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Neural networks (NNs) have proven to be a very powerful tool both for one-dimensional (1D) and two-dimensional (2D) direction of arrival (DOA) estimation. By avoiding complex and time-consuming mathematical calculations, NNs estimate DOAs almost instantaneously. This feature makes them very convenient for real-time applications. Further, unlike the well known MUSIC algorithm, neural network-based...
The fundamental problem in today's world when robots are becoming a part of daily routine is getting them to move from one desired place to another without any problem and collision. In doing so a robot needs to perceive its environment to efficiently navigate through it. The navigation is possible only if the robot differentiates between different scenarios around it in real time. In this paper we...
One approach for deaf signs recognition and classification is presented in the paper. It is assumed that the signs are presented in digital images. Recognition algorithm is consisted of several stages. At the beginning it is necessary to perform appropriate image processing in sense of segmentation and filtration of the input images. Aim is to detect arm position, i.e. sign of interest. For this purpose...
The research presented in this paper refers to classification of geometric shapes (cubes, pyramids and cylinders) using multilayer neural network. The input data of the algorithm are the images of shapes placed in different positions and distances from the camera. The classification is based on feature vectors that are obtained using methods of digital image processing. Feature vectors are inputs...
Modern studies of a cultural heritage objects are increasingly multidisciplinary. A variety of analytical techniques supported by pattern recognition methods can help in answering about the origin, dating or authenticity. Results of sourcing ceramics from three Neolithic sites in Serbia are shown in this paper. The procedure based on radial basic function networks (RBFN) was employed in ceramic characterization...
The paper presents a classification of the protein surface atom neighbourhoods from the hydrophobicity perspective. Hydrophobicity is the property which is considered around each surface atom. The actual hydrophobicity distribution on the atoms that form an atom's vicinity is replaced by an equivalent hydrophobicity density distribution, computed in a standardized octagonal pattern around the atom...
Complex-Valued Neural Networks are extensions of the classical Neural Networks. They have complex-valued weights, accept complex inputs and have more computational power than the classical ones. We discuss in this paper the training for Phase-Based Neurons, neural processing elements similar to Universal Binary Neurons, that uses as weights and bias complex numbers with unit magnitude, the phase being...
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...
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...
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