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Recently, with the obvious increasing number of cardiovascular disease, the automatic classification research of Electrocardiogram signals (ECG) has been playing a significantly important part in the clinical diagnosis of cardiovascular disease. In this paper, a 1D convolution neural network (CNN) based method is proposed to classify ECG signals. The proposed CNN model consists of five layers in addition...
In this paper the problem of improving the reliability of nonlinear dynamic objects fault diagnosing is presented. Model-based diagnostics nonparametric identification method is used. Diagnostic models are constructed on the base of Volterra kernels wavelet transforms. The effectiveness of the suggested diagnostic models based on Volterra kernels wavelet transforms is analyzed on the basis of simulation...
In this work, we have proposed a novel filtering approach based on empirical mode decomposition (EMD) and tunable-Q wavelet transform (TQWT) for the detection of epileptic seizure electroencephalogram (EEG) signals, which is termed as EMD-TQWT method. In this EMD-TQWT method, the intrinsic mode functions (IMFs) obtained from EEG signals using EMD method are considered as a set of amplitude modulated...
Objectives: Electroencephalogram (EEG) plays an important role in recording the activity of human brain. Identification of epileptic seizures can be done using EEG signals. Methods/ Statistical Analysis: In this work for classification of EEG signals a method known as Empirical mode decomposition (EMD) is used and compared with empirical wavelet transform (EWT) based method. Findings: In this paper...
This paper introduces a construction method of M-channel under-sampled spectral graph filter banks. They can be applied to any kind of undirected graphs, use arbitrary critically sampled or oversampled analysis filters, and obtain low redundancy, which is less than 1, regardless of the number of the analysis filters. We formulate the construction problem as a row subset selection method of the transform...
Magnetic resonance imaging (MRI) plays an important role in early diagnosis, which can accurately capture the disease variations of the anatomical brain structure. We propose a novel method for improving feature extraction performance from magnetic resonance images (MRI). This study presents a combination of multi-channel input and 3D convolutional neural network architecture which can reduce the...
This paper proposes a novel method for designing compactly supported biorthogonal graph wavelet filter banks with flat spectral responses. We firstly construct a class of biorthogonal graph filter banks by using the polynomial half-band kernels, and then present a design method for the polynomial half-band kernel. The proposed design method utilizes the PBP (Parametric Bernstein Polynomial), which...
Many diseases can be detected via performing the electrocardiogram diagnosis. Although the electrocardiograms can be acquired in the public clinics, hospitals or even at homes by using the existing devices, these existing devices are not tiny enough to carry on the bodies. Hence, it is difficult to acquire the electrocardiograms all the time. In order to acquire the electrocardiograms all the time,...
This paper addresses the problem of automatic scattering feature selection for signal classification. While features derived from group invariant scattering networks are quite effective for signal classification. We argue that scattering networks are not always the appropriate choice as they are not learned for the objective at hand. In this paper, we explore jointly learning a deep scattering convolution...
Medical imaging is playing a vital role for development of medical facilities in rural areas. It has reduced the gap between patients and doctors, especially in case of distance hospitals for image diagnosis. But in case of emergency data transmission it is difficult to transfer a 10MB of images and videos. They can take half an hour to communicate even at higher speed. Many times minor part of the...
The electrical conduction system of the heart has an elaborate structure. A small failure in this system can put the person's life in danger. In people with heart block disease the electrical signal that controls the heartbeat is partially or completely blocked from reaching the ventricles. Electrocardiograms are used in detection of heart blocks and thus in this paper we have used it for the assessment...
Blur kernel estimation is the vital step in the deblurring process for images. Though, the kernel has no unique solution, deblurring is a severely ill posed problem. The kernel may be point, linear or non linear. In most of the applications, we consider the blur kernel as a linear one, because it is easy to estimate. In this paper, we consider non linear blur kernels, which have more than one motion...
Recently, One Class Support Vector Machines (OCSVM) have been the subject of much research. This paper introduces a novel pattern classification approach for Broken Rotor Bar (BRB) detection in Induction Motors (IM), which combines Stationary Wavelet Packet Transform (SWPT) and OCSVM. Among all the kernels available to OCSVM, wavelet kernels are tuned to improve accuracy and detection time of fault...
Pedestrian detection has been always a challenging problem in computer vision. Numerous approaches based on features extraction and classification have been proposed over the years. In this paper, we present a novel pedestrian detection approach based on supervised classification. We propose here the use of basic statistical operators to adapt support vector regression (SVR) to binary classification...
In this paper, we suggest to utilize the reassigned method to improve a recognition of courant life audio signals based on the wavelet features. Reassigned approach generates a new time-frequency description by displacement spectrogram values away from their calculation location. Besides, reassignment method concentrates energy elements by displacing every time-frequency place to its group delay and...
In this paper, the Benchmark Simulation Model No. 1, which is designed for the purpose of simulating actual wastewater treatment processes, is introduced and implemented in SIMULINK environment. Then the partial least squares (PLS) model and its kernel version is studied, and wavelet transform is used to carry out the so called multi-scale kernel partial least squares (KPLS). By means of multi-scale...
Texture characterization of photographic papers is likely to provide scholars with valuable information regarding artistic practices. Currently, texture assessment remains mostly based on visual and manual inspections, implying long repetitive tasks prone to inter- and even intra-observer variability. Automated texture characterization and classification procedures are thus important tasks in historical...
Image classification is an important task within the field of computer vision. In this paper we propose a new wavelet network classifier (WNC) based on the cascaded architecture. This classifier is characterized by its new learning approach and its novel architecture which brings a novel robust test way. So, our contributions in this paper reside in two major points. The first one is the proposition...
Digital fundus photographs are often used to provide clinical diagnostic information about several pathologies such as diabetes, glaucoma, macular degeneration and vascular and neurologic disorders. To allow a precise analysis, digital fundus image quality should be assessed to evaluate if minimum requirements are present. Focus is one of the causes of low image quality. This paper describes a method...
Now, gait recognition for identification has received more and more attention from biometrics researchers. Gait Energy Image(GEI) is an efficient represent method and Gabor wavelet has many excellent property, so we use the Gabor wavelet to extract the amplitude and phase feature of GEI, research their recognition ability respectively, at last, fusion the two features in rank level to gait recognition...
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