In order to maintain average power levels as well as high range resolution, phasecoded
signals are used in radar and sonar signal processing. One of the most commonly
used phase-coded signals is the linear frequency modulated chirp waveform. Various
nonlinear frequency-modulated chirps are offered as alternatives to linear frequency
modulated chirp. In this paper a new nonlinear frequency modulated chirp waveform is
proposed. Properties like bandwidths, point spread functions and Fourier transforms are
given for the proposed chirp signal. Synthetic imagery for spotlight imaging geometry is
reconstructed by using the polar format and Stolt format processing techniques using the
linear frequency-modulated (LFM) and proposed nonlinear frequency modulated chirp.
Comparisons are presented, and it is shown that proposed waveform can improve the
sonar image resolution.
Multi-relational concept discovery aims to find the relational rules that best describe
the target concept. In this paper, we present a graph-based concept discovery method in Multi-
Relational Data Mining. Concept rule discovery aims at finding the definition of a specific
concept in terms of relations involving background knowledge. The proposed method is an
improvement over a state-of-the-art concept discovery system that uses both ILP and
conventional association rule mining techniques during concept discovery process. The
proposed method generates graph structures with respect to data that is initially stored in a
relational database and utilizes them to guide the concept induction process. A set of
experiments is conducted on data sets that belong to different learning problems. The results
show that the proposed method has promising results in comparison to state of the art
methods.
Numerical methods are commonly used in engineering where the analytical results
are not reached or as a support of experimental studies. Various techniques are being used
as a numeritical method as finite difference, finite volume or finite elements, etc. In this
study, numerical solutions are obtained for a circular fin of rectangular profile using finite
difference method, and the results are compared to the analytical solutions. It is seen that
the analytical solution and numerical results are found to be compatible.
The purpose of this study is to optimize multilayer perceptron (MLP) classifier and find optimal
ECG features to achieve better classification for automated sleep apnea detection. k-fold crossvalidation
technique was employed for classification of apneaic events on the apnea database
of the DREAMS project containing 12 whole-night Polysomnography (PSG) recordings
previously examined by an expert. To achieve the best possible performance with MLP, the
correlation feature selection method was utilized. The performance for apnea event diagnosis
after optimization of the features and the classifier resulted almost 10% in accuracy, %7 in
sensitivity and %13 in specificity.
The integration of ERP systems is a primary issue for management and
operation of enterprises. An enterprise resource planning (ERP) system is
regarded a solution approach for any organization. Future operation and
profitability of the enterprise or organization usually depends on selection
most suitable ERP system. ERP is an information system and arrange
different tools for management. This paper focuses on the ERP software
selection procedure for any governmental organization applying fuzzy rule
based decision making. Fuzzy rule based system depends on a rule
depository and components for accessing and running the rules of proposed
model. A governmental organization may request different solution
approaches for its requirements. This research proposes an effective
process to exploit what issues should be considered for ERP software
selection in order to enhance enterprise competitive advantages.
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