The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
Joint time-frequency representations of phonocardiograms enable fundamental characteristics of heart sounds and other physiological changes important for diagnostics to be more visible compared to time and frequency domain, separately. This paper discuss possibilities for relevant component extraction in such representations. Due to improved visual inspection and fast digital signal processing capabilities,...
The prediction of respiration-induced organ motion is crucial in some applications such as dynamic delivery of radiation dose. In this paper, we have proposed the novel approach to construct an acceleration-enhanced (AE) filter that is comprised of two independent adaptive channels. The filters use the adapted position and adapted acceleration, together with a weight factor to provide prediction for...
Change-point problems (or break point problems, disorder problems) can be considered one of the central issues of statistics, connecting asymptotic statistical theory and Monte Carlo methods, frequentist and Bayesian approaches, fixed and sequential procedures. In many real applications, observations are taken sequentially over time, or can be ordered with respect to some other criterion. The basic...
Usually, the focus of fMRI studies is the identification of brain regions that change level of activations as a response to specific stimuli. On the other hand, possible connectivity modeling between activated brain areas remains an open question. Based on fMRI data, in the last decades different methods for uncovering interactions between brain areas have been proposed. In this study we used the...
In this paper we propose the new multifractal measure inspired by sigmoid activation function usually used in neural networks. By using new measure the Hölder exponent and multifractal spectrum are determined in classical way. New measure is applied to image processing, especially in texture classification. It was shown that by changing the slope of the sigmoid function different details can be extracted...
this paper presents a brief methodology for determination of relevant parameters for automatic articulation regularity quantification of Serbian fricatives pronunciation. Using the known methods and algorithms as well as developing new ones for analysis of phoneme pronunciation process, authors used neural networks as a tool for modeling. Here we present a comparison of fricative articulation quality...
The paper tries to propose objectively existing hierarchy of control levels in Central Nervous System(CNS), based on already accomplished medical research results on Homo sapiens, i. e. Homo sapiens - sapiens CNS as well as the theory of control and the science of neural networks. Human CNS has been vague until 60 years ago. Although, there were intensive researches in the last 30 years worldwide,...
During the years image classification gained important significance in practice, especially in the fields of digital radiology, remote sensing, image retrieval, etc. Typical algorithm for image classification contains descriptor extraction phase, learning phase and testing phase. Testing phase calculates accuracy of the classifier based on predetermined set of labelled images. This paper analyse performance...
In this paper a novel learning based technique for single image super resolution (SR) is proposed. We model the relationship between available low resolution (LR) image and desired high resolution (HR) image as multi-scale markov random field (MSMRF). We re-formulate the SR problem in terms of learning the mapping between LR-MRF and HR-MRF, which is generally non-linear. Instead of learning MSMRF...
In this paper, we propose a learning based technique for imagedeblurring using artificial neural networks. We model the original image as Markov Random field and the blurred image as degraded version of the original MRF. We do not make any prior assumptions for the blur kernel and develop the proposed algorithm by taking into account the space varying nature of the blur kernel. We re-formulate the...
Emotional speech recognition (ESR) from the aspect of human-machine interaction (HCI) is a prerequisite for the framework of interacting partners within the HCI. This paper addresses the application of neural network (NN) in ESR. The performance of NN is tested using three different feature sets which are basis for ESR: prosodic features, spectral features and a set of their combination. The results...
As digital TV providers today offer hundreds of channels, TV viewers do not have problem with content availability, but with finding an interesting content in a reasonable time instead. In a situation like this, both the providers and the viewers would benefit from personalized TV program guides, the tools that would track and learn the viewers' preferences and then recommend them the content they...
In this paper, a mobile telemedicine application implemented for Android based devices is presented. The main application's functionality of ECG transmission is extended by real time ECG analysis, as well as real time analyze of acceleration data captured by embedded acceleration sensor. In this paper are presented efficient algorithms for ECG and acceleration data analysis. The ECG analysis is focused...
Sampling is a central topic in signal processing, communications, and in all fields where the world is analog and computation is digital. The question is simple: When does a countable set of measurements allow a perfect and stable representation of a class of signals?
One step ahead prediction of peak electricity loads based on artificial neural networks (ANN) is presented. Two architectures of ANNs were implemented to produce predictions that were used to generate the final value as an average. The time instants when daily peak loads occur are produced simultaneously. Examples will be given confirming both the feasibility of the method and the need for further...
The Static state estimation is widely used in power systems for real time monitoring and analysis. Standard methods, such as the weighted least squares (WLS) algorithm, require the computation of bus admittance and Jacobian matrices and the solution is found in an iterative process. This paper presents an alternative for the classic state estimation (SE) algorithms, which uses a multilayer perceptron...
Electrical transformers are the most important elements in the process of transmission and distribution of electricity. Depending on the size and position of the transformer, the sudden device failure can cause tremendous damage. Neural networks are widespread technique for transformer health monitoring. Neural Network Ensembles are an advanced neural technique that improves the accuracy and reliability...
Development of an effective maximum power point tracking (MPPT) algorithm is important in order to achieve maximum power point in a photovoltaic system (PV). In this study, a dynamic neural control (DNC) scheme is developed. The adaptation procedure is based on the back propagation learning law and is required only a priori knowledge, that's, the system output error. The feasibility of the proposed...
The generation of a small signal dynamic model of a solar cell was investigated. As a starting structure the usual one diode large signal dynamic model was used with known parameter values. A simple parallel linear RC circuit was used to represent the model while the element values were put to be functions of the illumination here represented by the photo-current. The element value versus photocurrent...
This paper is focused on the development of a neural solution to the residential buildings' heating design. Basically it is about a large and complex design formula which we propose to compute employing a Multilayer Perceptron. The experimental results presented in the fourth section prove neural network can be a good design tool in this area.
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.