Abstract: Alone with fast urbanization in world, traffic control
became a big issue in urban construction. Having an efficient and
reliable traffic control system is crucial to macro-traffic control.
Traffic signal is used to manage conflicting requirement by allocating
different sets of mutually compatible traffic movement during distinct
time interval. Many approaches have been made proposed to solve
this discrete stochastic problem. Recognizing the need to minimize
right-of-way impacts while efficiently handling the anticipated high
traffic volumes, the proposed alternative system gives effective
design. This model allows for increased traffic capacity and reduces
delays by eliminating a step in maneuvering through the freeway
interchange. The concept proposed in this paper involves
construction of bridges and ramps at intersection of four roads to
control the vehicular congestion and to prevent traffic breakdown.
Abstract: Subspace channel estimation methods have been
studied widely, where the subspace of the covariance matrix is
decomposed to separate the signal subspace from noise subspace. The
decomposition is normally done by using either the eigenvalue
decomposition (EVD) or the singular value decomposition (SVD) of
the auto-correlation matrix (ACM). However, the subspace
decomposition process is computationally expensive. This paper
considers the estimation of the multipath slow frequency hopping
(FH) channel using noise space based method. In particular, an
efficient method is proposed to estimate the multipath time delays by
applying multiple signal classification (MUSIC) algorithm which is
based on the null space extracted by the rank revealing LU (RRLU)
factorization. As a result, precise information is provided by the
RRLU about the numerical null space and the rank, (i.e., important
tool in linear algebra). The simulation results demonstrate the
effectiveness of the proposed novel method by approximately
decreasing the computational complexity to the half as compared
with RRQR methods keeping the same performance.
Abstract: The main objective of this paper is to provide a new
methodology for road safety assessment in Oman through the
development of suitable accident prediction models. GLM technique
with Poisson or NBR using SAS package was carried out to develop
these models. The paper utilized the accidents data of 31 un-signalized
T-intersections during three years. Five goodness-of-fit
measures were used to assess the overall quality of the developed
models. Two types of models were developed separately; the flow-based
models including only traffic exposure functions, and the full
models containing both exposure functions and other significant
geometry and traffic variables.
The results show that, traffic exposure functions produced much
better fit to the accident data. The most effective geometric variables
were major-road mean speed, minor-road 85th percentile speed,
major-road lane width, distance to the nearest junction, and right-turn
curb radius.
The developed models can be used for intersection treatment or
upgrading and specify the appropriate design parameters of T-intersections.
Finally, the models presented in this thesis reflect the intersection
conditions in Oman and could represent the typical conditions in
several countries in the middle east area, especially gulf countries.
Abstract: This paper provides a quantitative measure of the
time-varying multiunit neuronal spiking activity using an entropy
based approach. To verify the status embedded in the neuronal activity
of a population of neurons, the discrete wavelet transform (DWT) is
used to isolate the inherent spiking activity of MUA. Due to the
de-correlating property of DWT, the spiking activity would be
preserved while reducing the non-spiking component. By evaluating
the entropy of the wavelet coefficients of the de-noised MUA, a
multiresolution Shannon entropy (MRSE) of the MUA signal is
developed. The proposed entropy was tested in the analysis of both
simulated noisy MUA and actual MUA recorded from cortex in rodent
model. Simulation and experimental results demonstrate that the
dynamics of a population can be quantified by using the proposed
entropy.
Abstract: The paper presents an innovative networked radar
system for detection of obstacles in a railway level crossing scenario.
This Monitoring System (MS) is able to detect moving or still
obstacles within the railway level crossing area automatically,
avoiding the need of human presence for surveillance. The MS is also
connected to the National Railway Information and Signaling System
to communicate in real-time the level crossing status. The
architecture is compliant with the highest Safety Integrity Level
(SIL4) of the CENELEC standard. The number of radar sensors used
is configurable at set-up time and depends on how large the level
crossing area can be. At least two sensors are expected and up four
can be used for larger areas. The whole processing chain that
elaborates the output sensor signals, as well as the communication
interface, is fully-digital, was designed in VHDL code and
implemented onto a Xilinx Virtex 6.
Abstract: Employer branding is considered as a useful tool for
addressing the global-local problem facing complex organisations
that have operations scattered across the globe and face challenges of
dealing with the local environment alongside. Despite being an
established field of study within the Western developed world, there
is little empirical evidence concerning the relevance of employer
branding to global companies that operate in the under-developed
economies. This paper fills this gap by gaining rich insight into the
implementation of employer branding programs in a foreign
multinational operating in Pakistan dealing with the global-local
problem. The study is qualitative in nature and employs semistructured
and focus group interviews with senior/middle managers
and local frontline employees to deeply examine the phenomenon in
case organisation. Findings suggest that authenticity is required in
employer brands to enable them to respond to the local needs thereby
leading to the resolution of the global-local problem. However, the
role of signaling theory is key to the development of authentic
employer brands as it stresses on the need to establish an efficient and
effective signaling environment where in signals travel in both
directions (from signal designers to receivers and backwards) and
facilitate firms with the global-local problem. The paper also
identifies future avenues of research for the employer branding field.
Abstract: Today’s VLSI networks demands for high speed. And
in this work the compact form mathematical model for current mode
signalling in VLSI interconnects is presented.RLC interconnect line
is modelled using characteristic impedance of transmission line and
inductive effect. The on-chip inductance effect is dominant at lower
technology node is emulated into an equivalent resistance. First order
transfer function is designed using finite difference equation, Laplace
transform and by applying the boundary conditions at the source and
load termination. It has been observed that the dominant pole
determines system response and delay in the proposed model. The
novel proposed current mode model shows superior performance as
compared to voltage mode signalling. Analysis shows that current
mode signalling in VLSI interconnects provides 2.8 times better
delay performance than voltage mode. Secondly the damping factor
of a lumped RLC circuit is shown to be a useful figure of merit.
Abstract: Array-based gene expression analysis is a powerful
tool to profile expression of genes and to generate information on
therapeutic effects of new anti-cancer compounds. Anti-apoptotic
effect of thymoquinone was studied in MCF7 breast cancer cell line
using gene expression profiling with cDNA microarray. The purity
and yield of RNA samples were determined using RNeasyPlus Mini
kit. The Agilent RNA 6000 NanoLabChip kit evaluated the quantity
of the RNA samples. AffinityScript RT oligo-dT promoter primer
was used to generate cDNA strands. T7 RNA polymerase was used to
convert cDNA to cRNA. The cRNA samples and human universal
reference RNA were labelled with Cy-3-CTP and Cy-5-CTP,
respectively. Feature Extraction and GeneSpring softwares analysed
the data. The single experiment analysis revealed involvement of 64
pathways with up-regulated genes and 78 pathways with downregulated
genes. The MAPK and p38-MAPK pathways were
inhibited due to the up-regulation of PTPRR gene. The inhibition of
p38-MAPK suggested up-regulation of TGF-ß pathway. Inhibition of
p38-MAPK caused up-regulation of TP53 and down-regulation of
Bcl2 genes indicating involvement of intrinsic apoptotic pathway.
Down-regulation of CARD16 gene as an adaptor molecule regulated
CASP1 and suggested necrosis-like programmed cell death and
involvement of caspase in apoptosis. Furthermore, down-regulation
of GPCR, EGF-EGFR signalling pathways suggested reduction of
ER. Involvement of AhR pathway which control cytochrome P450
and glucuronidation pathways showed metabolism of Thymoquinone.
The findings showed differential expression of several genes in
apoptosis pathways with thymoquinone treatment in estrogen
receptor-positive breast cancer cells.
Abstract: Robotic surgery is used to enhance minimally invasive
surgical procedure. It provides greater degree of freedom for surgical
tools but lacks of haptic feedback system to provide sense of touch to
the surgeon. Surgical robots work on master-slave operation, where
user is a master and robotic arms are the slaves. Current, surgical
robots provide precise control of the surgical tools, but heavily rely
on visual feedback, which sometimes cause damage to the inner
organs. The goal of this research was to design and develop a realtime
Simulink based robotic system to study force feedback
mechanism during instrument-object interaction. Setup includes three
VelmexXSlide assembly (XYZ Stage) for three dimensional
movement, an end effector assembly for forceps, electronic circuit for
four strain gages, two Novint Falcon 3D gaming controllers,
microcontroller board with linear actuators, MATLAB and Simulink
toolboxes. Strain gages were calibrated using Imada Digital Force
Gauge device and tested with a hard-core wire to measure
instrument-object interaction in the range of 0-35N. Designed
Simulink model successfully acquires 3D coordinates from two
Novint Falcon controllers and transfer coordinates to the XYZ stage
and forceps. Simulink model also reads strain gages signal through
10-bit analog to digital converter resolution of a microcontroller
assembly in real time, converts voltage into force and feedback the
output signals to the Novint Falcon controller for force feedback
mechanism. Experimental setup allows user to change forward
kinematics algorithms to achieve the best-desired movement of the
XYZ stage and forceps. This project combines haptic technology
with surgical robot to provide sense of touch to the user controlling
forceps through machine-computer interface.
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: Electroencephalogram (EEG) is a noninvasive
technique that registers signals originating from the firing of neurons
in the brain. The Emotiv EEG Neuroheadset is a consumer product
comprised of 14 EEG channels and was used to record the reactions
of the neurons within the brain to two forms of stimuli in 10
participants. These stimuli consisted of auditory and visual formats
that provided directions of ‘right’ or ‘left.’ Participants were
instructed to raise their right or left arm in accordance with the
instruction given. A scenario in OpenViBE was generated to both
stimulate the participants while recording their data. In OpenViBE,
the Graz Motor BCI Stimulator algorithm was configured to govern
the duration and number of visual stimuli. Utilizing EEGLAB under
the cross platform MATLAB®, the electrodes most stimulated during
the study were defined. Data outputs from EEGLAB were analyzed
using IBM SPSS Statistics® Version 20. This aided in determining
the electrodes to use in the development of a brain-machine interface
(BMI) using real-time EEG signals from the Emotiv EEG
Neuroheadset. Signal processing and feature extraction were
accomplished via the Simulink® signal processing toolbox. An
Arduino™ Duemilanove microcontroller was used to link the Emotiv
EEG Neuroheadset and the right and left Mecha TE™ Hands.
Abstract: The present work analyses different parameters of end
milling to minimize the surface roughness for AISI D2 steel. D2 Steel
is generally used for stamping or forming dies, punches, forming
rolls, knives, slitters, shear blades, tools, scrap choppers, tyre
shredders etc. Surface roughness is one of the main indices that
determines the quality of machined products and is influenced by
various cutting parameters. In machining operations, achieving
desired surface quality by optimization of machining parameters, is a
challenging job. In case of mating components the surface roughness
become more essential and is influenced by the cutting parameters,
because, these quality structures are highly correlated and are
expected to be influenced directly or indirectly by the direct effect of
process parameters or their interactive effects (i.e. on process
environment). In this work, the effects of selected process parameters
on surface roughness and subsequent setting of parameters with the
levels have been accomplished by Taguchi’s parameter design
approach. The experiments have been performed as per the
combination of levels of different process parameters suggested by
L9 orthogonal array. Experimental investigation of the end milling of
AISI D2 steel with carbide tool by varying feed, speed and depth of
cut and the surface roughness has been measured using surface
roughness tester. Analyses of variance have been performed for mean
and signal-to-noise ratio to estimate the contribution of the different
process parameters on the process.
Abstract: In this paper, we regard as a coded transmission over a
frequency-selective channel. We plan to study analytically the
convergence of the turbo-detector using a maximum a posteriori
(MAP) equalizer and a MAP decoder. We demonstrate that the
densities of the maximum likelihood (ML) exchanged during the
iterations are e-symmetric and output-symmetric. Under the Gaussian
approximation, this property allows to execute a one-dimensional
scrutiny of the turbo-detector. By deriving the analytical terminology
of the ML distributions under the Gaussian approximation, we confirm
that the bit error rate (BER) performance of the turbo-detector
converges to the BER performance of the coded additive white
Gaussian noise (AWGN) channel at high signal to noise ratio (SNR),
for any frequency selective channel.
Abstract: This paper presents small signal stability study carried
over the 140-Bus, 31-Machine, 5-Area MEPE system and validated
on free and open source software: PSAT. Well-established linearalgebra
analysis, eigenvalue analysis, is employed to determine the
small signal dynamic behavior of test system. The aspects of local
and interarea oscillations which may affect the operation and
behavior of power system are analyzed. Eigenvalue analysis is carried
out to investigate the small signal behavior of test system and the
participation factors have been determined to identify the
participation of the states in the variation of different mode shapes.
Also, the variations in oscillatory modes are presented to observe the
damping performance of the test system.
Abstract: The 3D body movement signals captured during
human-human conversation include clues not only to the content of
people’s communication but also to their culture and personality.
This paper is concerned with automatic extraction of this information
from body movement signals. For the purpose of this research, we
collected a novel corpus from 27 subjects, arranged them into groups
according to their culture. We arranged each group into pairs and
each pair communicated with each other about different topics.
A state-of-art recognition system is applied to the problems of
person, culture, and topic recognition. We borrowed modeling,
classification, and normalization techniques from speech recognition.
We used Gaussian Mixture Modeling (GMM) as the main technique
for building our three systems, obtaining 77.78%, 55.47%, and
39.06% from the person, culture, and topic recognition systems
respectively. In addition, we combined the above GMM systems with
Support Vector Machines (SVM) to obtain 85.42%, 62.50%, and
40.63% accuracy for person, culture, and topic recognition
respectively.
Although direct comparison among these three recognition
systems is difficult, it seems that our person recognition system
performs best for both GMM and GMM-SVM, suggesting that intersubject
differences (i.e. subject’s personality traits) are a major
source of variation. When removing these traits from culture and
topic recognition systems using the Nuisance Attribute Projection
(NAP) and the Intersession Variability Compensation (ISVC)
techniques, we obtained 73.44% and 46.09% accuracy from culture
and topic recognition systems respectively.
Abstract: Speech Segmentation is the measure of the change
point detection for partitioning an input speech signal into regions
each of which accords to only one speaker. In this paper, we apply
two features based on multi-scale product (MP) of the clean speech,
namely the spectral centroid of MP, and the zero crossings rate of
MP. We focus on multi-scale product analysis as an important tool
for segmentation extraction. The MP is based on making the product
of the speech wavelet transform coefficients (WTC). We have
estimated our method on the Keele database. The results show the
effectiveness of our method. It indicates that the two features can find
word boundaries, and extracted the segments of the clean speech.
Abstract: Neurons in the nervous system communicate with
each other by producing electrical signals called spikes. To
investigate the physiological function of nervous system it is essential
to study the activity of neurons by detecting and sorting spikes in the
recorded signal. In this paper a method is proposed for considering
the spike sorting problem which is based on the nonlinear modeling
of spikes using exponential autoregressive model. The genetic
algorithm is utilized for model parameter estimation. In this regard
some selected model coefficients are used as features for sorting
purposes. For optimal selection of model coefficients, self-organizing
feature map is used. The results show that modeling of spikes with
nonlinear autoregressive model outperforms its linear counterpart.
Also the extracted features based on the coefficients of exponential
autoregressive model are better than wavelet based extracted features
and get more compact and well-separated clusters. In the case of
spikes different in small-scale structures where principal component
analysis fails to get separated clouds in the feature space, the
proposed method can obtain well-separated cluster which removes
the necessity of applying complex classifiers.
Abstract: The study of the electrical signals produced by neural
activities of human brain is called Electroencephalography. In this
paper, we propose an automatic and efficient EEG signal
classification approach. The proposed approach is used to classify the
EEG signal into two classes: epileptic seizure or not. In the proposed
approach, we start with extracting the features by applying Discrete
Wavelet Transform (DWT) in order to decompose the EEG signals
into sub-bands. These features, extracted from details and
approximation coefficients of DWT sub-bands, are used as input to
Principal Component Analysis (PCA). The classification is based on
reducing the feature dimension using PCA and deriving the supportvectors
using Support Vector Machine (SVM). The experimental are
performed on real and standard dataset. A very high level of
classification accuracy is obtained in the result of classification.
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: In this paper, the improvement by deconvolution of
the depth resolution in Secondary Ion Mass Spectrometry (SIMS)
analysis is considered. Indeed, we have developed a new Tikhonov-
Miller deconvolution algorithm where a priori model of the solution
is included. This is a denoisy and pre-deconvoluted signal obtained
from: firstly, by the application of wavelet shrinkage algorithm,
secondly by the introduction of the obtained denoisy signal in an
iterative deconvolution algorithm. In particular, we have focused the
light on the effect of the iterations number on the evolution of the
deconvoluted signals. The SIMS profiles are multilayers of Boron in
Silicon matrix.