Rational Dilation Wavelet Transform Research Articles

Optimized Rational Dilation Wavelet Transform for Automatic Imagined Speech Recognition

Imagined speech is a process in which a person imagines words without saying them. Electroencephalogram (EEG)-based brain-computer interfaces (BCI) systems help in automatically identifying imagined speech to facilitate persons with severe brain disorders. Extracting meaningful information from the raw EEG signal is a challenging task due to the nonstationary nature of EEG signals. Decomposing a signal into several sub-bands (SB) using rational dilation wavelet transform (RADWT) requires selecting predefined factual parameters, which is an arduous task. The main objective of this study is to propose an adaptive RADWT method capable of decomposing EEG signals by adaptively selecting the tuning parameters and classifying the EEG signals into distinct categories. The optimum tuning parameters of RADWT are obtained using particle swarm optimization and used to decompose the EEG signals into several SBs. Several statistical features are elicited from each SB and used to input six different machine learning algorithms. This work employs a 64-channel EEG dataset recorded from 15 healthy people for three categories: long words, short words, and vowels. The performance of the proposed AISR system is evaluated using seven performance evaluation metrics: accuracy, recall, precision, Cohen’s kappa, F1-score, and area-under-the-curve. The proposed system achieved the average classification accuracies of 87.26±1.12%, 89.23±0.95%, 95.5±0.68%, and 92.16±0.83% for long words, short-long words, short words, and vowels, respectively. When compared to the existing state-of-the-art, the proposed non-parametric decomposition approach and the Bagging algorithm achieved a 3%-5% improvement. The performance of the proposed method is validated using an open-access dataset.

Prediction of rotor slot width in induction motor using Dyadic wavelet transform and softmax regression

Abstract Predictive maintenance is required for Induction Motor (IM) to avoid sudden breakdown. In this paper, multimodal sensor data are used for prediction of the rotor slot width variation during runtime of IM. Moreover sensor data are visualized through visual image obtained using Scale Invariant Feature Transform (SIFT) and matching dictionary for various faults in motor such as overload, high speed, Broken Rotor bar (BRB), and unbalance magnetic pull. The multimodal sensor signals data acquired from different parts of three phase induction motor are analysed using various transforms such as Over Complete Rational Dilation Wavelet Transform (ORaDWT), Tuneable Q Wavelet Transform (TQWT), Polynomial Chirplet Transform (PCT) and Dyadic Wavelet Transform (Dyadic WT) for various fault conditions and rotor slot width mentioned during runtime condition of motor. The rotor slot width is predicted using Multiple Linear Regression (MLR), Polynomial Regression (PR), Logistic Regression (LR) and Soft-max Regression (SR) methods through the mean value and energy band of the acquired sensor signal. The Dyadic WT and SR perform better for rotor width prediction. The proposed method such as Dyadic WT and SR provides the prediction accuracy of about 95.2%. From experimental results the rotor slot width expansion more than 2% needs immediate attention to avoid breakdown of motor.

Induction motor’s rotor slot variation measurement using logistic regression

Rotor slots in induction motor expand due to thermal imbalance and create magnetic stress. Magnetic stress is a force that develops on the laminated surface of the rotor due to the curving or stretching magnetic flux. Traditional motor fault detection methods never measure magnetic stress on the rotor; a significant problem frequently arises in the motor. Magnetic stress is proportional to slot size variations in the rotor. High slot size variations on the laminated surface of the rotor lead to more curving and stretching magnetic flux and damage the rotor and stator, reducing their efficiency and induce harmonics. In this paper, the Average rotor Slot Size Variation (ASSV) in the rotor is predicted during the running condition of the motor through logistic regression. Logistic regression predicts ASSV by multimodal sensor signal sub-band energy values and measures rotor slot sizes from microscope images. Multimodal sensor signal is obtained from various sensors, such as vibration, temperature, current and Giant Magneto Resistance (GMR). Signal sub-band energy is obtained from Over complete Rational-Dilation Wavelet Transform (ORaDWT). From experimental results, ASSV is more than 75% from standard size, damaging the rotor and stator. The accuracy of ASSV prediction is about 92%.

Automated technique for carotid plaque characterisation and classification using RDWT in ultrasound images

ABSTRACT In this manuscript, a novel method of rational-dilation wavelet transform (RDWT) is proposed for carotid plaque characterisation and classification in Ultrasound images. RDWT is mainly utilised for image acquisition, pre-processing, feature extraction and ensemble classification in automated plaque classification. Here, the transition bands are constructed from the transition function. The statistical features, viz mean, standard deviation, skewness, Renyi entropy, energy are extracted from the sub-bands of RDWT. The Salp Swarm Algorithm (SSA) is mainly used for selecting the optimum features. In this for selecting optimum features using SSA algorithm two conditions are satisfied such as, in the first approach, mean, standard deviation, skewness are selected and then utilised for converting the continual version of salp swarm algorithm to binary. Subsequently, the crossover operator is utilised to select the Renyi entropy, energy features including transfer functions for replacing the average operator and enhancing the characteristics of research method. Plaque Classification uses K Nearest Neighbour (KNN), Probabilistic Neural Network (PNN) and Support Vector Machine (SVM) classifiers. Experimental outcomes show the efficiency of the proposed method depending on accuracy, specificity and sensitivity. The proposed method attains accuracy of 93%, sensitivity of 90% and specificity of 94% when likened to the existing techniques, such as KNN, PNN and SVM.

Anomaly Detection in Multichannel Data Using Sparse Representation in RADWT Frames

We introduce a new methodology for anomaly detection (AD) in multichannel fast oscillating signals based on nonparametric penalized regression. Assuming the signals share similar shapes and characteristics, the estimation procedures are based on the use of the Rational-Dilation Wavelet Transform (RADWT), equipped with a tunable Q-factor able to provide sparse representations of functions with different oscillations persistence. Under the standard hypothesis of Gaussian additive noise, we model the signals by the RADWT and the anomalies as additive in each signal. Then we perform AD imposing a double penalty on the multiple regression model we obtained, promoting group sparsity both on the regression coefficients and on the anomalies. The first constraint preserves a common structure on the underlying signal components; the second one aims to identify the presence/absence of anomalies. Numerical experiments show the performance of the proposed method in different synthetic scenarios as well as in a real case.

Non-Invasive Measurement of Glaucoma Disease at Earlier Stage Through GMR Sensor AH Biomagnetic Signal From Eye and RADWT Algorithm

In this paper, we propose a non-invasive method to detect glaucoma eye disease at the earlier stage from aqueous humor (AH) fluid’s biomagnetic signal in anterior chamber of eye. The AH fluid has biomagnetic emission because of magnetic material, such as sodium, potassium, chloride, bicarbonate, and protein. At present, the diagnosis of glaucoma was done at the later stage because of undetectable microvariation in the fundus image cup to disc size ratio measurement and because of similar symptoms in glaucoma eye and vision problem. In the proposed method, glaucoma can be detected through the acquisition of AH fluid biomagnetic signal from eye through giant magnetoresistance magnetometers (GMRs) sensor. The biomagnetic signal of AH fluid’s dynamic and static variation in eye is analyzed with the rational dilation wavelet transform (RADWT) algorithm. The variations of AH fluid is obtained through beta-blocker eye drops. From experiment, The AH fluid’s biomagnetic signal shows variations for the rate of dissipation of fluid in eye, such as normal and glaucoma eye, with and without eye drops. The AH biomagnetic signal correlates glaucoma disease with signal variance and RADWT subband energy for 50 glaucoma and 10 normal eye. The proposed method detect glaucoma eye in the early stage with 90% accuracy. The experimental result is validated with glaucoma measuring tonometer instrument. The earlier detection of glaucoma eye is measured through regression modeling.

Rational-Dilation Wavelet Transform Based Torque Estimation from Acoustic Signals for Fault Diagnosis in a Three-Phase Induction Motor

Condition monitoring of electric drives play a significant role for a safe working environment. Induction motors are widely used in industries and any fault in it leads to interruption in the process or complete shutdown of the equipment. In this paper, acoustic-based fault detection in a three-phase induction motor is done by estimating the torque from the acoustic signals released by the machine. The fault detection is possible as acoustic emission is different for faults such as single phasing, bearing cage damage, and broken rotor bars. The acoustic signals are processed using rational-dilation wavelet transform (RADWT) technique to extract the fault features and thereby diagnose the fault type. The torque estimation is done using multiple regression method by extracting the energy possessed in the processed acoustic signal and the faults are diagnosed precisely. An experimental setup comprising of a three-phase induction motor with brake drum loading is used to validate this approach. The RADWT has adjustable frequency resolution in comparison with other wavelet methods. When high Q-factor filters are employed in the RADWT, better representation of different faults are obtained in the decomposed sub-bands. In addition, characteristic frequencies of different faults are calculated analytically and validated by observing the frequencies in the FFT spectrum of acoustic fault signals.

Analysis of Sensor Trapped Power Quality Indices Using Empirical Wavelet Transform and Rational Dilation Wavelet Transform to Achieve High Accuracy and Frequency Resolution

Analysis of Sensor Trapped Power Quality Indices Using Empirical Wavelet Transform and Rational Dilation Wavelet Transform to Achieve High Accuracy and Frequency Resolution

Diagnosis of Bearing Outer Race Faults Using a Low-Cost Non-Contact Method with Advanced Wavelet Transforms

This study analyses the vibration of Squirrel Cage Induction Motor (SCIM) with bearing faults using high-frequency signal (Handheld UWB radar) and Software Phase Locked Loop algorithm (SPLL). The contact or non-contact methods perform condition monitoring of the SCIM. The proposed method is a non-contact technique to perform condition monitoring of the SCIM. The contact methods execute via vibration, instantaneous frequency, rotor speed and flux signals analysis; whereas non-contact methods accomplish via acoustic, current and stray flux measurement. The existing techniques suffer from the influence of adjoining electrical machines; require human expertise to mount sensors and analysing the signals. In this paper, a new, non-contact method proposed for bearing fault identification in the SCIM. The proposed method uses a high-frequency signal projected on the motor and the reflected signal captured. The signal obtained is analysed with an advanced signal processing algorithm like Rational Dilation Wavelet Transform (RDWT) to identify the faults in the SCIM. The signal energy at the fault frequency level increases from 4.72 % to 5.82 % with the increase in the number of the faults. The signal energy variation indicates the severity of the faults. From the experimental results, the bearing fault of the motor identified in the beginning stage of the fault.DOI: 10.5755/j01.eie.25.1.22735

Simultaneous nonparametric regression in RADWT dictionaries

A new technique for nonparametric regression of multichannel signals is presented. The technique is based on the use of the Rational-Dilation Wavelet Transform (RADWT), equipped with a tunable Q-factor able to provide sparse representations of functions with different oscillations persistence. In particular, two different frames are obtained by two RADWT with different Q-factors that give sparse representations of functions with low and high resonance. It is assumed that the signals are measured simultaneously on several independent channels and that they share the low resonance component and the spectral characteristics of the high resonance component. Then, a regression analysis is performed by means of the grouped lasso penalty. Furthermore, a result of asymptotic optimality of the estimator is presented using reasonable assumptions and exploiting recent results on group-lasso like procedures. Numerical experiments show the performance of the proposed method in different synthetic scenarios as well as in a real case example for the analysis and joint detection of sleep spindles and K-complex events for multiple electroencephalogram (EEG) signals.

Wheeze type classification using non-dyadic wavelet transform based optimal energy ratio technique

Background and objectiveWheezes in pulmonary sounds are anomalies which are often associated with obstructive type of lung diseases. The previous works on wheeze-type classification focused mainly on using fixed time-frequency/scale resolution based on Fourier and wavelet transforms. The main contribution of the proposed method, in which the time-scale resolution can be tuned according to the signal of interest, is to discriminate monophonic and polyphonic wheezes with higher accuracy than previously suggested time and time-frequency/scale based methods. MethodsAn optimal Rational Dilation Wavelet Transform (RADWT) based peak energy ratio (PER) parameter selection method is proposed to discriminate wheeze types. Previously suggested Quartile Frequency Ratios, Mean Crossing Irregularity, Multiple Signal Classification, Mel-frequency Cepstrum and Dyadic Discrete Wavelet Transform approaches are also applied and the superiority of the proposed method is demonstrated in leave-one-out (LOO) and leave-one-subject-out (LOSO) cross validation schemes with support vector machine (SVM), k nearest neighbor (k-NN) and extreme learning machine (ELM) classifiers. ResultsThe results show that the proposed RADWT based method outperforms the state-of-the-art time, frequency, time-frequency and time-scale domain approaches for all classifiers in both LOO and LOSO cross validation settings. The highest accuracy values are obtained as 86% and 82.9% in LOO and LOSO respectively when the proposed PER features are fed into SVM. ConclusionsIt is concluded that time and frequency domain characteristics of wheezes are not steady and hence, tunable time-scale representations are more successful in discriminating polyphonic and monophonic wheezes when compared with conventional fixed resolution representations.

Analysis of embolic signals with directional dual tree rational dilation wavelet transform.

The dyadic discrete wavelet transform (dyadic-DWT), which is based on fixed integer sampling factor, has been used before for processing piecewise smooth biomedical signals. However, the dyadic-DWT has poor frequency resolution due to the low-oscillatory nature of its wavelet bases and therefore, it is less effective in processing embolic signals (ESs). To process ESs more effectively, a wavelet transform having better frequency resolution than the dyadic-DWT is needed. Therefore, in this study two ESs, containing micro-emboli and artifact waveforms, are analyzed with the Directional Dual Tree Rational-Dilation Wavelet Transform (DDT-RADWT). The DDT-RADWT, which can be directly applied to quadrature signals, is based on rational dilation factors and has adjustable frequency resolution. The analyses are done for both low and high Q-factors. It is proved that, when high Q-factor filters are employed in the DDT-RADWT, clearer representations of ESs can be attained in decomposed sub-bands and artifacts can be successfully separated.

Innovative RDWT: a new DWT-based method with applications for seismic ground roll attenuation

The presence of noise in seismic data is inevitable. In land seismic data acquisition, ground roll noise masks reflection events so that observation of reflection events is not usually easy to interpret. It is the exploration seismologist's task to attenuate ground roll to improve the data quality and to enhance the signal-to-noise ratio. Investigations have suggested that the wavelet transform is an efficient tool for such a purpose. In this study, a new type of discrete wavelet transform, known as the rational-dilation wavelet transform (RDWT), is used to attenuate ground roll. Compared with the common DWTs, the RDWT offers a wide range of redundancies and Q-factors (wavelet centre frequency/bandwidth), to help the user choose an appropriate Q-factor, and hence provides more satisfactory results in ground roll attenuation while better preserving the signal. In this transform, the Q-factor is determined by selecting a number of parameters. True parameter selection results in better performance of ground roll attenuation. Depending on the nature of the ground roll, the parameters may vary in each shot-gather. Due to the over-completeness of the transformation, aliasing is less problematic compared to other DWTs. This paper discusses and indicates the advantages and capability of RDWT, by applying it to synthetic and real shot-gathered data with the purpose of ground roll attenuation, and compares the results with the application of f–k and band-pass filters.

Pan-sharpening of multi-spectral images using over-complete rational-dilation wavelet transform

A rational-dilation wavelet transform (RWT) based pan-sharpening method for multi-spectral (MS) images of various oscillatory nature is proposed. The previous multi-scale transforms, such as wavelets, curvelets and contourlets, decompose an image into channels with low constant Q-factors, and aren't suitable for pan-sharpening images with different behavior in frequency domain. The RWT as an over-complete scheme not only increases the sampling in spatial and frequency domain, but also provides a tunable Q-factor approach to be suitable for a given dataset. We studied its multi-scale decomposition scheme and the RWT based pan-sharpening method. The MS image pan-sharpening experiments show that this method using a better suitable parameter set can achieve a promising performance and often outperforms many other widely-used pan-sharpening methods both in visual quality and in term of evaluation indexes.