Wyniki wyszukiwania - Biblioteka Nauki

1

Super-resolution in Clinical Conditions : Deep Brain Stimulation Case Study

100%

Feschet F. , Lemaire J.-J.

Fundamenta Informaticae

|

2018

|

tom Vol. 163, nr 1

41--62

EN

Deep Brain Stimulation (DBS) has proven its efficiency in the treatment of Parkinson's disease or essential tremor. It requires precise localizations of targets for instance in the thalamus. Since deep brain structures have been shown to be hardly visible on T1 or T2 weighted imaging, most methods rely on atlas based comparison and registration. It is however possible to use direct targeting using a specific MRI sequence called WAIR (White Matter Attenuated Inversion Recovery) even on 1.5 Tesla MRI machine. The direct targeting facilitates the precise segmentation of deep brain structures needed to plan the trajectories of the electrodes for the DBS. But this remains a tedious delineation necessarily done by a neurosurgeon to avoid misinterpretation of the images. In this paper, we propose to build an isotropic super-resolution image for WAIR imaging to facilitate precise direct targeting of anatomical structures in the deep brain. We present a method to perform the reconstruction of a high resolution isotropic WAIR volume from three acquisitions performed on a volunteer subject. The method is based on transfinite interpolation in convex cells of an hyperplane arrangement. Our results show promising quality reconstruction for the computation of a super-resolution WAIR. It allows unambiguous segmentation of the deep brain to be used in DBS surgery.

2

Wavelet-based forecasting of ARIMA time series - an empirical comparison of different methods

100%

Schluter S. , Deuschle C.

Managerial Economics

|

2014

|

tom Vol. 15, no. 1

107--131

EN

By means of wavelet transform, an ARIMA time series can be split into different frequency components. In doing so, one is able to identify relevant patters within this time series, and there are different ways to utilize this feature to improve existing time series forecasting methods. However, despite a considerable amount of literature on the topic, there is hardly any work that compares the different wavelet-based methods with each other. In this paper, we try to close this gap. We test various wavelet-based methods on four data sets, each with its own characteristies. Eventually we come to the conclusion that using wavelets does improve forecasting quality especially for time horizons longer than one-day-ahead. However, there is no single superior method: either wavelet-based denoising or wavelet-based time series decomposition is best. Performance depends on the data set as well as the forecasting time horizon.

3

Wavelet-based forecasting of ARIMA time series - an empirical comparison of different methods

100%

Schlüter S. , Deuschle C.

Managerial Economics

|

2014

|

tom 15

|

nr 1

107-131

EN

By means of wavelet transform, an ARIMA time series can be split into different frequency components. In doing so, one is able to identify relevant patters within this time series, and there are different ways to utilize this feature to improve existing time series forecasting methods. However, despite a considerable amount of literature on the topic, there is hardly any work that compares the different wavelet-based methods with each other. In this paper, we try to close this gap. We test various wavelet-based methods on four data sets, each with its own characteristics. Eventually, we come to the conclusion that using wavelets does improve forecasting quality, especially for time horizons longer than one-day-ahead. However, there is no single superior method: either wavelet-based denoising or wavelet-based time series decomposition is best. Performance depends on the data set as well as the forecasting time horizon.

4

Microseismic event denoising via adaptive directional vector median filters

100%

Zheng J. , Lu J.-R. , Jiang T.-Q. , Liang Z.

Acta Geophysica

|

2017

|

tom Vol. 65, no. 1

47--54

EN

We present a novel denoising scheme via Radon transform-based adaptive vector directional median filters named adaptive directional vector median filter (AD-VMF) to suppress noise for microseismic downhole dataset. AD-VMF contains three major steps for microseismic downhole data processing: (i) applying Radon transform on the microseismic data to obtain the parameters of the waves, (ii) performing S-transform to determine the parameters for filters, and (iii) applying the parameters for vector median filter (VMF) to denoise the data. The steps (i) and (ii) can realize the automatic direction detection. The proposed algorithm is tested with synthetic and field datasets that were recorded with a vertical array of receivers. The P-wave and S-wave direct arrivals are properly denoised for poor signal-to-noise ratio (SNR) records. In the simulation case, we also evaluate the performance with mean square error (MSE) in terms of signal-to-noise ratio (SNR). The result shows that the distortion of the proposed method is very low; the SNR is even less than dB.

5

Dealing with charged-coupled device noise in thin-layer videodensitometry. Optimization of several image-denoising techniques

88%

Komsta Ł.

Acta Chromatographica

|

2009

|

tom Vol. 21, no. 3

355--367

EN

Charged-coupled device (CCD) noise can be a serious problem during videoscanning, especially when scanning dark plates with weakly fluorescent spots. The proper denoising of videoscans inside mathematical environments is a critical part of any advanced chemometric processing. The paper reports comparison and optimization of representative videoscan denoising by different techniques. Several kind of filters (averaging, circular, Gaussian, Savitzky-Golay, median, Wiener, FIR) and wavelet shrinkage (twelve mother wavelets from the Daubechies, Symmlet, and Coiflet family, five decomposition levels, and soft/hard thresholding) were optimized against noise autocorrelation or mean-squared error to the reference image. The reference image was obtained by grabbing and averaging 256 CCD frames. The median filter is the winner of the competition; other filters except Gaussian and wavelet shrinkage at high decomposition level are also sufficient and good ways of videoscan denoising. The Gaussian filter and wavelet shrinkage at low decomposition level performed worst and could not be recommended.

6

Improving Quality of Watermarked Medical Images Using Symmetric Dilated Convolution Neural Networks

88%

Pulgam N. D. , Shinde S. K.

Journal of Telecommunications and Information Technology

|

2023

|

tom nr 2

46--52

EN

Rapid development of online medical technologies raises questions about the security of the patient’s medical data.When patient records are encrypted and labeled with a watermark, they may be exchanged securely online. In order to avoid geometrical attacks aiming to steal the information, image quality must be maintained and patient data must be appropriately extracted from the encoded image. To ensure that watermarked images are more resistant to attacks (e.g. additive noise or geometric attacks), diﬀerent watermarking methods have been invented in the past. Additive noise causes visual distortion and render the potentially harmful diseases more diﬃcult to diagnose and analyze. Consequently, denoising is an important pre-processing method for obtaining superior outcomes in terms of clarity and noise reduction and allows to improve the quality of damaged medical images. Therefore, various publications have been studied to understand the denoising methods used to improve image quality. The ﬁndings indicate that deep learning and neural networks have recently contributed considerably to the advancement of image processing techniques. Consequently, a system has been created that makes use of machine learning to enhance the quality of damaged images and to facilitate the process of identifying speciﬁc diseases. Images, damaged in the course of an assault, are denoised using the suggested technique relying on a symmetric dilated convolution neural network. This improves the system’s resilience and establishes a secure environment for the exchange of data while maintaining secrecy.

7

Grey wolf optimization-based variational mode decomposition for magnetotelluric data combined with detrended fluctuation analysis

75%

Zhang X. , Li D. , Li J. , Li Y.

Acta Geophysica

|

2022

|

tom Vol. 70, no 1

111--120

EN

We present a magnetotelluric data denoising method that uses grey wolf optimization to optimize variational mode decomposition and combines it with detrended fluctuation analysis. First, envelope entropy is selected as the fitness function for grey wolf optimization and is used to determine the number of modes K and the penalty factor, which are the key parameters of the variational mode decomposition method. Then, the optimized variational mode decomposition method is used to decompose magnetotelluric data. Finally, the scaling exponent in detrended fluctuation analysis is used to determine the corresponding intrinsic mode function components to superimpose and reconstruct the useful magnetotelluric data. Extensive experiments and thorough analyses are performed on the synthetic data and field data. The results of the proposed method are compared with the results of the remote reference, variational mode decomposition, variational mode decomposition and matching pursuit, variational mode decomposition and detrended fluctuation analysis methods; the proposed method can improve the denoising performance and reliability of low-frequency magnetotelluric data. The reconstructed data are closer to the natural magnetotelluric data. The satisfactory performance in the results verifies the effectiveness of the design and optimization method.

8

Wybrane przykłady zastosowania transformaty falkowej do przetwarzania sygnałów biomedycznych

75%

Wilk B.

Pomiary Automatyka Kontrola

|

2003

|

tom R. 49, nr 7-8

29-31

PL

W artykule przedstawiono wybrane problemy dotyczące przetwarzania sygnałów biomedycznych oraz zaproponowano ich rozwiązanie za pomocą transformaty falkowej. Szczególną uwagę zwrócono na estymację trendu i detekcję charakterystycznych punktów sygnału oraz na eliminację szumu.

EN

In this paper some problems of biomedical signal processing are presented. For their solutions wavelet transform has been proposed. Special attention is paid to the estimation of signal trend, separation of signal commponents, detection of the characteristic points of signal, and noise removal.

9

Adaptive wavelet thresholding for image denoising using sure minimization and clustering of wavelet coefficients

75%

Fedak V. , Nakonechny A.

Czasopismo Techniczne. Elektrotechnika

|

2015

|

tom Y. 112, iss. 2-E

197--210

EN

Images and video are often coded using block-based discrete cosine transform (DCT) or discrete wavelet transform (DWT) which cause a great deal of visual distortions. In this paper, an extension of the intra-scale dependencies of wavelet coefficients is proposed to improve denoising performance. This method incorporates information on neighbouring wavelet coefficients that are inside of manually created clusters. Extensive experimental results are given to demonstrate the strength of the proposed method.

PL

Obrazy i nagrania wideo są często kodowane z użyciem blokowej dyskretnej transformacji kosinusowej (DCT) lub dyskretnej transformacji falkowej (DWT), które powodują znaczne zakłócenia wizualne. W niniejszej pracy proponuje się rozszerzenie zależności między współczynnikami falkowymi dotyczącymi skali w celu zmniejszenia zaszumienia sygnału zakodowanego. Zaproponowana metoda zakłada wykorzystanie informacji o sąsiadujących współczynnikach falkowych, które znajdują się wewnątrz manualnie utworzonego klastra. W artykule zaprezentowano obszerne wyniki doświadczalne w celu wykazania jakości proponowanej metody.

10

Objective Edge Similarity Metric for denoising applications in MR images

75%

Shanmugam A. , Rukmani Devi S.

Biocybernetics and Biomedical Engineering

|

2020

|

tom Vol. 40, no. 1

574--582

EN

Edge Similarity Metrics (ESMs) are necessary to objectively quantify the inadvertent blur at the edge pixels which occurs during denoising. They are helpful for evaluating edge-preserving capability of nonlinear filters. Most of the ESMs in literature, consider similarity of either strength of the edges or their direction individually. They lag in terms of concordance with subjective edge similarity ratings. An Objective Edge Similarity Metric (OESM) which considers all three attributes of edges; strength, direction and width together, is proposed in this paper. Pearson's Correlation shown by Gradient Magnitude Similarity Deviation (GMSD), Gradient Similarity Measure (GSM), Edge Strength Similarity Index Metric (ESSIM) and OESM with Subjective Edge Similarity Score (SESS) are ˗0.9669 ± 0.0028, 0.9566 ± 0.0053, 0.9507 ± 0.0057 and 0.9848 ± 0.0038, respectively. OESM is able to measure the degree of edge similarity between images more efficiently than GMSD, GSM and ESSIM. It reflects the perceptual edge similarity between images more accurately than GMSD, GSM and ESSIM.

11

Multiresolution image denosing based on wavelet transform

75%

Hassanien A. E. , El Henawy I. , Own H.

Machine Graphics and Vision

|

2001

|

tom Vol. 10, No. 2

221-230

EN

Wavelet-based image denoising is a very attractive tool for analysis and syntesis of functions. It enables us to divide a complicated function into several simpler ones and study them individually. In this paper, we presents a new image-denoising algorithm based on multiresolution local contrast entropy of wavelet coefficients. Depending on the propability distribution of the noise in the wavelet coeficients, a new adaptive threshold estimation algorithm is introduced. This threshold enables the proposed algorithm to adapt unknown smoothness of denoised images. The experiments performed confirm that the proposed algorithm is capable of achieving good results for additive white guassian noise.

12

Speech recognition by means of feature extraction method based on slope transformation assisted with denoising

63%

Dąbrowski A. , Drgas S.

Elektronika : konstrukcje, technologie, zastosowania

|

2007

|

tom Vol. 48, nr 4

61-64

EN

This paper describes a method for speech feature extraction using morphological signal processing based on the so-called "slope transformation". The proposed approach has been used to extract the signal upper spectral envelope. Results of experiments of the automatic speech recognition (ASR), which were undertaken to check the performance of the presented method, have shown some evident improvements of the effectiveness of recognition of isolated words, especially for women voices. The proposed method was combined with the speech enhancement and then evaluated. Results showed that for low signal-to-noise ratios the denoising algorithms used in the preprocessing stage bring additional recognition accuracy increase.

PL

W artykule przedstawiono metodę ekstrakcji cech mowy za pomocą morfologicznego przetwarzania sygnałów, wykorzystującego tzw. transformację nachyleniową. Zaproponowane ujęcie polega na wyznaczeniu górnej obwiedni widmowej. Rezultaty eksperymentów automatycznego rozpoznawania mowy, które przeprowadzono w celu zbadania skuteczności zaprezentowanej metody, wykazały poprawę efektywności rozpoznawania izolowanych słów, zwłaszcza w przypadku głosów żeńskich. Metodę rozpoznawania powiązano z poprawą jakości mowy, a następnie dopiero oceniano skuteczność rozpoznawania. Otrzymane rezultaty wskazały na poprawę dokładności rozpoznawania mowy po jej wstępnym odszumieniu.

13

Clinical evaluation of ECG signal wavelet denoising method

63%

Romaniuk P. , Chiżyński K.

Journal of Medical Informatics & Technologies

|

2001

|

tom Vol. 2, Part. 2

MT49--MT55

EN

This paper reports on clinical evaluation of a wavelet denoising algorithm applied to ECG signal. The denoising method first the signal into the wavelet domain, then processes non-linearly the coefficients, and reconstructs the signal back to the tie domain. The described evaluation procedure of the denoising method was designed to test the clinical methodology that relies on cardiologist diagnoses. The preliminary results of the test, performed on a small ECG data set, are presented and discussed.

14

Reconstruction of dynamics of SO2 concentration in troposphere based

63%

Gac J. M. , Petelczyc M.

Ecological Chemistry and Engineering. S

|

2019

|

tom Vol. 26, nr 1

59--68

EN

A method for the reconstruction of the dynamics of processes with discrete time, developed in our previous papers, has been applied for study the dynamics of concentration of sulfur dioxide in lower troposphere. For the analysis, recordings of sulfur dioxide concentration from four measurement stations located in Poland (two of them has been located in huge cities and two in rarely inhabited regions) were used. We managed to obtain the deterministic and stochastic component of this dynamics. In result, we estimate the lifetime of sulfur dioxide in troposphere and the increase of sulfur dioxide concentration influenced by anthropogenic sources.

15

Method for Identifying Micro-seismic P-Arrival by Time-frequency Analysis Using Intrinsic Time-Scale Decomposition

63%

Zhang R. , Zhang L.

Acta Geophysica

|

2015

|

tom Vol. 63, no. 2

468--485

EN

A method to identify the P-arrival of microseismic signals is proposed in this work, based on the algorithm of intrinsic timescale decomposition (ITD). Using the results of ITD decomposition of observed data, information of instantaneous amplitude and frequency can be determined. The improved ratio function of short-time average over long-time average and the information of instantaneous frequency are applied to the time-frequency-energy denoised signal for picking the P-arrival of the microseismic signal. We compared the proposed method with the wavelet transform method based on the denoised signal resulting from the best basis wavelet packet transform and the single-scale reconstruction of the wavelet transform. The comparison results showed that the new method is more effective and reliable for identifying P-arrivals of microseismic signals.

16

Komputerowe wspomaganie analizy obrazów sonograficznych szumów podwodnych

63%

Jerzman B. , Kiciński W.

Biuletyn Wojskowej Akademii Technicznej

|

2012

|

tom Vol. 61, nr 2

115-125

PL

Praca dotyczy sposobu poprawy jakości zobrazowania sygnałów pomiarowych obserwowanych w środowisku podwodnym. Istotą zaproponowanego sposobu przetwarzania sygnałów hydroakustycznych jest transformacja sygnałów pomiarowych za pomocą falki Malvara, zobrazowanie współczynników falkowych w postaci sonogramu oraz odszumianie obrazu sonograficznego z wykorzystaniem estymatorów jądrowych funkcji gęstości prawdopodobieństwa. Opracowany w środowisku MATLAB program, po wczytaniu sygnałów pomiarowych zapisanych w kodzie ascii, tworzy obraz sonograficzny stanu środowiska podwodnego, a następnie realizuje procedurę odszumiania, mającą na celu poprawę jego jakości. Działanie programu zweryfikowano na rzeczywistych krótkookresowych, szerokopasmowych sygnałach pomiarowych zarejestrowanych w środowisku podwodnym.

EN

The article deals with the problem of improving the quality of imaging the measurement signals observed in the underwater environment. The essence of the proposed method of hydroacoustic signal processing is: the transform using the Malvar wavelet, imaging of the wavelet coefficients as a sonogram and denoising the image using the kernel density estimate. The application written in MATLAB environment reads the signals from files saved in ascii format, builds the sonogram of the state of the underwater environment and proceeds with the image denoising. The research was conducted on the real transient and broadband measurement signals recorded under the water.

17

A wavelet-based approach for business protocol discovery of web services from log files

63%

Moudjari A. , Kezzouli I. , Talbi H. , Draa A.

Bulletin of the Polish Academy of Sciences. Technical Sciences

|

2019

|

tom Vol. 67, nr 3

535--546

EN

Recently, business protocol discovery has taken more attention in the field of web services. This activity permits a better description of the web service by giving information about its dynamics. The latter is not supported by theWSDL language which concerns only the static part. The problem is that the only information available to construct the dynamic part is the set of log files saving the runtime interaction of the web service with its clients. In this paper, a new approach based on the Discrete Wavelet Transformation (DWT) is proposed to discover the business protocol of web services. The DWT allows reducing the problem space while preserving essential information. It also overcomes the problem of noise in the log files. The proposed approach has been validated using artificially-generated log files.

18

An analytical method for the adaptive computation of threshold of gradient modulus in 2D anisotropic diffusion filter

63%

Joseph J. , Periyasamy R.

Biocybernetics and Biomedical Engineering

|

2017

|

tom Vol. 37, no. 1

1--10

EN

In spite of the extensive application of Anisotropic Diffusion (AD) filter in software packages for medical image analysis, denoising and edge preservation offered by it depends exclusively on the selection of the value of Threshold of Gradient Modulus (TGM). Tuning the TGM to its optimum value through trial and error is subjective and tiring. An analytical model to compute the optimum value of TGM adaptively from the mean gradient of the image itself is proposed in this article. The qualitative examination of the gradient and true edge maps of the original and restored Magnetic Resonance images revealed that analytically computed TGM ensures best trade-off between noise suppression and edge preservation.

19

A diagnostic algorithm diagnosing the failure of railway signal equipment

63%

Wu Y. , Cao D.

Diagnostyka

|

2021

|

tom Vol. 22, No. 4

33--38

EN

Failure of railway signal equipment can cause an impact on its normal operation, and it is necessary to make a timely diagnosis of the failure. In this study, the data of a railway bureau from 2016 to 2020 were studied as an example. Firstly, denoising and feature extraction were performed on the data; then the Adaptive Comprehensive Oversampling (ADASYN) method was used to synthesize minority class samples; finally, three algorithms, back-propagation neural network (BPNN), support vector machine (SVM) and C4.5 algorithms, were used for failure diagnosis. It was found that the three algorithms performed poorly in diagnosing the original data but performed significantly better in diagnosing the synthesized samples, among which the BPNN algorithm had the best performance. The average precision, recall rate and F1 score of the BPNN algorithm were 0.94, 0.92 and 0.93, respectively. The results verify the effectiveness of the BPNN algorithm for failure diagnosis, and the algorithm can be further promoted and applied in practice.

20

A seismic interpolation and denoising method with curvelet transform matching filter

63%

Yang H. , Long Y. , Lin J. , Zhang F. , Chen Z.

Acta Geophysica

|

2017

|

tom Vol. 65, no. 5

1029--1042

EN

A new seismic interpolation and denoising method with a curvelet transform matching filter, employing the fast iterative shrinkage thresholding algorithm (FISTA), is proposed. The approach treats the matching filter, seismic interpolation, and denoising all as the same inverse problem using an inversion iteration algorithm. The curvelet transform has a high sparseness and is useful for separating signal from noise, meaning that it can accurately solve the matching problem using FISTA. When applying the new method to a synthetic noisy data sets and a data sets with missing traces, the optimum matching result is obtained, noise is greatly suppressed, missing seismic data are filled by interpolation, and the waveform is highly consistent. We then verified the method by applying it to real data, yielding satisfactory results. The results show that the method can reconstruct missing traces in the case of low SNR (signal-to-noise ratio). The above three problems can be simultaneously solved via FISTA algorithm, and it will not only increase the processing efficiency but also improve SNR of the seismic data.