Wavelet

Speech compression is one area of digital signal processing that focusing on reducing the bit rate of the speech signal for transmission or storage without significant loss of quality. This paper presents a new design feature for speech Compression using Wavelet Packet Transform and Linear prediction Coding. The proposed algorithm uses best tree decomposition of wavelet packets, which develop features afterward compressed signal is compressed by Linear prediction Coding. The 4 points encoded vector is a full of information just like the original best tree’s structure. The implied scoring system makes BTE suitable for compression problems and we reach at 12 compression ratio. The performance of speech signal is measure on the basis of signal to noise ratio (SNR), mean square error (MSE) and peak signal to noise ratio (PSNR).

Artificial intelligence (AI) techniques are becoming useful as alternate approaches to conventional techniques or as components of integrated systems. They have been used to solve complicated practical problems in various areas and are becoming more and more popular nowadays. AItechniques have the following features: can learn from examples; are fault tolerant in the sense that they are able to handle noisy and incomplete data; are able to deal with non-linear problems; and once trained can perform prediction and generalization at high speed. AI-based systems are being developed and deployed worldwide in a myriad of applications, mainly because of their symbolic reasoning, flexibility and explanation capabilities. AI have been used and applied in different sectors, such as engineering, economics, medicine, military, marine, etc. They have also been applied for modeling, identification, optimization, prediction, forecasting, and control of complex systems. The main objective of this paper is to present an overview of the AI-techniques for sizing photovoltaic (PV) systems: stand-alone PVs, grid-connected PV systems, PV-wind hybrid systems, etc. Published literature presented in this paper show the potential of AI as a design tool for the optimal sizing of PV systems. Additionally, the advantage of using an AI-based sizing of PV systems is that it provides good optimization, especially in isolated areas, where the weather data are not always available. #

Head injury is a major reason for morbidity and mortality worldwide and traumatic head injuries represent the major cause of neurological disability to a clot or hematoma caused by Haemorrhage (ICH) and is the The most common cause of ICH normally reported in our country are road traffic accidents (RTA) followed by falls and assaults. India is a populous country with over a billion every 100,000 population with deprived of these doctors. The unavailability of these specialists is a grave concern to the w care to the nation. The mainstay in the diagnosis of an ICH is the CT (Computed Tomography) scan of the head which is the definitive tool for accurate diagnosis of an ICH following trauma and provides an objective assessment of structural damage to brain. Accurate segmentation of the haemorrhage. This study is on segment Keywords: Intracranial decomposition; Brain haemorrhage segmentation is the first step before detecting the been done on the brain haemorrhage detection using methods like Convolutional neural network other efficient and advanced deep learning techniques. But that is resource intensive. It is also nec efficient when there is a large dataset Hssayeni and colleagues multiple slices and made it public. Second, used deep learning methods to perform segmentation and got a dice coefficient of 31% which is good compared to and colleagues [12] propose entropy based automatic unsupervised brain intracranial haemorrhage segmentation which comprises of FCM clustering, thresholding and edge based active contour methods and they get a better result with the combination than FCM clustering and active use deep learning to diagnose brain haemorrhage. They have used LeNet, GoogleNet and Inception dataset consisting of 100 cases collected from 115 hospitals and discovered LeNet is the among the three. Arjun Majumdar and colleagues haemorrhage instead of Head injury is a major reason for morbidity and mortality worldwide and traumatic head injuries represent the major cause of neurological disability. A traumatic brain injury to a clot or hematoma caused by an accident or any other trauma. (ICH) and is the most common and serious consequence of head injury which can be life The most common cause of ICH normally reported in our country are road traffic accidents (RTA) followed by falls and assaults. India is a populous country with over a billion every 100,000 population with most of them in the urban setup, Indian rural population of more than 70% is deprived of these doctors. The unavailability of these specialists is a grave concern to the w care to the nation. The mainstay in the diagnosis of an ICH is the CT (Computed Tomography) scan of the head which is the definitive tool for accurate diagnosis of an ICH following trauma and provides an objective assessment of ctural damage to brain. Accurate segmentation of the. This study is on segmentation of the brain haemorrhage Intracranial haemorrhage; Discrete wavelet transforms I. RELATED WORK Brain haemorrhage segmentation is the first step before detecting the been done on the brain haemorrhage detection using methods like Convolutional neural network other efficient and advanced deep learning techniques. But that is resource intensive. It is also nec efficient when there is a large dataset, which is not easily available in case of brain haemorrhage. Hssayeni and colleagues [1][2] have contributed in two ways, they collected a new dataset of 82 CT scans with ade it public. Second, used deep learning methods to perform segmentation and got a dice coefficient of 31% which is good compared to other deep learning techniques on small datasets. Indrajeet Kumar propose entropy based automatic unsupervised brain intracranial haemorrhage segmentation which comprises of FCM clustering, thresholding and edge based active contour methods and they get a better result with the combination than FCM clustering and active contour methods alone. use deep learning to diagnose brain haemorrhage. They have used LeNet, GoogleNet and Inception dataset consisting of 100 cases collected from 115 hospitals and discovered LeNet is the among the three. Arjun Majumdar and colleagues [8] haemorrhage instead of traditional methods and achieve a Head injury is a major reason for morbidity and mortality worldwide and traumatic head injuries traumatic brain injury (TBI) is damage to the brain, secondary an accident or any other trauma. This hematoma is known as an Intracranial most common and serious consequence of head injury which can be life The most common cause of ICH normally reported in our country are road traffic accidents (RTA) followed by falls and assaults. India is a populous country with over a billion people and there is approximately one radiologist for of them in the urban setup, Indian rural population of more than 70% is deprived of these doctors. The unavailability of these specialists is a grave concern to the w care to the nation. The mainstay in the diagnosis of an ICH is the CT (Computed Tomography) scan of the head which is the definitive tool for accurate diagnosis of an ICH following trauma and provides an objective assessment of ctural damage to brain. Accurate segmentation of the haemorrhage is the first step before detecting the brain haemorrhage images using discrete wavelet transforms. iscrete wavelet transforms; Segmentation; RELATED WORK Brain haemorrhage segmentation is the first step before detecting the haemorrhage in the brain. A lot of work has been done on the brain haemorrhage detection using methods like Convolutional neural network other efficient and advanced deep learning techniques. But that is resource intensive. It is also nec which is not easily available in case of brain haemorrhage. have contributed in two ways, they collected a new dataset of 82 CT scans with ade it public. Second, used deep learning methods to perform segmentation and got a dice deep learning techniques on small datasets. Indrajeet Kumar propose entropy based automatic unsupervised brain intracranial haemorrhage segmentation which comprises of FCM clustering, thresholding and edge based active contour methods and they get a better result contour methods alone. Tong Duc Phong and colleagues use deep learning to diagnose brain haemorrhage. They have used LeNet, GoogleNet and Inception dataset consisting of 100 cases collected from 115 hospitals and discovered LeNet is the most time [8] use a modified version of U-Net to detect the brain traditional methods and achieve an overall specificity of 98.6% on the small dataset. Brain Haemorrhage Segmentation using Dircrete Wavelet Transform. the terms of the Creative Commons Attribution License; Which Permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source Head injury is a major reason for morbidity and mortality worldwide and traumatic head injuries (TBI) is damage to the brain, secondary This hematoma is known as an Intracranial most common and serious consequence of head injury which can be life-threatening. The most common cause of ICH normally reported in our country are road traffic accidents (RTA) followed by falls people and there is approximately one radiologist for of them in the urban setup, Indian rural population of more than 70% is deprived of these doctors. The unavailability of these specialists is a grave concern to the well-being of the health care to the nation. The mainstay in the diagnosis of an ICH is the CT (Computed Tomography) scan of the head which is the definitive tool for accurate diagnosis of an ICH following trauma and provides an objective assessment of is the first step before detecting the brain images using discrete wavelet transforms. Thresholding; Wavelet haemorrhage in the brain. A lot of work has been done on the brain haemorrhage detection using methods like Convolutional neural network [2][3][5][11] and other efficient and advanced deep learning techniques. But that is resource intensive. It is also necessary and which is not easily available in case of brain haemorrhage. Murtada D. have contributed in two ways, they collected a new dataset of 82 CT scans with ade it public. Second, used deep learning methods to perform segmentation and got a dice deep learning techniques on small datasets. Indrajeet Kumar propose entropy based automatic unsupervised brain intracranial haemorrhage segmentation which comprises of FCM clustering, thresholding and edge based active contour methods and they get a better result Tong Duc Phong and colleagues [13] use deep learning to diagnose brain haemorrhage. They have used LeNet, GoogleNet and Inception-ResNet and a most time-consuming model Net to detect the brain overall specificity of 98.6% on the small dataset.

This paper presents a passive analysis method for determining the spatio-temporal characteristics of sound fields in small rooms. The analysis finds an approximate directional reflectogram (ADR) which reveals the approximate arrival directions, time delays and amplitudes of the direct sound and early reflections without using a special or known sound source. A coincident microphone array is used to obtain directional recordings. The recordings are analysed by wavelet packet decomposition to determine the direction of the sound source and select wavelet packet coefficients to reconstruct the estimate of the direct sound. ADR is then computed via deconvolution using this estimate. Experiments have been carried out using synthesized recordings that were obtained from actual room impulse responses measured in two rooms for various source locations. The method estimates the source direction with a mean absolute error of about 7 • . Calculated ADRs provide a good estimate of the time delays and arrival directions of acoustical reflections, whereas the amplitudes differ slightly.

Elastic full waveform inversion of seismic reflection data represents a data-driven form of analysis leading to quantification of sub-surface parameters in depth. In previous studies attention has been given to P-wave data recorded in the marine environment, using either acoustic or elastic inversion schemes. In this paper we exploit both P-waves and mode-converted S-waves in the marine environment in the inversion for both P- and S-wave velocities by using wide-angle, multi-component, ocean-bottom cable seismic data. An elastic waveform inversion scheme operating in the time domain was used, allowing accurate modelling of the full wavefield, including the elastic amplitude variation with offset response of reflected arrivals and mode-converted events. A series of one- and two-dimensional synthetic examples are presented, demonstrating the ability to invert for and thereby to quantify both P- and S-wave velocities for different velocity models. In particular, for more realistic low velocity models, including a typically soft seabed, an effective strategy for inversion is proposed to exploit both P- and mode-converted PS-waves. Whilst P-wave events are exploited for inversion for P-wave velocity, examples show the contribution of both P- and PS-waves to the successful recovery of S-wave velocity.

Noninvasive method of revaluation of blood hemoglobin concentration based on diffuse scattering spectroscopy with wavelet fil- tering of photoelectric signal has been developed. Correlation coefficient with standard invasive methods of 0,94 has been obtained.
Предложена неинвазивная методика определения концентрации общего гемоглобина в крови на основе спектроскопии диффузного рассеяния света с применением фильтрации фотоэлектрического сигнала на основе вейвлет-анализа, позволяющая определять уровень общего гемоглобина в крови с коэффициентом корреляции 0,94

Fractal structures are found in biomedical time series from a wide range of physiological phenomena. The multifractal spectrum identifies the deviations in fractal structure within time periods with large and small fluctuations. The present tutorial is an introduction to multifractal detrended fluctuation analysis (MFDFA) that estimates the multifractal spectrum of biomedical time series. The tutorial presents MFDFA step-by-step in an interactive Matlab session. All Matlab tools needed are available in Introduction to MFDFA folder at the website www.ntnu.edu/inm/geri/software. MFDFA are introduced in Matlab code boxes where the reader can employ pieces of, or the entire MFDFA to example time series. After introducing MFDFA, the tutorial discusses the best practice of MFDFA in biomedical signal processing. The main aim of the tutorial is to give the reader a simple self-sustained guide to the implementation of MFDFA and interpretation of the resulting multifractal spectra.

This paper presents a review of the researches done on fault detection and tolerant control , main aim of the fault tolerant control and fault detection of induction motor is used the wavelet transform. Wavelet transform is much better tool for the fault diagnosis point of view and a overview of the wavelet types (continuous and discrete), machine faults detection methods and their validation. The software, generality of codes, one dimensional and two dimensional DWT and frequency characteristics components of healthy as well as faulty induction motor has explained. So Finally, stator short winding , shaft fault, bearing fault ,rotor broken bar and open winding are taken as a case study to show the better diagnosis of fault by using wavelet techniques.

In situ combustion is one of the approaches used for secondary recovery of heavy oil in which time-lapse seismic data might be used to track the production process. We have analyzed three time-lapse seismic surveys carried out at a time interval of one year during pre-and postcombustion phases of in situ combustion at the Balol field in India. Interpretation of these land time-lapse data using standard seismic amplitude differences in terms of reservoir changes during production and injection can be erroneous. The Balol data, in particular, lacked calibration and had poor repeatability. We have addressed these issues by demonstrating the applicability of independent prestack inversion of baseline and monitor surveys to derive elastic attributes that help to produce clearer images of fluid movement. Independent estimates of wavelets from the baseline and two monitor surveys were used in prestack inversion to estimate acoustic impedance, shear impedance, and Poisson's ratio. The results show unexpected movement of the thermal front away from the injection wells toward the north and northwest of the injection wells, farther from the production wells. These results have been confirmed by well production data from the field north and northwest of Balol.

Soft computing is likely to play an important role in science and engineering in the future. The successful applications of soft computing and the rapid growth suggest that the impact of soft computing will be felt increasingly in coming years. Soft Computing encourages the integration of soft computing techniques and tools into both everyday and advanced applications. This Open access peer-reviewed journal serves as a platform that fosters new applications for all scientists and engineers engaged in research and development in this fast growing field.

Fault Tolerant Control (FTC) systems are crucial in industry to ensure safe and reliable operation, especially of motor drives. This paper proposes the use of multiple controllers for a FTC system of an induction motor drive, selected based on a switching mechanism. The system switches between sensor vector control, sensorless vector control, closed-loop voltage by frequency (V/f) control and open loop V/f control. Vector control offers high performance, while V/f is a simple, low cost strategy with high speed and satisfactory performance. The faults dealt with are speed sensor failures, stator winding open circuits, shorts and minimum voltage faults. In the event of compound faults, a protection unit halts motor operation. The faults are detected using a wavelet index. For the sensorless vector control, a novel Boosted Model Reference Adaptive System (BMRAS) to estimate the motor speed is presented, which reduces tuning time. Both simulation results and experimental results with an induction motor drive show the scheme to be a fast and effective one for fault detection, while the control methods transition smoothly and ensure the effectiveness of the FTC system. The system is also shown to be flexible, reverting rapidly back to the dominant controller if the motor returns to a healthy state.

Image compression is now essential for applications such as transmission and storage in data bases. In this paper we review and discuss about the image compression, need of compression, its principles, and classes of compression and various algorithm of image compression. This paper attempts to give a recipe for selecting one of the popular image compression algorithms based on Wavelet, JPEG/DCT, VQ, and Fractal approaches. We review and discuss the advantages and disadvantages of these algorithms for compressing grayscale images, give an experimental comparison on 256×256 commonly used image of Lenna and one 400×400 fingerprint image.

A new technique for induction motor fault detection and diagnosis is presented. This technique, which has been experimentally verified in stationary and non-stationary motor conditions, is based on the convolution of wavelet-based functions with motor stator currents. These functions are tuned to specific fault frequencies taking into account motor speed and load torque, thus considering variable operation conditions of the motor. Based on this technique an automatic system for fault diagnosis is also presented, which is suited for easy software implementation Q3 .

In this paper the discriminatory potential of wavelet based texture features in classifying digital mammogram images is studied. The case sample analysed consists of 200 mammographic images, originating from the mini MIAS (Mammographic Image Analysis Society) database. Regions of interest (ROIs) of size 350 x 210 pixels is cropped and is subjected to texture analysis using the daubechies (db3), symlets(sym3), and biorthogonal (bio3.3, bio3.5, and bio3.7) waveletfilters. The combined feature vector is fed to a probabilistic neural network for the purpose of classification. An overall classification accuracy of 85% is attained which indicates that the proposed method can be employed as an efficient tool in computer-aided diagnosis of breast cancer to aid the radiologists.

Abstract-Electromyography (EMG) is a study of muscles function through analysis of electrical activity produced from muscles. This electrical activity which is displayed in form of signal is the result of neuromuscular activation associated with muscle contraction. The most common ...

The wavelet envisioned by Huygen's in diffraction phenomenon is re-interpreted as a Polarized Wave (PW) after passing through slit/hole/or biaxial crystals which removed the electric field component from the Electromagnetic Radiation (EM-R), the resulted wave is what is known as the Conical Diffraction (CD) beam, the PW is originated from the Circular Magnetic Field (CMF) produced by accelerated electrons, integrated with the Electric Field (EF) during the Flip-Flop (F-F) mechanism producing EM-R; hence the passing of light through a single hole/slit/biaxial crystals, resulted in a PW which reproduced as rings on the monitor screen in single wave diffraction, while the interference of two such PW in double slits experiment, produced constructive or destructive interference forming patches on the monitor screen; and the perceived electron diffraction is an enter of two CMF from an accelerated electron into two slits then emerged to interfere constructively or destructively, and appears as patches, in addition to the electron which entered and emerged from the slit with the intense CMF, the paper finally derived the origin of Planck' constant (h) for the second time; the logical interpretation of double slits diffraction will restore the common sense in the physical world, distorted by the pilot wave.

Attitude estimation systems often use two or more different sensors to increase reliability and accuracy. Although gyroscopes do not have problems like limited range, interference, and line of sight obscuration, they suffer from slow drift. On the other hand, inclinometers are drift-free but they are sensitive to transverse accelerations and have slow dynamics. This paper presents an extended Kalman filter (EKF)-based data fusion algorithm which utilizes the complementary noise profiles of these two types of sensors to extend their limits. To avoid complexities of dynamic modelling of the platform and its interaction with the environment, gyro modelling will be used to implement indirect (error state) form of the Kalman filter. The great advantage of this approach is its independence from the structure of the platform and its applicability to any system with a similar set of sensors. Separate bias formulation of the Kalman filter will be used to reduce the computational complexity of the algorithm. In addition, a systematic approach based on wavelet decomposition will be utilized to estimate noise covariances used in the Kalman filter formulation. This approach solves many of the convergence problems encountered in the implementation of EKF due to the choice of covariance matrices. Experimental implementation of the estimator shows the excellent performance of the filter.

In this thesis, we address two problems in medical image processing: (1) Lossless compression of 3D MR images (2) Modeling of fMRI time series for the estimation of haemodynamic response function (HRF).

For the lossless compression of 3D MR images, we propose two schemes. In the first scheme, a nonuniformly sized mesh is superimposed on the image, designed based on the content of the image. Affine transformations are used to estimate the mesh elements of a MR slice using the previous slice. The residue between the actual slice and the estimated slice is entropy coded. Context based entropy coding is used to enhance the performance of the entropy coding. The second is a wavelet based scheme wherein important functionalities like region of interest coding, progressive transmission and lossless compression capability are provided. 2D and 3D separable integer wavelet transforms are used for lossless compression. A context based entropy coding scheme is also used here to code the resulting bit stream. These two schemes are tested on two datasets and compared with other state-of-the-art schemes available in the literature. The bit rate obtained with our schemes of about 2 bits per voxel is comparable to the other schemes.

For estimating the HRF from fMRI data, we proposed two probability models. In the first model, the fMRI time series is assumed to be a stationary model. Probabilistic principal component analysis is used to model the time series. Using this model, one can represent trends and other confounding signals in the fMRI time series in terms of its dominant eigen vectors. The HRF function is modeled using both parametric and nonparametric models. In the second model, the time series is assumed to be non-stationary and is modeled as a fractional Brownian motion. We use the property that wavelet transform with sufficient number of vanishing moments decorrelates fBm processes. The methods are tested both on synthetic data at various SNRs and on a real null fMRI data. We compare these methods with an existing method, where the trends in the fMRI time series are modeled by polynomial bases. The proposed methods performed better with respect to the accuracy of HRF estimation. We also developed an algorithm which classifies fMRI voxels according to their HRF characteristics. This algorithm will be useful in the event related design paradigms. In this scheme, we model the class labels as a Markov random field and the class labels and HRFs are estimated iteratively using an expectation maximization algorithm. This algorithm is tested on a real null fMRI data.

Subspace-based techniques have become important in behaviour analysis, appearance modelling and tracking. Various vector and tensor subspace learning techniques are already known that perform their operations in offline as well as in an online manner. In this work, we have improved upon a tensor-based subspace learning by using fourth-order decomposition and wavelets so as to have an advanced adaptive algorithm for robust and efficient background modelling and tracking in coloured video sequences. The proposed algorithm known as fourth-order incremental tensor subspace learning algorithm uses the spatio-colour-temporal information by adaptive online update of the means and the eigen basis for each unfolding matrix using tensor decomposition to fourth-order image tensors. The proposed method employs the wavelet transformation to an optimum decomposition level in order to reduce the computational complexity by working on the approximate counterpart of the original scenes and also reduces noise in the given scene. Our tracking method is an unscented particle filter that utilises appearance knowledge and estimates the new state of the intended object. Various experiments have been performed to demonstrate the promising and convincing nature of the proposed method and the method works better than existing methods.

The most critical attribute of a power control strategy for a fuel cell (FC) and ultracapacitor (UC) based hybrid vehicular power system is the sharing of load demand between available power sources. Transients and rapid changes of the power demand may cause drying of the FC membrane resulting in degradation of FC lifetime. The load demand profile of a hybrid electric vehicle (HEV) is a nonstationary fluctuating signal and consists of transients. Wavelet transforms are suitable for analyzing and evaluating such signals for dynamic systems. This paper focuses on the integration of a developed novel-wavelet-based load sharing algorithm and dynamic model of the FC/UC hybrid vehicular power system in order to ensure efficient power flow control strategy. Index Terms-Fuel cell (FC), hybrid system, power management, ultracapacitor (UC), vehicular application, wavelet.

In this paper an attempt has been made to analyse different wavelet techniques for image compression. Both hand-designed and lifting based wavelets are considered. Hand designed wavelets considered in this work are Haar wavelet, Daubechie wavelet, Biorthognal wavelet, Demeyer wavelet, Coiflet wavelet and Symlet wavelet. Lifting based wavelet transforms considered are 5/3 and 9/7. Wide range of images, including both color and gray scale images were considered. These wavelet transforms are used to compress the test images competitively by using Set Partitioning In Hierarchical Trees (SPIHT) algorithm and by incorporating lifting concepts. Set
Partitioning In Hierarchical Trees is a new advanced algorithm based on wavelet transform which is gaining attention due to many potential commercial applications in the area of image compression. These algorithms resulted in practical advantages, such as, superior low bit rate performance, bit-level compression, progressive transmission by pixel, accuracy and resolution.The SPIHT coder is also a highly refined version of the EZW algorithm and is a powerful image compression algorithm, that produces an embedded bit stream form, in which the best reconstructed images shows a significant perceptual improvement as well as an increased PSNR.

In this work, automatic detection and diagnosis of gear condition monitoring technique is presented. The vibration signals in time domain wereobtained from a fault simulator apparatus from a healthy gear and an induced faulty gear. These time domain signals were processed using Laplace and Morlet wavelet based enveloped power spectrum to detect the faults in gears. The vibration signals obtained were filtered to enhance the signal components before the application of wavelet analysis. The time and frequency domain features extracted from Laplace wavelet based wavelet transform are used as input to ANN for gear fault classification. Genetic algorithm was used to optimize the wavelet and ANN classification parameters. The result shows the successful classification of ANN test process.

The NAC gene family encodes a large family of plant-specific transcription factors with diverse roles in various developmental processes and stress responses in plants. Creation of genome wide prediction tools for NAC proteins will have a significant impact on gene annotation in rice. In the present study, NACSVM, a tool for computational genome-scale prediction of NAC proteins in rice was developed integrating compositional and evolutionary information of NAC proteins. Initially, support vector machine (SVM)based modules were developed using combinatorial presence of diverse protein features such as traditional amino acid, dipeptide (i+1), tripeptide (i+2), four-parts composition and PSSM and an overall accuracy of 79%, 93%, 93%, 79% and 100% respectively was achieved. Later, two hybrid modules were developed based on amino acid, dipeptide and tripeptide composition, through which an overall accuracy of 83% and 79% was achieved. NACSVM was also evaluated using position-specific iterated-basic local alignment search tool which resulted in a lower accuracy of 50%. In order to benchmark NACSVM , the tool was evaluated using independent data test and cross validation methods. The different statistical analyses carried out revealed that the proposed algorithm is an useful tool for annotating NAC proteins in genome of rice.

In this study, normal EEG series recorded from healthy volunteers and epileptic EEG series recorded from patients within and without seizure are classified by using Multilayer Neural Network (MLNN) architectures with respect to several time domain entropy measures such as Shannon Entropy (ShanEn), Log Energy Entropy (LogEn), and Sample Entropy (Sampen). In tests, the MLNN is performed with several numbers of neurons for both one hidden layer and two hidden layers. The results show that segments in seizure have significantly lower entropy values than normal EEG series. This result indicates an important increase of EEG regularity in epilepsy patients. The LogEn approach, which has not been experienced in EEG classification yet, provides the most reliable features into the EEG classification with very low absolute error as 0.01. In particular, the MLNN can be proposed to distinguish the seizure activity from the seizure-free epileptic series where the LogEn values are considered as signal features that characterize the degree of EEG complexity. The highest classification accuracy is obtained for one hidden layer architecture.

In this paper, we have studied the effect of fatigue on walking gait during normal walking. Acceleration of COG in lateral, vertical and anterior/posterior directions were recorded and analyzed to study the effect of the fatigue. Increase in cadence and shortening of step length were observed after the fatigue. The results also showed an increase in the RMS value of acceleration in lateral direction affecting the lateral stability during walking. The variability analysis showed that the amplitude variability is increased in lateral, vertical and anterior/posterior directions after fatigue which can be related to the weakness of the lower extremity muscles. Frequency analysis revealed that higher frequency components in the acceleration increased due to the fatigue. An increase in the wavelet entropy after fatigue showed the increase in the disorder in the vertical and anterior/posterior accelerations.

In this study, whether the wavelet transform method is better for spectral analysis of the brain signals is investigated. For this purpose, as a spectral analysis tool, wavelet transform is compared with fast Fourier transform (FFT) applied to the electroencephalograms (EEG), which have been used in the previous studies. In addition, the time-domain characteristics of the wavelet transform are also detected. The comparison results show that the wavelet transform method is better in detecting brain diseases.

Baseline wandering can mask some important features of the Electrocardiogram (ECG) signal hence it is desirable to remove this noise for proper analysis and display of the ECG signal. This paper presents the implementation and evaluation of different methods to remove this noise. The parameters i.e. Power Spectral density (PSD), average Power & Signal to noise ratio (SNR) are calculated of signals to compare the performance of different filtering methods. IIR zero phase filtering has been proved efficient method for the removal of Baseline wander from ECG signal. The results have been concluded using Matlab software and MIT-BIH arrhythmia database.

In this paper, a fast wavelet denoising by Averaging Wavelet Method (AWM) for digital communications will be proposed. This method is appropriate for the low bit operational communication installation in microcontrollers, at industrial noisy environments, over long wires.

2 F. Abramovich, Т. С. Bailey and T. Sapatinas mathematical and algorithmic techniques used in statistics, did not originate from statisticians, nor with statistical applications in mind. The wavelet transform is a synthesis of ideas emerging over many years from different fields, notably ...

The forecasting of future value of water consumption in an urban area is highly complex and nonlinear. It often exhibits a high degree of spatial and temporal variability. It is a crucial factor for long-term sustainable management and improvement of the operation of urban water allocation system. This chapter will study the application of two preprocessing phase space reconstruction (PSR) and wavelet decomposition transform (WDT) methods to investigate the behavior of time series to forecast short-term water demand value of Kelowna City (BC, Canada). The research proposes two pre-process technique to improve the accuracy of the models. Artificial neural networks (ANNs), gene expression programming (GEP) and multi linear regression (MLR) methods are the tools that considered for forecasting the demand values. Evaluation of the tools is based on two steps with and without applying the pre-processing methods. Moreover, auto correlation function (ACF) is used to calculate the lag time. Correlation dimension is used to study the chaotic behavior of the dataset. The models’ relative performance is compared using three different fitness indexes; coefficient of determination (CD), root mean square error (RMSE)
and mean absolute error (MAE). The results showed how pre-processing combination of WDT and PSR improved the performance of the models in forecasting short-term demand values.

Image segmentation and edge detection are of great interest in image processing prior to image recognition step. Segmentation process has an important application in Military, Biomedical, space, and environmental applications. In this paper, we applied the spatial domain methods, Thresholding and Edge based methods (Roberts operator, Sobel operator, Prewitt operator, and Laplacian operator). In this research, we found that using Fourier Transform in edge detection applications yields to a bad results and has a serious drawback. Wavelet transform plays a very important role in the image processing analysis, for its fine results when it is used in multi-resolution, multi-scale modeling. Unlike Discrete Cosine transforms or Fourier transforms, wavelet transform offers a natural decomposition of images at multiple resolutions. The resulting representation of wavelet transform provides an attractive tradeoff between spatial and frequency resolution where the human visual system can be be...

Find article (software manual) at:  http://www.iaees.org/publications/journals/np/articles/2020-5(1-2)/1-Zhang-Abstract.asp                                                                                                                                               

Download software at:                       
http://www.iaees.org/publications/software/                                       

In present study, I developed a powerful Matlab-based software package, imageProcAnal (Version 1.0), for image processing and analysis. Several modules were available for uses. Functions to resize, crop, rotate, dilate, pixelate and watermark images are included in Basic module. Adjustment of image edge, contrast and gamma factor is available in Adjustment module. In Denoising module, four elementary denoising methods and their joint uses are provided for image filtering and denoising. In Edge and Contour Detection module, five methods for detection of image edge and contour are provided. Also three methods for image sharpening are available in Sharpening module. Segmentation module includes three methods for image segmentation. Connected components recognition can be made in Object Recognition module. In Analysis module, Pearson correlation measure was provided to align images. Pearson correlations and p values among images can be obtained. Mutiple methods across different modules can be sequentially and jointly used to achieve ideal image. Finally, some examples for image processing can be found in Demo module. In addition to conventional methods, I proposed two new methods for image segmentation, i.e., TDOS (Two-Dimensional Ordered Segmentation) and MWAS (Moving Windows Averaging Segmentation). In TDOS, which is based on two-dimensional ordered cluster analysis, the pixels belonging to the same segment (class) are adjoint and are not separated by any of remaining segments. MWAS is based on moving windows averaging method for transect boundary detection. Algorithms of TDOS and MWAS were given, and full Matlab codes of MWAS were given.

It will be shown by experimental results that light propagating at right angles to the direction of source movement co-moves with the source due to inertia of energy, thereby allowing vector light speed to exceed the propagation velocity c. Stated another way there is no terrestrial light aberration.

Elastic full waveform inversion of seismic reflection data represents a data-driven form of analysis leading to quantification of sub-surface parameters in depth. In previous studies attention has been given to P-wave data recorded in the marine environment, using either acoustic or elastic inversion schemes. In this paper we exploit both P-waves and mode-converted S-waves in the marine environment in the inversion for both P-and S-wave velocities by using wide-angle, multi-component, ocean-bottom cable seismic data. An elastic waveform inversion scheme operating in the time domain was used, allowing accurate modelling of the full wavefield, including the elastic amplitude variation with offset response of reflected arrivals and mode-converted events. A series of one-and two-dimensional synthetic examples are presented, demonstrating the ability to invert for and thereby to quantify both P-and S-wave velocities for different velocity models. In particular, for more realistic low velocity models, including a typically soft seabed, an effective strategy for inversion is proposed to exploit both P-and mode-converted PS-waves. Whilst P-wave events are exploited for inversion for P-wave velocity, examples show the contribution of both P-and PS-waves to the successful recovery of S-wave velocity.

With the recent emphasis given to E-security, automatic human identification has received significant attention. Iris based subject recognition has become especially important due to its high level of complexity which lends itself to high confidence recognition. In addition, the eye is well protected and generally does not change very much over extended periods of time. We implemented 'Iris Recognition' using Matlab for its ease in image manipulation and wavelet applications. Preprocessing of acquired Image is performed; afterwards the extraction of the texture of the Iris is obtained using Wavelets. The coded image is compared with the already coded iris in order to find a match or detect an imposter.