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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...
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...
Two practical inevitabilities for diagnostic systems are the abilities of incremental learning in non-stationary environments and diagnosing under the class imbalance condition. The class imbalance condition has been widely occurred in real applications where system usually works in the normal state and it is not easy to collect the representative patterns of faulty classes. This work aims to adapt...
Features selection (FS) techniques have an apparent need in many complex engineering applications especially the bearing fault diagnosis of low-speed industrial motor. The main goal of an FS algorithm is to select the most discriminant features subset from a high-dimension features vector that increases the model performance by reducing the redundant and irrelevant fault features. This paper proposes...
For cardiologists, the detection of cardiac abnormalities is a very delicate and crucial task for the treatment of a patient's condition. This task that requires electronic systems of medical assistance that is more precise, faster and reliable to help cardiologists to analyze and make the right decisions. These medical assistance systems tend to model the human expertise and perception using signal...
Sleep Apnea is a potentially serious sleep disorder in which you have one or more pauses in breathing or shallow breaths while you sleep. It is classified into 3 main types: Obstructive sleep apnea, Central sleep apnea, and Complex sleep apnea syndrome. Obstructive sleep apnea (OSA) represents 80% of the apnea cases which makes it the most common type. Polysomnography is the current traditional method...
Classifier allows the user to classify between different classes based on the features acquired. The goals and applications of different classifiers are different. As the feature selection is one of the important criteria. In this paper we introduce a method of ranking the features of one class with respect to another and it tells the user that in the training set which feature has higher ranking...
Currently, in the field of road safety, research is moving towards the use of electronic driving support systems that are capable of simulating human perception. These systems have more and more facilities, flexibilities and human development, to respond effectively against the delicate situations in the real world, which require the development of more efficient, fast, accurate signal processing...
Aiming at the problem of features instability in specific emitter identification, this paper presents a based on one-class classifier feature preprocessing algorithm to detect the unstable features. The algorithm takes advantage of one-class classifier of property that can describe the distribution of given data sets. Its basic steps include: Firstly, we divide the feature time series into N segments...
In this study, the experimental studies were carried out on a database containing the types of wood knot. After preprocessing on the images in the database, specific features to knot were obtained using wavelet moments feature extraction algorithm. Type description is carried out with KNN classification algorithm by selecting most distinguishing the approximation coefficients on these features. In...
In this work, an algorithm is introduced that classifies test images into their originated countries using composite faces generated according to different countries. Also aim to increase success rate at implementation process using three color channel (R-G-B), color feature vector and local standard deviation matrix. Algorithm used Kernel Principal Component Analysis with gauss kernel structure for...
In this work human action recognition problem was discussed in video sequences. Solution of the problem was studied in three stages. Firstly, points of interest were detected with preproccesing and these points which are called cuboids were declared in small windows, then feature extraction was performed and finally, human action is decided by using classification. Features extraction is not only...
Aim of this study is applying the ensemble classification methods over the stock market closing values, which can be assumed as time series and finding out the relation between the economy news. In order to keep the study back ground clear, the majority voting method has been applied over the three classification algorithms, which are the k-nearest neighborhood, support vector machine and the C4.5...
In this paper a system to detect arrhythmia by automatically classifying normal and two types of abnormal ECG signals is presented. ECG signals are first pre-processed to reduce the baseline drift, noise and other unwanted components that might be present in the signal. The autoregressive modelling of the signals is then applied to extract small set of signal features - coefficients of autoregressive...
Fraud detection is a critical problem affecting large financial companies that has increased due to the growth in credit card transactions. This paper presents a new method for automatic detection of frauds in credit card transactions based on non-linear signal processing. The proposed method consists of the following stages: feature extraction, training and classification, decision fusion, and result...
This paper aims at presenting a âcomputational costâ optimization method in an Automatic Music Genre Classification system. In such systems, the training and validation database is often enormous. Consequently, a system based on a nearest neighbor classifier suffers from high computational cost during the classification process. In such cases, a training instance clustering (per...
Brain Computer Interface (BCI) systems translate brain rhythms into signals comprehensible by computers. BCI has numerous applications in the clinical domain, the computer gaming, and the military. Real-time analysis of single trial brain signals is a challenging task, due to the low SNR of the incoming signals, added noise due to muscle artifacts, and trial-to-trial variability. In this work we present...
In this paper, a novel fast logo detection approach in document images is presented. Logos with separated parts usually can affect the logo detection process. To overcome this problem, some specifications of logos are considered. Our proposed method divided in three main sections. In the first section, a horizontal dilation operator is used to merge separated parts of logo in horizontal direction...
In this paper we are highlighting the signals that are not Fourier transformable and give its Fourier transform using PCA (Principle Component Analysis), lDA (linear Discriminant Analysis). Such signals are step signal, signum, etc. Basically Fourier transform transforms time domain signal into frequency domain and after transformation describes what frequencies original signal have. Principle Component...
J-DSP is a java-based object-oriented online programming environment developed at Arizona State University for education and research. This paper presents a collection of interactive Java modules for the purpose of introducing undergraduate and graduate students to feature extraction in music and audio signals. These tools enable online simulations of different algorithms that are being used in applications...
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