The uniqueness theorem is proved for the linearized problem describing radiation and scattering of time-harmonic water waves by a vertical shell having an arbitrary horizontal cross-section. The uniqueness holds for all frequencies, and various locations of the shell are possible: surface-piercing, totally immersed and bottom-standing. A version of integral equation technique is outlined for finding a solution.
A multi-head 1-way pushdown automaton with k heads is a pushdown automaton with k 1-way read heads on the input tape and a stack. It was previously shown that the deterministic variant of the model cannot accept all the context free languages. In this paper, we introduce a 2-tape, 2-head model namely Watson-Crick pushdown automata where the content of the second tape is determined using a complementarity relation, similar to Watson-Crick automata. We show computational powers of nondeterministic two-head pushdown automata and nondeterministic Watson-Crick pushdown automata are same. Moreover, deterministic Watson-Crick pushdown automata can accept all the context free languages.
Paper summarizes the results in the area of information physics that is a new progressively developing field of study trying to introduce basics of information variables into physics. New parameters, like wave information flow, wave information/knowledge content or wave information impedance, are first defined and then represented by wave probabilistic functions. Next, relations between newly defined parameters are used to compute information power or to build wave information circuits covering feedbacks, etc.
The paper presents the basic theory of wave probabilistic models together with their features. By introduction of the complementarity's principle between x-representation and k-representation the probability theory is completed for "structural" parameter which carries information about the changes of time series or random processes. The next feature of wave probabilistic models is the quantization principle or definition of probabilistic inclusion-exclusion rules.
The paper continues with the theory of wave probabilistic models and uses the inclusion-exclusion rule to describe quantum entanglement as a wave probabilities resonance principle. The achieved results are mathematically described and an illustrative example is shown to demonstrate the possible applications of the presented theory.
The paper presents a multi-output wavelet neural network (WNN) which, taking benefit of wavelets and neural networks, is able to accomplish data feature extraction and modeling. In this work, WNN is implemented with a feedforward one-hidden layer architecture, whose activation functions in its hidden layer neurons are wavelet functions, in our case, the first derivative of a Gaussian function. The network training is performed using a backpropagation algorithm, adjusting the connection weights along with the network parameters. This principle is applied to the simultaneous quantification of heavy metals present in liquid media, taking the cyclic voltammogram obtained with a modified epoxy-graphite composite sensor as departure information. The combination between processing tools and electrochemical sensors is already known as an electronic tongue.
Texture can be defined as a local statistical pattern of texture primitives in observer's domain of interest. Texture analysis such as segmentation plays a critical role in machine vision and pattern recognition applications. The widely applied areas are industrial automation, biomedical image processing and remote sensing. This paper describes a novel system for texture segmentation. We call this system Wavelet Oscillator Neural Networks (WONN). The proposed system is composed of two parts. A second-order statistical wavelet co-occurrence features are the first part of the proposed system and an oscillator neural network is in the second part of the system. The performance of the proposed system is tested on various texture mosaic images. The results of the proposed system are found to be satisfactory.
A new approach based on the implementation of probabilistic neural network (PNN) is presented for classification of electrocardiogram (ECG) beats. Four types of ECG beats (normal beat, congestive heart failure beat, ventricular tachyarrhythmia beat, atrial fibrillation beat) obtained from the Physiobank database were analyzed. The ECG signals were decomposed into time-frequency representations using discrete wavelet transform (DWT) and wavelet coefficients were calculated to represent the signals. The aim of the study is classification of the ECG beats by the combination of wavelet coefficients and PNN. The purpose is to determine an optimum classification scheme for this problem and also to infer clues about the extracted features. The present research demonstrated that the wavelet coefficients are the features which well represent the ECG signals and the PNN trained on these features achieved high classification accuracies.