Congestive heart failure (CHF) is a cardiac abnormality in which heart is not able to pump sufficient blood to meet the requirement of all the parts of the body. This study aims to diagnose the CHF accurately using heart rate variability (HRV) signals. The HRV signals are non-stationary and nonlinear in nature. We have used eigenvalue decomposition of Hankel matrix (EVDHM) method to analyze the HRV signals. The lowest frequency component (LFC) and the highest frequency component (HFC) are extracted from the eigenvalue decomposed components of HRV signals. After that, the mean and standard deviation in time domain, mean frequency calculated from Fourier-Bessel series expansion, k-nearest neighbor (k-NN) entropy, and correntropy features are evaluated from the decomposed components. The ranked features based on t-value are fed to least-squares support vector machine (LS-SVM) classifier with radial basis function (RBF) kernel for automated diagnosis of CHF HRV signals. The study is performed on three normal datasets and two CHF datasets. Our proposed system has yielded an accuracy of 93.33%, sensitivity of 91.41%, and specificity of 94.90% using 500 HRV samples. The automated toolkit can aid cardiac physicians in the accurate diagnosis of CHF patients to confirm their findings with our system. Hence, it will help to provide timely treatment for CHF patients and save life.
In this paper, a new technique for constructing low density parity check codes based on the Hankel matrix and circulant permutation matrices is proposed. The new codes are exempt of any cycle of length 4. To ensure that parity check bits can be recursively calculated with linear computational complexity, a dual-diagonal structure is applied to the parity check matrices of those codes. The proposed codes provide a very low encoding complexity and reduce the stored memory of the matrix H in which this matrix can be easily implemented comparing to others codes used in channel coding. The new LDPC codes are compared, by simulation, with uncoded bi-phase shift keying (BPSK). The result shows that the proposed codes perform very well over additive white Gaussian noise (AWGN) channels.
The analysis of the influence of transfer function zeros on the parameters of state feedback controllers has been conducted. If a transfer function of a control object has zeros which are located closely to poles, the control object tends to singularity, and the influence of the input control signal to the states of the control object becomes weaker. The problem of the state feedback controller synthesis becomes ill-conditioned, which leads to the appearance of extremely large state feedback coefficients. In this case, the state feedback coefficients are sensitive to the parameters of the control object. As a result, the parametric robustness of the control system is reduced. Known methods of structural analysis of control object models are included amongst different methods of the numerical evaluation of the controllability and the observability, as well as methods of the model order reduction. These methods have some disadvantages, such as dependence on the state space representation form of the control object, ignoring a part of the control object model. In this paper, some ways of the preliminary structural analysis of the state space models of control objects have been proposed. The singular (Hankel) matrix is proposed for analyzing the properties of control object models. The singular matrix is the invariant characteristic of the control object in various state space forms and it characterizes the property of the control object completeness. As a result of the research, it was found that the coefficients of the state feedback controller are inversely proportional to the determinant of the singular matrix, and the determinant of the singular matrix is equal to the resultant of the transfer function polynomials. Thus the value of the determinant of the singular matrix depends on the location of the zeros of the transfer function. The method of the structural transformation (decomposition) of the control object for the defining the need of the reducing the order of the control object model is proposed.
The signal resulting from magnetic resonance spectroscopy is occupied by noises and irregularities so in the further analysis preprocessing techniques have to be introduced. The main idea of the paper is to develop a model of a signal as a sum of harmonics and to find its parameters. Such an approach is based on singular value decomposition applied to the data arranged in the Hankel matrix (HSVD) and can be used in each step of preprocessing techniques. For that purpose a method has was tested on real phantom data.
PL
Sygnał pochodzący z badania spektroskopii rezonansu magnetycznego zawiera również liczne szumy oraz nieprawidłości, stąd aby zastosować wyniki jako narzędzie diagnostyczne należy wprowadzić kilka usprawnień. W tym celu stosuje się filtrowanie, korekcję linii bazowej, korekcję fazy, korekcję prądów wirowych oraz usuwanie niechcianych komponentów, które nazywa się przetwarzaniem wstępnym. W dalszej analizie bardzo ważna jest identyfikacja poszczególnych metabolitów, którą można otrzymać poprzez zamodelowanie sygnału. Głównym pomysłem przedstawionym w artykule jest rozwinięcie modelu sygnału jako sumy harmonicznych. Metoda polega na znalezieniu parametrów opisujących sygnał takich jak amplituda, przesunięcie fazowe, częstotliwości i współczynnik tłumienia. Takie podejście bazuje na rozkładzie według wartości osobliwych (SVD) zastosowanym na danych zawartych w macierzy Hankela (HSVD), który dekomponuje sygnał na sumę harmonicznych oraz wylicza potrzebne parametry. Autor zaproponował zastosowanie HSVD w technikach przetwarzania wstępnego. Artykuł opisuje główne kroki przetwarzania i rozwiązanie każdej części oparte na HSVD. Podsumowując można stwierdzić, iż HSVD stosuje się w dekompozycji sygnału ale może być również skutecznym narzędziem w przetwarzaniu wstępnym. Artykuł składa się z 6 rozdziałów, w tym wstępu, rozdziału opisującego HSVD, metody przetwarzania wstępnego i główne wyniki, wniosków i referencji. W artykule znajdują się 4 obrazki oraz 7 referencji.
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This paper presents a novel image encryption scheme based on Toeplitz matrix and Hankel matrix. According to the definition and the expression of Toeplitz matrix and Hankel matrix, we permutate the positions of image pixels to confuse the relationship between the plain-image and cipher-image, and construct a new key. To enhance security further, hyper-chaos system of L¨u and Chen is taken to change the grey distribution of image pixels simultaneously. Numerical experimental results demonstrate that the key space is large enough, and the key is sensitive to initial conditions to resist the brute-force attack in the proposed scheme. Additionally, the distribution of grey values in encrypted image has a random-like behavior to resist statistical analysis. The proposed encryption scheme in this paper can suggest a high secure and efficient way for real-time image encryption and transmission in practice.
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