Abstract: In this study, the Taguchi method was used to optimize the effect of HALO structure or halo implant variations on threshold voltage (VTH) and leakage current (ILeak) in 45nm p-type Metal Oxide Semiconductor Field Effect Transistors (MOSFETs) device. Besides halo implant dose, the other process parameters which used were Source/Drain (S/D) implant dose, oxide growth temperature and silicide anneal temperature. This work was done using TCAD simulator, consisting of a process simulator, ATHENA and device simulator, ATLAS. These two simulators were combined with Taguchi method to aid in design and optimize the process parameters. In this research, the most effective process parameters with respect to VTH and ILeak are halo implant dose (40%) and S/D implant dose (52%) respectively. Whereas the second ranking factor affecting VTH and ILeak are oxide growth temperature (32%) and halo implant dose (34%) respectively. The results show that after optimizations approaches is -0.157V at ILeak=0.195mA/μm.
Abstract: The ElectroEncephaloGram (EEG) is useful for
clinical diagnosis and biomedical research. EEG signals often
contain strong ElectroOculoGram (EOG) artifacts produced
by eye movements and eye blinks especially in EEG recorded
from frontal channels. These artifacts obscure the underlying
brain activity, making its visual or automated inspection
difficult. The goal of ocular artifact removal is to remove
ocular artifacts from the recorded EEG, leaving the underlying
background signals due to brain activity. In recent times,
Independent Component Analysis (ICA) algorithms have
demonstrated superior potential in obtaining the least
dependent source components. In this paper, the independent
components are obtained by using the JADE algorithm (best
separating algorithm) and are classified into either artifact
component or neural component. Neural Network is used for
the classification of the obtained independent components.
Neural Network requires input features that exactly represent
the true character of the input signals so that the neural
network could classify the signals based on those key
characters that differentiate between various signals. In this
work, Auto Regressive (AR) coefficients are used as the input
features for classification. Two neural network approaches
are used to learn classification rules from EEG data. First, a
Polynomial Neural Network (PNN) trained by GMDH (Group
Method of Data Handling) algorithm is used and secondly,
feed-forward neural network classifier trained by a standard
back-propagation algorithm is used for classification and the
results show that JADE-FNN performs better than JADEPNN.
Abstract: In single trial analysis, when using Principal
Component Analysis (PCA) to extract Visual Evoked Potential
(VEP) signals, the selection of principal components (PCs) is an
important issue. We propose a new method here that selects only
the appropriate PCs. We denote the method as selective eigen-rate
(SER). In the method, the VEP is reconstructed based on the rate
of the eigen-values of the PCs. When this technique is applied on
emulated VEP signals added with background
electroencephalogram (EEG), with a focus on extracting the
evoked P3 parameter, it is found to be feasible. The improvement
in signal to noise ratio (SNR) is superior to two other existing
methods of PC selection: Kaiser (KSR) and Residual Power (RP).
Though another PC selection method, Spectral Power Ratio (SPR)
gives a comparable SNR with high noise factors (i.e. EEGs), SER
give more impressive results in such cases. Next, we applied SER
method to real VEP signals to analyse the P3 responses for
matched and non-matched stimuli. The P3 parameters extracted
through our proposed SER method showed higher P3 response for
matched stimulus, which confirms to the existing neuroscience
knowledge. Single trial PCA using KSR and RP methods failed to
indicate any difference for the stimuli.
Abstract: The chemical degradation of dieldrin in ferric
sulfide and iron powder aqueous suspension was investigated
in laboratory batch type experiments. To identify the reaction
mechanism, reduced copper was used as reductant. More than
90% of dieldrin was degraded using both reaction systems after
29 days. Initial degradation rate of the pesticide using ferric
sulfide was superior to that using iron powder. The reaction
schemes were completely dissimilar even though the ferric ion
plays an important role in both reaction systems. In the case of
metallic iron powder, dieldrin undergoes partial dechlorination.
This reaction proceeded by reductive hydrodechlorination with
the generation of H+, which arise by oxidation of ferric iron.
This reductive reaction was accelerated by reductant but
mono-dechlorination intermediates were accumulated. On the
other hand, oxidative degradation was observed in the reaction
with ferric sulfide, and the stable chemical structure of dieldrin
was decomposed into water-soluble intermediates. These
reaction intermediates have no chemical structure of drin class.
This dehalogenation reaction assumes to occur via the adsorbed
hydroxyl radial generated on the surface of ferric sulfide.
Abstract: One approach to assess neural networks underlying the cognitive processes is to study Electroencephalography (EEG). It is relevant to detect various mental states and characterize the physiological changes that help to discriminate two situations. That is why an EEG (amplitude, synchrony) classification procedure is described, validated. The two situations are "eyes closed" and "eyes opened" in order to study the "alpha blocking response" phenomenon in the occipital area. The good classification rate between the two situations is 92.1 % (SD = 3.5%) The spatial distribution of a part of amplitude features that helps to discriminate the two situations are located in the occipital regions that permit to validate the localization method. Moreover amplitude features in frontal areas, "short distant" synchrony in frontal areas and "long distant" synchrony between frontal and occipital area also help to discriminate between the two situations. This procedure will be used for mental fatigue detection.
Abstract: Many studies have focused on the nonlinear analysis
of electroencephalography (EEG) mainly for the characterization of
epileptic brain states. It is assumed that at least two states of the
epileptic brain are possible: the interictal state characterized by a
normal apparently random, steady-state EEG ongoing activity; and
the ictal state that is characterized by paroxysmal occurrence of
synchronous oscillations and is generally called in neurology, a
seizure.
The spatial and temporal dynamics of the epileptogenic process is
still not clear completely especially the most challenging aspects of
epileptology which is the anticipation of the seizure. Despite all the
efforts we still don-t know how and when and why the seizure
occurs. However actual studies bring strong evidence that the
interictal-ictal state transition is not an abrupt phenomena. Findings
also indicate that it is possible to detect a preseizure phase.
Our approach is to use the neural network tool to detect interictal
states and to predict from those states the upcoming seizure ( ictal
state). Analysis of the EEG signal based on neural networks is used
for the classification of EEG as either seizure or non-seizure. By
applying prediction methods it will be possible to predict the
upcoming seizure from non-seizure EEG.
We will study the patients admitted to the epilepsy monitoring
unit for the purpose of recording their seizures. Preictal, ictal, and
post ictal EEG recordings are available on such patients for analysis
The system will be induced by taking a body of samples then
validate it using another. Distinct from the two first ones a third body
of samples is taken to test the network for the achievement of
optimum prediction. Several methods will be tried 'Backpropagation
ANN' and 'RBF'.
Abstract: The growth of the aquaculture industry has been
associated with negative environmental impacts through the
discharge of raw effluents into the adjacent receiving water bodies.
Macrophytes from natural saline lakes, which have adaptability to the
high salinity, can be suitable for saline effluent treatment. Eight
emergent species from natural saline area were planted in an
experimental gravel bed hydroponic mesocosm (GBH) which was
treated with effluent water from an intensive fish farm using
geothermal water. In order to examine the applicability of the
halophytes in treatment processes, we tested the relative efficacy of
total nitrogen (TN), total phosphorus (TP), potassium (K), sodium
(Na), magnesium (Mg) and calcium (Ca) removal for the saline
wastewater treatment. Four of the eight species, which were
Phragmites australis, Typha angustifolia, Glyceria maxima, Scirpus
lacustris spp. tabernaemontani could survive and contribute the
experimental treatment.
Abstract: A new approach based on the consideration that electroencephalogram (EEG) signals are chaotic signals was presented for automated diagnosis of electroencephalographic changes. This consideration was tested successfully using the nonlinear dynamics tools, like the computation of Lyapunov exponents. This paper presented the usage of statistics over the set of the Lyapunov exponents in order to reduce the dimensionality of the extracted feature vectors. Since classification is more accurate when the pattern is simplified through representation by important features, feature extraction and selection play an important role in classifying systems such as neural networks. Multilayer perceptron neural network (MLPNN) architectures were formulated and used as basis for detection of electroencephalographic changes. Three types of EEG signals (EEG signals recorded from healthy volunteers with eyes open, epilepsy patients in the epileptogenic zone during a seizure-free interval, and epilepsy patients during epileptic seizures) were classified. The selected Lyapunov exponents of the EEG signals were used as inputs of the MLPNN trained with Levenberg- Marquardt algorithm. The classification results confirmed that the proposed MLPNN has potential in detecting the electroencephalographic changes.
Abstract: Artifact rejection plays a key role in many signal processing applications. The artifacts are disturbance that can occur during the signal acquisition and that can alter the analysis of the signals themselves. Our aim is to automatically remove the artifacts, in particular from the Electroencephalographic (EEG) recordings. A technique for the automatic artifact rejection, based on the Independent Component Analysis (ICA) for the artifact extraction and on some high order statistics such as kurtosis and Shannon-s entropy, was proposed some years ago in literature. In this paper we try to enhance this technique proposing a new method based on the Renyi-s entropy. The performance of our method was tested and compared to the performance of the method in literature and the former proved to outperform the latter.
Abstract: Trihalomethanes (THMs) were among the first
disinfection byproducts to be discovered in chlorinated water. The
substances form during a reaction between chlorine and organic
matter in the water. Trihalomethanes are suspected to have negative
effects on birth such as, low birth weight, intrauterine growth
retardation in term births, as well as gestational age and preterm
delivery. There are also some evidences showing these by-products to
be mutagenic and carcinogenic, the greatest amount of evidence being
related to the bladder cancer. However, there exist inconsistencies
regarding such effects of THMs as different studies have provided
different results in this regard. The aim of the present study is to
provide a review of the related researches about the above mentioned
health effects of THMs.
Abstract: The goal of this work is to improve the efficiency and the reliability of the automatic artifact rejection, in particular from the Electroencephalographic (EEG) recordings. Artifact rejection is a key topic in signal processing. The artifacts are unwelcome signals that may occur during the signal acquisition and that may alter the analysis of the signals themselves. A technique for the automatic artifact rejection, based on the Independent Component Analysis (ICA) for the artifact extraction and on some high order statistics such as kurtosis and Shannon-s entropy, was proposed some years ago in literature. In this paper we enhance this technique introducing the Renyi-s entropy. The performance of our method was tested exploiting the Independent Component scalp maps and it was compared to the performance of the method in literature and it showed to outperform it.
Abstract: The voltage/current characteristics and the effect of
NO2 gas on the electrical conductivity of a PbPc gas sensor array is
investigated. The gas sensor is manufactured using vacuum
deposition of gold electrodes on sapphire substrate with the leadphathalocyanine
vacuum sublimed on the top of the gold electrodes.
Two versions of the PbPc gas sensor array are investigated. The
tested types differ in the gap sizes between the deposited gold
electrodes. The sensors are tested at different temperatures to account
for conductivity changes as the molecular adsorption/desorption rate
is affected by heat. The obtained results found to be encouraging as
the sensors shoed stability and sensitivity towards low concentration
of applied NO2 gas.
Abstract: It is known that an analog Hopfield neural network
with time delay can generate the outputs which are similar to the
human electroencephalogram. To gain deeper insights into the
mechanisms of rhythm generation by the Hopfield neural networks
and to study the effects of noise on their activities, we investigated
the behaviors of the networks with symmetric and asymmetric
interneuron connections. The neural network under the study consists
of 10 identical neurons. For symmetric (fully connected) networks all
interneuron connections aij = +1; the interneuron connections for
asymmetric networks form an upper triangular matrix with non-zero
entries aij = +1. The behavior of the network is described by 10
differential equations, which are solved numerically. The results of
simulations demonstrate some remarkable properties of a Hopfield
neural network, such as linear growth of outputs, dependence of
synchronization properties on the connection type, huge
amplification of oscillation by the external uniform noise, and the
capability of the neural network to transform one type of noise to
another.
Abstract: For stricter drinking water regulations in the future, reducing the humic acid and disinfection byproducts in raw water, namely, trihalomethanes (THMs) and haloacetic acids (HAAs) is worthy for research. To investigate the removal of waterborne organic material using a lab-scale of bio-activated carbon filter under different EBCT, the concentrations of humic acid prepared were 0.01, 0.03, 0.06, 0.12, 0.17, 0.23, and 0.29 mg/L. Then we conducted experiments using a pilot plant with in-field of the serially connected bio-activated carbon filters and hollow fiber membrane processes employed in traditional water purification plants. Results showed under low TOC conditions of humic acid in influent (0.69 to 1.03 mg TOC/L) with an EBCT of 30 min, 40 min, and 50 min, TOC removal rates increases with greater EBCT, attaining about 39 % removal rate. The removal rate of THMs and HAAs by BACF was 54.8 % and 89.0 %, respectively.
Abstract: An organic bulk heterojunction (BHJ) was fabricated using a blended film containing Copper (II) tetrakis(4-acumylphenoxy) phthalocyanine (Tc-CuPc) along with [6,6]-Phenyl C61 butyric acid methyl ester (PCBM). Weight ratio between Tc-CuPc and PCBM was 1:1. The electrical properties of Tc-CuPc: PCBM BHJ were examined. Rectifying nature of the BHJ was displayed by current-voltage (I-V) curves, recorded in dark and at various temperatures. At low voltages, conduction was ohmic succeeded by space-charge limiting current (SCLC) conduction at higher voltages in which exponential trap distribution was dominant. Series resistance, shunt resistance, ideality factor, effective barrier height and mobility at room temperature were found to be 526 4, 482 k4, 3.7, 0.17 eV and 2×10-7 cm2V-1s-1 respectively. Temperature effect towards different BHJ parameters was observed under dark condition.
Abstract: This study is to investigate the electroencephalogram (EEG) differences generated from a normal and Alzheimer-s disease (AD) sources. We also investigate the effects of brain tissue distortions due to AD on EEG. We develop a realistic head model from T1 weighted magnetic resonance imaging (MRI) using finite element method (FEM) for normal source (somatosensory cortex (SC) in parietal lobe) and AD sources (right amygdala (RA) and left amygdala (LA) in medial temporal lobe). Then, we compare the AD sourced EEGs to the SC sourced EEG for studying the nature of potential changes due to sources and 5% to 20% brain tissue distortions. We find an average of 0.15 magnification errors produced by AD sourced EEGs. Different brain tissue distortion models also generate the maximum 0.07 magnification. EEGs obtained from AD sources and different brain tissue distortion levels vary scalp potentials from normal source, and the electrodes residing in parietal and temporal lobes are more sensitive than other electrodes for AD sourced EEG.
Abstract: This paper presents a comparative study on
Vanadyl Phthalocyanine (VOPc) thin films deposited by thermal
evaporation and spin coating techniques. The samples
were prepared on cleaned glass substrates and annealed at
various temperatures ranging form 95oC to 155oC. To obtain
the morphological and structural properties of VOPc thin
films, X-ray diffraction (XRD) technique and atomic force
microscopy (AFM) have been implied. The AFM topographic
images show a very slight difference in the thermally grown
films, before and after annealing, however best results are
achieved for the spin-cast film annealed at 125oC. The XRD
spectra show no existence of the sharp peaks, suggesting the
material to be amorphous. The humps in the XRD patterns
indicate the presence of some crystallites.
Abstract: Urbanization and related anthropogenic modifications
cause extent of habitat fragmentation and directly lead to decline of
local biodiversity. Conservation biologists advocate corridor creation
as one approach to rescue biodiversity. Here we examine the utility of
roads as corridors in preserving plant diversity by investigating
roadside vegetation in Yellow River Delta (YRD), China. We
examined the spatio-temporal distribution pattern of plant species
richness, diversity and composition along roadside. The results
suggest that roads, as dispersal conduits, increase occurrence
probability of new settlers to a new area, meanwhile, roads accumulate
the greater propagule pressure and favourable survival condition
during operation phase. As a result, more species, including native and
alien plants, non- halophyte and halophyte species, threatened and
cosmopolitic species, were found prosperous at roadside. Roadside
may be a refuge for more species, and the pattern of vegetation
distribution is affected by road age and the distance from road verge.
Abstract: Diffuse viral encephalitis may lack fever and other cardinal signs of infection and hence its distinction from other acute encephalopathic illnesses is challenging. Often, the EEG changes seen routinely are nonspecific and reflect diffuse encephalopathic changes only. The aim of this study was to use nonlinear dynamic mathematical techniques for analyzing the EEG data in order to look for any characteristic diagnostic patterns in diffuse forms of encephalitis.It was diagnosed on clinical, imaging and cerebrospinal fluid criteria in three young male patients. Metabolic and toxic encephalopathies were ruled out through appropriate investigations. Digital EEGs were done on the 3rd to 5th day of onset. The digital EEGs of 5 male and 5 female age and sex matched healthy volunteers served as controls.Two sample t-test indicated that there was no statistically significant difference between the average values in amplitude between the two groups. However, the standard deviation (or variance) of the EEG signals at FP1-F7 and FP2-F8 are significantly higher for the patients than the normal subjects. The regularisation dimension is significantly less for the patients (average between 1.24-1.43) when compared to the normal persons (average between 1.41-1.63) for the EEG signals from all locations except for the Fz-Cz signal. Similarly the wavelet dimension is significantly less (P = 0.05*) for the patients (1.122) when compared to the normal person (1.458). EEGs are subdued in the case of the patients with presence of uniform patterns, manifested in the values of regularisation and wavelet dimensions, when compared to the normal person, indicating a decrease in chaotic nature.