filter. The aim was to improve the
accuracy of 3D models especially in urban areas, in order to assess if
satellite images are appropriate for a rapid evaluation of urban
environments.
The results showed that 3D models achieved by Pleiades tri-stereo
outperformed, both in terms of accuracy and detail, the result
obtained from a Geo-eye pair. The assessment was made with
reference digital surface models derived from high resolution aerial
photography. This could mean that tri-stereo images can be
successfully used for the proposed urban change analyses.
Abstract: Speech enhancement is a long standing problem with
numerous applications like teleconferencing, VoIP, hearing aids and
speech recognition. The motivation behind this research work is to
obtain a clean speech signal of higher quality by applying the optimal
noise cancellation technique. Real-time adaptive filtering algorithms
seem to be the best candidate among all categories of the speech
enhancement methods. In this paper, we propose a speech
enhancement method based on Recursive Least Squares (RLS)
adaptive filter of speech signals. Experiments were performed on
noisy data which was prepared by adding AWGN, Babble and Pink
noise to clean speech samples at -5dB, 0dB, 5dB and 10dB SNR
levels. We then compare the noise cancellation performance of
proposed RLS algorithm with existing NLMS algorithm in terms of
Mean Squared Error (MSE), Signal to Noise ratio (SNR) and SNR
Loss. Based on the performance evaluation, the proposed RLS
algorithm was found to be a better optimal noise cancellation
technique for speech signals.
Abstract: This paper presents modeling of an Alternating
Current (AC) Photovoltaic (PV) module using Matlab/Simulink. The
proposed AC-PV module model is simple, realistic, and application
oriented. The model is derived on module level as compared to cell
level directly from the information provided by the manufacturer data
sheet. DC-PV module, MPPT control, BC, VSI and LC filter, all
were treated as a single unit. The model accounts for changes in
variations of both irradiance and temperature. The AC-PV module
proposed model is simulated and the results are compared with the
datasheet projected numbers to validate model’s accuracy and
effectiveness. Implementation and results demonstrate simplicity and
accuracy, as well as reliability of the model.
Abstract: In this paper, we considered and applied parametric
modeling for some experimental data of dynamical system. In this
study, we investigated the different distribution of output
measurement from some dynamical systems. Also, with variance
processing in experimental data we obtained the region of
nonlinearity in experimental data and then identification of output
section is applied in different situation and data distribution. Finally,
the effect of the spanning the measurement such as variance to
identification and limitation of this approach is explained.
Abstract: Performance of different filtering approaches depends
on modeling of dynamical system and algorithm structure. For
modeling and smoothing the data the evaluation of posterior
distribution in different filtering approach should be chosen carefully.
In this paper different filtering approaches like filter KALMAN,
EKF, UKF, EKS and smoother RTS is simulated in some trajectory
tracking of path and accuracy and limitation of these approaches are
explained. Then probability of model with different filters is
compered and finally the effect of the noise variance to estimation is
described with simulations results.
Abstract: Leukaemia is a blood cancer disease that contributes
to the increment of mortality rate in Malaysia each year. There are
two main categories for leukaemia, which are acute and chronic
leukaemia. The production and development of acute leukaemia cells
occurs rapidly and uncontrollable. Therefore, if the identification of
acute leukaemia cells could be done fast and effectively, proper
treatment and medicine could be delivered. Due to the requirement of
prompt and accurate diagnosis of leukaemia, the current study has
proposed unsupervised pixel segmentation based on clustering
algorithm in order to obtain a fully segmented abnormal white blood
cell (blast) in acute leukaemia image. In order to obtain the
segmented blast, the current study proposed three clustering
algorithms which are k-means, fuzzy c-means and moving k-means
algorithms have been applied on the saturation component image.
Then, median filter and seeded region growing area extraction
algorithms have been applied, to smooth the region of segmented
blast and to remove the large unwanted regions from the image,
respectively. Comparisons among the three clustering algorithms are
made in order to measure the performance of each clustering
algorithm on segmenting the blast area. Based on the good sensitivity
value that has been obtained, the results indicate that moving kmeans
clustering algorithm has successfully produced the fully
segmented blast region in acute leukaemia image. Hence, indicating
that the resultant images could be helpful to haematologists for
further analysis of acute leukaemia.
Abstract: Motion Tracking and Stereo Vision are complicated,
albeit well-understood problems in computer vision. Existing
softwares that combine the two approaches to perform stereo motion
tracking typically employ complicated and computationally expensive
procedures. The purpose of this study is to create a simple and
effective solution capable of combining the two approaches. The
study aims to explore a strategy to combine the two techniques
of two-dimensional motion tracking using Kalman Filter; and depth
detection of object using Stereo Vision. In conventional approaches
objects in the scene of interest are observed using a single camera.
However for Stereo Motion Tracking; the scene of interest is
observed using video feeds from two calibrated cameras. Using two
simultaneous measurements from the two cameras a calculation for
the depth of the object from the plane containing the cameras is made.
The approach attempts to capture the entire three-dimensional spatial
information of each object at the scene and represent it through a
software estimator object. In discrete intervals, the estimator tracks
object motion in the plane parallel to plane containing cameras and
updates the perpendicular distance value of the object from the plane
containing the cameras as depth. The ability to efficiently track
the motion of objects in three-dimensional space using a simplified
approach could prove to be an indispensable tool in a variety of
surveillance scenarios. The approach may find application from high
security surveillance scenes such as premises of bank vaults, prisons
or other detention facilities; to low cost applications in supermarkets
and car parking lots.