This paper refers to the method of using the deep neural long-short-term memory (LSTM) network for the problem of electrocardiogram (ECG) signal classification. ECG signals contain a lot of subtle information analyzed by doctors to determine the type of heart dysfunction. Due to the large number of signal features that are difficult to identify, raw ECG data is usually not suitable for use in machine learning. The article presents how to transform individual ECG time series into spectral images for which two characteristics are determined, which are instantaneous frequency and spectral entropy. Feature extraction consists of converting the ECG signal into a series of spectral images using short-term Fourier transformation. Then the images were converted using Fourier transform again to two signals, which includes instantaneous frequency and spectral entropy. The data set transformed in this way was used to train the LSTM network. During the experiments, the LSTM networks were trained for both raw and spectrally transformed data. Then, the LSTM networks trained in this way were compared with each other. The obtained results prove that the transformation of input signals into images can be an effective method of improving the quality of classifiers based on deep learning.
This paper presents a new, nondestructive method of testing brick wall dampness in wall structures. The setup was used to determine the moisture in a specially built laboratory model. Topological methods and the gradient technique are used to optimize the approach. A forward model of a wall was constructed to solve the inverse problem resulting in moisture buildup inside the wall.
This article presents a new effective imaging method that can be applied in ultrasonic and radio tomography. The proposed method by changing the shape of the voxels leads to a substantial simplification of the algorithm at the cost of small approximations of the voxels. As proved in the work, these approximations do not have a significant impact on the readability of the image which is several times faster. Streszczenie. W pracy przedstawiono nową efektywną metodę obrazowania, która może mieć zastosowanie w transmisyjnej tomografii ultradźwiękowej lub radiowej. Proponowana metoda zmienia kształt voksela z sześciennego na kulisty dzięki czemu uzyskuje się znaczące uproszczenie algorytmu kosztem niewielkiej aproksymacji wokseli. Jak udowodniono w pracy, przyjęta aproksymacja nie ma znaczącego wpływu na wiarygodność obrazów, a jest kilkukrotnie bardziej efektywna. Efektywny algorytm do konstrukcji trójwymiarowych obrazów tomograficznych
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