Abstract: Formulation of biological profile is one of the modern roles of forensic anthropologist. The present study was conducted to estimate height using foot and shoeprint length of Malaysian population. The present work can be very useful information in the process of identification of individual in forensic cases based on shoeprint evidence. It can help to narrow down suspects and ease the police investigation. Besides, stature is important parameters in determining the partial identify of unidentified and mutilated bodies. Thus, this study can help the problem encountered in cases of mass disaster, massacre, explosions and assault cases. This is because it is very hard to identify parts of bodies in these cases where people are dismembered and become unrecognizable. Samples in this research were collected from 200 Malaysian adults (100 males and 100 females) with age ranging from 20 to 45 years old. In this research, shoeprint length were measured based on the print of the shoes made from the flat shoes. Other information like gender, foot length and height of subject were also recorded. The data was analyzed using IBM® SPSS Statistics 19 software. Results indicated that, foot length has a strong correlation with stature than shoeprint length for both sides of the feet. However, in the unknown, where the gender was undetermined have shown a better correlation in foot length and shoeprint length parameter compared to males and females analyzed separately. In addition, prediction equations are developed to estimate the stature using linear regression analysis of foot length and shoeprint length. However, foot lengths give better prediction than shoeprint length.
Abstract: The main objective of this paper is to estimate the cost of road traffic accidents in Egypt. The Human Capital (HC) approach, specifically the Gross-Loss-of-Output methodology, is adopted for estimation. Moreover, cost values obtained by previous national literature are updated using the inflation rates. The results indicate an estimated cost of road traffic accidents in Egypt of approximately 10 billion Egyptian Pounds (about $US 1.8 billion) for the year 2008. In addition, it is expected that this cost will rise in 2009 to 11.8 billion Egyptian Pounds (about $US 2.1 billion).
Abstract: A new method, based on the normal shrink and
modified version of Katssagelous and Lay, is proposed for multiscale
blind image restoration. The method deals with the noise and blur in
the images. It is shown that the normal shrink gives the highest S/N
(signal to noise ratio) for image denoising process. The multiscale
blind image restoration is divided in two sections. The first part of
this paper proposes normal shrink for image denoising and the
second part of paper proposes modified version of katssagelous and
Lay for blur estimation and the combination of both methods to reach
a multiscale blind image restoration.
Abstract: In this paper we present a novel approach for density estimation. The proposed approach is based on using the logistic regression model to get initial density estimation for the given empirical density. The empirical data does not exactly follow the logistic regression model, so, there will be a deviation between the empirical density and the density estimated using logistic regression model. This deviation may be positive and/or negative. In this paper we use a linear combination of Gaussian (LCG) with positive and negative components as a model for this deviation. Also, we will use the expectation maximization (EM) algorithm to estimate the parameters of LCG. Experiments on real images demonstrate the accuracy of our approach.
Abstract: The kinetic properties of enzymes are often reported
using the apparent KM and Vmax appropriate to the standard
Michaelis-Menten enzyme. However, this model is inappropriate to
enzymes that have more than one substrate or where the rate
expression does not apply for other reasons. Consequently, it is
desirable to have a means of estimating the appropriate kinetic
parameters from the apparent values of KM and Vmax reported for each
substrate. We provide a means of estimating the range within which
the parameters should lie and apply the method to data for glutamate
dehydrogenase from the nematode parasite of sheep Teladorsagia
circumcincta.
Abstract: The problem of controlling a two link robotic manipulator, consisting of a rotating and a prismatic links, is addressed. The actuations of both links are assumed to have unknown dead zone nonlinearities and friction torques modeled by LuGre friction model. Because of the existence of the unknown dead zone and friction torque at the actuations, unknown parameters and unmeasured states would appear to be part of the overall system dynamics that need for estimation. Unmeasured states observer, unknown parameters estimators, and robust adaptive control laws have been derived such that closed loop global stability is achieved. Simulation results have been performed to show the efficacy of the suggested approach.
Abstract: Maximal Ratio Combining (MRC) is considered the most complex combining technique as it requires channel coefficients estimation. It results in the lowest bit error rate (BER) compared to all other combining techniques. However the BER starts to deteriorate as errors are introduced in the channel coefficients estimation. A novel combining technique, termed Generalized Maximal Ratio Combining (GMRC) with a polynomial kernel, yields an identical BER as MRC with perfect channel estimation and a lower BER in the presence of channel estimation errors. We show that GMRC outperforms the optimal MRC scheme in general and we hereinafter introduce it to the scientific community as a new “supraoptimal" algorithm. Since diversity combining is especially effective in small femto- and pico-cells, internet-associated wireless peripheral systems are to benefit most from GMRC. As a result, many spinoff applications can be made to IP-based 4th generation networks.
Abstract: In this paper some procedures for building confidence intervals for the reliability in stress-strength models are discussed and empirically compared. The particular case of a bivariate normal setup is considered. The confidence intervals suggested are obtained employing approximations or asymptotic properties of maximum likelihood estimators. The coverage and the precision of these intervals are empirically checked through a simulation study. An application to real paired data is also provided.
Abstract: According to the interaction of inflation and
unemployment, expectation of the rate of inflation in Croatia is
estimated. The interaction between inflation and unemployment is
shown by model based on three first-order differential i.e. difference
equations: Phillips relation, adaptive expectations equation and
monetary-policy equation. The resulting equation is second order
differential i.e. difference equation which describes the time path of
inflation. The data of the rate of inflation and the rate of
unemployment are used for parameters estimation. On the basis of
the estimated time paths, the stability and convergence analysis is
done for the rate of inflation.
Abstract: In power systems, protective relays must filter their
inputs to remove undesirable quantities and retain signal quantities of
interest. This job must be performed accurate and fast. A new
method for filtering the undesirable components such as DC and
harmonic components associated with the fundamental system
signals. The method is s based on a dynamic filtering algorithm. The
filtering algorithm has many advantages over some other classical
methods. It can be used as dynamic on-line filter without the need of
parameters readjusting as in the case of classic filters. The proposed
filter is tested using different signals. Effects of number of samples
and sampling window size are discussed. Results obtained are
presented and discussed to show the algorithm capabilities.
Abstract: In this paper, a frequency-variation based method has
been proposed for transistor parameter estimation in a commonemitter
transistor amplifier circuit. We design an algorithm to estimate
the transistor parameters, based on noisy measurements of the output
voltage when the input voltage is a sine wave of variable frequency
and constant amplitude. The common emitter amplifier circuit has
been modelled using the transistor Ebers-Moll equations and the
perturbation technique has been used for separating the linear and
nonlinear parts of the Ebers-Moll equations. This model of the amplifier
has been used to determine the amplitude of the output sinusoid as
a function of the frequency and the parameter vector. Then, applying
the proposed method to the frequency components, the transistor
parameters have been estimated. As compared to the conventional
time-domain least squares method, the proposed method requires
much less data storage and it results in more accurate parameter
estimation, as it exploits the information in the time and frequency
domain, simultaneously. The proposed method can be utilized for
parameter estimation of an analog device in its operating range of
frequencies, as it uses data collected from different frequencies output
signals for parameter estimation.
Abstract: This article presents the results of a study conducted to identify operational risks for information systems (IS) with service-oriented architecture (SOA). Analysis of current approaches to risk and system error classifications revealed that the system error classes were never used for SOA risk estimation. Additionally system error classes are not normallyexperimentally supported with realenterprise error data. Through the study several categories of various existing error classifications systems are applied and three new error categories with sub-categories are identified. As a part of operational risks a new error classification scheme is proposed for SOA applications. It is based on errors of real information systems which are service providers for application with service-oriented architecture. The proposed classification approach has been used to classify SOA system errors for two different enterprises (oil and gas industry, metal and mining industry). In addition we have conducted a research to identify possible losses from operational risks.
Abstract: In manufacturing industries, development of measurement leads to increase the number of monitoring variables and eventually the importance of multivariate control comes to the fore. Statistical process control (SPC) is one of the most widely used as multivariate control chart. Nevertheless, SPC is restricted to apply in processes because its assumption of data as following specific distribution. Unfortunately, process data are composed by the mixture of several processes and it is hard to estimate as one certain distribution. To alternative conventional SPC, therefore, nonparametric control chart come into the picture because of the strength of nonparametric control chart, the absence of parameter estimation. SVDD based control chart is one of the nonparametric control charts having the advantage of flexible control boundary. However,basic concept of SVDD has been an oversight to the important of data characteristic, density distribution. Therefore, we proposed DW-SVDD (Density Weighted SVDD) to cover up the weakness of conventional SVDD. DW-SVDD makes a new attempt to consider dense of data as introducing the notion of density Weight. We extend as control chart using new proposed SVDD and a simulation study of various distributional data is conducted to demonstrate the improvement of performance.
Abstract: Eigenvector methods are gaining increasing acceptance in the area of spectrum estimation. This paper presents a successful attempt at testing and evaluating the performance of two of the most popular types of subspace techniques in determining the parameters of multiexponential signals with real decay constants buried in noise. In particular, MUSIC (Multiple Signal Classification) and minimum-norm techniques are examined. It is shown that these methods perform almost equally well on multiexponential signals with MUSIC displaying better defined peaks.
Abstract: In this paper we consider the problem of distributed adaptive estimation in wireless sensor networks for two different observation noise conditions. In the first case, we assume that there are some sensors with high observation noise variance (noisy sensors) in the network. In the second case, different variance for observation noise is assumed among the sensors which is more close to real scenario. In both cases, an initial estimate of each sensor-s observation noise is obtained. For the first case, we show that when there are such sensors in the network, the performance of conventional distributed adaptive estimation algorithms such as incremental distributed least mean square (IDLMS) algorithm drastically decreases. In addition, detecting and ignoring these sensors leads to a better performance in a sense of estimation. In the next step, we propose a simple algorithm to detect theses noisy sensors and modify the IDLMS algorithm to deal with noisy sensors. For the second case, we propose a new algorithm in which the step-size parameter is adjusted for each sensor according to its observation noise variance. As the simulation results show, the proposed methods outperforms the IDLMS algorithm in the same condition.
Abstract: Induction machine models used for steady-state and
transient analysis require machine parameters that are usually
considered design parameters or data. The knowledge of induction
machine parameters is very important for Indirect Field Oriented
Control (IFOC). A mismatched set of parameters will degrade the
response of speed and torque control. This paper presents an
improvement approach on rotor time constant adaptation in IFOC for
Induction Machines (IM). Our approach tends to improve the
estimation accuracy of the fundamental model for flux estimation.
Based on the reduced order of the IM model, the rotor fluxes and
rotor time constant are estimated using only the stator currents and
voltages. This reduced order model offers many advantages for real
time identification parameters of the IM.
Abstract: This paper presents a new algorithm for the channel estimation of the OFDM system based on a pilot signal for the new generation of high data rate communication systems. In orthogonal frequency division multiplexing (OFDM) systems over fast-varying fading channels, channel estimation and tracking is generally carried out by transmitting known pilot symbols in given positions of the frequency-time grid. In this paper, we propose to derive an improved algorithm based on the calculation of the mean and the variance of the adjacent pilot signals for a specific distribution of the pilot signals in the OFDM frequency-time grid then calculating of the entire unknown channel coefficients from the equation of the mean and the variance. Simulation results shows that the performance of the OFDM system increase as the length of the channel increase where the accuracy of the estimated channel will be increased using this low complexity algorithm, also the number of the pilot signal needed to be inserted in the OFDM signal will be reduced which lead to increase in the throughput of the signal over the OFDM system in compared with other type of the distribution such as Comb type and Block type channel estimation.
Abstract: The aim of this study was to remove the two principal
noises which disturb the surface electromyography signal
(Diaphragm). These signals are the electrocardiogram ECG artefact
and the power line interference artefact. The algorithm proposed
focuses on a new Lean Mean Square (LMS) Widrow adaptive
structure. These structures require a reference signal that is correlated
with the noise contaminating the signal. The noise references are
then extracted : first with a noise reference mathematically
constructed using two different cosine functions; 50Hz (the
fundamental) function and 150Hz (the first harmonic) function for
the power line interference and second with a matching pursuit
technique combined to an LMS structure for the ECG artefact
estimation. The two removal procedures are attained without the use
of supplementary electrodes. These techniques of filtering are
validated on real records of surface diaphragm electromyography
signal. The performance of the proposed methods was compared with
already conducted research results.
Abstract: This paper analyzes the patterns of the Monte Carlo
data for a large number of variables and minterms, in order to
characterize the circuit path length behavior. We propose models
that are determined by training process of shortest path length
derived from a wide range of binary decision diagram (BDD)
simulations. The creation of the model was done use of feed forward
neural network (NN) modeling methodology. Experimental results
for ISCAS benchmark circuits show an RMS error of 0.102 for the
shortest path length complexity estimation predicted by the NN
model (NNM). Use of such a model can help reduce the time
complexity of very large scale integrated (VLSI) circuitries and
related computer-aided design (CAD) tools that use BDDs.
Abstract: An array antenna system with innovative signal
processing can improve the resolution of a source direction of arrival
(DoA) estimation. High resolution techniques take the advantage of
array antenna structures to better process the incoming waves. They
also have the capability to identify the direction of multiple targets.
This paper investigates performance of the DOA estimation
algorithm namely; Capon and MUSIC on the uniform linear array
(ULA). The simulation results show that in Capon and MUSIC
algorithm the resolution of the DOA techniques improves as number
of snapshots, number of array elements, signal-to-noise ratio and
separation angle between the two sources θ increases.