In this study, a novel floating frequency dependent negative resistor (FDNR), floating inductor, floating capacitor and floating resistor simulator circuit employing two CBTAs and three passive components is proposed. The presented circuit can realize floating FDNR, inductor, capacitor or resistor depending on the passive component selection. Since the passive elements are all grounded, this ci...

Floating point arithmetic is widely used in many areas especially scientific computation and signal processing. The main objective of this paper is to reduce the power consumption and to increase the speed of execution and the implementation of floating point multiplier using sequential processing on the reconfigurable hardwareFloating Point (FP) addition, subtraction and multiplication are wid...

The regularized logistic regression classifier has shown good performance in problems where feature selection is critical, including our recent winning submissions to the ICANN2011 MEG mind reading challenge [Huttunen et al. 2011; Huttunen et al. 2012], and to the DREAM 6 AML classification challenge [Manninen et al. 2011]. The benefit of the method is that it includes an embedded feature selec...

The short-term load forecasting is an essential problem in energy system planning and operation. The accuracy of the forecasting models depends on the quality of the input information. The input variable selection allows to chose the most informative inputs which ensure the best forecasts. To improve the short-term load forecasting model based on the kernel regression four variable selection wr...

Protein SUMO modification is an important post-translational modification and the optimization of prediction methods remains a challenge. Here, by using Support Vector Machines algorithm (SVM), a novel computational method was developed for SUMO modification site prediction based on Sequential Forward Selection (SFS) of hundreds of amino acid properties, which are collected by Amino Acid Index ...

The selection of weights of the new hidden units for sequential feed-forward neural networks (FNNs) usually involves a non-linear optimization problem that cannot be solved analytically in the general case. A suboptimal solution is searched heuristically. Most models found in the literature choose the weights in the first layer that correspond to each hidden unit so that its associated output v...

Electroencephalogram (EEG) is one of the useful biological signals to distinguish different brain diseases and mental states. In recent years, detecting different emotional states from biological signals has been merged more attention by researchers and several feature extraction methods and classifiers are suggested to recognize emotions from EEG signals. In this research, we introduce an emot...

As core counts continue to grow in modern microarchitectures, automatic parallelization technologies are becoming increasingly important to fill the gap between hardware that has increased parallelism and software that is still designed for sequential execution. In previous research, we have proposed a novel dynamic binary parallelization scheme called T-DBP, which leverages hot traces to provi...

The problem of finding a feature subset that corresponds to a sufficiently small classification error is intensively studied in pattern recognition. This thesis focuses on wrapper approach to feature selection, which uses an estimate of classification error as the search criterion. A novel family of optimization methods is introduced, which is created through simple modifications of opinion for...