Extraction of coherent structures in a rotating turbulent flow experiment

The discrete wavelet packet transform (DWPT) and discrete wavelet transform (DWT) are used to extract and study the dynamics of coherent structures in a turbulent rotating fluid. Three-dimensional (3D) turbulence is generated by strong pumping through tubes at the bottom of a rotating tank (48.4 cm high, 39.4 cm diameter). This flow evolves toward two-dimensional (2D) turbulence with increasing height in the tank. Particle Image Velocimetry (PIV) measurements on the quasi-2D flow reveal many long-lived coherent vortices with a wide range of sizes. The vorticity fields exhibit vortex birth, merger, scattering, and destruction. We separate the flow into a low-entropy ``coherent'' and a high-entropy ``incoherent'' component by thresholding the coefficients of the DWPT and DWT of the vorticity fields. Similar thresholdings using the Fourier transform and JPEG compression together with the Okubo-Weiss criterion are also tested for comparison. We find that the DWPT and DWT yield similar results and are much more efficient at representing the total flow than a Fourier-based method. Only about 3% of the large-amplitude coefficients of the DWPT and DWT are necessary to represent the coherent component and preserve the vorticity probability density function, transport properties, and spatial and temporal correlations. The remaining small amplitude coefficients represent the incoherent component, which has near Gaussian vorticity PDF, contains no coherent structures, rapidly loses correlation in time, and does not contribute significantly to the transport properties of the flow. This suggests that one can describe and simulate such turbulent flow using a relatively small number of wavelet or wavelet packet modes.Comment: experimental work aprox 17 pages, 11 figures, accepted to appear in PRE, last few figures appear at the end. clarifications, added references, fixed typo

Monitoring the depth of anaesthesia using simplified electroencephalogram (EEG)

Anaesthesia is administered routinely every day in hospitals and medical facilities. Numerous methods have been devised and implemented for monitoring the depth of anaesthesia (DoA) in order to guarantee the safety of patients. Monitoring the depth of anaesthesia provides anaesthesia professionals with an additional method to assess anaesthetic effects and patient responses during surgery. The measurement of depth of anaesthesia benefits patients and helps anaesthetists such as 'reduction in primary anaesthetic use, reduction in emergence and recovery time, improved patient satisfaction and decreased incidence of intra-operative awareness and recall' (Kelley S. D.). Clinical practice uses autonomic signs such as heart rate, blood pressure, pupils, tears, and sweating to determine depth of anaesthesia. However, clinical assessment of DoA is not valuable in predicting the response to a noxious stimulusand may vary depending on disease, drugs and surgical technique. Currently available DoA monitoring devices have been criticised in the literature, such as being redundant (Schneider, 2004), not responsive to some anaesthetic agents (Barr G., 1999), and time delay (Pilge S., 2006). This research proposes new methods to monitor the depth of anaesthesia (DoA) based on simplified EEG signals. These EEG signals were analysed in both the time domain and the time-frequency domain. In the time domain, the Detrended Fluctuation Analysis (DFA), detrended moving average (DMA) and Chaos methods are modified to study the scaling behaviour of the EEG as a measure of the DoA. In the frequency domain, fast Fourier transform (FFT) and filter bank are used to identify difference states of anaesthesia. In the time-frequency domain, discrete wavelet transforms (DWT) and power spectral density (PSD) function are applied to pre-process EEG data and to monitor the DoA. Firstly, a new de-noising algorithm is proposed with a threshold TWE, which is a function of wavelet entropy and the window length m for an EEG segment. Secondly, the anaesthesia states are identified into awake, light, moderate, deep and very deep anaesthesia states. Finally, the DoA indices are computed using: Modified DFA method (MDFA I), Modified DFA-Lagrange method (MDFA II), Modified detrended moving average method (MDMA), Modified Chaos method, combined Chaos and MDMA method, Wavelet-power spectral density. Simulation results demonstrate that our new methods monitor the DoA in all anaesthesia states accurately. These proposed methods and indices present a good responsive to anaesthetic agent, reduce the time delay when patient’s hypnotic state changes (from 12 to 178 seconds), and can estimate a patient’s hypnotic state when signal quality is poor

SVM Classification of MRI Brain Images for Computer-Assisted Diagnosis

Magnetic Resonance Imaging is a powerful technique that helps in the diagnosis of various medical conditions. MRI Image pre-processing followed by detection of brain abnormalities, such as brain tumors, are considered in this work. These images are often corrupted by noise from various sources. The Discrete Wavelet Transforms (DWT) with details thresholding is used for efficient noise removal followed by edge detection and threshold segmentation of the denoised images. Segmented image features are then extracted using morphological operations. These features are finally used to train an improved Support Vector Machine classifier that uses a Gausssian radial basis function kernel. The performance of the classifier is evaluated and the results of the classification show that the proposed scheme accurately distinguishes normal brain images from the abnormal ones and benign lesions from malignant tumours. The accuracy of the classification is shown to be 100% which is superior to the results reported in the literature

A New Speech Enhancment algorithm in Hearing Aid based on Wavelet Transform

Voice Enhancement systems are used to remove background inference in a speech signal and are become an main component of modern hearing aid. In everyday life the speech communication is vivid uses for the hearing impaired and the numerous other applications. Speech is the fundamental means of human communication. After over thirty years of research enhancement algorithm. Which offer superior noise reduction over current methods? All speech enhancements suffer from distortion for the residual noise due to imperfect noise removal. It is always require to perform denoising in voice processing system operating in highly noise because of wavelet transform is one of the popular techniques used in signal enhancement, In the present paper wavelet thresholding and wavelet packet thresholding method have been used to decrease the noise from the voice signal. A simple threshold method is presented to compute the optimum threshold value. Mean square error(MSE) at different values of SNR is computed to method like traditional speech subtraction, wiener filtering method, spectral subtraction with MMSE etc.The result obtained is compared with the other voice enhancement algorithm given in various reference papers. In comparison to other traditional methods we get improved result in terms of SNR and MSE.Simulation done in MATLAB platform

Intelligent Hemorrhage Identification in Wireless Capsule Endoscopy Pictures Using AI Techniques.

Image segmentation in medical images is performed to extract valuable information from the images by concentrating on the region of interest. Mostly, the number of medical images generated from a diagnosis is large and not ideal to treat with traditional ways of segmentation using machine learning models due to their numerous and complex features. To obtain crucial features from this large set of images, deep learning is a good choice over traditional machine learning algorithms. Wireless capsule endoscopy images comprise normal and sick frames and often suffers with a big data imbalance ratio which is sometimes 1000:1 for normal and sick classes. They are also special type of confounding images due to movement of the (capsule) camera, organs and variations in luminance to capture the site texture inside the body. So, we have proposed an automatic deep learning model based to detect bleeding frames out of the WCE images. The proposed model is based on Convolutional Neural Network (CNN) and its performance is compared with state-of- the-art methods including Logistic Regression, Support Vector Machine, Artificial Neural Network and Random Forest. The proposed model reduces the computational burden by offering the automatic feature extraction. It has promising accuracy with an F1 score of 0.76

The Use of EEG Signals For Biometric Person Recognition

This work is devoted to investigating EEG-based biometric recognition systems. One potential advantage of using EEG signals for person recognition is the difficulty in generating artificial signals with biometric characteristics, thus making the spoofing of EEG-based biometric systems a challenging task. However, more works needs to be done to overcome certain drawbacks that currently prevent the adoption of EEG biometrics in real-life scenarios: 1) usually large number of employed sensors, 2) still relatively low recognition rates (compared with some other biometric modalities), 3) the template ageing effect. The existing shortcomings of EEG biometrics and their possible solutions are addressed from three main perspectives in the thesis: pre-processing, feature extraction and pattern classification. In pre-processing, task (stimuli) sensitivity and noise removal are investigated and discussed in separated chapters. For feature extraction, four novel features are proposed; for pattern classification, a new quality filtering method, and a novel instance-based learning algorithm are described in respective chapters. A self-collected database (Mobile Sensor Database) is employed to investigate some important biometric specified effects (e.g. the template ageing effect; using low-cost sensor for recognition). In the research for pre-processing, a training data accumulation scheme is developed, which improves the recognition performance by combining the data of different mental tasks for training; a new wavelet-based de-noising method is developed, its effectiveness in person identification is found to be considerable. Two novel features based on Empirical Mode Decomposition and Hilbert Transform are developed, which provided the best biometric performance amongst all the newly proposed features and other state-of-the-art features reported in the thesis; the other two newly developed wavelet-based features, while having slightly lower recognition accuracies, were computationally more efficient. The quality filtering algorithm is designed to employ the most informative EEG signal segments: experimental results indicate using a small subset of the available data for feature training could receive reasonable improvement in identification rate. The proposed instance-based template reconstruction learning algorithm has shown significant effectiveness when tested using both the publicly available and self-collected databases

A Comparative Analysis of Feature Extraction Techniques for EEG Signals from Alzheimer patients

This research deals with the study of Alzheimer Disease (AD). Electroencephalogram (EEG) signal is a clinical tool for the diagnosis and detection of AD. EEG signals are analyzed for the diagnosis of AD applying several linear and non-linear methods of signal processing. This work studies and implements several measures of EEG signal complexity and then compares the complexity features measured or extracted from EEG signals. Time domain analysis of EEG signals is performed using several signal processing techniques such as higher order moments, entropies and fractal dimension calculation using fractal analysis. Frequency domain analysis of EEG signals is performed using signal processing techniques such as Welch Power spectrum and Discrete Fourier Transform (DFT). EEG signal analysis using Wavelet Transform was also performed. Higher order moments, entropies, fractal dimension estimation using fractal analysis and Welch Power Spectrum are also implemented along with moving windows. This work also deals with the artifact removal or de-noising of EEG signals using a band pass filter. EEG signal data recorded from AD subjects and their respective age-matched control subjects are used to test the performance of the methods in diagnosing AD. In addition, this work outlines the drawbacks of the methods used and compares the methods for the best feature extraction techniques

Power Quality Disturbance Detection and Classification

Power quality (PQ) monitoring is an essential service that many utilities perform for their industrial and larger commercial customers. Detecting and classifying the different electrical disturbances which can cause PQ problems is a difficult task that requires a high level of engineering knowledge. The vast majority of the disturbances are non-stationary and transitory in nature subsequently it requires advanced instruments and procedures for the examination of PQ disturbances. In this work a hybrid procedure is utilized for describing PQ disturbances utilizing wavelet transform and fuzzy logic. A no of PQ occasions are produced and decomposed utilizing wavelet decomposition algorithm of wavelet transform for exact recognition of disturbances. It is likewise watched that when the PQ disturbances are contaminated with noise the identification gets to be troublesome and the feature vectors to be separated will contain a high amount of noise which may corrupt the characterization precision. Consequently a Wavelet based denoising system is proposed in this work before feature extraction process. Two extremely distinct features basic to all PQ disturbances like Energy and Total Harmonic Distortion (THD) are separated utilizing discrete wavelet transform and is nourished as inputs to the fuzzy expert system for precise recognition and order of different PQ disturbances. The fuzzy expert system classifies the PQ disturbances as well as demonstrates whether the disturbance is unadulterated or contains harmonics. A neural network based Power Quality Disturbance (PQD) recognition framework is additionally displayed executing Multilayer Feedforward Neural Network (MFNN)