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1

Application of the discrete linear chirp transform (DLCT) to estimate the parameters of multicomponent LFM signals

Fiołka Jerzy

Przegląd Elektrotechniczny

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2022

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R. 98, nr 6

45--49

EN

This paper is focused on method to estimate the parameters of multicomponent linear frequency modulation (LFM) signals. These nonstationary signals, which are often referred to as ”chirp”, are encountered in many fields such as communication, vibration analysis, radar systems. The presented method, which is based on the discrete linear chirp transform (DLCT), permits the chirp parameters to be precisely estimated. Its high performance, which was proven by the simulation results, coupled with its simplicity, makes this method useful for many applications.

PL

W artykule przedstawiono metodę estymacji parametrów wieloskładnikowych sygnałów z liniową modulacją częstotliwości. Z tego typu sygnałami mamy do czynienia w takich dziedzinach jak telekomunikacja, analiza drgań, systemy radarowe. Przedstawiona metoda, bazująca na DLCT (ang. Discrete linear chirp transform), pozwala na oszacowanie parametrów wspomnianych sygnałów. Jej wysoka skuteczność, potwierdzona wynikami symulacji, w połączeniu z prostotą, czyni metodę użyteczną w wielu zastosowaniach.

2

Accurate automated detection of congestive heart failure using eigenvalue decomposition based features extracted from HRV signals

Sharma Rishi Raj, Kumar Ashish, Pachori Ram Bilas, Acharya U. Rajendra

Biocybernetics and Biomedical Engineering

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2019

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Vol. 39, no. 2

312--327

EN

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.

3

Zastosowanie podejścia kontekstowego w diagnostyce technicznej

Timofiejczuk A.

Diagnostyka

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2004

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Vol. 30, T. 2

129--132

PL

Diagnozowanie maszyn i procesów przemysłowych polega zwykle na interpretacji dużych zbiorów cech sygnałów resztkowych rejestrowanych podczas ich obserwacji. W przypadku obiektów złożonych interpretacja ta jest często trudna. W referacie pokazano identyfikację stanu technicznego obiektu na podstawie kontekstowej analizy sceny dynamicznej, której obiektami są cechy sygnałów diagnostycznych.

EN

Diagnosing machinery and industrial process consists in interpretation of large sets of residual signal features. Signals are recorded during observation of an object. In case of complex objects this interpretation is often difficult. The paper deals with a method of identification of technical state of an object on the basis of dynamic scene analysis with the use of contexts. Scene objects are features of diagnostic signals.