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1

Anatomical and functional assessment of patency of the upper respiratory tract in selected respiratory disorders - Part 2

Zając Andrzej, Kukwa Andrzej, Barański Robert, Nitkiewicz Szymon, Zomkowska Edyta, Rybak Adam

Metrology and Measurement Systems

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2022

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Vol. 29, nr 3

429--454

EN

This article presents selected physical diagnostic methods used in otorhinolaryngology and results of their application. In addition to the applications of methods using the capabilities of selective sensors, selected methods of hybrid diagnostics were also presented - for assessment of parameters of respiratory processes, with polysomnography as an example of using both typical diagnostic methods dedicated to otolaryngology, as well as standard EEG and ECG methods. It has been shown that in some special cases of respiratory disorders, measurements of the air flow in the respiratory tract can be supplemented with pressure measurements in selected positions within the airways. The presented optical methods and diagnostic systems are very often used in the diagnosis of diseases not specific for otolaryngology occurring in the area of the head and neck. The presented material is the second part of the study discussing both standard and widely used diagnostic methods. All presented methods are dedicated to otolaryngology. This text is a continuation of the material published in No 4 of 2021 [1].

2

Comparative Analysis and Fusion of MRI and PET Images based on Wavelets for Clinical Diagnosis

Sebastian Jinu, King Gnana

International Journal of Electronics and Telecommunications

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2022

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Vol. 68. No. 4

867--873

EN

Nowadays, Medical imaging modalities like Magnetic Resonance Imaging (MRI), Positron Emission Tomography (PET), Single Photon Emission Tomography (SPECT), and Computed Tomography (CT) play a crucial role in clinical diagnosis and treatment planning. The images obtained from each of these modalities contain complementary information of the organ imaged. Image fusion algorithms are employed to bring all of this disparate information together into a single image, allowing doctors to diagnose disorders quickly. This paper proposes a novel technique for the fusion of MRI and PET images based on YUV color space and wavelet transform. Quality assessment based on entropy showed that the method can achieve promising results for medical image fusion. The paper has done a comparative analysis of the fusion of MRI and PET images using different wavelet families at various decomposition levels for the detection of brain tumors as well as Alzheimer’s disease. The quality assessment and visual analysis showed that the Dmey wavelet at decomposition level 3 is optimum for the fusion of MRI and PET images. This paper also compared the results of several fusion rules such as average, maximum, and minimum, finding that the maximum fusion rule outperformed the other two.

3

Selection of level and type of decomposition in predicting suspended sediment load using wavelet neural network

Reddy Beeram Satya Narayana, Pramada S. K., Roshni Thendiyath

Acta Geophysica

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2022

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Vol. 70, no 2

847--857

EN

The advancements in artificial intelligence play a significant role in solving the problems of researchers and engineers to develop prediction models with higher accuracy over the analytical and numerical models. The wavelet ensemble artificial intelligence model has a widespread application in forecasting hydrological datasets. The signal decomposition type, level and the mother wavelet affect the model performance in wavelet-based approaches. The present analysis focuses on studying the significance of the level and type of decomposition in wavelet transform for pre-processing the input variables to predict the target variable. In this work, to forecast seasonal suspended sediment load of the Kallada River basin in Kerala, two types of decomposition with decomposition levels ranging from 2 to 7 were adopted using seasonal flow data (wet and dry seasons). To rank the WANN models, compromise programming was adopted using the results based on statistical performance indicators and compared with the performance of the conventional FFNN model. From the accuracy assessment and ranking, type-2 with 5th level decomposition can capture the actual periodicity of the signal and predict the suspended sediment load with higher accuracy. It also shows the capability to predict the extreme events of time series.

4

Classical versus deep learning methods for anomaly detection in ECG using wavelet transformation

Osowski Stanislaw, Gołgowski Maciej

Przegląd Elektrotechniczny

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2021

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R. 97, nr 6

72--76

EN

The paper describes and compares two forms of wavelet transformation: discrete (DWT) and continuous (CWT) in the analysis of electrocardiograms (ECG) to detect the anomaly. The anomalies have been limited to two types: cardiac and congestive heart failure. Two independent approaches to the problem have been considered. One is based on discrete wavelet transformation and feature generation based on statistical parameters of the results of the transformed ECG signals. These descriptors, after selection, are delivered as the input attributes to different classifiers. The second approach applies continuous wavelet transformation of ECG signals and the resulting two-dimensional image formed in time-frequency dimensions represents the input to the convolutional neural network, which is responsible for the generation of the diagnostic features and final classification. The experiments have been performed on the publically available database Complex Physiologic Signals PhysioNet. The calculations have been done in Python. The results of both approaches: DWT and CWT have been discussed and compared.

PL

Artykuł predstawia dwa podejścia do wykrywania anomalii w sygnalach ECG. Jako anomalie rozważane są: arytmia i zastoinowa niewydolność serca. Podstawą analizy jest sygnał ECG poddany transformacji falkowej w dwu postaciach: transformacja dyskretna oraz transformacja ciągła. W przypadku transformacji dyskretnej sygnał ECG poddany jest dekompozycji falkowej na kilku poziomach a wyniki tej dekompozycji (sygnały szczegółowe i sygnał aproksymacyjny ostatniego poziomu) podlegają opisowi statystycznemu tworząc zbiór deskryptorów numerycznych – potencjalnych cech diagnostycznych. Po przeprowadzonej selekcji stanowią one atrybuty wejściowe dla zespołu 9 klasyfikatorów. W drugim podejściu sygnał ECG jest poddany ciągłej transformacji falkowej generując dwuwymiarową macierz w postaci obrazu. Zbiór takich obrazów podawany jest na wejście głębokiej sieci neuronowej CNN, która w jednej strukturze dokonuje jednocześnie generacji cech diagnostycznych i klasyfikacji. Eksperymenty numeryczne przeprowadzone zostały na ogólnie dostępnej bazie danych Complex Physiologic Signals PhysioNet. Wyniki eksperymentów wykazały przewagę podejścia wykorzystujacego dyskretną transformację falkową.

5

Application of wavelet transform for identification of single phase-to-ground fault in compensated networks

Osipov Dmitry S., Kovalev Vladimir Z., Arkhipova Olga V., Dolgikh Nadezda N.

Przegląd Elektrotechniczny

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2021

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R. 97, nr 6

34--38

EN

In a number of EU countries medium voltage networks operate in the compensated neutral mode. In that case an arc suppression coil is commonly shunted with a resistor. The most common type of damage to such networks is a single phase-to-ground fault. The paper presents the method for two stage identification of a line where the fault has occured. The first stage is based on the analysis of high frequency components arising under transients. At the first stage a continuous wavelet transform is used to find frequencies. The second stage involves an analysis of the steady-state mode of a single phase-to-ground fault. Based on the energy spectrum of higher harmonics a damaged line is detected. To determine the energy spectrum at the second stage of the work a wavelet packet transform is applied. Wavelet transform has a number of advantages compared with short-time Fourier transform (STFT), particularly when analyzing non-stationary modes. The proposed method can be implemented to organize digital protection against ground faults.

PL

Sieci średniego napięcia w wielu krajach UE działają w skompensowanym trybie neutralnym. W takim przypadku cewka gasząca łuk jest zwykle bocznikowana przez rezystor. Najczęstszym rodzajem uszkodzeń w takich sieciach jest zwarcie jednofazowe do ziemi. W artykule przedstawiono technikę dwuetapowej identyfikacji linii, na której nastąpiło uszkodzenie. Pierwszy etap opiera się na analizie składowych wysokiej częstotliwości powstających pod wpływem stanów nieustalonych. W pierwszym etapie do znalezienia częstotliwości używana jest ciągła transformata falkowa. Drugi etap obejmuje analizę stanu ustalonego pojedynczego zwarcia międzyfazowego. Na podstawie widma energii wyższych harmonicznych wykrywana jest uszkodzona linia. Do wyznaczenia widma energii w drugim etapie pracy stosuje się transformację pakietu falkowego. Transformacja falkowa ma wiele zalet w porównaniu z krótkotrwałą transformatą Fouriera, szczególnie w przypadku analizy modów niestacjonarnych. Zaproponowaną metodę można zaimplementować do organizacji cyfrowej ochrony przed zwarciami doziemnymi.

6

Anatomical and functional assessment of patency of the upper respiratory tract in selected respiratory disorders - Part 1

Kukwa Andrzej, Zając Andrzej, Barański Robert, Nitkiewicz Szymon, Kukwa Wojciech, Zomkowska Edyta, Rybak Adam

Metrology and Measurement Systems

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2021

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Vol. 28, nr 4

813--836

EN

The rapidly developing measurement techniques and emerging new physical methods are frequently used in otolaryngological diagnostics. A wide range of applied diagnostic methods constituted the basis for the review study aimed at presenting selected modern diagnostic methods and achieved diagnostic results to a wider group of users. In this part, the methods based on measuring the respiratory parameters of patients were analysed. Respiration is the most important and necessary action to support life and its effective duration. It is an actual gas exchange in the respiratory system consisting of removing CO2 and supplying O2. Gas exchange occurs in the alveoli, and an efficient respiratory tract allows for effective ventilation. The disruption in the work of the respiratory system leads to measurable disturbances in blood saturation and, consequently, hypoxia. Frequent, even short-term, recurrent hypoxia in any part of the body leads to multiple complications. This process is largely related to its duration and the processes that accompany it. The causes of hypoxia resulting from impaired patency of the respiratory tract and/or the absence of neuronal respiratory drive can be divided into the following groups depending on the cause: peripheral, central and/or of mixed origin. Causes of the peripheral form of these disorders are largely due to the impaired patency of the upper and/or lower respiratory tract. Therefore, early diagnosis and location of these disorders can be considered reversible and not a cause of complications. Slow, gradually increasing obstruction of the upper respiratory tract (URT) is not noticeable and becomes a slow killer. Hypoxic individuals in a large percentage of cases have a shorter life expectancy and, above all, deal with the consequences of hypoxia much sooner.

7

Gene prediction by the noise-assisted MEMD and wavelet transform for identifying the protein coding regions

Zheng Qian, Chen Tao, Zhou Wenxiang, Xie Lei, Su Hongye

Biocybernetics and Biomedical Engineering

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2021

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Vol. 41, no. 1

196--210

EN

The analysis of protein coding regions of DNA sequences is one of the most fundamental applications in bioinformatics. A number of model-independent approaches have been developed for differentiating between the protein-coding and non-protein-coding regions of DNA. However, these methods are often based on univariate analysis algorithms, which leads to the loss of joint information among four nucleotides of DNA. In this article, we introduce a method on basis of the noise-assisted multivariate empirical mode decomposition (NA-MEMD) and the modified Gabor-wavelet transform (MGWT). The NA-MEMD algorithm, as a multivariate analysis tool, is utilized to reconstruct the numerical analyzed sequence since it enables a matched-scale decomposition across all variables and eliminates the mode mixing. By virtues of NA-MEMD, the MGWT method achieves a stable improvement on the general identification performance. We compare our method with other Digital Signal Processing (DSP) methods on two representative DNA sequences and three benchmark datasets. The results reveal that our method can enhance the spectra of the analyzed sequences, and improve the robustness of MGWT to different DNA sequences, thus obtaining higher identification accuracies of protein coding regions over other applied methods. In addition, another comparative experiment with the model-dependent method (AUGUSTUS) on the recently proposed benchmark dataset G3PO verifies the superiority of model-independent methods (especially NA-MEMD-MGWT) for identifying coding regions of the poor-quality DNA sequences.

8

Automated detection of driver fatigue from electroencephalography through wavelet-based connectivity

Ahmadi Amirmasoud, Bazregarzadeh Hanieh, Kazemi Kamran

Biocybernetics and Biomedical Engineering

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2021

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Vol. 41, no. 1

316--332

EN

Background: Mental fatigue is one of the most causes of road accidents. Identification of biological tools and methods such as electroencephalogram (EEG) are invaluable to detect them at early stage in hazard situations. Methods: In this paper, an expert automatic method based on brain region connectivity for detecting fatigue is proposed. The recorded general data during driving in both fatigue (the last five minutes) and alert (at the beginning of driving) states are used in analyzing the method. In this process, the EEG data during continuous driving in one to two hours are noted. The new feature of Gaussian Copula Mutual Information (GCMI) based on wavelet coefficients is calculated to detect brain region connectivity. Classification for each subject is then done through selected optimal features using the support vector machine (SVM) with linear kernel. Results: The designed technique can classify trials with 98.1% accuracy. The most significant contributions to the selected features are the wavelet coefficients details 1_2 (corresponding to the Beta and Gamma frequency bands) in the central and temporal regions. In this paper, a new algorithm for channel selection is introduced that has been able to achieve 97.2% efficiency by selecting eight channels from 30 recorded channels. Conclusion: The obtained results from the classification are compared with other methods, and it is proved that the proposed method accuracy is higher from others at a significant level. The technique is completely automatic, while the calculation load could be reduced remarkably through selecting the optimal channels implementing in real-time systems.

9

Stress feld estimation by the geoid undulations of the Samos Kuşadası Bay and implications for seismogenic behavior

Oruç Bülent, Balkan Emir

Acta Geophysica

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2021

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Vol. 69, no. 3

1137--1149

EN

The strong earthquake with magnitude 6.9 occurred ofshore at the northernmost edge of the Samos Island and was strongly felt in the north Aegean islands and İzmir metropolitan city. In this study, the effective elastic thicknesses of the lithosphere and seismogenic layer thickness were correlated with each other in order to understand the nature of the earthquakes. We determined that the upper and lower depth limits of seismogenic layer are in a range of 5–15 km, meaning that only the upper crust is mostly involved in earthquakes in the study area. The fact that seismogenic layer and effective elastic thicknesses are close to each other indicates that the earthquake potential may be within the seismogenic layer. Following that, we estimate the stress feld from the geoid undulations as a proxy of gravity potential energy in order to analyze the amplitude and orientation of the stress vectors and seismogenic behavior implications. The discrete wavelet transform has been carried out to decompose the isostatic residual gravity anomalies into horizontal, vertical and diagonal detail coefcients. The results delineated edges of gravity anomalies that reveal some previously unknown features.

10

A second-order TV-based coupling model and an ADMM algorithm for MR image reconstruction

Zhou Bo, Yang Yu-Fei, Hu Bo-Xia

International Journal of Applied Mathematics and Computer Science

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2020

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Vol. 30, no. 1

113--122

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.

11

A machine learning approach to epileptic seizure prediction using Electroencephalogram (EEG) Signal

Savadkoohi Marzieh, Oladunni Timothy, Thompson Lara

Biocybernetics and Biomedical Engineering

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2020

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Vol. 40, no. 3

1328--1341

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.

12

Fault diagnosis of analog circuit based on wavelet transform and neural network

Wang Hui

Archives of Electrical Engineering

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2020

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Vol. 69, nr 1

175--185

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.

13

Novel hybrid approaches based on evolutionary strategy for streamfow forecasting in the Chellif River, Algeria

Zakhrouf Mousaab, Bouchelkia Hamid, Stamboul Madani, Kim Sungwon

Acta Geophysica

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2020

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Vol. 68, no. 1

167--180

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.

14

Automatic diagnosis of atrial fibrillation based on online RR interval analysis

Sokol Yevgen, Shapov Pavel, Shyshkin Mykhailo, Koval Sergiy, Tomashevskyi Roman

Zeszyty Naukowe Instytutu Pojazdów / Politechnika Warszawska

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2019

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z. 2/120

53--60

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.

15

Image compression and encryption algorithm with wavelet-transform-based 2D compressive sensing

Fan Jing-Hui, Liu Xian-Bao, Chen Yan-Bin

Optica Applicata

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2019

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Vol. 49, nr 3

461--472

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.

16

Application of Acoustic Signals for Rectifier Fault Detection in Brushless Synchronous Generator

Rahnama Mehdi, Vahedi Abolfazl

Archives of Acoustics

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2019

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Vol. 44, No. 2

267--276

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.

17

Spline-wavelet bent robust codes

Levina Alla, Ryaskin Gleb, Zikratov Igor

Annals of Computer Science and Information Systems

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2019

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Vol. 18

227--230

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.

18

Long term monthly streamfow forecasting in humid and semiarid regions

Fouchal Amel, Souag Gamane Doudja

Acta Geophysica

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2019

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Vol. 67, no. 4

1223--1240

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.

19

Effective denoising of magnetotelluric (MT) data using a combined wavelet method

Ling Zhenbao, Wang Peiyuan, Wan Yunxia, Li Tonglin

Acta Geophysica

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2019

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Vol. 67, no. 3

813--824

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.

20

Analiza możliwości wykorzystania transformacji falkowej do detekcji niezajętych pasm częstotliwości

Bronk K., Rutkowski D., Wereszko B., Wereszko K., Żurek K.

Przegląd Telekomunikacyjny + Wiadomości Telekomunikacyjne

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2018

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nr 6

463--466, CD

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.