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EN
Motivated by ideas from two-step models and combining second-order TV regularization in the LLT model, we propose a coupling model for MR image reconstruction. By applying the variables splitting technique, the split Bregman iterative scheme, and the alternating minimization method twice, we can divide the proposed model into several subproblems only related to second-order PDEs so as to avoid solving a fourth-order PDE. The solution of every subproblem is based on generalized shrinkage formulas, the shrink operator or the diagonalization technique of the Fourier transform, and hence can be obtained very easily. By means of the Barzilai–Borwein step size selection scheme, an ADMM type algorithm is proposed to solve the equations underlying the proposed model. The results of numerical implementation demonstrate the feasibility and effectiveness of the proposed model and algorithm.
EN
This study investigates the properties of the brain electrical activity from different recording regions and physiological states for seizure detection. Neurophysiologists will find the work useful in the timely and accurate detection of epileptic seizures of their patients. We explored the best way to detect meaningful patterns from an epileptic Electroencephalogram (EEG). Signals used in this work are 23.6 s segments of 100 single channel surface EEG recordings collected with the sampling rate of 173.61 Hz. The recorded signals are from five healthy volunteers with eyes closed and eyes open, and intracranial EEG recordings from five epilepsy patients during the seizure-free interval as well as epileptic seizures. Feature engineering was done using; i) feature extraction of each EEG wave in time, frequency and time-frequency domains via Butterworth filter, Fourier Transform and Wavelet Transform respectively and, ii) feature selection with T-test, and Sequential Forward Floating Selection (SFFS). SVM and KNN learning algorithms were applied to classify preprocessed EEG signal. Performance comparison was based on Accuracy, Sensitivity and Specificity. Our experiments showed that SVM has a slight edge over KNN.
EN
Analog circuits need more effective fault diagnosis methods. In this study, the fault diagnosis method of analog circuits was studied. The fault feature vectors were extracted by a wavelet transform and then classified by a generalized regression neural network (GRNN). In order to improve the classification performance, a wolf pack algorithm (WPA) was used to optimize the GRNN, and a WPA-GRNN diagnosis algorithm was obtained. Then a simulation experiment was carried out taking a Sallen–Key bandpass filter as an example. It was found from the experimental results that the WPA could achieve the preset accuracy in the eighth iteration and had a good optimization effect. In the comparison between the GRNN, genetic algorithm (GA)-GRNN and WPA-GRNN, the WPA-GRNN had the highest diagnostic accuracy, and moreover it had high accuracy in diagnosing a single fault than multiple faults, short training time, smaller error, and an average accuracy rate of 91%. The experimental results prove the effectiveness of the WPA-GRNN in fault diagnosis of analog circuits, which can make some contributions to the further development of the fault diagnosis of analog circuits.
EN
In this study, the feedforward neural networks (FFNNs) were proposed to forecast the multi-day-ahead streamfow. The parameters of FFNNs model were optimized utilizing genetic algorithm (GA). Moreover, discrete wavelet transform was utilized to enhance the accuracy of FFNNs model’s forecasting. Therefore, the wavelet-based feedforward neural networks (WFFNNs-GA) model was developed for the multi-day-ahead streamfow forecasting based on three evolutionary strategies [i.e., multi-input multi-output (MIMO), multi-input single-output (MISO), and multi-input several multi-output (MISMO)]. In addition, the developed models were evaluated utilizing fve diferent statistical indices including root mean squared error, signal-to-noise ratio, correlation coefcient, Nash–Sutclife efciency, and peak fow criteria. Results provided that the statistical values of WFFNNs-GA model based on MISMO evolutionary strategy were superior to those of WFFNNs-GA model based on MISO and MIMO evolutionary strategies for the multi-day-ahead streamfow forecasting. Results indicated that the performance of WFFNNs-GA model based on MISMO evolutionary strategy provided the best accuracy. Results also explained that the hybrid model suggested better performance compared with stand-alone model based on the corresponding evolutionary strategies. Therefore, the hybrid model can be an efcient and robust implement to forecast the multi-day-ahead streamfow in the Chellif River, Algeria.
5
Content available remote Automatic diagnosis of atrial fibrillation based on online RR interval analysis
EN
The article presents the results of research of the tachogram of the cardiac signal with areas of atrial fibrillation. Using the wavelet transform to analyse non-stationary processes, it was shown that dispersion decomposition is an instantaneous correlation between the wavelet spectrum and has two implementations: frequency non-stationarity models - for frequency and time estimates, the ability to specify a parameter that should be relevant for the onset of atrial fibrillation. The calculation of this parameter can be used to detect fibrillation during the online recording of RR intervals.
PL
W artykule przedstawiono wyniki badań tachogramu sygnału sercowego z obszarami migotania przedsionków. Wykorzystanie transformacji falkowej do analizy procesów niestacjonarnych wykazało, że dekompozycja dyspersji jest chwilową korelacją między widmem falkowym i ma dwie implementacje: modele niestacjonarne częstotliwości - dla szacunków częstotliwości i czasu, możliwość określenia parametru, który powinien mieć znaczenie dla początku migotania przedsionków. Obliczenie tego parametru może być wykorzystane do wykrycia migotania podczas rejestracji online odstępów RR.
EN
By combining a wavelet transform with chaos scrambling, an image compression and encryption algorithm based on 2D compressive sensing is designed. The wavelet transform is employed to obtain the sparse representation of a plaintext image. The sparse image is measured in two orthogonal directions by compressive sensing. Then, the result of 2D compressive sensing is confused by the Arnold transform and the random pixel scrambling. The combination of four-dimensional chaos and logistic map is exploited to generate the first row of the key-controlled circulant matrix. The proposed algorithm not only carries out image compression and encryption simultaneously, but also reduces the consumption of the key by controlling the generation of measurement matrix. Experimental results reveal that the proposed image compression and encryption algorithm is resistant to noise attacks with good compression performance and high key sensitivity.
EN
One of the most important issues that power companies face when trying to reduce time and cost maintenance is condition monitoring. In electricity market worldwide, a significant amount of electrical energy is produced by synchronous machines. One type of these machines is brushless synchronous generators in which the rectifier bridge is mounted on rotating shafts. Since bridge terminals are not accessible in this type of generators, it is difficult to detect the possible faults on the rectifier bridge. Therefore, in this paper, a method is proposed to facilitate the rectifier fault detection. The proposed method is then evaluated by applying two conventional kinds of faults on rectifier bridges including one diode open-circuit and two diode open-circuit (one phase open-circuit of the armature winding in the auxiliary generator in experimental set). To extract suitable features for fault detection, the wavelet transform has been used on recorded audio signals. For classifying faulty and healthy states, K-Nearest Neighbours (KNN) supervised classification method was used. The results show a good accuracy of the proposed method.
8
Content available remote Spline-wavelet bent robust codes
EN
This paper presents an application of spline-wavelet transformation and bent-functions for the construction of robust codes. To improve the non-linear properties of presented robust codes, bent-functions were used. Bent-functions ensure maximum non-linearity of functions, increasing the probability of detecting an error in the data channel. In the work different designs of codes based on wavelet transform and bent-functions are developed. The difference of constructions consists of using different grids for wavelet transformation and using different bent-functions. The developed robust codes have higher characteristics compared to existing. These codes can be used for ensuring the security of transmitted information.
EN
Long-term monthly streamfow forecasting has great importance in the water resource system planning. However, its modelling in extreme cases is difcult, especially in semiarid regions. The main purpose of this paper is to evaluate the accuracy of artifcial neural networks (ANNs) and hybrid wavelet-artifcial neural networks (WA-ANNs) for multi-step monthly streamfow forecasting in two diferent hydro-climatic regions in Northern Algeria. Diferent issues have been addressed, both those related to the model’s structure and those related to wavelet transform. The discrete wavelet transform has been used for the preprocessing of the input variables of the hybrid models, and the multi-step streamfow forecast was carried out by means of a recursive approach. The study demonstrated that WA-ANN models outperform the single ANN models for the two hydro-climatic regions. According to the performance criteria used, the results highlighted the ability of WAANN models with lagged streamfows, precipitations and evapotranspirations to forecast up to 19 months for the humid region with good accuracy [Nash–Sutclife criterion (Ns) equal 0.63], whereas, for the semiarid region, the introduction of evapotranspirations does not improve the model’s accuracy for long lead time (Ns less than 0.6 for all combinations used). The maximum lead time achieved, for the semiarid region, was about 13 months, with only lagged streamfows as inputs.
EN
Noise interference, especially from human noise, seriously affects the quality of magnetotelluric (MT) data. Strong human noise distorts the apparent resistivity curve, known as the near-source effect, causing poor reliability of MT data inversion. Based on analyzing the frequency characteristics of human noise resulting from the surrounding environment, a new waveletbased denoising method is proposed for both synthetic and real MT data in this paper. The new technique combines multiresolution analysis with a wavelet threshold algorithm based on Bayes estimation and has a remarkable effect on denoising at all band frequencies. The multi-resolution analysis method was employed to reduce long-period noise, and a wavelet threshold algorithm was used to eliminate strong high-frequency noise. In this research, the improved algorithm was assessed via simulated experiments and field measurements with regard to the reduction in human noises. This study demonstrates that the new denoising technique can increase the signal-to-noise ratio by at least 112% and provides an extensive analysis method for mineral resource exploration.
PL
Celem pracy była implementacja oraz wykonanie badań efektywności wybranej metody wykrywania niezajętych zasobów częstotliwości, opartej na wyznaczaniu entropii sygnału z wykorzystaniem analizy falkowej. W referacie przedstawiono podstawy teoretyczne rozważanych zagadnień, wyniki przeprowadzonych badań laboratoryjnych i ich analizę oraz wnioski.
EN
The aim of the paper is the implementation and efficiency evaluation of a method selected for the detection of the unused frequency resources. This method is based on the determination of signal entropy using wavelet analysis. The paper contains the theoretical fundamentals of the approach considered, the results obtained and their analysis as well as the final conclusions.
EN
The aim of this paper is to detect the single line to ground fault on the unit generator- transformer. A new ground fault detection scheme based on the extraction of energy and statistical parameters from wavelet transform based neural network is proposed. The faulty current signals obtained from a simulation were decomposed through wavelet analysis into various approximations and details. The simulation of the unit generator-transformer was carried out using the Sim-PowerSystem Blockset of MATLAB. The energy and statistical parameters analysis involved measured of the dispersion factors (range and standard deviation) of wavelet coefficients. Regarding the ANN performance, the errors in the SLGfault detection of ANN were under 1 %. The results indicate that the proposed algorithm was accurate enough in differentiating a single line to ground fault and un-fault for a unit generator-transformer.
PL
Przestawiono metodę detekcji nieprawidłowości w uziemieniu jednostki generator-transformator. W nowej metodzie wykorzystano transformatę falkową I sieć neuronową. Symulację przeproprowadzno wykorzystując Sim-PowerSystem Blockset of MATLAB. Uzyskano błąd pomiaru poniżej 1%.
EN
The main purpose of this paper is the evaluation of the developed image encryption algorithm based on wavelet decomposition of images. Encryption algorithms DES (Data Encryption Standard) and AES (Advanced Encryption Standard) are used only for encryption of detail coefficients of the wavelet decomposition and encrypted images are the result of the inverse wavelet transform. Compressed data is also examined in the encryption process. This encryption approach is implemented in Matlab environment.
PL
Głównym celem tego artykułu jest ocena opracowanego algorytmu szyfrowania obrazów opartego o dekompozycję falkową obrazów. Algorytmy szyfrowania DES (Data Encryption Standard) i AES (Advanced Encryption Standard) są wykorzystane do szyfrowania tylko współczynników detali dekompozycji falkowej a zaszyfrowane obrazy są wynikiem odwrotnej transformacji falkowej. Skompresowane dane są również badane w procesie szyfrowania. Ten proces szyfrowania jest implementowany w środowisku Matlab.
PL
Wyniki dotychczasowych badań naukowych prowadzonych w różnych jednostkach akademickich wskazują, że możliwa jest identyfikacja aktywności niektórych odbiorników energii elektrycznej w oparciu o analizę wysokoczęstotliwościowych zniekształceń sygnału powstających podczas pracy urządzenia. Metody te jednak nie uwzględniają informacji o czasie pojawienia się zniekształceń. W metodzie wykorzystano przekształcenie falkowe dla uzyskania precyzyjnej informacji o czasie wystąpienia zniekształceń, która pozwala na identyfikację aktywności niektórych urządzeń.
EN
According to literature it is possible to identify activities of some electric energy receivers based on analysis of their high frequency signal distortion. These methods do not take into account starting point of these distortions. In this paper, wavelet transform was used to acquire precise information about start time of distortions that in case of some devices enables identification of their activity.
PL
W artykule opisano algorytm wyznaczania sygnatur urządzeń elektrycznych poprzez charakteryzację stanów przejściowych występujących podczas włączania urządzeń. Do przetwarzania sygnału prądu wykorzystano transformatę falkową dedykowaną do analizy sygnałów niestacjonarnych. Przedstawiono sposób obliczania parametrów liczbowych charakteryzujących urządzenia oraz przykładowe wyniki badań eksperymentalnych. Zaproponowano metodę oceny jakości wyznaczonych sygnatur.
EN
The paper presents an algorithm for determining features of electrical appliances by characterizing switch-on transients. A wavelet transform dedicated to non-stationary signals analysis was used for current signal processing. The method of numeric parameters calculations characterizing devices and some results of experiments are presented. A method for patterns evaluation has been proposed.
16
Content available remote Composition of wavelet and Fourier transforms
EN
The paper presents the basic properties of the serial composition of two transformations: wavelet and Fourier. Two types of transformations were obtained because wavelet and Fourier transformations do not commute. The consequences of a phenomenon known as a "wavelet crime" are presented. Using wavelets with compact support in the frequency domain (e.g. Meyer wavelets) leads to the representation of signals as sparse matrices. Speech signals were used to test the presented transforms.
PL
W pracy przedstawione są podstawowe własności szeregowego złożenia dwóch transformacji: falkowej i Fouriera. Uzyskano dwa rodzaje transformacji ponieważ transformacje falkowe i Fouriera nie są przemienne. Przedstawione są konsekwencje zjawiska zwanego "przestępstwem falkowym". Zastosowanie falek ze zwartymi nośnikami w dziedzinie częstotliwości (np. falki Meyera) prowadzi do reprezentacji sygnałów w postaci macierzy rzadkich. Sygnały mowy zostały użyte do przetestowania przedstawionych transformacji.
EN
An expert system aided method of the blade-tip signal decomposition to the turbine blade vibration sources identification is presented. The method utilises a multivalued diagnostic model based on the discrete wavelet transform. Proposed algorithm consists of four stages: signal decomposition into low- and high-frequency components (approximations and details), approximations and details parameterization, multi-valued encoding of parameters obtained at the second stage, an expert system use of the turbine blade vibration sources identification.
PL
W artykule przedstawiono metodę ekspertowego wspomagania identyfikacji źródeł drgań łopatek turbiny na podstawie dekompozycji sygnału generowanego przez wierzchołki łopatek. Zastosowano wielowartościowy model diagnostyczno-decyzyjny uzyskany z wykorzystaniem transformaty falkowej. Proponowany algorytm metody składa się z czterech faz: falkowa dekompozycja sygnału na składowe niskoczęstotliwościowe (tzw. aproksymacje) i wysokoczęstotliwościowe (tzw. detale), parametryzacja aproksymacji i detali, wielowartościowe kodowanie parametrów uzyskanych w drugiej fazie, zastosowanie systemu ekspertowego do identyfikacji źródeł drgań łopatek.
EN
The result of the research on recognition and processing of violin sound is presented in the paper. This paper shows how to achieve higher quality of recorded sound, which is closer to human’s subjective perception, using signal processing and algorithms, which preserve playing techniques and individual style of the violinist. The filter, which provides the best result as well as perception and data analysis spectrum is a one-shot filter based on wavelet transform, with experimentally designated universal wavelet form for each technique. The work also delivers such information as safety and optimization way of processing audio parameters in the production code.
PL
W pracy przedstawiono wyniki badań nad rozpoznaniem i przetwarzaniem dźwięku skrzypiec w celu korekcji błędów wykonania, przy zachowaniu techniki gry i indywidualnego charakteru wykonania utworu przez muzyka. Podany jest sposób poprawy jakości subiektywnego odbioru nagranego dźwięku skrzypiec. Opisano zastosowanie transformaty falkowej do filtracji. Zoptymalizowano algorytmy i i sposób wyznaczania wyznaczanie parametrów. Wprowadzono innowacyjne techniki programowania i włączono je do podstawowych bibliotek transformaty falkowej w kodzie produkcyjnym.
EN
Non-destructive testing of engineering structures and elements in operation is one of the crucial steps in recently introduced design philosophies: damage tolerance and condition-based maintenance. Therefore, it is important to provide effective non-destructive testing methods, which are able to detect and identify a possible damage in early stage of its development. One effective testing method, which still gains its popularity in various industrial applications, is shearography. Although, shearography is sensitive to various types of structural damage and flaws, this sensitivity can be significantly improved by applying advanced post-processing algorithms to raw data obtained from measurements. An excellent candidate for such an improvement is the wavelet analysis, due to its very high sensitivity to smallest signal disturbances. This study presents results of comparative analysis of various wavelet transforms and various wavelets in order to analyse their sensitivity to damage. The improvement in damage detectability is verified using experimental data.
PL
Badania nieniszczące inżynierskich struktur i elementów będących w eksploatacji są jednym z najważniejszych kroków w obecnie zapoczątkowanych filozofii projektowania: tolerancji uszkodzenia oraz eksploatacji na podstawie stanu. Dlatego ważnym jest wykorzystanie efektywnych metod badań nieniszczących, które są zdolne do detekcji i identyfikacji możliwego uszkodzenia we wczesnej fazie jego rozwoju. Jedną z efektywnych metod badawczych, która wciąż zyskuje na popularności w różnych zastosowaniach przemysłowych, jest shearografia. Mimo że shearografia jest wrażliwa na różne rodzaje uszkodzeń i wad w strukturach, jej wrażliwość może być znacząco ulepszona poprzez zastosowanie zaawansowanych algorytmów przetwarzania do surowych danych wynikowych otrzymanych z pomiarów. Doskonałym kandydatem dla takiego polepszenia jest analiza falkowa ze względu na jej bardzo dużą wrażliwość na najmniejsze zaburzenia w sygnale. Niniejsza praca przedstawia wyniki analizy porównawczej różnych transformacji falkowych oraz różnych falek w celu analizy ich wrażliwości na uszkodzenia. Polepszenie wykrywalności uszkodzeń zostało zweryfikowane z wykorzystaniem danych eksperymentalnych.
EN
Glaucoma is a neuro-degenerative disorder of the eye and it leads to permanent blindness when untreated or detected in the later stage. The main cause of glaucoma is the damage of the optic nerve, which occurs due to the increase of eye pressure. Hence the early detection of this disease is critical in time and which can help to prevent further vision loss. The assessment of optic nerve head using fundus images is more beneficial than the raised intra ocular pressure assessment in population-based glaucoma screening. This work proposed a novel method for glaucoma identification based on time-invariant feature cup to disk ratio and anisotropic dual-tree complex wavelet transform features. Optic disk segmentation is done by using Fuzzy C-Means clustering method and Otsu's thresholding is used for optic cup segmentation. The results show the proposed method achieved an accuracy rate of 97.67% with 98% sensitivity using a multilayer perceptron model that is considered as clinically significant when compared to the existing works.
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