Journal articles: 'Daubechies wavelet of type 4'

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Ardianti, Chrisentia Widya, Rukun Santoso, and Sudarno Sudarno. "ANALISIS ARIMA DAN WAVELET UNTUK PERAMALAN HARGA CABAI MERAH BESAR DI JAWA TENGAH." Jurnal Gaussian 9, no. 3 (August 30, 2020): 247–62. http://dx.doi.org/10.14710/j.gauss.v9i3.28906.

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Time series is a type of data collected according to the sequence of times in a certain time span. Time series data can be used as a predictor of future conditions. Analysis of time series data, one of the ARIMA units, is a parametric method that requires an assumption to get valid results. Data stationarity is one of the factors that must be fulfilled. Wavelet is a non-parametric method that is able to represent time and frequency information simultaneously, so that it can analyze non-stationary data. This research presents forecasting the price of red chili in Central Java using ARIMA and wavelet with the approach of the Multiscale Autoregressive (MAR) model. The best model is the one with the smallest MSE value. The results showed that the ARIMA(0,1,1) model was said to be the best model with MSE = 2252142. However, because the assumption of normality is not fulfilled, an alternative process is done with wavelet. Wavelet approach results show that the MAR model Haar filter level (j) = 4 with MSE = 2175906 is better than Daubechies 4 filter 4 level (j) = 1 with MSE = 3999669. Therefore, the Haar wavelet is considered better in the time series analysis. Keyword : ARIMA, wavelet, MAR, forecasting, MSE

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Kaneko, Kenichi, Hitoshi Makabe, Kazuyuki Mito, Kazuyoshi Sakamoto, Yoshiya Kawanori, and Kiyoshi Yonemoto. "Characteristics of Lower Limb Muscle Activity in Elderly Persons After Ergometric Exercise." Gerontology and Geriatric Medicine 6 (January 2020): 233372142097980. http://dx.doi.org/10.1177/2333721420979800.

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This study examined the characteristics of lower limb muscle activity in elderly persons after ergometric pedaling exercise for 1 month. To determine the effect of the exercise, surface electromyography (SEMG) of lower limb muscles was subjected to Daubechies-4 wavelet transformation, and mean wavelet coefficients were compared with the pre-exercise coefficients and the post-exercise coefficients in each wavelet level. The characteristics of muscle activity after pedaling exercise were also compared between the elderly subjects and young subjects. For the elderly subjects, the mean wavelet coefficients were significantly decreased in the tibialis anterior and the gastrocnemius medialis at wavelet levels of 3, 4, and 5 (125–62.5, 62.5–31.25, and 31.25–15.625 Hz, respectively), by pedaling exercise. However, the mean power of wavelet levels of 2 and 3 (250–125 and 125–62.5 Hz) within the rectus femoris and the biceps femoris were significantly increased in the young subjects. The effect of pedaling exercise is different from the effects of heavy-resistance training. It was suggested that the muscle coordination, motor unit (MU) firing frequency, and firing fiber type of lower limb muscles are changed with the different characteristics between elderly and young persons by pedaling exercise for 1 month.

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Habsah Asman, Saidatul, and Ahmad Farid Abidin. "Comparative Study of Extension Mode Method in Reducing Border Distortion Effect for Transient Voltage Disturbance." Indonesian Journal of Electrical Engineering and Computer Science 6, no. 3 (June 1, 2017): 628. http://dx.doi.org/10.11591/ijeecs.v6.i3.pp628-637.

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Wavelet transform is an essential method for preprocessing and analyzing non-stationary signal of power quality disturbances. Recently, power quality disturbances cause various effect which reduce the accuracy of the signal such as border distortion. This paper is presenting the comparative study on extension mode scheme to reduce border distortion effect in Discrete Wavelet Transform. The three different method namely zero padding, smooth padding of order 1 and symmetrization mode have been carried to observe their capability on reducing border distortion effectively. The implementation of these modes has been carried out in Matlab Software version R2014a. The analysis is considering the decomposition coefficient at level 4 with mother wavelet type Daubechies. With the aid of soft- threshold function, the noise and unwanted signal is effectively removed to recover the original signal. The comparative study provides the best mode to reduce border distortion effect with the presence of transient voltage is smooth padding of order 1.

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Too, Jingwei, A. R. Abdullah, Norhashimah Mohd Saad, N. Mohd Ali, and H. Musa. "A Detail Study of Wavelet Families for EMG Pattern Recognition." International Journal of Electrical and Computer Engineering (IJECE) 8, no. 6 (December 1, 2018): 4221. http://dx.doi.org/10.11591/ijece.v8i6.pp4221-4229.

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Wavelet transform (WT) has recently drawn the attention of the researchers due to its potential in electromyography (EMG) recognition system. However, the optimal mother wavelet selection remains a challenge to the application of WT in EMG signal processing. This paper presents a detail study for different mother wavelet function in discrete wavelet transform (DWT) and continuous wavelet transform (CWT). Additionally, the performance of different mother wavelet in DWT and CWT at different decomposition level and scale are also investigated. The mean absolute value (MAV) and wavelength (WL) features are extracted from each CWT and reconstructed DWT wavelet coefficient. A popular machine learning method, support vector machine (SVM) is employed to classify the different types of hand movements. The results showed that the most suitable mother wavelet in CWT are Mexican hat and Symlet 6 at scale 16 and 32, respectively. On the other hand, Symlet 4 and Daubechies 4 at the second decomposition level are found to be the optimal wavelet in DWT. From the analysis, we deduced that Symlet 4 at the second decomposition level in DWT is the most suitable mother wavelet for accurate classification of EMG signals of different hand movements.

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Makandar, Aziz, and Anita Patrot. "Wavelet Statistical Feature based Malware Class Recognition and Classification using Supervised Learning Classifier." Oriental journal of computer science and technology 10, no. 2 (May 2, 2017): 400–406. http://dx.doi.org/10.13005/ojcst/10.02.20.

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Malware is a malicious instructions which may harm to the unauthorized private access through internet. The types of malware are incresing day to day life, it is a challenging task for the antivius vendors to predict and caught on access time. This paper aims to design an automated analysis system for malware classes based on the features extracted by Discrete Wavelet Transformation (DWT) and then by applying four level decomposition of malware. The proposed system works in three stages, pre-processing, feature extraction and classification. In preprocessing, input image is normalized in to 256x256 by applying wavelet we are denoising the image which helps to enhance the image. In feature extraction, DWT is used to decompose image into four level. For classification the support vector machine (SVM) classifiers are used to discriminate the malware classes with statistical features extracted from level 4 decomposition of DWT such as Daubechies (db4), Coiflet (coif5) and Bi-orthogonal (bior 2.8). Among these wavelet features the db4 features effectively classify the malware class type with high accuracy 91.05% and 92.53% respectively on both dataset. The analysis of proposed method conducted on two dataset and the results are promising.

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Ibrahim, Noraini, and Norhaiza Ahmad. "Comparative performance of support vector regressions for accurate streamflow predictions." Malaysian Journal of Fundamental and Applied Sciences 13, no. 4-1 (December 5, 2017): 325–30. http://dx.doi.org/10.11113/mjfas.v13n4-1.876.

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Obtaining accurate streamflow predictions can be challenging due to the inherent variabilities and complex nonlinear nature in streamflow generation processes. Support vector regression model is an effective forecasting tool to forecast streamflow as it is able to capture the nonlinearity in the data and attain the global optimum parameters in the forecasted model. However, the efficiency of SVR might be hindered by noise that typically exists in any hydrological time series data through random influences and inaccuracies in recording. Thus, this condition could compromise the quality of input data into SVR. In this study, we investigate the effectiveness of forecasting monthly streamflow data using different settings of SVR in two ways. First, we use different variations of wavelet denoising technique using different selections of wavelet decomposition levels and mother wavelets in order to preserve information and reduce distortion of the original time series. For this purpose, we measured the impact of six different wavelets on SVR namely Daubechies of type db3, db4, db5, db6 and db7 with two different levels of decomposition which are level 3 and level 4. There is more information that may contribute to better performance of the model when the decomposition level is increase. Then, the data are applied using radial basis function (RBF) by performing K-fold cross-validation to obtain the optimal parameter for kernel function in forecasting streamflow. We illustrate the methods using the monthly streamflow data observed at Segamat River in the state of Johor. The results demonstrated that SVR based wavelet denoising for 1-month lead time streamflow forecasting of type db5 with level 3 give better results using Gaussian (RBF) kernel function based on K-fold cross-validation compared to regular SVR. This implies that reduced variance in the denoising procedure and obtain optimal parameter in kernel function may improve forecasting accuracy.

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Rashid, Rakan Saadallah, and Jafar Ramadhan Mohammed. "Securing speech signals by watermarking binary images in the wavelet domain." Indonesian Journal of Electrical Engineering and Computer Science 18, no. 2 (May 1, 2020): 1096. http://dx.doi.org/10.11591/ijeecs.v18.i2.pp1096-1103.

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Digital watermarking is the process of embedding particular information into other signal data in such a way that the quality of the original data is maintained and secured. Watermarking can be performed on images, videos, texts, or audio to protect them from copyright violation. Among all of these types of watermarking, audio watermarking techniques are gaining more interest and becoming more challenging because the quality of such signals is highly affected by the watermarked code. This paper introduces some efficient approaches that have capability to maintain the signals’ quality and preserves the important features of the audio signals. Moreover, the proposed digital audio watermarking approaches are performed in the transform domain. These approaches are gaining more attention due to their robustness or resistance to the attackers. These transform domains include discrete cosine transform (DCT), short-term Fourier transform (STFT), and digital wavelet transform (DWT). Furthermore, the most digital wavelet transforms were found to be applicable for speech watermarking are the Haar and the Daubechies-4.

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Narendiranath Babu, T., N. Senthilnathan, Shailesh Pancholi, S. P. Nikhil Kumar, D. Rama Prabha, Noor Mohammed, and Razia Sultana Wahab. "Fault analysis on continuous variable transmission using DB-06 wavelet decomposition and fault classification using ANN." Journal of Intelligent & Fuzzy Systems 41, no. 1 (August 11, 2021): 1297–307. http://dx.doi.org/10.3233/jifs-210199.

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This study aims at developing a novel method for condition monitoring technique for detection and classification of developing faults and increase the working life of continuous variable transmission (CVT) using Daubechies Wavelet 06 (DB-06). The vibration data is collected for 4 different types of faults and healthy condition. Using a magnetic accelerometer and signal analyser, vibration data is collected from the system in the time-domain which is then used as input for a MATLAB code producing the plot of the frequency-domain signal. Maximum frequency is determined to diagnose the faults which are induced over three different belts. Collected data for large scale automotive system (CVT) is used to train the network and then it is tested based on random data points. Faults were classified using ANN with a classification rate of 90.8 %.

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Hendrawan, Muhammad Afif, Pramana Yoga Saputra, and Cahya Rahmad. "Identification of optimum segment in single channel EEG biometric system." Indonesian Journal of Electrical Engineering and Computer Science 23, no. 3 (September 1, 2021): 1847. http://dx.doi.org/10.11591/ijeecs.v23.i3.pp1847-1854.

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Nowadays, biometric modalities have gained popularity in security systems. Nevertheless, the conventional commercial-grade biometric system addresses some issues. The biggest problem is that they can be imposed by artificial biometrics. The electroencephalogram (EEG) is a possible solution. It is nearly impossible to replicate because it is dependent on human mental activity. Several studies have already demonstrated a high level of accuracy. However, it requires a large number of sensors and time to collect the signal. This study proposed a biometric system using single-channel EEG recorded during resting eyes open (EO) conditions. A total of 45 EEG signals from 9 subjects were collected. The EEG signal was segmented into 5 second lengths. The alpha band was used in this study. Discrete wavelet transform (DWT) with Daubechies type 4 (db4) was employed to extract the alpha band. Power spectral density (PSD) was extracted from each segment as the main feature. Linear discriminant analysis (LDA) and support vector machine (SVM) were used to classify the EEG signal. The proposed method achieved 86% accuracy using LDA only from the third segment. Therefore, this study showed that it is possible to utilize single-channel EEG during a resting EO state in a biometric system.

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Kapoor, Gaurav. "Fifteen phase transmission line protection using daubechies-4 wavelet transform." International Journal of Engineering, Science and Technology 12, no. 1 (April 30, 2020): 1–14. http://dx.doi.org/10.4314/ijest.v12i1.1.

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In this work, the daubechies-4 wavelet transform (Db4WT) is utilized for fault detection and faulted phase recognition in a fifteen phase transmission line (FPTL). The daubechies-4 wavelet transform has been widely investigated using the MATLAB model of a 765 kV, 200 km long fifteen phase transmission line. The fifteen phase fault currents measured by the current measurements blocks connected at the bus-1 of the fifteen phase transmission line are entered to the wavelet transform algorithm. The first level detail coefficients of the fifteen phase currents are then calculated. The fault detection is carried out with great correctness. Keywords: fault detection, faulty phase recognition, fifteen phase transmission line protection, wavelet transform.

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Alfian, Fiena Efliana, I. Gede Pasek Suta Wijaya, and Fitri Bimantoro. "Identifikasi Iris Mata Menggunakan Metode Wavelet Daubechies dan K-Nearest Neighbor." Jurnal Teknologi Informasi, Komputer, dan Aplikasinya (JTIKA ) 2, no. 1 (March 31, 2020): 1–10. http://dx.doi.org/10.29303/jtika.v2i1.76.

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Human iris has a very unique pattern which is different for each person so it is possible to use it as a basic of biometric recognition. To identify texture in an image, texture analysis method can be used. There is some texture analysis method, one of them is wavelet that extract the feature of image based on energy. In this research made a simulation to identified eyes iris based on Daubechies wavelet transform. First, the image of iris is segmented from eye image then enhanced with histogram equalization. Then used Daubechies wavelet method to get the energy value. The next step is recognition using K-Nearest Neighbor as the data classification. Three experiments are done in the research, those are influence of number of samples in database, influence of Daubechies wavelet transform level, and influence of the number of testing samples to calculate the level of False Positive Rate. As the result, the highest accuracy is achieved using Daubechies 8 level 3 with three samples iris image saved is 93,50%. Then, the lowest accuracy is achieved using Daubechies 4 level 1 and 3, and Daubechies 6 level 1 with one sample iris image saved is 91,50%. Keywords: biometric, human iris, texture analysis, Daubechies wavelet transform, K-Nearest Neighbor

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Dengfeng, Li, and Peng Silong. "A characterization ofN-dimensional daubechies type tensor product wavelet." Acta Mathematicae Applicatae Sinica 17, no. 3 (July 2001): 382–92. http://dx.doi.org/10.1007/bf02677383.

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Li, Dengfeng, and Guochang Wu. "Construction of a class of Daubechies type wavelet bases." Chaos, Solitons & Fractals 42, no. 1 (October 2009): 620–25. http://dx.doi.org/10.1016/j.chaos.2009.01.034.

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Khare, Manish, Rajneesh Kumar Srivastava, and Ashish Khare. "Moving object segmentation in Daubechies complex wavelet domain." Signal, Image and Video Processing 9, no. 3 (May 21, 2013): 635–50. http://dx.doi.org/10.1007/s11760-013-0496-4.

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Wojciechowska, Agnieszka. "A Remark on Wavelet Bases in Weighted Spaces." Journal of Function Spaces and Applications 2012 (2012): 1–10. http://dx.doi.org/10.1155/2012/328310.

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The paper deals with unconditional wavelet bases in weighted spaces. Inhomogeneous wavelets of Daubechies type are considered. Necessary and sufficient conditions for weights are found for which the wavelet system is an unconditional basis in weighted spaces in dependence on .

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Sun, Fengjie, and Chenkai Zhao. "Research and Modeling of Photovoltaic Array Channel Noise Characteristics." Energies 12, no. 7 (April 1, 2019): 1257. http://dx.doi.org/10.3390/en12071257.

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The photovoltaic array can be used as a medium for carrier communication to realize monitoring of photovoltaic components. Photovoltaic array channel noise, especially the pulse-type noise therein, seriously interferes carrier communication, so it is necessary to grasp the characteristics of the photovoltaic array channel noise. Photovoltaic array channel noise modeling is a key process when conducting anti-noise immunity tests of monitoring equipment. Based on the time-domain waveform of photovoltaic series channel noise which is measured in a photovoltaic power station, this paper proposes a photovoltaic array noise modeling method of Wavelet Peak-Type Markov chain, and studies the influence on modeling accuracy when different mother wavelets are adopted for modeling. From the simulation results, root mean square errors of the predicted output for Haar, Biorthogonal and Daubechies wavelet-based function modeling case are 0.9614 V, 1.4915 V and 0.7928 V, respectively, validating that Daubechies wavelet-based function is the best wavelet-based function of modeling. In the case that the peak of original noise reaches 20 V, the predicted mean absolute error of this model is only 0.4926 V, which not only verifies the applicability of the Wavelet Peak-Type Markov chain model to the photovoltaic array channel noise, but also verifies the applicability to the pulse-type noise.

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A.Mohamed, Muhidin, and Mohamed A. Deriche. "An Approach for ECG Feature Extraction using Daubechies 4 (DB4) Wavelet." International Journal of Computer Applications 96, no. 12 (June 18, 2014): 36–41. http://dx.doi.org/10.5120/16850-6712.

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Huda Ja’afar, Noor, and Afandi Ahmad. "Pipeline architectures of Three-dimensional daubechies wavelet transform using hybrid method." Indonesian Journal of Electrical Engineering and Computer Science 15, no. 1 (July 1, 2019): 240. http://dx.doi.org/10.11591/ijeecs.v15.i1.pp240-246.

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The application of three-dimensional (3-D) medical image compression systems uses several building blocks for its computationally intensive algorithms to perform matrix transformation operations. Complexity in addressing large medical volumes data has resulted in vast challenges from a hardware implementation perspective. This paper presents an approach towards very-large-scale-integration (VLSI) implementation of 3-D Daubechies wavelet transform for medical image compression. Discrete wavelet transform (DWT) algorithm is used to design the proposed architectures with pipelined direct mapping technique. Hybrid method use a combination of hardware description language (HDL) and G-code, where this method provides an advantage compared to traditional method. The proposed pipelined architectures are deployed for adaptive transformation process of medical image compression applications. The soft IP core design was targeted on to Xilinx field programmable gate array (FPGA) single board RIO (sbRIO 9632). Results obtained for 3-D DWT architecture using Daubechies 4-tap (Daub4) implementation exhibits promising results in terms of area, power consumption and maximum frequency compared to Daubechies 6-tap (Daub6).

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STASZEWSKI, W. J., and K. WORDEN. "WAVELET ANALYSIS OF TIME-SERIES: COHERENT STRUCTURES, CHAOS AND NOISE." International Journal of Bifurcation and Chaos 09, no. 03 (March 1999): 455–71. http://dx.doi.org/10.1142/s0218127499000304.

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The continuous and orthogonal wavelet transforms are used to analyze time-series data. The analysis involves signal decomposition into scale components using both Grossman–Morlet and Daubechies type wavelets. A number of simulated and experimental data vectors exhibiting different types of coherent structures, chaos and noise is analyzed. The study shows that wavelet analysis provides a unifying framework for the description of many phenomena in time-series.

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Sharma, Vijay, Devesh Srivastava, and Pratistha Mathur. "A Daubechies DWT Based Image Steganography Using Smoothing Operation." International Arab Journal of Information Technology 17, no. 2 (February 28, 2019): 154–61. http://dx.doi.org/10.34028/iajit/17/2/2.

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Steganography is a capability which conceals the top-secret information into cover media (e.g., digital images, sound files etc.,). This Paper presents a secure, higher embedding capacity Discrete Wavelet Transformation (DWT) based technique. Before embedding correlation in between cover and the secret image is increased by multiplying some variable (i.e., 1/k) to the secret image. In embedding process, the Daubechies DWT of both Arnold transformed secret and cover images are taken followed by alpha blending operation. Arnold is a type of scrambling process which increases the confidentiality of secret image and alpha blending is a type of mixing operation of two images, the alpha value indicates the amount of secret image is embedded into the cover image. Daubechies Inverse Discrete Wavelet Transformation (IDWT) of the resulting image is performed to obtain the stego image. Smoothing operation inspired by the Genetic Algorithm (GA) is used to improve the quality of stego-image by minimizing Mean square error and morphological operation is used to extract the image component from the extracted secret image. Simulation results of the proposed steganography technique are also presented. The projected method is calculated on different parameters of image visual quality measurements

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Deng, Yaohua, Sicheng Chen, Bingjing Li, Jiayuan Chen, and Liming Wu. "Study and Testing of Processing Trajectory Measurement Method of Flexible Workpiece." Mathematical Problems in Engineering 2013 (2013): 1–9. http://dx.doi.org/10.1155/2013/798274.

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Flexible workpiece includes the materials like elasticity spline, textile fabric, and polyurethane sponge, due to the fact that processing trajectory is composed by small arc or small line segment primitives and the deformation of the flexible workpiece during the processing trajectory, making the captured image of processing trajectory not clear, the edge of processing image over local uneven gray, and also the pixels of boundaries between the processing trajectory image edge and background organizations not obvious. This paper takes corner search of processing trajectory as the cut-in-point, slope angle curve of starting and terminal point of each primitive is also designed, put forward the search algorithm that regards Daubechies (4) as wavelet operator to conduct slope angle curve for multiple scales wavelet transform, by judging whether there is a point of the curve appears wavelet transform extremum to determine whether the point is a corner point based on wavelet edge modulus maxima extract principle. Finally, proposed a decomposition/reconstruction design method of FIR filters based on wavelet transform of processing image. Eight-tap transpose FIR filter is used to design the decomposition of Daubechies (4) and reconfigurable computing IP core. The IP core wavelet decomposition of the total time-consuming increases only 5.561% in comparsion with PC. Trajectory angle relative error is 2.2%, and the average measurement time is 212.38 ms.

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Mozgaleva, Marina, Pavel Akimov, and Mojtaba Aslami. "NUMERICAL SOLUTION OF THE PROBLEM FOR POISSON’S EQUATION WITH THE USE OF DAUBECHIES WAVELET DISCRETE-CONTINUAL FINITE ELEMENT METHOD." International Journal for Computational Civil and Structural Engineering 17, no. 4 (December 26, 2021): 123–33. http://dx.doi.org/10.22337/2587-9618-2021-17-4-123-133.

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Numerical solution of the problem for Poisson’s equation with the use of Daubechies wavelet discrete continual finite element method (specific version of wavelet-based discrete-continual finite element method) is under consideration in the distinctive paper. The operational initial continual and discrete-continual formulations of the problem are given, several aspects of finite element approximation are considered. Some information about the numerical implementation and an example of analysis are presented.

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Fearn, Tom, and Anthony M. C. Davies. "A Comparison of Fourier and Wavelet Transforms in the Processing of near Infrared Spectroscopic Data: Part 1. Data Compression." Journal of Near Infrared Spectroscopy 11, no. 1 (February 2003): 3–15. http://dx.doi.org/10.1255/jnirs.349.

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The advent of spectral imaging and recent trends towards the compilation of large spectral databases have caused renewed interest in the compression of near infrared spectra for purposes of storage. A comparison of approaches using Fourier and wavelet transforms shows that wavelets are generally, though not always, more efficient than Fourier at compressing near infrared spectra. The Daubechies extremal phase wavelet of order 4 is a good choice for this purpose.

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Ayrulu-Erdem, Birsel, and Billur Barshan. "Leg Motion Classification with Artificial Neural Networks Using Wavelet-Based Features of Gyroscope Signals." Sensors 11, no. 2 (January 28, 2011): 1721–43. http://dx.doi.org/10.3390/s110201721.

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We extract the informative features of gyroscope signals using the discrete wavelet transform (DWT) decomposition and provide them as input to multi-layer feed-forward artificial neural networks (ANNs) for leg motion classification. Since the DWT is based on correlating the analyzed signal with a prototype wavelet function, selection of the wavelet type can influence the performance of wavelet-based applications significantly. We also investigate the effect of selecting different wavelet families on classification accuracy and ANN complexity and provide a comparison between them. The maximum classification accuracy of 97.7% is achieved with the Daubechies wavelet of order 16 and the reverse bi-orthogonal (RBO) wavelet of order 3.1, both with similar ANN complexity. However, the RBO 3.1 wavelet is preferable because of its lower computational complexity in the DWTdecomposition and reconstruction.

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Toda, Hiroshi, and Zhong Zhang. "Orthonormal wavelet basis with arbitrary real dilation factor." International Journal of Wavelets, Multiresolution and Information Processing 14, no. 03 (May 2016): 1650010. http://dx.doi.org/10.1142/s0219691316500107.

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Daubechies posed the following problem in Ten Lectures on Wavelets (SIAM, Philadelphia, PA, 1992): “It is an open question whether there exist orthonormal wavelet bases (not necessarily associated with a multiresolution analysis), with good time-frequency localization, and with irrational [Formula: see text]” (that is, for an arbitrary irrational dilation factor [Formula: see text], with appropriate wavelet function [Formula: see text] and constant [Formula: see text], whether can [Formula: see text] construct an orthonormal wavelet basis with good time-frequency localization?). Our answer is “Yes”. In this paper, we introduce a new type of orthonormal wavelet basis having an arbitrary real dilation factor greater than 1. This orthonormal wavelet basis requires an infinite number of wavelet shapes when its dilation factor is irrational.

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Zou, L. H., A. P. Liu, X. Ma, C. Zhang, and K. Huang. "Synthesis of Vibration Waves Based on Wavelet Technology." Shock and Vibration 19, no. 3 (2012): 391–403. http://dx.doi.org/10.1155/2012/520254.

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A novel method to generate time series of vibration waves is proposed in the paper. Considering the frequency band energy as the criterion, synthesis formulas for fluctuating wind pressure and earthquake ground motion are developed in terms of Daubechies wavelet and Harr wavelet respectively. The wavelet reconstruction method is applicable to both stationary and non-stationary process simulation. Theoretically, for non-stationary (such as seismic) process synthesis, it has a better non-stationarity in time-frequency domain than the traditional trigonometric series. Influence of wavelet delamination number and wavelet function type is also analyzed. Numerical results show that the synthesis of vibration waves based on wavelet reconstruction method contains main components of vibration, and can reflect the main properties of practical vibrations.

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Vonghirandecha, P., P. Bhurayanontachai, S. Kansomkeat, and S. Intajag. "No-Reference Retinal Image Sharpness Metric Using Daubechies Wavelet Transform." International Journal of Circuits, Systems and Signal Processing 15 (August 26, 2021): 1064–71. http://dx.doi.org/10.46300/9106.2021.15.115.

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Retinal fundus images are increasingly used by ophthalmologists both manually and without human intervention for detecting ocular diseases. Poor quality retinal images could lead to misdiagnosis or delayed treatment. Hence, a picture quality index was a crucial measure to ensure that the obtained images from acquisition system were suitable for reliable medical diagnosis. In this paper, a no-reference retinal image quality assessment based on wavelet transform is presented. A multiresolution Daubechies (db2) wavelet at level 4 was employed to decompose an original image into detail, and approximation sub-bands for extracting the sharpness information. The sharpness quality index was calculated from the entropy of the sub-bands. The proposed measure was validated by using images from the High-Resolution Fundus (HRF) dataset. The experimental results show that the proposed index performed more consistent with human visual perception and outperformed the Abdel-Hamid et al method.

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Du, Xiao Qiang, Zhe He Yao, and Zi Chen Chen. "Wavelet Denoising of the Horizontal Vibration Signal for Identification of the Guide Rail Irregularity in Elevator." Key Engineering Materials 353-358 (September 2007): 2794–97. http://dx.doi.org/10.4028/www.scientific.net/kem.353-358.2794.

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Wavelet denoising method was introduced to improve the validity of identifying the guide rail irregularity in elevator. The horizontal acceleration signal measured from a test elevator was processed by wavelet denoising. Then the denoised signal was decomposed up to four levels using Daubechies 4 mother wavelet to identify the guide rail irregularity. The results indicate that the impulses due to the guide rail irregularity are prominent in wavelet decompositions. And the causes resulting in those characteristics of the vibration signal can be well revealed. So discrete wavelet transform (DWT) can be used as an effective tool for denoising the horizontal vibration signal of elevator and diagnosing faults in the guide rail.

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Cabrelli, Carlos A., and Ursula M. Molter. "Wavelet transform of the dilation equation." Journal of the Australian Mathematical Society. Series B. Applied Mathematics 37, no. 4 (April 1996): 474–89. http://dx.doi.org/10.1017/s033427000001081x.

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AbstractIn this article we study the dilation equation f(x) = ∑h ch f (2x − h) in ℒ2(R) using a wavelet approach. We see that the structure of Multiresolution Analysis adapts very well to the study of scaling functions. The equation is reduced to an equation in a subspace of ℒ2(R) of much lower resolution. This simpler equation is then “wavelet transformed” to obtain a discrete dilation equation. In particular we study the case of compactly supported solutions and we see that conditions for the existence of solutions are given by convergence of infinite products of matrices. These matrices are of the type obtained by Daubechies, and, when the analyzing wavelet is the Haar wavelet, they are exactly the same.

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Gao, Li Xin, Fen Lou Zhai, Bang Xi Hu, Jiang Hua Zhou, Jian Hua Chen, and Yong Gang Xiao. "The Identification Technology of Rolling Bearing Acoustic Emission Fault Pattern Based on Redundant Lifting Wavelet Packet and SVM." Applied Mechanics and Materials 52-54 (March 2011): 2033–38. http://dx.doi.org/10.4028/www.scientific.net/amm.52-54.2033.

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As the energy distribution in each frequency band of rolling bearing acoustic emission (AE) signal is related to its fault type, so we can use the redundant lifting wavelet packet to decompose the rolling bearing AE signal of different fault into different frequency band, combine energy in each frequency band together to be a feature vector of the Support Vector Machines (SVM), then being applied to identify the fault through SVM. This paper also compared the redundant lifting wavelet packet and Daubechies wavelet packet as well as the SVM and neural networks. The experimental result shows that for the fault pattern identification, the method that combines redundant lifting wavelet packet decomposition and SVM together can be effective.

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Zhai, Fen Lou, Li Xin Gao, Neng Chun Gong, Yong Gang Xu, and Ming Shi Feng. "The Identification Technology of Rolling Bearing Acoustic Emission Fault Pattern Based on Harmonic Wavelet Packet and SVM." Applied Mechanics and Materials 52-54 (March 2011): 2039–44. http://dx.doi.org/10.4028/www.scientific.net/amm.52-54.2039.

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As the energy distribution in each frequency band of rolling bearing acoustic emission (AE) signal is related to its fault type, so we can use the harmonic wavelet packet to decompose the rolling bearing AE signal of different fault into different frequency band, combine energy in each frequency band together to be a feature vector of the Support Vector Machines (SVM), then being applied to identify the fault through SVM. This paper also compared the Harmonic wavelet packet and Daubechies wavelet packet as well as the SVM and neural networks. The experimental result shows that for the fault pattern identification, the method that combines harmonic wavelet packet decomposition and SVM together can be effective.

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孙, 师阳. "Quantum Image Watermarking Algorithm Based on Daubechies D⁽⁴⁾ Wavelet Transform." Advances in Applied Mathematics 09, no. 12 (2020): 2292–300. http://dx.doi.org/10.12677/aam.2020.912268.

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Feng, Guan Ming, Liang Rong Zhu, and Zhuang Wen Wu. "Wavelet Transform Applied on Vehicle Air Flow Meter (AFM) Signal De-Noising." Applied Mechanics and Materials 188 (June 2012): 287–91. http://dx.doi.org/10.4028/www.scientific.net/amm.188.287.

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In engine compartment, it is difficult to eliminate the effect of interfering signals on vehicle AFM signal by the traditional processing method. According to the characteristic of AFM signal, this paper decomposed the noise signal for 4 layers by selecting Daubechies (db5) wavelet in Matlab based on wavelet theory. After decomposing, and then employed the soft-threshold de-noising method to eliminate noise. At last this paper used the inverse wavelet transform to reconstruct signals for signal de-noising and rebuilding. The test results show that this method can eliminate the noises of AFM signal effectively.

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Chau, F. T., T. M. Shih, J. B. Gao, and C. K. Chan. "Application of the Fast Wavelet Transform Method to Compress Ultraviolet-Visible Spectra." Applied Spectroscopy 50, no. 3 (March 1996): 339–48. http://dx.doi.org/10.1366/0003702963906320.

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Data compression methods based on the fast wavelet transform and the multiresolution signal decomposition algorithms were devised and applied to ultraviolet-visible absorption spectra. Wavelet functions of the Daubechies type were employed for the purpose. In addition, two data pretreatment procedures were proposed and used to cope with the side-lobe problem. The performance of these methods was evaluated by using both synthetic and experimental data. It was found that the storage space of the spectral information under study can be reduced significantly by using the suggested methods with good-quality spectra generated from the compressed data.

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Merzoug, Mustapha, Khalid Ait-Sghir, Abdelhamid Miloudi, and Paul Jean Dron. "Early Diagnosis of Spalling in the Gear Teeth." Advanced Materials Research 1016 (August 2014): 249–55. http://dx.doi.org/10.4028/www.scientific.net/amr.1016.249.

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The monitoring and vibratory analysis of gear transmission allow the prediction of a possible malfunction and breakdowns. As the gear transmission product non-stationary signals its treatment is too difficult with the usual tools of signal processing witch can product errors in its interpretation. As the characteristics of gear frequencies are predetermined, it is proposed to monitor (fault identification) using wavelet analysis. To simulate the signal to be analyzed, we intentionally introduced a spalling defect. We chose the Daubechies wavelet type which are the most used in diagnostic. The aim of this work is to try to control the various parameters related to the wavelet analysis for reliable and inexpensive detection, i.e., the order of the wavelet and level decomposition. The approach witch was previously used for bearings, consists on observing the kurtosis for several orders wavelet based on the default severity..

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Ali, Mohammed Nabih. "A wavelet-based method for MRI liver image denoising." Biomedical Engineering / Biomedizinische Technik 64, no. 6 (December 18, 2019): 699–709. http://dx.doi.org/10.1515/bmt-2018-0033.

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Abstract Image denoising stays be a standout amongst the primary issues in the field of image processing. Several image denoising algorithms utilizing wavelet transforms have been presented. This paper deals with the use of wavelet transform for magnetic resonance imaging (MRI) liver image denoising using selected wavelet families and thresholding methods with appropriate decomposition levels. Denoised MRI liver images are compared with the original images to conclude the most suitable parameters (wavelet family, level of decomposition and thresholding type) for the denoising process. The performance of our algorithm is evaluated using the signal-to-noise ratio (SNR), peak signal-to-noise ratio (PSNR) and mean square error (MSE). The results show that the Daubechies wavelet family of the tenth order with first and second of the levels of decomposition are the most optimal parameters for MRI liver image denoising.

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Codruta, Pricop. "Mother wavelet selection using signal energy for cracks detection in the rotation shafts." Scientific Bulletin of Naval Academy XXIII, no. 2 (December 15, 2020): 64–74. http://dx.doi.org/10.21279/1454-864x-20-i2-009.

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The mother wavelet greatly influences the wavelet analysis of a non-stationary and nonlinear recorded signal. Choosing mother wavelet must be done to determine cracks in rotating shafts so as to take into account the nature and type of information signals to be extracted from the signal. The difficulty in optimum selection of the mother wavelet is determined by their complex properties that determine different selection criteria. In the paper, several families of functions (Haar, Daubechies, Symlets, Coiflet, BiorSplines) were used for analysis and the proposed selection criterion is the energy dissipated on the frequency bands. Signal recordings were made on a stand to determine the presence of cracks in rotating shafts and their classification. For discrete decomposition of recorded signals (DWT) and the calculation of energy dissipated on the frequency bands the Matlab wavelet instrument was used.

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BEHERA, RATIKANTA, and MANI MEHRA. "APPROXIMATE SOLUTION OF MODIFIED CAMASSA–HOLM AND DEGASPERIS–PROCESI EQUATIONS USING WAVELET OPTIMIZED FINITE DIFFERENCE METHOD." International Journal of Wavelets, Multiresolution and Information Processing 11, no. 02 (March 2013): 1350019. http://dx.doi.org/10.1142/s0219691313500197.

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In this paper, we apply wavelet optimized finite difference method to solve modified Camassa–Holm and modified Degasperis–Procesi equations. The method is based on Daubechies wavelet with finite difference method on an arbitrary grid. The wavelet is used at regular intervals to adaptively select the grid points according to the local behaviour of the solution. The purpose of wavelet-based numerical methods for solving linear or nonlinear partial differential equations is to develop adaptive schemes, in order to achieve accuracy and computational efficiency. Since most of physical and scientific phenomena are modeled by nonlinear partial differential equations, but it is difficult to handle nonlinear partial differential equations analytically. So we need approximate solution to solve these type of partial differential equation. Numerical results are presented for approximating modified Camassa–Holm and modified Degasperis–Procesi equations, which demonstrate the advantages of this method.

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MARTIS, ROSHAN JOY, HARI PRASAD, CHANDAN CHAKRABORTY, and AJOY KUMAR RAY. "AUTOMATED DETECTION OF ATRIAL FLUTTER AND FIBRILLATION USING ECG SIGNALS IN WAVELET FRAMEWORK." Journal of Mechanics in Medicine and Biology 12, no. 05 (December 2012): 1240023. http://dx.doi.org/10.1142/s0219519412400234.

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In this paper, an electrocardiogram (ECG)-based pattern analysis methodology is presented for the detection of artrial flutter and atrial fibrillation using fractal dimension (FD) of continuous wavelet transform (CWT) coefficients of raw ECG signals, sample entropy of heart beat interval time series, and mean heart beat interval features. Accurate diagnosis of atrial tachyarrhythmias is important, as they have different therapeutic options and clinical decisions. In view of this, we have made an attempt to develop a discrimination mechanism between artrial flutter and atrial fibrillation. The methodology consists of mean heart beat interval detection using Pan Tompkins algorithm, calculation of sample entropy of heart beat interval time series, computation of box counting FD from CWT coefficients of raw ECG, statistical significance test, and subsequent pattern classification using different classifiers. Different wavelet basis functions like Daubechies-4, Daubechies-6, Symlet-2, Symlet-4, Symlet-6, Symlet-8, Coiflet-2, Coiflet-5, Biorthogonal-1.3, Biorthogonal-3.1, and Mayer wavelet have been used to compute CWT coefficients. Features are evaluated using statistical analysis and subsequently two-class pattern classification is done using unsupervised (k-means, fuzzy c-means, and Gaussian mixture model) and supervised (error back propagation neural network and support vector machine) techniques. In order to reduce the bias in choosing the training and testing set, k-fold cross validation is used. The obtained results are compared and discussed. It is found that the supervised classifiers provide higher accuracy in comparison to the set of unsupervised classifiers.

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Liu, Yi, Yi Li, Linchao Li, and Chunyan Chen. "Spatiotemporal Variability of Monthly and Annual Snow Depths in Xinjiang, China over 1961–2015 and the Potential Effects." Water 11, no. 8 (August 12, 2019): 1666. http://dx.doi.org/10.3390/w11081666.

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The spatiotemporal variability of snow depth supplies important information for snow disaster prevention. The monthly and annual snow depths and weather data (from Xinjiang Meteorological Observatory) at 102 meteorological stations in Xinjiang, China over 1961–2015 were used to analyze the spatiotemporal characteristics of snow depths from different aspects. The empirical orthogonal function (EOF), the modified Mann–Kendall method, Morlet wavelet, Daubechies wavelet decomposition and cross wavelet transform were applied to investigate the trend and significance, spatial structure, periods, decomposed series and coherence of monthly and annual snow depths. The results indicated that: (1) The value of EOF first spatial mode (EOF1) of the monthly and annual snow depths in north Xinjiang were larger than south Xinjiang, indicating greater variability of snow depths in north Xinjiang. (2) The change points of annual snow depth mainly occurred during 1969–1979 and 1980–1990. The annual snow depth of most sites showed increasing trends, but with different slope magnitudes. (3) The sites that had main periods of 2–8 and 9–14 years of monthly and annual snow depths (detected by the Morlet wavelet) mainly distributed in northern Xinjiang. The sites that had main periods of 15–20 years of monthly and annual snow depths mainly distributed in southwestern Xinjiang. (4) By using the Daubechies wavelet, the decomposed annual snow depth in entire Xinjiang tended to increase. (5) Through the cross wavelet transform, annual snow depths in entire Xinjiang had good correlations with annual precipitation or relative humidity, and showed a low negative correlation with minimum temperature or sunshine hours. In conclusion, the monthly and annual snow depths had comprehensive spatiotemporal variability but had overall increasing trend during 1961–2015.

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GIACCONE, SANTIAGO J., GUILLERMO R. BOSSIO, GUILLERMO O. GARCÍA, and JORGE A. SOLSONA. "WAVELET ANALYSIS FOR STATOR FAULT DETECTION IN INDUCTION MACHINES." International Journal of Wavelets, Multiresolution and Information Processing 09, no. 03 (May 2011): 361–74. http://dx.doi.org/10.1142/s0219691311004109.

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The main objective of the proposed analysis is the detection of inter-turn short circuits in the stator windings of an induction machine. The analysis of the space vector current modulus of an induction motor is presented in this paper. This analysis is based on Daubechies 8 wavelet with seven decomposition levels. The 5th decomposition-level detail signal for a 4 kHz sampling frequency is chosen as a fault indicator, based on simulation results that show different behaviors of the energy contained in the detail signals independent of the percentage of load and fault levels. Experimental results that validate the proposed strategy are also presented. These results also show that the strategy is in addition immune to load variations as well as to feeding voltage unbalances.

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Xia, Chunxu, and Chunguang Liu. "Identification and Representation of Multi-Pulse Near-Fault Strong Ground Motion Using Adaptive Wavelet Transform." Applied Sciences 9, no. 2 (January 12, 2019): 259. http://dx.doi.org/10.3390/app9020259.

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In order to identify the horizontal seismic motion owning the largest pulse energy, and represent the dominant pulse-like component embedded in this seismic motion, we used the adaptive wavelet transform algorithm in this paper. Fifteen candidate mother wavelets were evaluated to select the optimum wavelet based on the similarities between the candidate mother wavelet and the target seismic motion, evaluated by the minimum cross variance. This adaptive choosing algorithm for the optimum mother wavelet was invoked before identifying both the horizontal direction owning the largest pulse energy and every dominant pulse, which provides the optimum mother wavelet for the continuous wavelet transform. Each dominant pulse can be represented by its adaptively selected optimum mother wavelet. The results indicate that the identified multi-pulse component fits well with the seismic motion. In most cases, mother wavelets in one multi-pulse seismic motion were different from each other. For the Chi-Chi event (1999-Sep-20 17:47:16 UTC, Mw = 7.6), 62.26% of the qualified pulse-like earthquake motions lay in the horizontal direction ranging from ±15° to ±75°. The Daubechies 6 (db6) mother wavelet was the most frequently used type for both the first and second pulse components.

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Abidin, Zaenal, and Alamsyah Alamsyah. "Wavelet based approach for facial expression recognition." International Journal of Advances in Intelligent Informatics 1, no. 1 (March 31, 2015): 7. http://dx.doi.org/10.26555/ijain.v1i1.7.

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Facial expression recognition is one of the most active fields of research. Many facial expression recognition methods have been developed and implemented. Neural networks (NNs) have capability to undertake such pattern recognition tasks. The key factor of the use of NN is based on its characteristics. It is capable in conducting learning and generalizing, non-linear mapping, and parallel computation. Backpropagation neural networks (BPNNs) are the approach methods that mostly used. In this study, BPNNs were used as classifier to categorize facial expression images into seven-class of expressions which are anger, disgust, fear, happiness, sadness, neutral and surprise. For the purpose of feature extraction tasks, three discrete wavelet transforms were used to decompose images, namely Haar wavelet, Daubechies (4) wavelet and Coiflet (1) wavelet. To analyze the proposed method, a facial expression recognition system was built. The proposed method was tested on static images from JAFFE database.

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Xiang, Xin Jian, and Zhang Lin. "Arc-Fault Detection Method Research Based on Wavelet Transformation." Advanced Materials Research 646 (January 2013): 240–44. http://dx.doi.org/10.4028/www.scientific.net/amr.646.240.

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The arc-fault is the main reason that cause electric fires. The technology of arc-fault circuit interrupters (AFCI) is the new circuit protection technology and it could avoid arc-fault causing fire effectively. The appearance of arc-fault can not be predicted. The traditional time domain or frequency domain analysis method for arc-fault signal processing is not ideal because it’s inaccurate and not in time. This paper bases on characteristics of arc-fault signals and analyzes the series connection arc-fault signal by Daubechies wavelet transform in 4 orders. As a result, it can provide the characteristics of arc-fault and detect arc-fault effectively and timely. This method is confirmed reliability by the simulation result and provides the theoretical basis of the development of AFCI.

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Pothisarn, Chaichan, Jittiphong Klomjit, Atthapol Ngaopitakkul, Chaiyan Jettanasen, Dimas Anton Asfani, and I. Made Yulistya Negara. "Comparison of Various Mother Wavelets for Fault Classification in Electrical Systems." Applied Sciences 10, no. 4 (February 11, 2020): 1203. http://dx.doi.org/10.3390/app10041203.

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This paper presents a comparative study on mother wavelets using a fault type classification algorithm in a power system. The study aims to evaluate the performance of the protection algorithm by implementing different mother wavelets for signal analysis and determines a suitable mother wavelet for power system protection applications. The factors that influence the fault signal, such as the fault location, fault type, and inception angle, have been considered during testing. The algorithm operates by applying the discrete wavelet transform (DWT) to the three-phase current and zero-sequence signal obtained from the experimental setup. The DWT extracts high-frequency components from the signals during both the normal and fault states. The coefficients at scales 1–3 have been decomposed using different mother wavelets, such as Daubechies (db), symlets (sym), biorthogonal (bior), and Coiflets (coif). The results reveal different coefficient values for the different mother wavelets even though the behaviors are similar. The coefficient for any mother wavelet has the same behavior but does not have the same value. Therefore, this finding has shown that the mother wavelet has a significant impact on the accuracy of the fault classification algorithm.

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Z. Abdullah, A., M. Isa, S. N. M. Arshad, M. N. K. H. Rohani, H. S. A. Halim, A. N. Nanyan, and H. A. Hamid. "Wavelet based de-noising for on-site partial discharge measurement signal." Indonesian Journal of Electrical Engineering and Computer Science 16, no. 1 (October 1, 2019): 259. http://dx.doi.org/10.11591/ijeecs.v16.i1.pp259-266.

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This paper presents, wavelet based de-noising technique for on-site partial discharge (PD) measurement signal. The signal is measured from medium voltage power cable at 11 kV distribution substation. The best mother wavelet, decomposition level and the type of threshold for the de-noising technique are selected based on the signal to noise ratio (SNR) aggregation. The SNR aggregation is determined based on the minimum, maximum, mean and standard deviation parameters. The same standard de-noising procedure is applied for two different PD signals and the selection parameters are done based on the accuracy of de-noising analysis. The analysis is performed in MATLAB software environment and Daubechies 2 (db2) is found as the best mother wavelet at tenth decomposition levels with soft threshold type. This study is specifically performed to develop the de-noising procedure for on-site PD measurement. Overall results indicate that the right selection of the de-noising procedure will help to improve the PD signal detection from on–site measurement.

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Tong, Yaonan, Jingui Li, Yaohui Xu, and Lichen Cao. "Signal Denoising Method Based on Improved Wavelet Threshold Function for Microchip Electrophoresis C4D Equipment." Complexity 2020 (July 10, 2020): 1–11. http://dx.doi.org/10.1155/2020/6481317.

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A signal denoising method using improved wavelet threshold function is presented for microchip electrophoresis based on capacitively coupled contactless conductivity detection (ME-C4D) device. The evaluation results of denoising effect for the ME-C4D simulation signal show that using Daubechies 5 (db5) wavelet at a decomposition level 4 can produce the best performance. Furthermore, the denoising effect is compared with, as well as proved to be superior to, the existing techniques, such as Savitzky–Golay, Fast Fourier Transform, and soft threshold method. This method has been successfully applied to the self-developed ME-C4D equipment. After executing this method, the noise is cleanly removed, and the signal peak shape and peak area are well maintained.

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Dyarbirru, Zaurarista, and Syahroni Hidayat. "Metode Wavelet-MFCC dan Korelasi dalam Pengenalan Suara Digit." JTIM : Jurnal Teknologi Informasi dan Multimedia 2, no. 2 (August 21, 2020): 100–108. http://dx.doi.org/10.35746/jtim.v2i2.99.

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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.

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Ganesan, T., and Pothuraju Rajarajeswari. "Efficient Sensor Node Connectivity and Target Coverage using Genetic Algorithm with Daubechies 4 Lifting Wavelet Transform." International Journal of Communication Networks and Distributed Systems 1, no. 1 (2022): 1. http://dx.doi.org/10.1504/ijcnds.2022.10042210.

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Ganesan, T., and Pothuraju Rajarajeswari. "Efficient sensor node connectivity and target coverage using genetic algorithm with Daubechies 4 lifting wavelet transform." International Journal of Communication Networks and Distributed Systems 28, no. 3 (2022): 337. http://dx.doi.org/10.1504/ijcnds.2022.122170.

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Journal articles on the topic 'Daubechies wavelet of type 4'

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