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Finanicial market is characterized with complex, stochastic, nonstationary process and the development of effective models for prediction of a stock price is one of the important problems in finance. For analyzing nonlinear time-series, the importance of nonlinear models, such as neural networks (NNs) and fuzzy systems (FSs), has been increasing in recent years. Combining NNs, FSs and wavelets, FuzzyWavelet...
This paper presents an intelligent steganalysis method to investigate anomalies in Waveform Audio File Format (Wave). There are many images and audio file formats available to hide sensitive information without attracting attentions. Steganalysis is a set of techniques to reveal secrets in audio, video or other file formats. Image based stego analysis is fairly simple because hiding methods such as...
Accurate parametric identification of Linear Parameter-Varying (LPV) systems requires an optimal prior selection of model order and a set of functional dependencies for the parameterization of the model coefficients. In order to address this problem for linear regression models, a regressor shrinkage method, the Non-Negative Garrote (NNG) approach, has been proposed recently. This approach achieves...
This paper investigates the problem of mean-square asymptotic stability of uncertain neural networks with time-varying delay and stochastic noise. Based on generalized Finsler lemma and the linear matrix inequality (LMI) optimization technique, an improved delay-dependent stability criterion is developed. It is shown that the new stability criterion is less conservative and less computationally complex...
Robust execution of robotic tasks is a difficult problem. Improper execution could lead to very costly and difficult diagnosis of robot failures. In this paper a solution for the problem of dealing with noisy in robotics execution failures from a stochastic sampling (STOCHS) or probabilistic point of view is derived. Our experimental results show the STOCHS algorithm as more robust to noise compared...
This paper presents field results for a pollution estimation system based on ultrasound noise and Statistical AutoAssociative Artificial Neural Networks (SA³N²). The system extracts spectral information from the ultrasonic noise emitted by the corona discharges that occur nearby electric insulation, then correlates this information to a previously known pollution intensity situation. The entire acquisition...
With the demand of image quality, a novel algorithm named MW-SVM is proposed for the enhancement of digital images based on mathematical morphology, watershed algorithm and SVM to remove noises and increase articulation. The MW-SVM algorithm is adopted in embedded network video monitoring system to improve video image quality. Firstly, the principle of MW-SVM algorithm and the structure of network...
The aim of this paper is to analyze and compare two artificial neural network based approaches for parameter extractions of microwave transistor equivalent circuits including noise. In the first approach equivalent circuit parameters are determined from the operating conditions, whereas in the second approach equivalent circuit parameters are determined directly from the measured scattering and noise...
An empirical mode decomposition based recurrent Hermite neural network (ERHNN) prediction model is proposed to predict short-term traffic flow in this study. First, a recurrent Hermite neural network (RHNN) prediction model with different orthonormal Hermite polynomial basis functions (OHPBFs) as activation functions is introduced. Then, to further mitigate the influence of noise and improve the accuracy...
A task of edge detection in single-look synthetic aperture radar (SAR) images is considered. It is shown that edge detector performance can be improved by using an artificial neural network (NN). Selection of input local parameters is studied. As shown, it is reasonable to employ a limited number of elementary edge detectors that are the most informative (insensitive to noise) and that differ by the...
Filters are tools that can be used for precision control of digital measurements. Due to the filtering problem of tomographic projections of the soil following a nonlinear model during the measurement, the use of unscented Kalman filter with neural networks was useful to ensure an improvement in signal/noise ratio. Embedded systems may have limited accuracy in numerical calculations due to processing...
Hitherto, different efforts have been held for the recognition of emotional state of speakers. Most of these works are performed in clean environments. But, in the real world, there are different noise parameters such as cross-talk, car noise, awgn (especially in the transmission of sounds) and etc., which decrease the performance of classifiers. In this paper we look for features which have the best...
There are several techniques for image recognition. Among those methods, application of soft computing models on digital image has been considered to be an approach for a better result. The main objective of the present work is to provide a new approach for image recognition using Artificial Neural Networks. Initially an original gray scale intensity image has been taken for transformation. The Input...
In this paper, we consider a class of impulsive fuzzy Cohen-Grossberg neural networks with variable coefficients and time-varying delays. By establishing an L-operator differential inequality and using stochastic analysis technique, we obtain some new sufficient conditions for the pth moment exponential synchronization of the fuzzy Cohen-Grossberg neural networks under noise perturbation. Moreover,...
In this study we aim to develop a decision support application for predicting ICU mortality risk that starts with a clinical analysis of the problem that also leverages machine learning to help create an algorithm with good performance characteristics. By starting from a clear basis in clinical practice we hope to improve algorithm development and the transparency of the resulting system. We start...
This paper proposes a method to predict the effect of Bevacizumab therapy on Glioblastoma Multiform (GBM) tumors. The prediction is critical for effective treatment planning. The proposed method is developed and evaluated using Diffusion Tensor Imaging (DTI) and post-contrast T1-weighted Magnetic Resonance Images (pc-T1-MRI) of 14 patients with GBM tumors gathered before and after the treatment. First,...
In underwater signal processing the most important factor in quantifying the signatures of the radiating object is to decipher the signals which are prevalent in the ambient noise. Ambient noise is a complex and important phenomenon which greatly affects the listening capacity of instruments such as sonar in underwater environment. The ambient noise in sea is the overall combination of wind speed,...
This paper considers the optimal control of the logical control network with noisy inputs. The optimal control problem concerning the minority game (MG) with mixed population is formulated. In the game the producers are represented by the states of nodes in the Boolean control network, the speculators and the noise traders are identified as the controllable inputs and the noisy inputs of the nodes...
Plastics are used in a truly vast number of applications, and research is continously carried out to improve every aspect of the plastics industry. A recent study of laser transmission welding [1] required cross-sectional images of the weld's microstructure to be analyzed for the presence of pores, which are tiny bubbles that may form during the weld process. It is believed that the number and size...
Partial discharge (PD) is a common reason that causes electrical breakdown in high voltage underground XLPE cables. This paper proposes a concept of how to build an on-line, on-site system that is able to diagnose the severity of PD activities in XLPE cable as well as differentiate different types of PD signals. The system consists of magnetic probes, low noise amplifier, 3GSPS analog to digital converter...
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