Different Types Of Wavelets Research Articles

Sensitivity Study of Feature Vectors Formed on the Basis of Multiple Scale Transformations of Processed Images

Introduction. Under the conditions of globalization, transportation of oil products by tanker fleets becomes one of the causes of man-made disasters in the water areas of seas and oceans. In this context, environmental monitoring acquires particular significance as a tool for ensuring timely detection of negative consequences of man-made disasters. This task is facilitated by recognition of images obtained from unmanned aerial vehicles with selection of those depicting the traces of man-made accidents or their consequences.Aim. To develop approaches for carrying out automatic selection of input data obtained from unmanned aerial vehicles in the form of photo and video images at the preliminary stage of image recognition.Materials and methods. The theoretical part of the study employed a classification method based on pattern recognition theory. Mathematical processing and calculation were carried out in the MATLAB environment. Simulation was conducted using the MathCAD environment.Results. A series of experiments was conducted to select a basis for discrete wavelet transforms. Modeling was conducted using the study results of the sensitivity of the feature vectors of images formed on the basis of different types of wavelet transforms. A concept of image construction for the purposes of feature vector formation was developed.Conclusion. An approach to the formalization of images for the purposes of feature vector formation is proposed. A metric of their contrast estimation is substantiated. It was established that the sensitivity of a recognition system based on the representation of images in the form of discrete wavelet transform matrices depends not only on the type of the mother wavelet, but also on the value of the scale parameter.

An Empirical Selection of Wavelet for Near-Lossless Medical Image Compression.

Wavelets are defined as mathematical functions that segment the data into different frequency levels. We can easily capture the fine and coarse details of an image or signal referred to as a subband. And it also helps in subband thresholding to achieve good compression performance. In recent days in telemedicine services, the handling of medical images is prominently increasing and it leads to the demand for medical image compression. While compressing the medical images, we have to concentrate on the data that holds important information, and at the same time, it must retain the image quality. Near-Lossless compression plays an essential role to achieve a better compression ratio than lossy compression and provides better quality than lossless compression. In this paper, we analyzed the sub-banding of Discrete Wavelet Transform (DWT) using different types of wavelets and made an optimal selection of wavelets for subband thresholding to attain a good compression performance with an application to medical images. We used Set Partitioning In Hierarchical Trees (SPIHT) compression scheme to test the compression performance of different wavelets. The Peak Signal to Noise Ratio (PSNR), Bits Per Pixel (BPP), Compression Ratio, and percentage of number of zeros are used as metrics to assess the performance of all the selected wavelets. And to find out its efficiency in possessing the essential information of medical images, the subband of the selected wavelets is further utilized to devise the near-lossless compression scheme for medical images.

Methods of increasing the resolution of signals in optical systems

Optical observation is the most effective way to control the state of various objects. The dynamism of the objects of the observed scene makes it an urgent task to improve the algorithms for obtaining coordinate and non-coordinate information about the observed objects. The issues of increasing the accuracy of measuring the distance and increasing the resolution between the light objects observed by the optical system by the method of double continuous wavelet transformation are considered. It is shown that the use of the second continuous wavelet transform to the curves of the coefficients of the first transform leads to an increase in the maxima of the scalegram and the curves of the coefficients, providing the coordinate sensitivity of the position of the signals by more than two times. The use of different types of wavelets in each continuous wavelet transform of signals gives many options for constructing a processing algorithm and can be used for additional filtering of noise taking into account the characteristics of signals. In this regard, it is proposed to use parallel mathematical models and real signals in a neural network for determining the coordinates of signals and their characteristics, which leads to an increase in accuracy for each type of signal. The indicated approach can be used in systems for multiple signaling from different sources or for combining images in multi-position systems.

Optimal Classification of Emotions from Electroencephalography (EEG) Signals

It is incredibly challenging to build an intelligent algorithm for emotion recognition that can deliver high accuracy because electroencephalography (EEG) signals are not stationary, nonlinear, and noisy. First, decomposing the preprocessed EEG signals of the SEED dataset into five frequency bands: delta, theta, alpha, beta, and gamma, and then calculated their energy and entropy from the extracted features. Then Principal Component Analysis (PCA) method for feature reduction was performed. It is important to note that different types of wavelets transform (db6, db5, etc.) were tested, and hyperparameter tuning of classification models was done to obtain optimal accuracy. The next step is classifying the emotions into three states: -1(negative), 0(neutral), and +1(positive), and tested the dataset on two types of classification models, namely Random Forest and Support vector machine (SVM). SVM gives better performance compared to Random Forest with an accuracy of 80.74%.

COMPARATIVE STUDY OF WHITE GAUSSIAN NOISE REDUCTION FOR DIFFERENT SIGNALS USING WAVELET

The present work is an attempt to make a comparative study of the wavelet-based noise reduction algorithm. The algorithm has been developed and implemented in MATLAB GUI and then analyzed for different types of wavelets such as Coiflet, Daubechies, Symlet, and Biorthogonal with their different versions. This algorithm has been verified for different types of input signals from different domains. Various statistical aspects like Mean Absolute Error (MAE), Mean Squared Error (MSE), Signal to Noise Ratio (SNR), and Peak Signal to Noise Ratio (PSNR) are analyzed for this algorithm. It is observed that the developed algorithm for noise reduction using wavelet works very well for different types of wavelets.

INVESTIGATION OF ALGORITHMS OF DETECTING OF THE CHARACTERISTICS OF REMOTE OBJECTS IN OPTOELECTRONIC SYSTEMS BY THE METHOD OF WAVELET TRANSFORMATION

The issues of the accuracy of measuring the coordinates and size of objects observed by an optical system by the methods of single and double continuous wavelet transform in their image are considered. It is shown that the use of the second continuous wavelet transform to the curves of the coefficients of the first transform leads to an increase in the extrema of the scalegram and the smoothness of the curves of the coefficients, providing more than two times higher coordinate sensitivity of determining the position and orientation of objects. The use of different types of wavelets in each continuous wavelet transform of signals gives many options for the curves of the coefficients of the continuous wavelet transform and can be used for additional filtering of noise, taking into account the nature of objects. The parallel use of mathematical models and real objects in a neural network for determining the coordinates of signals and their characteristics is proposed, which leads to an increase in accuracy for each type of object, the possibility of constructing intelligent control devices for outer space. Using the example of an experimental installation of two synchronously movable optoelectronic systems, the accuracy of combining images of objects in multiplexing systems from different digital sources is demonstrated.

Visually meaningful image encryption based on universal embedding model

Visually meaningful image encryption (VMIE) means that a plain image can be encrypted into a visually meaningful cipher image (VMCI), which makes the secret more imperceptible than noise-like cipher images. Here, we first present a universal embedding model (UEM) and further present a new UEM-based VMIE algorithm. The plain image is pre-encrypted and then embedded into the integer wavelet subbands of the host image in a dynamic way. In order to avoid the overflow, a threshold limiting function is used to modify the range of pixel values of the host image. To adapt different types of wavelet transform subbands, a UEM is proposed to be used to embed the secret information domain into the host domain. Moreover, in the embedding process, a four-dimensional discrete chaotic system is used to ensure the security of embedding. The numbers of embedded bits in the four embedded domains can be adjusted flexibly. A traversal algorithm is designed to find the optimal numbers of embedded bits for different types of wavelet transform in order to achieve an optimal visual quality of cipher images. A matrix-bit-depth-conversion algorithm is designed where a large matrix with low bit depth can be transformed into a small matrix with high bit depth to meet the input requirements of UEM, which means that the size of the plain image is not limited to a quarter of the size of the host image. Simulation results and performance comparisons show that the proposed VMIE algorithm can achieve a better visual quality than existing VMIE algorithms.

CWT × DWT × DTWT × SDTWT: Clarifying terminologies and roles of different types of wavelet transforms

Wavelets have been placed at the forefront of scientific researches involving signal processing, applied mathematics, pattern recognition and related fields. Nevertheless, as we have observed, students and young researchers still make mistakes when referring to one of the most relevant tools for time–frequency signal analysis. Thus, this correspondence clarifies the terminologies and specific roles of four types of wavelet transforms: the continuous wavelet transform (CWT), the discrete wavelet transform (DWT), the discrete-time wavelet transform (DTWT) and the stationary discrete-time wavelet transform (SDTWT). We believe that, after reading this correspondence, readers will be able to correctly refer to, and identify, the most appropriate type of wavelet transform for a certain application, selecting relevant and accurate material for subsequent investigation.

Wavelet Transform Application Based on Matlab Simulation

Multiple Input Multiple Output (MIMO) and Orthogonal Frequency Division Multiplexing (OFDM) are two combined technologies to provide the spectral efficiency and high data rate required for 4G, 5G wireless systems. Discrete Wavelet Transform (DWT) is presented as an alternative for Fast Fourier Transform (FFT) since there is no necessity for Cyclic Prefix (CP) due to the overlapping properties of DWT. By a simple replacement of the FFT with DWT in MIMO OFDM system, an improvement of performances has been detected which leads to a new system scenario DWT-based MIMO-OFDM. In this thesis, such a system is simulated and the performance of Bit Error Rate (BER) is analyzed to determine the different types of wavelets in various channel condition with different modulations (4, 16, 64 -QAM). Therefore, DWT-based MIMO-OFDM in comparison with DFT-based MIMO-OFDM system was investigated using MATLAB simulation software. And besides, I would like to refer to use Neural Network algorithms to replace Wavelet transform in the next research.

Multi-resolution wavelet analysis for noise reduction in impulse puncture voltage measurements

This work describes an approach for digital processing of voltage impulse signals in puncture tests of insulators on high voltage laboratories, using wavelet transform based multi-resolution analysis. Quantifying the amplitude of such impulses is not a trivial task because they have typical magnitudes of hundreds of kilovolts and rise times of 100 ns to 200 ns. The processing is mainly necessary due to high frequency electromagnetic interferences caused by the impulse generation system, which may strongly affect the measurement accuracy and, consequently, the test result. This manuscript presents qualitative and quantitative results for different types of wavelets studied, as well as the best performance obtained by the filter. Software for signal processing and analysis was also developed for this application. Results are also presented using the wavelet filter for a pioneering inter-laboratory comparison between high voltage measuring systems used in impulse puncture tests of insulators.

A Relative Method of Video Coding using Different Wavelets with SPIHT

Lossy or lossless data compression is needed to Bring down the storage size and greater transmission rate. Wavelet transform is one of the ideal methods used for video compression. Different types of wavelet transforms are now available. In this paper, video is compressed by different wavelets and modified version of SPIHT. Totally seven types of wavelets are used here to compress the video, MSPIHT is used as encoding technique. They are (i) Video coding using duabechies wavelet and MSPIHT (VDM) (ii) Video coding using haar wavelet and MSPIHT (VHM) (iii) video coding using bi orthogonal wavelet and MSPIHT (VBM) (iv) video coding using symlet wavelet and MSPIHT (VSM) (v) video coding using coiflet wavelet and MSPIHT (VCM) (vi) video coding using demeyer wavelet and MSPIHT (VDMM) and (vii) video coding using Mexican hat wavelet and MSPIHT (VMHM). Then we calculate the PSNR and compression ratio for knowing the performance of the system.

Wavelet transform‐based seismic facies classification and modelling: application to a geothermal target horizon in the NE German Basin

ABSTRACTAt the geothermal test site near Groß Schönebeck (NE German Basin), a new 3D seismic reflection survey was conducted to study geothermal target layers at around 4 km depth and 150°C. We present a workflow for seismic facies classification and modelling which is applied to a prospective sandstone horizon within the Rotliegend formation. Signal attributes are calculated along the horizon using the continuous Morlet wavelet transform. We use a short mother wavelet to allow for the temporal resolution of the relatively short reflection signals to be analysed. Time‐frequency domain data patterns form the input of a neural network clustering using self‐organizing maps. Neural model patterns are adopted during iterative learning to simulate the information inherent in the input data. After training we determine a gradient function across the self‐organizing maps and apply an image processing technique called watershed segmentation. The result is a pattern clustering based on similarities in wavelet transform characteristics. Three different types of wavelet transform patterns were found for the sandstone horizon. We apply seismic waveform modelling to improve the understanding of the classification results. The modelling tests indicate that thickness variations have a much stronger influence on the wavelet transform response of the sandstone horizon compared with reasonable variations of seismic attenuation. In our interpretation, the assumed thickness variations could be a result of variable paleo‐topography during deposition of predominantly fluvial sediments. A distinct seismic facies distribution is interpreted as a system of thicker paleo‐channels deposited within a deepened landscape. The results provide constraints for the ongoing development of the geothermal test site.

Intelligent Fault Diagnosis of Bearings Based on Energy Levels in Frequency Bands Using Wavelet and Support Vector Machines (SVM)

In this paper, a new method was introduced for feature extraction and fault diagnosis in bearings based on wavelet packet decomposition and analysis of the energy in different frequency bands. This method decomposes a signal into different frequency bands using different types of wavelets and performs multi-resolution analysis to extract different features of the signals by choosing energy levels in different frequency bands. The support vector machines (SVM) technique was used for faults classifications. Daubechies, biorthogonal, coiflet, symlet, Meyer, and reverse Meyer wavelets were used for feature extraction. The most appropriate decomposition level and frequency band were selected by analyzing the variation in the signal’s energy level. The proposed approach was applied to the fault diagnosis of rolling bearings, and testing results showed that the proposed approach can reliably identify different fault categories and their severities. Moreover, the effectiveness of the proposed feature selection and fault diagnosis method was significant based on the similarity between the wavelet packet and the signal, and effectively reduced the influence of the signal noise on the classification results.

High-Intensity Focused Ultrasound Lesion Detection Using Adaptive Compressive Sensing Based on Empirical Mode Decomposition.

Background:The main goal of ultrasound therapy is to have clinical effects in the tissue without damage to the intervening and surrounding tissues. Treatments have been developed for both in vitro and in clinical applications. HIFU therapy is one of these. Non-invasive surgeries, such as HIFU, have been developed to treat tumors or to stop bleeding. In this approach, an adequate imaging method for monitoring and controlling the treatment is required.Methods:In this paper, an adaptive compressive sensing representation of ultrasound RF echo signals is presented based on empirical mode decomposition (EMD). According to the different numbers of intrinsic mode functions (IMFs) produced by the EMD, the ultrasound signals is adaptively compressive sampled in the source and then adaptively reconstructed in the receiver domains. In this paper, a new application of compressive sensing based on EMD (CS-EMD) in the monitoring of high-intensity focused ultrasound (HIFU) treatment is presented. Non-invasive surgeries such as HIFU have been developed for various therapeutic applications. In this technique, a suitable imaging method is necessary for monitoring of the treatment to achieve adequate treatment safety and efficacy. So far, several methods have been proposed, such as ultrasound radiofrequency (RF) signal processing techniques, and imaging methods such as X-ray, MRI, and ultrasound to monitor HIFU lesions.Results:In this paper, a CS-EMD method is used to detect the HIFU thermal lesion dimensions using different types of wavelet transform. The results of the processing on the real data demonstrate the potential for this technique in image-guided HIFU therapy.Conclusions:In this study, a new application of compressive sensing in the field of monitoring of the HIFU treatment is presented. To the best of our knowledge, so far no studies on compressive sensing have been carried out in the monitoring of the HIFU. Based on the results obtained, it was showed that the number of measurements and Intrinsic Mode Functions have the function of noise reduction. In addition, results were shown that the successful reconstruction of the compressive sensing signals can be gained using a threshold based algorithm. To this end, in this paper it was shown that by selecting an suitable number of measurements, the sparse transform, and a thresholding algorithm, we can achieve a more accurate detection of the HIFU thermal lesion size.

Genetik Algoritmalar Kullanarak EKG Vuru Sınıflandırması için Öznitelik Seçimi

In this study, genetic algorithm method was used to select the most suitable set of features for classification of arrhythmia types of heart beats. Normal, right branch block, left branch block and pace rhythm samples of electrocardiography (ECG) signals which obtained from the MIT-BIH cardiac arrhythmia database were used in the classification. Mean, standard deviation, energy and entropy of discrete wavelet transform (DWT) coefficients were proposed as the features for the classification. By using the proposed DWT method, 16 features which have high classification accuracy were obtained among the 208 feature sets constructed from 13 different wavelet types by applying the genetic algorithm method. It was observed that the features that increase accuracy can be detected by the genetic algorithm and the feature set obtained from the coefficients of the different types of wavelets selected at different levels show higher performance than the coefficients obtained from the standard individual wavelet in the ECG arrhythmia classification.

Mode shape-based damage identification for a reinforced concrete beam using wavelet coefficient differences and multiresolution analysis

In this paper, the structural mode shapes extracted from the finite element model of a simply supported reinforced concrete beam are employed for damage identification using different types of wavelets. To start with, the parity of signals, wavelets, and their convolution, that is, wavelet transform properties, are verified. In light of the mathematical modeling complexity of modal frequency, which relates to the localization and quantification of damage in the reinforced concrete beam, the maximum curves based on multiresolution wavelet transform coefficient differences and the corresponding theoretical assumptions are described and analyzed. It is concluded that the maximum curve reaches a peak value at a specific scale for a specific case, based upon which, a new mode shape based algorithm and damage index are proposed for damage identification. The accuracy of localization as well as the sensitivity of quantification is further discussed.

Applications of wavelet transform on power engineering problems — A brief review

The main scope of this paper is about the applications of wavelet transform (WT) on power engineering research. This review paper deals with the summary of applications of WT to different power system problems including transmission line faults detection, power quality issues, power forecasting, power measurements and power system stability. Different types of wavelet are used for the solution of various power engineering issues, and the selection of suitable wavelet for specific problems is also discussed in this paper with the support of literature.

Wavelets for the Maxwell’s equations: An overview

In recent years wavelets decompositions have been widely used in computational Maxwell’s curl equations, to effectively resolve complex problems. In this paper, we review different types of wavelets that we can consider, the Cohen–Daubechies–Feauveau biorthogonal wavelets, the orthogonal Daubechies wavelets and the Deslauries–Dubuc interpolating wavelets. We summarize the main features of these frameworks and we propose some possible future works.

EEG Characteristic Analysis of Sleep Spindle and K-Complex in Obstructive Sleep Apnea

This Paper Describes a Method for the Evaluation of Sleep Apnea, Namely, the Peak Signal-to-noise ratio (PSNR) of Wavelet Transformed Electroencephalography (EEG) Data. The Purpose of this Study was to Investigate EEG Properties with Regard to Differences between Sleep Spindles and K-complexes and to Characterize Obstructive Sleep Apnea According to Sleep Stage. We Examined Non-REM and REM Sleep in 20 Patients with OSA and Established a New Approach for Detecting Sleep Apnea Base on EEG Frequency Changes According to Sleep Stage During Sleep Apnea Events. For Frequency Bands Corresponding to A3 Decomposition with a Sampling Applied to the KC and the Sleep Spindle Signal. In this Paper, the KC and Sleep Spindle are Ccalculated using MSE and PSNR for 4 Types of Mother Wavelets. Wavelet Transform Coefficients Were Obtained Around Sleep Spindles in Order to Identify the Frequency Information that Changed During Obstructive Sleep Apnea. We also Investigated Whether Quantification Analysis of EEG During Sleep Apnea is Valuable for Analyzing Sleep Spindles and The K-complexes in Patients. First, Decomposition of the EEG Signal from Feature Data was Carried out using 4 Different Types of Wavelets, Namely, Daubechies 3, Symlet 4, Biorthogonal 2.8, and Coiflet 3. We Compared the PSNR Accuracy for Each Wavelet Function and Found that Mother Wavelets Daubechies 3 and Biorthogonal 2.8 Surpassed the other Wavelet Functions in Performance. We have Attempted to Improve the Computing Efficiency as it Selects the most Suitable Wavelet Function that can be used for Sleep Spindle, K-complex Signal Processing Efficiently and Accurate Decision with Lesser Computational Time.

Analysis on ECG Data Compression Using Wavelet Transform Technique

Although digital storage media is not expensive and computational power has exponentially increased in past few years, the possibility of electrocardiogram (ECG) compression still attracts the attention, due to the huge amount of data that has to be stored and transmitted. ECG compression methods can be classified into two categories; direct method and transform method. A wide range of compression techniques were based on different transformation techniques. In this work, transform based signal compression is proposed. This method is used to exploit the redundancy in the signal. Wavelet based compression is evaluated to find an optimal compression strategy for ECG data compression. The algorithm for the one-dimensional case is modified and it is applied to compress ECG data. A wavelet ECG data code based on Run-length encoding compression algorithm is proposed in this research. Wavelet based compression algorithms for one-dimensional signals are presented along with the results of compression ECG data. Firstly, ECG signals are decomposed by discrete wavelet transform (DWT). The decomposed signals are compressed using thresholding and run-length encoding. Global and local thresholding are employed in the research. Different types of wavelets such as daubechies, haar, coiflets and symlets are applied for decomposition. Finally the compressed signal is reconstructed. Different types of wavelets are applied and their performances are evaluated in terms of compression ratio (CR), percent root mean square difference (PRD). Compression using HAAR wavelet and local thresholding are found to be optimal in terms of compression ratio.