Dyadic Wavelet Research Articles

Metode Wavelet-MFCC dan Korelasi dalam Pengenalan Suara Digit

Voice is the sound emitted from living things. With the development of Automatic Speech Recognition (ASR) technology, voice can be used to make it easier for humans to do something. In the ASR extraction process the features have an important role in the recognition process. The feature extraction methods that are commonly applied to ASR are MFCC and Wavelet. Each of them has advantages and disadvantages. Therefore, this study will combine the wavelet feature extraction method and MFCC to maximize the existing advantages. The proposed method is called Wavelet-MFCC. Voice recognition method that does not use recommendations. Determination of system performance using the Word Recoginition Rate (WRR) method which is validated with the K-Fold Cross Validation with the number of folds is 5. The research dataset used is voice recording digits 0-9 in English. The results show that the digit speech recognition system that has been built gives the highest average value of 63% for digit 4 using wavelet daubechies DB3 and wavelet dyadic transform method. As for the comparison results of the wavelet decomposition method used, that the use of dyadic wavelet transformation is better than the wavelet package.

A novel integrated price and load forecasting method in smart grid environment based on multi-level structure

Prediction of load and price are two critical key in power system planning and operation. Most of the recent works in this area forecast the load and price signals separately but, a dynamic model in smart grid is evaluated while, the customers may have opportunity to react the proposed prices changing through shifting the electricity usages from expensive to cheaper hours. So, the load and price signals are coupled strongly which made the previous prediction models ineffective. In this research, a synthetic prediction approach has been proposed by considering the load and price signals, simultaneously. This method works as multi-input multi-output (MIMO) model based on least square support vector machine (LSSVM) forecast engine. Furthermore, a dyadic wavelet transform (DWT) is suggested in this approach to decompose the original signal into different small sub-signals. Beside of that, the modified mutual information (MMI) filter has been used to choose the best candidate input of forecast engine. The learning section is also coupled with novel modified optimization algorithm based on gravitational search algorithm (GSA) which called as modified GSA (MGSA). Finally, various forecasting errors have been considered as average mean absolute percentage error and error variance to get the comparison outcomes and performance of forecasting approaches. For this purpose, different markets have been considered as test case to show the efficiency of suggested approach.

Assessment of Structural Monitoring by Analyzing Some Modal Parameters: An Extended Inventory of Methods and Developments

The SHM (structural health monitoring) evaluation consists to determining the modes (resonances) of vibration characteristic of the structure and each of them is represented by its modal parameters which can be obtained experimentally and can be analyzed by different procedures. In the present paper (except subsections 2.1.1, 3.1.1) the coefficients (including the coefficient of displacement) are constant; in this regard, it is made an inventory of some methods of classical and non-classical mathematics with the specific computing scheme. All methods of classical mathematics that were considered, i.e., second-order linear nonhomogeneous differential equations, Laplace operational method, analytical conditional form, approximations with error evaluation (according to contraction principle) are inventoried and then developed—and the first two methods by comparison. As methods of the non-classical mathematics the dyadic wavelet method, the approximation (transform) fuzzy method and the grammatical evolution method are inventoried and then the first is developed. In addition, there are illustrated the calculus techniques by few examples and also computing wavelet coefficients for healthy and damaged structures. Notice that in the subsection 2.1.1 in which the coefficients (including the coefficient of displacement) are considered variable there are some transformations of (non)homogeneous equations in the scaled forms. In addition in the subsection 3.1.1 exact solutions, respectively approximations with error evaluation (according to contraction principle) relative to remarkable equations in literature (including the corresponding standard form) are obtained by using the results of 2.1.1.

Dyadic wavelet analysis of bender element signals in determining shear wave velocity

Determining shear wave velocity is a critical technique in bender element tests, as it can be readily affected by near-field effects, wave reflection, and other factors. This study proposes a new method based on the dyadic wavelet transform modulus maxima. Combining the local modulus maxima of dyadic wavelet transform approximate coefficients at fine decomposition levels and an appropriate threshold value, the proposed method can automatically detect the target point. For validation, a comparative study among the dyadic wavelet transform modulus maxima, peak-to-peak, first arrival, and cross-correlation methods was carried out using 140 sets of bender element signals. The comparison results show that the proposed method not only mitigates the adverse effects of near-field, later major peaks, and noise contamination, but is also more robust in estimating shear wave velocity.

Geometric distortion and mixed pixel elimination via TDYWT image enhancement for precise spatial measurement to avoid land survey error modeling

In remote sensing, land cover classification of vegetation and water area from satellite image play a vital role for rural and urban planning and development. Existing algorithms of land cover classification require more sample image datasets for training. For existing algorithms, land cover classification of vegetation and water area is a challenging task because of mixed pixel and geometric distortion over boundary and curvature region. Mixed pixel affects the precise classification and measurement of land cover. Geometric distortion arises due to frame of isotropic and angular selectivity during image acquisition and affects the contour of land cover. In this paper, the proposed transverse dyadic wavelet transform (TDyWT) enhances and classifies vegetation and water area in land cover from LANDSAT image without training datasets. The proposed TDyWT uses Haar wavelet for decomposition and Burt 5 × 7 wavelet for reconstruction. The TDyWT enhances the contour, curvature, and boundary of vegetation and water area in LANDSAT image due to reversible and lifting properties of wavelet. TDyWT removes geometric distortion and spatial scale error of mixed pixel. In traditional land surveying spatial scale error reduction eliminates through total station and error modeling techniques. From the results, the proposed TDyWT algorithm classifies the area of subclass of vegetation and water with the 95% of accuracy with respect to ground truth survey methods.

A Two-Step Denoising Strategy for Early-Stage Fault Diagnosis of Rolling Bearings

Empirical wavelet transform (EWT) is an adaptive tool for vibration signals processing and has been adopted for fault diagnosis of rolling bearings, while still suffers two weaknesses in detecting the weak, early-stage defects. First, adaptive and robust boundaries determination of the EWT segments is a challenge. Second, vestigial noise and unwanted vibrations still exist in the filtered signal, which may bury the weak fault signature and weaken the detection performance of early-stage faults. In this article, a two-step denoising strategy (TSDS) is proposed for early-stage fault detection, which includes frequency filtering and time-domain denoising steps. In the scheme, an automated EWT segmentation method is proposed for identifying each primary harmonic component; then, EWT-based correlated kurtogram is adopted to optimize the filtering frequency band such that to enhance the signal-to-noise ratio (SNR) of the fault information. Furthermore, the two-layer sliding-correlated kurtosis (TLSCK) algorithm is applied as the second step, which eliminates the vestigial noise and outputs pure periodic pulses, indicating the occurrence moments of the fault impulses. Thus, the weak, early-stage fault signature can be significantly enhanced and detected. A simulation study is conducted and validates the successfulness of the proposed scheme when the SNR is as low as −19 dB. A run-to-failure experiment verifies the effectiveness of the proposed method for weak faults identification, and comparison studies show that the proposed scheme could diagnosis bearing defects at much earlier moment than traditional methods like spectral kurtosis and dyadic wavelet transform.

Analysis on Wavelet Family based Channel Estimation for MIMO-OFDM System

Channel estimation for (MIMO-OFDM) is an important part for present and future generation broadband wireless communications. OFDM, which uses for the spaced subcarriers to improving the performance. The channel estimation schemes based on pilot reduces the transmission rate and spectral efficiency. Many conventional schemes of channel estimation are not effective in reducing noise. It leads to poor quality signals at receiver at final stage. To rectify this problem, in this paper a wavelet family based channel estimation technique, is proposed and analyzed. The proposed research aims to estimate channels and reconstruct signal via wavelet transform, dyadic wavelet transform and fractional spline wavelet transform which enhances the spectrum efficiency and transmission rate. Simulation results shows that the fractional spline wavelet transform performs well for channel estimation and data signal reconstruction.

Approximation Properties of Systems of Periodic Wavelets on the Cantor Group

We construct systems of periodic dyadic wavelets and study approximation properties of such systems by using the notion of a multiresolution analysis. We prove a Jackson type inequality for the spaces constituting the multiresolution analysis and obtain a periodic analogue of the Strang–Fix condition.

Epidermal antenna in palmar arch region for anaemia detection to avoid peripheral perfusion artifact in optical sensor during hemoglobin measurement

The anaemia disease diagnosed with various test such as complete blood count (CBC), ferritin measure, reticulocyte count, peripheral smear and blood gas analyser. The ferritin measure the storage and usage of iron in human body, reticulocyte count assess young red blood cell, and Peripheral smear analyse the cell colour, size, and shape of red blood cell through microscopic image. The blood gas analyser measures methemoglobin (MetHb) and carboxyhaemoglobin. Different test measure haemoglobin, since iron level show normal in blood for the patient with low total body iron. The iron deficiency never show symptom or sign in the human body. In this paper, we propose a new non-invasive method for anaemia measure through epidermal antenna over ulnar region of palm and acquire radiating signal from Haemoglobin protein. The acquired signal from epidermal antenna analysed with Transverse Dyadic Wavelet Transform for haemoglobin protein level in red blood cell. The epidermal antenna signal from ulnar region haemoglobin measurement avoids the peripheral perfusion artifact of optical or image sensor which arise during measurement. The experimental analysis show signal of epidermal antenna measure the haemoglobin more accurately, through regression modelling than existing optical and image based point of care device. Experimental result of haemoglobin measurement from ulnar region has validated with blood gas analyser.

Enhancement of Fault Feature Extraction from Displacement Signals by Suppressing Severe End Distortions via Sinusoidal Wave Reduction

Displacement signals, acquired by eddy current sensors, are extensively used in condition monitoring and health prognosis of electromechanical equipment. Owing to its sensitivity to low frequency components, the displacement signal often contains sinusoidal waves of high amplitudes. If the digitization of the sinusoidal wave does not satisfy the condition of full period sampling, an effect of severe end distortion (SED), in the form of impulsive features, is likely to occur because of boundary extensions in discrete wavelet decompositions. The SED effect will complicate the extraction of weak fault features if it is left untreated. In this paper, we investigate the mechanism of the SED effect using theories based on Fourier analysis and wavelet analysis. To enhance feature extraction performance from displacement signals in the presence of strong sinusoidal waves, a novel method, based on the Fourier basis and a compound wavelet dictionary, is proposed. In the procedure, ratio-based spectrum correction methods, using the rectangle window as well as the Hanning window, are employed to obtain an optimized reduction of strong sinusoidal waves. The residual signal is further decomposed by the compound wavelet dictionary which consists of dyadic wavelet packets and implicit wavelet packets. It was verified through numerical simulations that the reconstructed signal in each wavelet subspace can avoid severe end distortions. The proposed method was applied to case studies of an experimental test with rub impact fault and an engineering test with blade crack fault. The analysis results demonstrate the proposed method can effectively suppress the SED effect in displacement signal analysis, and therefore enhance the performance of wavelet analysis in extracting weak fault features.

A Condition Monitoring System Based On Dyadic Wavelet Transform Using Thermal Image

Wavelet analysis is broadly and effectively utilized in image processing and analysis. Because of the simple and fast algorithm, the dyadic wavelet transform has generally used. A dyadic wavelet transform gives an excellent output because of the different levels of the wavelet coefficient of the image. The proposed method presents an optimal value such as mean, standard deviation and entropy for the decomposition and reconstruction of the thermal image. The original results demonstrate which technique can find the induction motor faults clearly and effectively.

3D modified wavelet block tree coding for hyperspectral images

<p><span>A novel wavelet-based efficient hyperspectral image compression scheme for low memory sensors has been proposed. The proposed scheme uses the 3D dyadic wavelet transform to exploit intersubband and intrasubband correlation among the wavelet coefficients. By doing the reconstruction of the transform image cube, taking the difference between the frames, it increases the coding efficiency, reduces the memory requirement and complexity of the hyperspectral compression schemes in comparison with other state-of-the-art compression schemes.</span></p>

Design and implementation of a novel low complexity symmetric orthogonal wavelet filter‐bank

The design of wavelet filter-bank in real continuum domain demands infinite precision to achieve perfect reconstruction (PR). The computations with irrational coefficients in orthogonal filters require a large amount of hardware that result in increased power dissipation, huge memory requirements and reduction in speed of operation. This paper presents a new approach to obtain a complete dyadic (low complexity) 6-tap orthogonal symmetric wavelet filter-bank (FB) with near perfect reconstruction. This is achieved by slightly altering the PR conditions to make orthogonal filters symmetric and to obtain complete dyadic filter coefficients. The proposed wavelet FB has reduced dynamic power dissipation and significantly reduced adder and shifter count. The VLSI architecture of the proposed FB is designed and implemented on Kintex-7 FPGA. The architectural design is carried out using Verilog and functional simulation and synthesis are executed using Vivado 2016.4 software. The proposed FB dissipates 114mW dynamic power and requires only 5 adders and 3 shifters. The effectiveness of the proposed wavelet is verified in three different applications namely image compression, iris recognition system and orthogonal frequency division multiplexing (OFDM). The proposed wavelet FB gives comparable performance in image compression and attains superior performance in iris recognition system (feature extraction) and OFDM.

Centralized Wavelet Multiresolution for Exact Translation Invariant Processing of ECG Signals

Dyadic wavelet transform is useful in analyzing electrocardiogram (ECG) signals due to its fast computation and its multiresolution ability. In order to improve the feature extraction performance of dyadic wavelet transform, a new construction example of centralized multiresolution (CMR) is proposed. The proposed CMR example consists of two elements, namely, a dyadic part and a non-dyadic part. The dyadic part, based on the maximal overlap second generation wavelet packet transform (SGWPT), generates dyadic wavelet packets. The non-dyadic part engenders ensemble wavelet packets by postprocessing on the dyadic part. The produced wavelet packets and ensemble wavelet packets are combined to realize continued spectral refinement around fixed central analysis frequencies. Numerical simulation and a case study of ECG signal decomposition are utilized to validate the enhancements of the proposed CMR example. The processing results of the CMR example are compared with those of the dual tree complex wavelet transform and the conventional SGWPT. It is validated this CMR example achieves better feature extraction performances due to the presence of the exact translation invariance property.

A novel dyadic multiresolution wavelet image steganography using N-ary

Steganography plays an important role in information sharing. In this paper a wavelet based image steganography using Haar wavelet and an N-ary notation is proposed. The high and middle frequency coefficients are selected based on the Shannon entropy value to identify the best sub band for data hiding. Peak signal to noise ratio and Structure similarity index metric values are computed from the stego image. The empirical results show the proposed method provides good, better imperceptibility and data capacity with average peak signal to noise ratio 53.60 dB for 359,112 bits in terms of effectiveness. To prove this accomplishment of the method, several experiments were conducted and compared the results with existing works.

A novel dyadic multiresolution wavelet image steganography using N-ary

Steganography plays an important role in information sharing. In this paper a wavelet based image steganography using Haar wavelet and an N-ary notation is proposed. The high and middle frequency coefficients are selected based on the Shannon entropy value to identify the best sub band for data hiding. Peak signal to noise ratio and Structure similarity index metric values are computed from the stego image. The empirical results show the proposed method provides good, better imperceptibility and data capacity with average peak signal to noise ratio 53.60 dB for 359,112 bits in terms of effectiveness. To prove this accomplishment of the method, several experiments were conducted and compared the results with existing works.

A Fault Diagnosis Model for Rotating Machinery Using VWC and MSFLA‐SVM Based on Vibration Signal Analysis

Fault diagnosis of rotating machinery mainly includes fault feature extraction and fault classification. Vibration signal from the operation of machinery usually could help diagnosing the operational state of equipment. Different types of fault usually have different vibrational features, which are actually the basis of fault diagnosis. This paper proposes a novel fault diagnosis model, which extracts features by combining vibration severity, dyadic wavelet energy time‐spectrum, and coefficient power spectrum of the maximum wavelet energy level (VWC) at the feature extraction stage. At the stage of fault classification, we design a support vector machine (SVM) based on the modified shuffled frog‐leaping algorithm (MSFLA) for the accurate classifying machinery fault method. Specifically, we use the MSFLA method to optimize SVM parameters. MSFLA can avoid getting trapped into local optimum, speeding up convergence, and improving classification accuracy. Finally, we evaluate our model on real rotating machinery platform, which has four different states, i.e., normal state, eccentric axle fault (EAF), bearing pedestal fault (BPF), and sealing ring wear fault (SRWF). As demonstrated by the results, the VWC method is efficient in extracting vibration signal features of rotating machinery. Based on the extracted features, we further compare our classification method with other three fault classification methods, i.e., backpropagation neural network (BPNN), artificial chemical reaction optimization algorithm (ACROA‐SVM), and SFLA‐SVM. The experiment results show that MSFLA‐SVM achieves a much higher fault classification rate than BPNN, ACROA‐SVM, and SFLA‐SVM.

Rational-Orthogonal-Wavelet-Based Active Sonar Pulse and Detector Design

A family of rational orthogonal wavelets is explored in active sonar detection to achieve Doppler robustness and noise mitigation. The broadband active sonar system is classified as wavelet-based noncoherent subband energy detection. It features a flexible broadband sonar pulse design with low peak-to-average power ratio (PAPR), and a detector of low computation complexity via fast rational wavelet filter bank. The designed family of broadband pulses is based on a combination of multiple basis functions of the complex rational orthogonal wavelet (CROW). Differing from the conventional dyadic wavelet (with an integer scale factor of 2), the CROW has a rational scale factor and much higher resolution in the partition of the time-scale plane. This family of wavelets presents great flexibility in the system design. Design example is presented with minimized PAPR to serve for applications demanding the stealth. The sonar detector is based on the CROW analysis filter bank. Detection performance is analyzed regarding its resistance to Doppler noise and multipath by simulations using a geometric underwater acoustic channel model. The performance of the wavelet-based active sonar system is compared with a linear frequency modulated pulse-based system. The receiver operating characteristic curves demonstrate that the designed system is able to cope with severe multipath, high ambient noise, and severe Doppler dispersion. The framework provides possible solutions for underwater applications in low signal-to-noise ratio and varying mobility conditions.

A Robust Passive Blind Copy-Move Image Forgery Detection

Image forensic is the important research area which deals to verify the authenticity of the digital image. Copy-move forgery is common type of forgery used to modify the image. This paper proposes passive blind forensic copy-move forgery detection technique. The combination of dyadic wavelet transform (DyWT) and scale invariant feature transform (SIFT) is used. Finally, RANSAC is used to improve the detection accuracy. The performance of the algorithm is verified with 45 original/forged images considering various attacks, such as scaling, rotation, small object and multiple copy-move pasting. The experimental results reveal that the proposed technique identifies and locates the forged area accurately for all attacks. The comparison of the proposed method is done with existing methods in terms of detection accuracy, recall, and precision. The simulation results show that for the most of the cases the proposed method outperforms the existing methods.

Isolation and Identification of Compound Faults in Rotating Machinery via Adaptive Deep Filtering Technique

Compound defects commonly occur on rotating machinery under fatigue and heavy loads, and their fault signatures are coupled and easily buried in strong unwanted vibrations and background noise. The isolation and identification of the compound fault signatures are still a challenge especially when the transient impulses induced from the compound defects share common resonant frequency. In this paper, a data-driven, adaptive deep filtering technique which mainly contains filtering and isolating procedures is proposed for compound faults diagnosis. During the filtering procedure, an empirical wavelet transform (EWT) based correlated kurtogram is presented to adaptively obtain the proper spectral segments for filtering the vibration measurements, such that to enhance the signal-to-noise ratio (SNR) of compound faults in the filtered signals. Subsequently, during the isolation procedure, windowed correlated kurtosis (WCK) which outputs pure periodic pulses indicating the occurrence moments of interested fault impulses is presented to isolate each interested fault mode and to determine the defects number. The performance of the proposed technique is tested on simulated signals and validated via analyzing experimental measurements from high-speed locomotive bearings which suffer multiple damages. The results validate that the proposed method outperforms dyadic wavelet transform and spectral kurtogram in isolating and identifying compound faults in rotating machinery.