Abstract: We address the integer frequency offset (IFO)
estimation under the influence of the timing offset (TO) in orthogonal
frequency division multiplexing (OFDM) systems. Incorporating the
IFO and TO into the symbol set used to represent the received
OFDM symbol, we investigate the influence of the TO on the IFO,
and then, propose a combining method between two consecutive
OFDM correlations, reducing the influence. The proposed scheme
has almost the same complexity as that of the conventional
schemes, whereas it does not need the TO knowledge contrary to
the conventional schemes. From numerical results it is confirmed
that the proposed scheme is insensitive to the TO, consequently,
yielding an improvement of the IFO estimation performance over
the conventional schemes when the TO exists.
Abstract: The detection of moving objects from a video image
sequences is very important for object tracking, activity recognition,
and behavior understanding in video surveillance.
The most used approach for moving objects detection / tracking is
background subtraction algorithms. Many approaches have been
suggested for background subtraction. But, these are illumination
change sensitive and the solutions proposed to bypass this problem
are time consuming.
In this paper, we propose a robust yet computationally efficient
background subtraction approach and, mainly, focus on the ability to
detect moving objects on dynamic scenes, for possible applications in
complex and restricted access areas monitoring, where moving and
motionless persons must be reliably detected. It consists of three
main phases, establishing illumination changes invariance,
background/foreground modeling and morphological analysis for
noise removing.
We handle illumination changes using Contrast Limited Histogram
Equalization (CLAHE), which limits the intensity of each pixel to
user determined maximum. Thus, it mitigates the degradation due to
scene illumination changes and improves the visibility of the video
signal. Initially, the background and foreground images are extracted
from the video sequence. Then, the background and foreground
images are separately enhanced by applying CLAHE.
In order to form multi-modal backgrounds we model each channel
of a pixel as a mixture of K Gaussians (K=5) using Gaussian Mixture
Model (GMM). Finally, we post process the resulting binary
foreground mask using morphological erosion and dilation
transformations to remove possible noise.
For experimental test, we used a standard dataset to challenge the
efficiency and accuracy of the proposed method on a diverse set of
dynamic scenes.
Abstract: This paper addresses the issue of the autonomous
mobile robot (AMR) navigation task based on the hybrid control
modes. The novel hybrid control mode, based on multi-sensors
information by using the fuzzy approach, has been presented in this
research. The system operates in real time, is robust, enables the robot
to operate with imprecise knowledge, and takes into account the
physical limitations of the environment in which the robot moves,
obtaining satisfactory responses for a large number of different
situations. An experiment is simulated and carried out with a pioneer
mobile robot. From the experimental results, the effectiveness and
usefulness of the proposed AMR obstacle avoidance and navigation
scheme are confirmed. The experimental results show the feasibility,
and the control system has improved the navigation accuracy. The
implementation of the controller is robust, has a low execution time,
and allows an easy design and tuning of the fuzzy knowledge base.
Abstract: In this paper, we used data mining to extract
biomedical knowledge. In general, complex biomedical data
collected in studies of populations are treated by statistical methods,
although they are robust, they are not sufficient in themselves to
harness the potential wealth of data. For that you used in step two
learning algorithms: the Decision Trees and Support Vector Machine
(SVM). These supervised classification methods are used to make the
diagnosis of thyroid disease. In this context, we propose to promote
the study and use of symbolic data mining techniques.
Abstract: Operations, maintenance and reliability of wind
turbines have received much attention over the years due to the rapid
expansion of wind farms. This paper explores early fault diagnosis
technique for a 5MW wind turbine system subjected to multiple
faults, where genetic optimization algorithm is employed to make the
residual sensitive to the faults, but robust against disturbances. The
proposed technique has a potential to reduce the downtime mostly
caused by the breakdown of components and exploit the productivity
consistency by providing timely fault alarms. Simulation results show
the effectiveness of the robust fault detection methods used under
Matlab/Simulink/Gatool environment.
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: Near-infrared spectroscopy (NIRS) has been widely
used as a non-invasive method to measure brain activity, but it is
corrupted by baseline drift noise. Here we present a method to measure
regional cerebral blood flow as a derivative of NIRS output. We
investigate whether, when listening to languages, blood flow can
reasonably localize and represent regional brain activity or not. The
prefrontal blood flow distribution pattern when advanced
second-language listeners listened to a second language (L2) was most
similar to that when listening to their first language (L1) among the
patterns of mean and standard deviation. In experiments with 25
healthy subjects, the maximum blood flow was localized to the left
BA46 of advanced listeners. The blood flow presented is robust to
baseline drift and stably localizes regional brain activity.
Abstract: The design and plantwide control of an integrated
plant where the endothermic 1,4-butanediol dehydrogenation and the
exothermic furfural hydrogenation is simultaneously performed in a
single reactor is studied. The reactions can be carried out in an
adiabatic reactor using small hydrogen excess and with reduced
parameter sensitivity. The plant is robust and flexible enough to
allow different production rates of γ-butyrolactone and 2-methyl
furan, keeping high product purities. Rigorous steady state and
dynamic simulations performed in AspenPlus and AspenDynamics to
support the conclusions.
Abstract: An attempt has been made in the present
communication to elucidate the efficacy of robust ANOVA methods
to analyse horticultural field experimental data in the presence of
outliers. Results obtained fortify the use of robust ANOVA methods
as there was substantiate reduction in error mean square, and hence
the probability of committing Type I error, as compared to the regular
approach.
Abstract: This paper presents a combination of both robust
nonlinear controller and nonlinear controller for a class of nonlinear
4Y Octorotor UAV using Back-stepping and sliding mode controller.
The robustness against internal and external disturbance and
decoupling control are the merits of the proposed paper. The
proposed controller decouples the Octorotor dynamical system. The
controller is then applied to a 4Y Octortor UAV and its feature will
be shown.
Abstract: Environmental impacts of six 3D printers using
various materials were compared to determine if material choice
drove sustainability, or if other factors such as machine type, machine
size, or machine utilization dominate. Cradle-to-grave life-cycle
assessments were performed, comparing a commercial-scale FDM
machine printing in ABS plastic, a desktop FDM machine printing in
ABS, a desktop FDM machine printing in PET and PLA plastics, a
polyjet machine printing in its proprietary polymer, an SLA machine
printing in its polymer, and an inkjet machine hacked to print in salt
and dextrose. All scenarios were scored using ReCiPe Endpoint H
methodology to combine multiple impact categories, comparing
environmental impacts per part made for several scenarios per
machine. Results showed that most printers’ ecological impacts were
dominated by electricity use, not materials, and the changes in
electricity use due to different plastics was not significant compared
to variation from one machine to another. Variation in machine idle
time determined impacts per part most strongly. However, material
impacts were quite important for the inkjet printer hacked to print in
salt: In its optimal scenario, it had up to 1/38th the impacts coreper
part as the worst-performing machine in the same scenario. If salt
parts were infused with epoxy to make them more physically robust,
then much of this advantage disappeared, and material impacts
actually dominated or equaled electricity use. Future studies should
also measure DMLS and SLS processes / materials.
Abstract: Nowadays, Photovoltaic-PV Farms/ Parks and large
PV-Smart Grid Interface Schemes are emerging and commonly
utilized in Renewable Energy distributed generation. However, PVhybrid-
Dc-Ac Schemes using interface power electronic converters
usually has negative impact on power quality and stabilization of
modern electrical network under load excursions and network fault
conditions in smart grid. Consequently, robust FACTS based
interface schemes are required to ensure efficient energy utilization
and stabilization of bus voltages as well as limiting switching/fault
onrush current condition. FACTS devices are also used in smart grid-
Battery Interface and Storage Schemes with PV-Battery Storage
hybrid systems as an elegant alternative to renewable energy
utilization with backup battery storage for electric utility energy and
demand side management to provide needed energy and power
capacity under heavy load conditions. The paper presents a robust
interface PV-Li-Ion Battery Storage Interface Scheme for
Distribution/Utilization Low Voltage Interface using FACTS
stabilization enhancement and dynamic maximum PV power tracking
controllers.
Digital simulation and validation of the proposed scheme is done
using MATLAB/Simulink software environment for Low Voltage-
Distribution/Utilization system feeding a hybrid Linear-Motorized
inrush and nonlinear type loads from a DC-AC Interface VSC-6-
pulse Inverter Fed from the PV Park/Farm with a back-up Li-Ion
Storage Battery.
Abstract: In this paper, we present a robust algorithm to recognize extracted text from grocery product images captured by mobile phone cameras. Recognition of such text is challenging since text in grocery product images varies in its size, orientation,
style, illumination, and can suffer from perspective distortion.
Pre-processing is performed to make the characters scale and
rotation invariant. Since text degradations can not be appropriately
defined using well-known geometric transformations such
as translation, rotation, affine transformation and shearing, we
use the whole character black pixels as our feature vector.
Classification is performed with minimum distance classifier
using the maximum likelihood criterion, which delivers very
promising Character Recognition Rate (CRR) of 89%. We
achieve considerably higher Word Recognition Rate (WRR) of
99% when using lower level linguistic knowledge about product
words during the recognition process.
Abstract: Several of the practical industrial control processes are
multivariable processes. Due to the relation amid the variables
(interaction), delay in the loops, it is very intricate to design a
controller directly for these processes. So first, the interaction of the
variables is analyzed using Relative Normalized Gain Array
(RNGA), which considers the time constant, static gain and delay
time of the processes. Based on the effect of RNGA, relative gain
array (RGA) and NI, the pair (control configuration) of variables to
be controlled by decentralized control is selected. The equivalent
transfer function (ETF) of the process model is estimated as first
order process with delay using the corresponding elements in the
Relative gain array and Relative average residence time array
(RARTA) of the processes. Secondly, a decentralized Proportional-
Integral (PI) controller is designed for each ETF simply using
frequency response specifications. Finally, the performance and
robustness of the algorithm is comparing with existing related
approaches to validate the effectiveness of the projected algorithm.
Abstract: Steganography is the art and science that hides the information in an appropriate cover carrier like image, text, audio and video media. In this work the authors propose a new image based steganographic method for hiding information within the complex bit planes of the image. After slicing into bit planes the cover image is analyzed to extract the most complex planes in decreasing order based on their bit plane complexity. The complexity function next determines the complex noisy blocks of the chosen bit plane and finally pixel mapping method (PMM) has been used to embed secret bits into those regions of the bit plane. The novel approach of using pixel mapping method (PMM) in bit plane domain adaptively embeds data on most complex regions of image, provides high embedding capacity, better imperceptibility and resistance to steganalysis attack.
Abstract: This paper proposes a new technique to design a
fixed-structure robust loop shaping controller for the pneumatic
servosystem. In this paper, a new method based on a particle swarm
optimization (PSO) algorithm for tuning the weighting function
parameters to design an H∞ controller is presented. The PSO
algorithm is used to minimize the infinity norm of the transfer
function of the nominal closed loop system to obtain the optimal
parameters of the weighting functions. The optimal stability margin is
used as an objective in PSO for selecting the optimal weighting
parameters; it is shown that the proposed method can simplify the
design procedure of H∞ control to obtain optimal robust controller for
pneumatic servosystem. In addition, the order of the proposed
controller is much lower than that of the conventional robust loop
shaping controller, making it easy to implement in practical works.
Also two-degree-of-freedom (2DOF) control design procedure is
proposed to improve tracking performance in the face of noise and
disturbance. Result of simulations demonstrates the advantages of the
proposed controller in terms of simple structure and robustness
against plant perturbations and disturbances.
Abstract: Currently, seismic probabilistic risk assessments
(SPRA) for nuclear facilities use In-Structure Response Spectra
(ISRS) in the calculation of fragilities for systems and components.
ISRS are calculated via dynamic analyses of the host building
subjected to two orthogonal components of horizontal ground
motion. Each component is defined as the median motion in any
horizontal direction. Structural engineers applied the components
along selected X and Y Cartesian axes. The ISRS at different
locations in the building are also calculated in the X and Y directions.
The choice of the directions of X and Y are not specified by the
ground motion model with respect to geographic coordinates, and are
rather arbitrarily selected by the structural engineer. Normally, X and
Y coincide with the “principal” axes of the building, in the
understanding that this practice is generally conservative. For SPRA
purposes, however, it is desirable to remove any conservatism in the
estimates of median ISRS. This paper examines the effects of the
direction of horizontal seismic motion on the ISRS on typical nuclear
structure. We also evaluate the variability of ISRS calculated along
different horizontal directions. Our results indicate that some central
measures of the ISRS provide robust estimates that are practically
independent of the selection of the directions of the horizontal
Cartesian axes.
Abstract: Key frame extraction methods select the most
representative frames of a video, which can be used in different areas
of video processing such as video retrieval, video summary, and video
indexing. In this paper we present a novel approach for extracting key
frames from video sequences. The frame is characterized uniquely by
his contours which are represented by the dominant blocks. These
dominant blocks are located on the contours and its near textures.
When the video frames have a noticeable changement, its dominant
blocks changed, then we can extracte a key frame. The dominant
blocks of every frame is computed, and then feature vectors are
extracted from the dominant blocks image of each frame and arranged
in a feature matrix. Singular Value Decomposition is used to calculate
sliding windows ranks of those matrices. Finally the computed ranks
are traced and then we are able to extract key frames of a video.
Experimental results show that the proposed approach is robust
against a large range of digital effects used during shot transition.
Abstract: In this work, a Multi-Level Artificial Bee Colony
(called MLABC) for optimizing numerical test functions is presented.
In MLABC, two species are used. The first species employs n
colonies where each of them optimizes the complete solution vector.
The cooperation between these colonies is carried out by exchanging
information through a leader colony, which contains a set of elite
bees. The second species uses a cooperative approach in which the
complete solution vector is divided to k sub-vectors, and each of
these sub-vectors is optimized by a colony. The cooperation between
these colonies is carried out by compiling sub-vectors into the
complete solution vector. Finally, the cooperation between two
species is obtained by exchanging information. The proposed
algorithm is tested on a set of well-known test functions. The results
show that MLABC algorithm provides efficiency and robustness to
solve numerical functions.
Abstract: This paper proposes the designing direct adaptive
neural controller to apply for a class of a nonlinear pendulum
dynamic system. The radial basis function (RBF) neural adaptive
controller is robust in presence of external and internal uncertainties.
Both the effectiveness of the controller and robustness against
disturbances are importance of this paper. The simulation results
show the promising performance of the proposed controller.