Abstract: An approach and its implementation in 0.18 m CMOS process of the multichannel ASIC for capacitive (up to 30 pF) sensors are described in the paper. The main design aim was to study an analog data-driven architecture. The design was done for an analog derandomizing function of the 128 to 16 structure. That means that the ASIC structure should provide a parallel front-end readout of 128 input analog sensor signals and after the corresponding fast commutation with appropriate arbitration logic their processing by means of 16 output chains, including analog-to-digital conversion. The principal feature of the ASIC is a low power consumption within 2 mW/channel (including a 9-bit 20Ms/s ADC) at a maximum average channel hit rate not less than 150 kHz.
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: This paper deals with a portfolio selection problem
based on the possibility theory under the assumption that the returns
of assets are LR-type fuzzy numbers. A possibilistic portfolio model
with transaction costs is proposed, in which the possibilistic mean
value of the return is termed measure of investment return, and the
possibilistic variance of the return is termed measure of investment
risk. Due to considering transaction costs, the existing traditional
optimization algorithms usually fail to find the optimal solution
efficiently and heuristic algorithms can be the best method. Therefore,
a particle swarm optimization is designed to solve the corresponding
optimization problem. At last, a numerical example is given to
illustrate our proposed effective means and approaches.
Abstract: The main objective of Automatic Generation Control (AGC) is to balance the total system generation against system load losses so that the desired frequency and power interchange with neighboring systems is maintained. Any mismatch between generation and demand causes the system frequency to deviate from its nominal value. Thus high frequency deviation may lead to system collapse. This necessitates a very fast and accurate controller to maintain the nominal system frequency. This paper deals with a novel approach of artificial intelligence (AI) technique called Hybrid Neuro-Fuzzy (HNF) approach for an (AGC). The advantage of this controller is that it can handle the non-linearities at the same time it is faster than other conventional controllers. The effectiveness of the proposed controller in increasing the damping of local and inter area modes of oscillation is demonstrated in a two area interconnected power system. The result shows that intelligent controller is having improved dynamic response and at the same time faster than conventional controller.
Abstract: Considering payload, reliability, security and operational lifetime as major constraints in transmission of images we put forward in this paper a steganographic technique implemented at the physical layer. We suggest transmission of Halftoned images (payload constraint) in wireless sensor networks to reduce the amount of transmitted data. For low power and interference limited applications Turbo codes provide suitable reliability. Ensuring security is one of the highest priorities in many sensor networks. The Turbo Code structure apart from providing forward error correction can be utilized to provide for encryption. We first consider the Halftoned image and then the method of embedding a block of data (called secret) in this Halftoned image during the turbo encoding process is presented. The small modifications required at the turbo decoder end to extract the embedded data are presented next. The implementation complexity and the degradation of the BER (bit error rate) in the Turbo based stego system are analyzed. Using some of the entropy based crypt analytic techniques we show that the strength of our Turbo based stego system approaches that found in the OTPs (one time pad).
Abstract: Based on the fuzzy set theory this work develops two
adaptations of iterative methods that solve mathematical programming
problems with uncertainties in the objective function and in
the set of constraints. The first one uses the approach proposed by
Zimmermann to fuzzy linear programming problems as a basis and
the second one obtains cut levels and later maximizes the membership
function of fuzzy decision making using the bound search method.
We outline similarities between the two iterative methods studied.
Selected examples from the literature are presented to validate the
efficiency of the methods addressed.
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: Petrol Fuel Station (PFS) has potential hazards to the
people, asset, environment and reputation of an operating company.
Fire hazards, static electricity air pollution evoked by aliphatic and
aromatic organic compounds are major causes of accident/incident
occurrence at fuel station. Activities such as carelessness,
maintenance, housekeeping, slips trips and falls, transportation
hazard, major and minor injuries, robbery and snake bites has a
potential to create unsafe conditions. The level of risk of these
hazards varies according to location and country. The emphasis on
safety considerations by the government is variable all around the
world. Developed countries safety records are much better as
compared to developing countries safety statistics. There is no
significant approach available to highlight the unsafe acts and unsafe
conditions during operation and maintenance of fuel station. Fuel
station is the most commonly available facilities that contain
flammable and hazardous materials. Due to continuous operation of
fuel station they pose various hazards to people, environment and
assets of an organization. To control these hazards, there is a need for
specific approach. PFS operation is unique as compared to other
businesses. For smooth operations it demands an involvement of
operating company, contractor and operator group. This study will
focus to address hazard contributing factors that have a potential to
make PFS operation risky. One year data collected, 902 activities
analyzed, comparisons were made to highlight significant
contributing factors. The study will provide help and assistance to
PFS outlet marketing companies to make their fuel station operation
safer. It will help health safety and environment (HSE) professionals
to arrest the gap available related to safety matters at PFS.
Abstract: In this paper, a new learning approach for network
intrusion detection using naïve Bayesian classifier and ID3 algorithm
is presented, which identifies effective attributes from the training
dataset, calculates the conditional probabilities for the best attribute
values, and then correctly classifies all the examples of training and
testing dataset. Most of the current intrusion detection datasets are
dynamic, complex and contain large number of attributes. Some of
the attributes may be redundant or contribute little for detection
making. It has been successfully tested that significant attribute
selection is important to design a real world intrusion detection
systems (IDS). The purpose of this study is to identify effective
attributes from the training dataset to build a classifier for network
intrusion detection using data mining algorithms. The experimental
results on KDD99 benchmark intrusion detection dataset demonstrate
that this new approach achieves high classification rates and reduce
false positives using limited computational resources.
Abstract: One of the popular methods for recognition of facial
expressions such as happiness, sadness and surprise is based on
deformation of facial features. Motion vectors which show these
deformations can be specified by the optical flow. In this method, for
detecting emotions, the resulted set of motion vectors are compared
with standard deformation template that caused by facial expressions.
In this paper, a new method is introduced to compute the quantity of
likeness in order to make decision based on the importance of
obtained vectors from an optical flow approach. For finding the
vectors, one of the efficient optical flow method developed by
Gautama and VanHulle[17] is used. The suggested method has been
examined over Cohn-Kanade AU-Coded Facial Expression Database,
one of the most comprehensive collections of test images available.
The experimental results show that our method could correctly
recognize the facial expressions in 94% of case studies. The results
also show that only a few number of image frames (three frames) are
sufficient to detect facial expressions with rate of success of about
83.3%. This is a significant improvement over the available methods.
Abstract: This paper presents the results of the authors in designing, experimenting, assessing and transferring an innovative approach to energy education in secondary schools, aimed to enhance the quality of learning in terms of didactic curricula and pedagogic methods. The training is online delivered to youngsters via e-Books and portals specially designed for this purpose or by learning by doing via interactive games. An online educational methodology is available teachers.
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: This paper presents an approach based on the
adoption of a distributed cognition framework and a non parametric
multicriteria evaluation methodology (DEA) designed specifically to
compare e-commerce websites from the consumer/user viewpoint. In
particular, the framework considers a website relative efficiency as a
measure of its quality and usability. A website is modelled as a black
box capable to provide the consumer/user with a set of
functionalities. When the consumer/user interacts with the website to
perform a task, he/she is involved in a cognitive activity, sustaining a
cognitive cost to search, interpret and process information, and
experiencing a sense of satisfaction. The degree of ambiguity and
uncertainty he/she perceives and the needed search time determine
the effort size – and, henceforth, the cognitive cost amount – he/she
has to sustain to perform his/her task. On the contrary, task
performing and result achievement induce a sense of gratification,
satisfaction and usefulness. In total, 9 variables are measured,
classified in a set of 3 website macro-dimensions (user experience,
site navigability and structure). The framework is implemented to
compare 40 websites of businesses performing electronic commerce
in the information technology market. A questionnaire to collect
subjective judgements for the websites in the sample was purposely
designed and administered to 85 university students enrolled in
computer science and information systems engineering
undergraduate courses.
Abstract: This paper presents a hybrid approach for solving nqueen problem by combination of PSO and SA. PSO is a population based heuristic method that sometimes traps in local maximum. To solve this problem we can use SA. Although SA suffer from many iterations and long time convergence for solving some problems, By good adjusting initial parameters such as temperature and the length of temperature stages SA guarantees convergence. In this article we use discrete PSO (due to nature of n-queen problem) to achieve a good local maximum. Then we use SA to escape from local maximum. The experimental results show that our hybrid method in comparison of SA method converges to result faster, especially for high dimensions n-queen problems.
Abstract: The identification and elimination of bad
measurements is one of the basic functions of a robust state estimator
as bad data have the effect of corrupting the results of state
estimation according to the popular weighted least squares method.
However this is a difficult problem to handle especially when dealing
with multiple errors from the interactive conforming type. In this
paper, a self adaptive genetic based algorithm is proposed. The
algorithm utilizes the results of the classical linearized normal
residuals approach to tune the genetic operators thus instead of
making a randomized search throughout the whole search space it is
more likely to be a directed search thus the optimum solution is
obtained at very early stages(maximum of 5 generations). The
algorithm utilizes the accumulating databases of already computed
cases to reduce the computational burden to minimum. Tests are
conducted with reference to the standard IEEE test systems. Test
results are very promising.
Abstract: This work presents an approach for the measurement
of mutual inductance on near field inductive coupling. The mutual
inductance between inductive circuits allows the simulation of energy
transfer from reader to tag, that can be used in RFID and powerless
implantable devices. It also allows one to predict the maximum
voltage in the tag of the radio-frequency system.
Abstract: We consider linear regression models where both input data (the values of independent variables) and output data (the observations of the dependent variable) are interval-censored. We introduce a possibilistic generalization of the least squares estimator, so called OLS-set for the interval model. This set captures the impact of the loss of information on the OLS estimator caused by interval censoring and provides a tool for quantification of this effect. We study complexity-theoretic properties of the OLS-set. We also deal with restricted versions of the general interval linear regression model, in particular the crisp input – interval output model. We give an argument that natural descriptions of the OLS-set in the crisp input – interval output cannot be computed in polynomial time. Then we derive easily computable approximations for the OLS-set which can be used instead of the exact description. We illustrate the approach by an example.
Abstract: Electromyography (EMG) is the study of muscles function through analysis of electrical activity produced from muscles. This electrical activity which is displayed in the form of signal is the result of neuromuscular activation associated with muscle contraction. The most common techniques of EMG signal recording are by using surface and needle/wire electrode where the latter is usually used for interest in deep muscle. This paper will focus on surface electromyogram (SEMG) signal. During SEMG recording, several problems had to been countered such as noise, motion artifact and signal instability. Thus, various signal processing techniques had been implemented to produce a reliable signal for analysis. SEMG signal finds broad application particularly in biomedical field. It had been analyzed and studied for various interests such as neuromuscular disease, enhancement of muscular function and human-computer interface.
Abstract: This paper discusses the novel graphical approach for
stability analysis of multi induction motor drive controlled by a single
inverter. Stability issue arises in parallel connected induction motors
under unbalanced load conditions. The two powerful globally
accepted modeling and simulation software packages such as
MATLAB and LabVIEW are selected to perform the stability
analysis. The stability investigation is performed for different load
conditions and difference in stator and rotor resistances among the
two motors. It is very simple and effective than the techniques
presented to obtain the stability of the parallel connected induction
motor drive under unbalanced load conditions. Approximate transfer
functions are considered to model the induction motors, load
dynamics, speed controllers and inverter. Simulink library tools are
utilized to model the entire drive scheme in MATLAB. Stability
study is discussed in LabVIEW using control design and simulation
toolkits. Simulation results are illustrated for various running
conditions to demonstrate the effectiveness of the transfer function
method.
Abstract: Compared to oil production from microorganisms, little work has been performed for mixed culture of microalgae and yeast. In this article it is aimed to show high oil accumulation potential of mixed culture of microalgae Chlorella sp. KKU-S2 and oleaginous yeast Torulaspora maleeae Y30 using sugarcane molasses as substrate. The monoculture of T. maleeae Y30 grew faster than that of microalgae Chlorella sp. KKU-S2. In monoculture of yeast, a biomass of 6.4g/L with specific growth rate (m) of 0.265 (1/d) and lipid yield of 0.466g/L were obtained, while 2.53g/L of biomass with m of 0.133 (1/d) and lipid yield of 0.132g/L were obtained for monoculture of Chlorella sp. KKU-S2. The biomass concentration in the mixed culture of T. maleeae Y30 with Chlorella sp. KKU-S2 increased faster and was higher compared with that in the monoculture and mixed culture of microalgae. In mixed culture of microalgae Chlorella sp. KKU-S2 and C. vulgaris TISTR8580, a biomass of 3.47g/L and lipid yield of 0.123 g/L were obtained. In mixed culture of T. maleeae Y30 with Chlorella sp. KKU-S2, a maximum biomass of 7.33 g/L and lipid yield of 0.808g/L were obtained. Maximum cell yield coefficient (YX/S, 0.229g/L), specific yield of lipid (YP/X, 0.11g lipid/g cells) and volumetric lipid production rate (QP, 0.115 g/L/d) were obtained in mixed culture of yeast and microalgae. Clearly, T. maleeae Y30 and Chlorella sp. KKU-S2 use sugarcane molasses as organic nutrients efficiently in mixed culture under mixotrophic growth. The biomass productivity and lipid yield are notably enhanced in comparison with monoculture.