Academic literature on the topic 'Symlet wavelet of type 10'

Watermarking is currently investigated as an efficient and safe method of embedding additional patient or environment-related data into the electrocardiogram. This paper presents experimental work on the assessment of the loss of ECG (electrocardiogram signal) diagnostic quality from the industrial standard EN60601-2-25:2015 point of view. We implemented an original time-frequency watermarking technique with an adaptive beat-to-beat lead-independent data container design. We tested six wavelets, six coding bit depth values (including the automatic noise-dependent one) and two types of watermark content to find the conditions that are necessary for watermarked ECG to maintain the compliance with International Electrotechnical Commission (IEC) requirements for interpretation performance. Unlike other authors, we did not assess the differences of signal values, but errors in ECG wave delineation results. The results of a total of 7300 original and watermarked 10 s ECGs were statistically processed to reveal possible interpretation quality degradation due to watermarking. Finally we found (1) the Symlet of 11-th order as the best of the wavelets that were tested; (2) the important role of ECG wave delineation and noise tracking procedures; (3) the high influence of the watermark-to-noise similarity of amplitude and values distribution and (4) the stability of the watermarking capacity for different heart rates in atrial rhythms.

The wavelet thresholding method is an approximation method by reducing noise, which is known as the denoising process. This denoising process will remove noise while closed the important information in the data. In this research, the wavelet thresholding method is used to approximate the nonlinear function. The data used for the simulation is a representation of several functions that represent several events that often occur in the real world, which consists of the types of functions Blocks, Bumps, Doppler, and HeaviSine. Based on simulation results based on the indicator value of the Cross-Validation (CV), the best approximation of the nonlinear function using the wavelet thresholding method for the four simulation cases are: (i) the Blocks function is given by Haar wavelet with a soft of thresholding function and the 10-th resolution level ; (ii) the Doppler function is given on the 2-nd order of Symlets wavelet with a soft of thresholding function and the 10-th resolution level; (iii) the Bumps function is given on the 6-th order of Daubechies wavelet with a soft of thresholding function and the 10-th resolution level; and (iv) the HeaviSine function is given by the 3-rd order of Coiflet wavelet with a soft of thresholding function and the 7-th resolution level.

<p>Nowadays, iris recognition is widely used for personal identification and verification based on biometrical technology, especially in the smartphone arena. By having this iris recognition for identification and verification, the smartphone will be secured since every person have their own iris type. In this paper, we proposed an efficient iris recognition using Wavelet Packets and Hamming distance which has lightweight computational requirements while maintaining the accuracy. There are several steps needed in order to recognize the iris which are pre-processing the iris image consists of segmentation and normalization, extract the feature that available in the iris image and identify this image to see whether it match with the person or not. For comparison purposes, different types of wavelet bases will be compared, including symlets, discrete meyer, biorthogonals, daubechies, and coiflets. Performance of the proposed algorithm was tested on Chinese Academy of Sciences Institute of Automation (CASIA) iris image database. The optimum wavelet basis function obtained is symlet. Results showed that the accuracy of the proposed algorithm is 100% identification rate.</p>

<p>This paper presents an approach for the selection of mother wavelet for classification of EEG epilepsy signals .Wavelet transform is very popular for analyzing signals in time and frequency domain. But as there are various wavelet families exist and not a one fits to all, in this study author have experimented with 51 wavelets from six different families Haar(haar), Daubechies(Db), Symlet(Sym), Coiflets(Coif), Biorthogonal(Bior) and Discrete Meyer(Dmey). Optimal mother wavelet is selected on the basis of highest correlation between input signal and reconstructed signal. With Discrete wavelet transform four levels of decomposition have been used to create the five EEG rhythms delta, theta, alpha, beta and gamma. Five features kurtosis, skew, mean, standard deviation and relative power have been extracted from each decomposed level by using the optimal mother wavelet. Statistical significance of the extracted features has been computed by Mann Whitney U test with significance level p&lt;0.05 and statistical parameters sensitivity, specificity and accuracy for performance evaluation of the classifier have been computed. Results shown that out of six experimented wavelet families, five families with eight wavelets have qualified the correlation test. Out of five extracted feature relative power is more statistically significant for all type of classification and all EEG bands .Classifier used is support vector machine and accuracy of classifier lies in the range of 74% to 100 % for 14 classifications between different subsets.</p>

The heart is considered the most important organ of our body that controls the circulation of blood throughout the body. Measured heartbeat signals can be further analyzed in order to know the health condition of a person. The challenge of ECG signal measurement and analysis is how to remove the noises imposed on the signal that is interfered from many different sources, such as internal noise in sensor devices, power line interference, muscle activity, and body movements. This paper implemented wavelet transform to reduce the noise imposed on the ECG signal to get a closely actual heart signal. ECG data used in this research are three digitized recorded ECG data obtained from MIT-BIH Arrhythmia Database. The first step is generating the noisy ECG signal as the input system by adding 1W WGN signal into the original ECG signal. Then DWT is applied to extract the noisy ECG signal. Some DWT’s parameters, threshold selection (rule, type, rescaling), decomposition level, and desired wavelet family are varied to get the best denoised output signal. All results are recorded to be compared. Based on the results, the best DWT parameter for ECG signal denoising is obtained by Symlet wavelet when the decomposition level is set to 3, with soft thresholding, in rigrsure thresholding rule.

One of the requirements faced as a result of information technology development is memory and transmission efficiency. This requirement can be overcome with data compression. Compression is a method to obtain compact data with a smaller size but still maintaining similarity to the original data. Principal Component Analysis (PCA) is an algorithm in machine learning that is used to reduce dimensions. Dimensional reduction is a process of transforming high-dimensional data into new subspaces with lower dimensions. The goal is to use some principal components to represents the original data. Wavelet transformation represents a signal into a set of basic functions through filter analysis. Wavelets concentrate information into coefficients of approximation and coefficients of detail. Wavelet transform produces a lot of zero or close to zero coefficients that can be neglected so it can reduce storage space. In this research, we will propose the implementation of PCA and Wavelet for digital audio compression. The audio was performed with the .wav format. The compressed audio will be evaluated based on Peak Signal to Noise Ratio (PSNR) and Mean Square Error (MSE). The mean PSNR obtained when using a wavelet is 47.61601 dB with an average MSE of 3.76 x 10-5. Meanwhile, when using PCA, the PSNR average was 57.3962772 dB and the average MSE obtained was 4.59 x 10-5. Four out of five compressed audio had a larger PSNR and smaller MSE when using PCA. Thus, the Principal Component Analysis algorithm can be better used for audio compression than the level 1 of Symlet Wavelet Transformation.

Stationary Wavelet Transform (SWT), mother function Symlet 4, shows an effective method for Internal Solitary Waves (ISW) feature extraction and four parameters (soliton numbers, first crest length, wavelength and propagation direction) estimation by Sentinel-1A C-band Synthetic Aperture Radar (SAR) imagery in Lombok Strait. The ISW feature can be distinguished from other features using the SWT noise reduction process and simple thresholding methods. ISW feature extraction results by SAR images can show ISW characteristics more clearly and can be used as a basis for obtaining ISW spatial-temporal distribution maps in the Lombok Strait. Our estimation results show that the arc-like type of ISW in the Lombok Strait propagated to the north of the sill with the detected soliton numbers are 2 solitons per packets with a wavelength about 3.20 km and the first crest length varies about 60.27 km near the Kangean Island with the propagation direction about 50.38 degree to the North.

In the 5th generation (5G) and 6th generation (6G) of wireless mobile telecommunication networks, the requests for an elevated data rate with access to stationary as well as portable customers are going to be overwhelming. Mobile worldwide interoperability for microwave access (WiMAX) comes out as a favourable alternative that is intelligibly developed and more matured than wireless fidelity (Wi-Fi). Mobile WiMAX makes use of the orthogonal frequency division multiple access (OFDMA) technology for its two-way communication to enhance the system performance in fading environments making it more suitable for 5G applications. The diverse OFDM forms deliberated here are the fast Fourier transform- (FFT-) based WiMAX and discrete wavelet transform- (DWT-) based WiMAX. The suggested study exhibits the bit error rate (BER) and peak to average power ratio (PAPR) reduction by integrating different wavelet families, i.e., Haar, symlet, coiflet, and reverse biorthogonal over Rayleigh fading channel. The simulation results obtained by MATLAB depicts an improvement in PAPR reduction, and signal to noise ratio (SNR) requirement is also reduced by 6-12 dB by using DWT-incorporated WiMAX at a BER of 10-4.

Orthogonal Frequency Division Multiplexing (OFDM) is chosen as multiplexing techniques and broadly used in today’s radiocommunication environments to overcome spectrum insufficiency. With several superior advantages, however, OFDM is terribly affected by high peak to average power ratio (PAPR) due to offset frequency errors and local oscillator (LO) frequency synchronization errors. The high PAPR can cause nonlinear distortion, which outcomes in intermodulation and spectral leakage. This study aims to model the use of wavelet transform (discrete wavelet transform (DWT)) to replace Fourier transform (discrete Fourier transform (DFT)) that used in conventional OFDM, later in this paper is termed as DFT-OFDM. Clipping filtering techniques then applied to DWT-OFDM. The model was proposed to reduce PAPR in DFT-OFDM. The model was compared to DFT-OFDM using Matlab simulation method. The performance was evaluated using the Complementary Cumulative Distributive Function (CCDF) vs. PAPR. The results show that at PAPR 10-3for DFT-OFDM, it was produced PAPR of 10.6 dB whereas in DWT-OFDM, using Daubechies orde 7 (Daubechies7), Symlet orde 7 (Symlet7), Coiflet orde 2 (Coiflet2), were reached PAPR 4.8 dB, PAPR 3.3 dB, PAPR 3 dB, respectively. It means Coiflet2 providing the best PAPR reduction among other orthogonal wavelets. By applied clipping filtering to wavelet Coiflet2, it was produced PAPR of 2.9 dB for classical clipping and 2.8 dB for deep clipping. It show that wavelet Coiflet2 with deep clipping provided the best PAPR.

Brain-Computer Interface is the collaboration of the human brain and a device that controls the actions of a human using brain signals. Applications of brain-computer interface vary from the field of entertainment to medical. In this article, a novel Deceit Identification Test is proposed based on the Electroencephalogram signals to identify and analyze the human behavior. Deceit identification test is based on P300 signals, which have a positive peak from 300 ms to 1,000 ms of the stimulus onset. The aim of the experiment is to identify and classify P300 signals with good classification accuracy. For preprocessing, a band-pass filter is used to eliminate the artifacts. The feature extraction is carried out using “symlet” Wavelet Packet Transform (WPT). Deep Neural Network (DNN) with two autoencoders having 10 hidden layers each is applied as the classifier. A novel experiment is conducted for the collection of EEG data from the subjects. EEG signals of 30 subjects (15 guilty and 15 innocent) are recorded and analyzed during the experiment. BrainVision recorder and analyzer are used for recording and analyzing EEG signals. The model is trained for 90% of the dataset and tested for 10% of the dataset and accuracy of 95% is obtained.

Abstract Human’s motion and its mechanisms had become interesting in the last years, where the medecine’s field search for rehabilitation methods for handicapped persons. Other fields, like sport sciences, professional or military world, search to distinguish profiles and ways to train them with specific purposes. Besides, recent findings in neuroscience try to describe these mechanisms from an organic point of view. Until now, different researchs had given a model about control motor that describes how the union between the senses’s information allows adaptable movements. One of this sense is the proprioception, the sense which has a quite big factor in the orientation and position of the body, its members and joints. For this reason, research for new strategies to explore proprioception and improve the theories of human motion could be done by three different vias. At first, the sense is analysed in a case-study where three groups of persons are compared in a controlled enviroment with three experimental tasks. The subjects belong to each group by the kind of sport they do: sedentary, normal sportsmen (e.g. athletics, swimming) and martial sportmen (e.g. karate, judo). They are compared thinking about the following hypothesis: “Martial Sportmen have a better proprioception than of the other groups’s subjects: It could be due to the type of exercises they do in their sports as empirically, a contact sportsman shows significantly superior motor skills to the members of the other two groups. The second via are records from encephalogram (EEG) while the experimental tasks are doing. These records are analised a posteriori with a set of processing algorithms to extract characteristics about brain’s activity of the proprioception and motion control. Finally , the study tries to integrate graphic tools to make easy to understand final scientific results which allow us to explore the brain activity of the subjects through easy interfaces (e.g. space-time events, activity intensity, connectivity, specific neural netwoks or anormal activity). In the future, this application could be a complement to assist doctors, researchers, sports center specialists and anyone who must improve the health and movements of handicapped persons. Keywords: proprioception, EEG, assesment, rehabilitation.References: Röijezon, U., Clark, N.C., Treleaven, J. (2015). Proprioception in musculoskeletal rehabilitation. Part 1: Basic science and principles of assessment and clinical interventions. ManualTher.10.1016/j.math.2015.01.008. Röijezon, U., Clark, N.C., Treleaven, J. (2015). Proprioception in musculoskeletal rehabilitation. Part 2: Clinical assessment and intervention. Manual Ther.10.1016/j.math.2015.01.009. 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