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This paper aims to investigate the neural networking system. The signals to be studied have been taken from photonic sensors. For classification, a given signal is first transformed into different feature domains and then neural network is used to train the given dataset to form the network. Wavelet transform is used to extract the signal properties-skewness, kurtosis and entropy and Fourier Transform...
Automated Modulation Classification (AMC) shows great significance for any receiver that has little knowledge of the modulation scheme of the received signal. A useful digital signal modulation recognition scheme inspired by the deep auto-encoder network is proposed in this investigation. In our proposed method, there are two deep auto-encoder networks. The system extracts the original features of...
In this study, EEG records taken from healthy people with eyes open and eyes closed, EEG records taken from epileptic patients at the time of seizure and out of seizure were classified using Naive Bayes, K-Nearest Neighbor and Artificial Neural Networks methods. Feature vectors are obtained by using Daubechies wavelet transforms with different degrees and their effect on the classification success...
In order to overcome the impact of complex illumination environment and head movement, a novel eye state recognition algorithm is proposed in this paper, which is based on feature level fusion. Firstly, Pseudo Zernike feature was found can be used to overcome the impact of head movement and Gabor feature can be used to overcome the impact of illumination changing. Then we got the fusion feature by...
In order to guarantee the power quality and the highly efficient operation of the power network, a reliable operating condition recognition system of distribution networks is necessary. To solve the problem of multi-condition recognition, an operating condition recognition system based on the workflow of decision-making tree is proposed. Big data of waveforms acquired by an online recording system...
With the future development of substation, the research of power fault detection algorithm has very important theoretical significance and wide application prospects. In order to improve the recognition of power line fault detection, one modeling method based on sparse self-encoding neural network is proposed. The dB3 wavelet is used to decompose the fault signal, and then the sub-band energy is calculated...
This paper presents an effective approach for classification of power quality (PQ) disturbances based on wavelet transform (WT) and support vector machine (SVM). Wavelet transform was applied to disturbance signal in order to obtain decomposition coefficients at six levels that represents signal in time and frequency domain. Eight statistical methods were used to extract features that characterize...
Brain computer interface applications have big importance in becoming a bridge between the human brain and devices. The studies in this area increase every day with the use of different feature extractions and classification methods In this study, classification is done by Random Forest method using Data Set III presented in BCI Competiton 2003, and it has been shown that combining the Fast Walsh...
Brain computer interface systems are modeled to facilitate lives of patients who have not a problem in their cognitive functions but also can not move their muscles. The performance of such systems highly depends on features extracted from the Electrocorticography (ECoG) signals, selected classifiers for features and channels of ECoG signals. In this study, we proposed a novel method which provides...
In order to assist doctors in predicting the pathological information of hepatocellular carcinoma (HCC) using magnetic resonance imaging (MRI), we proposed an automatic histological grading method of HCC based on adaptive weighted multi-classifier fusion in this paper. First, five sets of texture features were extracted for each region of interest (ROI), corresponding to first order statistics, gray-level...
The electroencephalography (EEG) is the most essential tool for the diagnosis and the treatment of the epilepsy. It allows observing events strongly associated with epilepsy or epileptic spikes and locating the brain regions that cause the symptoms of epilepsy. This paper presents an automated classification of EEG signals for the detection of epileptic seizures with Single-Channel using the wavelet...
In this paper, a facial expression recognition algorithm based on Gabor and conditional random fields is proposed. Firstly, owing to the fact that in the existing databases, the number of people and images are relatively small, we established our own facial expression database, and some preprocessing methods are performed thereon. Secondly, Gabor features are extracted in five scales and eight directions...
The goal of the Deep learning methods is learning feature hierarchies with features from higher levels to lower level features of the hierarchy. The major contribution of this paper is to show how to extract features and train an image classification system on large-scale datasets. This method is an improvement of our recent work. The training is carried out by the combination of the most used methods...
Machining is the process that a kind of mechanical device change the dimensions or the performance of the workpiece, which has a great influence on the quality of the component. Manufacturing and processing enterprises always want to improve the passing rate and the life of the machining workpiece and reduce unnecessary costs during processing. This must strictly control machining process based mechanical...
In this paper a method for multiple human detection in the image has been presented. This method uses differential evolution (DE) algorithm to improve window position detection speed and HOG-LBP algorithm for feature extraction. Fitness function for DE algorithm is SVM and in the final state, a postprocessing on detected windows by DE algorithm is performed. This method has been tested on INRIA datasets...
In this paper, a new method for general work piece recognition based on Wavelet Neural Network is proposed. The composition of the experimental system is introduced and the operating principle is analyzed. The invariant moment is a highly concentrated image feature, which have the characteristics of invariant to translation and rotation. In the selection of the classifier, the Wavelet Neural Network...
In this study, EEG signals recorded from healthy individuals and EEG signals recorded from epileptic patients during epileptic seizures were classified. In the classification process, the Hilbert and wavelet transform were applied separately for the extraction of features from the EEG signals. The same statistical parameters were used in order to reduce the size of the feature vectors obtained via...
Texture can be characterized in different ways. Local texture facets are considered to be one of the useful approaches for texture analysis. Local texture facets comprise data about the texture behavior. The present approach extracts the texture primitive units (TPU) and texture primitive spectrum (TPS) for classification of the textures. The present paper derives a feature extraction algorithm based...
In computer vision system, texture refers to the characteristics of an object that appear on its surface. Texture classification is to classify textures in correct texture groups. The accuracy of texture image classification depends on quality of texture features and classification algorithm used. In this paper, Brodatz texture images are used as an experimental data. Features are extracted from texture...
According to the characteristics of infrared images of electrical equipment, a new image segmentation method based on wavelet transformation and fuzzy clustering is proposed in this paper. Because image segmentation based on the fuzzy C mean (FCM) clustering algorithm is easy to be affected by the initial clustering center and the clustering number, which often leads to the convergence of results...
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